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huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/whisper/test_processor_whisper.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import shutil import tempfile import unittest from transformers import WhisperTokenizer, is_speech_available from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio from .test_feature_extraction_whisper import floats_list if is_speech_available(): from transformers import WhisperFeatureExtractor, WhisperProcessor @require_torch @require_torchaudio @require_sentencepiece class WhisperProcessorTest(unittest.TestCase): def setUp(self): self.checkpoint = "openai/whisper-small.en" self.tmpdirname = tempfile.mkdtemp() def get_tokenizer(self, **kwargs): return WhisperTokenizer.from_pretrained(self.checkpoint, **kwargs) def get_feature_extractor(self, **kwargs): return WhisperFeatureExtractor.from_pretrained(self.checkpoint, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def test_save_load_pretrained_default(self): tokenizer = self.get_tokenizer() feature_extractor = self.get_feature_extractor() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) processor.save_pretrained(self.tmpdirname) processor = WhisperProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertIsInstance(processor.tokenizer, WhisperTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor) def test_save_load_pretrained_additional_features(self): processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0) processor = WhisperProcessor.from_pretrained( self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, WhisperTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor) def test_feature_extractor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) raw_speech = floats_list((3, 1000)) input_feat_extract = feature_extractor(raw_speech, return_tensors="np") input_processor = processor(raw_speech, return_tensors="np") for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2) def test_tokenizer(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "This is a test string" encoded_processor = processor(text=input_str) encoded_tok = tokenizer(input_str) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key], encoded_processor[key]) def test_tokenizer_decode(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] decoded_processor = processor.batch_decode(predicted_ids) decoded_tok = tokenizer.batch_decode(predicted_ids) self.assertListEqual(decoded_tok, decoded_processor) def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) self.assertListEqual( processor.model_input_names, feature_extractor.model_input_names, msg="`processor` and `feature_extractor` model input names do not match", )

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import shutil import tempfile import unittest from transformers import WhisperTokenizer, is_speech_available from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio from .test_feature_extraction_whisper import floats_list if is_speech_available(): from transformers import WhisperFeatureExtractor, WhisperProcessor @require_torch @require_torchaudio @require_sentencepiece class WhisperProcessorTest(unittest.TestCase): def setUp(self): self.checkpoint = "openai/whisper-small.en" self.tmpdirname = tempfile.mkdtemp() def get_tokenizer(self, **kwargs): return WhisperTokenizer.from_pretrained(self.checkpoint, **kwargs) def get_feature_extractor(self, **kwargs): return WhisperFeatureExtractor.from_pretrained(self.checkpoint, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def test_save_load_pretrained_default(self): tokenizer = self.get_tokenizer() feature_extractor = self.get_feature_extractor() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) processor.save_pretrained(self.tmpdirname) processor = WhisperProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertIsInstance(processor.tokenizer, WhisperTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor) def test_save_load_pretrained_additional_features(self): processor = WhisperProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0) processor = WhisperProcessor.from_pretrained( self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, WhisperTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, WhisperFeatureExtractor) def test_feature_extractor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) raw_speech = floats_list((3, 1000)) input_feat_extract = feature_extractor(raw_speech, return_tensors="np") input_processor = processor(raw_speech, return_tensors="np") for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2) def test_tokenizer(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "This is a test string" encoded_processor = processor(text=input_str) encoded_tok = tokenizer(input_str) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key], encoded_processor[key]) def test_tokenizer_decode(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] decoded_processor = processor.batch_decode(predicted_ids) decoded_tok = tokenizer.batch_decode(predicted_ids) self.assertListEqual(decoded_tok, decoded_processor) def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = WhisperProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) self.assertListEqual( processor.model_input_names, feature_extractor.model_input_names, msg="`processor` and `feature_extractor` model input names do not match", )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./utils/test_module/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/trocr/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/file_utils.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ File utilities: utilities related to download and cache models This module should not be update anymore and is only left for backward compatibility. """ from . import __version__ # Backward compatibility imports, to make sure all those objects can be found in file_utils from .utils import ( CLOUDFRONT_DISTRIB_PREFIX, CONFIG_NAME, DISABLE_TELEMETRY, DUMMY_INPUTS, DUMMY_MASK, ENV_VARS_TRUE_AND_AUTO_VALUES, ENV_VARS_TRUE_VALUES, FEATURE_EXTRACTOR_NAME, FLAX_WEIGHTS_NAME, HF_MODULES_CACHE, HUGGINGFACE_CO_PREFIX, HUGGINGFACE_CO_RESOLVE_ENDPOINT, MODEL_CARD_NAME, MULTIPLE_CHOICE_DUMMY_INPUTS, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, S3_BUCKET_PREFIX, SENTENCEPIECE_UNDERLINE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TORCH_FX_REQUIRED_VERSION, TRANSFORMERS_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, USE_JAX, USE_TF, USE_TORCH, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ContextManagers, DummyObject, EntryNotFoundError, ExplicitEnum, ModelOutput, PaddingStrategy, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, TensorType, _LazyModule, add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, cached_property, copy_func, default_cache_path, define_sagemaker_information, get_cached_models, get_file_from_repo, get_full_repo_name, has_file, http_user_agent, is_apex_available, is_bs4_available, is_coloredlogs_available, is_datasets_available, is_detectron2_available, is_faiss_available, is_flax_available, is_ftfy_available, is_in_notebook, is_ipex_available, is_librosa_available, is_offline_mode, is_onnx_available, is_pandas_available, is_phonemizer_available, is_protobuf_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_pytesseract_available, is_pytorch_quantization_available, is_rjieba_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_scipy_available, is_sentencepiece_available, is_sklearn_available, is_soundfile_availble, is_spacy_available, is_speech_available, is_tensor, is_tensorflow_probability_available, is_tf2onnx_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_bf16_available, is_torch_cuda_available, is_torch_fx_available, is_torch_fx_proxy, is_torch_onnx_dict_inputs_support_available, is_torch_tf32_available, is_torch_tpu_available, is_torchaudio_available, is_training_run_on_sagemaker, is_vision_available, replace_return_docstrings, requires_backends, tf_required, to_numpy, to_py_obj, torch_only_method, torch_required, torch_version, )

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ File utilities: utilities related to download and cache models This module should not be update anymore and is only left for backward compatibility. """ from . import __version__ # Backward compatibility imports, to make sure all those objects can be found in file_utils from .utils import ( CLOUDFRONT_DISTRIB_PREFIX, CONFIG_NAME, DISABLE_TELEMETRY, DUMMY_INPUTS, DUMMY_MASK, ENV_VARS_TRUE_AND_AUTO_VALUES, ENV_VARS_TRUE_VALUES, FEATURE_EXTRACTOR_NAME, FLAX_WEIGHTS_NAME, HF_MODULES_CACHE, HUGGINGFACE_CO_PREFIX, HUGGINGFACE_CO_RESOLVE_ENDPOINT, MODEL_CARD_NAME, MULTIPLE_CHOICE_DUMMY_INPUTS, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, S3_BUCKET_PREFIX, SENTENCEPIECE_UNDERLINE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TORCH_FX_REQUIRED_VERSION, TRANSFORMERS_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, USE_JAX, USE_TF, USE_TORCH, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ContextManagers, DummyObject, EntryNotFoundError, ExplicitEnum, ModelOutput, PaddingStrategy, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, TensorType, _LazyModule, add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, cached_property, copy_func, default_cache_path, define_sagemaker_information, get_cached_models, get_file_from_repo, get_full_repo_name, has_file, http_user_agent, is_apex_available, is_bs4_available, is_coloredlogs_available, is_datasets_available, is_detectron2_available, is_faiss_available, is_flax_available, is_ftfy_available, is_in_notebook, is_ipex_available, is_librosa_available, is_offline_mode, is_onnx_available, is_pandas_available, is_phonemizer_available, is_protobuf_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_pytesseract_available, is_pytorch_quantization_available, is_rjieba_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_scipy_available, is_sentencepiece_available, is_sklearn_available, is_soundfile_availble, is_spacy_available, is_speech_available, is_tensor, is_tensorflow_probability_available, is_tf2onnx_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_bf16_available, is_torch_cuda_available, is_torch_fx_available, is_torch_fx_proxy, is_torch_onnx_dict_inputs_support_available, is_torch_tf32_available, is_torch_tpu_available, is_torchaudio_available, is_training_run_on_sagemaker, is_vision_available, replace_return_docstrings, requires_backends, tf_required, to_numpy, to_py_obj, torch_only_method, torch_required, torch_version, )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/data2vec/test_modeling_data2vec_vision.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Data2VecVision model. """ import inspect import unittest from transformers import Data2VecVisionConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import ( MODEL_MAPPING, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation, Data2VecVisionModel, ) from transformers.models.data2vec.modeling_data2vec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import BeitFeatureExtractor class Data2VecVisionModelTester: def __init__( self, parent, vocab_size=100, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=4, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, out_indices=[0, 1, 2, 3], ): self.parent = parent self.vocab_size = 100 self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope self.out_indices = out_indices self.num_labels = num_labels # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None pixel_labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels, pixel_labels def get_config(self): return Data2VecVisionConfig( vocab_size=self.vocab_size, image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = Data2VecVisionModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) num_patches = (self.image_size // self.patch_size) ** 2 self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.type_sequence_label_size model = Data2VecVisionForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = Data2VecVisionForSemanticSegmentation(config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) result = model(pixel_values, labels=pixel_labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels, pixel_labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class Data2VecVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation) if is_torch_available() else () ) test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = Data2VecVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() def test_inputs_embeds(self): # Data2VecVision does not use inputs_embeds pass @require_torch_multi_gpu @unittest.skip( reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`" ) def test_multi_gpu_data_parallel_forward(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs) def test_training(self): if not self.model_tester.is_training: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: if model_class in [*get_values(MODEL_MAPPING)]: continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.model_tester.is_training: return config.use_cache = False config.return_dict = True for model_class in self.all_model_classes: if model_class in [*get_values(MODEL_MAPPING)] or not model_class.supports_gradient_checkpointing: continue # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING # this can then be incorporated into _prepare_for_class in test_modeling_common.py elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation": batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [self.model_tester.batch_size, height, width], device=torch_device ).long() model = model_class(config) model.gradient_checkpointing_enable() model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): # we skip lambda parameters as these require special initial values # determined by config.layer_scale_init_value if "lambda" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as semseg models tend to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = Data2VecVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class Data2VecVisionModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ( BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None ) @slow def test_inference_image_classification_head_imagenet_1k(self): model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to( torch_device ) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device) self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4)) expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]] self.assertEqual(logits[0].topk(2).indices.cpu().tolist(), expected_top2)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch Data2VecVision model. """ import inspect import unittest from transformers import Data2VecVisionConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import ( MODEL_MAPPING, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation, Data2VecVisionModel, ) from transformers.models.data2vec.modeling_data2vec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import BeitFeatureExtractor class Data2VecVisionModelTester: def __init__( self, parent, vocab_size=100, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=4, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, out_indices=[0, 1, 2, 3], ): self.parent = parent self.vocab_size = 100 self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope self.out_indices = out_indices self.num_labels = num_labels # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None pixel_labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels, pixel_labels def get_config(self): return Data2VecVisionConfig( vocab_size=self.vocab_size, image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = Data2VecVisionModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) num_patches = (self.image_size // self.patch_size) ** 2 self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.type_sequence_label_size model = Data2VecVisionForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = Data2VecVisionForSemanticSegmentation(config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) result = model(pixel_values, labels=pixel_labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels, pixel_labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class Data2VecVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation) if is_torch_available() else () ) test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = Data2VecVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() def test_inputs_embeds(self): # Data2VecVision does not use inputs_embeds pass @require_torch_multi_gpu @unittest.skip( reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`" ) def test_multi_gpu_data_parallel_forward(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs) def test_training(self): if not self.model_tester.is_training: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: if model_class in [*get_values(MODEL_MAPPING)]: continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.model_tester.is_training: return config.use_cache = False config.return_dict = True for model_class in self.all_model_classes: if model_class in [*get_values(MODEL_MAPPING)] or not model_class.supports_gradient_checkpointing: continue # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING # this can then be incorporated into _prepare_for_class in test_modeling_common.py elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation": batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [self.model_tester.batch_size, height, width], device=torch_device ).long() model = model_class(config) model.gradient_checkpointing_enable() model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): # we skip lambda parameters as these require special initial values # determined by config.layer_scale_init_value if "lambda" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as semseg models tend to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = Data2VecVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class Data2VecVisionModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ( BeitFeatureExtractor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None ) @slow def test_inference_image_classification_head_imagenet_1k(self): model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to( torch_device ) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device) self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4)) expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]] self.assertEqual(logits[0].topk(2).indices.cpu().tolist(), expected_top2)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/segformer/test_modeling_tf_segformer.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow SegFormer model. """ import inspect import unittest from typing import List, Tuple from transformers import SegformerConfig from transformers.file_utils import is_tf_available, is_vision_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel from transformers.models.segformer.modeling_tf_segformer import TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import SegformerFeatureExtractor class TFSegformerConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_attention_heads")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class TFSegformerModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, num_encoder_blocks=4, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], hidden_sizes=[16, 32, 64, 128], downsampling_rates=[1, 4, 8, 16], num_attention_heads=[1, 2, 4, 8], is_training=True, use_labels=True, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.sr_ratios = sr_ratios self.depths = depths self.hidden_sizes = hidden_sizes self.downsampling_rates = downsampling_rates self.num_attention_heads = num_attention_heads self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return SegformerConfig( image_size=self.image_size, num_channels=self.num_channels, num_encoder_blocks=self.num_encoder_blocks, depths=self.depths, hidden_sizes=self.hidden_sizes, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = TFSegformerModel(config=config) result = model(pixel_values, training=False) expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) ) def create_and_check_for_image_segmentation(self, config, pixel_values, labels): config.num_labels = self.num_labels model = TFSegformerForSemanticSegmentation(config) result = model(pixel_values, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) result = model(pixel_values, labels=labels, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict def prepare_config_and_inputs_for_keras_fit(self, for_segmentation: bool = False): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, seg_labels = config_and_inputs if for_segmentation: inputs_dict = {"pixel_values": pixel_values, "labels": seg_labels} else: inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))} return config, inputs_dict @require_tf class TFSegformerModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( (TFSegformerModel, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation) if is_tf_available() else () ) test_head_masking = False test_onnx = False test_pruning = False test_resize_embeddings = False def setUp(self): self.model_tester = TFSegformerModelTester(self) self.config_tester = TFSegformerConfigTester(self, config_class=SegformerConfig, has_text_modality=False) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip("SegFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods") def test_model_common_attributes(self): pass @unittest.skip("Test was written for TF 1.x and isn't really relevant here") def test_compile_tf_model(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions expected_num_attentions = sum(self.model_tester.depths) self.assertEqual(len(attentions), expected_num_attentions) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len], ) # verify the last attentions (last block, last layer) expected_seq_len = (self.model_tester.image_size // 32) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2 self.assertListEqual( list(attentions[-1].shape[-3:]), [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_encoder_blocks self.assertEqual(len(hidden_states), expected_num_layers) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:]), [ self.model_tester.hidden_sizes[0], self.model_tester.image_size // 4, self.model_tester.image_size // 4, ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( all(tf.equal(tuple_object, dict_object)), msg=( "Tuple and dict output are not equal. Difference:" f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}" ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) # todo: incorporate label support for semantic segmentation in `test_modeling_tf_common.py`. @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) def test_dataset_conversion(self): super().test_dataset_conversion() def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1): self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol)) @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) def test_keras_fit(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # Since `TFSegformerModel` cannot operate with the default `fit()` method. if model_class.__name__ != "TFSegformerModel": model = model_class(config) if getattr(model, "hf_compute_loss", None): super().test_keras_fit() def test_loss_computation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def apply(model): for_segmentation = True if model_class.__name__ == "TFSegformerForSemanticSegmentation" else False # The number of elements in the loss should be the same as the number of elements in the label _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit( for_segmentation=for_segmentation ) added_label = prepared_for_class[ sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0] ] loss_size = tf.size(added_label) # Test that model correctly compute the loss with kwargs possible_input_names = {"input_ids", "pixel_values", "input_features"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) loss = model(model_input, **prepared_for_class)[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": # Semantic segmentation loss is computed similarly as # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210. self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a dict _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit( for_segmentation=for_segmentation ) loss = model(**prepared_for_class)[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a tuple label_keys = prepared_for_class.keys() - inputs_dict.keys() signature = inspect.signature(model.call).parameters signature_names = list(signature.keys()) # Create a dictionary holding the location of the tensors in the tuple tuple_index_mapping = {0: input_name} for label_key in label_keys: label_key_index = signature_names.index(label_key) tuple_index_mapping[label_key_index] = label_key sorted_tuple_index_mapping = sorted(tuple_index_mapping.items()) # Initialize a list with their default values, update the values and convert to a tuple list_input = [] for name in signature_names: if name != "kwargs": list_input.append(signature[name].default) for index, value in sorted_tuple_index_mapping: list_input[index] = prepared_for_class[value] tuple_input = tuple(list_input) # Send to model loss = model(tuple_input[:-1])[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) for model_class in self.all_model_classes: # Since `TFSegformerModel` won't have labels against which we # could compute loss. if model_class.__name__ != "TFSegformerModel": model = model_class(config) apply(model) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as semseg models tend to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) @slow def test_model_from_pretrained(self): for model_name in TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFSegformerModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_tf class TFSegformerModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_segmentation_ade(self): # only resize + normalize feature_extractor = SegformerFeatureExtractor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") image = prepare_img() encoded_inputs = feature_extractor(images=image, return_tensors="tf") pixel_values = encoded_inputs.pixel_values outputs = model(pixel_values, training=False) expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant( [ [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]], [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]], [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]], ] ) tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-4) @slow def test_inference_image_segmentation_city(self): # only resize + normalize feature_extractor = SegformerFeatureExtractor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = TFSegformerForSemanticSegmentation.from_pretrained( "nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ) image = prepare_img() encoded_inputs = feature_extractor(images=image, return_tensors="tf") pixel_values = encoded_inputs.pixel_values outputs = model(pixel_values, training=False) expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant( [ [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]], [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]], [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]], ] ) tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-1)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow SegFormer model. """ import inspect import unittest from typing import List, Tuple from transformers import SegformerConfig from transformers.file_utils import is_tf_available, is_vision_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel from transformers.models.segformer.modeling_tf_segformer import TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import SegformerFeatureExtractor class TFSegformerConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_attention_heads")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class TFSegformerModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, num_encoder_blocks=4, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], hidden_sizes=[16, 32, 64, 128], downsampling_rates=[1, 4, 8, 16], num_attention_heads=[1, 2, 4, 8], is_training=True, use_labels=True, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.sr_ratios = sr_ratios self.depths = depths self.hidden_sizes = hidden_sizes self.downsampling_rates = downsampling_rates self.num_attention_heads = num_attention_heads self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return SegformerConfig( image_size=self.image_size, num_channels=self.num_channels, num_encoder_blocks=self.num_encoder_blocks, depths=self.depths, hidden_sizes=self.hidden_sizes, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = TFSegformerModel(config=config) result = model(pixel_values, training=False) expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) ) def create_and_check_for_image_segmentation(self, config, pixel_values, labels): config.num_labels = self.num_labels model = TFSegformerForSemanticSegmentation(config) result = model(pixel_values, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) result = model(pixel_values, labels=labels, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict def prepare_config_and_inputs_for_keras_fit(self, for_segmentation: bool = False): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, seg_labels = config_and_inputs if for_segmentation: inputs_dict = {"pixel_values": pixel_values, "labels": seg_labels} else: inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))} return config, inputs_dict @require_tf class TFSegformerModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( (TFSegformerModel, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation) if is_tf_available() else () ) test_head_masking = False test_onnx = False test_pruning = False test_resize_embeddings = False def setUp(self): self.model_tester = TFSegformerModelTester(self) self.config_tester = TFSegformerConfigTester(self, config_class=SegformerConfig, has_text_modality=False) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip("SegFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods") def test_model_common_attributes(self): pass @unittest.skip("Test was written for TF 1.x and isn't really relevant here") def test_compile_tf_model(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions expected_num_attentions = sum(self.model_tester.depths) self.assertEqual(len(attentions), expected_num_attentions) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len], ) # verify the last attentions (last block, last layer) expected_seq_len = (self.model_tester.image_size // 32) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2 self.assertListEqual( list(attentions[-1].shape[-3:]), [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_encoder_blocks self.assertEqual(len(hidden_states), expected_num_layers) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:]), [ self.model_tester.hidden_sizes[0], self.model_tester.image_size // 4, self.model_tester.image_size // 4, ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( all(tf.equal(tuple_object, dict_object)), msg=( "Tuple and dict output are not equal. Difference:" f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}" ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) # todo: incorporate label support for semantic segmentation in `test_modeling_tf_common.py`. @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) def test_dataset_conversion(self): super().test_dataset_conversion() def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1): self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol)) @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) def test_keras_fit(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # Since `TFSegformerModel` cannot operate with the default `fit()` method. if model_class.__name__ != "TFSegformerModel": model = model_class(config) if getattr(model, "hf_compute_loss", None): super().test_keras_fit() def test_loss_computation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def apply(model): for_segmentation = True if model_class.__name__ == "TFSegformerForSemanticSegmentation" else False # The number of elements in the loss should be the same as the number of elements in the label _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit( for_segmentation=for_segmentation ) added_label = prepared_for_class[ sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0] ] loss_size = tf.size(added_label) # Test that model correctly compute the loss with kwargs possible_input_names = {"input_ids", "pixel_values", "input_features"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) loss = model(model_input, **prepared_for_class)[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": # Semantic segmentation loss is computed similarly as # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210. self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a dict _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit( for_segmentation=for_segmentation ) loss = model(**prepared_for_class)[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a tuple label_keys = prepared_for_class.keys() - inputs_dict.keys() signature = inspect.signature(model.call).parameters signature_names = list(signature.keys()) # Create a dictionary holding the location of the tensors in the tuple tuple_index_mapping = {0: input_name} for label_key in label_keys: label_key_index = signature_names.index(label_key) tuple_index_mapping[label_key_index] = label_key sorted_tuple_index_mapping = sorted(tuple_index_mapping.items()) # Initialize a list with their default values, update the values and convert to a tuple list_input = [] for name in signature_names: if name != "kwargs": list_input.append(signature[name].default) for index, value in sorted_tuple_index_mapping: list_input[index] = prepared_for_class[value] tuple_input = tuple(list_input) # Send to model loss = model(tuple_input[:-1])[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) for model_class in self.all_model_classes: # Since `TFSegformerModel` won't have labels against which we # could compute loss. if model_class.__name__ != "TFSegformerModel": model = model_class(config) apply(model) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as semseg models tend to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) @slow def test_model_from_pretrained(self): for model_name in TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFSegformerModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_tf class TFSegformerModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_segmentation_ade(self): # only resize + normalize feature_extractor = SegformerFeatureExtractor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") image = prepare_img() encoded_inputs = feature_extractor(images=image, return_tensors="tf") pixel_values = encoded_inputs.pixel_values outputs = model(pixel_values, training=False) expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant( [ [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]], [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]], [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]], ] ) tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-4) @slow def test_inference_image_segmentation_city(self): # only resize + normalize feature_extractor = SegformerFeatureExtractor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = TFSegformerForSemanticSegmentation.from_pretrained( "nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ) image = prepare_img() encoded_inputs = feature_extractor(images=image, return_tensors="tf") pixel_values = encoded_inputs.pixel_values outputs = model(pixel_values, training=False) expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant( [ [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]], [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]], [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]], ] ) tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-1)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/legacy/seq2seq/download_wmt.py

#!/usr/bin/env python # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path import fire from tqdm import tqdm def download_wmt_dataset(src_lang="ro", tgt_lang="en", dataset="wmt16", save_dir=None) -> None: """Download a dataset using the datasets package and save it to the format expected by finetune.py Format of save_dir: train.source, train.target, val.source, val.target, test.source, test.target. Args: src_lang: <str> source language tgt_lang: <str> target language dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import datasets; print([d.id for d in datasets.list_datasets() if "wmt" in d.id])` save_dir: <str>, where to save the datasets, defaults to f'{dataset}-{src_lang}-{tgt_lang}' Usage: >>> download_wmt_dataset('ro', 'en', dataset='wmt16') # saves to wmt16-ro-en """ try: import datasets except (ModuleNotFoundError, ImportError): raise ImportError("run pip install datasets") pair = f"{src_lang}-{tgt_lang}" print(f"Converting {dataset}-{pair}") ds = datasets.load_dataset(dataset, pair) if save_dir is None: save_dir = f"{dataset}-{pair}" save_dir = Path(save_dir) save_dir.mkdir(exist_ok=True) for split in ds.keys(): print(f"Splitting {split} with {ds[split].num_rows} records") # to save to val.source, val.target like summary datasets fn = "val" if split == "validation" else split src_path = save_dir.joinpath(f"{fn}.source") tgt_path = save_dir.joinpath(f"{fn}.target") src_fp = src_path.open("w+") tgt_fp = tgt_path.open("w+") # reader is the bottleneck so writing one record at a time doesn't slow things down for x in tqdm(ds[split]): ex = x["translation"] src_fp.write(ex[src_lang] + "\n") tgt_fp.write(ex[tgt_lang] + "\n") print(f"Saved {dataset} dataset to {save_dir}") if __name__ == "__main__": fire.Fire(download_wmt_dataset)

#!/usr/bin/env python # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from pathlib import Path import fire from tqdm import tqdm def download_wmt_dataset(src_lang="ro", tgt_lang="en", dataset="wmt16", save_dir=None) -> None: """Download a dataset using the datasets package and save it to the format expected by finetune.py Format of save_dir: train.source, train.target, val.source, val.target, test.source, test.target. Args: src_lang: <str> source language tgt_lang: <str> target language dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import datasets; print([d.id for d in datasets.list_datasets() if "wmt" in d.id])` save_dir: <str>, where to save the datasets, defaults to f'{dataset}-{src_lang}-{tgt_lang}' Usage: >>> download_wmt_dataset('ro', 'en', dataset='wmt16') # saves to wmt16-ro-en """ try: import datasets except (ModuleNotFoundError, ImportError): raise ImportError("run pip install datasets") pair = f"{src_lang}-{tgt_lang}" print(f"Converting {dataset}-{pair}") ds = datasets.load_dataset(dataset, pair) if save_dir is None: save_dir = f"{dataset}-{pair}" save_dir = Path(save_dir) save_dir.mkdir(exist_ok=True) for split in ds.keys(): print(f"Splitting {split} with {ds[split].num_rows} records") # to save to val.source, val.target like summary datasets fn = "val" if split == "validation" else split src_path = save_dir.joinpath(f"{fn}.source") tgt_path = save_dir.joinpath(f"{fn}.target") src_fp = src_path.open("w+") tgt_fp = tgt_path.open("w+") # reader is the bottleneck so writing one record at a time doesn't slow things down for x in tqdm(ds[split]): ex = x["translation"] src_fp.write(ex[src_lang] + "\n") tgt_fp.write(ex[tgt_lang] + "\n") print(f"Saved {dataset} dataset to {save_dir}") if __name__ == "__main__": fire.Fire(download_wmt_dataset)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/longt5/convert_longt5x_checkpoint_to_flax.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert T5/LongT5X checkpoints from the original repository to JAX/FLAX model. This script is an extension of 'src/transformers/models/t5/convert_t5x_checkpoint_to_flax. """ import argparse from t5x import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path): config = AutoConfig.from_pretrained(config_name) flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=config) t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path) split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"] if config.model_type == "t5": encoder_attn_name = "SelfAttention" if config.model_type == "longt5" and config.encoder_attention_type == "local": encoder_attn_name = "LocalSelfAttention" elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": encoder_attn_name = "TransientGlobalSelfAttention" else: raise ValueError( "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`" " attribute with a value from ['local', 'transient-global]." ) # Encoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"] # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_global_layer_norm = t5x_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"] # Layer Normalization t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"] if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_encoder_layer_block = flax_model.params["encoder"]["block"][str(layer_index)]["layer"] flax_model_encoder_layer_block["0"][encoder_attn_name]["k"]["kernel"] = t5x_attention_key flax_model_encoder_layer_block["0"][encoder_attn_name]["o"]["kernel"] = t5x_attention_out flax_model_encoder_layer_block["0"][encoder_attn_name]["q"]["kernel"] = t5x_attention_query flax_model_encoder_layer_block["0"][encoder_attn_name]["v"]["kernel"] = t5x_attention_value flax_model_encoder_layer_block["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": flax_model_encoder_layer_block["0"][encoder_attn_name]["global_input_layer_norm"][ "weight" ] = t5x_global_layer_norm if split_mlp_wi: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_encoder_layer_block["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_encoder_layer_block["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm flax_model.params["encoder"]["block"][str(layer_index)]["layer"] = flax_model_encoder_layer_block # Only for layer 0: t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["relative_attention_bias"][ "embedding" ] = t5x_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_encoder_global_rel_embedding = t5x_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["global_relative_attention_bias"][ "embedding" ] = t5x_encoder_global_rel_embedding # Assigning t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"] flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm # Decoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"] # Layer Normalization t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][ "scale" ] # Encoder-Decoder-Attention t5x_enc_dec_attention_module = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"] t5x_enc_dec_attention_key = t5x_enc_dec_attention_module["key"]["kernel"] t5x_enc_dec_attention_out = t5x_enc_dec_attention_module["out"]["kernel"] t5x_enc_dec_attention_query = t5x_enc_dec_attention_module["query"]["kernel"] t5x_enc_dec_attention_value = t5x_enc_dec_attention_module["value"]["kernel"] # Layer Normalization t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"] # MLP if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_decoder_layer_block = flax_model.params["decoder"]["block"][str(layer_index)]["layer"] flax_model_decoder_layer_block["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key flax_model_decoder_layer_block["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out flax_model_decoder_layer_block["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query flax_model_decoder_layer_block["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value flax_model_decoder_layer_block["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm flax_model_decoder_layer_block["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key flax_model_decoder_layer_block["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out flax_model_decoder_layer_block["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query flax_model_decoder_layer_block["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value flax_model_decoder_layer_block["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm if split_mlp_wi: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_decoder_layer_block["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_decoder_layer_block["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm flax_model.params["decoder"]["block"][str(layer_index)]["layer"] = flax_model_decoder_layer_block # Decoder Normalization tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"] flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm # Only for layer 0: t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][ "embedding" ] = t5x_decoder_rel_embedding # Token Embeddings tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"] flax_model.params["shared"]["embedding"] = tx5_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in t5x_model["target"]["decoder"]: flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"] flax_model.save_pretrained(flax_dump_folder_path) print("T5X Model was sucessfully converted!") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint." ) parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.") parser.add_argument( "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model." ) args = parser.parse_args() convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert T5/LongT5X checkpoints from the original repository to JAX/FLAX model. This script is an extension of 'src/transformers/models/t5/convert_t5x_checkpoint_to_flax. """ import argparse from t5x import checkpoints from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path): config = AutoConfig.from_pretrained(config_name) flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=config) t5x_model = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path) split_mlp_wi = "wi_0" in t5x_model["target"]["encoder"]["layers_0"]["mlp"] if config.model_type == "t5": encoder_attn_name = "SelfAttention" if config.model_type == "longt5" and config.encoder_attention_type == "local": encoder_attn_name = "LocalSelfAttention" elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global": encoder_attn_name = "TransientGlobalSelfAttention" else: raise ValueError( "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`" " attribute with a value from ['local', 'transient-global]." ) # Encoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"] # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_global_layer_norm = t5x_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"] # Layer Normalization t5x_attention_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"] if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization t5x_mlp_layer_norm = t5x_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_encoder_layer_block = flax_model.params["encoder"]["block"][str(layer_index)]["layer"] flax_model_encoder_layer_block["0"][encoder_attn_name]["k"]["kernel"] = t5x_attention_key flax_model_encoder_layer_block["0"][encoder_attn_name]["o"]["kernel"] = t5x_attention_out flax_model_encoder_layer_block["0"][encoder_attn_name]["q"]["kernel"] = t5x_attention_query flax_model_encoder_layer_block["0"][encoder_attn_name]["v"]["kernel"] = t5x_attention_value flax_model_encoder_layer_block["0"]["layer_norm"]["weight"] = t5x_attention_layer_norm # Global input layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": flax_model_encoder_layer_block["0"][encoder_attn_name]["global_input_layer_norm"][ "weight" ] = t5x_global_layer_norm if split_mlp_wi: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_encoder_layer_block["1"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_encoder_layer_block["1"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_encoder_layer_block["1"]["layer_norm"]["weight"] = t5x_mlp_layer_norm flax_model.params["encoder"]["block"][str(layer_index)]["layer"] = flax_model_encoder_layer_block # Only for layer 0: t5x_encoder_rel_embedding = t5x_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["relative_attention_bias"][ "embedding" ] = t5x_encoder_rel_embedding # Side/global relative position_bias + layer norm if config.model_type == "longt5" and config.encoder_attention_type == "transient-global": t5x_encoder_global_rel_embedding = t5x_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T flax_model.params["encoder"]["block"]["0"]["layer"]["0"][encoder_attn_name]["global_relative_attention_bias"][ "embedding" ] = t5x_encoder_global_rel_embedding # Assigning t5x_encoder_norm = t5x_model["target"]["encoder"]["encoder_norm"]["scale"] flax_model.params["encoder"]["final_layer_norm"]["weight"] = t5x_encoder_norm # Decoder for layer_index in range(config.num_layers): layer_name = f"layers_{str(layer_index)}" # Self-Attention t5x_attention_key = t5x_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"] t5x_attention_out = t5x_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"] t5x_attention_query = t5x_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"] t5x_attention_value = t5x_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"] # Layer Normalization t5x_pre_attention_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][ "scale" ] # Encoder-Decoder-Attention t5x_enc_dec_attention_module = t5x_model["target"]["decoder"][layer_name]["encoder_decoder_attention"] t5x_enc_dec_attention_key = t5x_enc_dec_attention_module["key"]["kernel"] t5x_enc_dec_attention_out = t5x_enc_dec_attention_module["out"]["kernel"] t5x_enc_dec_attention_query = t5x_enc_dec_attention_module["query"]["kernel"] t5x_enc_dec_attention_value = t5x_enc_dec_attention_module["value"]["kernel"] # Layer Normalization t5x_cross_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"] # MLP if split_mlp_wi: t5x_mlp_wi_0 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"] t5x_mlp_wi_1 = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"] else: t5x_mlp_wi = t5x_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"] t5x_mlp_wo = t5x_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"] # Layer Normalization tx5_mlp_layer_norm = t5x_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"] # Assigning flax_model_decoder_layer_block = flax_model.params["decoder"]["block"][str(layer_index)]["layer"] flax_model_decoder_layer_block["0"]["SelfAttention"]["k"]["kernel"] = t5x_attention_key flax_model_decoder_layer_block["0"]["SelfAttention"]["o"]["kernel"] = t5x_attention_out flax_model_decoder_layer_block["0"]["SelfAttention"]["q"]["kernel"] = t5x_attention_query flax_model_decoder_layer_block["0"]["SelfAttention"]["v"]["kernel"] = t5x_attention_value flax_model_decoder_layer_block["0"]["layer_norm"]["weight"] = t5x_pre_attention_layer_norm flax_model_decoder_layer_block["1"]["EncDecAttention"]["k"]["kernel"] = t5x_enc_dec_attention_key flax_model_decoder_layer_block["1"]["EncDecAttention"]["o"]["kernel"] = t5x_enc_dec_attention_out flax_model_decoder_layer_block["1"]["EncDecAttention"]["q"]["kernel"] = t5x_enc_dec_attention_query flax_model_decoder_layer_block["1"]["EncDecAttention"]["v"]["kernel"] = t5x_enc_dec_attention_value flax_model_decoder_layer_block["1"]["layer_norm"]["weight"] = t5x_cross_layer_norm if split_mlp_wi: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_0"]["kernel"] = t5x_mlp_wi_0 flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi_1"]["kernel"] = t5x_mlp_wi_1 else: flax_model_decoder_layer_block["2"]["DenseReluDense"]["wi"]["kernel"] = t5x_mlp_wi flax_model_decoder_layer_block["2"]["DenseReluDense"]["wo"]["kernel"] = t5x_mlp_wo flax_model_decoder_layer_block["2"]["layer_norm"]["weight"] = tx5_mlp_layer_norm flax_model.params["decoder"]["block"][str(layer_index)]["layer"] = flax_model_decoder_layer_block # Decoder Normalization tx5_decoder_norm = t5x_model["target"]["decoder"]["decoder_norm"]["scale"] flax_model.params["decoder"]["final_layer_norm"]["weight"] = tx5_decoder_norm # Only for layer 0: t5x_decoder_rel_embedding = t5x_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T flax_model.params["decoder"]["block"]["0"]["layer"]["0"]["SelfAttention"]["relative_attention_bias"][ "embedding" ] = t5x_decoder_rel_embedding # Token Embeddings tx5_token_embeddings = t5x_model["target"]["token_embedder"]["embedding"] flax_model.params["shared"]["embedding"] = tx5_token_embeddings # LM Head (only in v1.1 and LongT5 checkpoints) if "logits_dense" in t5x_model["target"]["decoder"]: flax_model.params["lm_head"]["kernel"] = t5x_model["target"]["decoder"]["logits_dense"]["kernel"] flax_model.save_pretrained(flax_dump_folder_path) print("T5X Model was sucessfully converted!") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint." ) parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.") parser.add_argument( "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model." ) args = parser.parse_args() convert_t5x_checkpoint_to_flax(args.t5x_checkpoint_path, args.config_name, args.flax_dump_folder_path)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/auto/test_modeling_tf_auto.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import tempfile import unittest from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tensorflow_probability, require_tf, slow, ) from ..bert.test_modeling_bert import BertModelTester if is_tf_available(): from transformers import ( TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFFunnelBaseModel, TFFunnelModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, TFTapasForQuestionAnswering, ) from transformers.models.auto.modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, ) from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_tf_available(): class TFNewModel(TFBertModel): config_class = NewModelConfig @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModel.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) @slow def test_model_for_pretraining_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForPreTraining) @slow def test_model_for_causal_lm(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = TFAutoModelForCausalLM.from_pretrained(model_name) model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFGPT2LMHeadModel) @slow def test_lmhead_model_from_pretrained(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelWithLMHead.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_masked_lm(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFT5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) @slow def test_question_answering_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForQuestionAnswering.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForQuestionAnswering) @slow @require_tensorflow_probability def test_table_question_answering_model_from_pretrained(self): for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TFTapasForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_pretrained_with_tuple_values(self): # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, TFFunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = TFAutoModel.from_config(config) self.assertIsInstance(model, TFFunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = TFAutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, TFFunnelBaseModel) def test_new_model_registration(self): try: AutoConfig.register("new-model", NewModelConfig) auto_classes = [ TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSequenceClassification, TFAutoModelForTokenClassification, ] for auto_class in auto_classes: with self.subTest(auto_class.__name__): # Wrong config class will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFNewModel) auto_class.register(NewModelConfig, TFNewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFBertModel) # Now that the config is registered, it can be used as any other config with the auto-API tiny_config = BertModelTester(self).get_config() config = NewModelConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, TFNewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, TFNewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( TF_MODEL_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = TFAutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_model_file_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named tf_model.h5", ): _ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_pt_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"): _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0) # With a sharded checkpoint _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0)

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import tempfile import unittest from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tensorflow_probability, require_tf, slow, ) from ..bert.test_modeling_bert import BertModelTester if is_tf_available(): from transformers import ( TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFFunnelBaseModel, TFFunnelModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, TFTapasForQuestionAnswering, ) from transformers.models.auto.modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, ) from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_tf_available(): class TFNewModel(TFBertModel): config_class = NewModelConfig @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModel.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) @slow def test_model_for_pretraining_from_pretrained(self): model_name = "bert-base-cased" config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForPreTraining) @slow def test_model_for_causal_lm(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = TFAutoModelForCausalLM.from_pretrained(model_name) model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFGPT2LMHeadModel) @slow def test_lmhead_model_from_pretrained(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelWithLMHead.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_masked_lm(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFT5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) @slow def test_question_answering_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForQuestionAnswering.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForQuestionAnswering) @slow @require_tensorflow_probability def test_table_question_answering_model_from_pretrained(self): for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TFTapasForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_pretrained_with_tuple_values(self): # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, TFFunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = TFAutoModel.from_config(config) self.assertIsInstance(model, TFFunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = TFAutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, TFFunnelBaseModel) def test_new_model_registration(self): try: AutoConfig.register("new-model", NewModelConfig) auto_classes = [ TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSequenceClassification, TFAutoModelForTokenClassification, ] for auto_class in auto_classes: with self.subTest(auto_class.__name__): # Wrong config class will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFNewModel) auto_class.register(NewModelConfig, TFNewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFBertModel) # Now that the config is registered, it can be used as any other config with the auto-API tiny_config = BertModelTester(self).get_config() config = NewModelConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, TFNewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, TFNewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( TF_MODEL_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = TFAutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_model_file_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named tf_model.h5", ): _ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_pt_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"): _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0) # With a sharded checkpoint _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/nystromformer/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/mbart/modeling_flax_mbart.py

# coding=utf-8 # Copyright 2021, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax MBart model.""" import math import random from functools import partial from typing import Callable, Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput, FlaxSeq2SeqModelOutput, FlaxSeq2SeqQuestionAnsweringModelOutput, FlaxSeq2SeqSequenceClassifierOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_mbart import MBartConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/mbart-large-cc25" _CONFIG_FOR_DOC = "MBartConfig" _TOKENIZER_FOR_DOC = "MBartTokenizer" MBART_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`MBartConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ MBART_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ MBART_ENCODE_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ MBART_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int) -> jnp.ndarray: """ Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not have a single `decoder_start_token_id` in contrast to other Bart-like models. """ prev_output_tokens = np.array(input_ids).copy() if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` prev_output_tokens = np.where(prev_output_tokens == -100, pad_token_id, input_ids) index_of_eos = (np.where(prev_output_tokens != pad_token_id, 1, 0).sum(axis=-1) - 1).reshape(-1, 1) decoder_start_tokens = np.array( [prev_output_tokens[i, eos_idx] for i, eos_idx in enumerate(index_of_eos)], dtype=np.int32 ).squeeze() prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].copy() prev_output_tokens[:, 0] = decoder_start_tokens return prev_output_tokens # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->MBart class FlaxMBartAttention(nn.Module): config: MBartConfig embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxMBartEncoderLayer(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxMBartAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.encoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.fc1 = nn.Dense( self.config.encoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->MBart class FlaxMBartEncoderLayerCollection(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxMBartEncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers) ] self.layerdrop = self.config.encoder_layerdrop def __call__( self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for encoder_layer in self.layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions, deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxMBartDecoderLayer(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxMBartAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.encoder_attn = FlaxMBartAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.fc1 = nn.Dense( self.config.decoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->MBart class FlaxMBartDecoderLayerCollection(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxMBartDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers) ] self.layerdrop = self.config.decoder_layerdrop def __call__( self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartClassificationHead with Bart->MBart class FlaxMBartClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" config: MBartConfig inner_dim: int num_classes: int pooler_dropout: float dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense( self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.dropout = nn.Dropout(rate=self.pooler_dropout) self.out_proj = nn.Dense( self.num_classes, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__(self, hidden_states: jnp.ndarray, deterministic: bool): hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.dense(hidden_states) hidden_states = jnp.tanh(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.out_proj(hidden_states) return hidden_states class FlaxMBartEncoder(nn.Module): config: MBartConfig embed_tokens: nn.Embed dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_source_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0 # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = FlaxMBartEncoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_states = outputs[0] last_hidden_states = self.layer_norm(last_hidden_states) # update the last element in `hidden_states` after applying `layernorm` above hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_states,) if not return_dict: outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=last_hidden_states, hidden_states=hidden_states, attentions=outputs.attentions, ) class FlaxMBartDecoder(nn.Module): config: MBartConfig embed_tokens: nn.Embed dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = FlaxMBartDecoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale # embed positions positions = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_states = outputs[0] last_hidden_states = self.layer_norm(last_hidden_states) # update the last element in `hidden_states` after applying `layernorm` above hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_states,) if not return_dict: outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=last_hidden_states, hidden_states=hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModule with Bart->MBart class FlaxMBartModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.shared = nn.Embed( self.config.vocab_size, self.config.d_model, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.encoder = FlaxMBartEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared) self.decoder = FlaxMBartDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared) def _get_encoder_module(self): return self.encoder def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class FlaxMBartPreTrainedModel(FlaxPreTrainedModel): config_class = MBartConfig base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: MBartConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") # make sure initialization pass will work for FlaxMBartForSequenceClassificationModule input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id) attention_mask = jnp.ones_like(input_ids) decoder_input_ids = input_ids decoder_attention_mask = jnp.ones_like(input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->MBart def init_cache(self, batch_size, max_length, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings(MBART_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MBartConfig) def encode( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") >>> encoder_outputs = model.encode(**inputs) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs): encode_module = module._get_encoder_module() return encode_module(input_ids, attention_mask, position_ids, **kwargs) return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MBartConfig) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> last_decoder_hidden_states = outputs.last_hidden_state ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxMBartAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING) def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # prepare decoder inputs if decoder_input_ids is None: decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) @add_start_docstrings( "The bare MBart Model transformer outputting raw hidden-states without any specific head on top.", MBART_START_DOCSTRING, ) class FlaxMBartModel(FlaxMBartPreTrainedModel): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation module_class = FlaxMBartModule append_call_sample_docstring( FlaxMBartModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForConditionalGenerationModule with Bart->MBart class FlaxMBartForConditionalGenerationModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros def setup(self): self.model = FlaxMBartModule(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.model.shared.num_embeddings, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings)) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["shared"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) lm_logits += jax.lax.stop_gradient(self.final_logits_bias.astype(self.dtype)) if not return_dict: output = (lm_logits,) + outputs[1:] return output return FlaxSeq2SeqLMOutput( logits=lm_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( "The MMBart Model with a language modeling head. Can be used for summarization.", MBART_START_DOCSTRING ) class FlaxMBartForConditionalGeneration(FlaxMBartPreTrainedModel): module_class = FlaxMBartForConditionalGenerationModule dtype: jnp.dtype = jnp.float32 @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MBartConfig) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxMBartAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() outputs = decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = module.model.variables["params"]["shared"]["embedding"] lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = module.lm_head(hidden_states) lm_logits += module.final_logits_bias.astype(self.dtype) return lm_logits, outputs outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) if past_key_values is None: lm_logits, decoder_outputs = outputs else: (lm_logits, decoder_outputs), past = outputs if return_dict: outputs = FlaxCausalLMOutputWithCrossAttentions( logits=lm_logits, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, ) else: outputs = (lm_logits,) + decoder_outputs[1:] # add updated cache to model output if past_key_values is not None and return_dict: outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def prepare_inputs_for_generation( self, decoder_input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None, decoder_attention_mask: Optional[jnp.DeviceArray] = None, encoder_outputs=None, **kwargs ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "encoder_attention_mask": attention_mask, "decoder_attention_mask": extended_attention_mask, "decoder_position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING = r""" Returns: Summarization example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration, MBartConfig >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> ARTICLE_TO_SUMMARIZE = "Meine Freunde sind cool, aber sie essen zu viel Kuchen." >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np") >>> # Generate Summary >>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5).sequences >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)) ``` Mask filling example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> # de_DE is the language symbol id <LID> for German >>> TXT = "</s> Meine Freunde sind <mask> nett aber sie essen zu viel Kuchen. </s> de_DE" >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="np")["input_ids"] >>> logits = model(input_ids).logits >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero()[0].item() >>> probs = logits[0, masked_index].softmax(dim=0) >>> values, predictions = probs.topk(5) >>> tokenizer.decode(predictions).split() ``` """ overwrite_call_docstring( FlaxMBartForConditionalGeneration, MBART_INPUTS_DOCSTRING + FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING ) append_replace_return_docstrings( FlaxMBartForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForSequenceClassificationModule with Bart->MBart class FlaxMBartForSequenceClassificationModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 num_labels: Optional[int] = None def setup(self): self.model = FlaxMBartModule(config=self.config, dtype=self.dtype) self.classification_head = FlaxMBartClassificationHead( config=self.config, inner_dim=self.config.d_model, num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels, pooler_dropout=self.config.classifier_dropout, ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] # last hidden state eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0) # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer: if len(jnp.unique(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.") if any(eos_mask.sum(1) == 0): raise ValueError("There are missing <eos> tokens in input_ids") # Ensure to keep 1 only for the last <eos> token for each example eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6 eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0) sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1) logits = self.classification_head(sentence_representation, deterministic=deterministic) if not return_dict: output = (logits,) + outputs[1:] return output return FlaxSeq2SeqSequenceClassifierOutput( logits=logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ MBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MBART_START_DOCSTRING, ) class FlaxMBartForSequenceClassification(FlaxMBartPreTrainedModel): module_class = FlaxMBartForSequenceClassificationModule dtype = jnp.float32 append_call_sample_docstring( FlaxMBartForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqSequenceClassifierOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForQuestionAnsweringModule with Bart->MBart class FlaxMBartForQuestionAnsweringModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 num_labels = 2 def setup(self): self.model = FlaxMBartModule(config=self.config, dtype=self.dtype) self.qa_outputs = nn.Dense( self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: output = (start_logits, end_logits) + outputs[1:] return output return FlaxSeq2SeqQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ MBart Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, MBART_START_DOCSTRING, ) class FlaxMBartForQuestionAnswering(FlaxMBartPreTrainedModel): module_class = FlaxMBartForQuestionAnsweringModule dtype = jnp.float32 append_call_sample_docstring( FlaxMBartForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, )

# coding=utf-8 # Copyright 2021, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax MBart model.""" import math import random from functools import partial from typing import Callable, Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput, FlaxSeq2SeqModelOutput, FlaxSeq2SeqQuestionAnsweringModelOutput, FlaxSeq2SeqSequenceClassifierOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings from .configuration_mbart import MBartConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/mbart-large-cc25" _CONFIG_FOR_DOC = "MBartConfig" _TOKENIZER_FOR_DOC = "MBartTokenizer" MBART_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`MBartConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ MBART_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ MBART_ENCODE_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ MBART_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int) -> jnp.ndarray: """ Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not have a single `decoder_start_token_id` in contrast to other Bart-like models. """ prev_output_tokens = np.array(input_ids).copy() if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.") # replace possible -100 values in labels by `pad_token_id` prev_output_tokens = np.where(prev_output_tokens == -100, pad_token_id, input_ids) index_of_eos = (np.where(prev_output_tokens != pad_token_id, 1, 0).sum(axis=-1) - 1).reshape(-1, 1) decoder_start_tokens = np.array( [prev_output_tokens[i, eos_idx] for i, eos_idx in enumerate(index_of_eos)], dtype=np.int32 ).squeeze() prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].copy() prev_output_tokens[:, 0] = decoder_start_tokens return prev_output_tokens # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->MBart class FlaxMBartAttention(nn.Module): config: MBartConfig embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxMBartEncoderLayer(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxMBartAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.encoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.fc1 = nn.Dense( self.config.encoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->MBart class FlaxMBartEncoderLayerCollection(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxMBartEncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers) ] self.layerdrop = self.config.encoder_layerdrop def __call__( self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for encoder_layer in self.layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions, deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxMBartDecoderLayer(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxMBartAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.encoder_attn = FlaxMBartAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.fc1 = nn.Dense( self.config.decoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->MBart class FlaxMBartDecoderLayerCollection(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxMBartDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers) ] self.layerdrop = self.config.decoder_layerdrop def __call__( self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartClassificationHead with Bart->MBart class FlaxMBartClassificationHead(nn.Module): """Head for sentence-level classification tasks.""" config: MBartConfig inner_dim: int num_classes: int pooler_dropout: float dtype: jnp.dtype = jnp.float32 def setup(self): self.dense = nn.Dense( self.inner_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.dropout = nn.Dropout(rate=self.pooler_dropout) self.out_proj = nn.Dense( self.num_classes, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__(self, hidden_states: jnp.ndarray, deterministic: bool): hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.dense(hidden_states) hidden_states = jnp.tanh(hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = self.out_proj(hidden_states) return hidden_states class FlaxMBartEncoder(nn.Module): config: MBartConfig embed_tokens: nn.Embed dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_source_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0 # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = FlaxMBartEncoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_states = outputs[0] last_hidden_states = self.layer_norm(last_hidden_states) # update the last element in `hidden_states` after applying `layernorm` above hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_states,) if not return_dict: outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=last_hidden_states, hidden_states=hidden_states, attentions=outputs.attentions, ) class FlaxMBartDecoder(nn.Module): config: MBartConfig embed_tokens: nn.Embed dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 # MBart is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = nn.Embed( self.config.max_position_embeddings + self.offset, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = FlaxMBartDecoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale # embed positions positions = self.embed_positions(position_ids + self.offset) hidden_states = inputs_embeds + positions hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_states = outputs[0] last_hidden_states = self.layer_norm(last_hidden_states) # update the last element in `hidden_states` after applying `layernorm` above hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_states,) if not return_dict: outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=last_hidden_states, hidden_states=hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModule with Bart->MBart class FlaxMBartModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.shared = nn.Embed( self.config.vocab_size, self.config.d_model, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.encoder = FlaxMBartEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared) self.decoder = FlaxMBartDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared) def _get_encoder_module(self): return self.encoder def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class FlaxMBartPreTrainedModel(FlaxPreTrainedModel): config_class = MBartConfig base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: MBartConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") # make sure initialization pass will work for FlaxMBartForSequenceClassificationModule input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id) attention_mask = jnp.ones_like(input_ids) decoder_input_ids = input_ids decoder_attention_mask = jnp.ones_like(input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, )["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartPreTrainedModel.init_cache with Bart->MBart def init_cache(self, batch_size, max_length, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings(MBART_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=MBartConfig) def encode( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") >>> encoder_outputs = model.encode(**inputs) ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs): encode_module = module._get_encoder_module() return encode_module(input_ids, attention_mask, position_ids, **kwargs) return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=MBartConfig) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> last_decoder_hidden_states = outputs.last_hidden_state ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxMBartAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs @add_start_docstrings_to_model_forward(MBART_INPUTS_DOCSTRING) def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # prepare decoder inputs if decoder_input_ids is None: decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) @add_start_docstrings( "The bare MBart Model transformer outputting raw hidden-states without any specific head on top.", MBART_START_DOCSTRING, ) class FlaxMBartModel(FlaxMBartPreTrainedModel): config: MBartConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation module_class = FlaxMBartModule append_call_sample_docstring( FlaxMBartModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForConditionalGenerationModule with Bart->MBart class FlaxMBartForConditionalGenerationModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros def setup(self): self.model = FlaxMBartModule(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.model.shared.num_embeddings, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings)) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["shared"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) lm_logits += jax.lax.stop_gradient(self.final_logits_bias.astype(self.dtype)) if not return_dict: output = (lm_logits,) + outputs[1:] return output return FlaxSeq2SeqLMOutput( logits=lm_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( "The MMBart Model with a language modeling head. Can be used for summarization.", MBART_START_DOCSTRING ) class FlaxMBartForConditionalGeneration(FlaxMBartPreTrainedModel): module_class = FlaxMBartForConditionalGenerationModule dtype: jnp.dtype = jnp.float32 @add_start_docstrings(MBART_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=MBartConfig) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="jax") >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxMBartAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() outputs = decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = module.model.variables["params"]["shared"]["embedding"] lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = module.lm_head(hidden_states) lm_logits += module.final_logits_bias.astype(self.dtype) return lm_logits, outputs outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) if past_key_values is None: lm_logits, decoder_outputs = outputs else: (lm_logits, decoder_outputs), past = outputs if return_dict: outputs = FlaxCausalLMOutputWithCrossAttentions( logits=lm_logits, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, ) else: outputs = (lm_logits,) + decoder_outputs[1:] # add updated cache to model output if past_key_values is not None and return_dict: outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def prepare_inputs_for_generation( self, decoder_input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None, decoder_attention_mask: Optional[jnp.DeviceArray] = None, encoder_outputs=None, **kwargs ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "encoder_outputs": encoder_outputs, "encoder_attention_mask": attention_mask, "decoder_attention_mask": extended_attention_mask, "decoder_position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING = r""" Returns: Summarization example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration, MBartConfig >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> ARTICLE_TO_SUMMARIZE = "Meine Freunde sind cool, aber sie essen zu viel Kuchen." >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np") >>> # Generate Summary >>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=5).sequences >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)) ``` Mask filling example: ```python >>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration >>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25") >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-cc25") >>> # de_DE is the language symbol id <LID> for German >>> TXT = "</s> Meine Freunde sind <mask> nett aber sie essen zu viel Kuchen. </s> de_DE" >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="np")["input_ids"] >>> logits = model(input_ids).logits >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero()[0].item() >>> probs = logits[0, masked_index].softmax(dim=0) >>> values, predictions = probs.topk(5) >>> tokenizer.decode(predictions).split() ``` """ overwrite_call_docstring( FlaxMBartForConditionalGeneration, MBART_INPUTS_DOCSTRING + FLAX_MBART_CONDITIONAL_GENERATION_DOCSTRING ) append_replace_return_docstrings( FlaxMBartForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForSequenceClassificationModule with Bart->MBart class FlaxMBartForSequenceClassificationModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 num_labels: Optional[int] = None def setup(self): self.model = FlaxMBartModule(config=self.config, dtype=self.dtype) self.classification_head = FlaxMBartClassificationHead( config=self.config, inner_dim=self.config.d_model, num_classes=self.num_labels if self.num_labels is not None else self.config.num_labels, pooler_dropout=self.config.classifier_dropout, ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] # last hidden state eos_mask = jnp.where(input_ids == self.config.eos_token_id, 1, 0) # The first condition is necessary to overcome jax._src.errors.ConcretizationTypeError during JIT compilation if type(eos_mask) != jax.interpreters.partial_eval.DynamicJaxprTracer: if len(jnp.unique(eos_mask.sum(1))) > 1: raise ValueError("All examples must have the same number of <eos> tokens.") if any(eos_mask.sum(1) == 0): raise ValueError("There are missing <eos> tokens in input_ids") # Ensure to keep 1 only for the last <eos> token for each example eos_mask_noised = eos_mask + jnp.arange(eos_mask.shape[1]) * 1e-6 eos_mask = jnp.where(eos_mask_noised == eos_mask_noised.max(1).reshape(-1, 1), 1, 0) sentence_representation = jnp.einsum("ijk, ij -> ijk", hidden_states, eos_mask).sum(1) logits = self.classification_head(sentence_representation, deterministic=deterministic) if not return_dict: output = (logits,) + outputs[1:] return output return FlaxSeq2SeqSequenceClassifierOutput( logits=logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ MBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. """, MBART_START_DOCSTRING, ) class FlaxMBartForSequenceClassification(FlaxMBartPreTrainedModel): module_class = FlaxMBartForSequenceClassificationModule dtype = jnp.float32 append_call_sample_docstring( FlaxMBartForSequenceClassification, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqSequenceClassifierOutput, _CONFIG_FOR_DOC, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForQuestionAnsweringModule with Bart->MBart class FlaxMBartForQuestionAnsweringModule(nn.Module): config: MBartConfig dtype: jnp.dtype = jnp.float32 num_labels = 2 def setup(self): self.model = FlaxMBartModule(config=self.config, dtype=self.dtype) self.qa_outputs = nn.Dense( self.num_labels, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) sequence_output = outputs[0] logits = self.qa_outputs(sequence_output) start_logits, end_logits = jnp.split(logits, logits.shape[-1], axis=-1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) if not return_dict: output = (start_logits, end_logits) + outputs[1:] return output return FlaxSeq2SeqQuestionAnsweringModelOutput( start_logits=start_logits, end_logits=end_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( """ MBart Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). """, MBART_START_DOCSTRING, ) class FlaxMBartForQuestionAnswering(FlaxMBartPreTrainedModel): module_class = FlaxMBartForQuestionAnsweringModule dtype = jnp.float32 append_call_sample_docstring( FlaxMBartForQuestionAnswering, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqQuestionAnsweringModelOutput, _CONFIG_FOR_DOC, )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/longform-qa/eli5_utils.py

import functools import math import os # noqa: F401 from random import choice, randint from time import time import datasets # noqa: F401 import numpy as np import pandas as pd import torch import torch.utils.checkpoint as checkpoint from elasticsearch import Elasticsearch # noqa: F401 from elasticsearch.helpers import bulk, streaming_bulk # noqa: F401 from torch import nn from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler from tqdm import tqdm import faiss # noqa: F401 from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup pd.set_option("display.max_colwidth", None) ############### # Sparse index ############### def make_es_index_snippets(es_client, passages_dset, index_name="english_wiki_kilt_snippets_100w"): index_config = { "settings": { "number_of_shards": 1, "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}}, }, "mappings": { "properties": { "article_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"}, "section_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"}, "passage_text": {"type": "text", "analyzer": "standard", "similarity": "BM25"}, } }, } es_client.indices.create(index=index_name, body=index_config) number_of_docs = passages_dset.num_rows progress = tqdm(unit="docs", total=number_of_docs) successes = 0 def passage_generator(): for passage in passages_dset: yield passage # create the ES index for ok, action in streaming_bulk( client=es_client, index=index_name, actions=passage_generator(), ): progress.update(1) successes += ok print("Indexed %d documents" % (successes,)) def query_es_index(question, es_client, index_name="english_wiki_kilt_snippets_100w", n_results=10, min_length=20): q = question.lower() banned = ["how", "why", "what", "where", "which", "do", "does", "is", "?", "eli5", "eli5:"] q = " ".join([w for w in q.split() if w not in banned]) response = es_client.search( index=index_name, body={ "query": { "multi_match": { "query": q, "fields": ["article_title", "section_title", "passage_text^2"], "type": "cross_fields", } }, "size": 2 * n_results, }, ) hits = response["hits"]["hits"] support_doc = "<P> " + " <P> ".join([hit["_source"]["passage_text"] for hit in hits]) res_list = [dict([(k, hit["_source"][k]) for k in hit["_source"] if k != "passage_text"]) for hit in hits] for r, hit in zip(res_list, hits): r["passage_id"] = hit["_id"] r["score"] = hit["_score"] r["passage_text"] = hit["_source"]["passage_text"] res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results] return support_doc, res_list ############### # ELI5 retriever training ############### class ELI5DatasetQARetriver(Dataset): def __init__(self, examples_array, extra_answer_threshold=3, min_answer_length=64, training=True, n_samples=None): self.data = examples_array self.answer_thres = extra_answer_threshold self.min_length = min_answer_length self.training = training self.n_samples = self.data.num_rows if n_samples is None else n_samples def __len__(self): return self.n_samples def make_example(self, idx): example = self.data[idx] question = example["title"] if self.training: answers = [a for i, (a, sc) in enumerate(zip(example["answers"]["text"], example["answers"]["score"]))] answer_tab = choice(answers).split(" ") start_idx = randint(0, max(0, len(answer_tab) - self.min_length)) answer_span = " ".join(answer_tab[start_idx:]) else: answer_span = example["answers"]["text"][0] return (question, answer_span) def __getitem__(self, idx): return self.make_example(idx % self.data.num_rows) class RetrievalQAEmbedder(nn.Module): def __init__(self, sent_encoder, dim): super(RetrievalQAEmbedder, self).__init__() self.sent_encoder = sent_encoder self.output_dim = 128 self.project_q = nn.Linear(dim, self.output_dim, bias=False) self.project_a = nn.Linear(dim, self.output_dim, bias=False) self.ce_loss = nn.CrossEntropyLoss(reduction="mean") def embed_sentences_checkpointed(self, input_ids, attention_mask, checkpoint_batch_size=-1): # reproduces BERT forward pass with checkpointing if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: return self.sent_encoder(input_ids, attention_mask=attention_mask)[1] else: # prepare implicit variables device = input_ids.device input_shape = input_ids.size() token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) head_mask = [None] * self.sent_encoder.config.num_hidden_layers extended_attention_mask: torch.Tensor = self.sent_encoder.get_extended_attention_mask( attention_mask, input_shape ) # define function for checkpointing def partial_encode(*inputs): encoder_outputs = self.sent_encoder.encoder( inputs[0], attention_mask=inputs[1], head_mask=head_mask, ) sequence_output = encoder_outputs[0] pooled_output = self.sent_encoder.pooler(sequence_output) return pooled_output # run embedding layer on everything at once embedding_output = self.sent_encoder.embeddings( input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None ) # run encoding and pooling on one mini-batch at a time pooled_output_list = [] for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) pooled_output_list.append(pooled_output) return torch.cat(pooled_output_list, dim=0) def embed_questions(self, q_ids, q_mask, checkpoint_batch_size=-1): q_reps = self.embed_sentences_checkpointed(q_ids, q_mask, checkpoint_batch_size) return self.project_q(q_reps) def embed_answers(self, a_ids, a_mask, checkpoint_batch_size=-1): a_reps = self.embed_sentences_checkpointed(a_ids, a_mask, checkpoint_batch_size) return self.project_a(a_reps) def forward(self, q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=-1): device = q_ids.device q_reps = self.embed_questions(q_ids, q_mask, checkpoint_batch_size) a_reps = self.embed_answers(a_ids, a_mask, checkpoint_batch_size) compare_scores = torch.mm(q_reps, a_reps.t()) loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) loss = (loss_qa + loss_aq) / 2 return loss def make_qa_retriever_model(model_name="google/bert_uncased_L-8_H-512_A-8", from_file=None, device="cuda:0"): tokenizer = AutoTokenizer.from_pretrained(model_name) bert_model = AutoModel.from_pretrained(model_name).to(device) # run bert_model on a dummy batch to get output dimension d_ids = torch.LongTensor( [[bert_model.config.bos_token_id if bert_model.config.bos_token_id is not None else 1]] ).to(device) d_mask = torch.LongTensor([[1]]).to(device) sent_dim = bert_model(d_ids, attention_mask=d_mask)[1].shape[-1] qa_embedder = RetrievalQAEmbedder(bert_model, sent_dim).to(device) if from_file is not None: param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states qa_embedder.load_state_dict(param_dict["model"]) return tokenizer, qa_embedder def make_qa_retriever_batch(qa_list, tokenizer, max_len=64, device="cuda:0"): q_ls = [q for q, a in qa_list] a_ls = [a for q, a in qa_list] q_toks = tokenizer(q_ls, max_length=max_len, padding="max_length", truncation=True) q_ids, q_mask = ( torch.LongTensor(q_toks["input_ids"]).to(device), torch.LongTensor(q_toks["attention_mask"]).to(device), ) a_toks = tokenizer(a_ls, max_length=max_len, padding="max_length", truncation=True) a_ids, a_mask = ( torch.LongTensor(a_toks["input_ids"]).to(device), torch.LongTensor(a_toks["attention_mask"]).to(device), ) return (q_ids, q_mask, a_ids, a_mask) def train_qa_retriever_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0): model.train() # make iterator train_sampler = RandomSampler(dataset) model_collate_fn = functools.partial( make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() for step, batch in enumerate(epoch_iterator): q_ids, q_mask, a_ids, a_mask = batch pre_loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size) loss = pre_loss.sum() # optimizer loss.backward() optimizer.step() scheduler.step() model.zero_grad() # some printing within the epoch loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0 or step == 1: print( "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) loc_loss = 0 loc_steps = 0 def train_qa_retriever_joint_epoch(model, dataset_list, tokenizer, optimizer, scheduler, args, e=0): model.train() model_collate_fn = functools.partial( make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) # make iterator train_samplers = [RandomSampler(dataset) for dataset in dataset_list] data_loaders = [ DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) for dataset, train_sampler in zip(dataset_list, train_samplers) ] iterators = [iter(dloader) for dloader in data_loaders] joint_iter = zip(*iterators) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() for step, (batches,) in enumerate(zip(joint_iter)): for batch in batches: q_ids, q_mask, a_ids, a_mask = batch loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size) # optimizer loss.backward() optimizer.step() scheduler.step() model.zero_grad() # some printing within the epoch loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0: print( "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( e, step, len(dataset_list[0]) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) loc_loss = 0 loc_steps = 0 def evaluate_qa_retriever(model, dataset, tokenizer, args): model.eval() # make iterator eval_sampler = SequentialSampler(dataset) model_collate_fn = functools.partial( make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=eval_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) tot_loss = 0.0 with torch.no_grad(): for step, batch in enumerate(epoch_iterator): q_ids, q_mask, a_ids, a_mask = batch loss = model(q_ids, q_mask, a_ids, a_mask) tot_loss += loss.item() return tot_loss / (step + 1) def train_qa_retriever(qar_model, qar_tokenizer, qar_train_dset, qar_valid_dset, qar_args): qar_optimizer = AdamW(qar_model.parameters(), lr=qar_args.learning_rate, eps=1e-8) qar_scheduler = get_linear_schedule_with_warmup( qar_optimizer, num_warmup_steps=100, num_training_steps=(qar_args.num_epochs + 1) * math.ceil(len(qar_train_dset) / qar_args.batch_size), ) for e in range(qar_args.num_epochs): train_qa_retriever_epoch(qar_model, qar_train_dset, qar_tokenizer, qar_optimizer, qar_scheduler, qar_args, e) m_save_dict = { "model": qar_model.state_dict(), "optimizer": qar_optimizer.state_dict(), "scheduler": qar_scheduler.state_dict(), } print("Saving model {}".format(qar_args.model_save_name)) torch.save(m_save_dict, "{}_{}.pth".format(qar_args.model_save_name, e)) eval_loss = evaluate_qa_retriever(qar_model, qar_valid_dset, qar_tokenizer, qar_args) print("Evaluation loss epoch {:4d}: {:.3f}".format(e, eval_loss)) ############### # ELI5 seq2seq model training ############### class ELI5DatasetS2S(Dataset): def __init__( self, examples_array, make_doc_fun=None, extra_answer_threshold=3, document_cache=None, training=True ): self.training = training self.data = examples_array self.make_doc_function = make_doc_fun self.document_cache = {} if document_cache is None else document_cache assert not (make_doc_fun is None and document_cache is None) # make index of specific question-answer pairs from multi-answers if self.training: self.qa_id_list = [ (i, j) for i, qa in enumerate(self.data) for j, (a, sc) in enumerate(zip(qa["answers"]["text"], qa["answers"]["score"])) if j == 0 or sc >= extra_answer_threshold ] else: self.qa_id_list = [(i, 0) for i in range(self.data.num_rows)] def __len__(self): return len(self.qa_id_list) def make_example(self, idx): i, j = self.qa_id_list[idx] example = self.data[i] question = example["title"] + " " + example["selftext"] answer = example["answers"]["text"][j] q_id = example["q_id"] if self.make_doc_function is not None: self.document_cache[q_id] = self.document_cache.get(q_id, self.make_doc_function(example["title"])) document = self.document_cache[q_id] in_st = "question: {} context: {}".format( question.lower().replace(" --t--", "").strip(), document.lower().strip(), ) out_st = answer return (in_st, out_st) def __getitem__(self, idx): return self.make_example(idx) def make_qa_s2s_model(model_name="facebook/bart-large", from_file=None, device="cuda:0"): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) if from_file is not None: param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states model.load_state_dict(param_dict["model"]) return tokenizer, model def make_qa_s2s_batch(qa_list, tokenizer, max_len=64, max_a_len=360, device="cuda:0"): q_ls = [q for q, a in qa_list] a_ls = [a for q, a in qa_list] q_toks = tokenizer(q_ls, max_length=max_len, padding="max_length", truncation=True) q_ids, q_mask = ( torch.LongTensor(q_toks["input_ids"]).to(device), torch.LongTensor(q_toks["attention_mask"]).to(device), ) a_toks = tokenizer(a_ls, max_length=min(max_len, max_a_len), padding="max_length", truncation=True) a_ids, a_mask = ( torch.LongTensor(a_toks["input_ids"]).to(device), torch.LongTensor(a_toks["attention_mask"]).to(device), ) lm_labels = a_ids[:, 1:].contiguous().clone() lm_labels[a_mask[:, 1:].contiguous() == 0] = -100 model_inputs = { "input_ids": q_ids, "attention_mask": q_mask, "decoder_input_ids": a_ids[:, :-1].contiguous(), "lm_labels": lm_labels, } return model_inputs def train_qa_s2s_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0, curriculum=False): model.train() # make iterator if curriculum: train_sampler = SequentialSampler(dataset) else: train_sampler = RandomSampler(dataset) model_collate_fn = functools.partial( make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() for step, batch_inputs in enumerate(epoch_iterator): pre_loss = model(**batch_inputs)[0] loss = pre_loss.sum() / pre_loss.shape[0] loss.backward() # optimizer if step % args.backward_freq == 0: optimizer.step() scheduler.step() model.zero_grad() # some printing within the epoch loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0 or step == 1: print( "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) loc_loss = 0 loc_steps = 0 def eval_qa_s2s_epoch(model, dataset, tokenizer, args): model.eval() # make iterator train_sampler = SequentialSampler(dataset) model_collate_fn = functools.partial( make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() with torch.no_grad(): for step, batch_inputs in enumerate(epoch_iterator): pre_loss = model(**batch_inputs)[0] loss = pre_loss.sum() / pre_loss.shape[0] loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0: print( "{:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) print( "Total \t L: {:.3f} \t -- {:.3f}".format( loc_loss / loc_steps, time() - st_time, ) ) def train_qa_s2s(qa_s2s_model, qa_s2s_tokenizer, s2s_train_dset, s2s_valid_dset, s2s_args): s2s_optimizer = AdamW(qa_s2s_model.parameters(), lr=s2s_args.learning_rate, eps=1e-8) s2s_scheduler = get_linear_schedule_with_warmup( s2s_optimizer, num_warmup_steps=400, num_training_steps=(s2s_args.num_epochs + 1) * math.ceil(len(s2s_train_dset) / s2s_args.batch_size), ) for e in range(s2s_args.num_epochs): train_qa_s2s_epoch( qa_s2s_model, s2s_train_dset, qa_s2s_tokenizer, s2s_optimizer, s2s_scheduler, s2s_args, e, curriculum=(e == 0), ) m_save_dict = { "model": qa_s2s_model.state_dict(), "optimizer": s2s_optimizer.state_dict(), "scheduler": s2s_scheduler.state_dict(), } print("Saving model {}".format(s2s_args.model_save_name)) eval_qa_s2s_epoch(qa_s2s_model, s2s_valid_dset, qa_s2s_tokenizer, s2s_args) torch.save(m_save_dict, "{}_{}.pth".format(s2s_args.model_save_name, e)) # generate answer from input "question: ... context: <p> ..." def qa_s2s_generate( question_doc, qa_s2s_model, qa_s2s_tokenizer, num_answers=1, num_beams=None, min_len=64, max_len=256, do_sample=False, temp=1.0, top_p=None, top_k=None, max_input_length=512, device="cuda:0", ): model_inputs = make_qa_s2s_batch( [(question_doc, "A")], qa_s2s_tokenizer, max_input_length, device=device, ) n_beams = num_answers if num_beams is None else max(num_beams, num_answers) generated_ids = qa_s2s_model.generate( input_ids=model_inputs["input_ids"], attention_mask=model_inputs["attention_mask"], min_length=min_len, max_length=max_len, do_sample=do_sample, early_stopping=True, num_beams=1 if do_sample else n_beams, temperature=temp, top_k=top_k, top_p=top_p, eos_token_id=qa_s2s_tokenizer.eos_token_id, no_repeat_ngram_size=3, num_return_sequences=num_answers, decoder_start_token_id=qa_s2s_tokenizer.bos_token_id, ) return [qa_s2s_tokenizer.decode(ans_ids, skip_special_tokens=True).strip() for ans_ids in generated_ids] ############### # ELI5-trained retrieval model usage ############### def embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length=128, device="cuda:0"): a_toks = tokenizer(passages, max_length=max_length, padding="max_length", truncation=True) a_ids, a_mask = ( torch.LongTensor(a_toks["input_ids"]).to(device), torch.LongTensor(a_toks["attention_mask"]).to(device), ) with torch.no_grad(): a_reps = qa_embedder.embed_answers(a_ids, a_mask).cpu().type(torch.float) return a_reps.numpy() def embed_questions_for_retrieval(q_ls, tokenizer, qa_embedder, device="cuda:0"): q_toks = tokenizer(q_ls, max_length=128, padding="max_length", truncation=True) q_ids, q_mask = ( torch.LongTensor(q_toks["input_ids"]).to(device), torch.LongTensor(q_toks["attention_mask"]).to(device), ) with torch.no_grad(): q_reps = qa_embedder.embed_questions(q_ids, q_mask).cpu().type(torch.float) return q_reps.numpy() def make_qa_dense_index( qa_embedder, tokenizer, passages_dset, batch_size=512, max_length=128, index_name="kilt_passages_reps.dat", dtype="float32", device="cuda:0", ): st_time = time() fp = np.memmap(index_name, dtype=dtype, mode="w+", shape=(passages_dset.num_rows, 128)) n_batches = math.ceil(passages_dset.num_rows / batch_size) for i in range(n_batches): passages = [p for p in passages_dset[i * batch_size : (i + 1) * batch_size]["passage_text"]] reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length, device) fp[i * batch_size : (i + 1) * batch_size] = reps if i % 50 == 0: print(i, time() - st_time) def evaluate_retriever(qa_list, retriever_func, scoring_func, n_ret=10, verbose=False): total_retriever_time = 0.0 total_retriever_score = 0.0 st_time = time() for i, (question, answer) in enumerate(qa_list): r_time = time() retrieved_passages = retriever_func(question, n_ret) total_retriever_time += time() - r_time total_retriever_score += scoring_func(retrieved_passages, answer) if verbose and ((i + 1) % 500 == 0 or i <= 1): print( "{:03d}: S-{:.4f} T-{:.4f} | {:.2f}".format( i + 1, total_retriever_score / (i + 1), total_retriever_time / (i + 1), time() - st_time ) ) return {"idf_recall": total_retriever_score / (i + 1), "retrieval_time": total_retriever_time / (i + 1)} # build a support document for the question out of Wikipedia snippets def query_qa_dense_index( question, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20, device="cuda:0" ): q_rep = embed_questions_for_retrieval([question], tokenizer, qa_embedder, device=device) D, I = wiki_index.search(q_rep, 2 * n_results) res_passages = [wiki_passages[int(i)] for i in I[0]] support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results] for r, sc in zip(res_list, D[0]): r["score"] = float(sc) return support_doc, res_list def batch_query_qa_dense_index(questions, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10): q_rep = embed_questions_for_retrieval(questions, tokenizer, qa_embedder) D, I = wiki_index.search(q_rep, n_results) res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I] support_doc_lst = [ "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst ] all_res_lists = [] for res_passages, dl in zip(res_passages_lst, D): res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] for r, sc in zip(res_list, dl): r["score"] = float(sc) all_res_lists += [res_list[:]] return support_doc_lst, all_res_lists # find nearest neighbors of an answer or declarative text in Wikipedia snippets def query_qa_dense_index_nn(passage, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20): a_rep = embed_passages_for_retrieval([passage], tokenizer, qa_embedder) D, I = wiki_index.search(a_rep, 2 * n_results) res_passages = [wiki_passages[int(i)] for i in I[0]] support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results] for r, sc, i in zip(res_list, D[0], I[0]): r["passage_id"] = int(i) r["score"] = float(sc) return support_doc, res_list def batch_query_qa_dense_index_nn(passages, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10): a_reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder) D, I = wiki_index.search(a_reps, n_results) res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I] support_doc_lst = [ "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst ] all_res_lists = [] for res_passages, dl, il in zip(res_passages_lst, D, I): res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] for r, sc, i in zip(res_list, dl, il): r["passage_id"] = int(i) r["score"] = float(sc) all_res_lists += [res_list[:]] return support_doc_lst, all_res_lists

import functools import math import os # noqa: F401 from random import choice, randint from time import time import datasets # noqa: F401 import numpy as np import pandas as pd import torch import torch.utils.checkpoint as checkpoint from elasticsearch import Elasticsearch # noqa: F401 from elasticsearch.helpers import bulk, streaming_bulk # noqa: F401 from torch import nn from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler from tqdm import tqdm import faiss # noqa: F401 from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup pd.set_option("display.max_colwidth", None) ############### # Sparse index ############### def make_es_index_snippets(es_client, passages_dset, index_name="english_wiki_kilt_snippets_100w"): index_config = { "settings": { "number_of_shards": 1, "analysis": {"analyzer": {"stop_standard": {"type": "standard", " stopwords": "_english_"}}}, }, "mappings": { "properties": { "article_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"}, "section_title": {"type": "text", "analyzer": "standard", "similarity": "BM25"}, "passage_text": {"type": "text", "analyzer": "standard", "similarity": "BM25"}, } }, } es_client.indices.create(index=index_name, body=index_config) number_of_docs = passages_dset.num_rows progress = tqdm(unit="docs", total=number_of_docs) successes = 0 def passage_generator(): for passage in passages_dset: yield passage # create the ES index for ok, action in streaming_bulk( client=es_client, index=index_name, actions=passage_generator(), ): progress.update(1) successes += ok print("Indexed %d documents" % (successes,)) def query_es_index(question, es_client, index_name="english_wiki_kilt_snippets_100w", n_results=10, min_length=20): q = question.lower() banned = ["how", "why", "what", "where", "which", "do", "does", "is", "?", "eli5", "eli5:"] q = " ".join([w for w in q.split() if w not in banned]) response = es_client.search( index=index_name, body={ "query": { "multi_match": { "query": q, "fields": ["article_title", "section_title", "passage_text^2"], "type": "cross_fields", } }, "size": 2 * n_results, }, ) hits = response["hits"]["hits"] support_doc = "<P> " + " <P> ".join([hit["_source"]["passage_text"] for hit in hits]) res_list = [dict([(k, hit["_source"][k]) for k in hit["_source"] if k != "passage_text"]) for hit in hits] for r, hit in zip(res_list, hits): r["passage_id"] = hit["_id"] r["score"] = hit["_score"] r["passage_text"] = hit["_source"]["passage_text"] res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results] return support_doc, res_list ############### # ELI5 retriever training ############### class ELI5DatasetQARetriver(Dataset): def __init__(self, examples_array, extra_answer_threshold=3, min_answer_length=64, training=True, n_samples=None): self.data = examples_array self.answer_thres = extra_answer_threshold self.min_length = min_answer_length self.training = training self.n_samples = self.data.num_rows if n_samples is None else n_samples def __len__(self): return self.n_samples def make_example(self, idx): example = self.data[idx] question = example["title"] if self.training: answers = [a for i, (a, sc) in enumerate(zip(example["answers"]["text"], example["answers"]["score"]))] answer_tab = choice(answers).split(" ") start_idx = randint(0, max(0, len(answer_tab) - self.min_length)) answer_span = " ".join(answer_tab[start_idx:]) else: answer_span = example["answers"]["text"][0] return (question, answer_span) def __getitem__(self, idx): return self.make_example(idx % self.data.num_rows) class RetrievalQAEmbedder(nn.Module): def __init__(self, sent_encoder, dim): super(RetrievalQAEmbedder, self).__init__() self.sent_encoder = sent_encoder self.output_dim = 128 self.project_q = nn.Linear(dim, self.output_dim, bias=False) self.project_a = nn.Linear(dim, self.output_dim, bias=False) self.ce_loss = nn.CrossEntropyLoss(reduction="mean") def embed_sentences_checkpointed(self, input_ids, attention_mask, checkpoint_batch_size=-1): # reproduces BERT forward pass with checkpointing if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size: return self.sent_encoder(input_ids, attention_mask=attention_mask)[1] else: # prepare implicit variables device = input_ids.device input_shape = input_ids.size() token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) head_mask = [None] * self.sent_encoder.config.num_hidden_layers extended_attention_mask: torch.Tensor = self.sent_encoder.get_extended_attention_mask( attention_mask, input_shape ) # define function for checkpointing def partial_encode(*inputs): encoder_outputs = self.sent_encoder.encoder( inputs[0], attention_mask=inputs[1], head_mask=head_mask, ) sequence_output = encoder_outputs[0] pooled_output = self.sent_encoder.pooler(sequence_output) return pooled_output # run embedding layer on everything at once embedding_output = self.sent_encoder.embeddings( input_ids=input_ids, position_ids=None, token_type_ids=token_type_ids, inputs_embeds=None ) # run encoding and pooling on one mini-batch at a time pooled_output_list = [] for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)): b_embedding_output = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] b_attention_mask = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size] pooled_output = checkpoint.checkpoint(partial_encode, b_embedding_output, b_attention_mask) pooled_output_list.append(pooled_output) return torch.cat(pooled_output_list, dim=0) def embed_questions(self, q_ids, q_mask, checkpoint_batch_size=-1): q_reps = self.embed_sentences_checkpointed(q_ids, q_mask, checkpoint_batch_size) return self.project_q(q_reps) def embed_answers(self, a_ids, a_mask, checkpoint_batch_size=-1): a_reps = self.embed_sentences_checkpointed(a_ids, a_mask, checkpoint_batch_size) return self.project_a(a_reps) def forward(self, q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=-1): device = q_ids.device q_reps = self.embed_questions(q_ids, q_mask, checkpoint_batch_size) a_reps = self.embed_answers(a_ids, a_mask, checkpoint_batch_size) compare_scores = torch.mm(q_reps, a_reps.t()) loss_qa = self.ce_loss(compare_scores, torch.arange(compare_scores.shape[1]).to(device)) loss_aq = self.ce_loss(compare_scores.t(), torch.arange(compare_scores.shape[0]).to(device)) loss = (loss_qa + loss_aq) / 2 return loss def make_qa_retriever_model(model_name="google/bert_uncased_L-8_H-512_A-8", from_file=None, device="cuda:0"): tokenizer = AutoTokenizer.from_pretrained(model_name) bert_model = AutoModel.from_pretrained(model_name).to(device) # run bert_model on a dummy batch to get output dimension d_ids = torch.LongTensor( [[bert_model.config.bos_token_id if bert_model.config.bos_token_id is not None else 1]] ).to(device) d_mask = torch.LongTensor([[1]]).to(device) sent_dim = bert_model(d_ids, attention_mask=d_mask)[1].shape[-1] qa_embedder = RetrievalQAEmbedder(bert_model, sent_dim).to(device) if from_file is not None: param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states qa_embedder.load_state_dict(param_dict["model"]) return tokenizer, qa_embedder def make_qa_retriever_batch(qa_list, tokenizer, max_len=64, device="cuda:0"): q_ls = [q for q, a in qa_list] a_ls = [a for q, a in qa_list] q_toks = tokenizer(q_ls, max_length=max_len, padding="max_length", truncation=True) q_ids, q_mask = ( torch.LongTensor(q_toks["input_ids"]).to(device), torch.LongTensor(q_toks["attention_mask"]).to(device), ) a_toks = tokenizer(a_ls, max_length=max_len, padding="max_length", truncation=True) a_ids, a_mask = ( torch.LongTensor(a_toks["input_ids"]).to(device), torch.LongTensor(a_toks["attention_mask"]).to(device), ) return (q_ids, q_mask, a_ids, a_mask) def train_qa_retriever_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0): model.train() # make iterator train_sampler = RandomSampler(dataset) model_collate_fn = functools.partial( make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() for step, batch in enumerate(epoch_iterator): q_ids, q_mask, a_ids, a_mask = batch pre_loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size) loss = pre_loss.sum() # optimizer loss.backward() optimizer.step() scheduler.step() model.zero_grad() # some printing within the epoch loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0 or step == 1: print( "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) loc_loss = 0 loc_steps = 0 def train_qa_retriever_joint_epoch(model, dataset_list, tokenizer, optimizer, scheduler, args, e=0): model.train() model_collate_fn = functools.partial( make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) # make iterator train_samplers = [RandomSampler(dataset) for dataset in dataset_list] data_loaders = [ DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) for dataset, train_sampler in zip(dataset_list, train_samplers) ] iterators = [iter(dloader) for dloader in data_loaders] joint_iter = zip(*iterators) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() for step, (batches,) in enumerate(zip(joint_iter)): for batch in batches: q_ids, q_mask, a_ids, a_mask = batch loss = model(q_ids, q_mask, a_ids, a_mask, checkpoint_batch_size=args.checkpoint_batch_size) # optimizer loss.backward() optimizer.step() scheduler.step() model.zero_grad() # some printing within the epoch loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0: print( "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( e, step, len(dataset_list[0]) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) loc_loss = 0 loc_steps = 0 def evaluate_qa_retriever(model, dataset, tokenizer, args): model.eval() # make iterator eval_sampler = SequentialSampler(dataset) model_collate_fn = functools.partial( make_qa_retriever_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=eval_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) tot_loss = 0.0 with torch.no_grad(): for step, batch in enumerate(epoch_iterator): q_ids, q_mask, a_ids, a_mask = batch loss = model(q_ids, q_mask, a_ids, a_mask) tot_loss += loss.item() return tot_loss / (step + 1) def train_qa_retriever(qar_model, qar_tokenizer, qar_train_dset, qar_valid_dset, qar_args): qar_optimizer = AdamW(qar_model.parameters(), lr=qar_args.learning_rate, eps=1e-8) qar_scheduler = get_linear_schedule_with_warmup( qar_optimizer, num_warmup_steps=100, num_training_steps=(qar_args.num_epochs + 1) * math.ceil(len(qar_train_dset) / qar_args.batch_size), ) for e in range(qar_args.num_epochs): train_qa_retriever_epoch(qar_model, qar_train_dset, qar_tokenizer, qar_optimizer, qar_scheduler, qar_args, e) m_save_dict = { "model": qar_model.state_dict(), "optimizer": qar_optimizer.state_dict(), "scheduler": qar_scheduler.state_dict(), } print("Saving model {}".format(qar_args.model_save_name)) torch.save(m_save_dict, "{}_{}.pth".format(qar_args.model_save_name, e)) eval_loss = evaluate_qa_retriever(qar_model, qar_valid_dset, qar_tokenizer, qar_args) print("Evaluation loss epoch {:4d}: {:.3f}".format(e, eval_loss)) ############### # ELI5 seq2seq model training ############### class ELI5DatasetS2S(Dataset): def __init__( self, examples_array, make_doc_fun=None, extra_answer_threshold=3, document_cache=None, training=True ): self.training = training self.data = examples_array self.make_doc_function = make_doc_fun self.document_cache = {} if document_cache is None else document_cache assert not (make_doc_fun is None and document_cache is None) # make index of specific question-answer pairs from multi-answers if self.training: self.qa_id_list = [ (i, j) for i, qa in enumerate(self.data) for j, (a, sc) in enumerate(zip(qa["answers"]["text"], qa["answers"]["score"])) if j == 0 or sc >= extra_answer_threshold ] else: self.qa_id_list = [(i, 0) for i in range(self.data.num_rows)] def __len__(self): return len(self.qa_id_list) def make_example(self, idx): i, j = self.qa_id_list[idx] example = self.data[i] question = example["title"] + " " + example["selftext"] answer = example["answers"]["text"][j] q_id = example["q_id"] if self.make_doc_function is not None: self.document_cache[q_id] = self.document_cache.get(q_id, self.make_doc_function(example["title"])) document = self.document_cache[q_id] in_st = "question: {} context: {}".format( question.lower().replace(" --t--", "").strip(), document.lower().strip(), ) out_st = answer return (in_st, out_st) def __getitem__(self, idx): return self.make_example(idx) def make_qa_s2s_model(model_name="facebook/bart-large", from_file=None, device="cuda:0"): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) if from_file is not None: param_dict = torch.load(from_file) # has model weights, optimizer, and scheduler states model.load_state_dict(param_dict["model"]) return tokenizer, model def make_qa_s2s_batch(qa_list, tokenizer, max_len=64, max_a_len=360, device="cuda:0"): q_ls = [q for q, a in qa_list] a_ls = [a for q, a in qa_list] q_toks = tokenizer(q_ls, max_length=max_len, padding="max_length", truncation=True) q_ids, q_mask = ( torch.LongTensor(q_toks["input_ids"]).to(device), torch.LongTensor(q_toks["attention_mask"]).to(device), ) a_toks = tokenizer(a_ls, max_length=min(max_len, max_a_len), padding="max_length", truncation=True) a_ids, a_mask = ( torch.LongTensor(a_toks["input_ids"]).to(device), torch.LongTensor(a_toks["attention_mask"]).to(device), ) lm_labels = a_ids[:, 1:].contiguous().clone() lm_labels[a_mask[:, 1:].contiguous() == 0] = -100 model_inputs = { "input_ids": q_ids, "attention_mask": q_mask, "decoder_input_ids": a_ids[:, :-1].contiguous(), "lm_labels": lm_labels, } return model_inputs def train_qa_s2s_epoch(model, dataset, tokenizer, optimizer, scheduler, args, e=0, curriculum=False): model.train() # make iterator if curriculum: train_sampler = SequentialSampler(dataset) else: train_sampler = RandomSampler(dataset) model_collate_fn = functools.partial( make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() for step, batch_inputs in enumerate(epoch_iterator): pre_loss = model(**batch_inputs)[0] loss = pre_loss.sum() / pre_loss.shape[0] loss.backward() # optimizer if step % args.backward_freq == 0: optimizer.step() scheduler.step() model.zero_grad() # some printing within the epoch loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0 or step == 1: print( "{:2d} {:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( e, step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) loc_loss = 0 loc_steps = 0 def eval_qa_s2s_epoch(model, dataset, tokenizer, args): model.eval() # make iterator train_sampler = SequentialSampler(dataset) model_collate_fn = functools.partial( make_qa_s2s_batch, tokenizer=tokenizer, max_len=args.max_length, device="cuda:0" ) data_loader = DataLoader(dataset, batch_size=args.batch_size, sampler=train_sampler, collate_fn=model_collate_fn) epoch_iterator = tqdm(data_loader, desc="Iteration", disable=True) # accumulate loss since last print loc_steps = 0 loc_loss = 0.0 st_time = time() with torch.no_grad(): for step, batch_inputs in enumerate(epoch_iterator): pre_loss = model(**batch_inputs)[0] loss = pre_loss.sum() / pre_loss.shape[0] loc_loss += loss.item() loc_steps += 1 if step % args.print_freq == 0: print( "{:5d} of {:5d} \t L: {:.3f} \t -- {:.3f}".format( step, len(dataset) // args.batch_size, loc_loss / loc_steps, time() - st_time, ) ) print( "Total \t L: {:.3f} \t -- {:.3f}".format( loc_loss / loc_steps, time() - st_time, ) ) def train_qa_s2s(qa_s2s_model, qa_s2s_tokenizer, s2s_train_dset, s2s_valid_dset, s2s_args): s2s_optimizer = AdamW(qa_s2s_model.parameters(), lr=s2s_args.learning_rate, eps=1e-8) s2s_scheduler = get_linear_schedule_with_warmup( s2s_optimizer, num_warmup_steps=400, num_training_steps=(s2s_args.num_epochs + 1) * math.ceil(len(s2s_train_dset) / s2s_args.batch_size), ) for e in range(s2s_args.num_epochs): train_qa_s2s_epoch( qa_s2s_model, s2s_train_dset, qa_s2s_tokenizer, s2s_optimizer, s2s_scheduler, s2s_args, e, curriculum=(e == 0), ) m_save_dict = { "model": qa_s2s_model.state_dict(), "optimizer": s2s_optimizer.state_dict(), "scheduler": s2s_scheduler.state_dict(), } print("Saving model {}".format(s2s_args.model_save_name)) eval_qa_s2s_epoch(qa_s2s_model, s2s_valid_dset, qa_s2s_tokenizer, s2s_args) torch.save(m_save_dict, "{}_{}.pth".format(s2s_args.model_save_name, e)) # generate answer from input "question: ... context: <p> ..." def qa_s2s_generate( question_doc, qa_s2s_model, qa_s2s_tokenizer, num_answers=1, num_beams=None, min_len=64, max_len=256, do_sample=False, temp=1.0, top_p=None, top_k=None, max_input_length=512, device="cuda:0", ): model_inputs = make_qa_s2s_batch( [(question_doc, "A")], qa_s2s_tokenizer, max_input_length, device=device, ) n_beams = num_answers if num_beams is None else max(num_beams, num_answers) generated_ids = qa_s2s_model.generate( input_ids=model_inputs["input_ids"], attention_mask=model_inputs["attention_mask"], min_length=min_len, max_length=max_len, do_sample=do_sample, early_stopping=True, num_beams=1 if do_sample else n_beams, temperature=temp, top_k=top_k, top_p=top_p, eos_token_id=qa_s2s_tokenizer.eos_token_id, no_repeat_ngram_size=3, num_return_sequences=num_answers, decoder_start_token_id=qa_s2s_tokenizer.bos_token_id, ) return [qa_s2s_tokenizer.decode(ans_ids, skip_special_tokens=True).strip() for ans_ids in generated_ids] ############### # ELI5-trained retrieval model usage ############### def embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length=128, device="cuda:0"): a_toks = tokenizer(passages, max_length=max_length, padding="max_length", truncation=True) a_ids, a_mask = ( torch.LongTensor(a_toks["input_ids"]).to(device), torch.LongTensor(a_toks["attention_mask"]).to(device), ) with torch.no_grad(): a_reps = qa_embedder.embed_answers(a_ids, a_mask).cpu().type(torch.float) return a_reps.numpy() def embed_questions_for_retrieval(q_ls, tokenizer, qa_embedder, device="cuda:0"): q_toks = tokenizer(q_ls, max_length=128, padding="max_length", truncation=True) q_ids, q_mask = ( torch.LongTensor(q_toks["input_ids"]).to(device), torch.LongTensor(q_toks["attention_mask"]).to(device), ) with torch.no_grad(): q_reps = qa_embedder.embed_questions(q_ids, q_mask).cpu().type(torch.float) return q_reps.numpy() def make_qa_dense_index( qa_embedder, tokenizer, passages_dset, batch_size=512, max_length=128, index_name="kilt_passages_reps.dat", dtype="float32", device="cuda:0", ): st_time = time() fp = np.memmap(index_name, dtype=dtype, mode="w+", shape=(passages_dset.num_rows, 128)) n_batches = math.ceil(passages_dset.num_rows / batch_size) for i in range(n_batches): passages = [p for p in passages_dset[i * batch_size : (i + 1) * batch_size]["passage_text"]] reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder, max_length, device) fp[i * batch_size : (i + 1) * batch_size] = reps if i % 50 == 0: print(i, time() - st_time) def evaluate_retriever(qa_list, retriever_func, scoring_func, n_ret=10, verbose=False): total_retriever_time = 0.0 total_retriever_score = 0.0 st_time = time() for i, (question, answer) in enumerate(qa_list): r_time = time() retrieved_passages = retriever_func(question, n_ret) total_retriever_time += time() - r_time total_retriever_score += scoring_func(retrieved_passages, answer) if verbose and ((i + 1) % 500 == 0 or i <= 1): print( "{:03d}: S-{:.4f} T-{:.4f} | {:.2f}".format( i + 1, total_retriever_score / (i + 1), total_retriever_time / (i + 1), time() - st_time ) ) return {"idf_recall": total_retriever_score / (i + 1), "retrieval_time": total_retriever_time / (i + 1)} # build a support document for the question out of Wikipedia snippets def query_qa_dense_index( question, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20, device="cuda:0" ): q_rep = embed_questions_for_retrieval([question], tokenizer, qa_embedder, device=device) D, I = wiki_index.search(q_rep, 2 * n_results) res_passages = [wiki_passages[int(i)] for i in I[0]] support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results] for r, sc in zip(res_list, D[0]): r["score"] = float(sc) return support_doc, res_list def batch_query_qa_dense_index(questions, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10): q_rep = embed_questions_for_retrieval(questions, tokenizer, qa_embedder) D, I = wiki_index.search(q_rep, n_results) res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I] support_doc_lst = [ "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst ] all_res_lists = [] for res_passages, dl in zip(res_passages_lst, D): res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] for r, sc in zip(res_list, dl): r["score"] = float(sc) all_res_lists += [res_list[:]] return support_doc_lst, all_res_lists # find nearest neighbors of an answer or declarative text in Wikipedia snippets def query_qa_dense_index_nn(passage, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10, min_length=20): a_rep = embed_passages_for_retrieval([passage], tokenizer, qa_embedder) D, I = wiki_index.search(a_rep, 2 * n_results) res_passages = [wiki_passages[int(i)] for i in I[0]] support_doc = "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] res_list = [res for res in res_list if len(res["passage_text"].split()) > min_length][:n_results] for r, sc, i in zip(res_list, D[0], I[0]): r["passage_id"] = int(i) r["score"] = float(sc) return support_doc, res_list def batch_query_qa_dense_index_nn(passages, qa_embedder, tokenizer, wiki_passages, wiki_index, n_results=10): a_reps = embed_passages_for_retrieval(passages, tokenizer, qa_embedder) D, I = wiki_index.search(a_reps, n_results) res_passages_lst = [[wiki_passages[int(i)] for i in i_lst] for i_lst in I] support_doc_lst = [ "<P> " + " <P> ".join([p["passage_text"] for p in res_passages]) for res_passages in res_passages_lst ] all_res_lists = [] for res_passages, dl, il in zip(res_passages_lst, D, I): res_list = [dict([(k, p[k]) for k in wiki_passages.column_names]) for p in res_passages] for r, sc, i in zip(res_list, dl, il): r["passage_id"] = int(i) r["score"] = float(sc) all_res_lists += [res_list[:]] return support_doc_lst, all_res_lists

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/wav2vec2/tokenization_wav2vec2.py

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Wav2Vec2.""" import json import os import sys import warnings from dataclasses import dataclass from itertools import groupby from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list from ...tokenization_utils_base import AddedToken, BatchEncoding from ...utils import ( ModelOutput, PaddingStrategy, TensorType, add_end_docstrings, is_flax_available, is_tf_available, is_torch_available, logging, to_py_obj, ) logger = logging.get_logger(__name__) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp # noqa: F401 VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json", }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer_config.json" ), }, } # Wav2Vec2 has no max input length PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-base-960h": sys.maxsize} WAV2VEC2_KWARGS_DOCSTRING = r""" padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ ListOfDict = List[Dict[str, Union[int, str]]] @dataclass class Wav2Vec2CTCTokenizerOutput(ModelOutput): """ Output type of [` Wav2Vec2CTCTokenizer`], with transcription. Args: text (list of `str` or `str`): Decoded logits in text from. Usually the speech transcription. char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with produced text. word_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets can be used to compute time stamps for each word. """ text: Union[List[str], str] char_offsets: Union[List[ListOfDict], ListOfDict] = None word_offsets: Union[List[ListOfDict], ListOfDict] = None class Wav2Vec2CTCTokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2CTC tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to accept lowercase input and lowercase the output when decoding. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", replace_word_delimiter_char=" ", do_lower_case=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, word_delimiter_token=word_delimiter_token, replace_word_delimiter_char=replace_word_delimiter_char, **kwargs, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.replace_word_delimiter_char = replace_word_delimiter_char with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} # make sure that tokens made of several # characters are not split at tokenization for token in self.encoder.keys(): if len(token) > 1: self.unique_no_split_tokens.append(token) self._create_trie(self.unique_no_split_tokens) @property def word_delimiter_token(self) -> str: """ `str`: Word delimiter token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _tokenize(self, text, **kwargs): """ Converts a string in a sequence of tokens (string), using the tokenizer. """ if self.do_lower_case: text = text.upper() return list(text.replace(" ", self.word_delimiter_token)) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string( self, tokens: List[str], group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_char_offsets: bool = False, output_word_offsets: bool = False, ) -> Dict[str, Union[str, float]]: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ if len(tokens) == 0: return {"text": "", "char_offsets": [], "word_offsets": []} # group same tokens into non-repeating tokens in CTC style decoding if group_tokens: chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens))) else: chars = tokens char_repetitions = len(tokens) * [1] # filter self.pad_token which is used as CTC-blank token processed_chars = list(filter(lambda char: char != self.pad_token, chars)) # replace delimiter token processed_chars = [ self.replace_word_delimiter_char if char == self.word_delimiter_token else char for char in processed_chars ] # retrieve offsets char_offsets = word_offsets = None if output_char_offsets or output_word_offsets: char_offsets = self._compute_offsets(char_repetitions, chars, self.pad_token) if len(char_offsets) != len(processed_chars): raise ValueError( f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}" " have to be of the same length, but are: " f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:" f" {len(processed_chars)}" ) # set tokens to correct processed token for i, char in enumerate(processed_chars): char_offsets[i]["char"] = char # retrieve word offsets from character offsets word_offsets = None if output_word_offsets: word_offsets = self._get_word_offsets(char_offsets, self.replace_word_delimiter_char) # don't output chars if not set to True if not output_char_offsets: char_offsets = None # join to string join_char = " " if spaces_between_special_tokens else "" string = join_char.join(processed_chars).strip() if self.do_lower_case: string = string.lower() return {"text": string, "char_offsets": char_offsets, "word_offsets": word_offsets} @staticmethod def _compute_offsets( char_repetitions: List[int], chars: List[str], ctc_token: int ) -> List[Dict[str, Union[str, int]]]: end_indices = np.asarray(char_repetitions).cumsum() start_indices = np.concatenate(([0], end_indices[:-1])) offsets = [ {"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices) ] # filter out CTC token offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets)) return offsets @staticmethod def _get_word_offsets( offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " " ) -> Dict[str, Union[str, float]]: word_offsets = [] last_state = "SPACE" word = "" start_offset = 0 end_offset = 0 for i, offset in enumerate(offsets): char = offset["char"] state = "SPACE" if char == word_delimiter_char else "WORD" if state == last_state: # If we are in the same state as before, we simply repeat what we've done before end_offset = offset["end_offset"] word += char else: # Switching state if state == "SPACE": # Finishing a word word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) else: # Starting a new word start_offset = offset["start_offset"] end_offset = offset["end_offset"] word = char last_state = state if last_state == "WORD": word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) return word_offsets def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): if is_split_into_words: text = " " + text return (text, kwargs) def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_word_offsets: Optional[bool] = False, output_char_offsets: Optional[bool] = False, ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) string_output = self.convert_tokens_to_string( result, group_tokens=group_tokens, spaces_between_special_tokens=spaces_between_special_tokens, output_word_offsets=output_word_offsets, output_char_offsets=output_char_offsets, ) text = string_output["text"] if clean_up_tokenization_spaces: text = self.clean_up_tokenization(text) if output_word_offsets or output_char_offsets: return Wav2Vec2CTCTokenizerOutput( text=text, char_offsets=string_output["char_offsets"], word_offsets=string_output["word_offsets"], ) else: return text # overwritten from `tokenization_utils_base.py` because tokenizer can output # `ModelOutput` which should not be a list for batched output and # because we need docs for `output_char_offsets` here def batch_decode( self, sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> List[str]: """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. """ batch_decoded = [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) for seq in sequences ] if output_char_offsets or output_word_offsets: # transform list of dicts to dict of lists return Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]}) return batch_decoded # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets` # and `output_word_offsets` here def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the example below to better understand how to make use of `output_char_offsets`. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the example below to better understand how to make use of `output_word_offsets`. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. Example: ```python >>> # Let's see how to retrieve time steps for a model >>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC >>> from datasets import load_dataset >>> import datasets >>> import torch >>> # import model, feature extractor, tokenizer >>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h") >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h") >>> # load first sample of English common_voice >>> dataset = load_dataset("common_voice", "en", split="train", streaming=True) >>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000)) >>> dataset_iter = iter(dataset) >>> sample = next(dataset_iter) >>> # forward sample through model to get greedily predicted transcription ids >>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values >>> logits = model(input_values).logits[0] >>> pred_ids = torch.argmax(logits, axis=-1) >>> # retrieve word stamps (analogous commands for `output_char_offsets`) >>> outputs = tokenizer.decode(pred_ids, output_word_offsets=True) >>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate >>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate >>> word_offsets = [ ... { ... "word": d["word"], ... "start_time": round(d["start_offset"] * time_offset, 2), ... "end_time": round(d["end_offset"] * time_offset, 2), ... } ... for d in outputs.word_offsets ... ] >>> # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer: >>> # https://huggingface.co/datasets/common_voice/viewer/en/train >>> word_offsets[:3] [{'word': 'WHY', 'start_time': 1.42, 'end_time': 1.54}, {'word': 'DOES', 'start_time': 1.64, 'end_time': 1.9}, {'word': 'MILISANDRA', 'start_time': 2.26, 'end_time': 2.9}] ```""" # Convert inputs to python lists token_ids = to_py_obj(token_ids) return self._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,) def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: """ Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary. Args: new_tokens (`List[str]`or `List[tokenizers.AddedToken]`): Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by checking if the tokenizer assign the index of the `unk_token` to them). special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the tokens should be added as special tokens. Returns: `int`: The number of tokens actually added to the vocabulary. Example: ```python # Let's see how to increase the vocabulary of Bert model and tokenizer tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"]) print("We have added", num_added_toks, "tokens") # Note: resize_token_embeddings expects to receive the full size of the new vocabulary, i.e. the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) ```""" new_tokens = [str(tok) for tok in new_tokens] tokens_to_add = [] for token in new_tokens: assert isinstance(token, str) if not special_tokens and hasattr(self, "do_lower_case") and self.do_lower_case: token = token.lower() if ( token != self.unk_token and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and token not in tokens_to_add ): tokens_to_add.append(token) if self.verbose: logger.info(f"Adding {token} to the vocabulary") added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add)) added_tok_decoder = {v: k for k, v in added_tok_encoder.items()} self.added_tokens_encoder.update(added_tok_encoder) self.added_tokens_decoder.update(added_tok_decoder) # Make sure we don't split on any special tokens (even they were already in the vocab before) for token in tokens_to_add: if len(token) > 1: self._additional_special_tokens.append(AddedToken(token)) _insert_one_token_to_ordered_list(self.unique_no_split_tokens, token) self._create_trie(self.unique_no_split_tokens) return len(tokens_to_add) class Wav2Vec2Tokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2 tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the output when decoding. do_normalize (`bool`, *optional*, defaults to `False`): Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly improve the performance for some models, *e.g.*, [wav2vec2-lv60](https://huggingface.co/models?search=lv60). return_attention_mask (`bool`, *optional*, defaults to `False`): Whether or not [`~Wav2Vec2Tokenizer.__call__`] should return `attention_mask`. <Tip> Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` should be passed. For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be passed for batched inference. </Tip> **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json" }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer.json" ), }, } model_input_names = ["input_values", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", do_lower_case=False, do_normalize=False, return_attention_mask=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, do_normalize=do_normalize, return_attention_mask=return_attention_mask, word_delimiter_token=word_delimiter_token, **kwargs, ) warnings.warn( "The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use" " `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.", FutureWarning, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} @property def word_delimiter_token(self) -> str: """ `str`: Padding token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @add_end_docstrings(WAV2VEC2_KWARGS_DOCSTRING) def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], padding: Union[bool, str, PaddingStrategy] = False, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences. Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrayr or a list of list of float values. """ is_batched = bool( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list))) ) # make sure input is in list format if is_batched and not isinstance(raw_speech[0], np.ndarray): raw_speech = [np.asarray(speech) for speech in raw_speech] elif not is_batched and not isinstance(raw_speech, np.ndarray): raw_speech = np.asarray(raw_speech) # always return batch if not is_batched: raw_speech = [raw_speech] # zero-mean and unit-variance normalization if self.do_normalize: raw_speech = [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in raw_speech] # convert into correct format for padding encoded_inputs = BatchEncoding({"input_values": raw_speech}) padded_inputs = self.pad( encoded_inputs, padding=padding, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=self.return_attention_mask, return_tensors=return_tensors, verbose=verbose, ) return padded_inputs @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ # group same tokens into non-repeating tokens in CTC style decoding grouped_tokens = [token_group[0] for token_group in groupby(tokens)] # filter self.pad_token which is used as CTC-blank token filtered_tokens = list(filter(lambda token: token != self.pad_token, grouped_tokens)) # replace delimiter token string = "".join([" " if token == self.word_delimiter_token else token for token in filtered_tokens]).strip() if self.do_lower_case: string = string.lower() return string def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) text = self.convert_tokens_to_string(result) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,)

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Wav2Vec2.""" import json import os import sys import warnings from dataclasses import dataclass from itertools import groupby from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union import numpy as np from ...tokenization_utils import PreTrainedTokenizer, _insert_one_token_to_ordered_list from ...tokenization_utils_base import AddedToken, BatchEncoding from ...utils import ( ModelOutput, PaddingStrategy, TensorType, add_end_docstrings, is_flax_available, is_tf_available, is_torch_available, logging, to_py_obj, ) logger = logging.get_logger(__name__) if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf if is_flax_available(): import jax.numpy as jnp # noqa: F401 VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "tokenizer_config_file": "tokenizer_config.json", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json", }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer_config.json" ), }, } # Wav2Vec2 has no max input length PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/wav2vec2-base-960h": sys.maxsize} WAV2VEC2_KWARGS_DOCSTRING = r""" padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. pad_to_multiple_of (`int`, *optional*): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. verbose (`bool`, *optional*, defaults to `True`): Whether or not to print more information and warnings. """ ListOfDict = List[Dict[str, Union[int, str]]] @dataclass class Wav2Vec2CTCTokenizerOutput(ModelOutput): """ Output type of [` Wav2Vec2CTCTokenizer`], with transcription. Args: text (list of `str` or `str`): Decoded logits in text from. Usually the speech transcription. char_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded characters. In combination with sampling rate and model downsampling rate char offsets can be used to compute time stamps for each charater. Total logit score of the beam associated with produced text. word_offsets (list of `List[Dict[str, Union[int, str]]]` or `List[Dict[str, Union[int, str]]]`): Offsets of the decoded words. In combination with sampling rate and model downsampling rate word offsets can be used to compute time stamps for each word. """ text: Union[List[str], str] char_offsets: Union[List[ListOfDict], ListOfDict] = None word_offsets: Union[List[ListOfDict], ListOfDict] = None class Wav2Vec2CTCTokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2CTC tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to accept lowercase input and lowercase the output when decoding. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", replace_word_delimiter_char=" ", do_lower_case=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, word_delimiter_token=word_delimiter_token, replace_word_delimiter_char=replace_word_delimiter_char, **kwargs, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.replace_word_delimiter_char = replace_word_delimiter_char with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} # make sure that tokens made of several # characters are not split at tokenization for token in self.encoder.keys(): if len(token) > 1: self.unique_no_split_tokens.append(token) self._create_trie(self.unique_no_split_tokens) @property def word_delimiter_token(self) -> str: """ `str`: Word delimiter token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _tokenize(self, text, **kwargs): """ Converts a string in a sequence of tokens (string), using the tokenizer. """ if self.do_lower_case: text = text.upper() return list(text.replace(" ", self.word_delimiter_token)) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string( self, tokens: List[str], group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_char_offsets: bool = False, output_word_offsets: bool = False, ) -> Dict[str, Union[str, float]]: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ if len(tokens) == 0: return {"text": "", "char_offsets": [], "word_offsets": []} # group same tokens into non-repeating tokens in CTC style decoding if group_tokens: chars, char_repetitions = zip(*((token, len(list(group_iter))) for token, group_iter in groupby(tokens))) else: chars = tokens char_repetitions = len(tokens) * [1] # filter self.pad_token which is used as CTC-blank token processed_chars = list(filter(lambda char: char != self.pad_token, chars)) # replace delimiter token processed_chars = [ self.replace_word_delimiter_char if char == self.word_delimiter_token else char for char in processed_chars ] # retrieve offsets char_offsets = word_offsets = None if output_char_offsets or output_word_offsets: char_offsets = self._compute_offsets(char_repetitions, chars, self.pad_token) if len(char_offsets) != len(processed_chars): raise ValueError( f"`char_offsets`: {char_offsets} and `processed_tokens`: {processed_chars}" " have to be of the same length, but are: " f"`len(offsets)`: {len(char_offsets)} and `len(processed_tokens)`:" f" {len(processed_chars)}" ) # set tokens to correct processed token for i, char in enumerate(processed_chars): char_offsets[i]["char"] = char # retrieve word offsets from character offsets word_offsets = None if output_word_offsets: word_offsets = self._get_word_offsets(char_offsets, self.replace_word_delimiter_char) # don't output chars if not set to True if not output_char_offsets: char_offsets = None # join to string join_char = " " if spaces_between_special_tokens else "" string = join_char.join(processed_chars).strip() if self.do_lower_case: string = string.lower() return {"text": string, "char_offsets": char_offsets, "word_offsets": word_offsets} @staticmethod def _compute_offsets( char_repetitions: List[int], chars: List[str], ctc_token: int ) -> List[Dict[str, Union[str, int]]]: end_indices = np.asarray(char_repetitions).cumsum() start_indices = np.concatenate(([0], end_indices[:-1])) offsets = [ {"char": t, "start_offset": s, "end_offset": e} for t, s, e in zip(chars, start_indices, end_indices) ] # filter out CTC token offsets = list(filter(lambda offsets: offsets["char"] != ctc_token, offsets)) return offsets @staticmethod def _get_word_offsets( offsets: Dict[str, Union[str, float]], word_delimiter_char: str = " " ) -> Dict[str, Union[str, float]]: word_offsets = [] last_state = "SPACE" word = "" start_offset = 0 end_offset = 0 for i, offset in enumerate(offsets): char = offset["char"] state = "SPACE" if char == word_delimiter_char else "WORD" if state == last_state: # If we are in the same state as before, we simply repeat what we've done before end_offset = offset["end_offset"] word += char else: # Switching state if state == "SPACE": # Finishing a word word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) else: # Starting a new word start_offset = offset["start_offset"] end_offset = offset["end_offset"] word = char last_state = state if last_state == "WORD": word_offsets.append({"word": word, "start_offset": start_offset, "end_offset": end_offset}) return word_offsets def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): if is_split_into_words: text = " " + text return (text, kwargs) def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, group_tokens: bool = True, spaces_between_special_tokens: bool = False, output_word_offsets: Optional[bool] = False, output_char_offsets: Optional[bool] = False, ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) string_output = self.convert_tokens_to_string( result, group_tokens=group_tokens, spaces_between_special_tokens=spaces_between_special_tokens, output_word_offsets=output_word_offsets, output_char_offsets=output_char_offsets, ) text = string_output["text"] if clean_up_tokenization_spaces: text = self.clean_up_tokenization(text) if output_word_offsets or output_char_offsets: return Wav2Vec2CTCTokenizerOutput( text=text, char_offsets=string_output["char_offsets"], word_offsets=string_output["word_offsets"], ) else: return text # overwritten from `tokenization_utils_base.py` because tokenizer can output # `ModelOutput` which should not be a list for batched output and # because we need docs for `output_char_offsets` here def batch_decode( self, sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> List[str]: """ Convert a list of lists of token ids into a list of strings by calling decode. Args: sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_char_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the Example of [`~Wav2Vec2CTCTokenizer.decode`] to better understand how to make use of `output_word_offsets`. [`~Wav2Vec2CTCTokenizer.batch_decode`] works the same way with batched output. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. """ batch_decoded = [ self.decode( seq, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) for seq in sequences ] if output_char_offsets or output_word_offsets: # transform list of dicts to dict of lists return Wav2Vec2CTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]}) return batch_decoded # overwritten from `tokenization_utils_base.py` because we need docs for `output_char_offsets` # and `output_word_offsets` here def decode( self, token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, output_char_offsets: bool = False, output_word_offsets: bool = False, **kwargs ) -> str: """ Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special tokens and clean up tokenization spaces. Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`. Args: token_ids (`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`): List of tokenized input ids. Can be obtained using the `__call__` method. skip_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not to remove special tokens in the decoding. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): Whether or not to clean up the tokenization spaces. output_char_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output character offsets. Character offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed characters. <Tip> Please take a look at the example below to better understand how to make use of `output_char_offsets`. </Tip> output_word_offsets (`bool`, *optional*, defaults to `False`): Whether or not to output word offsets. Word offsets can be used in combination with the sampling rate and model downsampling rate to compute the time-stamps of transcribed words. <Tip> Please take a look at the example below to better understand how to make use of `output_word_offsets`. </Tip> kwargs (additional keyword arguments, *optional*): Will be passed to the underlying model specific decode method. Returns: `str` or [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`]: The list of decoded sentences. Will be a [`~models.wav2vec2.tokenization_wav2vec2.Wav2Vec2CTCTokenizerOutput`] when `output_char_offsets == True` or `output_word_offsets == True`. Example: ```python >>> # Let's see how to retrieve time steps for a model >>> from transformers import AutoTokenizer, AutoFeatureExtractor, AutoModelForCTC >>> from datasets import load_dataset >>> import datasets >>> import torch >>> # import model, feature extractor, tokenizer >>> model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h") >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h") >>> # load first sample of English common_voice >>> dataset = load_dataset("common_voice", "en", split="train", streaming=True) >>> dataset = dataset.cast_column("audio", datasets.Audio(sampling_rate=16_000)) >>> dataset_iter = iter(dataset) >>> sample = next(dataset_iter) >>> # forward sample through model to get greedily predicted transcription ids >>> input_values = feature_extractor(sample["audio"]["array"], return_tensors="pt").input_values >>> logits = model(input_values).logits[0] >>> pred_ids = torch.argmax(logits, axis=-1) >>> # retrieve word stamps (analogous commands for `output_char_offsets`) >>> outputs = tokenizer.decode(pred_ids, output_word_offsets=True) >>> # compute `time_offset` in seconds as product of downsampling ratio and sampling_rate >>> time_offset = model.config.inputs_to_logits_ratio / feature_extractor.sampling_rate >>> word_offsets = [ ... { ... "word": d["word"], ... "start_time": round(d["start_offset"] * time_offset, 2), ... "end_time": round(d["end_offset"] * time_offset, 2), ... } ... for d in outputs.word_offsets ... ] >>> # compare word offsets with audio `common_voice_en_100038.mp3` online on the dataset viewer: >>> # https://huggingface.co/datasets/common_voice/viewer/en/train >>> word_offsets[:3] [{'word': 'WHY', 'start_time': 1.42, 'end_time': 1.54}, {'word': 'DOES', 'start_time': 1.64, 'end_time': 1.9}, {'word': 'MILISANDRA', 'start_time': 2.26, 'end_time': 2.9}] ```""" # Convert inputs to python lists token_ids = to_py_obj(token_ids) return self._decode( token_ids=token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, output_char_offsets=output_char_offsets, output_word_offsets=output_word_offsets, **kwargs, ) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,) def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int: """ Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to it with indices starting from length of the current vocabulary. Args: new_tokens (`List[str]`or `List[tokenizers.AddedToken]`): Token(s) to add in vocabulary. A token is only added if it's not already in the vocabulary (tested by checking if the tokenizer assign the index of the `unk_token` to them). special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the tokens should be added as special tokens. Returns: `int`: The number of tokens actually added to the vocabulary. Example: ```python # Let's see how to increase the vocabulary of Bert model and tokenizer tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("facebook/wav2vec2-base-960h") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") num_added_toks = tokenizer.add_tokens(["new_tok1", "my_new-tok2"]) print("We have added", num_added_toks, "tokens") # Note: resize_token_embeddings expects to receive the full size of the new vocabulary, i.e. the length of the tokenizer. model.resize_token_embeddings(len(tokenizer)) ```""" new_tokens = [str(tok) for tok in new_tokens] tokens_to_add = [] for token in new_tokens: assert isinstance(token, str) if not special_tokens and hasattr(self, "do_lower_case") and self.do_lower_case: token = token.lower() if ( token != self.unk_token and self.convert_tokens_to_ids(token) == self.convert_tokens_to_ids(self.unk_token) and token not in tokens_to_add ): tokens_to_add.append(token) if self.verbose: logger.info(f"Adding {token} to the vocabulary") added_tok_encoder = dict((tok, len(self) + i) for i, tok in enumerate(tokens_to_add)) added_tok_decoder = {v: k for k, v in added_tok_encoder.items()} self.added_tokens_encoder.update(added_tok_encoder) self.added_tokens_decoder.update(added_tok_decoder) # Make sure we don't split on any special tokens (even they were already in the vocab before) for token in tokens_to_add: if len(token) > 1: self._additional_special_tokens.append(AddedToken(token)) _insert_one_token_to_ordered_list(self.unique_no_split_tokens, token) self._create_trie(self.unique_no_split_tokens) return len(tokens_to_add) class Wav2Vec2Tokenizer(PreTrainedTokenizer): """ Constructs a Wav2Vec2 tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. word_delimiter_token (`str`, *optional*, defaults to `"|"`): The token used for defining the end of a word. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the output when decoding. do_normalize (`bool`, *optional*, defaults to `False`): Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly improve the performance for some models, *e.g.*, [wav2vec2-lv60](https://huggingface.co/models?search=lv60). return_attention_mask (`bool`, *optional*, defaults to `False`): Whether or not [`~Wav2Vec2Tokenizer.__call__`] should return `attention_mask`. <Tip> Wav2Vec2 models that have set `config.feat_extract_norm == "group"`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), have **not** been trained using `attention_mask`. For such models, `input_values` should simply be padded with 0 and no `attention_mask` should be passed. For Wav2Vec2 models that have set `config.feat_extract_norm == "layer"`, such as [wav2vec2-lv60](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self), `attention_mask` should be passed for batched inference. </Tip> **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = { "vocab_file": { "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/vocab.json" }, "tokenizer_config_file": { "facebook/wav2vec2-base-960h": ( "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/tokenizer.json" ), }, } model_input_names = ["input_values", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", word_delimiter_token="|", do_lower_case=False, do_normalize=False, return_attention_mask=False, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, do_normalize=do_normalize, return_attention_mask=return_attention_mask, word_delimiter_token=word_delimiter_token, **kwargs, ) warnings.warn( "The class `Wav2Vec2Tokenizer` is deprecated and will be removed in version 5 of Transformers. Please use" " `Wav2Vec2Processor` or `Wav2Vec2CTCTokenizer` instead.", FutureWarning, ) self._word_delimiter_token = word_delimiter_token self.do_lower_case = do_lower_case self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} @property def word_delimiter_token(self) -> str: """ `str`: Padding token. Log an error if used while not having been set. """ if self._word_delimiter_token is None and self.verbose: logger.error("Using word_delimiter_token, but it is not set yet.") return None return str(self._word_delimiter_token) @property def word_delimiter_token_id(self) -> Optional[int]: """ `Optional[int]`: Id of the word_delimiter_token in the vocabulary. Returns `None` if the token has not been set. """ if self._word_delimiter_token is None: return None return self.convert_tokens_to_ids(self.word_delimiter_token) @word_delimiter_token.setter def word_delimiter_token(self, value): self._word_delimiter_token = value @word_delimiter_token_id.setter def word_delimiter_token_id(self, value): self._word_delimiter_token = self.convert_tokens_to_ids(value) @add_end_docstrings(WAV2VEC2_KWARGS_DOCSTRING) def __call__( self, raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], padding: Union[bool, str, PaddingStrategy] = False, max_length: Optional[int] = None, pad_to_multiple_of: Optional[int] = None, return_tensors: Optional[Union[str, TensorType]] = None, verbose: bool = True, **kwargs ) -> BatchEncoding: """ Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of sequences. Args: raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`): The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float values, a list of numpy arrayr or a list of list of float values. """ is_batched = bool( isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list))) ) # make sure input is in list format if is_batched and not isinstance(raw_speech[0], np.ndarray): raw_speech = [np.asarray(speech) for speech in raw_speech] elif not is_batched and not isinstance(raw_speech, np.ndarray): raw_speech = np.asarray(raw_speech) # always return batch if not is_batched: raw_speech = [raw_speech] # zero-mean and unit-variance normalization if self.do_normalize: raw_speech = [(x - np.mean(x)) / np.sqrt(np.var(x) + 1e-5) for x in raw_speech] # convert into correct format for padding encoded_inputs = BatchEncoding({"input_values": raw_speech}) padded_inputs = self.pad( encoded_inputs, padding=padding, max_length=max_length, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=self.return_attention_mask, return_tensors=return_tensors, verbose=verbose, ) return padded_inputs @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a connectionist-temporal-classification (CTC) output tokens into a single string. """ # group same tokens into non-repeating tokens in CTC style decoding grouped_tokens = [token_group[0] for token_group in groupby(tokens)] # filter self.pad_token which is used as CTC-blank token filtered_tokens = list(filter(lambda token: token != self.pad_token, grouped_tokens)) # replace delimiter token string = "".join([" " if token == self.word_delimiter_token else token for token in filtered_tokens]).strip() if self.do_lower_case: string = string.lower() return string def _decode( self, token_ids: List[int], skip_special_tokens: bool = False, clean_up_tokenization_spaces: bool = True, **kwargs ) -> str: """ special _decode function is needed for Wav2Vec2Tokenizer because added tokens should be treated exactly the same as tokens of the base vocabulary and therefore the function `convert_tokens_to_string` has to be called on the whole token list and not individually on added tokens """ filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens) result = [] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue result.append(token) text = self.convert_tokens_to_string(result) if clean_up_tokenization_spaces: clean_text = self.clean_up_tokenization(text) return clean_text else: return text def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") return (vocab_file,)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/modeling_flax_outputs.py

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, Optional, Tuple import flax import jax.numpy as jnp from .utils import ModelOutput @flax.struct.dataclass class FlaxBaseModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPast(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. past_key_values (`Dict[str, jnp.ndarray]`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None past_key_values: Optional[Dict[str, jnp.ndarray]] = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPooling(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None pooler_output: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ last_hidden_state: jnp.ndarray = None pooler_output: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPastAndCrossAttentions(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSeq2SeqModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxCausalLMOutputWithCrossAttentions(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `jnp.ndarray` tuples of length `config.n_layers`, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if `config.is_decoder = True`. Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxMaskedLMOutput(ModelOutput): """ Base class for masked language models outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None FlaxCausalLMOutput = FlaxMaskedLMOutput @flax.struct.dataclass class FlaxSeq2SeqLMOutput(ModelOutput): """ Base class for sequence-to-sequence language models outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxNextSentencePredictorOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: logits (`jnp.ndarray` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sentence classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSeq2SeqSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence sentence classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxMultipleChoiceModelOutput(ModelOutput): """ Base class for outputs of multiple choice models. Args: logits (`jnp.ndarray` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxTokenClassifierOutput(ModelOutput): """ Base class for outputs of token classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of question answering models. Args: start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ start_logits: jnp.ndarray = None end_logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSeq2SeqQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence question answering models. Args: start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ start_logits: jnp.ndarray = None end_logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None

# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, Optional, Tuple import flax import jax.numpy as jnp from .utils import ModelOutput @flax.struct.dataclass class FlaxBaseModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPast(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. past_key_values (`Dict[str, jnp.ndarray]`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None past_key_values: Optional[Dict[str, jnp.ndarray]] = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPooling(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None pooler_output: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ last_hidden_state: jnp.ndarray = None pooler_output: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxBaseModelOutputWithPastAndCrossAttentions(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSeq2SeqModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxCausalLMOutputWithCrossAttentions(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `jnp.ndarray` tuples of length `config.n_layers`, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if `config.is_decoder = True`. Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxMaskedLMOutput(ModelOutput): """ Base class for masked language models outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None FlaxCausalLMOutput = FlaxMaskedLMOutput @flax.struct.dataclass class FlaxSeq2SeqLMOutput(ModelOutput): """ Base class for sequence-to-sequence language models outputs. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxNextSentencePredictorOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: logits (`jnp.ndarray` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sentence classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSeq2SeqSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence sentence classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxMultipleChoiceModelOutput(ModelOutput): """ Base class for outputs of multiple choice models. Args: logits (`jnp.ndarray` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxTokenClassifierOutput(ModelOutput): """ Base class for outputs of token classification models. Args: logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of question answering models. Args: start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ start_logits: jnp.ndarray = None end_logits: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxSeq2SeqQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence question answering models. Args: start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). past_key_values (`tuple(tuple(jnp.ndarray))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(jnp.ndarray)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ start_logits: jnp.ndarray = None end_logits: jnp.ndarray = None past_key_values: Optional[Tuple[Tuple[jnp.ndarray]]] = None decoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None decoder_attentions: Optional[Tuple[jnp.ndarray]] = None cross_attentions: Optional[Tuple[jnp.ndarray]] = None encoder_last_hidden_state: Optional[jnp.ndarray] = None encoder_hidden_states: Optional[Tuple[jnp.ndarray]] = None encoder_attentions: Optional[Tuple[jnp.ndarray]] = None

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/speech_to_text_2/tokenization_speech_to_text_2.py

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Speech2Text2.""" import json import os from typing import Dict, List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "tokenizer_config_file": "tokenizer_config.json", "merges_file": "merges.txt", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/s2t-wav2vec2-large-en-de": ( "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json" ), }, "tokenizer_config_file": { "facebook/s2t-wav2vec2-large-en-de": ( "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json" ), }, "merges_file": { "facebook/s2t-wav2vec2-large-en-de": ( "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt" ), }, } BPE_TOKEN_MERGES = "</w>" BPE_TOKEN_VOCAB = "@@ " def get_pairs(word): """ Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs # Speech2Text2 has no max input length PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/s2t-wav2vec2-large-en-de": 1024} class Speech2Text2Tokenizer(PreTrainedTokenizer): """ Constructs a Speech2Text2Tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", pad_token="<pad>", eos_token="</s>", unk_token="<unk>", do_lower_case=False, merges_file=None, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, **kwargs, ) self.do_lower_case = do_lower_case with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} if merges_file is None: logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding.") self.bpe_ranks = None self.cache = None else: with open(merges_file, encoding="utf-8") as merges_handle: merges = merges_handle.read().split("\n")[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,) if token in self.cache: return self.cache[token] pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) if word == "\n " + BPE_TOKEN_MERGES: word = "\n" + BPE_TOKEN_MERGES if word.endswith(BPE_TOKEN_MERGES): word = word.replace(BPE_TOKEN_MERGES, "") word = word.replace(" ", BPE_TOKEN_VOCAB) self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" if self.bpe_ranks is None: raise ValueError( "This tokenizer was instantiated without a `merges.txt` file, so" " that it can only be used for decoding, not for encoding." "Make sure to provide `merges.txt` file at instantiation to enable " "encoding." ) if self.do_lower_case: text = text.lower() text = text.split() split_tokens = [] for token in text: if token: split_tokens.extend([t for t in self.bpe(token).split(" ")]) return split_tokens def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a list of output tokens into a single string. """ # combine tokens string = " ".join(tokens) # make sure @@ tokens are concatenated string = "".join(string.split(BPE_TOKEN_VOCAB)) return string def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merges_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 if self.bpe_ranks is None: return (vocab_file,) with open(merges_file, "w", encoding="utf-8") as writer: for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return (vocab_file, merges_file)

# coding=utf-8 # Copyright 2021 The Facebook Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization class for Speech2Text2.""" import json import os from typing import Dict, List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "tokenizer_config_file": "tokenizer_config.json", "merges_file": "merges.txt", } PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "facebook/s2t-wav2vec2-large-en-de": ( "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json" ), }, "tokenizer_config_file": { "facebook/s2t-wav2vec2-large-en-de": ( "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json" ), }, "merges_file": { "facebook/s2t-wav2vec2-large-en-de": ( "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt" ), }, } BPE_TOKEN_MERGES = "</w>" BPE_TOKEN_VOCAB = "@@ " def get_pairs(word): """ Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs # Speech2Text2 has no max input length PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"facebook/s2t-wav2vec2-large-en-de": 1024} class Speech2Text2Tokenizer(PreTrainedTokenizer): """ Constructs a Speech2Text2Tokenizer. This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to the superclass for more information regarding such methods. Args: vocab_file (`str`): File containing the vocabulary. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sentence token. eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sentence token. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. **kwargs Additional keyword arguments passed along to [`PreTrainedTokenizer`] """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", pad_token="<pad>", eos_token="</s>", unk_token="<unk>", do_lower_case=False, merges_file=None, **kwargs ): super().__init__( unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, do_lower_case=do_lower_case, **kwargs, ) self.do_lower_case = do_lower_case with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} if merges_file is None: logger.info(f"No merges files provided. {self.__class__.__name__} can only be used for decoding.") self.bpe_ranks = None self.cache = None else: with open(merges_file, encoding="utf-8") as merges_handle: merges = merges_handle.read().split("\n")[:-1] merges = [tuple(merge.split()[:2]) for merge in merges] self.bpe_ranks = dict(zip(merges, range(len(merges)))) self.cache = {} @property def vocab_size(self) -> int: return len(self.decoder) def get_vocab(self) -> Dict: return dict(self.encoder, **self.added_tokens_encoder) def bpe(self, token): word = tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,) if token in self.cache: return self.cache[token] pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) if word == "\n " + BPE_TOKEN_MERGES: word = "\n" + BPE_TOKEN_MERGES if word.endswith(BPE_TOKEN_MERGES): word = word.replace(BPE_TOKEN_MERGES, "") word = word.replace(" ", BPE_TOKEN_VOCAB) self.cache[token] = word return word def _tokenize(self, text): """Tokenize a string.""" if self.bpe_ranks is None: raise ValueError( "This tokenizer was instantiated without a `merges.txt` file, so" " that it can only be used for decoding, not for encoding." "Make sure to provide `merges.txt` file at instantiation to enable " "encoding." ) if self.do_lower_case: text = text.lower() text = text.split() split_tokens = [] for token in text: if token: split_tokens.extend([t for t in self.bpe(token).split(" ")]) return split_tokens def _convert_token_to_id(self, token: str) -> int: """Converts a token (str) in an index (integer) using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" result = self.decoder.get(index, self.unk_token) return result def convert_tokens_to_string(self, tokens: List[str]) -> str: """ Converts a list of output tokens into a single string. """ # combine tokens string = " ".join(tokens) # make sure @@ tokens are concatenated string = "".join(string.split(BPE_TOKEN_VOCAB)) return string def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merges_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 if self.bpe_ranks is None: return (vocab_file,) with open(merges_file, "w", encoding="utf-8") as writer: for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merges_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return (vocab_file, merges_file)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/opt/modeling_flax_opt.py

# coding=utf-8 # Copyright 2022 The Fairseq Authors and The Google Flax Team Authors And The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax OPT model.""" from functools import partial from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxMaskedLMOutput from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring from ...utils import add_start_docstrings, logging from .configuration_opt import OPTConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/opt-350m" _CONFIG_FOR_DOC = "OPTConfig" _TOKENIZER_FOR_DOC = "GPT2Tokenizer" OPT_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`OPTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ OPT_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->OPT class FlaxOPTAttention(nn.Module): config: OPTConfig embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxOPTDecoderLayer(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.hidden_size self.self_attn = FlaxOPTAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.num_attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.do_layer_norm_before = self.config.do_layer_norm_before self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.fc1 = nn.Dense( self.config.ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache, deterministic=deterministic, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states) # Fully Connected hidden_states_shape = hidden_states.shape hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1]) residual = hidden_states # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = (residual + hidden_states).reshape(hidden_states_shape) # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) return outputs class FlaxOPTDecoderLayerCollection(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxOPTDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] self.layerdrop = self.config.layerdrop def __call__( self, hidden_states, attention_mask, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) outputs = [hidden_states, all_hidden_states, all_self_attns] return outputs class FlaxOPTLearnedPositionalEmbedding(nn.Embed): """ This module learns positional embeddings up to a fixed maximum size. """ def setup(self): self.offset = 2 self.embedding = self.param( "embedding", self.embedding_init, (self.num_embeddings + self.offset, self.features), self.param_dtype ) def __call__(self, positions): """`input_ids_shape` is expected to be [bsz x seqlen].""" return super().__call__(positions + self.offset) class FlaxOPTDecoder(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation offset: int = 2 def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.hidden_size self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_tokens = nn.Embed( self.config.vocab_size, self.config.word_embed_proj_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.embed_positions = FlaxOPTLearnedPositionalEmbedding( self.config.max_position_embeddings, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) if self.config.word_embed_proj_dim != self.config.hidden_size: self.project_in = nn.Dense(self.config.hidden_size, use_bias=False) self.project_out = nn.Dense(self.config.word_embed_proj_dim, use_bias=False) else: self.project_in = None self.project_out = None # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility # with checkpoints that have been fine-tuned before transformers v4.20.1 # see https://github.com/facebookresearch/metaseq/pull/164 if self.config.do_layer_norm_before and not self.config._remove_final_layer_norm: self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) else: self.final_layer_norm = None self.layers = FlaxOPTDecoderLayerCollection(self.config, self.dtype) def __call__( self, input_ids, attention_mask, position_ids, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) if self.project_in is not None: inputs_embeds = self.project_in(inputs_embeds) positions = self.embed_positions(position_ids) hidden_states = inputs_embeds + positions hidden_state, all_hidden_states, attentions = self.layers( hidden_states, attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) if self.final_layer_norm is not None: hidden_state = self.final_layer_norm(hidden_state) if self.project_out is not None: hidden_state = self.project_out(hidden_state) if output_hidden_states: all_hidden_states += (hidden_state,) outputs = [hidden_state, all_hidden_states, attentions] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_state, hidden_states=all_hidden_states, attentions=attentions, ) class FlaxOPTPreTrainedModel(FlaxPreTrainedModel): config_class = OPTConfig base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: OPTConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} module_init_outputs = self.module.init( rngs, input_ids, attention_mask, position_ids, return_dict=False, ) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length), dtype="i4") attention_mask = jnp.ones_like(input_ids, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, params: dict = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, dropout_rng: PRNGKey = None, deterministic: bool = True, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: position_ids = (attention_mask.cumsum(axis=1) * attention_mask) - 1 # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxOPTAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] return outputs class FlaxOPTModule(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.decoder = FlaxOPTDecoder(self.config, dtype=self.dtype) def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, init_cache=False, ): decoder_outputs = self.decoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, init_cache=init_cache, ) if not return_dict: return decoder_outputs return FlaxBaseModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModel with Bart->OPT class FlaxOPTModel(FlaxOPTPreTrainedModel): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation module_class = FlaxOPTModule append_call_sample_docstring( FlaxOPTModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC ) @add_start_docstrings( "The bare OPT Model transformer outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING, ) class FlaxOPTForCausalLMModule(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.model = FlaxOPTModule(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__( self, input_ids, attention_mask, position_ids, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids, attention_mask, position_ids, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["decoder"]["embed_tokens"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + outputs[1:] return FlaxMaskedLMOutput( logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ OPT Model with a language modeling head on top (linear layer with weights tied to the input embeddings) e.g for autoregressive tasks. """, OPT_START_DOCSTRING, ) class FlaxOPTForCausalLM(FlaxOPTPreTrainedModel): module_class = FlaxOPTForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyway. # Thus, we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxOPTForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC, )

# coding=utf-8 # Copyright 2022 The Fairseq Authors and The Google Flax Team Authors And The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax OPT model.""" from functools import partial from typing import Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxMaskedLMOutput from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring from ...utils import add_start_docstrings, logging from .configuration_opt import OPTConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/opt-350m" _CONFIG_FOR_DOC = "OPTConfig" _TOKENIZER_FOR_DOC = "GPT2Tokenizer" OPT_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`OPTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ OPT_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`GPT2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->OPT class FlaxOPTAttention(nn.Module): config: OPTConfig embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxOPTDecoderLayer(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.hidden_size self.self_attn = FlaxOPTAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.num_attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.do_layer_norm_before = self.config.do_layer_norm_before self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.fc1 = nn.Dense( self.config.ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache, deterministic=deterministic, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.self_attn_layer_norm(hidden_states) # Fully Connected hidden_states_shape = hidden_states.shape hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1]) residual = hidden_states # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention if self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = (residual + hidden_states).reshape(hidden_states_shape) # 350m applies layer norm AFTER attention if not self.do_layer_norm_before: hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) return outputs class FlaxOPTDecoderLayerCollection(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxOPTDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] self.layerdrop = self.config.layerdrop def __call__( self, hidden_states, attention_mask, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) outputs = [hidden_states, all_hidden_states, all_self_attns] return outputs class FlaxOPTLearnedPositionalEmbedding(nn.Embed): """ This module learns positional embeddings up to a fixed maximum size. """ def setup(self): self.offset = 2 self.embedding = self.param( "embedding", self.embedding_init, (self.num_embeddings + self.offset, self.features), self.param_dtype ) def __call__(self, positions): """`input_ids_shape` is expected to be [bsz x seqlen].""" return super().__call__(positions + self.offset) class FlaxOPTDecoder(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation offset: int = 2 def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.hidden_size self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_tokens = nn.Embed( self.config.vocab_size, self.config.word_embed_proj_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.embed_positions = FlaxOPTLearnedPositionalEmbedding( self.config.max_position_embeddings, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) if self.config.word_embed_proj_dim != self.config.hidden_size: self.project_in = nn.Dense(self.config.hidden_size, use_bias=False) self.project_out = nn.Dense(self.config.word_embed_proj_dim, use_bias=False) else: self.project_in = None self.project_out = None # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility # with checkpoints that have been fine-tuned before transformers v4.20.1 # see https://github.com/facebookresearch/metaseq/pull/164 if self.config.do_layer_norm_before and not self.config._remove_final_layer_norm: self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) else: self.final_layer_norm = None self.layers = FlaxOPTDecoderLayerCollection(self.config, self.dtype) def __call__( self, input_ids, attention_mask, position_ids, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) if self.project_in is not None: inputs_embeds = self.project_in(inputs_embeds) positions = self.embed_positions(position_ids) hidden_states = inputs_embeds + positions hidden_state, all_hidden_states, attentions = self.layers( hidden_states, attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) if self.final_layer_norm is not None: hidden_state = self.final_layer_norm(hidden_state) if self.project_out is not None: hidden_state = self.project_out(hidden_state) if output_hidden_states: all_hidden_states += (hidden_state,) outputs = [hidden_state, all_hidden_states, attentions] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_state, hidden_states=all_hidden_states, attentions=attentions, ) class FlaxOPTPreTrainedModel(FlaxPreTrainedModel): config_class = OPTConfig base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: OPTConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} module_init_outputs = self.module.init( rngs, input_ids, attention_mask, position_ids, return_dict=False, ) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length), dtype="i4") attention_mask = jnp.ones_like(input_ids, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, params: dict = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, dropout_rng: PRNGKey = None, deterministic: bool = True, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: position_ids = (attention_mask.cumsum(axis=1) * attention_mask) - 1 # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxOPTAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] return outputs class FlaxOPTModule(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.decoder = FlaxOPTDecoder(self.config, dtype=self.dtype) def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, init_cache=False, ): decoder_outputs = self.decoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, init_cache=init_cache, ) if not return_dict: return decoder_outputs return FlaxBaseModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModel with Bart->OPT class FlaxOPTModel(FlaxOPTPreTrainedModel): config: OPTConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation module_class = FlaxOPTModule append_call_sample_docstring( FlaxOPTModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC ) @add_start_docstrings( "The bare OPT Model transformer outputting raw hidden-states without any specific head on top.", OPT_START_DOCSTRING, ) class FlaxOPTForCausalLMModule(nn.Module): config: OPTConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.model = FlaxOPTModule(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__( self, input_ids, attention_mask, position_ids, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids, attention_mask, position_ids, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["decoder"]["embed_tokens"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + outputs[1:] return FlaxMaskedLMOutput( logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ OPT Model with a language modeling head on top (linear layer with weights tied to the input embeddings) e.g for autoregressive tasks. """, OPT_START_DOCSTRING, ) class FlaxOPTForCausalLM(FlaxOPTPreTrainedModel): module_class = FlaxOPTForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyway. # Thus, we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxOPTForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC, )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/deberta_v2/tokenization_deberta_v2_fast.py

# coding=utf-8 # Copyright 2020 Microsoft and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization class for model DeBERTa.""" import os from shutil import copyfile from typing import Optional, Tuple from ...file_utils import is_sentencepiece_available from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging if is_sentencepiece_available(): from .tokenization_deberta_v2 import DebertaV2Tokenizer else: DebertaV2Tokenizer = None logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model", "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model", "microsoft/deberta-v2-xlarge-mnli": ( "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model" ), "microsoft/deberta-v2-xxlarge-mnli": ( "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model" ), } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/deberta-v2-xlarge": 512, "microsoft/deberta-v2-xxlarge": 512, "microsoft/deberta-v2-xlarge-mnli": 512, "microsoft/deberta-v2-xxlarge-mnli": 512, } PRETRAINED_INIT_CONFIGURATION = { "microsoft/deberta-v2-xlarge": {"do_lower_case": False}, "microsoft/deberta-v2-xxlarge": {"do_lower_case": False}, "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False}, "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False}, } class DebertaV2TokenizerFast(PreTrainedTokenizerFast): r""" Constructs a DeBERTa-v2 fast tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). Args: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the input when tokenizing. bos_token (`string`, *optional*, defaults to `"[CLS]"`): The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. eos_token (`string`, *optional*, defaults to `"[SEP]"`): The end of sequence token. When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. unk_token (`str`, *optional*, defaults to `"[UNK]"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = DebertaV2Tokenizer def __init__( self, vocab_file=None, tokenizer_file=None, do_lower_case=False, split_by_punct=False, bos_token="[CLS]", eos_token="[SEP]", unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs ) -> None: super().__init__( vocab_file, tokenizer_file=tokenizer_file, do_lower_case=do_lower_case, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, split_by_punct=split_by_punct, **kwargs, ) self.do_lower_case = do_lower_case self.split_by_punct = split_by_punct self.vocab_file = vocab_file self.can_save_slow_tokenizer = False if not self.vocab_file else True def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A DeBERTa sequence has the following format: - single sequence: [CLS] X [SEP] - pair of sequences: [CLS] A [SEP] B [SEP] Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not self.can_save_slow_tokenizer: raise ValueError( "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " "tokenizer." ) if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) return (out_vocab_file,)

# coding=utf-8 # Copyright 2020 Microsoft and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Fast Tokenization class for model DeBERTa.""" import os from shutil import copyfile from typing import Optional, Tuple from ...file_utils import is_sentencepiece_available from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging if is_sentencepiece_available(): from .tokenization_deberta_v2 import DebertaV2Tokenizer else: DebertaV2Tokenizer = None logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "spm.model", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model", "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model", "microsoft/deberta-v2-xlarge-mnli": ( "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model" ), "microsoft/deberta-v2-xxlarge-mnli": ( "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model" ), } } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "microsoft/deberta-v2-xlarge": 512, "microsoft/deberta-v2-xxlarge": 512, "microsoft/deberta-v2-xlarge-mnli": 512, "microsoft/deberta-v2-xxlarge-mnli": 512, } PRETRAINED_INIT_CONFIGURATION = { "microsoft/deberta-v2-xlarge": {"do_lower_case": False}, "microsoft/deberta-v2-xxlarge": {"do_lower_case": False}, "microsoft/deberta-v2-xlarge-mnli": {"do_lower_case": False}, "microsoft/deberta-v2-xxlarge-mnli": {"do_lower_case": False}, } class DebertaV2TokenizerFast(PreTrainedTokenizerFast): r""" Constructs a DeBERTa-v2 fast tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). Args: vocab_file (`str`): [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. do_lower_case (`bool`, *optional*, defaults to `False`): Whether or not to lowercase the input when tokenizing. bos_token (`string`, *optional*, defaults to `"[CLS]"`): The beginning of sequence token that was used during pre-training. Can be used a sequence classifier token. When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. eos_token (`string`, *optional*, defaults to `"[SEP]"`): The end of sequence token. When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. unk_token (`str`, *optional*, defaults to `"[UNK]"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. sep_token (`str`, *optional*, defaults to `"[SEP]"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. pad_token (`str`, *optional*, defaults to `"[PAD]"`): The token used for padding, for example when batching sequences of different lengths. cls_token (`str`, *optional*, defaults to `"[CLS]"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. mask_token (`str`, *optional*, defaults to `"[MASK]"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. sp_model_kwargs (`dict`, *optional*): Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = DebertaV2Tokenizer def __init__( self, vocab_file=None, tokenizer_file=None, do_lower_case=False, split_by_punct=False, bos_token="[CLS]", eos_token="[SEP]", unk_token="[UNK]", sep_token="[SEP]", pad_token="[PAD]", cls_token="[CLS]", mask_token="[MASK]", **kwargs ) -> None: super().__init__( vocab_file, tokenizer_file=tokenizer_file, do_lower_case=do_lower_case, bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, split_by_punct=split_by_punct, **kwargs, ) self.do_lower_case = do_lower_case self.split_by_punct = split_by_punct self.vocab_file = vocab_file self.can_save_slow_tokenizer = False if not self.vocab_file else True def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A DeBERTa sequence has the following format: - single sequence: [CLS] X [SEP] - pair of sequences: [CLS] A [SEP] B [SEP] Args: token_ids_0 (`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + token_ids_1 + sep def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. already_has_special_tokens (`bool`, *optional*, defaults to `False`): Whether or not the token list is already formatted with special tokens for the model. Returns: `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True ) if token_ids_1 is not None: return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] return [1] + ([0] * len(token_ids_0)) + [1] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A DeBERTa sequence pair mask has the following format: ``` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second sequence | ``` If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not self.can_save_slow_tokenizer: raise ValueError( "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow " "tokenizer." ) if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) return (out_vocab_file,)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/auto/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for sequence to sequence speech recognition. """ # You can also adapt this script on your own sequence to sequence speech # recognition task. Pointers for this are left as comments. import logging import os import sys from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Union import datasets import torch from datasets import DatasetDict, load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, HfArgumentParser, Seq2SeqTrainer, Seq2SeqTrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt") logger = logging.getLogger(__name__) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) feature_extractor_name: Optional[str] = field( default=None, metadata={"help": "feature extractor name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) freeze_encoder: bool = field( default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."} ) forced_decoder_ids: List[List[int]] = field( default=None, metadata={ "help": ( "A list of pairs of integers which indicates a mapping from generation indices to token indices " "that will be forced before sampling. For example, [[0, 123]] means the first generated token " "will always be a token of index 123." ) }, ) suppress_tokens: List[int] = field( default=None, metadata={"help": "A list of tokens that will be suppressed at generation."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: str = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) text_column: Optional[str] = field( default=None, metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"}, ) max_duration_in_seconds: float = field( default=20.0, metadata={ "help": ( "Truncate audio files that are longer than `max_duration_in_seconds` seconds to" " 'max_duration_in_seconds`" ) }, ) min_duration_in_seconds: float = field( default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"} ) preprocessing_only: bool = field( default=False, metadata={ "help": ( "Whether to only do data preprocessing and skip training. This is especially useful when data" " preprocessing errors out in distributed training due to timeout. In this case, one should run the" " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets" " can consequently be loaded in distributed training" ) }, ) train_split_name: str = field( default="train", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) eval_split_name: str = field( default="test", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) do_lower_case: bool = field( default=True, metadata={"help": "Whether the target text should be lower cased."}, ) language: str = field( default=None, metadata={ "help": ( "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning " "only. For English speech recognition, it should be set to `None`." ) }, ) task: str = field( default="transcribe", metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."}, ) @dataclass class DataCollatorSpeechSeq2SeqWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor ([`WhisperProcessor`]) The processor used for processing the data. decoder_start_token_id (`int`) The begin-of-sentence of the decoder. """ processor: Any decoder_start_token_id: int def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lengths and need # different padding methods model_input_name = self.processor.model_input_names[0] input_features = [{model_input_name: feature[model_input_name]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt") labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt") # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) # if bos token is appended in previous tokenization step, # cut bos token here as it's append later anyways if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item(): labels = labels[:, 1:] batch["labels"] = labels return batch def main(): # 1. Parse input arguments # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args) # 2. Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) datasets.utils.logging.set_verbosity(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() logger.info("Training/evaluation parameters %s", training_args) # 3. Detecting last checkpoint and eventually continue from last checkpoint last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed) # 4. Load dataset raw_datasets = DatasetDict() if training_args.do_train: raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=True if model_args.use_auth_token else None, ) if training_args.do_eval: raw_datasets["eval"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=True if model_args.use_auth_token else None, ) if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names: raise ValueError( f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--audio_column_name` to the correct audio column - one of " f"{', '.join(next(iter(raw_datasets.values())).column_names)}." ) if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names: raise ValueError( f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--text_column_name` to the correct text column - one of " f"{', '.join(next(iter(raw_datasets.values())).column_names)}." ) # 5. Load pretrained model, tokenizer, and feature extractor # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens}) feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) model = AutoModelForSpeechSeq2Seq.from_pretrained( model_args.model_name_or_path, config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) if model.config.decoder_start_token_id is None: raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") if model_args.freeze_feature_encoder: model.freeze_feature_encoder() if model_args.freeze_encoder: model.freeze_encoder() model.model.encoder.gradient_checkpointing = False if data_args.language is not None: # We only need to set the task id when the language is specified (i.e. in a multilingual setting) tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task) # 6. Resample speech dataset if necessary dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate if dataset_sampling_rate != feature_extractor.sampling_rate: raw_datasets = raw_datasets.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) # 7. Preprocessing the datasets. # We need to read the audio files as arrays and tokenize the targets. max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate audio_column_name = data_args.audio_column_name num_workers = data_args.preprocessing_num_workers text_column_name = data_args.text_column_name model_input_name = feature_extractor.model_input_names[0] do_lower_case = data_args.do_lower_case if data_args.max_train_samples is not None: raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples)) if data_args.max_eval_samples is not None: raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples)) def prepare_dataset(batch): # process audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) # process audio length batch[model_input_name] = inputs.get(model_input_name)[0] batch["input_length"] = len(sample["array"]) # process targets input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name] batch["labels"] = tokenizer(input_str).input_ids return batch with training_args.main_process_first(desc="dataset map pre-processing"): vectorized_datasets = raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=data_args.preprocessing_num_workers, desc="preprocess train dataset", ) # filter data that is shorter than min_input_length or longer than # max_input_length def is_audio_in_length_range(length): return length > min_input_length and length < max_input_length vectorized_datasets = vectorized_datasets.filter( is_audio_in_length_range, num_proc=num_workers, input_columns=["input_length"], ) # for large datasets it is advised to run the preprocessing on a # single machine first with `args.preprocessing_only` since there will mostly likely # be a timeout when running the script in distributed mode. # In a second step `args.preprocessing_only` can then be set to `False` to load the # cached dataset if data_args.preprocessing_only: cache = {k: v.cache_files for k, v in vectorized_datasets.items()} logger.info(f"Data preprocessing finished. Files cached at {cache}.") return # 8. Load Metric metric = evaluate.load("wer") def compute_metrics(pred): pred_ids = pred.predictions pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True) # we do not want to group tokens when computing the metrics label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True) wer = metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} # 9. Create a single speech processor if is_main_process(training_args.local_rank): # save feature extractor, tokenizer and config feature_extractor.save_pretrained(training_args.output_dir) tokenizer.save_pretrained(training_args.output_dir) config.save_pretrained(training_args.output_dir) processor = AutoProcessor.from_pretrained(training_args.output_dir) # 10. Define data collator data_collator = DataCollatorSpeechSeq2SeqWithPadding( processor=processor, decoder_start_token_id=model.config.decoder_start_token_id, ) # 11. Initialize Trainer trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=vectorized_datasets["train"] if training_args.do_train else None, eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None, tokenizer=feature_extractor, data_collator=data_collator, compute_metrics=compute_metrics if training_args.predict_with_generate else None, ) # 12. Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the feature extractor too for easy upload metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(vectorized_datasets["train"]) ) metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"])) trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # 13. Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate( metric_key_prefix="eval", max_length=training_args.generation_max_length, num_beams=training_args.generation_num_beams, ) max_eval_samples = ( data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"]) ) metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"])) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # 14. Write Training Stats kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition"} if data_args.dataset_name is not None: kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: kwargs["dataset_args"] = data_args.dataset_config_name kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: kwargs["dataset"] = data_args.dataset_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) return results if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for sequence to sequence speech recognition. """ # You can also adapt this script on your own sequence to sequence speech # recognition task. Pointers for this are left as comments. import logging import os import sys from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Union import datasets import torch from datasets import DatasetDict, load_dataset import evaluate import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForSpeechSeq2Seq, AutoProcessor, AutoTokenizer, HfArgumentParser, Seq2SeqTrainer, Seq2SeqTrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint, is_main_process from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt") logger = logging.getLogger(__name__) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) feature_extractor_name: Optional[str] = field( default=None, metadata={"help": "feature extractor name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) model_revision: str = field( default="main", metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "Will use the token generated when running `huggingface-cli login` (necessary to use this script " "with private models)." ) }, ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) freeze_encoder: bool = field( default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."} ) forced_decoder_ids: List[List[int]] = field( default=None, metadata={ "help": ( "A list of pairs of integers which indicates a mapping from generation indices to token indices " "that will be forced before sampling. For example, [[0, 123]] means the first generated token " "will always be a token of index 123." ) }, ) suppress_tokens: List[int] = field( default=None, metadata={"help": "A list of tokens that will be suppressed at generation."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: str = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) text_column: Optional[str] = field( default=None, metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ) }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"}, ) max_duration_in_seconds: float = field( default=20.0, metadata={ "help": ( "Truncate audio files that are longer than `max_duration_in_seconds` seconds to" " 'max_duration_in_seconds`" ) }, ) min_duration_in_seconds: float = field( default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"} ) preprocessing_only: bool = field( default=False, metadata={ "help": ( "Whether to only do data preprocessing and skip training. This is especially useful when data" " preprocessing errors out in distributed training due to timeout. In this case, one should run the" " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets" " can consequently be loaded in distributed training" ) }, ) train_split_name: str = field( default="train", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) eval_split_name: str = field( default="test", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" }, ) do_lower_case: bool = field( default=True, metadata={"help": "Whether the target text should be lower cased."}, ) language: str = field( default=None, metadata={ "help": ( "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning " "only. For English speech recognition, it should be set to `None`." ) }, ) task: str = field( default="transcribe", metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."}, ) @dataclass class DataCollatorSpeechSeq2SeqWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor ([`WhisperProcessor`]) The processor used for processing the data. decoder_start_token_id (`int`) The begin-of-sentence of the decoder. """ processor: Any decoder_start_token_id: int def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lengths and need # different padding methods model_input_name = self.processor.model_input_names[0] input_features = [{model_input_name: feature[model_input_name]} for feature in features] label_features = [{"input_ids": feature["labels"]} for feature in features] batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt") labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt") # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) # if bos token is appended in previous tokenization step, # cut bos token here as it's append later anyways if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item(): labels = labels[:, 1:] batch["labels"] = labels return batch def main(): # 1. Parse input arguments # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_speech_recognition_seq2seq", model_args, data_args) # 2. Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) log_level = training_args.get_process_log_level() logger.setLevel(log_level) datasets.utils.logging.set_verbosity(log_level) transformers.utils.logging.set_verbosity(log_level) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) logger.info(f"Training/evaluation parameters {training_args}") # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() logger.info("Training/evaluation parameters %s", training_args) # 3. Detecting last checkpoint and eventually continue from last checkpoint last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed) # 4. Load dataset raw_datasets = DatasetDict() if training_args.do_train: raw_datasets["train"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=True if model_args.use_auth_token else None, ) if training_args.do_eval: raw_datasets["eval"] = load_dataset( data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=True if model_args.use_auth_token else None, ) if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names: raise ValueError( f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--audio_column_name` to the correct audio column - one of " f"{', '.join(next(iter(raw_datasets.values())).column_names)}." ) if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names: raise ValueError( f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--text_column_name` to the correct text column - one of " f"{', '.join(next(iter(raw_datasets.values())).column_names)}." ) # 5. Load pretrained model, tokenizer, and feature extractor # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently config = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens}) feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) model = AutoModelForSpeechSeq2Seq.from_pretrained( model_args.model_name_or_path, config=config, cache_dir=model_args.cache_dir, revision=model_args.model_revision, use_auth_token=True if model_args.use_auth_token else None, ) if model.config.decoder_start_token_id is None: raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") if model_args.freeze_feature_encoder: model.freeze_feature_encoder() if model_args.freeze_encoder: model.freeze_encoder() model.model.encoder.gradient_checkpointing = False if data_args.language is not None: # We only need to set the task id when the language is specified (i.e. in a multilingual setting) tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task) # 6. Resample speech dataset if necessary dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate if dataset_sampling_rate != feature_extractor.sampling_rate: raw_datasets = raw_datasets.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate) ) # 7. Preprocessing the datasets. # We need to read the audio files as arrays and tokenize the targets. max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate audio_column_name = data_args.audio_column_name num_workers = data_args.preprocessing_num_workers text_column_name = data_args.text_column_name model_input_name = feature_extractor.model_input_names[0] do_lower_case = data_args.do_lower_case if data_args.max_train_samples is not None: raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples)) if data_args.max_eval_samples is not None: raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples)) def prepare_dataset(batch): # process audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) # process audio length batch[model_input_name] = inputs.get(model_input_name)[0] batch["input_length"] = len(sample["array"]) # process targets input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name] batch["labels"] = tokenizer(input_str).input_ids return batch with training_args.main_process_first(desc="dataset map pre-processing"): vectorized_datasets = raw_datasets.map( prepare_dataset, remove_columns=next(iter(raw_datasets.values())).column_names, num_proc=data_args.preprocessing_num_workers, desc="preprocess train dataset", ) # filter data that is shorter than min_input_length or longer than # max_input_length def is_audio_in_length_range(length): return length > min_input_length and length < max_input_length vectorized_datasets = vectorized_datasets.filter( is_audio_in_length_range, num_proc=num_workers, input_columns=["input_length"], ) # for large datasets it is advised to run the preprocessing on a # single machine first with `args.preprocessing_only` since there will mostly likely # be a timeout when running the script in distributed mode. # In a second step `args.preprocessing_only` can then be set to `False` to load the # cached dataset if data_args.preprocessing_only: cache = {k: v.cache_files for k, v in vectorized_datasets.items()} logger.info(f"Data preprocessing finished. Files cached at {cache}.") return # 8. Load Metric metric = evaluate.load("wer") def compute_metrics(pred): pred_ids = pred.predictions pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True) # we do not want to group tokens when computing the metrics label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True) wer = metric.compute(predictions=pred_str, references=label_str) return {"wer": wer} # 9. Create a single speech processor if is_main_process(training_args.local_rank): # save feature extractor, tokenizer and config feature_extractor.save_pretrained(training_args.output_dir) tokenizer.save_pretrained(training_args.output_dir) config.save_pretrained(training_args.output_dir) processor = AutoProcessor.from_pretrained(training_args.output_dir) # 10. Define data collator data_collator = DataCollatorSpeechSeq2SeqWithPadding( processor=processor, decoder_start_token_id=model.config.decoder_start_token_id, ) # 11. Initialize Trainer trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=vectorized_datasets["train"] if training_args.do_train else None, eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None, tokenizer=feature_extractor, data_collator=data_collator, compute_metrics=compute_metrics if training_args.predict_with_generate else None, ) # 12. Training if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the feature extractor too for easy upload metrics = train_result.metrics max_train_samples = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(vectorized_datasets["train"]) ) metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"])) trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # 13. Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate( metric_key_prefix="eval", max_length=training_args.generation_max_length, num_beams=training_args.generation_num_beams, ) max_eval_samples = ( data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"]) ) metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"])) trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # 14. Write Training Stats kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition"} if data_args.dataset_name is not None: kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: kwargs["dataset_args"] = data_args.dataset_config_name kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: kwargs["dataset"] = data_args.dataset_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) return results if __name__ == "__main__": main()

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/nystromformer/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available _import_structure = { "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_nystromformer"] = [ "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "NystromformerForMaskedLM", "NystromformerForMultipleChoice", "NystromformerForQuestionAnswering", "NystromformerForSequenceClassification", "NystromformerForTokenClassification", "NystromformerLayer", "NystromformerModel", "NystromformerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_nystromformer import ( NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, NystromformerForMaskedLM, NystromformerForMultipleChoice, NystromformerForQuestionAnswering, NystromformerForSequenceClassification, NystromformerForTokenClassification, NystromformerLayer, NystromformerModel, NystromformerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available _import_structure = { "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_nystromformer"] = [ "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "NystromformerForMaskedLM", "NystromformerForMultipleChoice", "NystromformerForQuestionAnswering", "NystromformerForSequenceClassification", "NystromformerForTokenClassification", "NystromformerLayer", "NystromformerModel", "NystromformerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_nystromformer import ( NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, NystromformerForMaskedLM, NystromformerForMultipleChoice, NystromformerForQuestionAnswering, NystromformerForSequenceClassification, NystromformerForTokenClassification, NystromformerLayer, NystromformerModel, NystromformerPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/videomae/modeling_videomae.py

# coding=utf-8 # Copyright 2022 Multimedia Computing Group, Nanjing University and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch VideoMAE (masked autoencoder) model.""" import collections.abc import math from copy import deepcopy from dataclasses import dataclass from typing import Optional, Set, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ...utils.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .configuration_videomae import VideoMAEConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VideoMAEConfig" _CHECKPOINT_FOR_DOC = "MCG-NJU/videomae-base" VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "MCG-NJU/videomae-base", # See all VideoMAE models at https://huggingface.co/models?filter=videomae ] @dataclass class VideoMAEDecoderOutput(ModelOutput): """ Class for VideoMAEDecoder's outputs, with potential hidden states and attentions. Args: logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class VideoMAEForPreTrainingOutput(ModelOutput): """ Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions. Args: loss (`torch.FloatTensor` of shape `(1,)`): Pixel reconstruction loss. logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None # sin-cos position encoding # https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 def get_sinusoid_encoding_table(n_position, d_hid): """Sinusoid position encoding table""" # TODO: make it with torch instead of numpy def get_position_angle_vec(position): return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 return torch.FloatTensor(sinusoid_table).unsqueeze(0) class VideoMAEEmbeddings(nn.Module): """ Construct the patch and position embeddings. """ def __init__(self, config): super().__init__() self.patch_embeddings = VideoMAEPatchEmbeddings(config) self.num_patches = self.patch_embeddings.num_patches # fixed sin-cos embedding self.position_embeddings = get_sinusoid_encoding_table(self.num_patches, config.hidden_size) self.config = config def forward(self, pixel_values, bool_masked_pos): # create patch embeddings embeddings = self.patch_embeddings(pixel_values) # add position embeddings embeddings = embeddings + self.position_embeddings.type_as(embeddings).to(embeddings.device).clone().detach() # only keep visible patches # ~bool_masked_pos means visible if bool_masked_pos is not None: batch_size, _, num_channels = embeddings.shape embeddings = embeddings[~bool_masked_pos] embeddings = embeddings.reshape(batch_size, -1, num_channels) return embeddings class VideoMAEPatchEmbeddings(nn.Module): """ Video to Patch Embedding. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. The seq_len (the number of patches) equals (number of frames // tubelet_size) * (height // patch_size) * (width // patch_size). """ def __init__(self, config): super().__init__() image_size = config.image_size patch_size = config.patch_size num_channels = config.num_channels hidden_size = config.hidden_size num_frames = config.num_frames tubelet_size = config.tubelet_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) self.image_size = image_size self.patch_size = patch_size self.tubelet_size = int(tubelet_size) num_patches = ( (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) * (num_frames // self.tubelet_size) ) self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv3d( in_channels=num_channels, out_channels=hidden_size, kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), stride=(self.tubelet_size, patch_size[0], patch_size[1]), ) def forward(self, pixel_values): batch_size, num_frames, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." ) # permute to (batch_size, num_channels, num_frames, height, width) pixel_values = pixel_values.permute(0, 2, 1, 3, 4) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings class VideoMAESelfAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False) if config.qkv_bias: self.q_bias = nn.Parameter(torch.zeros(self.all_head_size)) self.v_bias = nn.Parameter(torch.zeros(self.all_head_size)) else: self.q_bias = None self.v_bias = None self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: k_bias = torch.zeros_like(self.v_bias, requires_grad=False) if self.q_bias is not None else None keys = nn.functional.linear(input=hidden_states, weight=self.key.weight, bias=k_bias) values = nn.functional.linear(input=hidden_states, weight=self.value.weight, bias=self.v_bias) queries = nn.functional.linear(input=hidden_states, weight=self.query.weight, bias=self.q_bias) key_layer = self.transpose_for_scores(keys) value_layer = self.transpose_for_scores(values) query_layer = self.transpose_for_scores(queries) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->VideoMAE class VideoMAESelfOutput(nn.Module): """ The residual connection is defined in VideoMAELayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->VideoMAE class VideoMAEAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.attention = VideoMAESelfAttention(config) self.output = VideoMAESelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->VideoMAE class VideoMAEIntermediate(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->VideoMAE class VideoMAEOutput(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->VideoMAE class VideoMAELayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = VideoMAEAttention(config) self.intermediate = VideoMAEIntermediate(config) self.output = VideoMAEOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in VideoMAE, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in VideoMAE, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->VideoMAE class VideoMAEEncoder(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([VideoMAELayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class VideoMAEPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = VideoMAEConfig base_model_prefix = "videomae" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv3d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, VideoMAEEncoder): module.gradient_checkpointing = value VIDEOMAE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VideoMAEConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIDEOMAE_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`VideoMAEFeatureExtractor`]. See [`VideoMAEFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare VideoMAE Model transformer outputting raw hidden-states without any specific head on top.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEModel(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = VideoMAEEmbeddings(config) self.encoder = VideoMAEEncoder(config) if config.use_mean_pooling: self.layernorm = None else: self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEModel >>> from huggingface_hub import hf_hub_download >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base") >>> # prepare video for the model >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1568, 768] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values, bool_masked_pos) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if self.layernorm is not None: sequence_output = self.layernorm(sequence_output) if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class VideoMAEDecoder(nn.Module): def __init__(self, config, num_patches): super().__init__() decoder_num_labels = config.num_channels * config.tubelet_size * config.patch_size**2 decoder_config = deepcopy(config) decoder_config.hidden_size = config.decoder_hidden_size decoder_config.num_hidden_layers = config.decoder_num_hidden_layers decoder_config.num_attention_heads = config.decoder_num_attention_heads decoder_config.intermediate_size = config.decoder_intermediate_size self.decoder_layers = nn.ModuleList( [VideoMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)] ) self.norm = nn.LayerNorm(config.decoder_hidden_size) self.head = ( nn.Linear(config.decoder_hidden_size, decoder_num_labels) if decoder_num_labels > 0 else nn.Identity() ) self.gradient_checkpointing = False self.config = config def forward( self, hidden_states, return_token_num, output_attentions=False, output_hidden_states=False, return_dict=True, ): # apply Transformer layers (blocks) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.decoder_layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, None, ) else: layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if return_token_num > 0: hidden_states = hidden_states[:, -return_token_num:] # predictor projection hidden_states = self.norm(hidden_states) logits = self.head(hidden_states) if not return_dict: return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None) return VideoMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions) @add_start_docstrings( "The VideoMAE Model transformer with the decoder on top for self-supervised pre-training.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForPreTraining(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.videomae = VideoMAEModel(config) self.encoder_to_decoder = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=False) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size)) self.position_embeddings = get_sinusoid_encoding_table( self.videomae.embeddings.num_patches, config.decoder_hidden_size ) self.decoder = VideoMAEDecoder(config, num_patches=self.videomae.embeddings.num_patches) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=VideoMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, bool_masked_pos: torch.BoolTensor, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, VideoMAEForPreTrainingOutput]: r""" Returns: Examples: ```python >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForPreTraining >>> import numpy as np >>> import torch >>> num_frames = 16 >>> video = list(np.random.randn(16, 3, 224, 224)) >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") >>> pixel_values = feature_extractor(video, return_tensors="pt").pixel_values >>> num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2 >>> seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame >>> bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss = outputs.loss ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.encoder_to_decoder( sequence_output ) # [batch_size, num_visible_patches, decoder_hidden_size] batch_size, seq_len, num_channels = sequence_output.shape # we don't unshuffle the correct visible token order, but shuffle the position embeddings accordingly. if bool_masked_pos is None: raise ValueError("One must provided a boolean mask ") expanded_position_embeddings = self.position_embeddings.expand(batch_size, -1, -1).type_as(pixel_values) expanded_position_embeddings = expanded_position_embeddings.to(pixel_values.device).clone().detach() pos_emb_visible = expanded_position_embeddings[~bool_masked_pos].reshape(batch_size, -1, num_channels) pos_emb_mask = expanded_position_embeddings[bool_masked_pos].reshape(batch_size, -1, num_channels) # [batch_size, num_patches, decoder_hidden_size] x_full = torch.cat([sequence_output + pos_emb_visible, self.mask_token + pos_emb_mask], dim=1) # [batch_size, num_masked_patches, num_channels * patch_size * patch_size] decoder_outputs = self.decoder(x_full, pos_emb_mask.shape[1]) logits = decoder_outputs.logits loss = None with torch.no_grad(): # calculate the labels to be predicted # first, unnormalize the frames device = pixel_values.device mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, None, :, None, None] std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, None, :, None, None] frames = pixel_values * std + mean # in [0, 1] batch_size, time, num_channels, height, width = frames.shape tubelet_size, patch_size = self.config.tubelet_size, self.config.patch_size if self.config.norm_pix_loss: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate: frames = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size, num_channels, ) # step 4: normalize. The authors find that the mean is about 0.48 and standard deviation is about 0.08. frames_norm = (frames - frames.mean(dim=-2, keepdim=True)) / ( frames.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6 ) # step 5: reshape to (batch_size, T//ts * H//ps * W//ps, ts * ps * ps * C) videos_patch = frames_norm.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) else: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: (batch_size, T//ts, H//ps, W//ps, ts, ps, ps, C) frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate videos_patch = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) batch_size, _, num_channels = videos_patch.shape labels = videos_patch[bool_masked_pos].reshape(batch_size, -1, num_channels) loss_fct = MSELoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return VideoMAEForPreTrainingOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """VideoMAE Model transformer with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.""", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForVideoClassification(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.videomae = VideoMAEModel(config) # Classifier head self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean_pooling else None self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, ImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import torch >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... logits = outputs.logits >>> # model predicts one of the 400 Kinetics-400 classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) eating spaghetti ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] if self.fc_norm is not None: sequence_output = self.fc_norm(sequence_output.mean(1)) else: sequence_output = sequence_output[:, 0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

# coding=utf-8 # Copyright 2022 Multimedia Computing Group, Nanjing University and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch VideoMAE (masked autoencoder) model.""" import collections.abc import math from copy import deepcopy from dataclasses import dataclass from typing import Optional, Set, Tuple, Union import numpy as np import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from ...utils.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .configuration_videomae import VideoMAEConfig logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "VideoMAEConfig" _CHECKPOINT_FOR_DOC = "MCG-NJU/videomae-base" VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST = [ "MCG-NJU/videomae-base", # See all VideoMAE models at https://huggingface.co/models?filter=videomae ] @dataclass class VideoMAEDecoderOutput(ModelOutput): """ Class for VideoMAEDecoder's outputs, with potential hidden states and attentions. Args: logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class VideoMAEForPreTrainingOutput(ModelOutput): """ Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions. Args: loss (`torch.FloatTensor` of shape `(1,)`): Pixel reconstruction loss. logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None # sin-cos position encoding # https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 def get_sinusoid_encoding_table(n_position, d_hid): """Sinusoid position encoding table""" # TODO: make it with torch instead of numpy def get_position_angle_vec(position): return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 return torch.FloatTensor(sinusoid_table).unsqueeze(0) class VideoMAEEmbeddings(nn.Module): """ Construct the patch and position embeddings. """ def __init__(self, config): super().__init__() self.patch_embeddings = VideoMAEPatchEmbeddings(config) self.num_patches = self.patch_embeddings.num_patches # fixed sin-cos embedding self.position_embeddings = get_sinusoid_encoding_table(self.num_patches, config.hidden_size) self.config = config def forward(self, pixel_values, bool_masked_pos): # create patch embeddings embeddings = self.patch_embeddings(pixel_values) # add position embeddings embeddings = embeddings + self.position_embeddings.type_as(embeddings).to(embeddings.device).clone().detach() # only keep visible patches # ~bool_masked_pos means visible if bool_masked_pos is not None: batch_size, _, num_channels = embeddings.shape embeddings = embeddings[~bool_masked_pos] embeddings = embeddings.reshape(batch_size, -1, num_channels) return embeddings class VideoMAEPatchEmbeddings(nn.Module): """ Video to Patch Embedding. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. The seq_len (the number of patches) equals (number of frames // tubelet_size) * (height // patch_size) * (width // patch_size). """ def __init__(self, config): super().__init__() image_size = config.image_size patch_size = config.patch_size num_channels = config.num_channels hidden_size = config.hidden_size num_frames = config.num_frames tubelet_size = config.tubelet_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) self.image_size = image_size self.patch_size = patch_size self.tubelet_size = int(tubelet_size) num_patches = ( (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) * (num_frames // self.tubelet_size) ) self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv3d( in_channels=num_channels, out_channels=hidden_size, kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), stride=(self.tubelet_size, patch_size[0], patch_size[1]), ) def forward(self, pixel_values): batch_size, num_frames, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." ) # permute to (batch_size, num_channels, num_frames, height, width) pixel_values = pixel_values.permute(0, 2, 1, 3, 4) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings class VideoMAESelfAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False) if config.qkv_bias: self.q_bias = nn.Parameter(torch.zeros(self.all_head_size)) self.v_bias = nn.Parameter(torch.zeros(self.all_head_size)) else: self.q_bias = None self.v_bias = None self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: k_bias = torch.zeros_like(self.v_bias, requires_grad=False) if self.q_bias is not None else None keys = nn.functional.linear(input=hidden_states, weight=self.key.weight, bias=k_bias) values = nn.functional.linear(input=hidden_states, weight=self.value.weight, bias=self.v_bias) queries = nn.functional.linear(input=hidden_states, weight=self.query.weight, bias=self.q_bias) key_layer = self.transpose_for_scores(keys) value_layer = self.transpose_for_scores(values) query_layer = self.transpose_for_scores(queries) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs # Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->VideoMAE class VideoMAESelfOutput(nn.Module): """ The residual connection is defined in VideoMAELayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->VideoMAE class VideoMAEAttention(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.attention = VideoMAESelfAttention(config) self.output = VideoMAESelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs # Copied from transformers.models.vit.modeling_vit.ViTIntermediate ViT->VideoMAE class VideoMAEIntermediate(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTOutput ViT->VideoMAE class VideoMAEOutput(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states # Copied from transformers.models.vit.modeling_vit.ViTLayer with ViT->VideoMAE class VideoMAELayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = VideoMAEAttention(config) self.intermediate = VideoMAEIntermediate(config) self.output = VideoMAEOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in VideoMAE, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in VideoMAE, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs # Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->VideoMAE class VideoMAEEncoder(nn.Module): def __init__(self, config: VideoMAEConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([VideoMAELayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class VideoMAEPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = VideoMAEConfig base_model_prefix = "videomae" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv3d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, VideoMAEEncoder): module.gradient_checkpointing = value VIDEOMAE_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VideoMAEConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIDEOMAE_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`VideoMAEFeatureExtractor`]. See [`VideoMAEFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare VideoMAE Model transformer outputting raw hidden-states without any specific head on top.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEModel(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embeddings = VideoMAEEmbeddings(config) self.encoder = VideoMAEEncoder(config) if config.use_mean_pooling: self.layernorm = None else: self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutput]: r""" Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEModel >>> from huggingface_hub import hf_hub_download >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base") >>> # prepare video for the model >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1568, 768] ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings(pixel_values, bool_masked_pos) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] if self.layernorm is not None: sequence_output = self.layernorm(sequence_output) if not return_dict: return (sequence_output,) + encoder_outputs[1:] return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class VideoMAEDecoder(nn.Module): def __init__(self, config, num_patches): super().__init__() decoder_num_labels = config.num_channels * config.tubelet_size * config.patch_size**2 decoder_config = deepcopy(config) decoder_config.hidden_size = config.decoder_hidden_size decoder_config.num_hidden_layers = config.decoder_num_hidden_layers decoder_config.num_attention_heads = config.decoder_num_attention_heads decoder_config.intermediate_size = config.decoder_intermediate_size self.decoder_layers = nn.ModuleList( [VideoMAELayer(decoder_config) for _ in range(config.decoder_num_hidden_layers)] ) self.norm = nn.LayerNorm(config.decoder_hidden_size) self.head = ( nn.Linear(config.decoder_hidden_size, decoder_num_labels) if decoder_num_labels > 0 else nn.Identity() ) self.gradient_checkpointing = False self.config = config def forward( self, hidden_states, return_token_num, output_attentions=False, output_hidden_states=False, return_dict=True, ): # apply Transformer layers (blocks) all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.decoder_layers): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, None, ) else: layer_outputs = layer_module(hidden_states, head_mask=None, output_attentions=output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if return_token_num > 0: hidden_states = hidden_states[:, -return_token_num:] # predictor projection hidden_states = self.norm(hidden_states) logits = self.head(hidden_states) if not return_dict: return tuple(v for v in [logits, all_hidden_states, all_self_attentions] if v is not None) return VideoMAEDecoderOutput(logits=logits, hidden_states=all_hidden_states, attentions=all_self_attentions) @add_start_docstrings( "The VideoMAE Model transformer with the decoder on top for self-supervised pre-training.", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForPreTraining(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.videomae = VideoMAEModel(config) self.encoder_to_decoder = nn.Linear(config.hidden_size, config.decoder_hidden_size, bias=False) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.decoder_hidden_size)) self.position_embeddings = get_sinusoid_encoding_table( self.videomae.embeddings.num_patches, config.decoder_hidden_size ) self.decoder = VideoMAEDecoder(config, num_patches=self.videomae.embeddings.num_patches) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=VideoMAEForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: torch.FloatTensor, bool_masked_pos: torch.BoolTensor, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, VideoMAEForPreTrainingOutput]: r""" Returns: Examples: ```python >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForPreTraining >>> import numpy as np >>> import torch >>> num_frames = 16 >>> video = list(np.random.randn(16, 3, 224, 224)) >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") >>> pixel_values = feature_extractor(video, return_tensors="pt").pixel_values >>> num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2 >>> seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame >>> bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss = outputs.loss ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] sequence_output = self.encoder_to_decoder( sequence_output ) # [batch_size, num_visible_patches, decoder_hidden_size] batch_size, seq_len, num_channels = sequence_output.shape # we don't unshuffle the correct visible token order, but shuffle the position embeddings accordingly. if bool_masked_pos is None: raise ValueError("One must provided a boolean mask ") expanded_position_embeddings = self.position_embeddings.expand(batch_size, -1, -1).type_as(pixel_values) expanded_position_embeddings = expanded_position_embeddings.to(pixel_values.device).clone().detach() pos_emb_visible = expanded_position_embeddings[~bool_masked_pos].reshape(batch_size, -1, num_channels) pos_emb_mask = expanded_position_embeddings[bool_masked_pos].reshape(batch_size, -1, num_channels) # [batch_size, num_patches, decoder_hidden_size] x_full = torch.cat([sequence_output + pos_emb_visible, self.mask_token + pos_emb_mask], dim=1) # [batch_size, num_masked_patches, num_channels * patch_size * patch_size] decoder_outputs = self.decoder(x_full, pos_emb_mask.shape[1]) logits = decoder_outputs.logits loss = None with torch.no_grad(): # calculate the labels to be predicted # first, unnormalize the frames device = pixel_values.device mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, None, :, None, None] std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, None, :, None, None] frames = pixel_values * std + mean # in [0, 1] batch_size, time, num_channels, height, width = frames.shape tubelet_size, patch_size = self.config.tubelet_size, self.config.patch_size if self.config.norm_pix_loss: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate: frames = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size, num_channels, ) # step 4: normalize. The authors find that the mean is about 0.48 and standard deviation is about 0.08. frames_norm = (frames - frames.mean(dim=-2, keepdim=True)) / ( frames.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6 ) # step 5: reshape to (batch_size, T//ts * H//ps * W//ps, ts * ps * ps * C) videos_patch = frames_norm.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) else: # step 1: split up dimensions (time by tubelet_size, height by patch_size, width by patch_size) frames = frames.view( batch_size, time // tubelet_size, tubelet_size, num_channels, height // patch_size, patch_size, width // patch_size, patch_size, ) # step 2: move dimensions to concatenate: (batch_size, T//ts, H//ps, W//ps, ts, ps, ps, C) frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() # step 3: concatenate videos_patch = frames.view( batch_size, time // tubelet_size * height // patch_size * width // patch_size, tubelet_size * patch_size * patch_size * num_channels, ) batch_size, _, num_channels = videos_patch.shape labels = videos_patch[bool_masked_pos].reshape(batch_size, -1, num_channels) loss_fct = MSELoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return VideoMAEForPreTrainingOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """VideoMAE Model transformer with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet.""", VIDEOMAE_START_DOCSTRING, ) class VideoMAEForVideoClassification(VideoMAEPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.videomae = VideoMAEModel(config) # Classifier head self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean_pooling else None self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIDEOMAE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, ImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from decord import VideoReader, cpu >>> import torch >>> import numpy as np >>> from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> videoreader = VideoReader(file_path, num_threads=1, ctx=cpu(0)) >>> # sample 16 frames >>> videoreader.seek(0) >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=len(videoreader)) >>> video = videoreader.get_batch(indices).asnumpy() >>> feature_extractor = VideoMAEFeatureExtractor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> inputs = feature_extractor(list(video), return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... logits = outputs.logits >>> # model predicts one of the 400 Kinetics-400 classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) eating spaghetti ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.videomae( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = outputs[0] if self.fc_norm is not None: sequence_output = self.fc_norm(sequence_output.mean(1)) else: sequence_output = sequence_output[:, 0] logits = self.classifier(sequence_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/m2m_100/convert_m2m100_original_checkpoint_to_pytorch.py

# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import torch from torch import nn from transformers import M2M100Config, M2M100ForConditionalGeneration def remove_ignore_keys_(state_dict): ignore_keys = [ "encoder.version", "decoder.version", "model.encoder.version", "model.decoder.version", "decoder.output_projection.weight", "_float_tensor", "encoder.embed_positions._float_tensor", "decoder.embed_positions._float_tensor", ] for k in ignore_keys: state_dict.pop(k, None) def make_linear_from_emb(emb): vocab_size, emb_size = emb.weight.shape lin_layer = nn.Linear(vocab_size, emb_size, bias=False) lin_layer.weight.data = emb.weight.data return lin_layer def convert_fairseq_m2m100_checkpoint_from_disk(checkpoint_path): m2m_100 = torch.load(checkpoint_path, map_location="cpu") args = m2m_100["args"] or m2m_100["cfg"]["model"] state_dict = m2m_100["model"] remove_ignore_keys_(state_dict) vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0] config = M2M100Config( vocab_size=vocab_size, max_position_embeddings=1024, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, encoder_layerdrop=args.encoder_layerdrop, decoder_layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function="relu", ) state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"] model = M2M100ForConditionalGeneration(config) model.model.load_state_dict(state_dict, strict=False) model.lm_head = make_linear_from_emb(model.model.shared) return model if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.") parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") args = parser.parse_args() model = convert_fairseq_m2m100_checkpoint_from_disk(args.fairseq_pathß) model.save_pretrained(args.pytorch_dump_folder_path)

# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import torch from torch import nn from transformers import M2M100Config, M2M100ForConditionalGeneration def remove_ignore_keys_(state_dict): ignore_keys = [ "encoder.version", "decoder.version", "model.encoder.version", "model.decoder.version", "decoder.output_projection.weight", "_float_tensor", "encoder.embed_positions._float_tensor", "decoder.embed_positions._float_tensor", ] for k in ignore_keys: state_dict.pop(k, None) def make_linear_from_emb(emb): vocab_size, emb_size = emb.weight.shape lin_layer = nn.Linear(vocab_size, emb_size, bias=False) lin_layer.weight.data = emb.weight.data return lin_layer def convert_fairseq_m2m100_checkpoint_from_disk(checkpoint_path): m2m_100 = torch.load(checkpoint_path, map_location="cpu") args = m2m_100["args"] or m2m_100["cfg"]["model"] state_dict = m2m_100["model"] remove_ignore_keys_(state_dict) vocab_size = state_dict["encoder.embed_tokens.weight"].shape[0] config = M2M100Config( vocab_size=vocab_size, max_position_embeddings=1024, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, encoder_layerdrop=args.encoder_layerdrop, decoder_layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function="relu", ) state_dict["shared.weight"] = state_dict["decoder.embed_tokens.weight"] model = M2M100ForConditionalGeneration(config) model.model.load_state_dict(state_dict, strict=False) model.lm_head = make_linear_from_emb(model.model.shared) return model if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.") parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") args = parser.parse_args() model = convert_fairseq_m2m100_checkpoint_from_disk(args.fairseq_pathß) model.save_pretrained(args.pytorch_dump_folder_path)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/clip/modeling_flax_clip.py

# coding=utf-8 # Copyright 2021 The OpenAI Team Authors, The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Optional, Tuple, Union import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxBaseModelOutputWithPooling from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import ModelOutput, add_start_docstrings, logging from .configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig logger = logging.get_logger(__name__) CLIP_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`CLIPConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ CLIP_TEXT_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIP_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIP_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @flax.struct.dataclass class FlaxCLIPOutput(ModelOutput): """ Args: logits_per_image:(`jnp.ndarray` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`jnp.ndarray` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPTextModel`]. image_embeds(`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPVisionModel`]. text_model_output(`FlaxBaseModelOutputWithPooling`): The output of the [`FlaxCLIPTextModel`]. vision_model_output(`FlaxBaseModelOutputWithPooling`): The output of the [`FlaxCLIPVisionModel`]. """ logits_per_image: jnp.ndarray = None logits_per_text: jnp.ndarray = None text_embeds: jnp.ndarray = None image_embeds: jnp.ndarray = None text_model_output: FlaxBaseModelOutputWithPooling = None vision_model_output: FlaxBaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) class FlaxCLIPVisionEmbeddings(nn.Module): config: CLIPVisionConfig dtype: jnp.dtype = jnp.float32 def setup(self): embed_dim = self.config.hidden_size image_size = self.config.image_size patch_size = self.config.patch_size self.class_embedding = self.param("class_embedding", jax.nn.initializers.normal(stddev=0.02), (embed_dim,)) self.patch_embedding = nn.Conv( embed_dim, kernel_size=(patch_size, patch_size), strides=(patch_size, patch_size), padding="VALID", use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(), ) self.num_patches = (image_size // patch_size) ** 2 num_positions = self.num_patches + 1 self.position_embedding = nn.Embed(num_positions, embed_dim, embedding_init=jax.nn.initializers.normal()) self.position_ids = jnp.expand_dims(jnp.arange(0, num_positions, dtype="i4"), axis=0) def __call__(self, pixel_values): patch_embeds = self.patch_embedding(pixel_values) batch_size, height, width, channels = patch_embeds.shape patch_embeds = jnp.reshape(patch_embeds, (batch_size, height * width, channels)) class_embeds = jnp.expand_dims(self.class_embedding, axis=(0, 1)) class_embeds = jnp.tile(class_embeds, (batch_size, 1, 1)) embeddings = jnp.concatenate([class_embeds, patch_embeds], axis=1) embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings class FlaxCLIPTextEmbeddings(nn.Module): config: CLIPTextConfig dtype: jnp.dtype = jnp.float32 def setup(self): embed_dim = self.config.hidden_size self.token_embedding = nn.Embed(self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal()) self.position_embedding = nn.Embed( self.config.max_position_embeddings, embed_dim, embedding_init=jax.nn.initializers.normal() ) self.position_ids = jnp.expand_dims( jnp.arange(0, self.config.max_position_embeddings, dtype="i4"), axis=(0, 1) ) def __call__(self, input_ids, position_ids): input_embeds = self.token_embedding(input_ids.astype("i4")) position_embeds = self.position_embedding(position_ids.astype("i4")) embeddings = input_embeds + position_embeds return embeddings class FlaxCLIPAttention(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.embed_dim = self.config.hidden_size self.num_heads = self.config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = self.config.attention_dropout self.k_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.v_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.q_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.out_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.causal = isinstance(self.config, CLIPTextConfig) if self.causal: self.causal_mask = make_causal_mask(jnp.ones((1, self.config.max_position_embeddings), dtype="i4")) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) def __call__( self, hidden_states, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, ): query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query) key = self._split_heads(key) value = self._split_heads(value) causal_attention_mask = None if self.causal: query_length, key_length = query.shape[1], key.shape[1] causal_attention_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length] if attention_mask is not None and causal_attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) attention_mask = combine_masks(attention_mask, causal_attention_mask, dtype="i4") elif causal_attention_mask is not None: attention_mask = causal_attention_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) if attention_mask is not None: attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, -1e4).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query, key, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs class FlaxCLIPMLP(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.activation_fn = ACT2FN[self.config.hidden_act] self.fc1 = nn.Dense( self.config.intermediate_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01), ) self.fc2 = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) def __call__(self, hidden_states): hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states class FlaxCLIPEncoderLayer(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.self_attn = FlaxCLIPAttention(self.config, dtype=self.dtype) self.layer_norm1 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.mlp = FlaxCLIPMLP(self.config, dtype=self.dtype) self.layer_norm2 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, ): residual = hidden_states hidden_states = self.layer_norm1(hidden_states) attn_outputs = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, ) hidden_states = attn_outputs[0] hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += attn_outputs[1:] return outputs class FlaxCLIPLayerCollection(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = [ FlaxCLIPEncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states,) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxCLIPEncoder(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = FlaxCLIPLayerCollection(self.config, dtype=self.dtype) def __call__( self, inputs_embeds, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.layers( hidden_states=inputs_embeds, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class FlaxCLIPTextTransformer(nn.Module): config: CLIPTextConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.embeddings = FlaxCLIPTextEmbeddings(self.config, dtype=self.dtype) self.encoder = FlaxCLIPEncoder(self.config, dtype=self.dtype) self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.final_layer_norm(last_hidden_state) # text_embeds.shape = [batch_size, sequence_length, transformer.width] # take features from the EOS embedding (eos_token_id is the highest number in each sequence) pooled_output = last_hidden_state[jnp.arange(last_hidden_state.shape[0]), input_ids.argmax(axis=-1)] if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return FlaxBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class FlaxCLIPVisionTransformer(nn.Module): config: CLIPVisionConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.embeddings = FlaxCLIPVisionEmbeddings(self.config, dtype=self.dtype) self.pre_layrnorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.encoder = FlaxCLIPEncoder(self.config, dtype=self.dtype) self.post_layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__( self, pixel_values=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict: bool = True, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return FlaxBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class FlaxCLIPTextPreTrainedModel(FlaxPreTrainedModel): config_class = CLIPTextConfig module_class: nn.Module = None def __init__( self, config: CLIPTextConfig, input_shape=(1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) attention_mask = jnp.ones_like(input_ids) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, attention_mask, position_ids)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, attention_mask=None, position_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) class FlaxCLIPVisionPreTrainedModel(FlaxPreTrainedModel): config_class = CLIPVisionConfig main_input_name = "pixel_values" module_class: nn.Module = None def __init__( self, config: CLIPVisionConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if input_shape is None: input_shape = (1, config.image_size, config.image_size, 3) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor pixel_values = jax.random.normal(rng, input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, pixel_values)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) class FlaxCLIPPreTrainedModel(FlaxPreTrainedModel): config_class = CLIPConfig module_class: nn.Module = None def __init__( self, config: CLIPConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if input_shape is None: input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape[0], dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) attention_mask = jnp.ones_like(input_ids) pixel_values = jax.random.normal(rng, input_shape[1]) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, pixel_values, attention_mask=None, position_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(pixel_values, dtype=jnp.float32), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def get_text_features( self, input_ids, attention_mask=None, position_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False, ): r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Returns: text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPTextModel`]. Examples: ```python >>> from transformers import CLIPTokenizer, FlaxCLIPModel >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32") >>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np") >>> text_features = model.get_text_features(**inputs) ```""" if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, input_ids, attention_mask, position_ids, deterministic): text_outputs = module.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, ) pooled_output = text_outputs[1] text_features = module.text_projection(pooled_output) return text_features return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, method=_get_features, rngs=rngs, ) def get_image_features( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False ): r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. Returns: image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPVisionModel`] Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPProcessor, FlaxCLIPModel >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32") >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="np") >>> image_features = model.get_image_features(**inputs) ```""" pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, pixel_values, deterministic): vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) pooled_output = vision_outputs[1] # pooled_output image_features = module.visual_projection(pooled_output) return image_features return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, method=_get_features, rngs=rngs, ) class FlaxCLIPTextModule(nn.Module): config: CLIPTextConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.text_model = FlaxCLIPTextTransformer(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class FlaxCLIPTextModel(FlaxCLIPTextPreTrainedModel): module_class = FlaxCLIPTextModule FLAX_CLIP_TEXT_MODEL_DOCSTRING = """ Returns: Example: ```python >>> from transformers import CLIPTokenizer, FlaxCLIPTextModel >>> model = FlaxCLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") >>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooler_output = outputs.pooler_output # pooled (EOS token) states ``` """ overwrite_call_docstring(FlaxCLIPTextModel, CLIP_TEXT_INPUTS_DOCSTRING + FLAX_CLIP_TEXT_MODEL_DOCSTRING) append_replace_return_docstrings( FlaxCLIPTextModel, output_type=FlaxBaseModelOutputWithPooling, config_class=CLIPTextConfig ) class FlaxCLIPVisionModule(nn.Module): config: CLIPVisionConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.vision_model = FlaxCLIPVisionTransformer(self.config, dtype=self.dtype) def __call__( self, pixel_values, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class FlaxCLIPVisionModel(FlaxCLIPVisionPreTrainedModel): module_class = FlaxCLIPVisionModule FLAX_CLIP_VISION_MODEL_DOCSTRING = """ Returns: Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPProcessor, FlaxCLIPVisionModel >>> model = FlaxCLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32") >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="np") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooler_output = outputs.pooler_output # pooled CLS states ``` """ overwrite_call_docstring(FlaxCLIPVisionModel, CLIP_VISION_INPUTS_DOCSTRING + FLAX_CLIP_VISION_MODEL_DOCSTRING) append_replace_return_docstrings( FlaxCLIPVisionModel, output_type=FlaxBaseModelOutputWithPooling, config_class=CLIPVisionConfig ) class FlaxCLIPModule(nn.Module): config: CLIPConfig dtype: jnp.dtype = jnp.float32 def setup(self): text_config = self.config.text_config vision_config = self.config.vision_config self.projection_dim = self.config.projection_dim self.text_embed_dim = text_config.hidden_size self.vision_embed_dim = vision_config.hidden_size self.text_model = FlaxCLIPTextTransformer(text_config, dtype=self.dtype) self.vision_model = FlaxCLIPVisionTransformer(vision_config, dtype=self.dtype) self.visual_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.text_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.logit_scale = self.param( "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] ) def __call__( self, input_ids=None, pixel_values=None, attention_mask=None, position_ids=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = jnp.exp(self.logit_scale) logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale logits_per_image = logits_per_text.T if not return_dict: return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return FlaxCLIPOutput( logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) @add_start_docstrings(CLIP_START_DOCSTRING) class FlaxCLIPModel(FlaxCLIPPreTrainedModel): module_class = FlaxCLIPModule FLAX_CLIP_MODEL_DOCSTRING = """ Returns: Example: ```python >>> import jax >>> from PIL import Image >>> import requests >>> from transformers import CLIPProcessor, FlaxCLIPModel >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32") >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="np", padding=True ... ) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ``` """ overwrite_call_docstring(FlaxCLIPModel, CLIP_INPUTS_DOCSTRING + FLAX_CLIP_MODEL_DOCSTRING) append_replace_return_docstrings(FlaxCLIPModel, output_type=FlaxCLIPOutput, config_class=CLIPConfig)

# coding=utf-8 # Copyright 2021 The OpenAI Team Authors, The Google Flax Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Optional, Tuple, Union import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxBaseModelOutputWithPooling from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import ModelOutput, add_start_docstrings, logging from .configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig logger = logging.get_logger(__name__) CLIP_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models) This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`CLIPConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ CLIP_TEXT_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIP_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ CLIP_INPUTS_DOCSTRING = r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @flax.struct.dataclass class FlaxCLIPOutput(ModelOutput): """ Args: logits_per_image:(`jnp.ndarray` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`jnp.ndarray` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPTextModel`]. image_embeds(`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPVisionModel`]. text_model_output(`FlaxBaseModelOutputWithPooling`): The output of the [`FlaxCLIPTextModel`]. vision_model_output(`FlaxBaseModelOutputWithPooling`): The output of the [`FlaxCLIPVisionModel`]. """ logits_per_image: jnp.ndarray = None logits_per_text: jnp.ndarray = None text_embeds: jnp.ndarray = None image_embeds: jnp.ndarray = None text_model_output: FlaxBaseModelOutputWithPooling = None vision_model_output: FlaxBaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) class FlaxCLIPVisionEmbeddings(nn.Module): config: CLIPVisionConfig dtype: jnp.dtype = jnp.float32 def setup(self): embed_dim = self.config.hidden_size image_size = self.config.image_size patch_size = self.config.patch_size self.class_embedding = self.param("class_embedding", jax.nn.initializers.normal(stddev=0.02), (embed_dim,)) self.patch_embedding = nn.Conv( embed_dim, kernel_size=(patch_size, patch_size), strides=(patch_size, patch_size), padding="VALID", use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(), ) self.num_patches = (image_size // patch_size) ** 2 num_positions = self.num_patches + 1 self.position_embedding = nn.Embed(num_positions, embed_dim, embedding_init=jax.nn.initializers.normal()) self.position_ids = jnp.expand_dims(jnp.arange(0, num_positions, dtype="i4"), axis=0) def __call__(self, pixel_values): patch_embeds = self.patch_embedding(pixel_values) batch_size, height, width, channels = patch_embeds.shape patch_embeds = jnp.reshape(patch_embeds, (batch_size, height * width, channels)) class_embeds = jnp.expand_dims(self.class_embedding, axis=(0, 1)) class_embeds = jnp.tile(class_embeds, (batch_size, 1, 1)) embeddings = jnp.concatenate([class_embeds, patch_embeds], axis=1) embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings class FlaxCLIPTextEmbeddings(nn.Module): config: CLIPTextConfig dtype: jnp.dtype = jnp.float32 def setup(self): embed_dim = self.config.hidden_size self.token_embedding = nn.Embed(self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal()) self.position_embedding = nn.Embed( self.config.max_position_embeddings, embed_dim, embedding_init=jax.nn.initializers.normal() ) self.position_ids = jnp.expand_dims( jnp.arange(0, self.config.max_position_embeddings, dtype="i4"), axis=(0, 1) ) def __call__(self, input_ids, position_ids): input_embeds = self.token_embedding(input_ids.astype("i4")) position_embeds = self.position_embedding(position_ids.astype("i4")) embeddings = input_embeds + position_embeds return embeddings class FlaxCLIPAttention(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.embed_dim = self.config.hidden_size self.num_heads = self.config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = self.config.attention_dropout self.k_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.v_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.q_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.out_proj = nn.Dense(self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) self.causal = isinstance(self.config, CLIPTextConfig) if self.causal: self.causal_mask = make_causal_mask(jnp.ones((1, self.config.max_position_embeddings), dtype="i4")) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) def __call__( self, hidden_states, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, ): query = self.q_proj(hidden_states) key = self.k_proj(hidden_states) value = self.v_proj(hidden_states) query = self._split_heads(query) key = self._split_heads(key) value = self._split_heads(value) causal_attention_mask = None if self.causal: query_length, key_length = query.shape[1], key.shape[1] causal_attention_mask = self.causal_mask[:, :, key_length - query_length : key_length, :key_length] if attention_mask is not None and causal_attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) attention_mask = combine_masks(attention_mask, causal_attention_mask, dtype="i4") elif causal_attention_mask is not None: attention_mask = causal_attention_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) if attention_mask is not None: attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, -1e4).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query, key, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) outputs = (attn_output, attn_weights) if output_attentions else (attn_output,) return outputs class FlaxCLIPMLP(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.activation_fn = ACT2FN[self.config.hidden_act] self.fc1 = nn.Dense( self.config.intermediate_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01), ) self.fc2 = nn.Dense(self.config.hidden_size, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.01)) def __call__(self, hidden_states): hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states class FlaxCLIPEncoderLayer(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.self_attn = FlaxCLIPAttention(self.config, dtype=self.dtype) self.layer_norm1 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.mlp = FlaxCLIPMLP(self.config, dtype=self.dtype) self.layer_norm2 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__( self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, ): residual = hidden_states hidden_states = self.layer_norm1(hidden_states) attn_outputs = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, ) hidden_states = attn_outputs[0] hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += attn_outputs[1:] return outputs class FlaxCLIPLayerCollection(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = [ FlaxCLIPEncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states,) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxCLIPEncoder(nn.Module): config: Union[CLIPTextConfig, CLIPVisionConfig] dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = FlaxCLIPLayerCollection(self.config, dtype=self.dtype) def __call__( self, inputs_embeds, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.layers( hidden_states=inputs_embeds, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class FlaxCLIPTextTransformer(nn.Module): config: CLIPTextConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.embeddings = FlaxCLIPTextEmbeddings(self.config, dtype=self.dtype) self.encoder = FlaxCLIPEncoder(self.config, dtype=self.dtype) self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.final_layer_norm(last_hidden_state) # text_embeds.shape = [batch_size, sequence_length, transformer.width] # take features from the EOS embedding (eos_token_id is the highest number in each sequence) pooled_output = last_hidden_state[jnp.arange(last_hidden_state.shape[0]), input_ids.argmax(axis=-1)] if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return FlaxBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class FlaxCLIPVisionTransformer(nn.Module): config: CLIPVisionConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.embeddings = FlaxCLIPVisionEmbeddings(self.config, dtype=self.dtype) self.pre_layrnorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.encoder = FlaxCLIPEncoder(self.config, dtype=self.dtype) self.post_layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__( self, pixel_values=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict: bool = True, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) encoder_outputs = self.encoder( inputs_embeds=hidden_states, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] pooled_output = last_hidden_state[:, 0, :] pooled_output = self.post_layernorm(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return FlaxBaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class FlaxCLIPTextPreTrainedModel(FlaxPreTrainedModel): config_class = CLIPTextConfig module_class: nn.Module = None def __init__( self, config: CLIPTextConfig, input_shape=(1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) attention_mask = jnp.ones_like(input_ids) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, attention_mask, position_ids)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, attention_mask=None, position_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) class FlaxCLIPVisionPreTrainedModel(FlaxPreTrainedModel): config_class = CLIPVisionConfig main_input_name = "pixel_values" module_class: nn.Module = None def __init__( self, config: CLIPVisionConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if input_shape is None: input_shape = (1, config.image_size, config.image_size, 3) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor pixel_values = jax.random.normal(rng, input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, pixel_values)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) class FlaxCLIPPreTrainedModel(FlaxPreTrainedModel): config_class = CLIPConfig module_class: nn.Module = None def __init__( self, config: CLIPConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): if input_shape is None: input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3)) module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensor input_ids = jnp.zeros(input_shape[0], dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape[0]) attention_mask = jnp.ones_like(input_ids) pixel_values = jax.random.normal(rng, input_shape[1]) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_ids, pixel_values, attention_mask, position_ids)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def __call__( self, input_ids, pixel_values, attention_mask=None, position_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(pixel_values, dtype=jnp.float32), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, output_attentions, output_hidden_states, return_dict, rngs=rngs, ) def get_text_features( self, input_ids, attention_mask=None, position_ids=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False, ): r""" Args: input_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`CLIPTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) Returns: text_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPTextModel`]. Examples: ```python >>> from transformers import CLIPTokenizer, FlaxCLIPModel >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32") >>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np") >>> text_features = model.get_text_features(**inputs) ```""" if position_ids is None: position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) if attention_mask is None: attention_mask = jnp.ones_like(input_ids) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, input_ids, attention_mask, position_ids, deterministic): text_outputs = module.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, ) pooled_output = text_outputs[1] text_features = module.text_projection(pooled_output) return text_features return self.module.apply( {"params": params or self.params}, jnp.array(input_ids, dtype="i4"), jnp.array(attention_mask, dtype="i4"), jnp.array(position_ids, dtype="i4"), not train, method=_get_features, rngs=rngs, ) def get_image_features( self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False ): r""" Args: pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for details. Returns: image_features (`jnp.ndarray` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlaxCLIPVisionModel`] Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPProcessor, FlaxCLIPModel >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32") >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="np") >>> image_features = model.get_image_features(**inputs) ```""" pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _get_features(module, pixel_values, deterministic): vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic) pooled_output = vision_outputs[1] # pooled_output image_features = module.visual_projection(pooled_output) return image_features return self.module.apply( {"params": params or self.params}, jnp.array(pixel_values, dtype=jnp.float32), not train, method=_get_features, rngs=rngs, ) class FlaxCLIPTextModule(nn.Module): config: CLIPTextConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.text_model = FlaxCLIPTextTransformer(self.config, dtype=self.dtype) def __call__( self, input_ids, attention_mask, position_ids, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class FlaxCLIPTextModel(FlaxCLIPTextPreTrainedModel): module_class = FlaxCLIPTextModule FLAX_CLIP_TEXT_MODEL_DOCSTRING = """ Returns: Example: ```python >>> from transformers import CLIPTokenizer, FlaxCLIPTextModel >>> model = FlaxCLIPTextModel.from_pretrained("openai/clip-vit-base-patch32") >>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32") >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="np") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooler_output = outputs.pooler_output # pooled (EOS token) states ``` """ overwrite_call_docstring(FlaxCLIPTextModel, CLIP_TEXT_INPUTS_DOCSTRING + FLAX_CLIP_TEXT_MODEL_DOCSTRING) append_replace_return_docstrings( FlaxCLIPTextModel, output_type=FlaxBaseModelOutputWithPooling, config_class=CLIPTextConfig ) class FlaxCLIPVisionModule(nn.Module): config: CLIPVisionConfig dtype: jnp.dtype = jnp.float32 def setup(self): self.vision_model = FlaxCLIPVisionTransformer(self.config, dtype=self.dtype) def __call__( self, pixel_values, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): return self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class FlaxCLIPVisionModel(FlaxCLIPVisionPreTrainedModel): module_class = FlaxCLIPVisionModule FLAX_CLIP_VISION_MODEL_DOCSTRING = """ Returns: Example: ```python >>> from PIL import Image >>> import requests >>> from transformers import CLIPProcessor, FlaxCLIPVisionModel >>> model = FlaxCLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32") >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="np") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooler_output = outputs.pooler_output # pooled CLS states ``` """ overwrite_call_docstring(FlaxCLIPVisionModel, CLIP_VISION_INPUTS_DOCSTRING + FLAX_CLIP_VISION_MODEL_DOCSTRING) append_replace_return_docstrings( FlaxCLIPVisionModel, output_type=FlaxBaseModelOutputWithPooling, config_class=CLIPVisionConfig ) class FlaxCLIPModule(nn.Module): config: CLIPConfig dtype: jnp.dtype = jnp.float32 def setup(self): text_config = self.config.text_config vision_config = self.config.vision_config self.projection_dim = self.config.projection_dim self.text_embed_dim = text_config.hidden_size self.vision_embed_dim = vision_config.hidden_size self.text_model = FlaxCLIPTextTransformer(text_config, dtype=self.dtype) self.vision_model = FlaxCLIPVisionTransformer(vision_config, dtype=self.dtype) self.visual_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.text_projection = nn.Dense( self.projection_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(0.02), use_bias=False, ) self.logit_scale = self.param( "logit_scale", lambda _, shape: jnp.ones(shape) * self.config.logit_scale_init_value, [] ) def __call__( self, input_ids=None, pixel_values=None, attention_mask=None, position_ids=None, deterministic: bool = True, output_attentions=None, output_hidden_states=None, return_dict=None, ): return_dict = return_dict if return_dict is not None else self.config.return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] image_embeds = self.visual_projection(image_embeds) text_embeds = text_outputs[1] text_embeds = self.text_projection(text_embeds) # normalized features image_embeds = image_embeds / jnp.linalg.norm(image_embeds, axis=-1, keepdims=True) text_embeds = text_embeds / jnp.linalg.norm(text_embeds, axis=-1, keepdims=True) # cosine similarity as logits logit_scale = jnp.exp(self.logit_scale) logits_per_text = jnp.matmul(text_embeds, image_embeds.T) * logit_scale logits_per_image = logits_per_text.T if not return_dict: return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return FlaxCLIPOutput( logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) @add_start_docstrings(CLIP_START_DOCSTRING) class FlaxCLIPModel(FlaxCLIPPreTrainedModel): module_class = FlaxCLIPModule FLAX_CLIP_MODEL_DOCSTRING = """ Returns: Example: ```python >>> import jax >>> from PIL import Image >>> import requests >>> from transformers import CLIPProcessor, FlaxCLIPModel >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32") >>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="np", padding=True ... ) >>> outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score >>> probs = jax.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities ``` """ overwrite_call_docstring(FlaxCLIPModel, CLIP_INPUTS_DOCSTRING + FLAX_CLIP_MODEL_DOCSTRING) append_replace_return_docstrings(FlaxCLIPModel, output_type=FlaxCLIPOutput, config_class=CLIPConfig)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/utils/test_cli.py

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import shutil import unittest from unittest.mock import patch from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test class CLITest(unittest.TestCase): @patch("sys.argv", ["fakeprogrampath", "env"]) def test_cli_env(self): # test transformers-cli env import transformers.commands.transformers_cli with CaptureStd() as cs: transformers.commands.transformers_cli.main() self.assertIn("Python version", cs.out) self.assertIn("Platform", cs.out) self.assertIn("Using distributed or parallel set-up in script?", cs.out) @is_pt_tf_cross_test @patch( "sys.argv", ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"] ) def test_cli_pt_to_tf(self): import transformers.commands.transformers_cli shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj", ignore_errors=True) # cleans potential past runs transformers.commands.transformers_cli.main() # The original repo has no TF weights -- if they exist, they were created by the CLI self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"))

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import shutil import unittest from unittest.mock import patch from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test class CLITest(unittest.TestCase): @patch("sys.argv", ["fakeprogrampath", "env"]) def test_cli_env(self): # test transformers-cli env import transformers.commands.transformers_cli with CaptureStd() as cs: transformers.commands.transformers_cli.main() self.assertIn("Python version", cs.out) self.assertIn("Platform", cs.out) self.assertIn("Using distributed or parallel set-up in script?", cs.out) @is_pt_tf_cross_test @patch( "sys.argv", ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"] ) def test_cli_pt_to_tf(self): import transformers.commands.transformers_cli shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj", ignore_errors=True) # cleans potential past runs transformers.commands.transformers_cli.main() # The original repo has no TF weights -- if they exist, they were created by the CLI self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"))

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/unispeech_sat/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = { "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_unispeech_sat"] = [ "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechSatForAudioFrameClassification", "UniSpeechSatForCTC", "UniSpeechSatForPreTraining", "UniSpeechSatForSequenceClassification", "UniSpeechSatForXVector", "UniSpeechSatModel", "UniSpeechSatPreTrainedModel", ] if TYPE_CHECKING: from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_unispeech_sat import ( UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, UniSpeechSatPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = { "configuration_unispeech_sat": ["UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_unispeech_sat"] = [ "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechSatForAudioFrameClassification", "UniSpeechSatForCTC", "UniSpeechSatForPreTraining", "UniSpeechSatForSequenceClassification", "UniSpeechSatForXVector", "UniSpeechSatModel", "UniSpeechSatPreTrainedModel", ] if TYPE_CHECKING: from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_unispeech_sat import ( UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, UniSpeechSatPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./docker/transformers-pytorch-tpu/docker-entrypoint.sh

#!/bin/bash source ~/.bashrc echo "running docker-entrypoint.sh" conda activate container echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS echo "printed TPU info" export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}" exec "$@"#!/bin/bash

#!/bin/bash source ~/.bashrc echo "running docker-entrypoint.sh" conda activate container echo $KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS echo "printed TPU info" export XRT_TPU_CONFIG="tpu_worker;0;${KUBE_GOOGLE_CLOUD_TPU_ENDPOINTS:7}" exec "$@"#!/bin/bash

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/bert_generation/test_tokenization_bert_generation.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import BertGenerationTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin SPIECE_UNDERLINE = "▁" SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece class BertGenerationTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = BertGenerationTokenizer test_rust_tokenizer = False test_sentencepiece = True def setUp(self): super().setUp() tokenizer = BertGenerationTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.save_pretrained(self.tmpdirname) def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "<s>" token_id = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "<unk>") self.assertEqual(vocab_keys[1], "<s>") self.assertEqual(vocab_keys[-1], "<pad>") self.assertEqual(len(vocab_keys), 1_002) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_000) def test_full_tokenizer(self): tokenizer = BertGenerationTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", ".", ], ) @cached_property def big_tokenizer(self): return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder") @slow def test_tokenization_base_easy_symbols(self): symbols = "Hello World!" original_tokenizer_encodings = [18536, 2260, 101] self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols)) @slow def test_tokenization_base_hard_symbols(self): symbols = ( 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will' " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth" ) original_tokenizer_encodings = [ 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, ] self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols)) @require_torch @slow def test_torch_encode_plus_sent_to_model(self): import torch from transformers import BertGenerationConfig, BertGenerationEncoder # Build sequence first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10] sequence = " ".join(first_ten_tokens) encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt", return_token_type_ids=False) batch_encoded_sequence = self.big_tokenizer.batch_encode_plus( [sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False ) config = BertGenerationConfig() model = BertGenerationEncoder(config) assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size with torch.no_grad(): model(**encoded_sequence) model(**batch_encoded_sequence) @slow def test_tokenizer_integration(self): # fmt: off expected_encoding = {'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="google/bert_for_seq_generation_L-24_bbc_encoder", revision="c817d1fd1be2ffa69431227a1fe320544943d4db", )

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import BertGenerationTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin SPIECE_UNDERLINE = "▁" SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece class BertGenerationTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = BertGenerationTokenizer test_rust_tokenizer = False test_sentencepiece = True def setUp(self): super().setUp() tokenizer = BertGenerationTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.save_pretrained(self.tmpdirname) def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "<s>" token_id = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "<unk>") self.assertEqual(vocab_keys[1], "<s>") self.assertEqual(vocab_keys[-1], "<pad>") self.assertEqual(len(vocab_keys), 1_002) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_000) def test_full_tokenizer(self): tokenizer = BertGenerationTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", ".", ], ) ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual( ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4], ) back_tokens = tokenizer.convert_ids_to_tokens(ids) self.assertListEqual( back_tokens, [ SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", ".", ], ) @cached_property def big_tokenizer(self): return BertGenerationTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder") @slow def test_tokenization_base_easy_symbols(self): symbols = "Hello World!" original_tokenizer_encodings = [18536, 2260, 101] self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols)) @slow def test_tokenization_base_hard_symbols(self): symbols = ( 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will' " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth" ) original_tokenizer_encodings = [ 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, ] self.assertListEqual(original_tokenizer_encodings, self.big_tokenizer.encode(symbols)) @require_torch @slow def test_torch_encode_plus_sent_to_model(self): import torch from transformers import BertGenerationConfig, BertGenerationEncoder # Build sequence first_ten_tokens = list(self.big_tokenizer.get_vocab().keys())[:10] sequence = " ".join(first_ten_tokens) encoded_sequence = self.big_tokenizer.encode_plus(sequence, return_tensors="pt", return_token_type_ids=False) batch_encoded_sequence = self.big_tokenizer.batch_encode_plus( [sequence + " " + sequence], return_tensors="pt", return_token_type_ids=False ) config = BertGenerationConfig() model = BertGenerationEncoder(config) assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size with torch.no_grad(): model(**encoded_sequence) model(**batch_encoded_sequence) @slow def test_tokenizer_integration(self): # fmt: off expected_encoding = {'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="google/bert_for_seq_generation_L-24_bbc_encoder", revision="c817d1fd1be2ffa69431227a1fe320544943d4db", )

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/encoder_decoder/configuration_encoder_decoder.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class EncoderDecoderConfig(PretrainedConfig): r""" [`EncoderDecoderConfig`] is the configuration class to store the configuration of a [`EncoderDecoderModel`]. It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: kwargs (*optional*): Dictionary of keyword arguments. Notably: - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the encoder config. - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the decoder config. Examples: ```python >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel >>> # Initializing a BERT bert-base-uncased style configuration >>> config_encoder = BertConfig() >>> config_decoder = BertConfig() >>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder) >>> # Initializing a Bert2Bert model from the bert-base-uncased style configurations >>> model = EncoderDecoderModel(config=config) >>> # Accessing the model configuration >>> config_encoder = model.config.encoder >>> config_decoder = model.config.decoder >>> # set decoder config to causal lm >>> config_decoder.is_decoder = True >>> config_decoder.add_cross_attention = True >>> # Saving the model, including its configuration >>> model.save_pretrained("my-model") >>> # loading model and config from pretrained folder >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained("my-model") >>> model = EncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) ```""" model_type = "encoder-decoder" is_composition = True def __init__(self, **kwargs): super().__init__(**kwargs) assert ( "encoder" in kwargs and "decoder" in kwargs ), "Config has to be initialized with encoder and decoder config" encoder_config = kwargs.pop("encoder") encoder_model_type = encoder_config.pop("model_type") decoder_config = kwargs.pop("decoder") decoder_model_type = decoder_config.pop("model_type") from ..auto.configuration_auto import AutoConfig self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config) self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config) self.is_encoder_decoder = True @classmethod def from_encoder_decoder_configs( cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs ) -> PretrainedConfig: r""" Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and decoder model configuration. Returns: [`EncoderDecoderConfig`]: An instance of a configuration object """ logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") decoder_config.is_decoder = True decoder_config.add_cross_attention = True return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs) def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default *to_dict()* from *PretrainedConfig*. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) output["encoder"] = self.encoder.to_dict() output["decoder"] = self.decoder.to_dict() output["model_type"] = self.__class__.model_type return output

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class EncoderDecoderConfig(PretrainedConfig): r""" [`EncoderDecoderConfig`] is the configuration class to store the configuration of a [`EncoderDecoderModel`]. It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: kwargs (*optional*): Dictionary of keyword arguments. Notably: - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the encoder config. - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines the decoder config. Examples: ```python >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel >>> # Initializing a BERT bert-base-uncased style configuration >>> config_encoder = BertConfig() >>> config_decoder = BertConfig() >>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder) >>> # Initializing a Bert2Bert model from the bert-base-uncased style configurations >>> model = EncoderDecoderModel(config=config) >>> # Accessing the model configuration >>> config_encoder = model.config.encoder >>> config_decoder = model.config.decoder >>> # set decoder config to causal lm >>> config_decoder.is_decoder = True >>> config_decoder.add_cross_attention = True >>> # Saving the model, including its configuration >>> model.save_pretrained("my-model") >>> # loading model and config from pretrained folder >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained("my-model") >>> model = EncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config) ```""" model_type = "encoder-decoder" is_composition = True def __init__(self, **kwargs): super().__init__(**kwargs) assert ( "encoder" in kwargs and "decoder" in kwargs ), "Config has to be initialized with encoder and decoder config" encoder_config = kwargs.pop("encoder") encoder_model_type = encoder_config.pop("model_type") decoder_config = kwargs.pop("decoder") decoder_model_type = decoder_config.pop("model_type") from ..auto.configuration_auto import AutoConfig self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config) self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config) self.is_encoder_decoder = True @classmethod def from_encoder_decoder_configs( cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs ) -> PretrainedConfig: r""" Instantiate a [`EncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model configuration and decoder model configuration. Returns: [`EncoderDecoderConfig`]: An instance of a configuration object """ logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") decoder_config.is_decoder = True decoder_config.add_cross_attention = True return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs) def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default *to_dict()* from *PretrainedConfig*. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) output["encoder"] = self.encoder.to_dict() output["decoder"] = self.decoder.to_dict() output["model_type"] = self.__class__.model_type return output

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/wav2vec2/requirements.txt

transformers datasets torch>=1.5.0 torchaudio jiwer==2.2.0 lang-trans==0.6.0 librosa==0.8.0

transformers datasets torch>=1.5.0 torchaudio jiwer==2.2.0 lang-trans==0.6.0 librosa==0.8.0

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./docs/source/en/model_doc/table-transformer.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Table Transformer ## Overview The Table Transformer model was proposed in [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://arxiv.org/abs/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents, as well as table structure recognition and functional analysis. The authors train 2 [DETR](detr) models, one for table detection and one for table structure recognition, dubbed Table Transformers. The abstract from the paper is the following: *Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any special customization for these tasks.* Tips: - The authors released 2 models, one for [table detection](https://huggingface.co/microsoft/table-transformer-detection) in documents, one for [table structure recognition](https://huggingface.co/microsoft/table-transformer-structure-recognition) (the task of recognizing the individual rows, columns etc. in a table). - One can use the [`AutoFeatureExtractor`] API to prepare images and optional targets for the model. This will load a [`DetrFeatureExtractor`] behind the scenes. - A demo notebook for the Table Transformer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Table Transformer). <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/table_transformer_architecture.jpeg" alt="drawing" width="600"/> <small> Table detection and table structure recognition clarified. Taken from the <a href="https://arxiv.org/abs/2110.00061">original paper</a>. </small> This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/table-transformer). ## TableTransformerConfig [[autodoc]] TableTransformerConfig ## TableTransformerModel [[autodoc]] TableTransformerModel - forward ## TableTransformerForObjectDetection [[autodoc]] TableTransformerForObjectDetection - forward

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Table Transformer ## Overview The Table Transformer model was proposed in [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://arxiv.org/abs/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents, as well as table structure recognition and functional analysis. The authors train 2 [DETR](detr) models, one for table detection and one for table structure recognition, dubbed Table Transformers. The abstract from the paper is the following: *Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any special customization for these tasks.* Tips: - The authors released 2 models, one for [table detection](https://huggingface.co/microsoft/table-transformer-detection) in documents, one for [table structure recognition](https://huggingface.co/microsoft/table-transformer-structure-recognition) (the task of recognizing the individual rows, columns etc. in a table). - One can use the [`AutoFeatureExtractor`] API to prepare images and optional targets for the model. This will load a [`DetrFeatureExtractor`] behind the scenes. - A demo notebook for the Table Transformer can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Table Transformer). <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/table_transformer_architecture.jpeg" alt="drawing" width="600"/> <small> Table detection and table structure recognition clarified. Taken from the <a href="https://arxiv.org/abs/2110.00061">original paper</a>. </small> This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/table-transformer). ## TableTransformerConfig [[autodoc]] TableTransformerConfig ## TableTransformerModel [[autodoc]] TableTransformerModel - forward ## TableTransformerForObjectDetection [[autodoc]] TableTransformerForObjectDetection - forward

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./utils/check_table.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import collections import importlib.util import os import re # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_table.py TRANSFORMERS_PATH = "src/transformers" PATH_TO_DOCS = "docs/source/en" REPO_PATH = "." def _find_text_in_file(filename, start_prompt, end_prompt): """ Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty lines. """ with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start prompt. start_index = 0 while not lines[start_index].startswith(start_prompt): start_index += 1 start_index += 1 end_index = start_index while not lines[end_index].startswith(end_prompt): end_index += 1 end_index -= 1 while len(lines[start_index]) <= 1: start_index += 1 while len(lines[end_index]) <= 1: end_index -= 1 end_index += 1 return "".join(lines[start_index:end_index]), start_index, end_index, lines # Add here suffixes that are used to identify models, separated by | ALLOWED_MODEL_SUFFIXES = "Model|Encoder|Decoder|ForConditionalGeneration" # Regexes that match TF/Flax/PT model names. _re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") _re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes. _re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(TRANSFORMERS_PATH, "__init__.py"), submodule_search_locations=[TRANSFORMERS_PATH], ) transformers_module = spec.loader.load_module() # Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python def camel_case_split(identifier): "Split a camelcased `identifier` into words." matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier) return [m.group(0) for m in matches] def _center_text(text, width): text_length = 2 if text == "✅" or text == "❌" else len(text) left_indent = (width - text_length) // 2 right_indent = width - text_length - left_indent return " " * left_indent + text + " " * right_indent def get_model_table_from_auto_modules(): """Generates an up-to-date model table from the content of the auto modules.""" # Dictionary model names to config. config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES model_name_to_config = { name: config_maping_names[code] for code, name in transformers_module.MODEL_NAMES_MAPPING.items() if code in config_maping_names } model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()} # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax. slow_tokenizers = collections.defaultdict(bool) fast_tokenizers = collections.defaultdict(bool) pt_models = collections.defaultdict(bool) tf_models = collections.defaultdict(bool) flax_models = collections.defaultdict(bool) # Let's lookup through all transformers object (once). for attr_name in dir(transformers_module): lookup_dict = None if attr_name.endswith("Tokenizer"): lookup_dict = slow_tokenizers attr_name = attr_name[:-9] elif attr_name.endswith("TokenizerFast"): lookup_dict = fast_tokenizers attr_name = attr_name[:-13] elif _re_tf_models.match(attr_name) is not None: lookup_dict = tf_models attr_name = _re_tf_models.match(attr_name).groups()[0] elif _re_flax_models.match(attr_name) is not None: lookup_dict = flax_models attr_name = _re_flax_models.match(attr_name).groups()[0] elif _re_pt_models.match(attr_name) is not None: lookup_dict = pt_models attr_name = _re_pt_models.match(attr_name).groups()[0] if lookup_dict is not None: while len(attr_name) > 0: if attr_name in model_name_to_prefix.values(): lookup_dict[attr_name] = True break # Try again after removing the last word in the name attr_name = "".join(camel_case_split(attr_name)[:-1]) # Let's build that table! model_names = list(model_name_to_config.keys()) model_names.sort(key=str.lower) columns = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"] # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side). widths = [len(c) + 2 for c in columns] widths[0] = max([len(name) for name in model_names]) + 2 # Build the table per se table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n" # Use ":-----:" format to center-aligned table cell texts table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n" check = {True: "✅", False: "❌"} for name in model_names: prefix = model_name_to_prefix[name] line = [ name, check[slow_tokenizers[prefix]], check[fast_tokenizers[prefix]], check[pt_models[prefix]], check[tf_models[prefix]], check[flax_models[prefix]], ] table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n" return table def check_model_table(overwrite=False): """Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`.""" current_table, start_index, end_index, lines = _find_text_in_file( filename=os.path.join(PATH_TO_DOCS, "index.mdx"), start_prompt="<!--This table is updated automatically from the auto modules", end_prompt="<!-- End table-->", ) new_table = get_model_table_from_auto_modules() if current_table != new_table: if overwrite: with open(os.path.join(PATH_TO_DOCS, "index.mdx"), "w", encoding="utf-8", newline="\n") as f: f.writelines(lines[:start_index] + [new_table] + lines[end_index:]) else: raise ValueError( "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this." ) def has_onnx(model_type): """ Returns whether `model_type` is supported by ONNX (by checking if there is an ONNX config) or not. """ config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING if model_type not in config_mapping: return False config = config_mapping[model_type] config_module = config.__module__ module = transformers_module for part in config_module.split(".")[1:]: module = getattr(module, part) config_name = config.__name__ onnx_config_name = config_name.replace("Config", "OnnxConfig") return hasattr(module, onnx_config_name) def get_onnx_model_list(): """ Return the list of models supporting ONNX. """ config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING model_names = config_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING onnx_model_types = [model_type for model_type in config_mapping.keys() if has_onnx(model_type)] onnx_model_names = [model_names[model_type] for model_type in onnx_model_types] onnx_model_names.sort(key=lambda x: x.upper()) return "\n".join([f"- {name}" for name in onnx_model_names]) + "\n" def check_onnx_model_list(overwrite=False): """Check the model list in the serialization.mdx is consistent with the state of the lib and maybe `overwrite`.""" current_list, start_index, end_index, lines = _find_text_in_file( filename=os.path.join(PATH_TO_DOCS, "serialization.mdx"), start_prompt="<!--This table is automatically generated by `make fix-copies`, do not fill manually!-->", end_prompt="In the next two sections, we'll show you how to:", ) new_list = get_onnx_model_list() if current_list != new_list: if overwrite: with open(os.path.join(PATH_TO_DOCS, "serialization.mdx"), "w", encoding="utf-8", newline="\n") as f: f.writelines(lines[:start_index] + [new_list] + lines[end_index:]) else: raise ValueError("The list of ONNX-supported models needs an update. Run `make fix-copies` to fix this.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_model_table(args.fix_and_overwrite) check_onnx_model_list(args.fix_and_overwrite)

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import collections import importlib.util import os import re # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_table.py TRANSFORMERS_PATH = "src/transformers" PATH_TO_DOCS = "docs/source/en" REPO_PATH = "." def _find_text_in_file(filename, start_prompt, end_prompt): """ Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty lines. """ with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() # Find the start prompt. start_index = 0 while not lines[start_index].startswith(start_prompt): start_index += 1 start_index += 1 end_index = start_index while not lines[end_index].startswith(end_prompt): end_index += 1 end_index -= 1 while len(lines[start_index]) <= 1: start_index += 1 while len(lines[end_index]) <= 1: end_index -= 1 end_index += 1 return "".join(lines[start_index:end_index]), start_index, end_index, lines # Add here suffixes that are used to identify models, separated by | ALLOWED_MODEL_SUFFIXES = "Model|Encoder|Decoder|ForConditionalGeneration" # Regexes that match TF/Flax/PT model names. _re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") _re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes. _re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)") # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(TRANSFORMERS_PATH, "__init__.py"), submodule_search_locations=[TRANSFORMERS_PATH], ) transformers_module = spec.loader.load_module() # Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python def camel_case_split(identifier): "Split a camelcased `identifier` into words." matches = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)", identifier) return [m.group(0) for m in matches] def _center_text(text, width): text_length = 2 if text == "✅" or text == "❌" else len(text) left_indent = (width - text_length) // 2 right_indent = width - text_length - left_indent return " " * left_indent + text + " " * right_indent def get_model_table_from_auto_modules(): """Generates an up-to-date model table from the content of the auto modules.""" # Dictionary model names to config. config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES model_name_to_config = { name: config_maping_names[code] for code, name in transformers_module.MODEL_NAMES_MAPPING.items() if code in config_maping_names } model_name_to_prefix = {name: config.replace("Config", "") for name, config in model_name_to_config.items()} # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax. slow_tokenizers = collections.defaultdict(bool) fast_tokenizers = collections.defaultdict(bool) pt_models = collections.defaultdict(bool) tf_models = collections.defaultdict(bool) flax_models = collections.defaultdict(bool) # Let's lookup through all transformers object (once). for attr_name in dir(transformers_module): lookup_dict = None if attr_name.endswith("Tokenizer"): lookup_dict = slow_tokenizers attr_name = attr_name[:-9] elif attr_name.endswith("TokenizerFast"): lookup_dict = fast_tokenizers attr_name = attr_name[:-13] elif _re_tf_models.match(attr_name) is not None: lookup_dict = tf_models attr_name = _re_tf_models.match(attr_name).groups()[0] elif _re_flax_models.match(attr_name) is not None: lookup_dict = flax_models attr_name = _re_flax_models.match(attr_name).groups()[0] elif _re_pt_models.match(attr_name) is not None: lookup_dict = pt_models attr_name = _re_pt_models.match(attr_name).groups()[0] if lookup_dict is not None: while len(attr_name) > 0: if attr_name in model_name_to_prefix.values(): lookup_dict[attr_name] = True break # Try again after removing the last word in the name attr_name = "".join(camel_case_split(attr_name)[:-1]) # Let's build that table! model_names = list(model_name_to_config.keys()) model_names.sort(key=str.lower) columns = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"] # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side). widths = [len(c) + 2 for c in columns] widths[0] = max([len(name) for name in model_names]) + 2 # Build the table per se table = "|" + "|".join([_center_text(c, w) for c, w in zip(columns, widths)]) + "|\n" # Use ":-----:" format to center-aligned table cell texts table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]) + "|\n" check = {True: "✅", False: "❌"} for name in model_names: prefix = model_name_to_prefix[name] line = [ name, check[slow_tokenizers[prefix]], check[fast_tokenizers[prefix]], check[pt_models[prefix]], check[tf_models[prefix]], check[flax_models[prefix]], ] table += "|" + "|".join([_center_text(l, w) for l, w in zip(line, widths)]) + "|\n" return table def check_model_table(overwrite=False): """Check the model table in the index.rst is consistent with the state of the lib and maybe `overwrite`.""" current_table, start_index, end_index, lines = _find_text_in_file( filename=os.path.join(PATH_TO_DOCS, "index.mdx"), start_prompt="<!--This table is updated automatically from the auto modules", end_prompt="<!-- End table-->", ) new_table = get_model_table_from_auto_modules() if current_table != new_table: if overwrite: with open(os.path.join(PATH_TO_DOCS, "index.mdx"), "w", encoding="utf-8", newline="\n") as f: f.writelines(lines[:start_index] + [new_table] + lines[end_index:]) else: raise ValueError( "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this." ) def has_onnx(model_type): """ Returns whether `model_type` is supported by ONNX (by checking if there is an ONNX config) or not. """ config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING if model_type not in config_mapping: return False config = config_mapping[model_type] config_module = config.__module__ module = transformers_module for part in config_module.split(".")[1:]: module = getattr(module, part) config_name = config.__name__ onnx_config_name = config_name.replace("Config", "OnnxConfig") return hasattr(module, onnx_config_name) def get_onnx_model_list(): """ Return the list of models supporting ONNX. """ config_mapping = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING model_names = config_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING onnx_model_types = [model_type for model_type in config_mapping.keys() if has_onnx(model_type)] onnx_model_names = [model_names[model_type] for model_type in onnx_model_types] onnx_model_names.sort(key=lambda x: x.upper()) return "\n".join([f"- {name}" for name in onnx_model_names]) + "\n" def check_onnx_model_list(overwrite=False): """Check the model list in the serialization.mdx is consistent with the state of the lib and maybe `overwrite`.""" current_list, start_index, end_index, lines = _find_text_in_file( filename=os.path.join(PATH_TO_DOCS, "serialization.mdx"), start_prompt="<!--This table is automatically generated by `make fix-copies`, do not fill manually!-->", end_prompt="In the next two sections, we'll show you how to:", ) new_list = get_onnx_model_list() if current_list != new_list: if overwrite: with open(os.path.join(PATH_TO_DOCS, "serialization.mdx"), "w", encoding="utf-8", newline="\n") as f: f.writelines(lines[:start_index] + [new_list] + lines[end_index:]) else: raise ValueError("The list of ONNX-supported models needs an update. Run `make fix-copies` to fix this.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.") args = parser.parse_args() check_model_table(args.fix_and_overwrite) check_onnx_model_list(args.fix_and_overwrite)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./docs/README.md

<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Generating the documentation To generate the documentation, you first have to build it. Several packages are necessary to build the doc, you can install them with the following command, at the root of the code repository: ```bash pip install -e ".[docs]" ``` Then you need to install our special tool that builds the documentation: ```bash pip install git+https://github.com/huggingface/doc-builder ``` --- **NOTE** You only need to generate the documentation to inspect it locally (if you're planning changes and want to check how they look before committing for instance). You don't have to commit the built documentation. --- ## Building the documentation Once you have setup the `doc-builder` and additional packages, you can generate the documentation by typing the following command: ```bash doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build ``` You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite Markdown editor. ## Previewing the documentation To preview the docs, first install the `watchdog` module with: ```bash pip install watchdog ``` Then run the following command: ```bash doc-builder preview {package_name} {path_to_docs} ``` For example: ```bash doc-builder preview transformers docs/source/en/ ``` The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives. --- **NOTE** The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again). --- ## Adding a new element to the navigation bar Accepted files are Markdown (.md or .mdx). Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/main/docs/source/_toctree.yml) file. ## Renaming section headers and moving sections It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information. Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor. So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file: ``` Sections that were moved: [ <a href="#section-b">Section A</a><a id="section-a"></a> ] ``` and of course, if you moved it to another file, then: ``` Sections that were moved: [ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ] ``` Use the relative style to link to the new file so that the versioned docs continue to work. For an example of a rich moved section set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.mdx). ## Writing Documentation - Specification The `huggingface/transformers` documentation follows the [Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings, although we can write them directly in Markdown. ### Adding a new tutorial Adding a new tutorial or section is done in two steps: - Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md). - Link that file in `./source/_toctree.yml` on the correct toc-tree. Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or four. ### Translating When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). ### Adding a new model When adding a new model: - Create a file `xxx.mdx` or under `./source/model_doc` (don't hesitate to copy an existing file as template). - Link that file in `./source/_toctree.yml`. - Write a short overview of the model: - Overview with paper & authors - Paper abstract - Tips and tricks and how to use it best - Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow. The order is generally: - Configuration, - Tokenizer - PyTorch base model - PyTorch head models - TensorFlow base model - TensorFlow head models - Flax base model - Flax head models These classes should be added using our Markdown syntax. Usually as follows: ``` ## XXXConfig [[autodoc]] XXXConfig ``` This will include every public method of the configuration that is documented. If for some reason you wish for a method not to be displayed in the documentation, you can do so by specifying which methods should be in the docs: ``` ## XXXTokenizer [[autodoc]] XXXTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ``` If you just want to add a method that is not documented (for instance magic methods like `__call__` are not documented by default) you can put the list of methods to add in a list that contains `all`: ``` ## XXXTokenizer [[autodoc]] XXXTokenizer - all - __call__ ``` ### Writing source documentation Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names and objects like True, None, or any strings should usually be put in `code`. When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or function to be in the main package. If you want to create a link to some internal class or function, you need to provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with `utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description. The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\]. #### Defining arguments in a method Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its description: ``` Args: n_layers (`int`): The number of layers of the model. ``` If the description is too long to fit in one line, another indentation is necessary before writing the description after the argument. Here's an example showcasing everything so far: ``` Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and [`~PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) ``` For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the following signature: ``` def my_function(x: str = None, a: float = 1): ``` then its documentation should look like this: ``` Args: x (`str`, *optional*): This argument controls ... a (`float`, *optional*, defaults to 1): This argument is used to ... ``` Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even if the first line describing your argument type and its default gets long, you can't break it on several lines. You can however write as many lines as you want in the indented description (see the example above with `input_ids`). #### Writing a multi-line code block Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown: ```` ``` # first line of code # second line # etc ``` ```` We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test the results to stay consistent with the library. #### Writing a return block The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation. The first line should be the type of the return, followed by a line return. No need to indent further for the elements building the return. Here's an example of a single value return: ``` Returns: `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token. ``` Here's an example of a tuple return, comprising several objects: ``` Returns: `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs: - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` -- Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss. - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). ``` #### Adding an image Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images to this dataset. ## Styling the docstring We have an automatic script running with the `make style` comment that will make sure that: - the docstrings fully take advantage of the line width - all code examples are formatted using black, like the code of the Transformers library This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's recommended to commit your changes before running `make style`, so you can revert the changes done by that script easily. # Testing documentation examples Good documentation often comes with an example of how a specific function or class should be used. Each model class should contain at least one example showcasing how to use this model class in inference. *E.g.* the class [Wav2Vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC) includes an example of how to transcribe speech to text in the [docstring of its forward function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward). ## Writing documentation examples The syntax for Example docstrings can look as follows: ``` Example: ```python >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC >>> from datasets import load_dataset >>> import torch >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") >>> dataset = dataset.sort("id") >>> sampling_rate = dataset.features["audio"].sampling_rate >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> # audio file is decoded on the fly >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_ids = torch.argmax(logits, dim=-1) >>> # transcribe speech >>> transcription = processor.batch_decode(predicted_ids) >>> transcription[0] 'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL' ``` ``` The docstring should give a minimal, clear example of how the respective model is to be used in inference and also include the expected (ideally sensible) output. Often, readers will try out the example before even going through the function or class definitions. Therefore, it is of utmost importance that the example works as expected. ## Docstring testing To do so each example should be included in the doctests. We use pytests' [doctest integration](https://docs.pytest.org/doctest.html) to verify that all of our examples run correctly. For Transformers, the doctests are run on a daily basis via GitHub Actions as can be seen [here](https://github.com/huggingface/transformers/actions/workflows/doctests.yml). To include your example in the daily doctests, you need to add the filename that contains the example docstring to the [documentation_tests.txt](../utils/documentation_tests.txt). ### For Python files You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files): ```bash python utils/prepare_for_doc_test.py src docs ``` If you work on a specific python module, say `modeling_wav2vec2.py`, you can run the command as follows (to avoid the unnecessary temporary changes in irrelevant files): ```bash python utils/prepare_for_doc_test.py src/transformers/utils/doc.py src/transformers/models/wav2vec2/modeling_wav2vec2.py ``` (`utils/doc.py` should always be included) Then you can run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance: ```bash pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure ``` If you want to isolate a specific docstring, just add `::` after the file name then type the whole path of the function/class/method whose docstring you want to test. For instance, here is how to just test the forward method of `Wav2Vec2ForCTC`: ```bash pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure ``` Once you're done, you can run the following command (still from the root of the repository) to undo the changes made by the first command before committing: ```bash python utils/prepare_for_doc_test.py src docs --remove_new_line ``` ### For Markdown files You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files): ```bash python utils/prepare_for_doc_test.py src docs ``` Then you can test locally a given file with this command (here testing the quicktour): ```bash pytest --doctest-modules docs/source/quicktour.mdx -sv --doctest-continue-on-failure --doctest-glob="*.mdx" ``` Once you're done, you can run the following command (still from the root of the repository) to undo the changes made by the first command before committing: ```bash python utils/prepare_for_doc_test.py src docs --remove_new_line ``` ### Writing doctests Here are a few tips to help you debug the doctests and make them pass: - The outputs of the code need to match the expected output **exactly**, so make sure you have the same outputs. In particular doctest will see a difference between single quotes and double quotes, or a missing parenthesis. The only exceptions to that rule are: * whitespace: one give whitespace (space, tabulation, new line) is equivalent to any number of whitespace, so you can add new lines where there are spaces to make your output more readable. * numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configured to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits). - Don't leave a block of code that is very long to execute. If you can't make it fast, you can either not use the doctest syntax on it (so that it's ignored), or if you want to use the doctest syntax to show the results, you can add a comment `# doctest: +SKIP` at the end of the lines of code too long to execute - Each line of code that produces a result needs to have that result written below. You can ignore an output if you don't want to show it in your code example by adding a comment ` # doctest: +IGNORE_RESULT` at the end of the line of code producing it.

<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Generating the documentation To generate the documentation, you first have to build it. Several packages are necessary to build the doc, you can install them with the following command, at the root of the code repository: ```bash pip install -e ".[docs]" ``` Then you need to install our special tool that builds the documentation: ```bash pip install git+https://github.com/huggingface/doc-builder ``` --- **NOTE** You only need to generate the documentation to inspect it locally (if you're planning changes and want to check how they look before committing for instance). You don't have to commit the built documentation. --- ## Building the documentation Once you have setup the `doc-builder` and additional packages, you can generate the documentation by typing the following command: ```bash doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build ``` You can adapt the `--build_dir` to set any temporary folder that you prefer. This command will create it and generate the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite Markdown editor. ## Previewing the documentation To preview the docs, first install the `watchdog` module with: ```bash pip install watchdog ``` Then run the following command: ```bash doc-builder preview {package_name} {path_to_docs} ``` For example: ```bash doc-builder preview transformers docs/source/en/ ``` The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives. --- **NOTE** The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again). --- ## Adding a new element to the navigation bar Accepted files are Markdown (.md or .mdx). Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/main/docs/source/_toctree.yml) file. ## Renaming section headers and moving sections It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information. Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor. So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file: ``` Sections that were moved: [ <a href="#section-b">Section A</a><a id="section-a"></a> ] ``` and of course, if you moved it to another file, then: ``` Sections that were moved: [ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ] ``` Use the relative style to link to the new file so that the versioned docs continue to work. For an example of a rich moved section set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.mdx). ## Writing Documentation - Specification The `huggingface/transformers` documentation follows the [Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings, although we can write them directly in Markdown. ### Adding a new tutorial Adding a new tutorial or section is done in two steps: - Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md). - Link that file in `./source/_toctree.yml` on the correct toc-tree. Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or four. ### Translating When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). ### Adding a new model When adding a new model: - Create a file `xxx.mdx` or under `./source/model_doc` (don't hesitate to copy an existing file as template). - Link that file in `./source/_toctree.yml`. - Write a short overview of the model: - Overview with paper & authors - Paper abstract - Tips and tricks and how to use it best - Add the classes that should be linked in the model. This generally includes the configuration, the tokenizer, and every model of that class (the base model, alongside models with additional heads), both in PyTorch and TensorFlow. The order is generally: - Configuration, - Tokenizer - PyTorch base model - PyTorch head models - TensorFlow base model - TensorFlow head models - Flax base model - Flax head models These classes should be added using our Markdown syntax. Usually as follows: ``` ## XXXConfig [[autodoc]] XXXConfig ``` This will include every public method of the configuration that is documented. If for some reason you wish for a method not to be displayed in the documentation, you can do so by specifying which methods should be in the docs: ``` ## XXXTokenizer [[autodoc]] XXXTokenizer - build_inputs_with_special_tokens - get_special_tokens_mask - create_token_type_ids_from_sequences - save_vocabulary ``` If you just want to add a method that is not documented (for instance magic methods like `__call__` are not documented by default) you can put the list of methods to add in a list that contains `all`: ``` ## XXXTokenizer [[autodoc]] XXXTokenizer - all - __call__ ``` ### Writing source documentation Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names and objects like True, None, or any strings should usually be put in `code`. When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or function to be in the main package. If you want to create a link to some internal class or function, you need to provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with `utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description. The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\]. #### Defining arguments in a method Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its description: ``` Args: n_layers (`int`): The number of layers of the model. ``` If the description is too long to fit in one line, another indentation is necessary before writing the description after the argument. Here's an example showcasing everything so far: ``` Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and [`~PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) ``` For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the following signature: ``` def my_function(x: str = None, a: float = 1): ``` then its documentation should look like this: ``` Args: x (`str`, *optional*): This argument controls ... a (`float`, *optional*, defaults to 1): This argument is used to ... ``` Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even if the first line describing your argument type and its default gets long, you can't break it on several lines. You can however write as many lines as you want in the indented description (see the example above with `input_ids`). #### Writing a multi-line code block Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown: ```` ``` # first line of code # second line # etc ``` ```` We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test the results to stay consistent with the library. #### Writing a return block The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation. The first line should be the type of the return, followed by a line return. No need to indent further for the elements building the return. Here's an example of a single value return: ``` Returns: `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token. ``` Here's an example of a tuple return, comprising several objects: ``` Returns: `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs: - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` -- Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss. - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). ``` #### Adding an image Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images to this dataset. ## Styling the docstring We have an automatic script running with the `make style` comment that will make sure that: - the docstrings fully take advantage of the line width - all code examples are formatted using black, like the code of the Transformers library This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's recommended to commit your changes before running `make style`, so you can revert the changes done by that script easily. # Testing documentation examples Good documentation often comes with an example of how a specific function or class should be used. Each model class should contain at least one example showcasing how to use this model class in inference. *E.g.* the class [Wav2Vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC) includes an example of how to transcribe speech to text in the [docstring of its forward function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward). ## Writing documentation examples The syntax for Example docstrings can look as follows: ``` Example: ```python >>> from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC >>> from datasets import load_dataset >>> import torch >>> dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") >>> dataset = dataset.sort("id") >>> sampling_rate = dataset.features["audio"].sampling_rate >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h") >>> model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") >>> # audio file is decoded on the fly >>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt") >>> with torch.no_grad(): ... logits = model(**inputs).logits >>> predicted_ids = torch.argmax(logits, dim=-1) >>> # transcribe speech >>> transcription = processor.batch_decode(predicted_ids) >>> transcription[0] 'MISTER QUILTER IS THE APOSTLE OF THE MIDDLE CLASSES AND WE ARE GLAD TO WELCOME HIS GOSPEL' ``` ``` The docstring should give a minimal, clear example of how the respective model is to be used in inference and also include the expected (ideally sensible) output. Often, readers will try out the example before even going through the function or class definitions. Therefore, it is of utmost importance that the example works as expected. ## Docstring testing To do so each example should be included in the doctests. We use pytests' [doctest integration](https://docs.pytest.org/doctest.html) to verify that all of our examples run correctly. For Transformers, the doctests are run on a daily basis via GitHub Actions as can be seen [here](https://github.com/huggingface/transformers/actions/workflows/doctests.yml). To include your example in the daily doctests, you need to add the filename that contains the example docstring to the [documentation_tests.txt](../utils/documentation_tests.txt). ### For Python files You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files): ```bash python utils/prepare_for_doc_test.py src docs ``` If you work on a specific python module, say `modeling_wav2vec2.py`, you can run the command as follows (to avoid the unnecessary temporary changes in irrelevant files): ```bash python utils/prepare_for_doc_test.py src/transformers/utils/doc.py src/transformers/models/wav2vec2/modeling_wav2vec2.py ``` (`utils/doc.py` should always be included) Then you can run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance: ```bash pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure ``` If you want to isolate a specific docstring, just add `::` after the file name then type the whole path of the function/class/method whose docstring you want to test. For instance, here is how to just test the forward method of `Wav2Vec2ForCTC`: ```bash pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure ``` Once you're done, you can run the following command (still from the root of the repository) to undo the changes made by the first command before committing: ```bash python utils/prepare_for_doc_test.py src docs --remove_new_line ``` ### For Markdown files You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files): ```bash python utils/prepare_for_doc_test.py src docs ``` Then you can test locally a given file with this command (here testing the quicktour): ```bash pytest --doctest-modules docs/source/quicktour.mdx -sv --doctest-continue-on-failure --doctest-glob="*.mdx" ``` Once you're done, you can run the following command (still from the root of the repository) to undo the changes made by the first command before committing: ```bash python utils/prepare_for_doc_test.py src docs --remove_new_line ``` ### Writing doctests Here are a few tips to help you debug the doctests and make them pass: - The outputs of the code need to match the expected output **exactly**, so make sure you have the same outputs. In particular doctest will see a difference between single quotes and double quotes, or a missing parenthesis. The only exceptions to that rule are: * whitespace: one give whitespace (space, tabulation, new line) is equivalent to any number of whitespace, so you can add new lines where there are spaces to make your output more readable. * numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configured to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits). - Don't leave a block of code that is very long to execute. If you can't make it fast, you can either not use the doctest syntax on it (so that it's ignored), or if you want to use the doctest syntax to show the results, you can add a comment `# doctest: +SKIP` at the end of the lines of code too long to execute - Each line of code that produces a result needs to have that result written below. You can ignore an output if you don't want to show it in your code example by adding a comment ` # doctest: +IGNORE_RESULT` at the end of the line of code producing it.

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/switch_transformers/__init__.py

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/levit/image_processing_levit.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for LeViT.""" from typing import Dict, Iterable, List, Optional, Union import numpy as np from transformers.utils.generic import TensorType from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images, ) from ...utils import logging logger = logging.get_logger(__name__) class LevitImageProcessor(BaseImageProcessor): r""" Constructs a LeViT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]`, *optional*, defaults to `{"shortest_edge": 224}`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width, size["shortest_egde"])`. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether or not to center crop the input to `(crop_size["height"], crop_size["width"])`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired image size after `center_crop`. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`List[int]`, defaults to `[0.229, 0.224, 0.225]`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`List[int]`, defaults to `[0.485, 0.456, 0.406]`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN, image_std: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD, **kwargs ) -> None: super().__init__(**kwargs) size = size if size is not None else {"shortest_edge": 224} size = get_size_dict(size, default_to_square=False) crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} crop_size = get_size_dict(crop_size, param_name="crop_size") self.do_resize = do_resize self.size = size self.resample = resample self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Resize an image. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width, size["shortest_egde"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size_dict = get_size_dict(size, default_to_square=False) # size_dict is a dict with either keys "height" and "width" or "shortest_edge" if "shortest_edge" in size: shortest_edge = int((256 / 224) * size["shortest_edge"]) output_size = get_resize_output_image_size(image, size=shortest_edge, default_to_square=False) size_dict = {"height": output_size[0], "width": output_size[1]} if "height" not in size_dict or "width" not in size_dict: raise ValueError( f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}" ) return resize( image, size=(size_dict["height"], size_dict["width"]), resample=resample, data_format=data_format, **kwargs ) def center_crop( self, image: np.ndarray, size: Dict[str, int], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Center crop an image. Args: image (`np.ndarray`): Image to center crop. size (`Dict[str, int]`): Dict `{"height": int, "width": int}` specifying the size of the output image after cropping. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}") return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs) def rescale( self, image: np.ndarray, scale: Union[int, float], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Rescale an image by a scale factor. image = image * scale. Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return rescale(image, scale=scale, data_format=data_format, **kwargs) def normalize( self, image: np.ndarray, mean: Union[float, List[float]], std: Union[float, List[float]], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Normalize an image. image = (image - image_mean) / image_std. Args: image (`np.ndarray`): Image to normalize. mean (`float` or `List[float]`): Image mean. std (`float` or `List[float]`): Image standard deviation. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs) def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, do_center_crop: Optional[bool] = None, crop_size: Optional[Dict[str, int]] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, Iterable[float]]] = None, image_std: Optional[Union[float, Iterable[float]]] = None, return_tensors: Optional[TensorType] = None, data_format: ChannelDimension = ChannelDimension.FIRST, **kwargs, ) -> BatchFeature: """ Preprocess an image or batch of images to be used as input to a LeViT model. Args: images (`ImageInput`): Image or batch of images to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the output image after center cropping. Crops images to (crop_size["height"], crop_size["width"]). do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Factor to rescale the image pixel values by. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image pixel values by `image_mean` and `image_std`. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Mean to normalize the image pixel values by. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Standard deviation to normalize the image pixel values by. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. """ do_resize = do_resize if do_resize is not None else self.do_resize resample = resample if resample is not None else self.resample do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std size = size if size is not None else self.size size = get_size_dict(size, default_to_square=False) crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size") if not is_batched(images): images = [images] if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if do_resize and size is None: raise ValueError("Size must be specified if do_resize is True.") if do_center_crop and crop_size is None: raise ValueError("Crop size must be specified if do_center_crop is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_normalize and (image_mean is None or image_std is None): raise ValueError("Image mean and std must be specified if do_normalize is True.") # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_resize: images = [self.resize(image, size, resample) for image in images] if do_center_crop: images = [self.center_crop(image, crop_size) for image in images] if do_rescale: images = [self.rescale(image, rescale_factor) for image in images] if do_normalize: images = [self.normalize(image, image_mean, image_std) for image in images] images = [to_channel_dimension_format(image, data_format) for image in images] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Image processor class for LeViT.""" from typing import Dict, Iterable, List, Optional, Union import numpy as np from transformers.utils.generic import TensorType from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, ChannelDimension, ImageInput, PILImageResampling, is_batched, to_numpy_array, valid_images, ) from ...utils import logging logger = logging.get_logger(__name__) class LevitImageProcessor(BaseImageProcessor): r""" Constructs a LeViT image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the `do_resize` parameter in the `preprocess` method. size (`Dict[str, int]`, *optional*, defaults to `{"shortest_edge": 224}`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width, size["shortest_egde"])`. Can be overridden by the `size` parameter in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the `preprocess` method. do_center_crop (`bool`, *optional*, defaults to `True`): Whether or not to center crop the input to `(crop_size["height"], crop_size["width"])`. Can be overridden by the `do_center_crop` parameter in the `preprocess` method. crop_size (`Dict`, *optional*, defaults to `{"height": 224, "width": 224}`): Desired image size after `center_crop`. Can be overridden by the `crop_size` parameter in the `preprocess` method. do_rescale (`bool`, *optional*, defaults to `True`): Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess` method. image_mean (`List[int]`, defaults to `[0.229, 0.224, 0.225]`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`List[int]`, defaults to `[0.485, 0.456, 0.406]`): Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method. """ model_input_names = ["pixel_values"] def __init__( self, do_resize: bool = True, size: Dict[str, int] = None, resample: PILImageResampling = PILImageResampling.BICUBIC, do_center_crop: bool = True, crop_size: Dict[str, int] = None, do_rescale: bool = True, rescale_factor: Union[int, float] = 1 / 255, do_normalize: bool = True, image_mean: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN, image_std: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD, **kwargs ) -> None: super().__init__(**kwargs) size = size if size is not None else {"shortest_edge": 224} size = get_size_dict(size, default_to_square=False) crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224} crop_size = get_size_dict(crop_size, param_name="crop_size") self.do_resize = do_resize self.size = size self.resample = resample self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD def resize( self, image: np.ndarray, size: Dict[str, int], resample: PILImageResampling = PILImageResampling.BICUBIC, data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Resize an image. If size is a dict with keys "width" and "height", the image will be resized to `(size["height"], size["width"])`. If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to `int(c * (256/224))`. The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to `(size["shortest_egde"] * height / width, size["shortest_egde"])`. Args: image (`np.ndarray`): Image to resize. size (`Dict[str, int]`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size_dict = get_size_dict(size, default_to_square=False) # size_dict is a dict with either keys "height" and "width" or "shortest_edge" if "shortest_edge" in size: shortest_edge = int((256 / 224) * size["shortest_edge"]) output_size = get_resize_output_image_size(image, size=shortest_edge, default_to_square=False) size_dict = {"height": output_size[0], "width": output_size[1]} if "height" not in size_dict or "width" not in size_dict: raise ValueError( f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}" ) return resize( image, size=(size_dict["height"], size_dict["width"]), resample=resample, data_format=data_format, **kwargs ) def center_crop( self, image: np.ndarray, size: Dict[str, int], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Center crop an image. Args: image (`np.ndarray`): Image to center crop. size (`Dict[str, int]`): Dict `{"height": int, "width": int}` specifying the size of the output image after cropping. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ size = get_size_dict(size) if "height" not in size or "width" not in size: raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}") return center_crop(image, size=(size["height"], size["width"]), data_format=data_format, **kwargs) def rescale( self, image: np.ndarray, scale: Union[int, float], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Rescale an image by a scale factor. image = image * scale. Args: image (`np.ndarray`): Image to rescale. scale (`int` or `float`): Scale to apply to the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return rescale(image, scale=scale, data_format=data_format, **kwargs) def normalize( self, image: np.ndarray, mean: Union[float, List[float]], std: Union[float, List[float]], data_format: Optional[Union[str, ChannelDimension]] = None, **kwargs ) -> np.ndarray: """ Normalize an image. image = (image - image_mean) / image_std. Args: image (`np.ndarray`): Image to normalize. mean (`float` or `List[float]`): Image mean. std (`float` or `List[float]`): Image standard deviation. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. """ return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs) def preprocess( self, images: ImageInput, do_resize: Optional[bool] = None, size: Optional[Dict[str, int]] = None, resample: PILImageResampling = None, do_center_crop: Optional[bool] = None, crop_size: Optional[Dict[str, int]] = None, do_rescale: Optional[bool] = None, rescale_factor: Optional[float] = None, do_normalize: Optional[bool] = None, image_mean: Optional[Union[float, Iterable[float]]] = None, image_std: Optional[Union[float, Iterable[float]]] = None, return_tensors: Optional[TensorType] = None, data_format: ChannelDimension = ChannelDimension.FIRST, **kwargs, ) -> BatchFeature: """ Preprocess an image or batch of images to be used as input to a LeViT model. Args: images (`ImageInput`): Image or batch of images to preprocess. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`Dict[str, int]`, *optional*, defaults to `self.size`): Size of the output image after resizing. If size is a dict with keys "width" and "height", the image will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value i.e, if height > width, then image will be rescaled to (size * height / width, size). resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`): Whether to center crop the image. crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`): Size of the output image after center cropping. Crops images to (crop_size["height"], crop_size["width"]). do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Factor to rescale the image pixel values by. do_normalize (`bool`, *optional*, defaults to `self.do_normalize`): Whether to normalize the image pixel values by `image_mean` and `image_std`. image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`): Mean to normalize the image pixel values by. image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`): Standard deviation to normalize the image pixel values by. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. If unset, the channel dimension format of the input image is used. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. """ do_resize = do_resize if do_resize is not None else self.do_resize resample = resample if resample is not None else self.resample do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop do_rescale = do_rescale if do_rescale is not None else self.do_rescale rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor do_normalize = do_normalize if do_normalize is not None else self.do_normalize image_mean = image_mean if image_mean is not None else self.image_mean image_std = image_std if image_std is not None else self.image_std size = size if size is not None else self.size size = get_size_dict(size, default_to_square=False) crop_size = crop_size if crop_size is not None else self.crop_size crop_size = get_size_dict(crop_size, param_name="crop_size") if not is_batched(images): images = [images] if not valid_images(images): raise ValueError( "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " "torch.Tensor, tf.Tensor or jax.ndarray." ) if do_resize and size is None: raise ValueError("Size must be specified if do_resize is True.") if do_center_crop and crop_size is None: raise ValueError("Crop size must be specified if do_center_crop is True.") if do_rescale and rescale_factor is None: raise ValueError("Rescale factor must be specified if do_rescale is True.") if do_normalize and (image_mean is None or image_std is None): raise ValueError("Image mean and std must be specified if do_normalize is True.") # All transformations expect numpy arrays. images = [to_numpy_array(image) for image in images] if do_resize: images = [self.resize(image, size, resample) for image in images] if do_center_crop: images = [self.center_crop(image, crop_size) for image in images] if do_rescale: images = [self.rescale(image, rescale_factor) for image in images] if do_normalize: images = [self.normalize(image, image_mean, image_std) for image in images] images = [to_channel_dimension_format(image, data_format) for image in images] data = {"pixel_values": images} return BatchFeature(data=data, tensor_type=return_tensors)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/pytorch/question-answering/trainer_seq2seq_qa.py

# coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A subclass of `Trainer` specific to Question-Answering tasks """ import math import time from typing import Dict, List, Optional from torch.utils.data import Dataset from transformers import Seq2SeqTrainer, is_torch_tpu_available from transformers.trainer_utils import PredictionOutput, speed_metrics if is_torch_tpu_available(check_device=False): import torch_xla.core.xla_model as xm import torch_xla.debug.metrics as met class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer): def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs): super().__init__(*args, **kwargs) self.eval_examples = eval_examples self.post_process_function = post_process_function # def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"): def evaluate( self, eval_dataset: Optional[Dataset] = None, eval_examples=None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "eval", **gen_kwargs, ) -> Dict[str, float]: gen_kwargs = gen_kwargs.copy() gen_kwargs["max_length"] = ( gen_kwargs["max_length"] if gen_kwargs.get("max_length") is not None else self.args.generation_max_length ) gen_kwargs["num_beams"] = ( gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams ) self._gen_kwargs = gen_kwargs eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset eval_dataloader = self.get_eval_dataloader(eval_dataset) eval_examples = self.eval_examples if eval_examples is None else eval_examples # Temporarily disable metric computation, we will do it in the loop here. compute_metrics = self.compute_metrics self.compute_metrics = None start_time = time.time() eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop try: output = eval_loop( eval_dataloader, description="Evaluation", # No point gathering the predictions if there are no metrics, otherwise we defer to # self.args.prediction_loss_only prediction_loss_only=True if compute_metrics is None else None, ignore_keys=ignore_keys, ) finally: self.compute_metrics = compute_metrics total_batch_size = self.args.eval_batch_size * self.args.world_size output.metrics.update( speed_metrics( metric_key_prefix, start_time, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size), ) ) if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save: # Only the main node write the results by default eval_preds = self.post_process_function(eval_examples, eval_dataset, output) metrics = self.compute_metrics(eval_preds) # Prefix all keys with metric_key_prefix + '_' for key in list(metrics.keys()): if not key.startswith(f"{metric_key_prefix}_"): metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) output.metrics.update(metrics) else: metrics = {} if self.args.should_log: # Only the main node log the results by default self.log(metrics) if self.args.tpu_metrics_debug or self.args.debug: # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.) xm.master_print(met.metrics_report()) self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics) return metrics def predict( self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test", **gen_kwargs ): self._gen_kwargs = gen_kwargs.copy() predict_dataloader = self.get_test_dataloader(predict_dataset) # Temporarily disable metric computation, we will do it in the loop here. compute_metrics = self.compute_metrics self.compute_metrics = None eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop try: output = eval_loop( predict_dataloader, description="Prediction", # No point gathering the predictions if there are no metrics, otherwise we defer to # self.args.prediction_loss_only prediction_loss_only=True if compute_metrics is None else None, ignore_keys=ignore_keys, ) finally: self.compute_metrics = compute_metrics if self.post_process_function is None or self.compute_metrics is None: return output predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict") metrics = self.compute_metrics(predictions) # Prefix all keys with metric_key_prefix + '_' for key in list(metrics.keys()): if not key.startswith(f"{metric_key_prefix}_"): metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics)

# coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A subclass of `Trainer` specific to Question-Answering tasks """ import math import time from typing import Dict, List, Optional from torch.utils.data import Dataset from transformers import Seq2SeqTrainer, is_torch_tpu_available from transformers.trainer_utils import PredictionOutput, speed_metrics if is_torch_tpu_available(check_device=False): import torch_xla.core.xla_model as xm import torch_xla.debug.metrics as met class QuestionAnsweringSeq2SeqTrainer(Seq2SeqTrainer): def __init__(self, *args, eval_examples=None, post_process_function=None, **kwargs): super().__init__(*args, **kwargs) self.eval_examples = eval_examples self.post_process_function = post_process_function # def evaluate(self, eval_dataset=None, eval_examples=None, ignore_keys=None, metric_key_prefix: str = "eval"): def evaluate( self, eval_dataset: Optional[Dataset] = None, eval_examples=None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "eval", **gen_kwargs, ) -> Dict[str, float]: gen_kwargs = gen_kwargs.copy() gen_kwargs["max_length"] = ( gen_kwargs["max_length"] if gen_kwargs.get("max_length") is not None else self.args.generation_max_length ) gen_kwargs["num_beams"] = ( gen_kwargs["num_beams"] if gen_kwargs.get("num_beams") is not None else self.args.generation_num_beams ) self._gen_kwargs = gen_kwargs eval_dataset = self.eval_dataset if eval_dataset is None else eval_dataset eval_dataloader = self.get_eval_dataloader(eval_dataset) eval_examples = self.eval_examples if eval_examples is None else eval_examples # Temporarily disable metric computation, we will do it in the loop here. compute_metrics = self.compute_metrics self.compute_metrics = None start_time = time.time() eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop try: output = eval_loop( eval_dataloader, description="Evaluation", # No point gathering the predictions if there are no metrics, otherwise we defer to # self.args.prediction_loss_only prediction_loss_only=True if compute_metrics is None else None, ignore_keys=ignore_keys, ) finally: self.compute_metrics = compute_metrics total_batch_size = self.args.eval_batch_size * self.args.world_size output.metrics.update( speed_metrics( metric_key_prefix, start_time, num_samples=output.num_samples, num_steps=math.ceil(output.num_samples / total_batch_size), ) ) if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save: # Only the main node write the results by default eval_preds = self.post_process_function(eval_examples, eval_dataset, output) metrics = self.compute_metrics(eval_preds) # Prefix all keys with metric_key_prefix + '_' for key in list(metrics.keys()): if not key.startswith(f"{metric_key_prefix}_"): metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) output.metrics.update(metrics) else: metrics = {} if self.args.should_log: # Only the main node log the results by default self.log(metrics) if self.args.tpu_metrics_debug or self.args.debug: # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.) xm.master_print(met.metrics_report()) self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics) return metrics def predict( self, predict_dataset, predict_examples, ignore_keys=None, metric_key_prefix: str = "test", **gen_kwargs ): self._gen_kwargs = gen_kwargs.copy() predict_dataloader = self.get_test_dataloader(predict_dataset) # Temporarily disable metric computation, we will do it in the loop here. compute_metrics = self.compute_metrics self.compute_metrics = None eval_loop = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop try: output = eval_loop( predict_dataloader, description="Prediction", # No point gathering the predictions if there are no metrics, otherwise we defer to # self.args.prediction_loss_only prediction_loss_only=True if compute_metrics is None else None, ignore_keys=ignore_keys, ) finally: self.compute_metrics = compute_metrics if self.post_process_function is None or self.compute_metrics is None: return output predictions = self.post_process_function(predict_examples, predict_dataset, output.predictions, "predict") metrics = self.compute_metrics(predictions) # Prefix all keys with metric_key_prefix + '_' for key in list(metrics.keys()): if not key.startswith(f"{metric_key_prefix}_"): metrics[f"{metric_key_prefix}_{key}"] = metrics.pop(key) return PredictionOutput(predictions=predictions.predictions, label_ids=predictions.label_ids, metrics=metrics)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./tests/models/layoutlmv3/test_processor_layoutlmv3.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import shutil import tempfile import unittest from typing import List import numpy as np from transformers import PreTrainedTokenizer, PreTrainedTokenizerBase, PreTrainedTokenizerFast from transformers.models.layoutlmv3 import LayoutLMv3Tokenizer, LayoutLMv3TokenizerFast from transformers.models.layoutlmv3.tokenization_layoutlmv3 import VOCAB_FILES_NAMES from transformers.testing_utils import require_pytesseract, require_tokenizers, require_torch, slow from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_pytesseract_available if is_pytesseract_available(): from PIL import Image from transformers import LayoutLMv3FeatureExtractor, LayoutLMv3Processor @require_pytesseract @require_tokenizers class LayoutLMv3ProcessorTest(unittest.TestCase): tokenizer_class = LayoutLMv3Tokenizer rust_tokenizer_class = LayoutLMv3TokenizerFast def setUp(self): # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", ] self.tmpdirname = tempfile.mkdtemp() vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": "<unk>"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) feature_extractor_map = { "do_resize": True, "size": 224, "apply_ocr": True, } self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME) with open(self.feature_extraction_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(feature_extractor_map) + "\n") def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer: return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast: return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_tokenizers(self, **kwargs) -> List[PreTrainedTokenizerBase]: return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)] def get_feature_extractor(self, **kwargs): return LayoutLMv3FeatureExtractor.from_pretrained(self.tmpdirname, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def prepare_image_inputs(self): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. """ image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] return image_inputs def test_save_load_pretrained_default(self): feature_extractor = self.get_feature_extractor() tokenizers = self.get_tokenizers() for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(self.tmpdirname) processor = LayoutLMv3Processor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertIsInstance(processor.tokenizer, (LayoutLMv3Tokenizer, LayoutLMv3TokenizerFast)) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor.feature_extractor, LayoutLMv3FeatureExtractor) def test_save_load_pretrained_additional_features(self): processor = LayoutLMv3Processor(feature_extractor=self.get_feature_extractor(), tokenizer=self.get_tokenizer()) processor.save_pretrained(self.tmpdirname) # slow tokenizer tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30) processor = LayoutLMv3Processor.from_pretrained( self.tmpdirname, use_fast=False, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30 ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, LayoutLMv3Tokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, LayoutLMv3FeatureExtractor) # fast tokenizer tokenizer_add_kwargs = self.get_rust_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30) processor = LayoutLMv3Processor.from_pretrained( self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30 ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, LayoutLMv3TokenizerFast) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, LayoutLMv3FeatureExtractor) def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = LayoutLMv3Processor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" image_input = self.prepare_image_inputs() # add extra args inputs = processor(text=input_str, images=image_input, return_codebook_pixels=False, return_image_mask=False) self.assertListEqual(list(inputs.keys()), processor.model_input_names) # different use cases tests @require_torch @require_pytesseract class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase): @cached_property def get_images(self): # we verify our implementation on 2 document images from the DocVQA dataset from datasets import load_dataset ds = load_dataset("hf-internal-testing/fixtures_docvqa", split="test") image_1 = Image.open(ds[0]["file"]).convert("RGB") image_2 = Image.open(ds[1]["file"]).convert("RGB") return image_1, image_2 @cached_property def get_tokenizers(self): slow_tokenizer = LayoutLMv3Tokenizer.from_pretrained("microsoft/layoutlmv3-base", add_visual_labels=False) fast_tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base", add_visual_labels=False) return [slow_tokenizer, fast_tokenizer] @slow def test_processor_case_1(self): # case 1: document image classification (training, inference) + token classification (inference), apply_ocr = True feature_extractor = LayoutLMv3FeatureExtractor() tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched input_feat_extract = feature_extractor(images[0], return_tensors="pt") input_processor = processor(images[0], return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify image self.assertAlmostEqual( input_feat_extract["pixel_values"].sum(), input_processor["pixel_values"].sum(), delta=1e-2 ) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "<s> 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer</s>" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched input_feat_extract = feature_extractor(images, return_tensors="pt") input_processor = processor(images, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify images self.assertAlmostEqual( input_feat_extract["pixel_values"].sum(), input_processor["pixel_values"].sum(), delta=1e-2 ) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "<s> 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) @slow def test_processor_case_2(self): # case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] input_processor = processor(images[0], words, boxes=boxes, return_tensors="pt") # verify keys expected_keys = ["input_ids", "bbox", "attention_mask", "pixel_values"] actual_keys = list(input_processor.keys()) for key in expected_keys: self.assertIn(key, actual_keys) # verify input_ids expected_decoding = "<s> hello world</s>" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] input_processor = processor(images, words, boxes=boxes, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> hello world</s><pad><pad><pad>" decoding = processor.decode(input_processor.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [ [0, 0, 0, 0], [3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [0, 0, 0, 0], ] self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow def test_processor_case_3(self): # case 3: token classification (training), apply_ocr=False feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched words = ["weirdly", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] word_labels = [1, 2] input_processor = processor(images[0], words, boxes=boxes, word_labels=word_labels, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> weirdly world</s>" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify labels expected_labels = [-100, 1, -100, 2, -100] self.assertListEqual(input_processor.labels.squeeze().tolist(), expected_labels) # batched words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] word_labels = [[1, 2], [6, 3, 10, 2]] input_processor = processor( images, words, boxes=boxes, word_labels=word_labels, padding=True, return_tensors="pt" ) # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> my name is niels</s>" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [ [0, 0, 0, 0], [3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [0, 0, 0, 0], ] self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) # verify labels expected_labels = [-100, 6, 3, 10, 2, -100, -100] self.assertListEqual(input_processor.labels[1].tolist(), expected_labels) @slow def test_processor_case_4(self): # case 4: visual question answering (inference), apply_ocr=True feature_extractor = LayoutLMv3FeatureExtractor() tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched question = "What's his name?" input_processor = processor(images[0], question, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "<s> What's his name?</s></s> 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer</s>" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] input_processor = processor( images, questions, padding="max_length", max_length=20, truncation=True, return_tensors="pt" ) # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids # this was obtained with Tesseract 4.1.1 expected_decoding = "<s> what's the time</s></s> 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC</s>" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox # fmt: off expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [74, 136, 161, 158], [0, 0, 0, 0]] # noqa: E231 # fmt: on self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow def test_processor_case_5(self): # case 5: visual question answering (inference), apply_ocr=False feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched question = "What's his name?" words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] input_processor = processor(images[0], question, words, boxes, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> What's his name?</s></s> hello world</s>" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] input_processor = processor(images, questions, words, boxes, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> How old is he?</s></s> hello world</s><pad><pad>" decoding = processor.decode(input_processor.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) expected_decoding = "<s> what's the time</s></s> my name is niels</s>" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [[6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [0, 0, 0, 0]] self.assertListEqual(input_processor.bbox[1].tolist()[-5:], expected_bbox)

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import shutil import tempfile import unittest from typing import List import numpy as np from transformers import PreTrainedTokenizer, PreTrainedTokenizerBase, PreTrainedTokenizerFast from transformers.models.layoutlmv3 import LayoutLMv3Tokenizer, LayoutLMv3TokenizerFast from transformers.models.layoutlmv3.tokenization_layoutlmv3 import VOCAB_FILES_NAMES from transformers.testing_utils import require_pytesseract, require_tokenizers, require_torch, slow from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_pytesseract_available if is_pytesseract_available(): from PIL import Image from transformers import LayoutLMv3FeatureExtractor, LayoutLMv3Processor @require_pytesseract @require_tokenizers class LayoutLMv3ProcessorTest(unittest.TestCase): tokenizer_class = LayoutLMv3Tokenizer rust_tokenizer_class = LayoutLMv3TokenizerFast def setUp(self): # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", ] self.tmpdirname = tempfile.mkdtemp() vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": "<unk>"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) feature_extractor_map = { "do_resize": True, "size": 224, "apply_ocr": True, } self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME) with open(self.feature_extraction_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(feature_extractor_map) + "\n") def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer: return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs) -> PreTrainedTokenizerFast: return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) def get_tokenizers(self, **kwargs) -> List[PreTrainedTokenizerBase]: return [self.get_tokenizer(**kwargs), self.get_rust_tokenizer(**kwargs)] def get_feature_extractor(self, **kwargs): return LayoutLMv3FeatureExtractor.from_pretrained(self.tmpdirname, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def prepare_image_inputs(self): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. """ image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)] image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] return image_inputs def test_save_load_pretrained_default(self): feature_extractor = self.get_feature_extractor() tokenizers = self.get_tokenizers() for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(self.tmpdirname) processor = LayoutLMv3Processor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertIsInstance(processor.tokenizer, (LayoutLMv3Tokenizer, LayoutLMv3TokenizerFast)) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor.feature_extractor, LayoutLMv3FeatureExtractor) def test_save_load_pretrained_additional_features(self): processor = LayoutLMv3Processor(feature_extractor=self.get_feature_extractor(), tokenizer=self.get_tokenizer()) processor.save_pretrained(self.tmpdirname) # slow tokenizer tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30) processor = LayoutLMv3Processor.from_pretrained( self.tmpdirname, use_fast=False, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30 ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, LayoutLMv3Tokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, LayoutLMv3FeatureExtractor) # fast tokenizer tokenizer_add_kwargs = self.get_rust_tokenizer(bos_token="(BOS)", eos_token="(EOS)") feature_extractor_add_kwargs = self.get_feature_extractor(do_resize=False, size=30) processor = LayoutLMv3Processor.from_pretrained( self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_resize=False, size=30 ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, LayoutLMv3TokenizerFast) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, LayoutLMv3FeatureExtractor) def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = LayoutLMv3Processor(tokenizer=tokenizer, feature_extractor=feature_extractor) input_str = "lower newer" image_input = self.prepare_image_inputs() # add extra args inputs = processor(text=input_str, images=image_input, return_codebook_pixels=False, return_image_mask=False) self.assertListEqual(list(inputs.keys()), processor.model_input_names) # different use cases tests @require_torch @require_pytesseract class LayoutLMv3ProcessorIntegrationTests(unittest.TestCase): @cached_property def get_images(self): # we verify our implementation on 2 document images from the DocVQA dataset from datasets import load_dataset ds = load_dataset("hf-internal-testing/fixtures_docvqa", split="test") image_1 = Image.open(ds[0]["file"]).convert("RGB") image_2 = Image.open(ds[1]["file"]).convert("RGB") return image_1, image_2 @cached_property def get_tokenizers(self): slow_tokenizer = LayoutLMv3Tokenizer.from_pretrained("microsoft/layoutlmv3-base", add_visual_labels=False) fast_tokenizer = LayoutLMv3TokenizerFast.from_pretrained("microsoft/layoutlmv3-base", add_visual_labels=False) return [slow_tokenizer, fast_tokenizer] @slow def test_processor_case_1(self): # case 1: document image classification (training, inference) + token classification (inference), apply_ocr = True feature_extractor = LayoutLMv3FeatureExtractor() tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched input_feat_extract = feature_extractor(images[0], return_tensors="pt") input_processor = processor(images[0], return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify image self.assertAlmostEqual( input_feat_extract["pixel_values"].sum(), input_processor["pixel_values"].sum(), delta=1e-2 ) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "<s> 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer</s>" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched input_feat_extract = feature_extractor(images, return_tensors="pt") input_processor = processor(images, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify images self.assertAlmostEqual( input_feat_extract["pixel_values"].sum(), input_processor["pixel_values"].sum(), delta=1e-2 ) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "<s> 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC’s Brands: An Asset for the Nation The consumer needs and aspirations they fulfil, the benefit they generate for millions across ITC’s value chains, the future-ready capabilities that support them, and the value that they create for the country, have made ITC’s brands national assets, adding to India’s competitiveness. It is ITC’s aspiration to be the No 1 FMCG player in the country, driven by its new FMCG businesses. A recent Nielsen report has highlighted that ITC's new FMCG businesses are the fastest growing among the top consumer goods companies operating in India. ITC takes justifiable pride that, along with generating economic value, these celebrated Indian brands also drive the creation of larger societal capital through the virtuous cycle of sustainable and inclusive growth. DI WILLS * ; LOVE DELIGHTFULLY SOFT SKIN? aia Ans Source: https://www.industrydocuments.ucsf.edu/docs/snbx0223</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) @slow def test_processor_case_2(self): # case 2: document image classification (training, inference) + token classification (inference), apply_ocr=False feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] input_processor = processor(images[0], words, boxes=boxes, return_tensors="pt") # verify keys expected_keys = ["input_ids", "bbox", "attention_mask", "pixel_values"] actual_keys = list(input_processor.keys()) for key in expected_keys: self.assertIn(key, actual_keys) # verify input_ids expected_decoding = "<s> hello world</s>" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] input_processor = processor(images, words, boxes=boxes, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> hello world</s><pad><pad><pad>" decoding = processor.decode(input_processor.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [ [0, 0, 0, 0], [3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [0, 0, 0, 0], ] self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow def test_processor_case_3(self): # case 3: token classification (training), apply_ocr=False feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched words = ["weirdly", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] word_labels = [1, 2] input_processor = processor(images[0], words, boxes=boxes, word_labels=word_labels, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> weirdly world</s>" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify labels expected_labels = [-100, 1, -100, 2, -100] self.assertListEqual(input_processor.labels.squeeze().tolist(), expected_labels) # batched words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] word_labels = [[1, 2], [6, 3, 10, 2]] input_processor = processor( images, words, boxes=boxes, word_labels=word_labels, padding=True, return_tensors="pt" ) # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "labels", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> my name is niels</s>" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [ [0, 0, 0, 0], [3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [0, 0, 0, 0], ] self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) # verify labels expected_labels = [-100, 6, 3, 10, 2, -100, -100] self.assertListEqual(input_processor.labels[1].tolist(), expected_labels) @slow def test_processor_case_4(self): # case 4: visual question answering (inference), apply_ocr=True feature_extractor = LayoutLMv3FeatureExtractor() tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched question = "What's his name?" input_processor = processor(images[0], question, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids # this was obtained with Tesseract 4.1.1 # fmt: off expected_decoding = "<s> What's his name?</s></s> 11:14 to 11:39 a.m 11:39 to 11:44 a.m. 11:44 a.m. to 12:25 p.m. 12:25 to 12:58 p.m. 12:58 to 4:00 p.m. 2:00 to 5:00 p.m. Coffee Break Coffee will be served for men and women in the lobby adjacent to exhibit area. Please move into exhibit area. (Exhibits Open) TRRF GENERAL SESSION (PART |) Presiding: Lee A. Waller TRRF Vice President “Introductory Remarks” Lee A. Waller, TRRF Vice Presi- dent Individual Interviews with TRRF Public Board Members and Sci- entific Advisory Council Mem- bers Conducted by TRRF Treasurer Philip G. Kuehn to get answers which the public refrigerated warehousing industry is looking for. Plus questions from the floor. Dr. Emil M. Mrak, University of Cal- ifornia, Chairman, TRRF Board; Sam R. Cecil, University of Georgia College of Agriculture; Dr. Stanley Charm, Tufts University School of Medicine; Dr. Robert H. Cotton, ITT Continental Baking Company; Dr. Owen Fennema, University of Wis- consin; Dr. Robert E. Hardenburg, USDA. Questions and Answers Exhibits Open Capt. Jack Stoney Room TRRF Scientific Advisory Council Meeting Ballroom Foyer</s>" # noqa: E231 # fmt: on decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] input_processor = processor( images, questions, padding="max_length", max_length=20, truncation=True, return_tensors="pt" ) # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids # this was obtained with Tesseract 4.1.1 expected_decoding = "<s> what's the time</s></s> 7 ITC Limited REPORT AND ACCOUNTS 2013 ITC</s>" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox # fmt: off expected_bbox = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 45, 67, 80], [72, 56, 109, 67], [72, 56, 109, 67], [116, 56, 189, 67], [198, 59, 253, 66], [257, 59, 285, 66], [289, 59, 365, 66], [289, 59, 365, 66], [289, 59, 365, 66], [372, 59, 407, 66], [74, 136, 161, 158], [74, 136, 161, 158], [0, 0, 0, 0]] # noqa: E231 # fmt: on self.assertListEqual(input_processor.bbox[1].tolist(), expected_bbox) @slow def test_processor_case_5(self): # case 5: visual question answering (inference), apply_ocr=False feature_extractor = LayoutLMv3FeatureExtractor(apply_ocr=False) tokenizers = self.get_tokenizers images = self.get_images for tokenizer in tokenizers: processor = LayoutLMv3Processor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched question = "What's his name?" words = ["hello", "world"] boxes = [[1, 2, 3, 4], [5, 6, 7, 8]] input_processor = processor(images[0], question, words, boxes, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> What's his name?</s></s> hello world</s>" decoding = processor.decode(input_processor.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] words = [["hello", "world"], ["my", "name", "is", "niels"]] boxes = [[[1, 2, 3, 4], [5, 6, 7, 8]], [[3, 2, 5, 1], [6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3]]] input_processor = processor(images, questions, words, boxes, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "bbox", "input_ids", "pixel_values"] actual_keys = sorted(list(input_processor.keys())) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s> How old is he?</s></s> hello world</s><pad><pad>" decoding = processor.decode(input_processor.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) expected_decoding = "<s> what's the time</s></s> my name is niels</s>" decoding = processor.decode(input_processor.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify bbox expected_bbox = [[6, 7, 4, 2], [3, 9, 2, 4], [1, 1, 2, 3], [1, 1, 2, 3], [0, 0, 0, 0]] self.assertListEqual(input_processor.bbox[1].tolist()[-5:], expected_bbox)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./docs/source/en/model_doc/trocr.mdx

<!--Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # TrOCR ## Overview The TrOCR model was proposed in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to perform [optical character recognition (OCR)](https://en.wikipedia.org/wiki/Optical_character_recognition). The abstract from the paper is the following: *Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition tasks.* <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/trocr_architecture.jpg" alt="drawing" width="600"/> <small> TrOCR architecture. Taken from the <a href="https://arxiv.org/abs/2109.10282">original paper</a>. </small> Please refer to the [`VisionEncoderDecoder`] class on how to use this model. This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm/tree/6f60612e7cc86a2a1ae85c47231507a587ab4e01/trocr). Tips: - The quickest way to get started with TrOCR is by checking the [tutorial notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/TrOCR), which show how to use the model at inference time as well as fine-tuning on custom data. - TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results on both printed (e.g. the [SROIE dataset](https://paperswithcode.com/dataset/sroie) and handwritten (e.g. the [IAM Handwriting dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database>) text recognition tasks. For more information, see the [official models](https://huggingface.co/models?other=trocr>). - TrOCR is always used within the [VisionEncoderDecoder](vision-encoder-decoder) framework. ## Inference TrOCR's [`VisionEncoderDecoder`] model accepts images as input and makes use of [`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image. The [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] class is responsible for preprocessing the input image and [`RobertaTokenizer`/`XLMRobertaTokenizer`] decodes the generated target tokens to the target string. The [`TrOCRProcessor`] wraps [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] and [`RobertaTokenizer`/`XLMRobertaTokenizer`] into a single instance to both extract the input features and decode the predicted token ids. - Step-by-step Optical Character Recognition (OCR) ``` py >>> from transformers import TrOCRProcessor, VisionEncoderDecoderModel >>> import requests >>> from PIL import Image >>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") >>> # load image from the IAM dataset >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB") >>> pixel_values = processor(image, return_tensors="pt").pixel_values >>> generated_ids = model.generate(pixel_values) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` See the [model hub](https://huggingface.co/models?filter=trocr) to look for TrOCR checkpoints. ## TrOCRConfig [[autodoc]] TrOCRConfig ## TrOCRProcessor [[autodoc]] TrOCRProcessor - __call__ - from_pretrained - save_pretrained - batch_decode - decode ## TrOCRForCausalLM [[autodoc]] TrOCRForCausalLM - forward

<!--Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # TrOCR ## Overview The TrOCR model was proposed in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to perform [optical character recognition (OCR)](https://en.wikipedia.org/wiki/Optical_character_recognition). The abstract from the paper is the following: *Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition tasks.* <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/trocr_architecture.jpg" alt="drawing" width="600"/> <small> TrOCR architecture. Taken from the <a href="https://arxiv.org/abs/2109.10282">original paper</a>. </small> Please refer to the [`VisionEncoderDecoder`] class on how to use this model. This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm/tree/6f60612e7cc86a2a1ae85c47231507a587ab4e01/trocr). Tips: - The quickest way to get started with TrOCR is by checking the [tutorial notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/TrOCR), which show how to use the model at inference time as well as fine-tuning on custom data. - TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results on both printed (e.g. the [SROIE dataset](https://paperswithcode.com/dataset/sroie) and handwritten (e.g. the [IAM Handwriting dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database>) text recognition tasks. For more information, see the [official models](https://huggingface.co/models?other=trocr>). - TrOCR is always used within the [VisionEncoderDecoder](vision-encoder-decoder) framework. ## Inference TrOCR's [`VisionEncoderDecoder`] model accepts images as input and makes use of [`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image. The [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] class is responsible for preprocessing the input image and [`RobertaTokenizer`/`XLMRobertaTokenizer`] decodes the generated target tokens to the target string. The [`TrOCRProcessor`] wraps [`ViTFeatureExtractor`/`DeiTFeatureExtractor`] and [`RobertaTokenizer`/`XLMRobertaTokenizer`] into a single instance to both extract the input features and decode the predicted token ids. - Step-by-step Optical Character Recognition (OCR) ``` py >>> from transformers import TrOCRProcessor, VisionEncoderDecoderModel >>> import requests >>> from PIL import Image >>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") >>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") >>> # load image from the IAM dataset >>> url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" >>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB") >>> pixel_values = processor(image, return_tensors="pt").pixel_values >>> generated_ids = model.generate(pixel_values) >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` See the [model hub](https://huggingface.co/models?filter=trocr) to look for TrOCR checkpoints. ## TrOCRConfig [[autodoc]] TrOCRConfig ## TrOCRProcessor [[autodoc]] TrOCRProcessor - __call__ - from_pretrained - save_pretrained - batch_decode - decode ## TrOCRForCausalLM [[autodoc]] TrOCRForCausalLM - forward

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./docs/source/de/model_sharing.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Ein Modell teilen Die letzten beiden Tutorials haben gezeigt, wie man ein Modell mit PyTorch, Keras und 🤗 Accelerate für verteilte Setups feinabstimmen kann. Der nächste Schritt besteht darin, Ihr Modell mit der Community zu teilen! Bei Hugging Face glauben wir an den offenen Austausch von Wissen und Ressourcen, um künstliche Intelligenz für alle zu demokratisieren. Wir ermutigen Sie, Ihr Modell mit der Community zu teilen, um anderen zu helfen, Zeit und Ressourcen zu sparen. In diesem Tutorial lernen Sie zwei Methoden kennen, wie Sie ein trainiertes oder verfeinertes Modell auf dem [Model Hub](https://huggingface.co/models) teilen können: - Programmgesteuertes Übertragen Ihrer Dateien auf den Hub. - Ziehen Sie Ihre Dateien per Drag-and-Drop über die Weboberfläche in den Hub. <iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <Tip> Um ein Modell mit der Öffentlichkeit zu teilen, benötigen Sie ein Konto auf [huggingface.co](https://huggingface.co/join). Sie können auch einer bestehenden Organisation beitreten oder eine neue Organisation gründen. </Tip> ## Repository-Funktionen Jedes Repository im Model Hub verhält sich wie ein typisches GitHub-Repository. Unsere Repositorys bieten Versionierung, Commit-Historie und die Möglichkeit, Unterschiede zu visualisieren. Die integrierte Versionierung des Model Hub basiert auf Git und [git-lfs](https://git-lfs.github.com/). Mit anderen Worten: Sie können ein Modell als ein Repository behandeln, was eine bessere Zugriffskontrolle und Skalierbarkeit ermöglicht. Die Versionskontrolle ermöglicht *Revisionen*, eine Methode zum Anheften einer bestimmten Version eines Modells mit einem Commit-Hash, Tag oder Branch. Folglich können Sie eine bestimmte Modellversion mit dem Parameter "Revision" laden: ```py >>> model = AutoModel.from_pretrained( ... "julien-c/EsperBERTo-small", revision="v2.0.1" # tag name, or branch name, or commit hash ... ) ``` Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können die Commit-Historie sowie die Unterschiede einsehen:  ## Einrichtung Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig `~/.cache/`) gespeichert: ```bash huggingface-cli login ``` Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub. ```bash pip install huggingface_hub ``` Verwenden Sie dann `notebook_login`, um sich beim Hub anzumelden, und folgen Sie dem Link [hier](https://huggingface.co/settings/token), um ein Token für die Anmeldung zu generieren: ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## Ein Modell für alle Frameworks konvertieren Um sicherzustellen, dass Ihr Modell von jemandem verwendet werden kann, der mit einem anderen Framework arbeitet, empfehlen wir Ihnen, Ihr Modell sowohl mit PyTorch- als auch mit TensorFlow-Checkpoints zu konvertieren und hochzuladen. Während Benutzer immer noch in der Lage sind, Ihr Modell von einem anderen Framework zu laden, wenn Sie diesen Schritt überspringen, wird es langsamer sein, weil 🤗 Transformers den Checkpoint on-the-fly konvertieren müssen. Die Konvertierung eines Checkpoints für ein anderes Framework ist einfach. Stellen Sie sicher, dass Sie PyTorch und TensorFlow installiert haben (siehe [hier](installation) für Installationsanweisungen), und finden Sie dann das spezifische Modell für Ihre Aufgabe in dem anderen Framework. <frameworkcontent> <pt> Geben Sie `from_tf=True` an, um einen Prüfpunkt von TensorFlow nach PyTorch zu konvertieren: ```py >>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True) >>> pt_model.save_pretrained("path/to/awesome-name-you-picked") ``` </pt> <tf> Geben Sie `from_pt=True` an, um einen Prüfpunkt von PyTorch nach TensorFlow zu konvertieren: ```py >>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True) ``` Dann können Sie Ihr neues TensorFlow-Modell mit seinem neuen Checkpoint speichern: ```py >>> tf_model.save_pretrained("path/to/awesome-name-you-picked") ``` </tf> <jax> Wenn ein Modell in Flax verfügbar ist, können Sie auch einen Kontrollpunkt von PyTorch nach Flax konvertieren: ```py >>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained( ... "path/to/awesome-name-you-picked", from_pt=True ... ) ``` </jax> </frameworkcontent> ## Ein Modell während des Trainings hochladen <frameworkcontent> <pt> <Youtube id="Z1-XMy-GNLQ"/> Die Weitergabe eines Modells an den Hub ist so einfach wie das Hinzufügen eines zusätzlichen Parameters oder Rückrufs. Erinnern Sie sich an das [Feinabstimmungs-Tutorial](training), in der Klasse [`TrainingArguments`] geben Sie Hyperparameter und zusätzliche Trainingsoptionen an. Eine dieser Trainingsoptionen beinhaltet die Möglichkeit, ein Modell direkt an den Hub zu pushen. Setzen Sie `push_to_hub=True` in Ihrer [`TrainingArguments`]: ```py >>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True) ``` Übergeben Sie Ihre Trainingsargumente wie gewohnt an [`Trainer`]: ```py >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... ) ``` Nach der Feinabstimmung Ihres Modells rufen Sie [`~transformers.Trainer.push_to_hub`] auf [`Trainer`] auf, um das trainierte Modell an den Hub zu übertragen. Transformers fügt sogar automatisch Trainings-Hyperparameter, Trainingsergebnisse und Framework-Versionen zu Ihrer Modellkarte hinzu! ```py >>> trainer.push_to_hub() ``` </pt> <tf> Geben Sie ein Modell mit [`PushToHubCallback`] an den Hub weiter. In der [`PushToHubCallback`] Funktion, fügen Sie hinzu: - Ein Ausgabeverzeichnis für Ihr Modell. - Einen Tokenizer. - Die `hub_model_id`, die Ihr Hub-Benutzername und Modellname ist. ```py >>> from transformers.keras.callbacks import PushToHubCallback >>> push_to_hub_callback = PushToHubCallback( ... output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model" ... ) ``` Fügen Sie den Callback zu [`fit`](https://keras.io/api/models/model_training_apis/) hinzu, und 🤗 Transformers wird das trainierte Modell an den Hub weiterleiten: ```py >>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback) ``` </tf> </frameworkcontent> ## Verwenden Sie die Funktion `push_to_hub`. Sie können `push_to_hub` auch direkt für Ihr Modell aufrufen, um es in den Hub hochzuladen. Geben Sie den Namen Ihres Modells in "push_to_hub" an: ```py >>> pt_model.push_to_hub("my-awesome-model") ``` Dadurch wird ein Repository unter Ihrem Benutzernamen mit dem Modellnamen `my-awesome-model` erstellt. Benutzer können nun Ihr Modell mit der Funktion `from_pretrained` laden: ```py >>> from transformers import AutoModel >>> model = AutoModel.from_pretrained("your_username/my-awesome-model") ``` Wenn Sie zu einer Organisation gehören und Ihr Modell stattdessen unter dem Namen der Organisation pushen wollen, fügen Sie diesen einfach zur `repo_id` hinzu: ```py >>> pt_model.push_to_hub("my-awesome-org/my-awesome-model") ``` Die Funktion "push_to_hub" kann auch verwendet werden, um andere Dateien zu einem Modell-Repository hinzuzufügen. Zum Beispiel kann man einen Tokenizer zu einem Modell-Repository hinzufügen: ```py >>> tokenizer.push_to_hub("my-awesome-model") ``` Oder vielleicht möchten Sie die TensorFlow-Version Ihres fein abgestimmten PyTorch-Modells hinzufügen: ```py >>> tf_model.push_to_hub("my-awesome-model") ``` Wenn Sie nun zu Ihrem Hugging Face-Profil navigieren, sollten Sie Ihr neu erstelltes Modell-Repository sehen. Wenn Sie auf die Registerkarte **Dateien** klicken, werden alle Dateien angezeigt, die Sie in das Repository hochgeladen haben. Weitere Einzelheiten zum Erstellen und Hochladen von Dateien in ein Repository finden Sie in der Hub-Dokumentation [hier](https://huggingface.co/docs/hub/how-to-upstream). ## Hochladen mit der Weboberfläche Benutzer, die einen no-code Ansatz bevorzugen, können ein Modell über das Webinterface des Hubs hochladen. Besuchen Sie [huggingface.co/new](https://huggingface.co/new) um ein neues Repository zu erstellen:  Fügen Sie von hier aus einige Informationen über Ihr Modell hinzu: - Wählen Sie den **Besitzer** des Repositorys. Dies können Sie selbst oder eine der Organisationen sein, denen Sie angehören. - Wählen Sie einen Namen für Ihr Modell, der auch der Name des Repositorys sein wird. - Wählen Sie, ob Ihr Modell öffentlich oder privat ist. - Geben Sie die Lizenzverwendung für Ihr Modell an. Klicken Sie nun auf die Registerkarte **Dateien** und klicken Sie auf die Schaltfläche **Datei hinzufügen**, um eine neue Datei in Ihr Repository hochzuladen. Ziehen Sie dann eine Datei per Drag-and-Drop hoch und fügen Sie eine Übergabemeldung hinzu.  ## Hinzufügen einer Modellkarte Um sicherzustellen, dass die Benutzer die Fähigkeiten, Grenzen, möglichen Verzerrungen und ethischen Aspekte Ihres Modells verstehen, fügen Sie bitte eine Modellkarte zu Ihrem Repository hinzu. Die Modellkarte wird in der Datei `README.md` definiert. Sie können eine Modellkarte hinzufügen, indem Sie: * Manuelles Erstellen und Hochladen einer "README.md"-Datei. * Klicken Sie auf die Schaltfläche **Modellkarte bearbeiten** in Ihrem Modell-Repository. Werfen Sie einen Blick auf die DistilBert [model card](https://huggingface.co/distilbert-base-uncased) als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei "README.md" einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation [hier](https://huggingface.co/docs/hub/models-cards).

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Ein Modell teilen Die letzten beiden Tutorials haben gezeigt, wie man ein Modell mit PyTorch, Keras und 🤗 Accelerate für verteilte Setups feinabstimmen kann. Der nächste Schritt besteht darin, Ihr Modell mit der Community zu teilen! Bei Hugging Face glauben wir an den offenen Austausch von Wissen und Ressourcen, um künstliche Intelligenz für alle zu demokratisieren. Wir ermutigen Sie, Ihr Modell mit der Community zu teilen, um anderen zu helfen, Zeit und Ressourcen zu sparen. In diesem Tutorial lernen Sie zwei Methoden kennen, wie Sie ein trainiertes oder verfeinertes Modell auf dem [Model Hub](https://huggingface.co/models) teilen können: - Programmgesteuertes Übertragen Ihrer Dateien auf den Hub. - Ziehen Sie Ihre Dateien per Drag-and-Drop über die Weboberfläche in den Hub. <iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <Tip> Um ein Modell mit der Öffentlichkeit zu teilen, benötigen Sie ein Konto auf [huggingface.co](https://huggingface.co/join). Sie können auch einer bestehenden Organisation beitreten oder eine neue Organisation gründen. </Tip> ## Repository-Funktionen Jedes Repository im Model Hub verhält sich wie ein typisches GitHub-Repository. Unsere Repositorys bieten Versionierung, Commit-Historie und die Möglichkeit, Unterschiede zu visualisieren. Die integrierte Versionierung des Model Hub basiert auf Git und [git-lfs](https://git-lfs.github.com/). Mit anderen Worten: Sie können ein Modell als ein Repository behandeln, was eine bessere Zugriffskontrolle und Skalierbarkeit ermöglicht. Die Versionskontrolle ermöglicht *Revisionen*, eine Methode zum Anheften einer bestimmten Version eines Modells mit einem Commit-Hash, Tag oder Branch. Folglich können Sie eine bestimmte Modellversion mit dem Parameter "Revision" laden: ```py >>> model = AutoModel.from_pretrained( ... "julien-c/EsperBERTo-small", revision="v2.0.1" # tag name, or branch name, or commit hash ... ) ``` Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können die Commit-Historie sowie die Unterschiede einsehen:  ## Einrichtung Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig `~/.cache/`) gespeichert: ```bash huggingface-cli login ``` Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub. ```bash pip install huggingface_hub ``` Verwenden Sie dann `notebook_login`, um sich beim Hub anzumelden, und folgen Sie dem Link [hier](https://huggingface.co/settings/token), um ein Token für die Anmeldung zu generieren: ```py >>> from huggingface_hub import notebook_login >>> notebook_login() ``` ## Ein Modell für alle Frameworks konvertieren Um sicherzustellen, dass Ihr Modell von jemandem verwendet werden kann, der mit einem anderen Framework arbeitet, empfehlen wir Ihnen, Ihr Modell sowohl mit PyTorch- als auch mit TensorFlow-Checkpoints zu konvertieren und hochzuladen. Während Benutzer immer noch in der Lage sind, Ihr Modell von einem anderen Framework zu laden, wenn Sie diesen Schritt überspringen, wird es langsamer sein, weil 🤗 Transformers den Checkpoint on-the-fly konvertieren müssen. Die Konvertierung eines Checkpoints für ein anderes Framework ist einfach. Stellen Sie sicher, dass Sie PyTorch und TensorFlow installiert haben (siehe [hier](installation) für Installationsanweisungen), und finden Sie dann das spezifische Modell für Ihre Aufgabe in dem anderen Framework. <frameworkcontent> <pt> Geben Sie `from_tf=True` an, um einen Prüfpunkt von TensorFlow nach PyTorch zu konvertieren: ```py >>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True) >>> pt_model.save_pretrained("path/to/awesome-name-you-picked") ``` </pt> <tf> Geben Sie `from_pt=True` an, um einen Prüfpunkt von PyTorch nach TensorFlow zu konvertieren: ```py >>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True) ``` Dann können Sie Ihr neues TensorFlow-Modell mit seinem neuen Checkpoint speichern: ```py >>> tf_model.save_pretrained("path/to/awesome-name-you-picked") ``` </tf> <jax> Wenn ein Modell in Flax verfügbar ist, können Sie auch einen Kontrollpunkt von PyTorch nach Flax konvertieren: ```py >>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained( ... "path/to/awesome-name-you-picked", from_pt=True ... ) ``` </jax> </frameworkcontent> ## Ein Modell während des Trainings hochladen <frameworkcontent> <pt> <Youtube id="Z1-XMy-GNLQ"/> Die Weitergabe eines Modells an den Hub ist so einfach wie das Hinzufügen eines zusätzlichen Parameters oder Rückrufs. Erinnern Sie sich an das [Feinabstimmungs-Tutorial](training), in der Klasse [`TrainingArguments`] geben Sie Hyperparameter und zusätzliche Trainingsoptionen an. Eine dieser Trainingsoptionen beinhaltet die Möglichkeit, ein Modell direkt an den Hub zu pushen. Setzen Sie `push_to_hub=True` in Ihrer [`TrainingArguments`]: ```py >>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True) ``` Übergeben Sie Ihre Trainingsargumente wie gewohnt an [`Trainer`]: ```py >>> trainer = Trainer( ... model=model, ... args=training_args, ... train_dataset=small_train_dataset, ... eval_dataset=small_eval_dataset, ... compute_metrics=compute_metrics, ... ) ``` Nach der Feinabstimmung Ihres Modells rufen Sie [`~transformers.Trainer.push_to_hub`] auf [`Trainer`] auf, um das trainierte Modell an den Hub zu übertragen. Transformers fügt sogar automatisch Trainings-Hyperparameter, Trainingsergebnisse und Framework-Versionen zu Ihrer Modellkarte hinzu! ```py >>> trainer.push_to_hub() ``` </pt> <tf> Geben Sie ein Modell mit [`PushToHubCallback`] an den Hub weiter. In der [`PushToHubCallback`] Funktion, fügen Sie hinzu: - Ein Ausgabeverzeichnis für Ihr Modell. - Einen Tokenizer. - Die `hub_model_id`, die Ihr Hub-Benutzername und Modellname ist. ```py >>> from transformers.keras.callbacks import PushToHubCallback >>> push_to_hub_callback = PushToHubCallback( ... output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model" ... ) ``` Fügen Sie den Callback zu [`fit`](https://keras.io/api/models/model_training_apis/) hinzu, und 🤗 Transformers wird das trainierte Modell an den Hub weiterleiten: ```py >>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback) ``` </tf> </frameworkcontent> ## Verwenden Sie die Funktion `push_to_hub`. Sie können `push_to_hub` auch direkt für Ihr Modell aufrufen, um es in den Hub hochzuladen. Geben Sie den Namen Ihres Modells in "push_to_hub" an: ```py >>> pt_model.push_to_hub("my-awesome-model") ``` Dadurch wird ein Repository unter Ihrem Benutzernamen mit dem Modellnamen `my-awesome-model` erstellt. Benutzer können nun Ihr Modell mit der Funktion `from_pretrained` laden: ```py >>> from transformers import AutoModel >>> model = AutoModel.from_pretrained("your_username/my-awesome-model") ``` Wenn Sie zu einer Organisation gehören und Ihr Modell stattdessen unter dem Namen der Organisation pushen wollen, fügen Sie diesen einfach zur `repo_id` hinzu: ```py >>> pt_model.push_to_hub("my-awesome-org/my-awesome-model") ``` Die Funktion "push_to_hub" kann auch verwendet werden, um andere Dateien zu einem Modell-Repository hinzuzufügen. Zum Beispiel kann man einen Tokenizer zu einem Modell-Repository hinzufügen: ```py >>> tokenizer.push_to_hub("my-awesome-model") ``` Oder vielleicht möchten Sie die TensorFlow-Version Ihres fein abgestimmten PyTorch-Modells hinzufügen: ```py >>> tf_model.push_to_hub("my-awesome-model") ``` Wenn Sie nun zu Ihrem Hugging Face-Profil navigieren, sollten Sie Ihr neu erstelltes Modell-Repository sehen. Wenn Sie auf die Registerkarte **Dateien** klicken, werden alle Dateien angezeigt, die Sie in das Repository hochgeladen haben. Weitere Einzelheiten zum Erstellen und Hochladen von Dateien in ein Repository finden Sie in der Hub-Dokumentation [hier](https://huggingface.co/docs/hub/how-to-upstream). ## Hochladen mit der Weboberfläche Benutzer, die einen no-code Ansatz bevorzugen, können ein Modell über das Webinterface des Hubs hochladen. Besuchen Sie [huggingface.co/new](https://huggingface.co/new) um ein neues Repository zu erstellen:  Fügen Sie von hier aus einige Informationen über Ihr Modell hinzu: - Wählen Sie den **Besitzer** des Repositorys. Dies können Sie selbst oder eine der Organisationen sein, denen Sie angehören. - Wählen Sie einen Namen für Ihr Modell, der auch der Name des Repositorys sein wird. - Wählen Sie, ob Ihr Modell öffentlich oder privat ist. - Geben Sie die Lizenzverwendung für Ihr Modell an. Klicken Sie nun auf die Registerkarte **Dateien** und klicken Sie auf die Schaltfläche **Datei hinzufügen**, um eine neue Datei in Ihr Repository hochzuladen. Ziehen Sie dann eine Datei per Drag-and-Drop hoch und fügen Sie eine Übergabemeldung hinzu.  ## Hinzufügen einer Modellkarte Um sicherzustellen, dass die Benutzer die Fähigkeiten, Grenzen, möglichen Verzerrungen und ethischen Aspekte Ihres Modells verstehen, fügen Sie bitte eine Modellkarte zu Ihrem Repository hinzu. Die Modellkarte wird in der Datei `README.md` definiert. Sie können eine Modellkarte hinzufügen, indem Sie: * Manuelles Erstellen und Hochladen einer "README.md"-Datei. * Klicken Sie auf die Schaltfläche **Modellkarte bearbeiten** in Ihrem Modell-Repository. Werfen Sie einen Blick auf die DistilBert [model card](https://huggingface.co/distilbert-base-uncased) als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei "README.md" einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation [hier](https://huggingface.co/docs/hub/models-cards).

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./docs/source/en/contributing.md

<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Contribute to 🤗 Transformers Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable. It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you. However you choose to contribute, please be mindful and respect our [code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md). **This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).** ## Ways to contribute There are several ways you can contribute to 🤗 Transformers: * Fix outstanding issues with the existing code. * Submit issues related to bugs or desired new features. * Implement new models. * Contribute to the examples or to the documentation. If you don't know where to start, there is a special [Good First Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of open issues that are beginner-friendly and help you start contributing to open-source. Just comment in the issue that you'd like to work on it. For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀 > All contributions are equally valuable to the community. 🥰 ## Fixing outstanding issues If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md/#create-a-pull-request) and open a Pull Request! ## Submitting a bug-related issue or feature request Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback. ### Did you find a bug? The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter. Before you report an issue, we would really appreciate it if you could **make sure the bug was not already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask on the [forum](https://discuss.huggingface.co/) first. This helps us respond quicker to fixing issues related to the library versus general questions. Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it: * Your **OS type and version** and **Python**, **PyTorch** and **TensorFlow** versions when applicable. * A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s. * The *full* traceback if an exception is raised. * Attach any other additional information, like screenshots, you think may help. To get the OS and software versions automatically, run the following command: ```bash transformers-cli env ``` You can also run the same command from the root of the repository: ```bash python src/transformers/commands/transformers_cli.py env ``` ### Do you want a new feature? If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe: 1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? Whatever it is, we'd love to hear about it! 2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you. 3. Provide a *code snippet* that demonstrates the features usage. 4. If the feature is related to a paper, please include a link. If your issue is well written we're already 80% of the way there by the time you create it. We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue. ## Do you want to implement a new model? New models are constantly released and if you want to implement a new model, please provide the following information * A short description of the model and link to the paper. * Link to the implementation if it is open-sourced. * Link to the model weights if they are available. If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers! We have added a [detailed guide and templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with adding a new model, and we also have a more technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model). ## Do you want to add documentation? We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested! For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs). ## Create a Pull Request Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback. You will need basic `git` proficiency to contribute to 🤗 Transformers. While `git` is not the easiest tool to use, it has the greatest manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro Git](https://git-scm.com/book/en/v2) is a very good reference. You'll need **[Python 3.7]((https://github.com/huggingface/transformers/blob/main/setup.py#L426))** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing: 1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code under your GitHub user account. 2. Clone your fork to your local disk, and add the base repository as a remote: ```bash $ git clone git@github.com:<your Github handle>/transformers.git $ cd transformers $ git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Create a new branch to hold your development changes: ```bash $ git checkout -b a-descriptive-name-for-my-changes ``` 🚨 **Do not** work on the `main` branch! 4. Set up a development environment by running the following command in a virtual environment: ```bash $ pip install -e ".[dev]" ``` If 🤗 Transformers was already installed in the virtual environment, remove it with `pip uninstall transformers` before reinstalling it in editable mode with the `-e` flag. Depending on your OS, you may need to install some external libraries as well if the `pip` installation fails. For macOS, you will likely need [MeCab](https://taku910.github.io/mecab/) which can be installed from Homebrew: ```bash brew install mecab ``` 5. Develop the features on your branch. As you work on your code, you should make sure the test suite passes. Run the tests impacted by your changes like this: ```bash $ pytest tests/<TEST_TO_RUN>.py ``` For more information about tests, check out the [Testing](https://huggingface.co/docs/transformers/testing) guide. 🤗 Transformers relies on `black` and `isort` to format its source code consistently. After you make changes, apply automatic style corrections and code verifications that can't be automated in one go with: ```bash $ make fixup ``` This target is also optimized to only work with files modified by the PR you're working on. If you prefer to run the checks one after the other, the following command applies the style corrections: ```bash $ make style ``` 🤗 Transformers also uses `flake8` and a few custom scripts to check for coding mistakes. Quality controls are run by the CI, but you can run the same checks with: ```bash $ make quality ``` Finally, we have a lot of scripts to make sure we didn't forget to update some files when adding a new model. You can run these scripts with: ```bash $ make repo-consistency ``` To learn more about those checks and how to fix any issues with them, check out the [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. If you're modifying documents under `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check make sure you install the documentation builder: ```bash $ pip install ".[docs]" ``` Run the following command from the root of the repository: ```bash $ doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build ``` This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request. Once you're happy with your changes, add changed files with `git add` and record your changes locally with `git commit`: ```bash $ git add modified_file.py $ git commit ``` Please remember to write [good commit messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made! To keep your copy of the code up to date with the original repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer: ```bash $ git fetch upstream $ git rebase upstream/main ``` Push your changes to your branch: ```bash $ git push -u origin a-descriptive-name-for-my-changes ``` If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally. 6. Now you can go to your fork of the repository on GitHub and click on **Pull request** to open a pull request. Make sure you tick off all the boxes in our [checklist](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md/#pull-request-checklist) below. When you're ready, you can send your changes to the project maintainers for review. 7. It's ok if maintainers request changes, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request. ### Pull request checklist ☐ The pull request title should summarize your contribution.<br> ☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it).<br> ☐ To indicate a work in progress please prefix the title with `[WIP]`. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged. ☐ Make sure existing tests pass.<br> ☐ If adding a new feature, also add tests for it.<br> - If you are adding a new model, make sure you use `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests. - If you are adding new `@slow` tests, make sure they pass using `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`. - If you are adding a new tokenizer, write tests and make sure `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes. CircleCI does not run the slow tests, but GitHub Actions does every night!<br> ☐ All public methods must have informative docstrings (see [`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py) for an example).<br> ☐ Due to the rapidly growing repository, don't add any images, videos and other non-text files that'll significantly weigh down the repository. Instead, use a Hub repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) to host these files and reference them by URL. We recommend placing documentation related images in the following repository: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). You can open a PR on this dataset repostitory and ask a Hugging Face member to merge it. For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. ### Tests An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the [examples](https://github.com/huggingface/transformers/tree/main/examples) folder. We like `pytest` and `pytest-xdist` because it's faster. From the root of the repository, specify a *path to a subfolder or a test file* to run the test. ```bash $ python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model ``` Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory: ```bash $ pip install -r examples/xxx/requirements.txt # only needed the first time $ python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)! You can also specify a smaller set of tests in order to test only the feature you're working on. By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to `yes` to run them. This will download many gigabytes of models so make sure you have enough disk space, a good internet connection or a lot of patience! <Tip warning={true}> Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time! </Tip> ```bash $ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model $ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` Like the slow tests, custom tokenizer tests are skipped but you can set the `RUN_CUSTOM_TOKENIZERS` environment variable to `yes` to run them. 🤗 Transformers uses `pytest` as a test runner only. It doesn't use any `pytest`-specific features in the test suite itself. This means `unittest` is fully supported. Here's how to run tests with `unittest`: ```bash $ python -m unittest discover -s tests -t . -v $ python -m unittest discover -s examples -t examples -v ``` ### Style guide For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification) for more information. ### Develop on Windows On Windows (unless you're working in [Windows Subsytem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings: ```bash git config core.autocrlf input ``` One way to run the `make` command on Windows is with MSYS2: 1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`. 2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu). 3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`. 4. Add `C:\msys64\usr\bin` to your PATH environment variable. You can now use `make` from any terminal (Powershell, cmd.exe, etc.)! 🎉 ### Sync a forked repository with upstream main (the Hugging Face repository) When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs. 1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. 2. If a PR is absolutely necessary, use the following steps after checking out your branch: ```bash $ git checkout -b your-branch-for-syncing $ git pull --squash --no-commit upstream main $ git commit -m '<your message without GitHub references>' $ git push --set-upstream origin your-branch-for-syncing ```

<!--- Copyright 2020 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Contribute to 🤗 Transformers Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable. It also helps us if you spread the word! Reference the library in blog posts about the awesome projects it made possible, shout out on Twitter every time it has helped you, or simply ⭐️ the repository to say thank you. However you choose to contribute, please be mindful and respect our [code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md). **This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).** ## Ways to contribute There are several ways you can contribute to 🤗 Transformers: * Fix outstanding issues with the existing code. * Submit issues related to bugs or desired new features. * Implement new models. * Contribute to the examples or to the documentation. If you don't know where to start, there is a special [Good First Issue](https://github.com/huggingface/transformers/contribute) listing. It will give you a list of open issues that are beginner-friendly and help you start contributing to open-source. Just comment in the issue that you'd like to work on it. For something slightly more challenging, you can also take a look at the [Good Second Issue](https://github.com/huggingface/transformers/labels/Good%20Second%20Issue) list. In general though, if you feel like you know what you're doing, go for it and we'll help you get there! 🚀 > All contributions are equally valuable to the community. 🥰 ## Fixing outstanding issues If you notice an issue with the existing code and have a fix in mind, feel free to [start contributing](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md/#create-a-pull-request) and open a Pull Request! ## Submitting a bug-related issue or feature request Do your best to follow these guidelines when submitting a bug-related issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback. ### Did you find a bug? The 🤗 Transformers library is robust and reliable thanks to users who report the problems they encounter. Before you report an issue, we would really appreciate it if you could **make sure the bug was not already reported** (use the search bar on GitHub under Issues). Your issue should also be related to bugs in the library itself, and not your code. If you're unsure whether the bug is in your code or the library, please ask on the [forum](https://discuss.huggingface.co/) first. This helps us respond quicker to fixing issues related to the library versus general questions. Once you've confirmed the bug hasn't already been reported, please include the following information in your issue so we can quickly resolve it: * Your **OS type and version** and **Python**, **PyTorch** and **TensorFlow** versions when applicable. * A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s. * The *full* traceback if an exception is raised. * Attach any other additional information, like screenshots, you think may help. To get the OS and software versions automatically, run the following command: ```bash transformers-cli env ``` You can also run the same command from the root of the repository: ```bash python src/transformers/commands/transformers_cli.py env ``` ### Do you want a new feature? If there is a new feature you'd like to see in 🤗 Transformers, please open an issue and describe: 1. What is the *motivation* behind this feature? Is it related to a problem or frustration with the library? Is it a feature related to something you need for a project? Is it something you worked on and think it could benefit the community? Whatever it is, we'd love to hear about it! 2. Describe your requested feature in as much detail as possible. The more you can tell us about it, the better we'll be able to help you. 3. Provide a *code snippet* that demonstrates the features usage. 4. If the feature is related to a paper, please include a link. If your issue is well written we're already 80% of the way there by the time you create it. We have added [templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with your issue. ## Do you want to implement a new model? New models are constantly released and if you want to implement a new model, please provide the following information * A short description of the model and link to the paper. * Link to the implementation if it is open-sourced. * Link to the model weights if they are available. If you are willing to contribute the model yourself, let us know so we can help you add it to 🤗 Transformers! We have added a [detailed guide and templates](https://github.com/huggingface/transformers/tree/main/templates) to help you get started with adding a new model, and we also have a more technical guide for [how to add a model to 🤗 Transformers](https://huggingface.co/docs/transformers/add_new_model). ## Do you want to add documentation? We're always looking for improvements to the documentation that make it more clear and accurate. Please let us know how the documentation can be improved such as typos and any content that is missing, unclear or inaccurate. We'll be happy to make the changes or help you make a contribution if you're interested! For more details about how to generate, build, and write the documentation, take a look at the documentation [README](https://github.com/huggingface/transformers/tree/main/docs). ## Create a Pull Request Before writing any code, we strongly advise you to search through the existing PRs or issues to make sure nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback. You will need basic `git` proficiency to contribute to 🤗 Transformers. While `git` is not the easiest tool to use, it has the greatest manual. Type `git --help` in a shell and enjoy! If you prefer books, [Pro Git](https://git-scm.com/book/en/v2) is a very good reference. You'll need **[Python 3.7]((https://github.com/huggingface/transformers/blob/main/setup.py#L426))** or above to contribute to 🤗 Transformers. Follow the steps below to start contributing: 1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the **[Fork](https://github.com/huggingface/transformers/fork)** button on the repository's page. This creates a copy of the code under your GitHub user account. 2. Clone your fork to your local disk, and add the base repository as a remote: ```bash $ git clone git@github.com:<your Github handle>/transformers.git $ cd transformers $ git remote add upstream https://github.com/huggingface/transformers.git ``` 3. Create a new branch to hold your development changes: ```bash $ git checkout -b a-descriptive-name-for-my-changes ``` 🚨 **Do not** work on the `main` branch! 4. Set up a development environment by running the following command in a virtual environment: ```bash $ pip install -e ".[dev]" ``` If 🤗 Transformers was already installed in the virtual environment, remove it with `pip uninstall transformers` before reinstalling it in editable mode with the `-e` flag. Depending on your OS, you may need to install some external libraries as well if the `pip` installation fails. For macOS, you will likely need [MeCab](https://taku910.github.io/mecab/) which can be installed from Homebrew: ```bash brew install mecab ``` 5. Develop the features on your branch. As you work on your code, you should make sure the test suite passes. Run the tests impacted by your changes like this: ```bash $ pytest tests/<TEST_TO_RUN>.py ``` For more information about tests, check out the [Testing](https://huggingface.co/docs/transformers/testing) guide. 🤗 Transformers relies on `black` and `isort` to format its source code consistently. After you make changes, apply automatic style corrections and code verifications that can't be automated in one go with: ```bash $ make fixup ``` This target is also optimized to only work with files modified by the PR you're working on. If you prefer to run the checks one after the other, the following command applies the style corrections: ```bash $ make style ``` 🤗 Transformers also uses `flake8` and a few custom scripts to check for coding mistakes. Quality controls are run by the CI, but you can run the same checks with: ```bash $ make quality ``` Finally, we have a lot of scripts to make sure we didn't forget to update some files when adding a new model. You can run these scripts with: ```bash $ make repo-consistency ``` To learn more about those checks and how to fix any issues with them, check out the [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. If you're modifying documents under `docs/source` directory, make sure the documentation can still be built. This check will also run in the CI when you open a pull request. To run a local check make sure you install the documentation builder: ```bash $ pip install ".[docs]" ``` Run the following command from the root of the repository: ```bash $ doc-builder build transformers docs/source/en --build_dir ~/tmp/test-build ``` This will build the documentation in the `~/tmp/test-build` folder where you can inspect the generated Markdown files with your favorite editor. You can also preview the docs on GitHub when you open a pull request. Once you're happy with your changes, add changed files with `git add` and record your changes locally with `git commit`: ```bash $ git add modified_file.py $ git commit ``` Please remember to write [good commit messages](https://chris.beams.io/posts/git-commit/) to clearly communicate the changes you made! To keep your copy of the code up to date with the original repository, rebase your branch on `upstream/branch` *before* you open a pull request or if requested by a maintainer: ```bash $ git fetch upstream $ git rebase upstream/main ``` Push your changes to your branch: ```bash $ git push -u origin a-descriptive-name-for-my-changes ``` If you've already opened a pull request, you'll need to force push with the `--force` flag. Otherwise, if the pull request hasn't been opened yet, you can just push your changes normally. 6. Now you can go to your fork of the repository on GitHub and click on **Pull request** to open a pull request. Make sure you tick off all the boxes in our [checklist](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md/#pull-request-checklist) below. When you're ready, you can send your changes to the project maintainers for review. 7. It's ok if maintainers request changes, it happens to our core contributors too! So everyone can see the changes in the pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request. ### Pull request checklist ☐ The pull request title should summarize your contribution.<br> ☐ If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people viewing the issue know you are working on it).<br> ☐ To indicate a work in progress please prefix the title with `[WIP]`. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged. ☐ Make sure existing tests pass.<br> ☐ If adding a new feature, also add tests for it.<br> - If you are adding a new model, make sure you use `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` to trigger the common tests. - If you are adding new `@slow` tests, make sure they pass using `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`. - If you are adding a new tokenizer, write tests and make sure `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes. CircleCI does not run the slow tests, but GitHub Actions does every night!<br> ☐ All public methods must have informative docstrings (see [`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py) for an example).<br> ☐ Due to the rapidly growing repository, don't add any images, videos and other non-text files that'll significantly weigh down the repository. Instead, use a Hub repository such as [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) to host these files and reference them by URL. We recommend placing documentation related images in the following repository: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images). You can open a PR on this dataset repostitory and ask a Hugging Face member to merge it. For more information about the checks run on a pull request, take a look at our [Checks on a Pull Request](https://huggingface.co/docs/transformers/pr_checks) guide. ### Tests An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests](https://github.com/huggingface/transformers/tree/main/tests) folder and examples tests in the [examples](https://github.com/huggingface/transformers/tree/main/examples) folder. We like `pytest` and `pytest-xdist` because it's faster. From the root of the repository, specify a *path to a subfolder or a test file* to run the test. ```bash $ python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model ``` Similarly, for the `examples` directory, specify a *path to a subfolder or test file* to run the test. For example, the following command tests the text classification subfolder in the PyTorch `examples` directory: ```bash $ pip install -r examples/xxx/requirements.txt # only needed the first time $ python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` In fact, this is actually how our `make test` and `make test-examples` commands are implemented (not including the `pip install`)! You can also specify a smaller set of tests in order to test only the feature you're working on. By default, slow tests are skipped but you can set the `RUN_SLOW` environment variable to `yes` to run them. This will download many gigabytes of models so make sure you have enough disk space, a good internet connection or a lot of patience! <Tip warning={true}> Remember to specify a *path to a subfolder or a test file* to run the test. Otherwise, you'll run all the tests in the `tests` or `examples` folder, which will take a very long time! </Tip> ```bash $ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model $ RUN_SLOW=yes python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification ``` Like the slow tests, custom tokenizer tests are skipped but you can set the `RUN_CUSTOM_TOKENIZERS` environment variable to `yes` to run them. 🤗 Transformers uses `pytest` as a test runner only. It doesn't use any `pytest`-specific features in the test suite itself. This means `unittest` is fully supported. Here's how to run tests with `unittest`: ```bash $ python -m unittest discover -s tests -t . -v $ python -m unittest discover -s examples -t examples -v ``` ### Style guide For documentation strings, 🤗 Transformers follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html). Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification) for more information. ### Develop on Windows On Windows (unless you're working in [Windows Subsytem for Linux](https://learn.microsoft.com/en-us/windows/wsl/) or WSL), you need to configure git to transform Windows `CRLF` line endings to Linux `LF` line endings: ```bash git config core.autocrlf input ``` One way to run the `make` command on Windows is with MSYS2: 1. [Download MSYS2](https://www.msys2.org/), and we assume it's installed in `C:\msys64`. 2. Open the command line `C:\msys64\msys2.exe` (it should be available from the **Start** menu). 3. Run in the shell: `pacman -Syu` and install `make` with `pacman -S make`. 4. Add `C:\msys64\usr\bin` to your PATH environment variable. You can now use `make` from any terminal (Powershell, cmd.exe, etc.)! 🎉 ### Sync a forked repository with upstream main (the Hugging Face repository) When updating the main branch of a forked repository, please follow these steps to avoid pinging the upstream repository which adds reference notes to each upstream PR, and sends unnecessary notifications to the developers involved in these PRs. 1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main. 2. If a PR is absolutely necessary, use the following steps after checking out your branch: ```bash $ git checkout -b your-branch-for-syncing $ git pull --squash --no-commit upstream main $ git commit -m '<your message without GitHub references>' $ git push --set-upstream origin your-branch-for-syncing ```

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/tensorflow/language-modeling/README.md

<!--- Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Language modelling examples This folder contains some scripts showing examples of *language model pre-training* with the 🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. The two scripts have almost identical arguments, but they differ in the type of LM they train - a causal language model (like GPT) or a masked language model (like BERT). Masked language models generally train more quickly and perform better when fine-tuned on new tasks with a task-specific output head, like text classification. However, their ability to generate text is weaker than causal language models. ## Pre-training versus fine-tuning These scripts can be used to both *pre-train* a language model completely from scratch, as well as to *fine-tune* a language model on text from your domain of interest. To start with an existing pre-trained language model you can use the `--model_name_or_path` argument, or to train from scratch you can use the `--model_type` argument to indicate the class of model architecture to initialize. ### Multi-GPU and TPU usage By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs can also be used by passing the name of the TPU resource with the `--tpu` argument. ## run_mlm.py This script trains a masked language model. ### Example command ``` python run_mlm.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ --dataset_name wikitext \ --dataset_config_name wikitext-103-raw-v1 ``` When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation. ``` python run_mlm.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ --train_file train_file_path ``` ## run_clm.py This script trains a causal language model. ### Example command ``` python run_clm.py \ --model_name_or_path distilgpt2 \ --output_dir output \ --dataset_name wikitext \ --dataset_config_name wikitext-103-raw-v1 ``` When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation. ``` python run_clm.py \ --model_name_or_path distilgpt2 \ --output_dir output \ --train_file train_file_path ```

<!--- Copyright 2021 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> # Language modelling examples This folder contains some scripts showing examples of *language model pre-training* with the 🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects. The two scripts have almost identical arguments, but they differ in the type of LM they train - a causal language model (like GPT) or a masked language model (like BERT). Masked language models generally train more quickly and perform better when fine-tuned on new tasks with a task-specific output head, like text classification. However, their ability to generate text is weaker than causal language models. ## Pre-training versus fine-tuning These scripts can be used to both *pre-train* a language model completely from scratch, as well as to *fine-tune* a language model on text from your domain of interest. To start with an existing pre-trained language model you can use the `--model_name_or_path` argument, or to train from scratch you can use the `--model_type` argument to indicate the class of model architecture to initialize. ### Multi-GPU and TPU usage By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs can also be used by passing the name of the TPU resource with the `--tpu` argument. ## run_mlm.py This script trains a masked language model. ### Example command ``` python run_mlm.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ --dataset_name wikitext \ --dataset_config_name wikitext-103-raw-v1 ``` When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation. ``` python run_mlm.py \ --model_name_or_path distilbert-base-cased \ --output_dir output \ --train_file train_file_path ``` ## run_clm.py This script trains a causal language model. ### Example command ``` python run_clm.py \ --model_name_or_path distilgpt2 \ --output_dir output \ --dataset_name wikitext \ --dataset_config_name wikitext-103-raw-v1 ``` When using a custom dataset, the validation file can be separately passed as an input argument. Otherwise some split (customizable) of training data is used as validation. ``` python run_clm.py \ --model_name_or_path distilgpt2 \ --output_dir output \ --train_file train_file_path ```

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/fsner/src/fsner/__init__.py

from .model import FSNERModel from .tokenizer_utils import FSNERTokenizerUtils __all__ = ["FSNERModel", "FSNERTokenizerUtils"]

from .model import FSNERModel from .tokenizer_utils import FSNERTokenizerUtils __all__ = ["FSNERModel", "FSNERTokenizerUtils"]

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/swin/convert_swin_simmim_to_pytorch.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swin SimMIM checkpoints from the original repository. URL: https://github.com/microsoft/Swin-Transformer/blob/main/MODELHUB.md#simmim-pretrained-swin-v1-models""" import argparse import torch from PIL import Image import requests from transformers import SwinConfig, SwinForMaskedImageModeling, ViTFeatureExtractor def get_swin_config(model_name): config = SwinConfig(image_size=192) if "base" in model_name: window_size = 6 embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) elif "large" in model_name: window_size = 12 embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) else: raise ValueError("Model not supported, only supports base and large variants") config.window_size = window_size config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads return config def rename_key(name): if "encoder.mask_token" in name: name = name.replace("encoder.mask_token", "embeddings.mask_token") if "encoder.patch_embed.proj" in name: name = name.replace("encoder.patch_embed.proj", "embeddings.patch_embeddings.projection") if "encoder.patch_embed.norm" in name: name = name.replace("encoder.patch_embed.norm", "embeddings.norm") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if name == "encoder.norm.weight": name = "layernorm.weight" if name == "encoder.norm.bias": name = "layernorm.bias" if "decoder" in name: pass else: name = "swin." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "attn_mask" in key: pass elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[2]) block_num = int(key_split[4]) dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[ dim : dim * 2, : ] orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[ :dim ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[ -dim: ] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swin_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub): state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] config = get_swin_config(model_name) model = SwinForMaskedImageModeling(config) model.eval() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" feature_extractor = ViTFeatureExtractor(size={"height": 192, "width": 192}) image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits print(outputs.keys()) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and feature extractor for {model_name} to hub") model.push_to_hub(f"microsoft/{model_name}") feature_extractor.push_to_hub(f"microsoft/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="swin-base-simmim-window6-192", type=str, choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"], help="Name of the Swin SimMIM model you'd like to convert.", ) parser.add_argument( "--checkpoint_path", default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth", type=str, help="Path to the original PyTorch checkpoint (.pth file).", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swin SimMIM checkpoints from the original repository. URL: https://github.com/microsoft/Swin-Transformer/blob/main/MODELHUB.md#simmim-pretrained-swin-v1-models""" import argparse import torch from PIL import Image import requests from transformers import SwinConfig, SwinForMaskedImageModeling, ViTFeatureExtractor def get_swin_config(model_name): config = SwinConfig(image_size=192) if "base" in model_name: window_size = 6 embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) elif "large" in model_name: window_size = 12 embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) else: raise ValueError("Model not supported, only supports base and large variants") config.window_size = window_size config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads return config def rename_key(name): if "encoder.mask_token" in name: name = name.replace("encoder.mask_token", "embeddings.mask_token") if "encoder.patch_embed.proj" in name: name = name.replace("encoder.patch_embed.proj", "embeddings.patch_embeddings.projection") if "encoder.patch_embed.norm" in name: name = name.replace("encoder.patch_embed.norm", "embeddings.norm") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if name == "encoder.norm.weight": name = "layernorm.weight" if name == "encoder.norm.bias": name = "layernorm.bias" if "decoder" in name: pass else: name = "swin." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "attn_mask" in key: pass elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[2]) block_num = int(key_split[4]) dim = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight"] = val[ dim : dim * 2, : ] orig_state_dict[ f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias"] = val[ :dim ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[f"swin.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias"] = val[ -dim: ] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swin_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path, push_to_hub): state_dict = torch.load(checkpoint_path, map_location="cpu")["model"] config = get_swin_config(model_name) model = SwinForMaskedImageModeling(config) model.eval() new_state_dict = convert_state_dict(state_dict, model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" feature_extractor = ViTFeatureExtractor(size={"height": 192, "width": 192}) image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs).logits print(outputs.keys()) print("Looks ok!") if pytorch_dump_folder_path is not None: print(f"Saving model {model_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) if push_to_hub: print(f"Pushing model and feature extractor for {model_name} to hub") model.push_to_hub(f"microsoft/{model_name}") feature_extractor.push_to_hub(f"microsoft/{model_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="swin-base-simmim-window6-192", type=str, choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"], help="Name of the Swin SimMIM model you'd like to convert.", ) parser.add_argument( "--checkpoint_path", default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth", type=str, help="Path to the original PyTorch checkpoint (.pth file).", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub." ) args = parser.parse_args() convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/convert_slow_tokenizers_checkpoints_to_fast.py

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert slow tokenizers checkpoints in fast (serialization format of the `tokenizers` library)""" import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS} def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download): if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES: raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.") if tokenizer_name is None: tokenizer_names = TOKENIZER_CLASSES else: tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")} logger.info(f"Loading tokenizer classes: {tokenizer_names}") for tokenizer_name in tokenizer_names: tokenizer_class = TOKENIZER_CLASSES[tokenizer_name] add_prefix = True if checkpoint_name is None: checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys()) else: checkpoint_names = [checkpoint_name] logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}") for checkpoint in checkpoint_names: logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}") # Load tokenizer tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download) # Save fast tokenizer logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}") # For organization names we create sub-directories if "/" in checkpoint: checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/") dump_path_full = os.path.join(dump_path, checkpoint_directory) elif add_prefix: checkpoint_prefix_name = checkpoint dump_path_full = dump_path else: checkpoint_prefix_name = None dump_path_full = dump_path logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}") if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]: file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint] next_char = file_path.split(checkpoint)[-1][0] if next_char == "/": dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name) checkpoint_prefix_name = None logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}") file_names = tokenizer.save_pretrained( dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name ) logger.info(f"=> File names {file_names}") for file_name in file_names: if not file_name.endswith("tokenizer.json"): os.remove(file_name) logger.info(f"=> removing {file_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files." ) parser.add_argument( "--tokenizer_name", default=None, type=str, help=( f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will " "download and convert all the checkpoints from AWS." ), ) parser.add_argument( "--checkpoint_name", default=None, type=str, help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.", ) parser.add_argument( "--force_download", action="store_true", help="Re-download checkpoints.", ) args = parser.parse_args() convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert slow tokenizers checkpoints in fast (serialization format of the `tokenizers` library)""" import argparse import os import transformers from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS from .utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS} def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download): if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES: raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.") if tokenizer_name is None: tokenizer_names = TOKENIZER_CLASSES else: tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")} logger.info(f"Loading tokenizer classes: {tokenizer_names}") for tokenizer_name in tokenizer_names: tokenizer_class = TOKENIZER_CLASSES[tokenizer_name] add_prefix = True if checkpoint_name is None: checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys()) else: checkpoint_names = [checkpoint_name] logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}") for checkpoint in checkpoint_names: logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}") # Load tokenizer tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download) # Save fast tokenizer logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}") # For organization names we create sub-directories if "/" in checkpoint: checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/") dump_path_full = os.path.join(dump_path, checkpoint_directory) elif add_prefix: checkpoint_prefix_name = checkpoint dump_path_full = dump_path else: checkpoint_prefix_name = None dump_path_full = dump_path logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}") if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]: file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint] next_char = file_path.split(checkpoint)[-1][0] if next_char == "/": dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name) checkpoint_prefix_name = None logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}") file_names = tokenizer.save_pretrained( dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name ) logger.info(f"=> File names {file_names}") for file_name in file_names: if not file_name.endswith("tokenizer.json"): os.remove(file_name) logger.info(f"=> removing {file_name}") if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files." ) parser.add_argument( "--tokenizer_name", default=None, type=str, help=( f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will " "download and convert all the checkpoints from AWS." ), ) parser.add_argument( "--checkpoint_name", default=None, type=str, help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.", ) parser.add_argument( "--force_download", action="store_true", help="Re-download checkpoints.", ) args = parser.parse_args() convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/pytorch/_tests_requirements.txt

tensorboard scikit-learn seqeval psutil sacrebleu >= 1.4.12 git+https://github.com/huggingface/accelerate@main#egg=accelerate rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.13.3 fire pytest conllu sentencepiece != 0.1.92 protobuf torchvision jiwer librosa evaluate >= 0.2.0

tensorboard scikit-learn seqeval psutil sacrebleu >= 1.4.12 git+https://github.com/huggingface/accelerate@main#egg=accelerate rouge-score tensorflow_datasets matplotlib git-python==1.0.3 faiss-cpu streamlit elasticsearch nltk pandas datasets >= 1.13.3 fire pytest conllu sentencepiece != 0.1.92 protobuf torchvision jiwer librosa evaluate >= 0.2.0

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./docs/source/en/perf_infer_gpu_one.mdx

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the --> # Efficient Inference on a Single GPU This document will be completed soon with information on how to infer on a single GPU. In the meantime you can check out [the guide for training on a single GPU](perf_train_gpu_one) and [the guide for inference on CPUs](perf_infer_cpu). ## `bitsandbytes` integration for Int8 mixed-precision matrix decomposition Note that this feature is also totally applicable in a multi GPU setup as well. From the paper [`LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale`](https://arxiv.org/abs/2208.07339), we support HuggingFace integration for all models in the Hub with a few lines of code. The method reduce `nn.Linear` size by 2 for `float16` and `bfloat16` weights and by 4 for `float32` weights, with close to no impact to the quality by operating on the outliers in half-precision.  Int8 mixed-precision matrix decomposition works by separating a matrix multiplication into two streams: (1) a systematic feature outlier stream matrix multiplied in fp16 (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no predictive degradation is possible for very large models. For more details regarding the method, check out the [paper](https://arxiv.org/abs/2208.07339) or our [blogpost about the integration](https://huggingface.co/blog/hf-bitsandbytes-integration).  Note, that you would require a GPU to run mixed-8bit models as the kernels have been compiled for GPUs only. Make sure that you have enough GPU memory to store the quarter (or half if your model weights are in half precision) of the model before using this feature. Below are some notes to help you use this module, or follow the demos on [Google colab](#colab-demos). ### Requirements - Make sure you run that on NVIDIA GPUs that support 8-bit tensor cores (Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100). - Install the correct version of `bitsandbytes` by running: `pip install bitsandbytes>=0.31.5` - Install `accelerate` `pip install accelerate>=0.12.0` ### Running mixed-int8 models - single GPU setup After installing the required libraries, the way to load your mixed 8-bit model is as follows: ```py model_name = "bigscience/bloom-2b5" model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) ``` ### Running mixed-int8 models - multi GPU setup The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup): ```py model_name = "bigscience/bloom-2b5" model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) ``` But you can control the GPU RAM you want to allocate on each GPU using `accelerate`. Use the `max_memory` argument as follows: ```py max_memory_mapping = {0: "1GB", 1: "2GB"} model_name = "bigscience/bloom-3b" model_8bit = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping ) ``` In this example, the first GPU will use 1GB of memory and the second 2GB. ### Colab demos With this method you can infer on models that were not possible to infer on a Google Colab before. Check out the demo for running T5-11b (42GB in fp32)! Using 8-bit quantization on Google Colab: [](https://colab.research.google.com/drive/1YORPWx4okIHXnjW7MSAidXN29mPVNT7F?usp=sharing) Or this demo for BLOOM-3B: [](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4?usp=sharing)

<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the --> # Efficient Inference on a Single GPU This document will be completed soon with information on how to infer on a single GPU. In the meantime you can check out [the guide for training on a single GPU](perf_train_gpu_one) and [the guide for inference on CPUs](perf_infer_cpu). ## `bitsandbytes` integration for Int8 mixed-precision matrix decomposition Note that this feature is also totally applicable in a multi GPU setup as well. From the paper [`LLM.int8() : 8-bit Matrix Multiplication for Transformers at Scale`](https://arxiv.org/abs/2208.07339), we support HuggingFace integration for all models in the Hub with a few lines of code. The method reduce `nn.Linear` size by 2 for `float16` and `bfloat16` weights and by 4 for `float32` weights, with close to no impact to the quality by operating on the outliers in half-precision.  Int8 mixed-precision matrix decomposition works by separating a matrix multiplication into two streams: (1) a systematic feature outlier stream matrix multiplied in fp16 (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no predictive degradation is possible for very large models. For more details regarding the method, check out the [paper](https://arxiv.org/abs/2208.07339) or our [blogpost about the integration](https://huggingface.co/blog/hf-bitsandbytes-integration).  Note, that you would require a GPU to run mixed-8bit models as the kernels have been compiled for GPUs only. Make sure that you have enough GPU memory to store the quarter (or half if your model weights are in half precision) of the model before using this feature. Below are some notes to help you use this module, or follow the demos on [Google colab](#colab-demos). ### Requirements - Make sure you run that on NVIDIA GPUs that support 8-bit tensor cores (Turing, Ampere or newer architectures - e.g. T4, RTX20s RTX30s, A40-A100). - Install the correct version of `bitsandbytes` by running: `pip install bitsandbytes>=0.31.5` - Install `accelerate` `pip install accelerate>=0.12.0` ### Running mixed-int8 models - single GPU setup After installing the required libraries, the way to load your mixed 8-bit model is as follows: ```py model_name = "bigscience/bloom-2b5" model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) ``` ### Running mixed-int8 models - multi GPU setup The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup): ```py model_name = "bigscience/bloom-2b5" model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True) ``` But you can control the GPU RAM you want to allocate on each GPU using `accelerate`. Use the `max_memory` argument as follows: ```py max_memory_mapping = {0: "1GB", 1: "2GB"} model_name = "bigscience/bloom-3b" model_8bit = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory_mapping ) ``` In this example, the first GPU will use 1GB of memory and the second 2GB. ### Colab demos With this method you can infer on models that were not possible to infer on a Google Colab before. Check out the demo for running T5-11b (42GB in fp32)! Using 8-bit quantization on Google Colab: [](https://colab.research.google.com/drive/1YORPWx4okIHXnjW7MSAidXN29mPVNT7F?usp=sharing) Or this demo for BLOOM-3B: [](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4?usp=sharing)

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/legacy/token-classification/README.md

## Token classification Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/main/examples/legacy/token-classification/run_ner.py). The following examples are covered in this section: * NER on the GermEval 2014 (German NER) dataset * Emerging and Rare Entities task: WNUT’17 (English NER) dataset Details and results for the fine-tuning provided by @stefan-it. ### GermEval 2014 (German NER) dataset #### Data (Download and pre-processing steps) Data can be obtained from the [GermEval 2014](https://sites.google.com/site/germeval2014ner/data) shared task page. Here are the commands for downloading and pre-processing train, dev and test datasets. The original data format has four (tab-separated) columns, in a pre-processing step only the two relevant columns (token and outer span NER annotation) are extracted: ```bash curl -L 'https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P' \ | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp curl -L 'https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm' \ | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp curl -L 'https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH' \ | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp ``` The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`. One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s. The `preprocess.py` script located in the `scripts` folder a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached). Let's define some variables that we need for further pre-processing steps and training the model: ```bash export MAX_LENGTH=128 export BERT_MODEL=bert-base-multilingual-cased ``` Run the pre-processing script on training, dev and test datasets: ```bash python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt ``` The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used: ```bash cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt ``` #### Prepare the run Additional environment variables must be set: ```bash export OUTPUT_DIR=germeval-model export BATCH_SIZE=32 export NUM_EPOCHS=3 export SAVE_STEPS=750 export SEED=1 ``` #### Run the Pytorch version To start training, just run: ```bash python3 run_ner.py --data_dir ./ \ --labels ./labels.txt \ --model_name_or_path $BERT_MODEL \ --output_dir $OUTPUT_DIR \ --max_seq_length $MAX_LENGTH \ --num_train_epochs $NUM_EPOCHS \ --per_device_train_batch_size $BATCH_SIZE \ --save_steps $SAVE_STEPS \ --seed $SEED \ --do_train \ --do_eval \ --do_predict ``` If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets. #### JSON-based configuration file Instead of passing all parameters via commandline arguments, the `run_ner.py` script also supports reading parameters from a json-based configuration file: ```json { "data_dir": ".", "labels": "./labels.txt", "model_name_or_path": "bert-base-multilingual-cased", "output_dir": "germeval-model", "max_seq_length": 128, "num_train_epochs": 3, "per_device_train_batch_size": 32, "save_steps": 750, "seed": 1, "do_train": true, "do_eval": true, "do_predict": true } ``` It must be saved with a `.json` extension and can be used by running `python3 run_ner.py config.json`. #### Evaluation Evaluation on development dataset outputs the following for our example: ```bash 10/04/2019 00:42:06 - INFO - __main__ - ***** Eval results ***** 10/04/2019 00:42:06 - INFO - __main__ - f1 = 0.8623348017621146 10/04/2019 00:42:06 - INFO - __main__ - loss = 0.07183869666975543 10/04/2019 00:42:06 - INFO - __main__ - precision = 0.8467916366258111 10/04/2019 00:42:06 - INFO - __main__ - recall = 0.8784592370979806 ``` On the test dataset the following results could be achieved: ```bash 10/04/2019 00:42:42 - INFO - __main__ - ***** Eval results ***** 10/04/2019 00:42:42 - INFO - __main__ - f1 = 0.8614389652384803 10/04/2019 00:42:42 - INFO - __main__ - loss = 0.07064602487454782 10/04/2019 00:42:42 - INFO - __main__ - precision = 0.8604651162790697 10/04/2019 00:42:42 - INFO - __main__ - recall = 0.8624150210424085 ``` #### Run the Tensorflow 2 version To start training, just run: ```bash python3 run_tf_ner.py --data_dir ./ \ --labels ./labels.txt \ --model_name_or_path $BERT_MODEL \ --output_dir $OUTPUT_DIR \ --max_seq_length $MAX_LENGTH \ --num_train_epochs $NUM_EPOCHS \ --per_device_train_batch_size $BATCH_SIZE \ --save_steps $SAVE_STEPS \ --seed $SEED \ --do_train \ --do_eval \ --do_predict ``` Such as the Pytorch version, if your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets. #### Evaluation Evaluation on development dataset outputs the following for our example: ```bash precision recall f1-score support LOCderiv 0.7619 0.6154 0.6809 52 PERpart 0.8724 0.8997 0.8858 4057 OTHpart 0.9360 0.9466 0.9413 711 ORGpart 0.7015 0.6989 0.7002 269 LOCpart 0.7668 0.8488 0.8057 496 LOC 0.8745 0.9191 0.8963 235 ORGderiv 0.7723 0.8571 0.8125 91 OTHderiv 0.4800 0.6667 0.5581 18 OTH 0.5789 0.6875 0.6286 16 PERderiv 0.5385 0.3889 0.4516 18 PER 0.5000 0.5000 0.5000 2 ORG 0.0000 0.0000 0.0000 3 micro avg 0.8574 0.8862 0.8715 5968 macro avg 0.8575 0.8862 0.8713 5968 ``` On the test dataset the following results could be achieved: ```bash precision recall f1-score support PERpart 0.8847 0.8944 0.8896 9397 OTHpart 0.9376 0.9353 0.9365 1639 ORGpart 0.7307 0.7044 0.7173 697 LOC 0.9133 0.9394 0.9262 561 LOCpart 0.8058 0.8157 0.8107 1150 ORG 0.0000 0.0000 0.0000 8 OTHderiv 0.5882 0.4762 0.5263 42 PERderiv 0.6571 0.5227 0.5823 44 OTH 0.4906 0.6667 0.5652 39 ORGderiv 0.7016 0.7791 0.7383 172 LOCderiv 0.8256 0.6514 0.7282 109 PER 0.0000 0.0000 0.0000 11 micro avg 0.8722 0.8774 0.8748 13869 macro avg 0.8712 0.8774 0.8740 13869 ``` ### Emerging and Rare Entities task: WNUT’17 (English NER) dataset Description of the WNUT’17 task from the [shared task website](http://noisy-text.github.io/2017/index.html): > The WNUT’17 shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. > Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on > them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Six labels are available in the dataset. An overview can be found on this [page](http://noisy-text.github.io/2017/files/). #### Data (Download and pre-processing steps) The dataset can be downloaded from the [official GitHub](https://github.com/leondz/emerging_entities_17) repository. The following commands show how to prepare the dataset for fine-tuning: ```bash mkdir -p data_wnut_17 curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/wnut17train.conll' | tr '\t' ' ' > data_wnut_17/train.txt.tmp curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/emerging.dev.conll' | tr '\t' ' ' > data_wnut_17/dev.txt.tmp curl -L 'https://raw.githubusercontent.com/leondz/emerging_entities_17/master/emerging.test.annotated' | tr '\t' ' ' > data_wnut_17/test.txt.tmp ``` Let's define some variables that we need for further pre-processing steps: ```bash export MAX_LENGTH=128 export BERT_MODEL=bert-large-cased ``` Here we use the English BERT large model for fine-tuning. The `preprocess.py` scripts splits longer sentences into smaller ones (once the max. subtoken length is reached): ```bash python3 scripts/preprocess.py data_wnut_17/train.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/train.txt python3 scripts/preprocess.py data_wnut_17/dev.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/dev.txt python3 scripts/preprocess.py data_wnut_17/test.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/test.txt ``` In the last pre-processing step, the `labels.txt` file needs to be generated. This file contains all available labels: ```bash cat data_wnut_17/train.txt data_wnut_17/dev.txt data_wnut_17/test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > data_wnut_17/labels.txt ``` #### Run the Pytorch version Fine-tuning with the PyTorch version can be started using the `run_ner.py` script. In this example we use a JSON-based configuration file. This configuration file looks like: ```json { "data_dir": "./data_wnut_17", "labels": "./data_wnut_17/labels.txt", "model_name_or_path": "bert-large-cased", "output_dir": "wnut-17-model-1", "max_seq_length": 128, "num_train_epochs": 3, "per_device_train_batch_size": 32, "save_steps": 425, "seed": 1, "do_train": true, "do_eval": true, "do_predict": true, "fp16": false } ``` If your GPU supports half-precision training, please set `fp16` to `true`. Save this JSON-based configuration under `wnut_17.json`. The fine-tuning can be started with `python3 run_ner_old.py wnut_17.json`. #### Evaluation Evaluation on development dataset outputs the following: ```bash 05/29/2020 23:33:44 - INFO - __main__ - ***** Eval results ***** 05/29/2020 23:33:44 - INFO - __main__ - eval_loss = 0.26505235286212275 05/29/2020 23:33:44 - INFO - __main__ - eval_precision = 0.7008264462809918 05/29/2020 23:33:44 - INFO - __main__ - eval_recall = 0.507177033492823 05/29/2020 23:33:44 - INFO - __main__ - eval_f1 = 0.5884802220680084 05/29/2020 23:33:44 - INFO - __main__ - epoch = 3.0 ``` On the test dataset the following results could be achieved: ```bash 05/29/2020 23:33:44 - INFO - transformers.trainer - ***** Running Prediction ***** 05/29/2020 23:34:02 - INFO - __main__ - eval_loss = 0.30948806500973547 05/29/2020 23:34:02 - INFO - __main__ - eval_precision = 0.5840108401084011 05/29/2020 23:34:02 - INFO - __main__ - eval_recall = 0.3994439295644115 05/29/2020 23:34:02 - INFO - __main__ - eval_f1 = 0.47440836543753434 ``` WNUT’17 is a very difficult task. Current state-of-the-art results on this dataset can be found [here](https://nlpprogress.com/english/named_entity_recognition.html).

## Token classification Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/main/examples/legacy/token-classification/run_ner.py). The following examples are covered in this section: * NER on the GermEval 2014 (German NER) dataset * Emerging and Rare Entities task: WNUT’17 (English NER) dataset Details and results for the fine-tuning provided by @stefan-it. ### GermEval 2014 (German NER) dataset #### Data (Download and pre-processing steps) Data can be obtained from the [GermEval 2014](https://sites.google.com/site/germeval2014ner/data) shared task page. Here are the commands for downloading and pre-processing train, dev and test datasets. The original data format has four (tab-separated) columns, in a pre-processing step only the two relevant columns (token and outer span NER annotation) are extracted: ```bash curl -L 'https://drive.google.com/uc?export=download&id=1Jjhbal535VVz2ap4v4r_rN1UEHTdLK5P' \ | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp curl -L 'https://drive.google.com/uc?export=download&id=1ZfRcQThdtAR5PPRjIDtrVP7BtXSCUBbm' \ | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp curl -L 'https://drive.google.com/uc?export=download&id=1u9mb7kNJHWQCWyweMDRMuTFoOHOfeBTH' \ | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp ``` The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`. One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s. The `preprocess.py` script located in the `scripts` folder a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached). Let's define some variables that we need for further pre-processing steps and training the model: ```bash export MAX_LENGTH=128 export BERT_MODEL=bert-base-multilingual-cased ``` Run the pre-processing script on training, dev and test datasets: ```bash python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt ``` The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used: ```bash cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt ``` #### Prepare the run Additional environment variables must be set: ```bash export OUTPUT_DIR=germeval-model export BATCH_SIZE=32 export NUM_EPOCHS=3 export SAVE_STEPS=750 export SEED=1 ``` #### Run the Pytorch version To start training, just run: ```bash python3 run_ner.py --data_dir ./ \ --labels ./labels.txt \ --model_name_or_path $BERT_MODEL \ --output_dir $OUTPUT_DIR \ --max_seq_length $MAX_LENGTH \ --num_train_epochs $NUM_EPOCHS \ --per_device_train_batch_size $BATCH_SIZE \ --save_steps $SAVE_STEPS \ --seed $SEED \ --do_train \ --do_eval \ --do_predict ``` If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets. #### JSON-based configuration file Instead of passing all parameters via commandline arguments, the `run_ner.py` script also supports reading parameters from a json-based configuration file: ```json { "data_dir": ".", "labels": "./labels.txt", "model_name_or_path": "bert-base-multilingual-cased", "output_dir": "germeval-model", "max_seq_length": 128, "num_train_epochs": 3, "per_device_train_batch_size": 32, "save_steps": 750, "seed": 1, "do_train": true, "do_eval": true, "do_predict": true } ``` It must be saved with a `.json` extension and can be used by running `python3 run_ner.py config.json`. #### Evaluation Evaluation on development dataset outputs the following for our example: ```bash 10/04/2019 00:42:06 - INFO - __main__ - ***** Eval results ***** 10/04/2019 00:42:06 - INFO - __main__ - f1 = 0.8623348017621146 10/04/2019 00:42:06 - INFO - __main__ - loss = 0.07183869666975543 10/04/2019 00:42:06 - INFO - __main__ - precision = 0.8467916366258111 10/04/2019 00:42:06 - INFO - __main__ - recall = 0.8784592370979806 ``` On the test dataset the following results could be achieved: ```bash 10/04/2019 00:42:42 - INFO - __main__ - ***** Eval results ***** 10/04/2019 00:42:42 - INFO - __main__ - f1 = 0.8614389652384803 10/04/2019 00:42:42 - INFO - __main__ - loss = 0.07064602487454782 10/04/2019 00:42:42 - INFO - __main__ - precision = 0.8604651162790697 10/04/2019 00:42:42 - INFO - __main__ - recall = 0.8624150210424085 ``` #### Run the Tensorflow 2 version To start training, just run: ```bash python3 run_tf_ner.py --data_dir ./ \ --labels ./labels.txt \ --model_name_or_path $BERT_MODEL \ --output_dir $OUTPUT_DIR \ --max_seq_length $MAX_LENGTH \ --num_train_epochs $NUM_EPOCHS \ --per_device_train_batch_size $BATCH_SIZE \ --save_steps $SAVE_STEPS \ --seed $SEED \ --do_train \ --do_eval \ --do_predict ``` Such as the Pytorch version, if your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets. #### Evaluation Evaluation on development dataset outputs the following for our example: ```bash precision recall f1-score support LOCderiv 0.7619 0.6154 0.6809 52 PERpart 0.8724 0.8997 0.8858 4057 OTHpart 0.9360 0.9466 0.9413 711 ORGpart 0.7015 0.6989 0.7002 269 LOCpart 0.7668 0.8488 0.8057 496 LOC 0.8745 0.9191 0.8963 235 ORGderiv 0.7723 0.8571 0.8125 91 OTHderiv 0.4800 0.6667 0.5581 18 OTH 0.5789 0.6875 0.6286 16 PERderiv 0.5385 0.3889 0.4516 18 PER 0.5000 0.5000 0.5000 2 ORG 0.0000 0.0000 0.0000 3 micro avg 0.8574 0.8862 0.8715 5968 macro avg 0.8575 0.8862 0.8713 5968 ``` On the test dataset the following results could be achieved: ```bash precision recall f1-score support PERpart 0.8847 0.8944 0.8896 9397 OTHpart 0.9376 0.9353 0.9365 1639 ORGpart 0.7307 0.7044 0.7173 697 LOC 0.9133 0.9394 0.9262 561 LOCpart 0.8058 0.8157 0.8107 1150 ORG 0.0000 0.0000 0.0000 8 OTHderiv 0.5882 0.4762 0.5263 42 PERderiv 0.6571 0.5227 0.5823 44 OTH 0.4906 0.6667 0.5652 39 ORGderiv 0.7016 0.7791 0.7383 172 LOCderiv 0.8256 0.6514 0.7282 109 PER 0.0000 0.0000 0.0000 11 micro avg 0.8722 0.8774 0.8748 13869 macro avg 0.8712 0.8774 0.8740 13869 ``` ### Emerging and Rare Entities task: WNUT’17 (English NER) dataset Description of the WNUT’17 task from the [shared task website](http://noisy-text.github.io/2017/index.html): > The WNUT’17 shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. > Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on > them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Six labels are available in the dataset. An overview can be found on this [page](http://noisy-text.github.io/2017/files/). #### Data (Download and pre-processing steps) The dataset can be downloaded from the [official GitHub](https://github.com/leondz/emerging_entities_17) repository. The following commands show how to prepare the dataset for fine-tuning: ```bash mkdir -p data_wnut_17 curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/wnut17train.conll' | tr '\t' ' ' > data_wnut_17/train.txt.tmp curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/emerging.dev.conll' | tr '\t' ' ' > data_wnut_17/dev.txt.tmp curl -L 'https://raw.githubusercontent.com/leondz/emerging_entities_17/master/emerging.test.annotated' | tr '\t' ' ' > data_wnut_17/test.txt.tmp ``` Let's define some variables that we need for further pre-processing steps: ```bash export MAX_LENGTH=128 export BERT_MODEL=bert-large-cased ``` Here we use the English BERT large model for fine-tuning. The `preprocess.py` scripts splits longer sentences into smaller ones (once the max. subtoken length is reached): ```bash python3 scripts/preprocess.py data_wnut_17/train.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/train.txt python3 scripts/preprocess.py data_wnut_17/dev.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/dev.txt python3 scripts/preprocess.py data_wnut_17/test.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/test.txt ``` In the last pre-processing step, the `labels.txt` file needs to be generated. This file contains all available labels: ```bash cat data_wnut_17/train.txt data_wnut_17/dev.txt data_wnut_17/test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > data_wnut_17/labels.txt ``` #### Run the Pytorch version Fine-tuning with the PyTorch version can be started using the `run_ner.py` script. In this example we use a JSON-based configuration file. This configuration file looks like: ```json { "data_dir": "./data_wnut_17", "labels": "./data_wnut_17/labels.txt", "model_name_or_path": "bert-large-cased", "output_dir": "wnut-17-model-1", "max_seq_length": 128, "num_train_epochs": 3, "per_device_train_batch_size": 32, "save_steps": 425, "seed": 1, "do_train": true, "do_eval": true, "do_predict": true, "fp16": false } ``` If your GPU supports half-precision training, please set `fp16` to `true`. Save this JSON-based configuration under `wnut_17.json`. The fine-tuning can be started with `python3 run_ner_old.py wnut_17.json`. #### Evaluation Evaluation on development dataset outputs the following: ```bash 05/29/2020 23:33:44 - INFO - __main__ - ***** Eval results ***** 05/29/2020 23:33:44 - INFO - __main__ - eval_loss = 0.26505235286212275 05/29/2020 23:33:44 - INFO - __main__ - eval_precision = 0.7008264462809918 05/29/2020 23:33:44 - INFO - __main__ - eval_recall = 0.507177033492823 05/29/2020 23:33:44 - INFO - __main__ - eval_f1 = 0.5884802220680084 05/29/2020 23:33:44 - INFO - __main__ - epoch = 3.0 ``` On the test dataset the following results could be achieved: ```bash 05/29/2020 23:33:44 - INFO - transformers.trainer - ***** Running Prediction ***** 05/29/2020 23:34:02 - INFO - __main__ - eval_loss = 0.30948806500973547 05/29/2020 23:34:02 - INFO - __main__ - eval_precision = 0.5840108401084011 05/29/2020 23:34:02 - INFO - __main__ - eval_recall = 0.3994439295644115 05/29/2020 23:34:02 - INFO - __main__ - eval_f1 = 0.47440836543753434 ``` WNUT’17 is a very difficult task. Current state-of-the-art results on this dataset can be found [here](https://nlpprogress.com/english/named_entity_recognition.html).

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./examples/research_projects/distillation/training_configs/distilbert-base-cased.json

{ "activation": "gelu", "attention_dropout": 0.1, "dim": 768, "dropout": 0.1, "hidden_dim": 3072, "initializer_range": 0.02, "max_position_embeddings": 512, "n_heads": 12, "n_layers": 6, "sinusoidal_pos_embds": true, "tie_weights_": true, "vocab_size": 28996 }

{ "activation": "gelu", "attention_dropout": 0.1, "dim": 768, "dropout": 0.1, "hidden_dim": 3072, "initializer_range": 0.02, "max_position_embeddings": 512, "n_heads": 12, "n_layers": 6, "sinusoidal_pos_embds": true, "tie_weights_": true, "vocab_size": 28996 }

huggingface/transformers

TF: add test for `PushToHubCallback`

# What does this PR do? Adds a test to TF's `PushToHubCallback`

gante

3a780cc57a7c1dc79a1bb0f787d0887adfbc6ab0

2062c28552319c2e637f4b6b467bd82ce8bfdd8e

TF: add test for `PushToHubCallback`. # What does this PR do? Adds a test to TF's `PushToHubCallback`

./src/transformers/models/transfo_xl/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"], "tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_transfo_xl"] = [ "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "AdaptiveEmbedding", "TransfoXLForSequenceClassification", "TransfoXLLMHeadModel", "TransfoXLModel", "TransfoXLPreTrainedModel", "load_tf_weights_in_transfo_xl", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_transfo_xl"] = [ "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAdaptiveEmbedding", "TFTransfoXLForSequenceClassification", "TFTransfoXLLMHeadModel", "TFTransfoXLMainLayer", "TFTransfoXLModel", "TFTransfoXLPreTrainedModel", ] if TYPE_CHECKING: from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_transfo_xl import ( TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, AdaptiveEmbedding, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel, TransfoXLPreTrainedModel, load_tf_weights_in_transfo_xl, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_transfo_xl import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFAdaptiveEmbedding, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLMainLayer, TFTransfoXLModel, TFTransfoXLPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"], "tokenization_transfo_xl": ["TransfoXLCorpus", "TransfoXLTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_transfo_xl"] = [ "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "AdaptiveEmbedding", "TransfoXLForSequenceClassification", "TransfoXLLMHeadModel", "TransfoXLModel", "TransfoXLPreTrainedModel", "load_tf_weights_in_transfo_xl", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_transfo_xl"] = [ "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAdaptiveEmbedding", "TFTransfoXLForSequenceClassification", "TFTransfoXLLMHeadModel", "TFTransfoXLMainLayer", "TFTransfoXLModel", "TFTransfoXLPreTrainedModel", ] if TYPE_CHECKING: from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_transfo_xl import ( TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, AdaptiveEmbedding, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel, TransfoXLPreTrainedModel, load_tf_weights_in_transfo_xl, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_transfo_xl import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFAdaptiveEmbedding, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLMainLayer, TFTransfoXLModel, TFTransfoXLPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # When adding a new object to this init, remember to add it twice: once inside the `_import_structure` dictionary and # once inside the `if TYPE_CHECKING` branch. The `TYPE_CHECKING` should have import statements as usual, but they are # only there for type checking. The `_import_structure` is a dictionary submodule to list of object names, and is used # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names # in the namespace without actually importing anything (and especially none of the backends). __version__ = "4.25.0.dev0" from typing import TYPE_CHECKING # Check the dependencies satisfy the minimal versions required. from . import dependency_versions_check from .utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_sentencepiece_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_vision_available, logging, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Base objects, independent of any specific backend _import_structure = { "benchmark": [], "commands": [], "configuration_utils": ["PretrainedConfig"], "convert_graph_to_onnx": [], "convert_slow_tokenizers_checkpoints_to_fast": [], "convert_tf_hub_seq_to_seq_bert_to_pytorch": [], "data": [ "DataProcessor", "InputExample", "InputFeatures", "SingleSentenceClassificationProcessor", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", ], "data.data_collator": [ "DataCollator", "DataCollatorForLanguageModeling", "DataCollatorForPermutationLanguageModeling", "DataCollatorForSeq2Seq", "DataCollatorForSOP", "DataCollatorForTokenClassification", "DataCollatorForWholeWordMask", "DataCollatorWithPadding", "DefaultDataCollator", "default_data_collator", ], "data.metrics": [], "data.processors": [], "debug_utils": [], "dependency_versions_check": [], "dependency_versions_table": [], "dynamic_module_utils": [], "feature_extraction_sequence_utils": ["SequenceFeatureExtractor"], "feature_extraction_utils": ["BatchFeature", "FeatureExtractionMixin"], "file_utils": [], "generation": [], "hf_argparser": ["HfArgumentParser"], "integrations": [ "is_clearml_available", "is_comet_available", "is_neptune_available", "is_optuna_available", "is_ray_available", "is_ray_tune_available", "is_sigopt_available", "is_tensorboard_available", "is_wandb_available", ], "modelcard": ["ModelCard"], "modeling_tf_pytorch_utils": [ "convert_tf_weight_name_to_pt_weight_name", "load_pytorch_checkpoint_in_tf2_model", "load_pytorch_model_in_tf2_model", "load_pytorch_weights_in_tf2_model", "load_tf2_checkpoint_in_pytorch_model", "load_tf2_model_in_pytorch_model", "load_tf2_weights_in_pytorch_model", ], "models": [], # Models "models.albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig"], "models.auto": [ "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "FEATURE_EXTRACTOR_MAPPING", "IMAGE_PROCESSOR_MAPPING", "MODEL_NAMES_MAPPING", "PROCESSOR_MAPPING", "TOKENIZER_MAPPING", "AutoConfig", "AutoFeatureExtractor", "AutoImageProcessor", "AutoProcessor", "AutoTokenizer", ], "models.bart": ["BartConfig", "BartTokenizer"], "models.barthez": [], "models.bartpho": [], "models.beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig"], "models.bert": [ "BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BasicTokenizer", "BertConfig", "BertTokenizer", "WordpieceTokenizer", ], "models.bert_generation": ["BertGenerationConfig"], "models.bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"], "models.bertweet": ["BertweetTokenizer"], "models.big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig"], "models.bigbird_pegasus": [ "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdPegasusConfig", ], "models.blenderbot": ["BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotConfig", "BlenderbotTokenizer"], "models.blenderbot_small": [ "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotSmallConfig", "BlenderbotSmallTokenizer", ], "models.bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig"], "models.bort": [], "models.byt5": ["ByT5Tokenizer"], "models.camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"], "models.canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig", "CanineTokenizer"], "models.clip": [ "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPConfig", "CLIPProcessor", "CLIPTextConfig", "CLIPTokenizer", "CLIPVisionConfig", ], "models.clipseg": [ "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPSegConfig", "CLIPSegProcessor", "CLIPSegTextConfig", "CLIPSegVisionConfig", ], "models.codegen": ["CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodeGenConfig", "CodeGenTokenizer"], "models.conditional_detr": ["CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConditionalDetrConfig"], "models.convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertTokenizer"], "models.convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig"], "models.cpm": [], "models.ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig", "CTRLTokenizer"], "models.cvt": ["CVT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CvtConfig"], "models.data2vec": [ "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig", "Data2VecTextConfig", "Data2VecVisionConfig", ], "models.deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaTokenizer"], "models.deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config"], "models.decision_transformer": ["DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "DecisionTransformerConfig"], "models.deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"], "models.deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig"], "models.detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"], "models.dialogpt": [], "models.distilbert": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig", "DistilBertTokenizer"], "models.dit": [], "models.donut": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutProcessor", "DonutSwinConfig"], "models.dpr": [ "DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig", "DPRContextEncoderTokenizer", "DPRQuestionEncoderTokenizer", "DPRReaderOutput", "DPRReaderTokenizer", ], "models.dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"], "models.electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraTokenizer"], "models.encoder_decoder": ["EncoderDecoderConfig"], "models.ernie": [ "ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", ], "models.esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig", "EsmTokenizer"], "models.flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertTokenizer"], "models.flava": [ "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlavaConfig", "FlavaImageCodebookConfig", "FlavaImageConfig", "FlavaMultimodalConfig", "FlavaTextConfig", ], "models.fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"], "models.fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig", "FSMTTokenizer"], "models.funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig", "FunnelTokenizer"], "models.glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"], "models.gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2Tokenizer"], "models.gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig"], "models.gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"], "models.gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"], "models.gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig"], "models.groupvit": [ "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GroupViTConfig", "GroupViTTextConfig", "GroupViTVisionConfig", ], "models.herbert": ["HerbertTokenizer"], "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"], "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"], "models.imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"], "models.jukebox": [ "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "JukeboxConfig", "JukeboxPriorConfig", "JukeboxTokenizer", "JukeboxVQVAEConfig", ], "models.layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMTokenizer"], "models.layoutlmv2": [ "LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config", "LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor", "LayoutLMv2Processor", "LayoutLMv2Tokenizer", ], "models.layoutlmv3": [ "LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv3Config", "LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor", "LayoutLMv3Processor", "LayoutLMv3Tokenizer", ], "models.layoutxlm": ["LayoutXLMProcessor"], "models.led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig", "LEDTokenizer"], "models.levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig"], "models.lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"], "models.longformer": ["LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongformerConfig", "LongformerTokenizer"], "models.longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config"], "models.luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig", "LukeTokenizer"], "models.lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig", "LxmertTokenizer"], "models.m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config"], "models.marian": ["MarianConfig"], "models.markuplm": [ "MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig", "MarkupLMFeatureExtractor", "MarkupLMProcessor", "MarkupLMTokenizer", ], "models.maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"], "models.mbart": ["MBartConfig"], "models.mbart50": [], "models.mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig", "MCTCTProcessor"], "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"], "models.megatron_gpt2": [], "models.mluke": [], "models.mmbt": ["MMBTConfig"], "models.mobilebert": ["MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileBertConfig", "MobileBertTokenizer"], "models.mobilenet_v2": ["MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileNetV2Config"], "models.mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig"], "models.mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig", "MPNetTokenizer"], "models.mt5": ["MT5Config"], "models.mvp": ["MvpConfig", "MvpTokenizer"], "models.nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"], "models.nllb": [], "models.nystromformer": [ "NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig", ], "models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"], "models.opt": ["OPTConfig"], "models.owlvit": [ "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OwlViTConfig", "OwlViTProcessor", "OwlViTTextConfig", "OwlViTVisionConfig", ], "models.pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig", "PegasusTokenizer"], "models.pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"], "models.perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverTokenizer"], "models.phobert": ["PhobertTokenizer"], "models.plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"], "models.poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"], "models.prophetnet": ["PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ProphetNetConfig", "ProphetNetTokenizer"], "models.qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"], "models.rag": ["RagConfig", "RagRetriever", "RagTokenizer"], "models.realm": ["REALM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RealmConfig", "RealmTokenizer"], "models.reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"], "models.regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"], "models.rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig"], "models.resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig"], "models.retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer"], "models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"], "models.roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig", "RoCBertTokenizer"], "models.roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerTokenizer"], "models.segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig"], "models.sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"], "models.sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"], "models.speech_encoder_decoder": ["SpeechEncoderDecoderConfig"], "models.speech_to_text": [ "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig", ], "models.speech_to_text_2": [ "SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config", "Speech2Text2Processor", "Speech2Text2Tokenizer", ], "models.splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig", "SplinterTokenizer"], "models.squeezebert": ["SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SqueezeBertConfig", "SqueezeBertTokenizer"], "models.swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig"], "models.swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"], "models.switch_transformers": ["SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwitchTransformersConfig"], "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"], "models.table_transformer": ["TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TableTransformerConfig"], "models.tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig", "TapasTokenizer"], "models.tapex": ["TapexTokenizer"], "models.time_series_transformer": [ "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimeSeriesTransformerConfig", ], "models.trajectory_transformer": [ "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrajectoryTransformerConfig", ], "models.transfo_xl": [ "TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig", "TransfoXLCorpus", "TransfoXLTokenizer", ], "models.trocr": [ "TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig", "TrOCRProcessor", ], "models.unispeech": [ "UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig", ], "models.unispeech_sat": [ "UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig", ], "models.van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"], "models.videomae": ["VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "VideoMAEConfig"], "models.vilt": [ "VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig", "ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor", ], "models.vision_encoder_decoder": ["VisionEncoderDecoderConfig"], "models.vision_text_dual_encoder": ["VisionTextDualEncoderConfig", "VisionTextDualEncoderProcessor"], "models.visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"], "models.vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"], "models.vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"], "models.vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"], "models.wav2vec2": [ "WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config", "Wav2Vec2CTCTokenizer", "Wav2Vec2FeatureExtractor", "Wav2Vec2Processor", "Wav2Vec2Tokenizer", ], "models.wav2vec2_conformer": [ "WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2ConformerConfig", ], "models.wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"], "models.wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"], "models.wavlm": [ "WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig", ], "models.whisper": [ "WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperFeatureExtractor", "WhisperProcessor", "WhisperTokenizer", ], "models.x_clip": [ "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "XCLIPConfig", "XCLIPProcessor", "XCLIPTextConfig", "XCLIPVisionConfig", ], "models.xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"], "models.xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMTokenizer"], "models.xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"], "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"], "models.xlm_roberta_xl": ["XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaXLConfig"], "models.xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"], "models.yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig"], "models.yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"], "onnx": [], "pipelines": [ "AudioClassificationPipeline", "AutomaticSpeechRecognitionPipeline", "Conversation", "ConversationalPipeline", "CsvPipelineDataFormat", "DepthEstimationPipeline", "DocumentQuestionAnsweringPipeline", "FeatureExtractionPipeline", "FillMaskPipeline", "ImageClassificationPipeline", "ImageSegmentationPipeline", "ImageToTextPipeline", "JsonPipelineDataFormat", "NerPipeline", "ObjectDetectionPipeline", "PipedPipelineDataFormat", "Pipeline", "PipelineDataFormat", "QuestionAnsweringPipeline", "SummarizationPipeline", "TableQuestionAnsweringPipeline", "Text2TextGenerationPipeline", "TextClassificationPipeline", "TextGenerationPipeline", "TokenClassificationPipeline", "TranslationPipeline", "VisualQuestionAnsweringPipeline", "ZeroShotClassificationPipeline", "ZeroShotImageClassificationPipeline", "ZeroShotObjectDetectionPipeline", "pipeline", ], "processing_utils": ["ProcessorMixin"], "testing_utils": [], "tokenization_utils": ["PreTrainedTokenizer"], "tokenization_utils_base": [ "AddedToken", "BatchEncoding", "CharSpan", "PreTrainedTokenizerBase", "SpecialTokensMixin", "TokenSpan", ], "trainer_callback": [ "DefaultFlowCallback", "EarlyStoppingCallback", "PrinterCallback", "ProgressCallback", "TrainerCallback", "TrainerControl", "TrainerState", ], "trainer_utils": ["EvalPrediction", "IntervalStrategy", "SchedulerType", "enable_full_determinism", "set_seed"], "training_args": ["TrainingArguments"], "training_args_seq2seq": ["Seq2SeqTrainingArguments"], "training_args_tf": ["TFTrainingArguments"], "utils": [ "CONFIG_NAME", "MODEL_CARD_NAME", "PYTORCH_PRETRAINED_BERT_CACHE", "PYTORCH_TRANSFORMERS_CACHE", "SPIECE_UNDERLINE", "TF2_WEIGHTS_NAME", "TF_WEIGHTS_NAME", "TRANSFORMERS_CACHE", "WEIGHTS_NAME", "TensorType", "add_end_docstrings", "add_start_docstrings", "is_apex_available", "is_datasets_available", "is_faiss_available", "is_flax_available", "is_phonemizer_available", "is_psutil_available", "is_py3nvml_available", "is_pyctcdecode_available", "is_safetensors_available", "is_scipy_available", "is_sentencepiece_available", "is_sklearn_available", "is_speech_available", "is_tensorflow_text_available", "is_tf_available", "is_timm_available", "is_tokenizers_available", "is_torch_available", "is_torch_tpu_available", "is_vision_available", "logging", ], "utils.bitsandbytes": [], } # sentencepiece-backed objects try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_sentencepiece_objects _import_structure["utils.dummy_sentencepiece_objects"] = [ name for name in dir(dummy_sentencepiece_objects) if not name.startswith("_") ] else: _import_structure["models.albert"].append("AlbertTokenizer") _import_structure["models.barthez"].append("BarthezTokenizer") _import_structure["models.bartpho"].append("BartphoTokenizer") _import_structure["models.bert_generation"].append("BertGenerationTokenizer") _import_structure["models.big_bird"].append("BigBirdTokenizer") _import_structure["models.camembert"].append("CamembertTokenizer") _import_structure["models.cpm"].append("CpmTokenizer") _import_structure["models.deberta_v2"].append("DebertaV2Tokenizer") _import_structure["models.fnet"].append("FNetTokenizer") _import_structure["models.layoutxlm"].append("LayoutXLMTokenizer") _import_structure["models.m2m_100"].append("M2M100Tokenizer") _import_structure["models.marian"].append("MarianTokenizer") _import_structure["models.mbart"].append("MBartTokenizer") _import_structure["models.nllb"].append("NllbTokenizer") _import_structure["models.mbart50"].append("MBart50Tokenizer") _import_structure["models.mluke"].append("MLukeTokenizer") _import_structure["models.mt5"].append("MT5Tokenizer") _import_structure["models.pegasus"].append("PegasusTokenizer") _import_structure["models.plbart"].append("PLBartTokenizer") _import_structure["models.reformer"].append("ReformerTokenizer") _import_structure["models.rembert"].append("RemBertTokenizer") _import_structure["models.speech_to_text"].append("Speech2TextTokenizer") _import_structure["models.t5"].append("T5Tokenizer") _import_structure["models.xglm"].append("XGLMTokenizer") _import_structure["models.xlm_prophetnet"].append("XLMProphetNetTokenizer") _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer") _import_structure["models.xlnet"].append("XLNetTokenizer") # tokenizers-backed objects try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tokenizers_objects _import_structure["utils.dummy_tokenizers_objects"] = [ name for name in dir(dummy_tokenizers_objects) if not name.startswith("_") ] else: # Fast tokenizers structure _import_structure["models.albert"].append("AlbertTokenizerFast") _import_structure["models.bart"].append("BartTokenizerFast") _import_structure["models.barthez"].append("BarthezTokenizerFast") _import_structure["models.bert"].append("BertTokenizerFast") _import_structure["models.big_bird"].append("BigBirdTokenizerFast") _import_structure["models.blenderbot"].append("BlenderbotTokenizerFast") _import_structure["models.blenderbot_small"].append("BlenderbotSmallTokenizerFast") _import_structure["models.bloom"].append("BloomTokenizerFast") _import_structure["models.camembert"].append("CamembertTokenizerFast") _import_structure["models.clip"].append("CLIPTokenizerFast") _import_structure["models.codegen"].append("CodeGenTokenizerFast") _import_structure["models.convbert"].append("ConvBertTokenizerFast") _import_structure["models.cpm"].append("CpmTokenizerFast") _import_structure["models.deberta"].append("DebertaTokenizerFast") _import_structure["models.deberta_v2"].append("DebertaV2TokenizerFast") _import_structure["models.distilbert"].append("DistilBertTokenizerFast") _import_structure["models.dpr"].extend( ["DPRContextEncoderTokenizerFast", "DPRQuestionEncoderTokenizerFast", "DPRReaderTokenizerFast"] ) _import_structure["models.electra"].append("ElectraTokenizerFast") _import_structure["models.fnet"].append("FNetTokenizerFast") _import_structure["models.funnel"].append("FunnelTokenizerFast") _import_structure["models.gpt2"].append("GPT2TokenizerFast") _import_structure["models.gpt_neox"].append("GPTNeoXTokenizerFast") _import_structure["models.gpt_neox_japanese"].append("GPTNeoXJapaneseTokenizer") _import_structure["models.herbert"].append("HerbertTokenizerFast") _import_structure["models.layoutlm"].append("LayoutLMTokenizerFast") _import_structure["models.layoutlmv2"].append("LayoutLMv2TokenizerFast") _import_structure["models.layoutlmv3"].append("LayoutLMv3TokenizerFast") _import_structure["models.layoutxlm"].append("LayoutXLMTokenizerFast") _import_structure["models.led"].append("LEDTokenizerFast") _import_structure["models.longformer"].append("LongformerTokenizerFast") _import_structure["models.lxmert"].append("LxmertTokenizerFast") _import_structure["models.markuplm"].append("MarkupLMTokenizerFast") _import_structure["models.mbart"].append("MBartTokenizerFast") _import_structure["models.mbart50"].append("MBart50TokenizerFast") _import_structure["models.mobilebert"].append("MobileBertTokenizerFast") _import_structure["models.mpnet"].append("MPNetTokenizerFast") _import_structure["models.mt5"].append("MT5TokenizerFast") _import_structure["models.mvp"].append("MvpTokenizerFast") _import_structure["models.nllb"].append("NllbTokenizerFast") _import_structure["models.openai"].append("OpenAIGPTTokenizerFast") _import_structure["models.pegasus"].append("PegasusTokenizerFast") _import_structure["models.realm"].append("RealmTokenizerFast") _import_structure["models.reformer"].append("ReformerTokenizerFast") _import_structure["models.rembert"].append("RemBertTokenizerFast") _import_structure["models.retribert"].append("RetriBertTokenizerFast") _import_structure["models.roberta"].append("RobertaTokenizerFast") _import_structure["models.roformer"].append("RoFormerTokenizerFast") _import_structure["models.splinter"].append("SplinterTokenizerFast") _import_structure["models.squeezebert"].append("SqueezeBertTokenizerFast") _import_structure["models.t5"].append("T5TokenizerFast") _import_structure["models.xglm"].append("XGLMTokenizerFast") _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizerFast") _import_structure["models.xlnet"].append("XLNetTokenizerFast") _import_structure["tokenization_utils_fast"] = ["PreTrainedTokenizerFast"] try: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_sentencepiece_and_tokenizers_objects _import_structure["utils.dummy_sentencepiece_and_tokenizers_objects"] = [ name for name in dir(dummy_sentencepiece_and_tokenizers_objects) if not name.startswith("_") ] else: _import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"] # Speech-specific objects try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_speech_objects _import_structure["utils.dummy_speech_objects"] = [ name for name in dir(dummy_speech_objects) if not name.startswith("_") ] else: _import_structure["models.mctct"].append("MCTCTFeatureExtractor") _import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor") # Tensorflow-text-specific objects try: if not is_tensorflow_text_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tensorflow_text_objects _import_structure["utils.dummy_tensorflow_text_objects"] = [ name for name in dir(dummy_tensorflow_text_objects) if not name.startswith("_") ] else: _import_structure["models.bert"].append("TFBertTokenizer") try: if not (is_sentencepiece_available() and is_speech_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_sentencepiece_and_speech_objects _import_structure["utils.dummy_sentencepiece_and_speech_objects"] = [ name for name in dir(dummy_sentencepiece_and_speech_objects) if not name.startswith("_") ] else: _import_structure["models.speech_to_text"].append("Speech2TextProcessor") # Vision-specific objects try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_vision_objects _import_structure["utils.dummy_vision_objects"] = [ name for name in dir(dummy_vision_objects) if not name.startswith("_") ] else: _import_structure["image_processing_utils"] = ["ImageProcessingMixin"] _import_structure["image_transforms"] = ["rescale", "resize", "to_pil_image"] _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"] _import_structure["models.beit"].extend(["BeitFeatureExtractor", "BeitImageProcessor"]) _import_structure["models.clip"].extend(["CLIPFeatureExtractor", "CLIPImageProcessor"]) _import_structure["models.convnext"].extend(["ConvNextFeatureExtractor", "ConvNextImageProcessor"]) _import_structure["models.deformable_detr"].append("DeformableDetrFeatureExtractor") _import_structure["models.deit"].extend(["DeiTFeatureExtractor", "DeiTImageProcessor"]) _import_structure["models.detr"].append("DetrFeatureExtractor") _import_structure["models.conditional_detr"].append("ConditionalDetrFeatureExtractor") _import_structure["models.donut"].append("DonutFeatureExtractor") _import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"]) _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaProcessor", "FlavaImageProcessor"]) _import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"]) _import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"]) _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"]) _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"]) _import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"]) _import_structure["models.maskformer"].append("MaskFormerFeatureExtractor") _import_structure["models.mobilenet_v2"].extend(["MobileNetV2FeatureExtractor", "MobileNetV2ImageProcessor"]) _import_structure["models.mobilevit"].extend(["MobileViTFeatureExtractor", "MobileViTImageProcessor"]) _import_structure["models.owlvit"].append("OwlViTFeatureExtractor") _import_structure["models.perceiver"].extend(["PerceiverFeatureExtractor", "PerceiverImageProcessor"]) _import_structure["models.poolformer"].extend(["PoolFormerFeatureExtractor", "PoolFormerImageProcessor"]) _import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"]) _import_structure["models.videomae"].extend(["VideoMAEFeatureExtractor", "VideoMAEImageProcessor"]) _import_structure["models.vilt"].extend(["ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor"]) _import_structure["models.vit"].extend(["ViTFeatureExtractor", "ViTImageProcessor"]) _import_structure["models.yolos"].extend(["YolosFeatureExtractor"]) # Timm-backed objects try: if not (is_timm_available() and is_vision_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_timm_and_vision_objects _import_structure["utils.dummy_timm_and_vision_objects"] = [ name for name in dir(dummy_timm_and_vision_objects) if not name.startswith("_") ] else: _import_structure["models.deformable_detr"].extend( [ "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ] ) _import_structure["models.detr"].extend( [ "DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DetrForObjectDetection", "DetrForSegmentation", "DetrModel", "DetrPreTrainedModel", ] ) _import_structure["models.table_transformer"].extend( [ "TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TableTransformerForObjectDetection", "TableTransformerModel", "TableTransformerPreTrainedModel", ] ) _import_structure["models.conditional_detr"].extend( [ "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "ConditionalDetrForObjectDetection", "ConditionalDetrForSegmentation", "ConditionalDetrModel", "ConditionalDetrPreTrainedModel", ] ) # PyTorch-backed objects try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_pt_objects _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")] else: _import_structure["activations"] = [] _import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"] _import_structure["benchmark.benchmark_args"] = ["PyTorchBenchmarkArguments"] _import_structure["data.datasets"] = [ "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "SquadDataset", "SquadDataTrainingArguments", "TextDataset", "TextDatasetForNextSentencePrediction", ] _import_structure["deepspeed"] = [] _import_structure["generation_utils"] = [] _import_structure["generation"].extend( [ "Constraint", "ConstraintListState", "DisjunctiveConstraint", "PhrasalConstraint", "BeamScorer", "BeamSearchScorer", "ConstrainedBeamSearchScorer", "ForcedBOSTokenLogitsProcessor", "ForcedEOSTokenLogitsProcessor", "HammingDiversityLogitsProcessor", "InfNanRemoveLogitsProcessor", "LogitsProcessor", "LogitsProcessorList", "LogitsWarper", "MinLengthLogitsProcessor", "NoBadWordsLogitsProcessor", "NoRepeatNGramLogitsProcessor", "PrefixConstrainedLogitsProcessor", "RepetitionPenaltyLogitsProcessor", "TemperatureLogitsWarper", "TopKLogitsWarper", "TopPLogitsWarper", "TypicalLogitsWarper", "MaxLengthCriteria", "MaxTimeCriteria", "StoppingCriteria", "StoppingCriteriaList", "GenerationMixin", "top_k_top_p_filtering", ] ) _import_structure["modeling_outputs"] = [] _import_structure["modeling_utils"] = ["PreTrainedModel"] # PyTorch models structure _import_structure["models.roc_bert"].extend( [ "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RoCBertForMaskedLM", "RoCBertForCausalLM", "RoCBertForMultipleChoice", "RoCBertForQuestionAnswering", "RoCBertForSequenceClassification", "RoCBertForTokenClassification", "RoCBertLayer", "RoCBertModel", "RoCBertForPreTraining", "RoCBertPreTrainedModel", "load_tf_weights_in_roc_bert", ] ) _import_structure["models.time_series_transformer"].extend( [ "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TimeSeriesTransformerForPrediction", "TimeSeriesTransformerModel", "TimeSeriesTransformerPreTrainedModel", ] ) _import_structure["models.albert"].extend( [ "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "AlbertForMaskedLM", "AlbertForMultipleChoice", "AlbertForPreTraining", "AlbertForQuestionAnswering", "AlbertForSequenceClassification", "AlbertForTokenClassification", "AlbertModel", "AlbertPreTrainedModel", "load_tf_weights_in_albert", ] ) _import_structure["models.auto"].extend( [ "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", "MODEL_FOR_AUDIO_XVECTOR_MAPPING", "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", "MODEL_FOR_CAUSAL_LM_MAPPING", "MODEL_FOR_CTC_MAPPING", "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "MODEL_FOR_MASKED_LM_MAPPING", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "MODEL_FOR_OBJECT_DETECTION_MAPPING", "MODEL_FOR_PRETRAINING_MAPPING", "MODEL_FOR_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", "MODEL_FOR_VISION_2_SEQ_MAPPING", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", "MODEL_MAPPING", "MODEL_WITH_LM_HEAD_MAPPING", "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", "AutoModel", "AutoModelForAudioClassification", "AutoModelForAudioFrameClassification", "AutoModelForAudioXVector", "AutoModelForCausalLM", "AutoModelForCTC", "AutoModelForDocumentQuestionAnswering", "AutoModelForDepthEstimation", "AutoModelForImageClassification", "AutoModelForImageSegmentation", "AutoModelForInstanceSegmentation", "AutoModelForMaskedImageModeling", "AutoModelForMaskedLM", "AutoModelForMultipleChoice", "AutoModelForNextSentencePrediction", "AutoModelForObjectDetection", "AutoModelForPreTraining", "AutoModelForQuestionAnswering", "AutoModelForSemanticSegmentation", "AutoModelForSeq2SeqLM", "AutoModelForSequenceClassification", "AutoModelForSpeechSeq2Seq", "AutoModelForTableQuestionAnswering", "AutoModelForTokenClassification", "AutoModelForVideoClassification", "AutoModelForVision2Seq", "AutoModelForVisualQuestionAnswering", "AutoModelWithLMHead", "AutoModelForZeroShotObjectDetection", ] ) _import_structure["models.bart"].extend( [ "BART_PRETRAINED_MODEL_ARCHIVE_LIST", "BartForCausalLM", "BartForConditionalGeneration", "BartForQuestionAnswering", "BartForSequenceClassification", "BartModel", "BartPretrainedModel", "PretrainedBartModel", ] ) _import_structure["models.mvp"].extend( [ "MVP_PRETRAINED_MODEL_ARCHIVE_LIST", "MvpForCausalLM", "MvpForConditionalGeneration", "MvpForQuestionAnswering", "MvpForSequenceClassification", "MvpModel", "MvpPreTrainedModel", ] ) _import_structure["models.beit"].extend( [ "BEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "BeitForImageClassification", "BeitForMaskedImageModeling", "BeitForSemanticSegmentation", "BeitModel", "BeitPreTrainedModel", ] ) _import_structure["models.bert"].extend( [ "BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "BertForMaskedLM", "BertForMultipleChoice", "BertForNextSentencePrediction", "BertForPreTraining", "BertForQuestionAnswering", "BertForSequenceClassification", "BertForTokenClassification", "BertLayer", "BertLMHeadModel", "BertModel", "BertPreTrainedModel", "load_tf_weights_in_bert", ] ) _import_structure["models.bert_generation"].extend( [ "BertGenerationDecoder", "BertGenerationEncoder", "BertGenerationPreTrainedModel", "load_tf_weights_in_bert_generation", ] ) _import_structure["models.big_bird"].extend( [ "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdForCausalLM", "BigBirdForMaskedLM", "BigBirdForMultipleChoice", "BigBirdForPreTraining", "BigBirdForQuestionAnswering", "BigBirdForSequenceClassification", "BigBirdForTokenClassification", "BigBirdLayer", "BigBirdModel", "BigBirdPreTrainedModel", "load_tf_weights_in_big_bird", ] ) _import_structure["models.bigbird_pegasus"].extend( [ "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdPegasusForCausalLM", "BigBirdPegasusForConditionalGeneration", "BigBirdPegasusForQuestionAnswering", "BigBirdPegasusForSequenceClassification", "BigBirdPegasusModel", "BigBirdPegasusPreTrainedModel", ] ) _import_structure["models.bloom"].extend( [ "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST", "BloomForCausalLM", "BloomModel", "BloomPreTrainedModel", "BloomForSequenceClassification", "BloomForTokenClassification", "BloomForQuestionAnswering", ] ) _import_structure["models.blenderbot"].extend( [ "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST", "BlenderbotForCausalLM", "BlenderbotForConditionalGeneration", "BlenderbotModel", "BlenderbotPreTrainedModel", ] ) _import_structure["models.blenderbot_small"].extend( [ "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST", "BlenderbotSmallForCausalLM", "BlenderbotSmallForConditionalGeneration", "BlenderbotSmallModel", "BlenderbotSmallPreTrainedModel", ] ) _import_structure["models.camembert"].extend( [ "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "CamembertForCausalLM", "CamembertForMaskedLM", "CamembertForMultipleChoice", "CamembertForQuestionAnswering", "CamembertForSequenceClassification", "CamembertForTokenClassification", "CamembertModel", "CamembertPreTrainedModel", ] ) _import_structure["models.canine"].extend( [ "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST", "CanineForMultipleChoice", "CanineForQuestionAnswering", "CanineForSequenceClassification", "CanineForTokenClassification", "CanineLayer", "CanineModel", "CaninePreTrainedModel", "load_tf_weights_in_canine", ] ) _import_structure["models.clip"].extend( [ "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPModel", "CLIPPreTrainedModel", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", ] ) _import_structure["models.clipseg"].extend( [ "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPSegModel", "CLIPSegPreTrainedModel", "CLIPSegTextModel", "CLIPSegVisionModel", "CLIPSegForImageSegmentation", ] ) _import_structure["models.x_clip"].extend( [ "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "XCLIPModel", "XCLIPPreTrainedModel", "XCLIPTextModel", "XCLIPVisionModel", ] ) _import_structure["models.convbert"].extend( [ "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvBertForMaskedLM", "ConvBertForMultipleChoice", "ConvBertForQuestionAnswering", "ConvBertForSequenceClassification", "ConvBertForTokenClassification", "ConvBertLayer", "ConvBertModel", "ConvBertPreTrainedModel", "load_tf_weights_in_convbert", ] ) _import_structure["models.convnext"].extend( [ "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvNextForImageClassification", "ConvNextModel", "ConvNextPreTrainedModel", ] ) _import_structure["models.ctrl"].extend( [ "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", "CTRLForSequenceClassification", "CTRLLMHeadModel", "CTRLModel", "CTRLPreTrainedModel", ] ) _import_structure["models.cvt"].extend( [ "CVT_PRETRAINED_MODEL_ARCHIVE_LIST", "CvtForImageClassification", "CvtModel", "CvtPreTrainedModel", ] ) _import_structure["models.data2vec"].extend( [ "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST", "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST", "Data2VecAudioForAudioFrameClassification", "Data2VecAudioForCTC", "Data2VecAudioForSequenceClassification", "Data2VecAudioForXVector", "Data2VecAudioModel", "Data2VecAudioPreTrainedModel", "Data2VecTextForCausalLM", "Data2VecTextForMaskedLM", "Data2VecTextForMultipleChoice", "Data2VecTextForQuestionAnswering", "Data2VecTextForSequenceClassification", "Data2VecTextForTokenClassification", "Data2VecTextModel", "Data2VecTextPreTrainedModel", "Data2VecVisionForImageClassification", "Data2VecVisionForSemanticSegmentation", "Data2VecVisionModel", "Data2VecVisionPreTrainedModel", ] ) _import_structure["models.deberta"].extend( [ "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "DebertaForMaskedLM", "DebertaForQuestionAnswering", "DebertaForSequenceClassification", "DebertaForTokenClassification", "DebertaModel", "DebertaPreTrainedModel", ] ) _import_structure["models.deberta_v2"].extend( [ "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "DebertaV2ForMaskedLM", "DebertaV2ForMultipleChoice", "DebertaV2ForQuestionAnswering", "DebertaV2ForSequenceClassification", "DebertaV2ForTokenClassification", "DebertaV2Model", "DebertaV2PreTrainedModel", ] ) _import_structure["models.decision_transformer"].extend( [ "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "DecisionTransformerGPT2Model", "DecisionTransformerGPT2PreTrainedModel", "DecisionTransformerModel", "DecisionTransformerPreTrainedModel", ] ) _import_structure["models.deit"].extend( [ "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "DeiTForImageClassification", "DeiTForImageClassificationWithTeacher", "DeiTForMaskedImageModeling", "DeiTModel", "DeiTPreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "DistilBertForMaskedLM", "DistilBertForMultipleChoice", "DistilBertForQuestionAnswering", "DistilBertForSequenceClassification", "DistilBertForTokenClassification", "DistilBertModel", "DistilBertPreTrainedModel", ] ) _import_structure["models.donut"].extend( [ "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "DonutSwinModel", "DonutSwinPreTrainedModel", ] ) _import_structure["models.dpr"].extend( [ "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPRContextEncoder", "DPRPretrainedContextEncoder", "DPRPreTrainedModel", "DPRPretrainedQuestionEncoder", "DPRPretrainedReader", "DPRQuestionEncoder", "DPRReader", ] ) _import_structure["models.dpt"].extend( [ "DPT_PRETRAINED_MODEL_ARCHIVE_LIST", "DPTForDepthEstimation", "DPTForSemanticSegmentation", "DPTModel", "DPTPreTrainedModel", ] ) _import_structure["models.electra"].extend( [ "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "ElectraForCausalLM", "ElectraForMaskedLM", "ElectraForMultipleChoice", "ElectraForPreTraining", "ElectraForQuestionAnswering", "ElectraForSequenceClassification", "ElectraForTokenClassification", "ElectraModel", "ElectraPreTrainedModel", "load_tf_weights_in_electra", ] ) _import_structure["models.encoder_decoder"].append("EncoderDecoderModel") _import_structure["models.ernie"].extend( [ "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST", "ErnieForCausalLM", "ErnieForMaskedLM", "ErnieForMultipleChoice", "ErnieForNextSentencePrediction", "ErnieForPreTraining", "ErnieForQuestionAnswering", "ErnieForSequenceClassification", "ErnieForTokenClassification", "ErnieModel", "ErniePreTrainedModel", ] ) _import_structure["models.esm"].extend( [ "ESM_PRETRAINED_MODEL_ARCHIVE_LIST", "EsmFoldPreTrainedModel", "EsmForMaskedLM", "EsmForProteinFolding", "EsmForSequenceClassification", "EsmForTokenClassification", "EsmModel", "EsmPreTrainedModel", ] ) _import_structure["models.flaubert"].extend( [ "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "FlaubertForMultipleChoice", "FlaubertForQuestionAnswering", "FlaubertForQuestionAnsweringSimple", "FlaubertForSequenceClassification", "FlaubertForTokenClassification", "FlaubertModel", "FlaubertWithLMHeadModel", "FlaubertPreTrainedModel", ] ) _import_structure["models.flava"].extend( [ "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST", "FlavaForPreTraining", "FlavaImageCodebook", "FlavaImageModel", "FlavaModel", "FlavaMultimodalModel", "FlavaPreTrainedModel", "FlavaTextModel", ] ) _import_structure["models.fnet"].extend( [ "FNET_PRETRAINED_MODEL_ARCHIVE_LIST", "FNetForMaskedLM", "FNetForMultipleChoice", "FNetForNextSentencePrediction", "FNetForPreTraining", "FNetForQuestionAnswering", "FNetForSequenceClassification", "FNetForTokenClassification", "FNetLayer", "FNetModel", "FNetPreTrainedModel", ] ) _import_structure["models.fsmt"].extend(["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]) _import_structure["models.funnel"].extend( [ "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "FunnelBaseModel", "FunnelForMaskedLM", "FunnelForMultipleChoice", "FunnelForPreTraining", "FunnelForQuestionAnswering", "FunnelForSequenceClassification", "FunnelForTokenClassification", "FunnelModel", "FunnelPreTrainedModel", "load_tf_weights_in_funnel", ] ) _import_structure["models.glpn"].extend( [ "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST", "GLPNForDepthEstimation", "GLPNModel", "GLPNPreTrainedModel", ] ) _import_structure["models.gpt2"].extend( [ "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "GPT2DoubleHeadsModel", "GPT2ForSequenceClassification", "GPT2ForTokenClassification", "GPT2LMHeadModel", "GPT2Model", "GPT2PreTrainedModel", "load_tf_weights_in_gpt2", ] ) _import_structure["models.gpt_neo"].extend( [ "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoForCausalLM", "GPTNeoForSequenceClassification", "GPTNeoModel", "GPTNeoPreTrainedModel", "load_tf_weights_in_gpt_neo", ] ) _import_structure["models.gpt_neox"].extend( [ "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXForCausalLM", "GPTNeoXLayer", "GPTNeoXModel", "GPTNeoXPreTrainedModel", ] ) _import_structure["models.gpt_neox_japanese"].extend( [ "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXJapaneseForCausalLM", "GPTNeoXJapaneseLayer", "GPTNeoXJapaneseModel", "GPTNeoXJapanesePreTrainedModel", ] ) _import_structure["models.gptj"].extend( [ "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTJForCausalLM", "GPTJForQuestionAnswering", "GPTJForSequenceClassification", "GPTJModel", "GPTJPreTrainedModel", ] ) _import_structure["models.groupvit"].extend( [ "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "GroupViTModel", "GroupViTPreTrainedModel", "GroupViTTextModel", "GroupViTVisionModel", ] ) _import_structure["models.codegen"].extend( [ "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST", "CodeGenForCausalLM", "CodeGenModel", "CodeGenPreTrainedModel", ] ) _import_structure["models.hubert"].extend( [ "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "HubertForCTC", "HubertForSequenceClassification", "HubertModel", "HubertPreTrainedModel", ] ) _import_structure["models.ibert"].extend( [ "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "IBertForMaskedLM", "IBertForMultipleChoice", "IBertForQuestionAnswering", "IBertForSequenceClassification", "IBertForTokenClassification", "IBertModel", "IBertPreTrainedModel", ] ) _import_structure["models.imagegpt"].extend( [ "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST", "ImageGPTForCausalImageModeling", "ImageGPTForImageClassification", "ImageGPTModel", "ImageGPTPreTrainedModel", "load_tf_weights_in_imagegpt", ] ) _import_structure["models.jukebox"].extend( [ "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST", "JukeboxModel", "JukeboxPreTrainedModel", "JukeboxVQVAE", "JukeboxPrior", ] ) _import_structure["models.layoutlm"].extend( [ "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMForMaskedLM", "LayoutLMForSequenceClassification", "LayoutLMForTokenClassification", "LayoutLMForQuestionAnswering", "LayoutLMModel", "LayoutLMPreTrainedModel", ] ) _import_structure["models.layoutlmv2"].extend( [ "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMv2ForQuestionAnswering", "LayoutLMv2ForSequenceClassification", "LayoutLMv2ForTokenClassification", "LayoutLMv2Model", "LayoutLMv2PreTrainedModel", ] ) _import_structure["models.layoutlmv3"].extend( [ "LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMv3ForQuestionAnswering", "LayoutLMv3ForSequenceClassification", "LayoutLMv3ForTokenClassification", "LayoutLMv3Model", "LayoutLMv3PreTrainedModel", ] ) _import_structure["models.led"].extend( [ "LED_PRETRAINED_MODEL_ARCHIVE_LIST", "LEDForConditionalGeneration", "LEDForQuestionAnswering", "LEDForSequenceClassification", "LEDModel", "LEDPreTrainedModel", ] ) _import_structure["models.levit"].extend( [ "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "LevitForImageClassification", "LevitForImageClassificationWithTeacher", "LevitModel", "LevitPreTrainedModel", ] ) _import_structure["models.longformer"].extend( [ "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "LongformerForMaskedLM", "LongformerForMultipleChoice", "LongformerForQuestionAnswering", "LongformerForSequenceClassification", "LongformerForTokenClassification", "LongformerModel", "LongformerPreTrainedModel", "LongformerSelfAttention", ] ) _import_structure["models.longt5"].extend( [ "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST", "LongT5EncoderModel", "LongT5ForConditionalGeneration", "LongT5Model", "LongT5PreTrainedModel", ] ) _import_structure["models.luke"].extend( [ "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "LukeForMultipleChoice", "LukeForQuestionAnswering", "LukeForSequenceClassification", "LukeForTokenClassification", "LukeForMaskedLM", "LukeModel", "LukePreTrainedModel", ] ) _import_structure["models.lxmert"].extend( [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] ) _import_structure["models.m2m_100"].extend( [ "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST", "M2M100ForConditionalGeneration", "M2M100Model", "M2M100PreTrainedModel", ] ) _import_structure["models.marian"].extend(["MarianForCausalLM", "MarianModel", "MarianMTModel"]) _import_structure["models.maskformer"].extend( [ "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel", ] ) _import_structure["models.markuplm"].extend( [ "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST", "MarkupLMForQuestionAnswering", "MarkupLMForSequenceClassification", "MarkupLMForTokenClassification", "MarkupLMModel", "MarkupLMPreTrainedModel", ] ) _import_structure["models.mbart"].extend( [ "MBartForCausalLM", "MBartForConditionalGeneration", "MBartForQuestionAnswering", "MBartForSequenceClassification", "MBartModel", "MBartPreTrainedModel", ] ) _import_structure["models.mctct"].extend( [ "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST", "MCTCTForCTC", "MCTCTModel", "MCTCTPreTrainedModel", ] ) _import_structure["models.megatron_bert"].extend( [ "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "MegatronBertForCausalLM", "MegatronBertForMaskedLM", "MegatronBertForMultipleChoice", "MegatronBertForNextSentencePrediction", "MegatronBertForPreTraining", "MegatronBertForQuestionAnswering", "MegatronBertForSequenceClassification", "MegatronBertForTokenClassification", "MegatronBertModel", "MegatronBertPreTrainedModel", ] ) _import_structure["models.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]) _import_structure["models.mobilebert"].extend( [ "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileBertForMaskedLM", "MobileBertForMultipleChoice", "MobileBertForNextSentencePrediction", "MobileBertForPreTraining", "MobileBertForQuestionAnswering", "MobileBertForSequenceClassification", "MobileBertForTokenClassification", "MobileBertLayer", "MobileBertModel", "MobileBertPreTrainedModel", "load_tf_weights_in_mobilebert", ] ) _import_structure["models.mobilenet_v2"].extend( [ "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileNetV2ForImageClassification", "MobileNetV2ForSemanticSegmentation", "MobileNetV2Model", "MobileNetV2PreTrainedModel", "load_tf_weights_in_mobilenet_v2", ] ) _import_structure["models.mobilevit"].extend( [ "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileViTForImageClassification", "MobileViTForSemanticSegmentation", "MobileViTModel", "MobileViTPreTrainedModel", ] ) _import_structure["models.mpnet"].extend( [ "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", "MPNetForMaskedLM", "MPNetForMultipleChoice", "MPNetForQuestionAnswering", "MPNetForSequenceClassification", "MPNetForTokenClassification", "MPNetLayer", "MPNetModel", "MPNetPreTrainedModel", ] ) _import_structure["models.mt5"].extend(["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model"]) _import_structure["models.nezha"].extend( [ "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST", "NezhaForMaskedLM", "NezhaForPreTraining", "NezhaForNextSentencePrediction", "NezhaForMultipleChoice", "NezhaForQuestionAnswering", "NezhaForSequenceClassification", "NezhaForTokenClassification", "NezhaModel", "NezhaPreTrainedModel", ] ) _import_structure["models.nystromformer"].extend( [ "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "NystromformerForMaskedLM", "NystromformerForMultipleChoice", "NystromformerForQuestionAnswering", "NystromformerForSequenceClassification", "NystromformerForTokenClassification", "NystromformerLayer", "NystromformerModel", "NystromformerPreTrainedModel", ] ) _import_structure["models.openai"].extend( [ "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OpenAIGPTDoubleHeadsModel", "OpenAIGPTForSequenceClassification", "OpenAIGPTLMHeadModel", "OpenAIGPTModel", "OpenAIGPTPreTrainedModel", "load_tf_weights_in_openai_gpt", ] ) _import_structure["models.opt"].extend( [ "OPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OPTForCausalLM", "OPTModel", "OPTPreTrainedModel", "OPTForSequenceClassification", "OPTForQuestionAnswering", ] ) _import_structure["models.owlvit"].extend( [ "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "OwlViTModel", "OwlViTPreTrainedModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", ] ) _import_structure["models.pegasus"].extend( ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"] ) _import_structure["models.pegasus_x"].extend( [ "PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST", "PegasusXForConditionalGeneration", "PegasusXModel", "PegasusXPreTrainedModel", ] ) _import_structure["models.perceiver"].extend( [ "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST", "PerceiverForImageClassificationConvProcessing", "PerceiverForImageClassificationFourier", "PerceiverForImageClassificationLearned", "PerceiverForMaskedLM", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "PerceiverForSequenceClassification", "PerceiverLayer", "PerceiverModel", "PerceiverPreTrainedModel", ] ) _import_structure["models.plbart"].extend( [ "PLBART_PRETRAINED_MODEL_ARCHIVE_LIST", "PLBartForCausalLM", "PLBartForConditionalGeneration", "PLBartForSequenceClassification", "PLBartModel", "PLBartPreTrainedModel", ] ) _import_structure["models.poolformer"].extend( [ "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "PoolFormerForImageClassification", "PoolFormerModel", "PoolFormerPreTrainedModel", ] ) _import_structure["models.prophetnet"].extend( [ "PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ProphetNetDecoder", "ProphetNetEncoder", "ProphetNetForCausalLM", "ProphetNetForConditionalGeneration", "ProphetNetModel", "ProphetNetPreTrainedModel", ] ) _import_structure["models.qdqbert"].extend( [ "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "QDQBertForMaskedLM", "QDQBertForMultipleChoice", "QDQBertForNextSentencePrediction", "QDQBertForQuestionAnswering", "QDQBertForSequenceClassification", "QDQBertForTokenClassification", "QDQBertLayer", "QDQBertLMHeadModel", "QDQBertModel", "QDQBertPreTrainedModel", "load_tf_weights_in_qdqbert", ] ) _import_structure["models.rag"].extend( ["RagModel", "RagPreTrainedModel", "RagSequenceForGeneration", "RagTokenForGeneration"] ) _import_structure["models.realm"].extend( [ "REALM_PRETRAINED_MODEL_ARCHIVE_LIST", "RealmEmbedder", "RealmForOpenQA", "RealmKnowledgeAugEncoder", "RealmPreTrainedModel", "RealmReader", "RealmRetriever", "RealmScorer", "load_tf_weights_in_realm", ] ) _import_structure["models.reformer"].extend( [ "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "ReformerAttention", "ReformerForMaskedLM", "ReformerForQuestionAnswering", "ReformerForSequenceClassification", "ReformerLayer", "ReformerModel", "ReformerModelWithLMHead", "ReformerPreTrainedModel", ] ) _import_structure["models.regnet"].extend( [ "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "RegNetForImageClassification", "RegNetModel", "RegNetPreTrainedModel", ] ) _import_structure["models.rembert"].extend( [ "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RemBertForCausalLM", "RemBertForMaskedLM", "RemBertForMultipleChoice", "RemBertForQuestionAnswering", "RemBertForSequenceClassification", "RemBertForTokenClassification", "RemBertLayer", "RemBertModel", "RemBertPreTrainedModel", "load_tf_weights_in_rembert", ] ) _import_structure["models.resnet"].extend( [ "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ResNetForImageClassification", "ResNetModel", "ResNetPreTrainedModel", ] ) _import_structure["models.retribert"].extend( ["RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RetriBertModel", "RetriBertPreTrainedModel"] ) _import_structure["models.roberta"].extend( [ "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaForCausalLM", "RobertaForMaskedLM", "RobertaForMultipleChoice", "RobertaForQuestionAnswering", "RobertaForSequenceClassification", "RobertaForTokenClassification", "RobertaModel", "RobertaPreTrainedModel", ] ) _import_structure["models.lilt"].extend( [ "LILT_PRETRAINED_MODEL_ARCHIVE_LIST", "LiltForQuestionAnswering", "LiltForSequenceClassification", "LiltForTokenClassification", "LiltModel", "LiltPreTrainedModel", ] ) _import_structure["models.roformer"].extend( [ "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "RoFormerForCausalLM", "RoFormerForMaskedLM", "RoFormerForMultipleChoice", "RoFormerForQuestionAnswering", "RoFormerForSequenceClassification", "RoFormerForTokenClassification", "RoFormerLayer", "RoFormerModel", "RoFormerPreTrainedModel", "load_tf_weights_in_roformer", ] ) _import_structure["models.segformer"].extend( [ "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "SegformerDecodeHead", "SegformerForImageClassification", "SegformerForSemanticSegmentation", "SegformerLayer", "SegformerModel", "SegformerPreTrainedModel", ] ) _import_structure["models.sew"].extend( [ "SEW_PRETRAINED_MODEL_ARCHIVE_LIST", "SEWForCTC", "SEWForSequenceClassification", "SEWModel", "SEWPreTrainedModel", ] ) _import_structure["models.sew_d"].extend( [ "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST", "SEWDForCTC", "SEWDForSequenceClassification", "SEWDModel", "SEWDPreTrainedModel", ] ) _import_structure["models.speech_encoder_decoder"].extend(["SpeechEncoderDecoderModel"]) _import_structure["models.speech_to_text"].extend( [ "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "Speech2TextForConditionalGeneration", "Speech2TextModel", "Speech2TextPreTrainedModel", ] ) _import_structure["models.whisper"].extend( [ "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "WhisperForConditionalGeneration", "WhisperModel", "WhisperPreTrainedModel", ] ) _import_structure["models.speech_to_text_2"].extend(["Speech2Text2ForCausalLM", "Speech2Text2PreTrainedModel"]) _import_structure["models.splinter"].extend( [ "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST", "SplinterForPreTraining", "SplinterForQuestionAnswering", "SplinterLayer", "SplinterModel", "SplinterPreTrainedModel", ] ) _import_structure["models.squeezebert"].extend( [ "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "SqueezeBertForMaskedLM", "SqueezeBertForMultipleChoice", "SqueezeBertForQuestionAnswering", "SqueezeBertForSequenceClassification", "SqueezeBertForTokenClassification", "SqueezeBertModel", "SqueezeBertModule", "SqueezeBertPreTrainedModel", ] ) _import_structure["models.swin"].extend( [ "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "SwinForImageClassification", "SwinForMaskedImageModeling", "SwinModel", "SwinPreTrainedModel", ] ) _import_structure["models.swinv2"].extend( [ "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST", "Swinv2ForImageClassification", "Swinv2ForMaskedImageModeling", "Swinv2Model", "Swinv2PreTrainedModel", ] ) _import_structure["models.tapas"].extend( [ "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TapasForMaskedLM", "TapasForQuestionAnswering", "TapasForSequenceClassification", "TapasModel", "TapasPreTrainedModel", "load_tf_weights_in_tapas", ] ) _import_structure["models.t5"].extend( [ "T5_PRETRAINED_MODEL_ARCHIVE_LIST", "T5EncoderModel", "T5ForConditionalGeneration", "T5Model", "T5PreTrainedModel", "load_tf_weights_in_t5", ] ) _import_structure["models.switch_transformers"].extend( [ "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST", "SwitchTransformersEncoderModel", "SwitchTransformersForConditionalGeneration", "SwitchTransformersModel", "SwitchTransformersPreTrainedModel", "SwitchTransformersTop1Router", "SwitchTransformersSparseMLP", ] ) _import_structure["models.trajectory_transformer"].extend( [ "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TrajectoryTransformerModel", "TrajectoryTransformerPreTrainedModel", ] ) _import_structure["models.transfo_xl"].extend( [ "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "AdaptiveEmbedding", "TransfoXLForSequenceClassification", "TransfoXLLMHeadModel", "TransfoXLModel", "TransfoXLPreTrainedModel", "load_tf_weights_in_transfo_xl", ] ) _import_structure["models.trocr"].extend( ["TROCR_PRETRAINED_MODEL_ARCHIVE_LIST", "TrOCRForCausalLM", "TrOCRPreTrainedModel"] ) _import_structure["models.unispeech"].extend( [ "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechForCTC", "UniSpeechForPreTraining", "UniSpeechForSequenceClassification", "UniSpeechModel", "UniSpeechPreTrainedModel", ] ) _import_structure["models.unispeech_sat"].extend( [ "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechSatForAudioFrameClassification", "UniSpeechSatForCTC", "UniSpeechSatForPreTraining", "UniSpeechSatForSequenceClassification", "UniSpeechSatForXVector", "UniSpeechSatModel", "UniSpeechSatPreTrainedModel", ] ) _import_structure["models.van"].extend( [ "VAN_PRETRAINED_MODEL_ARCHIVE_LIST", "VanForImageClassification", "VanModel", "VanPreTrainedModel", ] ) _import_structure["models.vilt"].extend( [ "VILT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViltForImageAndTextRetrieval", "ViltForImagesAndTextClassification", "ViltForTokenClassification", "ViltForMaskedLM", "ViltForQuestionAnswering", "ViltLayer", "ViltModel", "ViltPreTrainedModel", ] ) _import_structure["models.vision_encoder_decoder"].extend(["VisionEncoderDecoderModel"]) _import_structure["models.vision_text_dual_encoder"].extend(["VisionTextDualEncoderModel"]) _import_structure["models.visual_bert"].extend( [ "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "VisualBertForMultipleChoice", "VisualBertForPreTraining", "VisualBertForQuestionAnswering", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertLayer", "VisualBertModel", "VisualBertPreTrainedModel", ] ) _import_structure["models.vit"].extend( [ "VIT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTForImageClassification", "ViTForMaskedImageModeling", "ViTModel", "ViTPreTrainedModel", ] ) _import_structure["models.vit_mae"].extend( [ "VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTMAEForPreTraining", "ViTMAELayer", "ViTMAEModel", "ViTMAEPreTrainedModel", ] ) _import_structure["models.vit_msn"].extend( [ "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTMSNModel", "ViTMSNForImageClassification", "ViTMSNPreTrainedModel", ] ) _import_structure["models.videomae"].extend( [ "VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST", "VideoMAEForPreTraining", "VideoMAEModel", "VideoMAEPreTrainedModel", "VideoMAEForVideoClassification", ] ) _import_structure["models.wav2vec2"].extend( [ "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", "Wav2Vec2ForAudioFrameClassification", "Wav2Vec2ForCTC", "Wav2Vec2ForMaskedLM", "Wav2Vec2ForPreTraining", "Wav2Vec2ForSequenceClassification", "Wav2Vec2ForXVector", "Wav2Vec2Model", "Wav2Vec2PreTrainedModel", ] ) _import_structure["models.wav2vec2_conformer"].extend( [ "WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "Wav2Vec2ConformerForAudioFrameClassification", "Wav2Vec2ConformerForCTC", "Wav2Vec2ConformerForPreTraining", "Wav2Vec2ConformerForSequenceClassification", "Wav2Vec2ConformerForXVector", "Wav2Vec2ConformerModel", "Wav2Vec2ConformerPreTrainedModel", ] ) _import_structure["models.wavlm"].extend( [ "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST", "WavLMForAudioFrameClassification", "WavLMForCTC", "WavLMForSequenceClassification", "WavLMForXVector", "WavLMModel", "WavLMPreTrainedModel", ] ) _import_structure["models.xglm"].extend( [ "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XGLMForCausalLM", "XGLMModel", "XGLMPreTrainedModel", ] ) _import_structure["models.xlm"].extend( [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] ) _import_structure["models.xlm_prophetnet"].extend( [ "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMProphetNetDecoder", "XLMProphetNetEncoder", "XLMProphetNetForCausalLM", "XLMProphetNetForConditionalGeneration", "XLMProphetNetModel", "XLMProphetNetPreTrainedModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMRobertaForCausalLM", "XLMRobertaForMaskedLM", "XLMRobertaForMultipleChoice", "XLMRobertaForQuestionAnswering", "XLMRobertaForSequenceClassification", "XLMRobertaForTokenClassification", "XLMRobertaModel", "XLMRobertaPreTrainedModel", ] ) _import_structure["models.xlm_roberta_xl"].extend( [ "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMRobertaXLForCausalLM", "XLMRobertaXLForMaskedLM", "XLMRobertaXLForMultipleChoice", "XLMRobertaXLForQuestionAnswering", "XLMRobertaXLForSequenceClassification", "XLMRobertaXLForTokenClassification", "XLMRobertaXLModel", "XLMRobertaXLPreTrainedModel", ] ) _import_structure["models.xlnet"].extend( [ "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", "XLNetForMultipleChoice", "XLNetForQuestionAnswering", "XLNetForQuestionAnsweringSimple", "XLNetForSequenceClassification", "XLNetForTokenClassification", "XLNetLMHeadModel", "XLNetModel", "XLNetPreTrainedModel", "load_tf_weights_in_xlnet", ] ) _import_structure["models.yolos"].extend( [ "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST", "YolosForObjectDetection", "YolosModel", "YolosPreTrainedModel", ] ) _import_structure["models.yoso"].extend( [ "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST", "YosoForMaskedLM", "YosoForMultipleChoice", "YosoForQuestionAnswering", "YosoForSequenceClassification", "YosoForTokenClassification", "YosoLayer", "YosoModel", "YosoPreTrainedModel", ] ) _import_structure["optimization"] = [ "Adafactor", "AdamW", "get_constant_schedule", "get_constant_schedule_with_warmup", "get_cosine_schedule_with_warmup", "get_cosine_with_hard_restarts_schedule_with_warmup", "get_linear_schedule_with_warmup", "get_polynomial_decay_schedule_with_warmup", "get_scheduler", ] _import_structure["pytorch_utils"] = ["Conv1D", "apply_chunking_to_forward", "prune_layer"] _import_structure["sagemaker"] = [] _import_structure["trainer"] = ["Trainer"] _import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"] _import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"] # TensorFlow-backed objects try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tf_objects _import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")] else: _import_structure["activations_tf"] = [] _import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"] _import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"] _import_structure["generation_tf_utils"] = [] _import_structure["generation"].extend( [ "TFForcedBOSTokenLogitsProcessor", "TFForcedEOSTokenLogitsProcessor", "TFLogitsProcessor", "TFLogitsProcessorList", "TFLogitsWarper", "TFMinLengthLogitsProcessor", "TFNoBadWordsLogitsProcessor", "TFNoRepeatNGramLogitsProcessor", "TFRepetitionPenaltyLogitsProcessor", "TFTemperatureLogitsWarper", "TFTopKLogitsWarper", "TFTopPLogitsWarper", "TFGenerationMixin", "tf_top_k_top_p_filtering", ] ) _import_structure["keras_callbacks"] = ["KerasMetricCallback", "PushToHubCallback"] _import_structure["modeling_tf_outputs"] = [] _import_structure["modeling_tf_utils"] = [ "TFPreTrainedModel", "TFSequenceSummary", "TFSharedEmbeddings", "shape_list", ] # TensorFlow models structure _import_structure["models.albert"].extend( [ "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAlbertForMaskedLM", "TFAlbertForMultipleChoice", "TFAlbertForPreTraining", "TFAlbertForQuestionAnswering", "TFAlbertForSequenceClassification", "TFAlbertForTokenClassification", "TFAlbertMainLayer", "TFAlbertModel", "TFAlbertPreTrainedModel", ] ) _import_structure["models.auto"].extend( [ "TF_MODEL_FOR_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "TF_MODEL_FOR_MASKED_LM_MAPPING", "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "TF_MODEL_FOR_PRETRAINING_MAPPING", "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", "TF_MODEL_MAPPING", "TF_MODEL_WITH_LM_HEAD_MAPPING", "TFAutoModel", "TFAutoModelForCausalLM", "TFAutoModelForImageClassification", "TFAutoModelForMaskedLM", "TFAutoModelForMultipleChoice", "TFAutoModelForNextSentencePrediction", "TFAutoModelForPreTraining", "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForQuestionAnswering", "TFAutoModelForSemanticSegmentation", "TFAutoModelForSeq2SeqLM", "TFAutoModelForSequenceClassification", "TFAutoModelForSpeechSeq2Seq", "TFAutoModelForTableQuestionAnswering", "TFAutoModelForTokenClassification", "TFAutoModelForVision2Seq", "TFAutoModelWithLMHead", ] ) _import_structure["models.bart"].extend(["TFBartForConditionalGeneration", "TFBartModel", "TFBartPretrainedModel"]) _import_structure["models.bert"].extend( [ "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFBertEmbeddings", "TFBertForMaskedLM", "TFBertForMultipleChoice", "TFBertForNextSentencePrediction", "TFBertForPreTraining", "TFBertForQuestionAnswering", "TFBertForSequenceClassification", "TFBertForTokenClassification", "TFBertLMHeadModel", "TFBertMainLayer", "TFBertModel", "TFBertPreTrainedModel", ] ) _import_structure["models.blenderbot"].extend( ["TFBlenderbotForConditionalGeneration", "TFBlenderbotModel", "TFBlenderbotPreTrainedModel"] ) _import_structure["models.blenderbot_small"].extend( ["TFBlenderbotSmallForConditionalGeneration", "TFBlenderbotSmallModel", "TFBlenderbotSmallPreTrainedModel"] ) _import_structure["models.camembert"].extend( [ "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCamembertForCausalLM", "TFCamembertForMaskedLM", "TFCamembertForMultipleChoice", "TFCamembertForQuestionAnswering", "TFCamembertForSequenceClassification", "TFCamembertForTokenClassification", "TFCamembertModel", "TFCamembertPreTrainedModel", ] ) _import_structure["models.clip"].extend( [ "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCLIPModel", "TFCLIPPreTrainedModel", "TFCLIPTextModel", "TFCLIPVisionModel", ] ) _import_structure["models.convbert"].extend( [ "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFConvBertForMaskedLM", "TFConvBertForMultipleChoice", "TFConvBertForQuestionAnswering", "TFConvBertForSequenceClassification", "TFConvBertForTokenClassification", "TFConvBertLayer", "TFConvBertModel", "TFConvBertPreTrainedModel", ] ) _import_structure["models.convnext"].extend( [ "TFConvNextForImageClassification", "TFConvNextModel", "TFConvNextPreTrainedModel", ] ) _import_structure["models.ctrl"].extend( [ "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCTRLForSequenceClassification", "TFCTRLLMHeadModel", "TFCTRLModel", "TFCTRLPreTrainedModel", ] ) _import_structure["models.cvt"].extend( [ "TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCvtForImageClassification", "TFCvtModel", "TFCvtPreTrainedModel", ] ) _import_structure["models.data2vec"].extend( [ "TFData2VecVisionForImageClassification", "TFData2VecVisionForSemanticSegmentation", "TFData2VecVisionModel", "TFData2VecVisionPreTrainedModel", ] ) _import_structure["models.deberta"].extend( [ "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDebertaForMaskedLM", "TFDebertaForQuestionAnswering", "TFDebertaForSequenceClassification", "TFDebertaForTokenClassification", "TFDebertaModel", "TFDebertaPreTrainedModel", ] ) _import_structure["models.deberta_v2"].extend( [ "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDebertaV2ForMaskedLM", "TFDebertaV2ForQuestionAnswering", "TFDebertaV2ForSequenceClassification", "TFDebertaV2ForTokenClassification", "TFDebertaV2Model", "TFDebertaV2PreTrainedModel", ] ) _import_structure["models.deit"].extend( [ "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDeiTForImageClassification", "TFDeiTForImageClassificationWithTeacher", "TFDeiTForMaskedImageModeling", "TFDeiTModel", "TFDeiTPreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDistilBertForMaskedLM", "TFDistilBertForMultipleChoice", "TFDistilBertForQuestionAnswering", "TFDistilBertForSequenceClassification", "TFDistilBertForTokenClassification", "TFDistilBertMainLayer", "TFDistilBertModel", "TFDistilBertPreTrainedModel", ] ) _import_structure["models.dpr"].extend( [ "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDPRContextEncoder", "TFDPRPretrainedContextEncoder", "TFDPRPretrainedQuestionEncoder", "TFDPRPretrainedReader", "TFDPRQuestionEncoder", "TFDPRReader", ] ) _import_structure["models.electra"].extend( [ "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFElectraForMaskedLM", "TFElectraForMultipleChoice", "TFElectraForPreTraining", "TFElectraForQuestionAnswering", "TFElectraForSequenceClassification", "TFElectraForTokenClassification", "TFElectraModel", "TFElectraPreTrainedModel", ] ) _import_structure["models.encoder_decoder"].append("TFEncoderDecoderModel") _import_structure["models.esm"].extend( [ "ESM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFEsmForMaskedLM", "TFEsmForSequenceClassification", "TFEsmForTokenClassification", "TFEsmModel", "TFEsmPreTrainedModel", ] ) _import_structure["models.flaubert"].extend( [ "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFFlaubertForMultipleChoice", "TFFlaubertForQuestionAnsweringSimple", "TFFlaubertForSequenceClassification", "TFFlaubertForTokenClassification", "TFFlaubertModel", "TFFlaubertPreTrainedModel", "TFFlaubertWithLMHeadModel", ] ) _import_structure["models.funnel"].extend( [ "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFFunnelBaseModel", "TFFunnelForMaskedLM", "TFFunnelForMultipleChoice", "TFFunnelForPreTraining", "TFFunnelForQuestionAnswering", "TFFunnelForSequenceClassification", "TFFunnelForTokenClassification", "TFFunnelModel", "TFFunnelPreTrainedModel", ] ) _import_structure["models.gpt2"].extend( [ "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFGPT2DoubleHeadsModel", "TFGPT2ForSequenceClassification", "TFGPT2LMHeadModel", "TFGPT2MainLayer", "TFGPT2Model", "TFGPT2PreTrainedModel", ] ) _import_structure["models.gptj"].extend( [ "TFGPTJForCausalLM", "TFGPTJForQuestionAnswering", "TFGPTJForSequenceClassification", "TFGPTJModel", "TFGPTJPreTrainedModel", ] ) _import_structure["models.groupvit"].extend( [ "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFGroupViTModel", "TFGroupViTPreTrainedModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", ] ) _import_structure["models.hubert"].extend( [ "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFHubertForCTC", "TFHubertModel", "TFHubertPreTrainedModel", ] ) _import_structure["models.layoutlm"].extend( [ "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLayoutLMForMaskedLM", "TFLayoutLMForSequenceClassification", "TFLayoutLMForQuestionAnswering", "TFLayoutLMForTokenClassification", "TFLayoutLMMainLayer", "TFLayoutLMModel", "TFLayoutLMPreTrainedModel", ] ) _import_structure["models.layoutlmv3"].extend( [ "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLayoutLMv3ForQuestionAnswering", "TFLayoutLMv3ForSequenceClassification", "TFLayoutLMv3ForTokenClassification", "TFLayoutLMv3Model", "TFLayoutLMv3PreTrainedModel", ] ) _import_structure["models.led"].extend(["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]) _import_structure["models.longformer"].extend( [ "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLongformerForMaskedLM", "TFLongformerForMultipleChoice", "TFLongformerForQuestionAnswering", "TFLongformerForSequenceClassification", "TFLongformerForTokenClassification", "TFLongformerModel", "TFLongformerPreTrainedModel", "TFLongformerSelfAttention", ] ) _import_structure["models.lxmert"].extend( [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] ) _import_structure["models.marian"].extend(["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]) _import_structure["models.mbart"].extend( ["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"] ) _import_structure["models.mobilebert"].extend( [ "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMobileBertForMaskedLM", "TFMobileBertForMultipleChoice", "TFMobileBertForNextSentencePrediction", "TFMobileBertForPreTraining", "TFMobileBertForQuestionAnswering", "TFMobileBertForSequenceClassification", "TFMobileBertForTokenClassification", "TFMobileBertMainLayer", "TFMobileBertModel", "TFMobileBertPreTrainedModel", ] ) _import_structure["models.mobilevit"].extend( [ "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMobileViTPreTrainedModel", "TFMobileViTModel", "TFMobileViTForImageClassification", "TFMobileViTForSemanticSegmentation", ] ) _import_structure["models.mpnet"].extend( [ "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMPNetForMaskedLM", "TFMPNetForMultipleChoice", "TFMPNetForQuestionAnswering", "TFMPNetForSequenceClassification", "TFMPNetForTokenClassification", "TFMPNetMainLayer", "TFMPNetModel", "TFMPNetPreTrainedModel", ] ) _import_structure["models.mt5"].extend(["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]) _import_structure["models.openai"].extend( [ "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFOpenAIGPTDoubleHeadsModel", "TFOpenAIGPTForSequenceClassification", "TFOpenAIGPTLMHeadModel", "TFOpenAIGPTMainLayer", "TFOpenAIGPTModel", "TFOpenAIGPTPreTrainedModel", ] ) _import_structure["models.opt"].extend( [ "TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel", ] ) _import_structure["models.pegasus"].extend( ["TFPegasusForConditionalGeneration", "TFPegasusModel", "TFPegasusPreTrainedModel"] ) _import_structure["models.rag"].extend( [ "TFRagModel", "TFRagPreTrainedModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", ] ) _import_structure["models.regnet"].extend( [ "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRegNetForImageClassification", "TFRegNetModel", "TFRegNetPreTrainedModel", ] ) _import_structure["models.rembert"].extend( [ "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRemBertForCausalLM", "TFRemBertForMaskedLM", "TFRemBertForMultipleChoice", "TFRemBertForQuestionAnswering", "TFRemBertForSequenceClassification", "TFRemBertForTokenClassification", "TFRemBertLayer", "TFRemBertModel", "TFRemBertPreTrainedModel", ] ) _import_structure["models.resnet"].extend( [ "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFResNetForImageClassification", "TFResNetModel", "TFResNetPreTrainedModel", ] ) _import_structure["models.roberta"].extend( [ "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaForCausalLM", "TFRobertaForMaskedLM", "TFRobertaForMultipleChoice", "TFRobertaForQuestionAnswering", "TFRobertaForSequenceClassification", "TFRobertaForTokenClassification", "TFRobertaMainLayer", "TFRobertaModel", "TFRobertaPreTrainedModel", ] ) _import_structure["models.roformer"].extend( [ "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRoFormerForCausalLM", "TFRoFormerForMaskedLM", "TFRoFormerForMultipleChoice", "TFRoFormerForQuestionAnswering", "TFRoFormerForSequenceClassification", "TFRoFormerForTokenClassification", "TFRoFormerLayer", "TFRoFormerModel", "TFRoFormerPreTrainedModel", ] ) _import_structure["models.segformer"].extend( [ "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSegformerDecodeHead", "TFSegformerForImageClassification", "TFSegformerForSemanticSegmentation", "TFSegformerModel", "TFSegformerPreTrainedModel", ] ) _import_structure["models.speech_to_text"].extend( [ "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSpeech2TextForConditionalGeneration", "TFSpeech2TextModel", "TFSpeech2TextPreTrainedModel", ] ) _import_structure["models.swin"].extend( [ "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSwinForImageClassification", "TFSwinForMaskedImageModeling", "TFSwinModel", "TFSwinPreTrainedModel", ] ) _import_structure["models.t5"].extend( [ "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST", "TFT5EncoderModel", "TFT5ForConditionalGeneration", "TFT5Model", "TFT5PreTrainedModel", ] ) _import_structure["models.tapas"].extend( [ "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TFTapasForMaskedLM", "TFTapasForQuestionAnswering", "TFTapasForSequenceClassification", "TFTapasModel", "TFTapasPreTrainedModel", ] ) _import_structure["models.transfo_xl"].extend( [ "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAdaptiveEmbedding", "TFTransfoXLForSequenceClassification", "TFTransfoXLLMHeadModel", "TFTransfoXLMainLayer", "TFTransfoXLModel", "TFTransfoXLPreTrainedModel", ] ) _import_structure["models.vision_encoder_decoder"].extend(["TFVisionEncoderDecoderModel"]) _import_structure["models.vit"].extend( [ "TFViTForImageClassification", "TFViTModel", "TFViTPreTrainedModel", ] ) _import_structure["models.vit_mae"].extend( [ "TFViTMAEForPreTraining", "TFViTMAEModel", "TFViTMAEPreTrainedModel", ] ) _import_structure["models.wav2vec2"].extend( [ "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFWav2Vec2ForCTC", "TFWav2Vec2Model", "TFWav2Vec2PreTrainedModel", ] ) _import_structure["models.whisper"].extend( [ "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFWhisperForConditionalGeneration", "TFWhisperModel", "TFWhisperPreTrainedModel", ] ) _import_structure["models.xglm"].extend( [ "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXGLMForCausalLM", "TFXGLMModel", "TFXGLMPreTrainedModel", ] ) _import_structure["models.xlm"].extend( [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMRobertaForMaskedLM", "TFXLMRobertaForMultipleChoice", "TFXLMRobertaForQuestionAnswering", "TFXLMRobertaForSequenceClassification", "TFXLMRobertaForTokenClassification", "TFXLMRobertaModel", ] ) _import_structure["models.xlnet"].extend( [ "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLNetForMultipleChoice", "TFXLNetForQuestionAnsweringSimple", "TFXLNetForSequenceClassification", "TFXLNetForTokenClassification", "TFXLNetLMHeadModel", "TFXLNetMainLayer", "TFXLNetModel", "TFXLNetPreTrainedModel", ] ) _import_structure["optimization_tf"] = ["AdamWeightDecay", "GradientAccumulator", "WarmUp", "create_optimizer"] _import_structure["tf_utils"] = [] _import_structure["trainer_tf"] = ["TFTrainer"] # FLAX-backed objects try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_flax_objects _import_structure["utils.dummy_flax_objects"] = [ name for name in dir(dummy_flax_objects) if not name.startswith("_") ] else: _import_structure["generation_flax_utils"] = [] _import_structure["generation"].extend( [ "FlaxForcedBOSTokenLogitsProcessor", "FlaxForcedEOSTokenLogitsProcessor", "FlaxLogitsProcessor", "FlaxLogitsProcessorList", "FlaxLogitsWarper", "FlaxMinLengthLogitsProcessor", "FlaxTemperatureLogitsWarper", "FlaxTopKLogitsWarper", "FlaxTopPLogitsWarper", "FlaxGenerationMixin", ] ) _import_structure["modeling_flax_outputs"] = [] _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"] _import_structure["models.albert"].extend( [ "FlaxAlbertForMaskedLM", "FlaxAlbertForMultipleChoice", "FlaxAlbertForPreTraining", "FlaxAlbertForQuestionAnswering", "FlaxAlbertForSequenceClassification", "FlaxAlbertForTokenClassification", "FlaxAlbertModel", "FlaxAlbertPreTrainedModel", ] ) _import_structure["models.auto"].extend( [ "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_MASKED_LM_MAPPING", "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "FLAX_MODEL_FOR_PRETRAINING_MAPPING", "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", "FLAX_MODEL_MAPPING", "FlaxAutoModel", "FlaxAutoModelForCausalLM", "FlaxAutoModelForImageClassification", "FlaxAutoModelForMaskedLM", "FlaxAutoModelForMultipleChoice", "FlaxAutoModelForNextSentencePrediction", "FlaxAutoModelForPreTraining", "FlaxAutoModelForQuestionAnswering", "FlaxAutoModelForSeq2SeqLM", "FlaxAutoModelForSequenceClassification", "FlaxAutoModelForTokenClassification", "FlaxAutoModelForVision2Seq", ] ) # Flax models structure _import_structure["models.bart"].extend( [ "FlaxBartDecoderPreTrainedModel", "FlaxBartForCausalLM", "FlaxBartForConditionalGeneration", "FlaxBartForQuestionAnswering", "FlaxBartForSequenceClassification", "FlaxBartModel", "FlaxBartPreTrainedModel", ] ) _import_structure["models.beit"].extend( [ "FlaxBeitForImageClassification", "FlaxBeitForMaskedImageModeling", "FlaxBeitModel", "FlaxBeitPreTrainedModel", ] ) _import_structure["models.bert"].extend( [ "FlaxBertForCausalLM", "FlaxBertForMaskedLM", "FlaxBertForMultipleChoice", "FlaxBertForNextSentencePrediction", "FlaxBertForPreTraining", "FlaxBertForQuestionAnswering", "FlaxBertForSequenceClassification", "FlaxBertForTokenClassification", "FlaxBertModel", "FlaxBertPreTrainedModel", ] ) _import_structure["models.big_bird"].extend( [ "FlaxBigBirdForCausalLM", "FlaxBigBirdForMaskedLM", "FlaxBigBirdForMultipleChoice", "FlaxBigBirdForPreTraining", "FlaxBigBirdForQuestionAnswering", "FlaxBigBirdForSequenceClassification", "FlaxBigBirdForTokenClassification", "FlaxBigBirdModel", "FlaxBigBirdPreTrainedModel", ] ) _import_structure["models.blenderbot"].extend( ["FlaxBlenderbotForConditionalGeneration", "FlaxBlenderbotModel", "FlaxBlenderbotPreTrainedModel"] ) _import_structure["models.blenderbot_small"].extend( [ "FlaxBlenderbotSmallForConditionalGeneration", "FlaxBlenderbotSmallModel", "FlaxBlenderbotSmallPreTrainedModel", ] ) _import_structure["models.clip"].extend( [ "FlaxCLIPModel", "FlaxCLIPPreTrainedModel", "FlaxCLIPTextModel", "FlaxCLIPTextPreTrainedModel", "FlaxCLIPVisionModel", "FlaxCLIPVisionPreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "FlaxDistilBertForMaskedLM", "FlaxDistilBertForMultipleChoice", "FlaxDistilBertForQuestionAnswering", "FlaxDistilBertForSequenceClassification", "FlaxDistilBertForTokenClassification", "FlaxDistilBertModel", "FlaxDistilBertPreTrainedModel", ] ) _import_structure["models.electra"].extend( [ "FlaxElectraForCausalLM", "FlaxElectraForMaskedLM", "FlaxElectraForMultipleChoice", "FlaxElectraForPreTraining", "FlaxElectraForQuestionAnswering", "FlaxElectraForSequenceClassification", "FlaxElectraForTokenClassification", "FlaxElectraModel", "FlaxElectraPreTrainedModel", ] ) _import_structure["models.encoder_decoder"].append("FlaxEncoderDecoderModel") _import_structure["models.gpt2"].extend(["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]) _import_structure["models.gpt_neo"].extend( ["FlaxGPTNeoForCausalLM", "FlaxGPTNeoModel", "FlaxGPTNeoPreTrainedModel"] ) _import_structure["models.gptj"].extend(["FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel"]) _import_structure["models.longt5"].extend( ["FlaxLongT5ForConditionalGeneration", "FlaxLongT5Model", "FlaxLongT5PreTrainedModel"] ) _import_structure["models.marian"].extend( [ "FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel", ] ) _import_structure["models.mbart"].extend( [ "FlaxMBartForConditionalGeneration", "FlaxMBartForQuestionAnswering", "FlaxMBartForSequenceClassification", "FlaxMBartModel", "FlaxMBartPreTrainedModel", ] ) _import_structure["models.mt5"].extend(["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]) _import_structure["models.opt"].extend( [ "FlaxOPTForCausalLM", "FlaxOPTModel", "FlaxOPTPreTrainedModel", ] ) _import_structure["models.pegasus"].extend( [ "FlaxPegasusForConditionalGeneration", "FlaxPegasusModel", "FlaxPegasusPreTrainedModel", ] ) _import_structure["models.roberta"].extend( [ "FlaxRobertaForCausalLM", "FlaxRobertaForMaskedLM", "FlaxRobertaForMultipleChoice", "FlaxRobertaForQuestionAnswering", "FlaxRobertaForSequenceClassification", "FlaxRobertaForTokenClassification", "FlaxRobertaModel", "FlaxRobertaPreTrainedModel", ] ) _import_structure["models.roformer"].extend( [ "FlaxRoFormerForMaskedLM", "FlaxRoFormerForMultipleChoice", "FlaxRoFormerForQuestionAnswering", "FlaxRoFormerForSequenceClassification", "FlaxRoFormerForTokenClassification", "FlaxRoFormerModel", "FlaxRoFormerPreTrainedModel", ] ) _import_structure["models.speech_encoder_decoder"].append("FlaxSpeechEncoderDecoderModel") _import_structure["models.t5"].extend( ["FlaxT5EncoderModel", "FlaxT5ForConditionalGeneration", "FlaxT5Model", "FlaxT5PreTrainedModel"] ) _import_structure["models.vision_encoder_decoder"].append("FlaxVisionEncoderDecoderModel") _import_structure["models.vision_text_dual_encoder"].extend(["FlaxVisionTextDualEncoderModel"]) _import_structure["models.vit"].extend(["FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel"]) _import_structure["models.wav2vec2"].extend( ["FlaxWav2Vec2ForCTC", "FlaxWav2Vec2ForPreTraining", "FlaxWav2Vec2Model", "FlaxWav2Vec2PreTrainedModel"] ) _import_structure["models.xglm"].extend( [ "FlaxXGLMForCausalLM", "FlaxXGLMModel", "FlaxXGLMPreTrainedModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "FlaxXLMRobertaForMaskedLM", "FlaxXLMRobertaForMultipleChoice", "FlaxXLMRobertaForQuestionAnswering", "FlaxXLMRobertaForSequenceClassification", "FlaxXLMRobertaForTokenClassification", "FlaxXLMRobertaModel", ] ) # Direct imports for type-checking if TYPE_CHECKING: # Configuration from .configuration_utils import PretrainedConfig # Data from .data import ( DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor, SquadExample, SquadFeatures, SquadV1Processor, SquadV2Processor, glue_compute_metrics, glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels, squad_convert_examples_to_features, xnli_compute_metrics, xnli_output_modes, xnli_processors, xnli_tasks_num_labels, ) from .data.data_collator import ( DataCollator, DataCollatorForLanguageModeling, DataCollatorForPermutationLanguageModeling, DataCollatorForSeq2Seq, DataCollatorForSOP, DataCollatorForTokenClassification, DataCollatorForWholeWordMask, DataCollatorWithPadding, DefaultDataCollator, default_data_collator, ) from .feature_extraction_sequence_utils import SequenceFeatureExtractor # Feature Extractor from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .hf_argparser import HfArgumentParser # Integrations from .integrations import ( is_clearml_available, is_comet_available, is_neptune_available, is_optuna_available, is_ray_available, is_ray_tune_available, is_sigopt_available, is_tensorboard_available, is_wandb_available, ) # Model Cards from .modelcard import ModelCard # TF 2.0 <=> PyTorch conversion utilities from .modeling_tf_pytorch_utils import ( convert_tf_weight_name_to_pt_weight_name, load_pytorch_checkpoint_in_tf2_model, load_pytorch_model_in_tf2_model, load_pytorch_weights_in_tf2_model, load_tf2_checkpoint_in_pytorch_model, load_tf2_model_in_pytorch_model, load_tf2_weights_in_pytorch_model, ) from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig from .models.auto import ( ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, MODEL_NAMES_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoConfig, AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer, ) from .models.bart import BartConfig, BartTokenizer from .models.beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig from .models.bert import ( BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BasicTokenizer, BertConfig, BertTokenizer, WordpieceTokenizer, ) from .models.bert_generation import BertGenerationConfig from .models.bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer from .models.bertweet import BertweetTokenizer from .models.big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig from .models.bigbird_pegasus import BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdPegasusConfig from .models.blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotTokenizer from .models.blenderbot_small import ( BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotSmallConfig, BlenderbotSmallTokenizer, ) from .models.bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig from .models.byt5 import ByT5Tokenizer from .models.camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig from .models.canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig, CanineTokenizer from .models.clip import ( CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPConfig, CLIPProcessor, CLIPTextConfig, CLIPTokenizer, CLIPVisionConfig, ) from .models.clipseg import ( CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig, ) from .models.codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenTokenizer from .models.conditional_detr import CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, ConditionalDetrConfig from .models.convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertTokenizer from .models.convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig from .models.ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, CTRLTokenizer from .models.cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig from .models.data2vec import ( DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP, Data2VecAudioConfig, Data2VecTextConfig, Data2VecVisionConfig, ) from .models.deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaTokenizer from .models.deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config from .models.decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, DecisionTransformerConfig, ) from .models.deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig from .models.deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig from .models.detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer from .models.donut import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutProcessor, DonutSwinConfig from .models.dpr import ( DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig, DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderOutput, DPRReaderTokenizer, ) from .models.dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig from .models.electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraTokenizer from .models.encoder_decoder import EncoderDecoderConfig from .models.ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig from .models.esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig, EsmTokenizer from .models.flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertTokenizer from .models.flava import ( FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) from .models.fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig from .models.fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig, FSMTTokenizer from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer from .models.glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer from .models.gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig from .models.gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig from .models.gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig from .models.gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig from .models.groupvit import ( GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig, ) from .models.herbert import HerbertTokenizer from .models.hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig from .models.ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig from .models.imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig from .models.jukebox import ( JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP, JukeboxConfig, JukeboxPriorConfig, JukeboxTokenizer, JukeboxVQVAEConfig, ) from .models.layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMTokenizer from .models.layoutlmv2 import ( LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv2Config, LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor, LayoutLMv2Processor, LayoutLMv2Tokenizer, ) from .models.layoutlmv3 import ( LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv3Config, LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor, LayoutLMv3Processor, LayoutLMv3Tokenizer, ) from .models.layoutxlm import LayoutXLMProcessor from .models.led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig, LEDTokenizer from .models.levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig from .models.lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer from .models.longt5 import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongT5Config from .models.luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig, LukeTokenizer from .models.lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig, LxmertTokenizer from .models.m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config from .models.marian import MarianConfig from .models.markuplm import ( MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig, MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer, ) from .models.maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig from .models.mbart import MBartConfig from .models.mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig, MCTCTProcessor from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig from .models.mmbt import MMBTConfig from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer from .models.mobilenet_v2 import MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileNetV2Config from .models.mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig from .models.mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig, MPNetTokenizer from .models.mt5 import MT5Config from .models.mvp import MvpConfig, MvpTokenizer from .models.nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig from .models.nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer from .models.opt import OPTConfig from .models.owlvit import ( OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, OwlViTConfig, OwlViTProcessor, OwlViTTextConfig, OwlViTVisionConfig, ) from .models.pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig, PegasusTokenizer from .models.pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig from .models.perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverTokenizer from .models.phobert import PhobertTokenizer from .models.plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig from .models.poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig from .models.prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig, ProphetNetTokenizer from .models.qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig from .models.rag import RagConfig, RagRetriever, RagTokenizer from .models.realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig, RealmTokenizer from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig from .models.regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig from .models.rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig from .models.resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer from .models.roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig, RoCBertTokenizer from .models.roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerTokenizer from .models.segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig from .models.sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig from .models.sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig from .models.speech_encoder_decoder import SpeechEncoderDecoderConfig from .models.speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig from .models.speech_to_text_2 import ( SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2Text2Config, Speech2Text2Processor, Speech2Text2Tokenizer, ) from .models.splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig, SplinterTokenizer from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer from .models.swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig from .models.swinv2 import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, Swinv2Config from .models.switch_transformers import SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP, SwitchTransformersConfig from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config from .models.table_transformer import TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TableTransformerConfig from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer from .models.tapex import TapexTokenizer from .models.time_series_transformer import ( TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimeSeriesTransformerConfig, ) from .models.trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TrajectoryTransformerConfig, ) from .models.transfo_xl import ( TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig, TransfoXLCorpus, TransfoXLTokenizer, ) from .models.trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig, TrOCRProcessor from .models.unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig from .models.unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig from .models.van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig from .models.videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig from .models.vilt import ( VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig, ViltFeatureExtractor, ViltImageProcessor, ViltProcessor, ) from .models.vision_encoder_decoder import VisionEncoderDecoderConfig from .models.vision_text_dual_encoder import VisionTextDualEncoderConfig, VisionTextDualEncoderProcessor from .models.visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig from .models.vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig from .models.vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig from .models.vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig from .models.wav2vec2 import ( WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2Tokenizer, ) from .models.wav2vec2_conformer import WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2ConformerConfig from .models.wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizer from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM from .models.wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig from .models.whisper import ( WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperFeatureExtractor, WhisperProcessor, WhisperTokenizer, ) from .models.x_clip import ( XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, XCLIPConfig, XCLIPProcessor, XCLIPTextConfig, XCLIPVisionConfig, ) from .models.xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig from .models.xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMTokenizer from .models.xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig from .models.xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig from .models.xlm_roberta_xl import XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaXLConfig from .models.xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig from .models.yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig from .models.yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig # Pipelines from .pipelines import ( AudioClassificationPipeline, AutomaticSpeechRecognitionPipeline, Conversation, ConversationalPipeline, CsvPipelineDataFormat, DepthEstimationPipeline, DocumentQuestionAnsweringPipeline, FeatureExtractionPipeline, FillMaskPipeline, ImageClassificationPipeline, ImageSegmentationPipeline, ImageToTextPipeline, JsonPipelineDataFormat, NerPipeline, ObjectDetectionPipeline, PipedPipelineDataFormat, Pipeline, PipelineDataFormat, QuestionAnsweringPipeline, SummarizationPipeline, TableQuestionAnsweringPipeline, Text2TextGenerationPipeline, TextClassificationPipeline, TextGenerationPipeline, TokenClassificationPipeline, TranslationPipeline, VisualQuestionAnsweringPipeline, ZeroShotClassificationPipeline, ZeroShotImageClassificationPipeline, ZeroShotObjectDetectionPipeline, pipeline, ) from .processing_utils import ProcessorMixin # Tokenization from .tokenization_utils import PreTrainedTokenizer from .tokenization_utils_base import ( AddedToken, BatchEncoding, CharSpan, PreTrainedTokenizerBase, SpecialTokensMixin, TokenSpan, ) # Trainer from .trainer_callback import ( DefaultFlowCallback, EarlyStoppingCallback, PrinterCallback, ProgressCallback, TrainerCallback, TrainerControl, TrainerState, ) from .trainer_utils import EvalPrediction, IntervalStrategy, SchedulerType, enable_full_determinism, set_seed from .training_args import TrainingArguments from .training_args_seq2seq import Seq2SeqTrainingArguments from .training_args_tf import TFTrainingArguments # Files and general utilities from .utils import ( CONFIG_NAME, MODEL_CARD_NAME, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TRANSFORMERS_CACHE, WEIGHTS_NAME, TensorType, add_end_docstrings, add_start_docstrings, is_apex_available, is_datasets_available, is_faiss_available, is_flax_available, is_phonemizer_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_safetensors_available, is_scipy_available, is_sentencepiece_available, is_sklearn_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_tpu_available, is_vision_available, logging, ) try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_sentencepiece_objects import * else: from .models.albert import AlbertTokenizer from .models.barthez import BarthezTokenizer from .models.bartpho import BartphoTokenizer from .models.bert_generation import BertGenerationTokenizer from .models.big_bird import BigBirdTokenizer from .models.camembert import CamembertTokenizer from .models.cpm import CpmTokenizer from .models.deberta_v2 import DebertaV2Tokenizer from .models.fnet import FNetTokenizer from .models.layoutxlm import LayoutXLMTokenizer from .models.m2m_100 import M2M100Tokenizer from .models.marian import MarianTokenizer from .models.mbart import MBart50Tokenizer, MBartTokenizer from .models.mluke import MLukeTokenizer from .models.mt5 import MT5Tokenizer from .models.nllb import NllbTokenizer from .models.pegasus import PegasusTokenizer from .models.plbart import PLBartTokenizer from .models.reformer import ReformerTokenizer from .models.rembert import RemBertTokenizer from .models.speech_to_text import Speech2TextTokenizer from .models.t5 import T5Tokenizer from .models.xglm import XGLMTokenizer from .models.xlm_prophetnet import XLMProphetNetTokenizer from .models.xlm_roberta import XLMRobertaTokenizer from .models.xlnet import XLNetTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_tokenizers_objects import * else: # Fast tokenizers imports from .models.albert import AlbertTokenizerFast from .models.bart import BartTokenizerFast from .models.barthez import BarthezTokenizerFast from .models.bert import BertTokenizerFast from .models.big_bird import BigBirdTokenizerFast from .models.blenderbot import BlenderbotTokenizerFast from .models.blenderbot_small import BlenderbotSmallTokenizerFast from .models.bloom import BloomTokenizerFast from .models.camembert import CamembertTokenizerFast from .models.clip import CLIPTokenizerFast from .models.codegen import CodeGenTokenizerFast from .models.convbert import ConvBertTokenizerFast from .models.cpm import CpmTokenizerFast from .models.deberta import DebertaTokenizerFast from .models.deberta_v2 import DebertaV2TokenizerFast from .models.distilbert import DistilBertTokenizerFast from .models.dpr import DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast, DPRReaderTokenizerFast from .models.electra import ElectraTokenizerFast from .models.fnet import FNetTokenizerFast from .models.funnel import FunnelTokenizerFast from .models.gpt2 import GPT2TokenizerFast from .models.gpt_neox import GPTNeoXTokenizerFast from .models.gpt_neox_japanese import GPTNeoXJapaneseTokenizer from .models.herbert import HerbertTokenizerFast from .models.layoutlm import LayoutLMTokenizerFast from .models.layoutlmv2 import LayoutLMv2TokenizerFast from .models.layoutlmv3 import LayoutLMv3TokenizerFast from .models.layoutxlm import LayoutXLMTokenizerFast from .models.led import LEDTokenizerFast from .models.longformer import LongformerTokenizerFast from .models.lxmert import LxmertTokenizerFast from .models.markuplm import MarkupLMTokenizerFast from .models.mbart import MBartTokenizerFast from .models.mbart50 import MBart50TokenizerFast from .models.mobilebert import MobileBertTokenizerFast from .models.mpnet import MPNetTokenizerFast from .models.mt5 import MT5TokenizerFast from .models.mvp import MvpTokenizerFast from .models.nllb import NllbTokenizerFast from .models.openai import OpenAIGPTTokenizerFast from .models.pegasus import PegasusTokenizerFast from .models.realm import RealmTokenizerFast from .models.reformer import ReformerTokenizerFast from .models.rembert import RemBertTokenizerFast from .models.retribert import RetriBertTokenizerFast from .models.roberta import RobertaTokenizerFast from .models.roformer import RoFormerTokenizerFast from .models.splinter import SplinterTokenizerFast from .models.squeezebert import SqueezeBertTokenizerFast from .models.t5 import T5TokenizerFast from .models.xglm import XGLMTokenizerFast from .models.xlm_roberta import XLMRobertaTokenizerFast from .models.xlnet import XLNetTokenizerFast from .tokenization_utils_fast import PreTrainedTokenizerFast try: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummies_sentencepiece_and_tokenizers_objects import * else: from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_speech_objects import * else: from .models.mctct import MCTCTFeatureExtractor from .models.speech_to_text import Speech2TextFeatureExtractor try: if not is_tensorflow_text_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_tensorflow_text_objects import * else: from .models.bert import TFBertTokenizer try: if not (is_speech_available() and is_sentencepiece_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_sentencepiece_and_speech_objects import * else: from .models.speech_to_text import Speech2TextProcessor try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_vision_objects import * else: from .image_processing_utils import ImageProcessingMixin from .image_transforms import rescale, resize, to_pil_image from .image_utils import ImageFeatureExtractionMixin from .models.beit import BeitFeatureExtractor, BeitImageProcessor from .models.clip import CLIPFeatureExtractor, CLIPImageProcessor from .models.conditional_detr import ConditionalDetrFeatureExtractor from .models.convnext import ConvNextFeatureExtractor, ConvNextImageProcessor from .models.deformable_detr import DeformableDetrFeatureExtractor from .models.deit import DeiTFeatureExtractor, DeiTImageProcessor from .models.detr import DetrFeatureExtractor from .models.donut import DonutFeatureExtractor from .models.dpt import DPTFeatureExtractor, DPTImageProcessor from .models.flava import FlavaFeatureExtractor, FlavaImageProcessor, FlavaProcessor from .models.glpn import GLPNFeatureExtractor, GLPNImageProcessor from .models.imagegpt import ImageGPTFeatureExtractor, ImageGPTImageProcessor from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor from .models.layoutlmv3 import LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor from .models.levit import LevitFeatureExtractor, LevitImageProcessor from .models.maskformer import MaskFormerFeatureExtractor from .models.mobilenet_v2 import MobileNetV2FeatureExtractor, MobileNetV2ImageProcessor from .models.mobilevit import MobileViTFeatureExtractor, MobileViTImageProcessor from .models.owlvit import OwlViTFeatureExtractor from .models.perceiver import PerceiverFeatureExtractor, PerceiverImageProcessor from .models.poolformer import PoolFormerFeatureExtractor, PoolFormerImageProcessor from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor from .models.videomae import VideoMAEFeatureExtractor, VideoMAEImageProcessor from .models.vilt import ViltFeatureExtractor, ViltImageProcessor, ViltProcessor from .models.vit import ViTFeatureExtractor, ViTImageProcessor from .models.yolos import YolosFeatureExtractor # Modeling try: if not (is_timm_available() and is_vision_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_timm_and_vision_objects import * else: from .models.conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrModel, ConditionalDetrPreTrainedModel, ) from .models.deformable_detr import ( DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DeformableDetrForObjectDetection, DeformableDetrModel, DeformableDetrPreTrainedModel, ) from .models.detr import ( DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DetrForObjectDetection, DetrForSegmentation, DetrModel, DetrPreTrainedModel, ) from .models.table_transformer import ( TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TableTransformerForObjectDetection, TableTransformerModel, TableTransformerPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * else: # Benchmarks from .benchmark.benchmark import PyTorchBenchmark from .benchmark.benchmark_args import PyTorchBenchmarkArguments from .data.datasets import ( GlueDataset, GlueDataTrainingArguments, LineByLineTextDataset, LineByLineWithRefDataset, LineByLineWithSOPTextDataset, SquadDataset, SquadDataTrainingArguments, TextDataset, TextDatasetForNextSentencePrediction, ) from .generation import ( BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer, Constraint, ConstraintListState, DisjunctiveConstraint, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, GenerationMixin, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitsProcessor, LogitsProcessorList, LogitsWarper, MaxLengthCriteria, MaxTimeCriteria, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PhrasalConstraint, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, StoppingCriteria, StoppingCriteriaList, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, top_k_top_p_filtering, ) from .modeling_utils import PreTrainedModel # PyTorch model imports from .models.albert import ( ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, AlbertForMaskedLM, AlbertForMultipleChoice, AlbertForPreTraining, AlbertForQuestionAnswering, AlbertForSequenceClassification, AlbertForTokenClassification, AlbertModel, AlbertPreTrainedModel, load_tf_weights_in_albert, ) from .models.auto import ( MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_CTC_MAPPING, MODEL_FOR_DEPTH_ESTIMATION_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_FOR_VISION_2_SEQ_MAPPING, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, AutoModel, AutoModelForAudioClassification, AutoModelForAudioFrameClassification, AutoModelForAudioXVector, AutoModelForCausalLM, AutoModelForCTC, AutoModelForDepthEstimation, AutoModelForDocumentQuestionAnswering, AutoModelForImageClassification, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, AutoModelForMaskedImageModeling, AutoModelForMaskedLM, AutoModelForMultipleChoice, AutoModelForNextSentencePrediction, AutoModelForObjectDetection, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSemanticSegmentation, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForSpeechSeq2Seq, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelForVideoClassification, AutoModelForVision2Seq, AutoModelForVisualQuestionAnswering, AutoModelForZeroShotObjectDetection, AutoModelWithLMHead, ) from .models.bart import ( BART_PRETRAINED_MODEL_ARCHIVE_LIST, BartForCausalLM, BartForConditionalGeneration, BartForQuestionAnswering, BartForSequenceClassification, BartModel, BartPretrainedModel, PretrainedBartModel, ) from .models.beit import ( BEIT_PRETRAINED_MODEL_ARCHIVE_LIST, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitModel, BeitPreTrainedModel, ) from .models.bert import ( BERT_PRETRAINED_MODEL_ARCHIVE_LIST, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLayer, BertLMHeadModel, BertModel, BertPreTrainedModel, load_tf_weights_in_bert, ) from .models.bert_generation import ( BertGenerationDecoder, BertGenerationEncoder, BertGenerationPreTrainedModel, load_tf_weights_in_bert_generation, ) from .models.big_bird import ( BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdForCausalLM, BigBirdForMaskedLM, BigBirdForMultipleChoice, BigBirdForPreTraining, BigBirdForQuestionAnswering, BigBirdForSequenceClassification, BigBirdForTokenClassification, BigBirdLayer, BigBirdModel, BigBirdPreTrainedModel, load_tf_weights_in_big_bird, ) from .models.bigbird_pegasus import ( BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdPegasusForCausalLM, BigBirdPegasusForConditionalGeneration, BigBirdPegasusForQuestionAnswering, BigBirdPegasusForSequenceClassification, BigBirdPegasusModel, BigBirdPegasusPreTrainedModel, ) from .models.blenderbot import ( BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotForCausalLM, BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotPreTrainedModel, ) from .models.blenderbot_small import ( BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotSmallForCausalLM, BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallPreTrainedModel, ) from .models.bloom import ( BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel, BloomPreTrainedModel, ) from .models.camembert import ( CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, CamembertForCausalLM, CamembertForMaskedLM, CamembertForMultipleChoice, CamembertForQuestionAnswering, CamembertForSequenceClassification, CamembertForTokenClassification, CamembertModel, CamembertPreTrainedModel, ) from .models.canine import ( CANINE_PRETRAINED_MODEL_ARCHIVE_LIST, CanineForMultipleChoice, CanineForQuestionAnswering, CanineForSequenceClassification, CanineForTokenClassification, CanineLayer, CanineModel, CaninePreTrainedModel, load_tf_weights_in_canine, ) from .models.clip import ( CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPModel, CLIPPreTrainedModel, CLIPTextModel, CLIPTextModelWithProjection, CLIPVisionModel, CLIPVisionModelWithProjection, ) from .models.clipseg import ( CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegPreTrainedModel, CLIPSegTextModel, CLIPSegVisionModel, ) from .models.codegen import ( CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST, CodeGenForCausalLM, CodeGenModel, CodeGenPreTrainedModel, ) from .models.convbert import ( CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, ConvBertForMaskedLM, ConvBertForMultipleChoice, ConvBertForQuestionAnswering, ConvBertForSequenceClassification, ConvBertForTokenClassification, ConvBertLayer, ConvBertModel, ConvBertPreTrainedModel, load_tf_weights_in_convbert, ) from .models.convnext import ( CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST, ConvNextForImageClassification, ConvNextModel, ConvNextPreTrainedModel, ) from .models.ctrl import ( CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, CTRLPreTrainedModel, ) from .models.cvt import ( CVT_PRETRAINED_MODEL_ARCHIVE_LIST, CvtForImageClassification, CvtModel, CvtPreTrainedModel, ) from .models.data2vec import ( DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST, DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST, Data2VecAudioForAudioFrameClassification, Data2VecAudioForCTC, Data2VecAudioForSequenceClassification, Data2VecAudioForXVector, Data2VecAudioModel, Data2VecAudioPreTrainedModel, Data2VecTextForCausalLM, Data2VecTextForMaskedLM, Data2VecTextForMultipleChoice, Data2VecTextForQuestionAnswering, Data2VecTextForSequenceClassification, Data2VecTextForTokenClassification, Data2VecTextModel, Data2VecTextPreTrainedModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation, Data2VecVisionModel, Data2VecVisionPreTrainedModel, ) from .models.deberta import ( DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, DebertaForMaskedLM, DebertaForQuestionAnswering, DebertaForSequenceClassification, DebertaForTokenClassification, DebertaModel, DebertaPreTrainedModel, ) from .models.deberta_v2 import ( DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST, DebertaV2ForMaskedLM, DebertaV2ForMultipleChoice, DebertaV2ForQuestionAnswering, DebertaV2ForSequenceClassification, DebertaV2ForTokenClassification, DebertaV2Model, DebertaV2PreTrainedModel, ) from .models.decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, DecisionTransformerGPT2Model, DecisionTransformerGPT2PreTrainedModel, DecisionTransformerModel, DecisionTransformerPreTrainedModel, ) from .models.deit import ( DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, DeiTForImageClassification, DeiTForImageClassificationWithTeacher, DeiTForMaskedImageModeling, DeiTModel, DeiTPreTrainedModel, ) from .models.distilbert import ( DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, DistilBertModel, DistilBertPreTrainedModel, ) from .models.donut import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, DonutSwinModel, DonutSwinPreTrainedModel from .models.dpr import ( DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, DPRContextEncoder, DPRPretrainedContextEncoder, DPRPreTrainedModel, DPRPretrainedQuestionEncoder, DPRPretrainedReader, DPRQuestionEncoder, DPRReader, ) from .models.dpt import ( DPT_PRETRAINED_MODEL_ARCHIVE_LIST, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel, DPTPreTrainedModel, ) from .models.electra import ( ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, ElectraForCausalLM, ElectraForMaskedLM, ElectraForMultipleChoice, ElectraForPreTraining, ElectraForQuestionAnswering, ElectraForSequenceClassification, ElectraForTokenClassification, ElectraModel, ElectraPreTrainedModel, load_tf_weights_in_electra, ) from .models.encoder_decoder import EncoderDecoderModel from .models.ernie import ( ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST, ErnieForCausalLM, ErnieForMaskedLM, ErnieForMultipleChoice, ErnieForNextSentencePrediction, ErnieForPreTraining, ErnieForQuestionAnswering, ErnieForSequenceClassification, ErnieForTokenClassification, ErnieModel, ErniePreTrainedModel, ) from .models.esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, EsmFoldPreTrainedModel, EsmForMaskedLM, EsmForProteinFolding, EsmForSequenceClassification, EsmForTokenClassification, EsmModel, EsmPreTrainedModel, ) from .models.flaubert import ( FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, FlaubertForMultipleChoice, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertModel, FlaubertPreTrainedModel, FlaubertWithLMHeadModel, ) from .models.flava import ( FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST, FlavaForPreTraining, FlavaImageCodebook, FlavaImageModel, FlavaModel, FlavaMultimodalModel, FlavaPreTrainedModel, FlavaTextModel, ) from .models.fnet import ( FNET_PRETRAINED_MODEL_ARCHIVE_LIST, FNetForMaskedLM, FNetForMultipleChoice, FNetForNextSentencePrediction, FNetForPreTraining, FNetForQuestionAnswering, FNetForSequenceClassification, FNetForTokenClassification, FNetLayer, FNetModel, FNetPreTrainedModel, ) from .models.fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel from .models.funnel import ( FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, FunnelBaseModel, FunnelForMaskedLM, FunnelForMultipleChoice, FunnelForPreTraining, FunnelForQuestionAnswering, FunnelForSequenceClassification, FunnelForTokenClassification, FunnelModel, FunnelPreTrainedModel, load_tf_weights_in_funnel, ) from .models.glpn import ( GLPN_PRETRAINED_MODEL_ARCHIVE_LIST, GLPNForDepthEstimation, GLPNModel, GLPNPreTrainedModel, ) from .models.gpt2 import ( GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, GPT2DoubleHeadsModel, GPT2ForSequenceClassification, GPT2ForTokenClassification, GPT2LMHeadModel, GPT2Model, GPT2PreTrainedModel, load_tf_weights_in_gpt2, ) from .models.gpt_neo import ( GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoForCausalLM, GPTNeoForSequenceClassification, GPTNeoModel, GPTNeoPreTrainedModel, load_tf_weights_in_gpt_neo, ) from .models.gpt_neox import ( GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXForCausalLM, GPTNeoXLayer, GPTNeoXModel, GPTNeoXPreTrainedModel, ) from .models.gpt_neox_japanese import ( GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseLayer, GPTNeoXJapaneseModel, GPTNeoXJapanesePreTrainedModel, ) from .models.gptj import ( GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST, GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, GPTJPreTrainedModel, ) from .models.groupvit import ( GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, GroupViTModel, GroupViTPreTrainedModel, GroupViTTextModel, GroupViTVisionModel, ) from .models.hubert import ( HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, HubertForCTC, HubertForSequenceClassification, HubertModel, HubertPreTrainedModel, ) from .models.ibert import ( IBERT_PRETRAINED_MODEL_ARCHIVE_LIST, IBertForMaskedLM, IBertForMultipleChoice, IBertForQuestionAnswering, IBertForSequenceClassification, IBertForTokenClassification, IBertModel, IBertPreTrainedModel, ) from .models.imagegpt import ( IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST, ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel, ImageGPTPreTrainedModel, load_tf_weights_in_imagegpt, ) from .models.jukebox import ( JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST, JukeboxModel, JukeboxPreTrainedModel, JukeboxPrior, JukeboxVQVAE, ) from .models.layoutlm import ( LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMForMaskedLM, LayoutLMForQuestionAnswering, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMModel, LayoutLMPreTrainedModel, ) from .models.layoutlmv2 import ( LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMv2ForQuestionAnswering, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2Model, LayoutLMv2PreTrainedModel, ) from .models.layoutlmv3 import ( LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMv3ForQuestionAnswering, LayoutLMv3ForSequenceClassification, LayoutLMv3ForTokenClassification, LayoutLMv3Model, LayoutLMv3PreTrainedModel, ) from .models.led import ( LED_PRETRAINED_MODEL_ARCHIVE_LIST, LEDForConditionalGeneration, LEDForQuestionAnswering, LEDForSequenceClassification, LEDModel, LEDPreTrainedModel, ) from .models.levit import ( LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, LevitForImageClassification, LevitForImageClassificationWithTeacher, LevitModel, LevitPreTrainedModel, ) from .models.lilt import ( LILT_PRETRAINED_MODEL_ARCHIVE_LIST, LiltForQuestionAnswering, LiltForSequenceClassification, LiltForTokenClassification, LiltModel, LiltPreTrainedModel, ) from .models.longformer import ( LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, LongformerForMaskedLM, LongformerForMultipleChoice, LongformerForQuestionAnswering, LongformerForSequenceClassification, LongformerForTokenClassification, LongformerModel, LongformerPreTrainedModel, LongformerSelfAttention, ) from .models.longt5 import ( LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST, LongT5EncoderModel, LongT5ForConditionalGeneration, LongT5Model, LongT5PreTrainedModel, ) from .models.luke import ( LUKE_PRETRAINED_MODEL_ARCHIVE_LIST, LukeForEntityClassification, LukeForEntityPairClassification, LukeForEntitySpanClassification, LukeForMaskedLM, LukeForMultipleChoice, LukeForQuestionAnswering, LukeForSequenceClassification, LukeForTokenClassification, LukeModel, LukePreTrainedModel, ) from .models.lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) from .models.m2m_100 import ( M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST, M2M100ForConditionalGeneration, M2M100Model, M2M100PreTrainedModel, ) from .models.marian import MarianForCausalLM, MarianModel, MarianMTModel from .models.markuplm import ( MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST, MarkupLMForQuestionAnswering, MarkupLMForSequenceClassification, MarkupLMForTokenClassification, MarkupLMModel, MarkupLMPreTrainedModel, ) from .models.maskformer import ( MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskFormerForInstanceSegmentation, MaskFormerModel, MaskFormerPreTrainedModel, ) from .models.mbart import ( MBartForCausalLM, MBartForConditionalGeneration, MBartForQuestionAnswering, MBartForSequenceClassification, MBartModel, MBartPreTrainedModel, ) from .models.mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel from .models.megatron_bert import ( MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, MegatronBertForCausalLM, MegatronBertForMaskedLM, MegatronBertForMultipleChoice, MegatronBertForNextSentencePrediction, MegatronBertForPreTraining, MegatronBertForQuestionAnswering, MegatronBertForSequenceClassification, MegatronBertForTokenClassification, MegatronBertModel, MegatronBertPreTrainedModel, ) from .models.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings from .models.mobilebert import ( MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileBertForMaskedLM, MobileBertForMultipleChoice, MobileBertForNextSentencePrediction, MobileBertForPreTraining, MobileBertForQuestionAnswering, MobileBertForSequenceClassification, MobileBertForTokenClassification, MobileBertLayer, MobileBertModel, MobileBertPreTrainedModel, load_tf_weights_in_mobilebert, ) from .models.mobilenet_v2 import ( MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST, MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation, MobileNetV2Model, MobileNetV2PreTrainedModel, load_tf_weights_in_mobilenet_v2, ) from .models.mobilevit import ( MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel, MobileViTPreTrainedModel, ) from .models.mpnet import ( MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, MPNetForMaskedLM, MPNetForMultipleChoice, MPNetForQuestionAnswering, MPNetForSequenceClassification, MPNetForTokenClassification, MPNetLayer, MPNetModel, MPNetPreTrainedModel, ) from .models.mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model from .models.mvp import ( MVP_PRETRAINED_MODEL_ARCHIVE_LIST, MvpForCausalLM, MvpForConditionalGeneration, MvpForQuestionAnswering, MvpForSequenceClassification, MvpModel, MvpPreTrainedModel, ) from .models.nezha import ( NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST, NezhaForMaskedLM, NezhaForMultipleChoice, NezhaForNextSentencePrediction, NezhaForPreTraining, NezhaForQuestionAnswering, NezhaForSequenceClassification, NezhaForTokenClassification, NezhaModel, NezhaPreTrainedModel, ) from .models.nystromformer import ( NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, NystromformerForMaskedLM, NystromformerForMultipleChoice, NystromformerForQuestionAnswering, NystromformerForSequenceClassification, NystromformerForTokenClassification, NystromformerLayer, NystromformerModel, NystromformerPreTrainedModel, ) from .models.openai import ( OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification, OpenAIGPTLMHeadModel, OpenAIGPTModel, OpenAIGPTPreTrainedModel, load_tf_weights_in_openai_gpt, ) from .models.opt import ( OPT_PRETRAINED_MODEL_ARCHIVE_LIST, OPTForCausalLM, OPTForQuestionAnswering, OPTForSequenceClassification, OPTModel, OPTPreTrainedModel, ) from .models.owlvit import ( OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST, OwlViTForObjectDetection, OwlViTModel, OwlViTPreTrainedModel, OwlViTTextModel, OwlViTVisionModel, ) from .models.pegasus import ( PegasusForCausalLM, PegasusForConditionalGeneration, PegasusModel, PegasusPreTrainedModel, ) from .models.pegasus_x import ( PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST, PegasusXForConditionalGeneration, PegasusXModel, PegasusXPreTrainedModel, ) from .models.perceiver import ( PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST, PerceiverForImageClassificationConvProcessing, PerceiverForImageClassificationFourier, PerceiverForImageClassificationLearned, PerceiverForMaskedLM, PerceiverForMultimodalAutoencoding, PerceiverForOpticalFlow, PerceiverForSequenceClassification, PerceiverLayer, PerceiverModel, PerceiverPreTrainedModel, ) from .models.plbart import ( PLBART_PRETRAINED_MODEL_ARCHIVE_LIST, PLBartForCausalLM, PLBartForConditionalGeneration, PLBartForSequenceClassification, PLBartModel, PLBartPreTrainedModel, ) from .models.poolformer import ( POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, PoolFormerForImageClassification, PoolFormerModel, PoolFormerPreTrainedModel, ) from .models.prophetnet import ( PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, ProphetNetDecoder, ProphetNetEncoder, ProphetNetForCausalLM, ProphetNetForConditionalGeneration, ProphetNetModel, ProphetNetPreTrainedModel, ) from .models.qdqbert import ( QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST, QDQBertForMaskedLM, QDQBertForMultipleChoice, QDQBertForNextSentencePrediction, QDQBertForQuestionAnswering, QDQBertForSequenceClassification, QDQBertForTokenClassification, QDQBertLayer, QDQBertLMHeadModel, QDQBertModel, QDQBertPreTrainedModel, load_tf_weights_in_qdqbert, ) from .models.rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration from .models.realm import ( REALM_PRETRAINED_MODEL_ARCHIVE_LIST, RealmEmbedder, RealmForOpenQA, RealmKnowledgeAugEncoder, RealmPreTrainedModel, RealmReader, RealmRetriever, RealmScorer, load_tf_weights_in_realm, ) from .models.reformer import ( REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ReformerAttention, ReformerForMaskedLM, ReformerForQuestionAnswering, ReformerForSequenceClassification, ReformerLayer, ReformerModel, ReformerModelWithLMHead, ReformerPreTrainedModel, ) from .models.regnet import ( REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, RegNetForImageClassification, RegNetModel, RegNetPreTrainedModel, ) from .models.rembert import ( REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RemBertForCausalLM, RemBertForMaskedLM, RemBertForMultipleChoice, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForTokenClassification, RemBertLayer, RemBertModel, RemBertPreTrainedModel, load_tf_weights_in_rembert, ) from .models.resnet import ( RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, ResNetForImageClassification, ResNetModel, ResNetPreTrainedModel, ) from .models.retribert import RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RetriBertModel, RetriBertPreTrainedModel from .models.roberta import ( ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaForCausalLM, RobertaForMaskedLM, RobertaForMultipleChoice, RobertaForQuestionAnswering, RobertaForSequenceClassification, RobertaForTokenClassification, RobertaModel, RobertaPreTrainedModel, ) from .models.roc_bert import ( ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, RoCBertForCausalLM, RoCBertForMaskedLM, RoCBertForMultipleChoice, RoCBertForPreTraining, RoCBertForQuestionAnswering, RoCBertForSequenceClassification, RoCBertForTokenClassification, RoCBertLayer, RoCBertModel, RoCBertPreTrainedModel, load_tf_weights_in_roc_bert, ) from .models.roformer import ( ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, RoFormerForCausalLM, RoFormerForMaskedLM, RoFormerForMultipleChoice, RoFormerForQuestionAnswering, RoFormerForSequenceClassification, RoFormerForTokenClassification, RoFormerLayer, RoFormerModel, RoFormerPreTrainedModel, load_tf_weights_in_roformer, ) from .models.segformer import ( SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, SegformerDecodeHead, SegformerForImageClassification, SegformerForSemanticSegmentation, SegformerLayer, SegformerModel, SegformerPreTrainedModel, ) from .models.sew import ( SEW_PRETRAINED_MODEL_ARCHIVE_LIST, SEWForCTC, SEWForSequenceClassification, SEWModel, SEWPreTrainedModel, ) from .models.sew_d import ( SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST, SEWDForCTC, SEWDForSequenceClassification, SEWDModel, SEWDPreTrainedModel, ) from .models.speech_encoder_decoder import SpeechEncoderDecoderModel from .models.speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextPreTrainedModel, ) from .models.speech_to_text_2 import Speech2Text2ForCausalLM, Speech2Text2PreTrainedModel from .models.splinter import ( SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST, SplinterForPreTraining, SplinterForQuestionAnswering, SplinterLayer, SplinterModel, SplinterPreTrainedModel, ) from .models.squeezebert import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, SqueezeBertModule, SqueezeBertPreTrainedModel, ) from .models.swin import ( SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel, SwinPreTrainedModel, ) from .models.swinv2 import ( SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST, Swinv2ForImageClassification, Swinv2ForMaskedImageModeling, Swinv2Model, Swinv2PreTrainedModel, ) from .models.switch_transformers import ( SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST, SwitchTransformersEncoderModel, SwitchTransformersForConditionalGeneration, SwitchTransformersModel, SwitchTransformersPreTrainedModel, SwitchTransformersSparseMLP, SwitchTransformersTop1Router, ) from .models.t5 import ( T5_PRETRAINED_MODEL_ARCHIVE_LIST, T5EncoderModel, T5ForConditionalGeneration, T5Model, T5PreTrainedModel, load_tf_weights_in_t5, ) from .models.tapas import ( TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasPreTrainedModel, load_tf_weights_in_tapas, ) from .models.time_series_transformer import ( TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TimeSeriesTransformerForPrediction, TimeSeriesTransformerModel, TimeSeriesTransformerPreTrainedModel, ) from .models.trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TrajectoryTransformerModel, TrajectoryTransformerPreTrainedModel, ) from .models.transfo_xl import ( TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, AdaptiveEmbedding, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel, TransfoXLPreTrainedModel, load_tf_weights_in_transfo_xl, ) from .models.trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel from .models.unispeech import ( UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechForCTC, UniSpeechForPreTraining, UniSpeechForSequenceClassification, UniSpeechModel, UniSpeechPreTrainedModel, ) from .models.unispeech_sat import ( UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, UniSpeechSatPreTrainedModel, ) from .models.van import ( VAN_PRETRAINED_MODEL_ARCHIVE_LIST, VanForImageClassification, VanModel, VanPreTrainedModel, ) from .models.videomae import ( VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST, VideoMAEForPreTraining, VideoMAEForVideoClassification, VideoMAEModel, VideoMAEPreTrainedModel, ) from .models.vilt import ( VILT_PRETRAINED_MODEL_ARCHIVE_LIST, ViltForImageAndTextRetrieval, ViltForImagesAndTextClassification, ViltForMaskedLM, ViltForQuestionAnswering, ViltForTokenClassification, ViltLayer, ViltModel, ViltPreTrainedModel, ) from .models.vision_encoder_decoder import VisionEncoderDecoderModel from .models.vision_text_dual_encoder import VisionTextDualEncoderModel from .models.visual_bert import ( VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, VisualBertForMultipleChoice, VisualBertForPreTraining, VisualBertForQuestionAnswering, VisualBertForRegionToPhraseAlignment, VisualBertForVisualReasoning, VisualBertLayer, VisualBertModel, VisualBertPreTrainedModel, ) from .models.vit import ( VIT_PRETRAINED_MODEL_ARCHIVE_LIST, ViTForImageClassification, ViTForMaskedImageModeling, ViTModel, ViTPreTrainedModel, ) from .models.vit_mae import ( VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST, ViTMAEForPreTraining, ViTMAELayer, ViTMAEModel, ViTMAEPreTrainedModel, ) from .models.vit_msn import ( VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST, ViTMSNForImageClassification, ViTMSNModel, ViTMSNPreTrainedModel, ) from .models.wav2vec2 import ( WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2ForAudioFrameClassification, Wav2Vec2ForCTC, Wav2Vec2ForMaskedLM, Wav2Vec2ForPreTraining, Wav2Vec2ForSequenceClassification, Wav2Vec2ForXVector, Wav2Vec2Model, Wav2Vec2PreTrainedModel, ) from .models.wav2vec2_conformer import ( WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2ConformerPreTrainedModel, ) from .models.wavlm import ( WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST, WavLMForAudioFrameClassification, WavLMForCTC, WavLMForSequenceClassification, WavLMForXVector, WavLMModel, WavLMPreTrainedModel, ) from .models.whisper import ( WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, WhisperForConditionalGeneration, WhisperModel, WhisperPreTrainedModel, ) from .models.x_clip import ( XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST, XCLIPModel, XCLIPPreTrainedModel, XCLIPTextModel, XCLIPVisionModel, ) from .models.xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel from .models.xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) from .models.xlm_prophetnet import ( XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, XLMProphetNetDecoder, XLMProphetNetEncoder, XLMProphetNetForCausalLM, XLMProphetNetForConditionalGeneration, XLMProphetNetModel, XLMProphetNetPreTrainedModel, ) from .models.xlm_roberta import ( XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, XLMRobertaForCausalLM, XLMRobertaForMaskedLM, XLMRobertaForMultipleChoice, XLMRobertaForQuestionAnswering, XLMRobertaForSequenceClassification, XLMRobertaForTokenClassification, XLMRobertaModel, XLMRobertaPreTrainedModel, ) from .models.xlm_roberta_xl import ( XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST, XLMRobertaXLForCausalLM, XLMRobertaXLForMaskedLM, XLMRobertaXLForMultipleChoice, XLMRobertaXLForQuestionAnswering, XLMRobertaXLForSequenceClassification, XLMRobertaXLForTokenClassification, XLMRobertaXLModel, XLMRobertaXLPreTrainedModel, ) from .models.xlnet import ( XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, XLNetForMultipleChoice, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForSequenceClassification, XLNetForTokenClassification, XLNetLMHeadModel, XLNetModel, XLNetPreTrainedModel, load_tf_weights_in_xlnet, ) from .models.yolos import ( YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST, YolosForObjectDetection, YolosModel, YolosPreTrainedModel, ) from .models.yoso import ( YOSO_PRETRAINED_MODEL_ARCHIVE_LIST, YosoForMaskedLM, YosoForMultipleChoice, YosoForQuestionAnswering, YosoForSequenceClassification, YosoForTokenClassification, YosoLayer, YosoModel, YosoPreTrainedModel, ) # Optimization from .optimization import ( Adafactor, AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, ) from .pytorch_utils import Conv1D, apply_chunking_to_forward, prune_layer # Trainer from .trainer import Trainer from .trainer_pt_utils import torch_distributed_zero_first from .trainer_seq2seq import Seq2SeqTrainer # TensorFlow try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # Import the same objects as dummies to get them in the namespace. # They will raise an import error if the user tries to instantiate / use them. from .utils.dummy_tf_objects import * else: from .benchmark.benchmark_args_tf import TensorFlowBenchmarkArguments # Benchmarks from .benchmark.benchmark_tf import TensorFlowBenchmark from .generation import ( TFForcedBOSTokenLogitsProcessor, TFForcedEOSTokenLogitsProcessor, TFGenerationMixin, TFLogitsProcessor, TFLogitsProcessorList, TFLogitsWarper, TFMinLengthLogitsProcessor, TFNoBadWordsLogitsProcessor, TFNoRepeatNGramLogitsProcessor, TFRepetitionPenaltyLogitsProcessor, TFTemperatureLogitsWarper, TFTopKLogitsWarper, TFTopPLogitsWarper, tf_top_k_top_p_filtering, ) from .keras_callbacks import KerasMetricCallback, PushToHubCallback from .modeling_tf_layoutlm import ( TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFLayoutLMForMaskedLM, TFLayoutLMForQuestionAnswering, TFLayoutLMForSequenceClassification, TFLayoutLMForTokenClassification, TFLayoutLMMainLayer, TFLayoutLMModel, TFLayoutLMPreTrainedModel, ) from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list # TensorFlow model imports from .models.albert import ( TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFAlbertForMaskedLM, TFAlbertForMultipleChoice, TFAlbertForPreTraining, TFAlbertForQuestionAnswering, TFAlbertForSequenceClassification, TFAlbertForTokenClassification, TFAlbertMainLayer, TFAlbertModel, TFAlbertPreTrainedModel, ) from .models.auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForDocumentQuestionAnswering, TFAutoModelForImageClassification, TFAutoModelForMaskedLM, TFAutoModelForMultipleChoice, TFAutoModelForNextSentencePrediction, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSemanticSegmentation, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForSpeechSeq2Seq, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelForVision2Seq, TFAutoModelWithLMHead, ) from .models.bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel from .models.bert import ( TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFBertEmbeddings, TFBertForMaskedLM, TFBertForMultipleChoice, TFBertForNextSentencePrediction, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertForTokenClassification, TFBertLMHeadModel, TFBertMainLayer, TFBertModel, TFBertPreTrainedModel, ) from .models.blenderbot import ( TFBlenderbotForConditionalGeneration, TFBlenderbotModel, TFBlenderbotPreTrainedModel, ) from .models.blenderbot_small import ( TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel, TFBlenderbotSmallPreTrainedModel, ) from .models.camembert import ( TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFCamembertForCausalLM, TFCamembertForMaskedLM, TFCamembertForMultipleChoice, TFCamembertForQuestionAnswering, TFCamembertForSequenceClassification, TFCamembertForTokenClassification, TFCamembertModel, TFCamembertPreTrainedModel, ) from .models.clip import ( TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, TFCLIPModel, TFCLIPPreTrainedModel, TFCLIPTextModel, TFCLIPVisionModel, ) from .models.convbert import ( TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFConvBertForMaskedLM, TFConvBertForMultipleChoice, TFConvBertForQuestionAnswering, TFConvBertForSequenceClassification, TFConvBertForTokenClassification, TFConvBertLayer, TFConvBertModel, TFConvBertPreTrainedModel, ) from .models.convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel from .models.ctrl import ( TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, TFCTRLForSequenceClassification, TFCTRLLMHeadModel, TFCTRLModel, TFCTRLPreTrainedModel, ) from .models.cvt import ( TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST, TFCvtForImageClassification, TFCvtModel, TFCvtPreTrainedModel, ) from .models.data2vec import ( TFData2VecVisionForImageClassification, TFData2VecVisionForSemanticSegmentation, TFData2VecVisionModel, TFData2VecVisionPreTrainedModel, ) from .models.deberta import ( TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFDebertaForMaskedLM, TFDebertaForQuestionAnswering, TFDebertaForSequenceClassification, TFDebertaForTokenClassification, TFDebertaModel, TFDebertaPreTrainedModel, ) from .models.deberta_v2 import ( TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST, TFDebertaV2ForMaskedLM, TFDebertaV2ForQuestionAnswering, TFDebertaV2ForSequenceClassification, TFDebertaV2ForTokenClassification, TFDebertaV2Model, TFDebertaV2PreTrainedModel, ) from .models.deit import ( TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, TFDeiTModel, TFDeiTPreTrainedModel, ) from .models.distilbert import ( TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDistilBertForMaskedLM, TFDistilBertForMultipleChoice, TFDistilBertForQuestionAnswering, TFDistilBertForSequenceClassification, TFDistilBertForTokenClassification, TFDistilBertMainLayer, TFDistilBertModel, TFDistilBertPreTrainedModel, ) from .models.dpr import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, TFDPRContextEncoder, TFDPRPretrainedContextEncoder, TFDPRPretrainedQuestionEncoder, TFDPRPretrainedReader, TFDPRQuestionEncoder, TFDPRReader, ) from .models.electra import ( TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, TFElectraForMaskedLM, TFElectraForMultipleChoice, TFElectraForPreTraining, TFElectraForQuestionAnswering, TFElectraForSequenceClassification, TFElectraForTokenClassification, TFElectraModel, TFElectraPreTrainedModel, ) from .models.encoder_decoder import TFEncoderDecoderModel from .models.esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, TFEsmForMaskedLM, TFEsmForSequenceClassification, TFEsmForTokenClassification, TFEsmModel, TFEsmPreTrainedModel, ) from .models.flaubert import ( TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFFlaubertForMultipleChoice, TFFlaubertForQuestionAnsweringSimple, TFFlaubertForSequenceClassification, TFFlaubertForTokenClassification, TFFlaubertModel, TFFlaubertPreTrainedModel, TFFlaubertWithLMHeadModel, ) from .models.funnel import ( TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, TFFunnelBaseModel, TFFunnelForMaskedLM, TFFunnelForMultipleChoice, TFFunnelForPreTraining, TFFunnelForQuestionAnswering, TFFunnelForSequenceClassification, TFFunnelForTokenClassification, TFFunnelModel, TFFunnelPreTrainedModel, ) from .models.gpt2 import ( TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, TFGPT2DoubleHeadsModel, TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2MainLayer, TFGPT2Model, TFGPT2PreTrainedModel, ) from .models.gptj import ( TFGPTJForCausalLM, TFGPTJForQuestionAnswering, TFGPTJForSequenceClassification, TFGPTJModel, TFGPTJPreTrainedModel, ) from .models.groupvit import ( TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFGroupViTModel, TFGroupViTPreTrainedModel, TFGroupViTTextModel, TFGroupViTVisionModel, ) from .models.hubert import ( TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFHubertForCTC, TFHubertModel, TFHubertPreTrainedModel, ) from .models.layoutlmv3 import ( TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, TFLayoutLMv3ForQuestionAnswering, TFLayoutLMv3ForSequenceClassification, TFLayoutLMv3ForTokenClassification, TFLayoutLMv3Model, TFLayoutLMv3PreTrainedModel, ) from .models.led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel from .models.longformer import ( TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFLongformerForMaskedLM, TFLongformerForMultipleChoice, TFLongformerForQuestionAnswering, TFLongformerForSequenceClassification, TFLongformerForTokenClassification, TFLongformerModel, TFLongformerPreTrainedModel, TFLongformerSelfAttention, ) from .models.lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) from .models.marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel from .models.mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel from .models.mobilebert import ( TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileBertForMaskedLM, TFMobileBertForMultipleChoice, TFMobileBertForNextSentencePrediction, TFMobileBertForPreTraining, TFMobileBertForQuestionAnswering, TFMobileBertForSequenceClassification, TFMobileBertForTokenClassification, TFMobileBertMainLayer, TFMobileBertModel, TFMobileBertPreTrainedModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel, TFMobileViTPreTrainedModel, ) from .models.mpnet import ( TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification, TFMPNetForTokenClassification, TFMPNetMainLayer, TFMPNetModel, TFMPNetPreTrainedModel, ) from .models.mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model from .models.openai import ( TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification, TFOpenAIGPTLMHeadModel, TFOpenAIGPTMainLayer, TFOpenAIGPTModel, TFOpenAIGPTPreTrainedModel, ) from .models.opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel from .models.pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel from .models.rag import TFRagModel, TFRagPreTrainedModel, TFRagSequenceForGeneration, TFRagTokenForGeneration from .models.regnet import ( TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel, TFRegNetPreTrainedModel, ) from .models.rembert import ( TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForMultipleChoice, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertLayer, TFRemBertModel, TFRemBertPreTrainedModel, ) from .models.resnet import ( TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFResNetForImageClassification, TFResNetModel, TFResNetPreTrainedModel, ) from .models.roberta import ( TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForMultipleChoice, TFRobertaForQuestionAnswering, TFRobertaForSequenceClassification, TFRobertaForTokenClassification, TFRobertaMainLayer, TFRobertaModel, TFRobertaPreTrainedModel, ) from .models.roformer import ( TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForMultipleChoice, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerLayer, TFRoFormerModel, TFRoFormerPreTrainedModel, ) from .models.segformer import ( TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFSegformerDecodeHead, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel, TFSegformerPreTrainedModel, ) from .models.speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel, TFSpeech2TextPreTrainedModel, ) from .models.swin import ( TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, TFSwinForImageClassification, TFSwinForMaskedImageModeling, TFSwinModel, TFSwinPreTrainedModel, ) from .models.t5 import ( TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST, TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model, TFT5PreTrainedModel, ) from .models.tapas import ( TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TFTapasForMaskedLM, TFTapasForQuestionAnswering, TFTapasForSequenceClassification, TFTapasModel, TFTapasPreTrainedModel, ) from .models.transfo_xl import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFAdaptiveEmbedding, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLMainLayer, TFTransfoXLModel, TFTransfoXLPreTrainedModel, ) from .models.vision_encoder_decoder import TFVisionEncoderDecoderModel from .models.vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel from .models.vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel from .models.wav2vec2 import ( TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, TFWav2Vec2ForCTC, TFWav2Vec2Model, TFWav2Vec2PreTrainedModel, ) from .models.whisper import ( TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, TFWhisperForConditionalGeneration, TFWhisperModel, TFWhisperPreTrainedModel, ) from .models.xglm import ( TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXGLMForCausalLM, TFXGLMModel, TFXGLMPreTrainedModel, ) from .models.xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) from .models.xlm_roberta import ( TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMRobertaForMaskedLM, TFXLMRobertaForMultipleChoice, TFXLMRobertaForQuestionAnswering, TFXLMRobertaForSequenceClassification, TFXLMRobertaForTokenClassification, TFXLMRobertaModel, ) from .models.xlnet import ( TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLNetForMultipleChoice, TFXLNetForQuestionAnsweringSimple, TFXLNetForSequenceClassification, TFXLNetForTokenClassification, TFXLNetLMHeadModel, TFXLNetMainLayer, TFXLNetModel, TFXLNetPreTrainedModel, ) # Optimization from .optimization_tf import AdamWeightDecay, GradientAccumulator, WarmUp, create_optimizer # Trainer from .trainer_tf import TFTrainer try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # Import the same objects as dummies to get them in the namespace. # They will raise an import error if the user tries to instantiate / use them. from .utils.dummy_flax_objects import * else: from .generation import ( FlaxForcedBOSTokenLogitsProcessor, FlaxForcedEOSTokenLogitsProcessor, FlaxGenerationMixin, FlaxLogitsProcessor, FlaxLogitsProcessorList, FlaxLogitsWarper, FlaxMinLengthLogitsProcessor, FlaxTemperatureLogitsWarper, FlaxTopKLogitsWarper, FlaxTopPLogitsWarper, ) from .modeling_flax_utils import FlaxPreTrainedModel # Flax model imports from .models.albert import ( FlaxAlbertForMaskedLM, FlaxAlbertForMultipleChoice, FlaxAlbertForPreTraining, FlaxAlbertForQuestionAnswering, FlaxAlbertForSequenceClassification, FlaxAlbertForTokenClassification, FlaxAlbertModel, FlaxAlbertPreTrainedModel, ) from .models.auto import ( FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, FLAX_MODEL_FOR_PRETRAINING_MAPPING, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, FLAX_MODEL_MAPPING, FlaxAutoModel, FlaxAutoModelForCausalLM, FlaxAutoModelForImageClassification, FlaxAutoModelForMaskedLM, FlaxAutoModelForMultipleChoice, FlaxAutoModelForNextSentencePrediction, FlaxAutoModelForPreTraining, FlaxAutoModelForQuestionAnswering, FlaxAutoModelForSeq2SeqLM, FlaxAutoModelForSequenceClassification, FlaxAutoModelForTokenClassification, FlaxAutoModelForVision2Seq, ) from .models.bart import ( FlaxBartDecoderPreTrainedModel, FlaxBartForCausalLM, FlaxBartForConditionalGeneration, FlaxBartForQuestionAnswering, FlaxBartForSequenceClassification, FlaxBartModel, FlaxBartPreTrainedModel, ) from .models.beit import ( FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel, FlaxBeitPreTrainedModel, ) from .models.bert import ( FlaxBertForCausalLM, FlaxBertForMaskedLM, FlaxBertForMultipleChoice, FlaxBertForNextSentencePrediction, FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification, FlaxBertForTokenClassification, FlaxBertModel, FlaxBertPreTrainedModel, ) from .models.big_bird import ( FlaxBigBirdForCausalLM, FlaxBigBirdForMaskedLM, FlaxBigBirdForMultipleChoice, FlaxBigBirdForPreTraining, FlaxBigBirdForQuestionAnswering, FlaxBigBirdForSequenceClassification, FlaxBigBirdForTokenClassification, FlaxBigBirdModel, FlaxBigBirdPreTrainedModel, ) from .models.blenderbot import ( FlaxBlenderbotForConditionalGeneration, FlaxBlenderbotModel, FlaxBlenderbotPreTrainedModel, ) from .models.blenderbot_small import ( FlaxBlenderbotSmallForConditionalGeneration, FlaxBlenderbotSmallModel, FlaxBlenderbotSmallPreTrainedModel, ) from .models.clip import ( FlaxCLIPModel, FlaxCLIPPreTrainedModel, FlaxCLIPTextModel, FlaxCLIPTextPreTrainedModel, FlaxCLIPVisionModel, FlaxCLIPVisionPreTrainedModel, ) from .models.distilbert import ( FlaxDistilBertForMaskedLM, FlaxDistilBertForMultipleChoice, FlaxDistilBertForQuestionAnswering, FlaxDistilBertForSequenceClassification, FlaxDistilBertForTokenClassification, FlaxDistilBertModel, FlaxDistilBertPreTrainedModel, ) from .models.electra import ( FlaxElectraForCausalLM, FlaxElectraForMaskedLM, FlaxElectraForMultipleChoice, FlaxElectraForPreTraining, FlaxElectraForQuestionAnswering, FlaxElectraForSequenceClassification, FlaxElectraForTokenClassification, FlaxElectraModel, FlaxElectraPreTrainedModel, ) from .models.encoder_decoder import FlaxEncoderDecoderModel from .models.gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel from .models.gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel from .models.gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel from .models.longt5 import FlaxLongT5ForConditionalGeneration, FlaxLongT5Model, FlaxLongT5PreTrainedModel from .models.marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel from .models.mbart import ( FlaxMBartForConditionalGeneration, FlaxMBartForQuestionAnswering, FlaxMBartForSequenceClassification, FlaxMBartModel, FlaxMBartPreTrainedModel, ) from .models.mt5 import FlaxMT5EncoderModel, FlaxMT5ForConditionalGeneration, FlaxMT5Model from .models.opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel from .models.pegasus import FlaxPegasusForConditionalGeneration, FlaxPegasusModel, FlaxPegasusPreTrainedModel from .models.roberta import ( FlaxRobertaForCausalLM, FlaxRobertaForMaskedLM, FlaxRobertaForMultipleChoice, FlaxRobertaForQuestionAnswering, FlaxRobertaForSequenceClassification, FlaxRobertaForTokenClassification, FlaxRobertaModel, FlaxRobertaPreTrainedModel, ) from .models.roformer import ( FlaxRoFormerForMaskedLM, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerModel, FlaxRoFormerPreTrainedModel, ) from .models.speech_encoder_decoder import FlaxSpeechEncoderDecoderModel from .models.t5 import FlaxT5EncoderModel, FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel from .models.vision_encoder_decoder import FlaxVisionEncoderDecoderModel from .models.vision_text_dual_encoder import FlaxVisionTextDualEncoderModel from .models.vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel from .models.wav2vec2 import ( FlaxWav2Vec2ForCTC, FlaxWav2Vec2ForPreTraining, FlaxWav2Vec2Model, FlaxWav2Vec2PreTrainedModel, ) from .models.xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel from .models.xlm_roberta import ( FlaxXLMRobertaForMaskedLM, FlaxXLMRobertaForMultipleChoice, FlaxXLMRobertaForQuestionAnswering, FlaxXLMRobertaForSequenceClassification, FlaxXLMRobertaForTokenClassification, FlaxXLMRobertaModel, ) else: import sys sys.modules[__name__] = _LazyModule( __name__, globals()["__file__"], _import_structure, module_spec=__spec__, extra_objects={"__version__": __version__}, ) if not is_tf_available() and not is_torch_available() and not is_flax_available(): logger.warning( "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. " "Models won't be available and only tokenizers, configuration " "and file/data utilities can be used." )

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # When adding a new object to this init, remember to add it twice: once inside the `_import_structure` dictionary and # once inside the `if TYPE_CHECKING` branch. The `TYPE_CHECKING` should have import statements as usual, but they are # only there for type checking. The `_import_structure` is a dictionary submodule to list of object names, and is used # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names # in the namespace without actually importing anything (and especially none of the backends). __version__ = "4.25.0.dev0" from typing import TYPE_CHECKING # Check the dependencies satisfy the minimal versions required. from . import dependency_versions_check from .utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_sentencepiece_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_vision_available, logging, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Base objects, independent of any specific backend _import_structure = { "benchmark": [], "commands": [], "configuration_utils": ["PretrainedConfig"], "convert_graph_to_onnx": [], "convert_slow_tokenizers_checkpoints_to_fast": [], "convert_tf_hub_seq_to_seq_bert_to_pytorch": [], "data": [ "DataProcessor", "InputExample", "InputFeatures", "SingleSentenceClassificationProcessor", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", ], "data.data_collator": [ "DataCollator", "DataCollatorForLanguageModeling", "DataCollatorForPermutationLanguageModeling", "DataCollatorForSeq2Seq", "DataCollatorForSOP", "DataCollatorForTokenClassification", "DataCollatorForWholeWordMask", "DataCollatorWithPadding", "DefaultDataCollator", "default_data_collator", ], "data.metrics": [], "data.processors": [], "debug_utils": [], "dependency_versions_check": [], "dependency_versions_table": [], "dynamic_module_utils": [], "feature_extraction_sequence_utils": ["SequenceFeatureExtractor"], "feature_extraction_utils": ["BatchFeature", "FeatureExtractionMixin"], "file_utils": [], "generation": [], "hf_argparser": ["HfArgumentParser"], "integrations": [ "is_clearml_available", "is_comet_available", "is_neptune_available", "is_optuna_available", "is_ray_available", "is_ray_tune_available", "is_sigopt_available", "is_tensorboard_available", "is_wandb_available", ], "modelcard": ["ModelCard"], "modeling_tf_pytorch_utils": [ "convert_tf_weight_name_to_pt_weight_name", "load_pytorch_checkpoint_in_tf2_model", "load_pytorch_model_in_tf2_model", "load_pytorch_weights_in_tf2_model", "load_tf2_checkpoint_in_pytorch_model", "load_tf2_model_in_pytorch_model", "load_tf2_weights_in_pytorch_model", ], "models": [], # Models "models.albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig"], "models.auto": [ "ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "FEATURE_EXTRACTOR_MAPPING", "IMAGE_PROCESSOR_MAPPING", "MODEL_NAMES_MAPPING", "PROCESSOR_MAPPING", "TOKENIZER_MAPPING", "AutoConfig", "AutoFeatureExtractor", "AutoImageProcessor", "AutoProcessor", "AutoTokenizer", ], "models.bart": ["BartConfig", "BartTokenizer"], "models.barthez": [], "models.bartpho": [], "models.beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig"], "models.bert": [ "BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BasicTokenizer", "BertConfig", "BertTokenizer", "WordpieceTokenizer", ], "models.bert_generation": ["BertGenerationConfig"], "models.bert_japanese": ["BertJapaneseTokenizer", "CharacterTokenizer", "MecabTokenizer"], "models.bertweet": ["BertweetTokenizer"], "models.big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig"], "models.bigbird_pegasus": [ "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdPegasusConfig", ], "models.blenderbot": ["BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotConfig", "BlenderbotTokenizer"], "models.blenderbot_small": [ "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "BlenderbotSmallConfig", "BlenderbotSmallTokenizer", ], "models.bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig"], "models.bort": [], "models.byt5": ["ByT5Tokenizer"], "models.camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"], "models.canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig", "CanineTokenizer"], "models.clip": [ "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPConfig", "CLIPProcessor", "CLIPTextConfig", "CLIPTokenizer", "CLIPVisionConfig", ], "models.clipseg": [ "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPSegConfig", "CLIPSegProcessor", "CLIPSegTextConfig", "CLIPSegVisionConfig", ], "models.codegen": ["CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodeGenConfig", "CodeGenTokenizer"], "models.conditional_detr": ["CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConditionalDetrConfig"], "models.convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertTokenizer"], "models.convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig"], "models.cpm": [], "models.ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig", "CTRLTokenizer"], "models.cvt": ["CVT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CvtConfig"], "models.data2vec": [ "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig", "Data2VecTextConfig", "Data2VecVisionConfig", ], "models.deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaTokenizer"], "models.deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config"], "models.decision_transformer": ["DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "DecisionTransformerConfig"], "models.deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"], "models.deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig"], "models.detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig"], "models.dialogpt": [], "models.distilbert": ["DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DistilBertConfig", "DistilBertTokenizer"], "models.dit": [], "models.donut": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutProcessor", "DonutSwinConfig"], "models.dpr": [ "DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig", "DPRContextEncoderTokenizer", "DPRQuestionEncoderTokenizer", "DPRReaderOutput", "DPRReaderTokenizer", ], "models.dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"], "models.electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraTokenizer"], "models.encoder_decoder": ["EncoderDecoderConfig"], "models.ernie": [ "ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", ], "models.esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig", "EsmTokenizer"], "models.flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertTokenizer"], "models.flava": [ "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlavaConfig", "FlavaImageCodebookConfig", "FlavaImageConfig", "FlavaMultimodalConfig", "FlavaTextConfig", ], "models.fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"], "models.fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig", "FSMTTokenizer"], "models.funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig", "FunnelTokenizer"], "models.glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"], "models.gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2Tokenizer"], "models.gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig"], "models.gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"], "models.gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"], "models.gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig"], "models.groupvit": [ "GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GroupViTConfig", "GroupViTTextConfig", "GroupViTVisionConfig", ], "models.herbert": ["HerbertTokenizer"], "models.hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"], "models.ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig"], "models.imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig"], "models.jukebox": [ "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "JukeboxConfig", "JukeboxPriorConfig", "JukeboxTokenizer", "JukeboxVQVAEConfig", ], "models.layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMTokenizer"], "models.layoutlmv2": [ "LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config", "LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor", "LayoutLMv2Processor", "LayoutLMv2Tokenizer", ], "models.layoutlmv3": [ "LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv3Config", "LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor", "LayoutLMv3Processor", "LayoutLMv3Tokenizer", ], "models.layoutxlm": ["LayoutXLMProcessor"], "models.led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig", "LEDTokenizer"], "models.levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig"], "models.lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"], "models.longformer": ["LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongformerConfig", "LongformerTokenizer"], "models.longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config"], "models.luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig", "LukeTokenizer"], "models.lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig", "LxmertTokenizer"], "models.m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config"], "models.marian": ["MarianConfig"], "models.markuplm": [ "MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarkupLMConfig", "MarkupLMFeatureExtractor", "MarkupLMProcessor", "MarkupLMTokenizer", ], "models.maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"], "models.mbart": ["MBartConfig"], "models.mbart50": [], "models.mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig", "MCTCTProcessor"], "models.megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"], "models.megatron_gpt2": [], "models.mluke": [], "models.mmbt": ["MMBTConfig"], "models.mobilebert": ["MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileBertConfig", "MobileBertTokenizer"], "models.mobilenet_v2": ["MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileNetV2Config"], "models.mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig"], "models.mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig", "MPNetTokenizer"], "models.mt5": ["MT5Config"], "models.mvp": ["MvpConfig", "MvpTokenizer"], "models.nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"], "models.nllb": [], "models.nystromformer": [ "NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig", ], "models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"], "models.opt": ["OPTConfig"], "models.owlvit": [ "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OwlViTConfig", "OwlViTProcessor", "OwlViTTextConfig", "OwlViTVisionConfig", ], "models.pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig", "PegasusTokenizer"], "models.pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"], "models.perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverTokenizer"], "models.phobert": ["PhobertTokenizer"], "models.plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"], "models.poolformer": ["POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PoolFormerConfig"], "models.prophetnet": ["PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ProphetNetConfig", "ProphetNetTokenizer"], "models.qdqbert": ["QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "QDQBertConfig"], "models.rag": ["RagConfig", "RagRetriever", "RagTokenizer"], "models.realm": ["REALM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RealmConfig", "RealmTokenizer"], "models.reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"], "models.regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"], "models.rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig"], "models.resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig"], "models.retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer"], "models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"], "models.roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig", "RoCBertTokenizer"], "models.roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerTokenizer"], "models.segformer": ["SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SegformerConfig"], "models.sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"], "models.sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"], "models.speech_encoder_decoder": ["SpeechEncoderDecoderConfig"], "models.speech_to_text": [ "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig", ], "models.speech_to_text_2": [ "SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2Text2Config", "Speech2Text2Processor", "Speech2Text2Tokenizer", ], "models.splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig", "SplinterTokenizer"], "models.squeezebert": ["SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "SqueezeBertConfig", "SqueezeBertTokenizer"], "models.swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig"], "models.swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"], "models.switch_transformers": ["SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwitchTransformersConfig"], "models.t5": ["T5_PRETRAINED_CONFIG_ARCHIVE_MAP", "T5Config"], "models.table_transformer": ["TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TableTransformerConfig"], "models.tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig", "TapasTokenizer"], "models.tapex": ["TapexTokenizer"], "models.time_series_transformer": [ "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimeSeriesTransformerConfig", ], "models.trajectory_transformer": [ "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrajectoryTransformerConfig", ], "models.transfo_xl": [ "TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig", "TransfoXLCorpus", "TransfoXLTokenizer", ], "models.trocr": [ "TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig", "TrOCRProcessor", ], "models.unispeech": [ "UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig", ], "models.unispeech_sat": [ "UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechSatConfig", ], "models.van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"], "models.videomae": ["VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "VideoMAEConfig"], "models.vilt": [ "VILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViltConfig", "ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor", ], "models.vision_encoder_decoder": ["VisionEncoderDecoderConfig"], "models.vision_text_dual_encoder": ["VisionTextDualEncoderConfig", "VisionTextDualEncoderProcessor"], "models.visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"], "models.vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig"], "models.vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"], "models.vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"], "models.wav2vec2": [ "WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config", "Wav2Vec2CTCTokenizer", "Wav2Vec2FeatureExtractor", "Wav2Vec2Processor", "Wav2Vec2Tokenizer", ], "models.wav2vec2_conformer": [ "WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2ConformerConfig", ], "models.wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"], "models.wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"], "models.wavlm": [ "WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig", ], "models.whisper": [ "WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperFeatureExtractor", "WhisperProcessor", "WhisperTokenizer", ], "models.x_clip": [ "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "XCLIPConfig", "XCLIPProcessor", "XCLIPTextConfig", "XCLIPVisionConfig", ], "models.xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"], "models.xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMTokenizer"], "models.xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"], "models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"], "models.xlm_roberta_xl": ["XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaXLConfig"], "models.xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"], "models.yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig"], "models.yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"], "onnx": [], "pipelines": [ "AudioClassificationPipeline", "AutomaticSpeechRecognitionPipeline", "Conversation", "ConversationalPipeline", "CsvPipelineDataFormat", "DepthEstimationPipeline", "DocumentQuestionAnsweringPipeline", "FeatureExtractionPipeline", "FillMaskPipeline", "ImageClassificationPipeline", "ImageSegmentationPipeline", "ImageToTextPipeline", "JsonPipelineDataFormat", "NerPipeline", "ObjectDetectionPipeline", "PipedPipelineDataFormat", "Pipeline", "PipelineDataFormat", "QuestionAnsweringPipeline", "SummarizationPipeline", "TableQuestionAnsweringPipeline", "Text2TextGenerationPipeline", "TextClassificationPipeline", "TextGenerationPipeline", "TokenClassificationPipeline", "TranslationPipeline", "VisualQuestionAnsweringPipeline", "ZeroShotClassificationPipeline", "ZeroShotImageClassificationPipeline", "ZeroShotObjectDetectionPipeline", "pipeline", ], "processing_utils": ["ProcessorMixin"], "testing_utils": [], "tokenization_utils": ["PreTrainedTokenizer"], "tokenization_utils_base": [ "AddedToken", "BatchEncoding", "CharSpan", "PreTrainedTokenizerBase", "SpecialTokensMixin", "TokenSpan", ], "trainer_callback": [ "DefaultFlowCallback", "EarlyStoppingCallback", "PrinterCallback", "ProgressCallback", "TrainerCallback", "TrainerControl", "TrainerState", ], "trainer_utils": ["EvalPrediction", "IntervalStrategy", "SchedulerType", "enable_full_determinism", "set_seed"], "training_args": ["TrainingArguments"], "training_args_seq2seq": ["Seq2SeqTrainingArguments"], "training_args_tf": ["TFTrainingArguments"], "utils": [ "CONFIG_NAME", "MODEL_CARD_NAME", "PYTORCH_PRETRAINED_BERT_CACHE", "PYTORCH_TRANSFORMERS_CACHE", "SPIECE_UNDERLINE", "TF2_WEIGHTS_NAME", "TF_WEIGHTS_NAME", "TRANSFORMERS_CACHE", "WEIGHTS_NAME", "TensorType", "add_end_docstrings", "add_start_docstrings", "is_apex_available", "is_datasets_available", "is_faiss_available", "is_flax_available", "is_phonemizer_available", "is_psutil_available", "is_py3nvml_available", "is_pyctcdecode_available", "is_safetensors_available", "is_scipy_available", "is_sentencepiece_available", "is_sklearn_available", "is_speech_available", "is_tensorflow_text_available", "is_tf_available", "is_timm_available", "is_tokenizers_available", "is_torch_available", "is_torch_tpu_available", "is_vision_available", "logging", ], "utils.bitsandbytes": [], } # sentencepiece-backed objects try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_sentencepiece_objects _import_structure["utils.dummy_sentencepiece_objects"] = [ name for name in dir(dummy_sentencepiece_objects) if not name.startswith("_") ] else: _import_structure["models.albert"].append("AlbertTokenizer") _import_structure["models.barthez"].append("BarthezTokenizer") _import_structure["models.bartpho"].append("BartphoTokenizer") _import_structure["models.bert_generation"].append("BertGenerationTokenizer") _import_structure["models.big_bird"].append("BigBirdTokenizer") _import_structure["models.camembert"].append("CamembertTokenizer") _import_structure["models.cpm"].append("CpmTokenizer") _import_structure["models.deberta_v2"].append("DebertaV2Tokenizer") _import_structure["models.fnet"].append("FNetTokenizer") _import_structure["models.layoutxlm"].append("LayoutXLMTokenizer") _import_structure["models.m2m_100"].append("M2M100Tokenizer") _import_structure["models.marian"].append("MarianTokenizer") _import_structure["models.mbart"].append("MBartTokenizer") _import_structure["models.nllb"].append("NllbTokenizer") _import_structure["models.mbart50"].append("MBart50Tokenizer") _import_structure["models.mluke"].append("MLukeTokenizer") _import_structure["models.mt5"].append("MT5Tokenizer") _import_structure["models.pegasus"].append("PegasusTokenizer") _import_structure["models.plbart"].append("PLBartTokenizer") _import_structure["models.reformer"].append("ReformerTokenizer") _import_structure["models.rembert"].append("RemBertTokenizer") _import_structure["models.speech_to_text"].append("Speech2TextTokenizer") _import_structure["models.t5"].append("T5Tokenizer") _import_structure["models.xglm"].append("XGLMTokenizer") _import_structure["models.xlm_prophetnet"].append("XLMProphetNetTokenizer") _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizer") _import_structure["models.xlnet"].append("XLNetTokenizer") # tokenizers-backed objects try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tokenizers_objects _import_structure["utils.dummy_tokenizers_objects"] = [ name for name in dir(dummy_tokenizers_objects) if not name.startswith("_") ] else: # Fast tokenizers structure _import_structure["models.albert"].append("AlbertTokenizerFast") _import_structure["models.bart"].append("BartTokenizerFast") _import_structure["models.barthez"].append("BarthezTokenizerFast") _import_structure["models.bert"].append("BertTokenizerFast") _import_structure["models.big_bird"].append("BigBirdTokenizerFast") _import_structure["models.blenderbot"].append("BlenderbotTokenizerFast") _import_structure["models.blenderbot_small"].append("BlenderbotSmallTokenizerFast") _import_structure["models.bloom"].append("BloomTokenizerFast") _import_structure["models.camembert"].append("CamembertTokenizerFast") _import_structure["models.clip"].append("CLIPTokenizerFast") _import_structure["models.codegen"].append("CodeGenTokenizerFast") _import_structure["models.convbert"].append("ConvBertTokenizerFast") _import_structure["models.cpm"].append("CpmTokenizerFast") _import_structure["models.deberta"].append("DebertaTokenizerFast") _import_structure["models.deberta_v2"].append("DebertaV2TokenizerFast") _import_structure["models.distilbert"].append("DistilBertTokenizerFast") _import_structure["models.dpr"].extend( ["DPRContextEncoderTokenizerFast", "DPRQuestionEncoderTokenizerFast", "DPRReaderTokenizerFast"] ) _import_structure["models.electra"].append("ElectraTokenizerFast") _import_structure["models.fnet"].append("FNetTokenizerFast") _import_structure["models.funnel"].append("FunnelTokenizerFast") _import_structure["models.gpt2"].append("GPT2TokenizerFast") _import_structure["models.gpt_neox"].append("GPTNeoXTokenizerFast") _import_structure["models.gpt_neox_japanese"].append("GPTNeoXJapaneseTokenizer") _import_structure["models.herbert"].append("HerbertTokenizerFast") _import_structure["models.layoutlm"].append("LayoutLMTokenizerFast") _import_structure["models.layoutlmv2"].append("LayoutLMv2TokenizerFast") _import_structure["models.layoutlmv3"].append("LayoutLMv3TokenizerFast") _import_structure["models.layoutxlm"].append("LayoutXLMTokenizerFast") _import_structure["models.led"].append("LEDTokenizerFast") _import_structure["models.longformer"].append("LongformerTokenizerFast") _import_structure["models.lxmert"].append("LxmertTokenizerFast") _import_structure["models.markuplm"].append("MarkupLMTokenizerFast") _import_structure["models.mbart"].append("MBartTokenizerFast") _import_structure["models.mbart50"].append("MBart50TokenizerFast") _import_structure["models.mobilebert"].append("MobileBertTokenizerFast") _import_structure["models.mpnet"].append("MPNetTokenizerFast") _import_structure["models.mt5"].append("MT5TokenizerFast") _import_structure["models.mvp"].append("MvpTokenizerFast") _import_structure["models.nllb"].append("NllbTokenizerFast") _import_structure["models.openai"].append("OpenAIGPTTokenizerFast") _import_structure["models.pegasus"].append("PegasusTokenizerFast") _import_structure["models.realm"].append("RealmTokenizerFast") _import_structure["models.reformer"].append("ReformerTokenizerFast") _import_structure["models.rembert"].append("RemBertTokenizerFast") _import_structure["models.retribert"].append("RetriBertTokenizerFast") _import_structure["models.roberta"].append("RobertaTokenizerFast") _import_structure["models.roformer"].append("RoFormerTokenizerFast") _import_structure["models.splinter"].append("SplinterTokenizerFast") _import_structure["models.squeezebert"].append("SqueezeBertTokenizerFast") _import_structure["models.t5"].append("T5TokenizerFast") _import_structure["models.xglm"].append("XGLMTokenizerFast") _import_structure["models.xlm_roberta"].append("XLMRobertaTokenizerFast") _import_structure["models.xlnet"].append("XLNetTokenizerFast") _import_structure["tokenization_utils_fast"] = ["PreTrainedTokenizerFast"] try: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_sentencepiece_and_tokenizers_objects _import_structure["utils.dummy_sentencepiece_and_tokenizers_objects"] = [ name for name in dir(dummy_sentencepiece_and_tokenizers_objects) if not name.startswith("_") ] else: _import_structure["convert_slow_tokenizer"] = ["SLOW_TO_FAST_CONVERTERS", "convert_slow_tokenizer"] # Speech-specific objects try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_speech_objects _import_structure["utils.dummy_speech_objects"] = [ name for name in dir(dummy_speech_objects) if not name.startswith("_") ] else: _import_structure["models.mctct"].append("MCTCTFeatureExtractor") _import_structure["models.speech_to_text"].append("Speech2TextFeatureExtractor") # Tensorflow-text-specific objects try: if not is_tensorflow_text_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tensorflow_text_objects _import_structure["utils.dummy_tensorflow_text_objects"] = [ name for name in dir(dummy_tensorflow_text_objects) if not name.startswith("_") ] else: _import_structure["models.bert"].append("TFBertTokenizer") try: if not (is_sentencepiece_available() and is_speech_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_sentencepiece_and_speech_objects _import_structure["utils.dummy_sentencepiece_and_speech_objects"] = [ name for name in dir(dummy_sentencepiece_and_speech_objects) if not name.startswith("_") ] else: _import_structure["models.speech_to_text"].append("Speech2TextProcessor") # Vision-specific objects try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_vision_objects _import_structure["utils.dummy_vision_objects"] = [ name for name in dir(dummy_vision_objects) if not name.startswith("_") ] else: _import_structure["image_processing_utils"] = ["ImageProcessingMixin"] _import_structure["image_transforms"] = ["rescale", "resize", "to_pil_image"] _import_structure["image_utils"] = ["ImageFeatureExtractionMixin"] _import_structure["models.beit"].extend(["BeitFeatureExtractor", "BeitImageProcessor"]) _import_structure["models.clip"].extend(["CLIPFeatureExtractor", "CLIPImageProcessor"]) _import_structure["models.convnext"].extend(["ConvNextFeatureExtractor", "ConvNextImageProcessor"]) _import_structure["models.deformable_detr"].append("DeformableDetrFeatureExtractor") _import_structure["models.deit"].extend(["DeiTFeatureExtractor", "DeiTImageProcessor"]) _import_structure["models.detr"].append("DetrFeatureExtractor") _import_structure["models.conditional_detr"].append("ConditionalDetrFeatureExtractor") _import_structure["models.donut"].append("DonutFeatureExtractor") _import_structure["models.dpt"].extend(["DPTFeatureExtractor", "DPTImageProcessor"]) _import_structure["models.flava"].extend(["FlavaFeatureExtractor", "FlavaProcessor", "FlavaImageProcessor"]) _import_structure["models.glpn"].extend(["GLPNFeatureExtractor", "GLPNImageProcessor"]) _import_structure["models.imagegpt"].extend(["ImageGPTFeatureExtractor", "ImageGPTImageProcessor"]) _import_structure["models.layoutlmv2"].extend(["LayoutLMv2FeatureExtractor", "LayoutLMv2ImageProcessor"]) _import_structure["models.layoutlmv3"].extend(["LayoutLMv3FeatureExtractor", "LayoutLMv3ImageProcessor"]) _import_structure["models.levit"].extend(["LevitFeatureExtractor", "LevitImageProcessor"]) _import_structure["models.maskformer"].append("MaskFormerFeatureExtractor") _import_structure["models.mobilenet_v2"].extend(["MobileNetV2FeatureExtractor", "MobileNetV2ImageProcessor"]) _import_structure["models.mobilevit"].extend(["MobileViTFeatureExtractor", "MobileViTImageProcessor"]) _import_structure["models.owlvit"].append("OwlViTFeatureExtractor") _import_structure["models.perceiver"].extend(["PerceiverFeatureExtractor", "PerceiverImageProcessor"]) _import_structure["models.poolformer"].extend(["PoolFormerFeatureExtractor", "PoolFormerImageProcessor"]) _import_structure["models.segformer"].extend(["SegformerFeatureExtractor", "SegformerImageProcessor"]) _import_structure["models.videomae"].extend(["VideoMAEFeatureExtractor", "VideoMAEImageProcessor"]) _import_structure["models.vilt"].extend(["ViltFeatureExtractor", "ViltImageProcessor", "ViltProcessor"]) _import_structure["models.vit"].extend(["ViTFeatureExtractor", "ViTImageProcessor"]) _import_structure["models.yolos"].extend(["YolosFeatureExtractor"]) # Timm-backed objects try: if not (is_timm_available() and is_vision_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_timm_and_vision_objects _import_structure["utils.dummy_timm_and_vision_objects"] = [ name for name in dir(dummy_timm_and_vision_objects) if not name.startswith("_") ] else: _import_structure["models.deformable_detr"].extend( [ "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DeformableDetrForObjectDetection", "DeformableDetrModel", "DeformableDetrPreTrainedModel", ] ) _import_structure["models.detr"].extend( [ "DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "DetrForObjectDetection", "DetrForSegmentation", "DetrModel", "DetrPreTrainedModel", ] ) _import_structure["models.table_transformer"].extend( [ "TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TableTransformerForObjectDetection", "TableTransformerModel", "TableTransformerPreTrainedModel", ] ) _import_structure["models.conditional_detr"].extend( [ "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", "ConditionalDetrForObjectDetection", "ConditionalDetrForSegmentation", "ConditionalDetrModel", "ConditionalDetrPreTrainedModel", ] ) # PyTorch-backed objects try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_pt_objects _import_structure["utils.dummy_pt_objects"] = [name for name in dir(dummy_pt_objects) if not name.startswith("_")] else: _import_structure["activations"] = [] _import_structure["benchmark.benchmark"] = ["PyTorchBenchmark"] _import_structure["benchmark.benchmark_args"] = ["PyTorchBenchmarkArguments"] _import_structure["data.datasets"] = [ "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "SquadDataset", "SquadDataTrainingArguments", "TextDataset", "TextDatasetForNextSentencePrediction", ] _import_structure["deepspeed"] = [] _import_structure["generation_utils"] = [] _import_structure["generation"].extend( [ "Constraint", "ConstraintListState", "DisjunctiveConstraint", "PhrasalConstraint", "BeamScorer", "BeamSearchScorer", "ConstrainedBeamSearchScorer", "ForcedBOSTokenLogitsProcessor", "ForcedEOSTokenLogitsProcessor", "HammingDiversityLogitsProcessor", "InfNanRemoveLogitsProcessor", "LogitsProcessor", "LogitsProcessorList", "LogitsWarper", "MinLengthLogitsProcessor", "NoBadWordsLogitsProcessor", "NoRepeatNGramLogitsProcessor", "PrefixConstrainedLogitsProcessor", "RepetitionPenaltyLogitsProcessor", "TemperatureLogitsWarper", "TopKLogitsWarper", "TopPLogitsWarper", "TypicalLogitsWarper", "MaxLengthCriteria", "MaxTimeCriteria", "StoppingCriteria", "StoppingCriteriaList", "GenerationMixin", "top_k_top_p_filtering", ] ) _import_structure["modeling_outputs"] = [] _import_structure["modeling_utils"] = ["PreTrainedModel"] # PyTorch models structure _import_structure["models.roc_bert"].extend( [ "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RoCBertForMaskedLM", "RoCBertForCausalLM", "RoCBertForMultipleChoice", "RoCBertForQuestionAnswering", "RoCBertForSequenceClassification", "RoCBertForTokenClassification", "RoCBertLayer", "RoCBertModel", "RoCBertForPreTraining", "RoCBertPreTrainedModel", "load_tf_weights_in_roc_bert", ] ) _import_structure["models.time_series_transformer"].extend( [ "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TimeSeriesTransformerForPrediction", "TimeSeriesTransformerModel", "TimeSeriesTransformerPreTrainedModel", ] ) _import_structure["models.albert"].extend( [ "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "AlbertForMaskedLM", "AlbertForMultipleChoice", "AlbertForPreTraining", "AlbertForQuestionAnswering", "AlbertForSequenceClassification", "AlbertForTokenClassification", "AlbertModel", "AlbertPreTrainedModel", "load_tf_weights_in_albert", ] ) _import_structure["models.auto"].extend( [ "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", "MODEL_FOR_AUDIO_XVECTOR_MAPPING", "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", "MODEL_FOR_CAUSAL_LM_MAPPING", "MODEL_FOR_CTC_MAPPING", "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "MODEL_FOR_MASKED_LM_MAPPING", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "MODEL_FOR_OBJECT_DETECTION_MAPPING", "MODEL_FOR_PRETRAINING_MAPPING", "MODEL_FOR_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", "MODEL_FOR_VISION_2_SEQ_MAPPING", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", "MODEL_MAPPING", "MODEL_WITH_LM_HEAD_MAPPING", "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", "AutoModel", "AutoBackbone", "AutoModelForAudioClassification", "AutoModelForAudioFrameClassification", "AutoModelForAudioXVector", "AutoModelForCausalLM", "AutoModelForCTC", "AutoModelForDocumentQuestionAnswering", "AutoModelForDepthEstimation", "AutoModelForImageClassification", "AutoModelForImageSegmentation", "AutoModelForInstanceSegmentation", "AutoModelForMaskedImageModeling", "AutoModelForMaskedLM", "AutoModelForMultipleChoice", "AutoModelForNextSentencePrediction", "AutoModelForObjectDetection", "AutoModelForPreTraining", "AutoModelForQuestionAnswering", "AutoModelForSemanticSegmentation", "AutoModelForSeq2SeqLM", "AutoModelForSequenceClassification", "AutoModelForSpeechSeq2Seq", "AutoModelForTableQuestionAnswering", "AutoModelForTokenClassification", "AutoModelForVideoClassification", "AutoModelForVision2Seq", "AutoModelForVisualQuestionAnswering", "AutoModelWithLMHead", "AutoModelForZeroShotObjectDetection", ] ) _import_structure["models.bart"].extend( [ "BART_PRETRAINED_MODEL_ARCHIVE_LIST", "BartForCausalLM", "BartForConditionalGeneration", "BartForQuestionAnswering", "BartForSequenceClassification", "BartModel", "BartPretrainedModel", "PretrainedBartModel", ] ) _import_structure["models.mvp"].extend( [ "MVP_PRETRAINED_MODEL_ARCHIVE_LIST", "MvpForCausalLM", "MvpForConditionalGeneration", "MvpForQuestionAnswering", "MvpForSequenceClassification", "MvpModel", "MvpPreTrainedModel", ] ) _import_structure["models.beit"].extend( [ "BEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "BeitForImageClassification", "BeitForMaskedImageModeling", "BeitForSemanticSegmentation", "BeitModel", "BeitPreTrainedModel", ] ) _import_structure["models.bert"].extend( [ "BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "BertForMaskedLM", "BertForMultipleChoice", "BertForNextSentencePrediction", "BertForPreTraining", "BertForQuestionAnswering", "BertForSequenceClassification", "BertForTokenClassification", "BertLayer", "BertLMHeadModel", "BertModel", "BertPreTrainedModel", "load_tf_weights_in_bert", ] ) _import_structure["models.bert_generation"].extend( [ "BertGenerationDecoder", "BertGenerationEncoder", "BertGenerationPreTrainedModel", "load_tf_weights_in_bert_generation", ] ) _import_structure["models.big_bird"].extend( [ "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdForCausalLM", "BigBirdForMaskedLM", "BigBirdForMultipleChoice", "BigBirdForPreTraining", "BigBirdForQuestionAnswering", "BigBirdForSequenceClassification", "BigBirdForTokenClassification", "BigBirdLayer", "BigBirdModel", "BigBirdPreTrainedModel", "load_tf_weights_in_big_bird", ] ) _import_structure["models.bigbird_pegasus"].extend( [ "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST", "BigBirdPegasusForCausalLM", "BigBirdPegasusForConditionalGeneration", "BigBirdPegasusForQuestionAnswering", "BigBirdPegasusForSequenceClassification", "BigBirdPegasusModel", "BigBirdPegasusPreTrainedModel", ] ) _import_structure["models.bloom"].extend( [ "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST", "BloomForCausalLM", "BloomModel", "BloomPreTrainedModel", "BloomForSequenceClassification", "BloomForTokenClassification", "BloomForQuestionAnswering", ] ) _import_structure["models.blenderbot"].extend( [ "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST", "BlenderbotForCausalLM", "BlenderbotForConditionalGeneration", "BlenderbotModel", "BlenderbotPreTrainedModel", ] ) _import_structure["models.blenderbot_small"].extend( [ "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST", "BlenderbotSmallForCausalLM", "BlenderbotSmallForConditionalGeneration", "BlenderbotSmallModel", "BlenderbotSmallPreTrainedModel", ] ) _import_structure["models.camembert"].extend( [ "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "CamembertForCausalLM", "CamembertForMaskedLM", "CamembertForMultipleChoice", "CamembertForQuestionAnswering", "CamembertForSequenceClassification", "CamembertForTokenClassification", "CamembertModel", "CamembertPreTrainedModel", ] ) _import_structure["models.canine"].extend( [ "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST", "CanineForMultipleChoice", "CanineForQuestionAnswering", "CanineForSequenceClassification", "CanineForTokenClassification", "CanineLayer", "CanineModel", "CaninePreTrainedModel", "load_tf_weights_in_canine", ] ) _import_structure["models.clip"].extend( [ "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPModel", "CLIPPreTrainedModel", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", ] ) _import_structure["models.clipseg"].extend( [ "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPSegModel", "CLIPSegPreTrainedModel", "CLIPSegTextModel", "CLIPSegVisionModel", "CLIPSegForImageSegmentation", ] ) _import_structure["models.x_clip"].extend( [ "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "XCLIPModel", "XCLIPPreTrainedModel", "XCLIPTextModel", "XCLIPVisionModel", ] ) _import_structure["models.convbert"].extend( [ "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvBertForMaskedLM", "ConvBertForMultipleChoice", "ConvBertForQuestionAnswering", "ConvBertForSequenceClassification", "ConvBertForTokenClassification", "ConvBertLayer", "ConvBertModel", "ConvBertPreTrainedModel", "load_tf_weights_in_convbert", ] ) _import_structure["models.convnext"].extend( [ "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "ConvNextForImageClassification", "ConvNextModel", "ConvNextPreTrainedModel", ] ) _import_structure["models.ctrl"].extend( [ "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", "CTRLForSequenceClassification", "CTRLLMHeadModel", "CTRLModel", "CTRLPreTrainedModel", ] ) _import_structure["models.cvt"].extend( [ "CVT_PRETRAINED_MODEL_ARCHIVE_LIST", "CvtForImageClassification", "CvtModel", "CvtPreTrainedModel", ] ) _import_structure["models.data2vec"].extend( [ "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST", "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST", "Data2VecAudioForAudioFrameClassification", "Data2VecAudioForCTC", "Data2VecAudioForSequenceClassification", "Data2VecAudioForXVector", "Data2VecAudioModel", "Data2VecAudioPreTrainedModel", "Data2VecTextForCausalLM", "Data2VecTextForMaskedLM", "Data2VecTextForMultipleChoice", "Data2VecTextForQuestionAnswering", "Data2VecTextForSequenceClassification", "Data2VecTextForTokenClassification", "Data2VecTextModel", "Data2VecTextPreTrainedModel", "Data2VecVisionForImageClassification", "Data2VecVisionForSemanticSegmentation", "Data2VecVisionModel", "Data2VecVisionPreTrainedModel", ] ) _import_structure["models.deberta"].extend( [ "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "DebertaForMaskedLM", "DebertaForQuestionAnswering", "DebertaForSequenceClassification", "DebertaForTokenClassification", "DebertaModel", "DebertaPreTrainedModel", ] ) _import_structure["models.deberta_v2"].extend( [ "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "DebertaV2ForMaskedLM", "DebertaV2ForMultipleChoice", "DebertaV2ForQuestionAnswering", "DebertaV2ForSequenceClassification", "DebertaV2ForTokenClassification", "DebertaV2Model", "DebertaV2PreTrainedModel", ] ) _import_structure["models.decision_transformer"].extend( [ "DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "DecisionTransformerGPT2Model", "DecisionTransformerGPT2PreTrainedModel", "DecisionTransformerModel", "DecisionTransformerPreTrainedModel", ] ) _import_structure["models.deit"].extend( [ "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "DeiTForImageClassification", "DeiTForImageClassificationWithTeacher", "DeiTForMaskedImageModeling", "DeiTModel", "DeiTPreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "DistilBertForMaskedLM", "DistilBertForMultipleChoice", "DistilBertForQuestionAnswering", "DistilBertForSequenceClassification", "DistilBertForTokenClassification", "DistilBertModel", "DistilBertPreTrainedModel", ] ) _import_structure["models.donut"].extend( [ "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "DonutSwinModel", "DonutSwinPreTrainedModel", ] ) _import_structure["models.dpr"].extend( [ "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "DPRContextEncoder", "DPRPretrainedContextEncoder", "DPRPreTrainedModel", "DPRPretrainedQuestionEncoder", "DPRPretrainedReader", "DPRQuestionEncoder", "DPRReader", ] ) _import_structure["models.dpt"].extend( [ "DPT_PRETRAINED_MODEL_ARCHIVE_LIST", "DPTForDepthEstimation", "DPTForSemanticSegmentation", "DPTModel", "DPTPreTrainedModel", ] ) _import_structure["models.electra"].extend( [ "ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "ElectraForCausalLM", "ElectraForMaskedLM", "ElectraForMultipleChoice", "ElectraForPreTraining", "ElectraForQuestionAnswering", "ElectraForSequenceClassification", "ElectraForTokenClassification", "ElectraModel", "ElectraPreTrainedModel", "load_tf_weights_in_electra", ] ) _import_structure["models.encoder_decoder"].append("EncoderDecoderModel") _import_structure["models.ernie"].extend( [ "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST", "ErnieForCausalLM", "ErnieForMaskedLM", "ErnieForMultipleChoice", "ErnieForNextSentencePrediction", "ErnieForPreTraining", "ErnieForQuestionAnswering", "ErnieForSequenceClassification", "ErnieForTokenClassification", "ErnieModel", "ErniePreTrainedModel", ] ) _import_structure["models.esm"].extend( [ "ESM_PRETRAINED_MODEL_ARCHIVE_LIST", "EsmFoldPreTrainedModel", "EsmForMaskedLM", "EsmForProteinFolding", "EsmForSequenceClassification", "EsmForTokenClassification", "EsmModel", "EsmPreTrainedModel", ] ) _import_structure["models.flaubert"].extend( [ "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "FlaubertForMultipleChoice", "FlaubertForQuestionAnswering", "FlaubertForQuestionAnsweringSimple", "FlaubertForSequenceClassification", "FlaubertForTokenClassification", "FlaubertModel", "FlaubertWithLMHeadModel", "FlaubertPreTrainedModel", ] ) _import_structure["models.flava"].extend( [ "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST", "FlavaForPreTraining", "FlavaImageCodebook", "FlavaImageModel", "FlavaModel", "FlavaMultimodalModel", "FlavaPreTrainedModel", "FlavaTextModel", ] ) _import_structure["models.fnet"].extend( [ "FNET_PRETRAINED_MODEL_ARCHIVE_LIST", "FNetForMaskedLM", "FNetForMultipleChoice", "FNetForNextSentencePrediction", "FNetForPreTraining", "FNetForQuestionAnswering", "FNetForSequenceClassification", "FNetForTokenClassification", "FNetLayer", "FNetModel", "FNetPreTrainedModel", ] ) _import_structure["models.fsmt"].extend(["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]) _import_structure["models.funnel"].extend( [ "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "FunnelBaseModel", "FunnelForMaskedLM", "FunnelForMultipleChoice", "FunnelForPreTraining", "FunnelForQuestionAnswering", "FunnelForSequenceClassification", "FunnelForTokenClassification", "FunnelModel", "FunnelPreTrainedModel", "load_tf_weights_in_funnel", ] ) _import_structure["models.glpn"].extend( [ "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST", "GLPNForDepthEstimation", "GLPNModel", "GLPNPreTrainedModel", ] ) _import_structure["models.gpt2"].extend( [ "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "GPT2DoubleHeadsModel", "GPT2ForSequenceClassification", "GPT2ForTokenClassification", "GPT2LMHeadModel", "GPT2Model", "GPT2PreTrainedModel", "load_tf_weights_in_gpt2", ] ) _import_structure["models.gpt_neo"].extend( [ "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoForCausalLM", "GPTNeoForSequenceClassification", "GPTNeoModel", "GPTNeoPreTrainedModel", "load_tf_weights_in_gpt_neo", ] ) _import_structure["models.gpt_neox"].extend( [ "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXForCausalLM", "GPTNeoXLayer", "GPTNeoXModel", "GPTNeoXPreTrainedModel", ] ) _import_structure["models.gpt_neox_japanese"].extend( [ "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXJapaneseForCausalLM", "GPTNeoXJapaneseLayer", "GPTNeoXJapaneseModel", "GPTNeoXJapanesePreTrainedModel", ] ) _import_structure["models.gptj"].extend( [ "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTJForCausalLM", "GPTJForQuestionAnswering", "GPTJForSequenceClassification", "GPTJModel", "GPTJPreTrainedModel", ] ) _import_structure["models.groupvit"].extend( [ "GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "GroupViTModel", "GroupViTPreTrainedModel", "GroupViTTextModel", "GroupViTVisionModel", ] ) _import_structure["models.codegen"].extend( [ "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST", "CodeGenForCausalLM", "CodeGenModel", "CodeGenPreTrainedModel", ] ) _import_structure["models.hubert"].extend( [ "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "HubertForCTC", "HubertForSequenceClassification", "HubertModel", "HubertPreTrainedModel", ] ) _import_structure["models.ibert"].extend( [ "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "IBertForMaskedLM", "IBertForMultipleChoice", "IBertForQuestionAnswering", "IBertForSequenceClassification", "IBertForTokenClassification", "IBertModel", "IBertPreTrainedModel", ] ) _import_structure["models.imagegpt"].extend( [ "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST", "ImageGPTForCausalImageModeling", "ImageGPTForImageClassification", "ImageGPTModel", "ImageGPTPreTrainedModel", "load_tf_weights_in_imagegpt", ] ) _import_structure["models.jukebox"].extend( [ "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST", "JukeboxModel", "JukeboxPreTrainedModel", "JukeboxVQVAE", "JukeboxPrior", ] ) _import_structure["models.layoutlm"].extend( [ "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMForMaskedLM", "LayoutLMForSequenceClassification", "LayoutLMForTokenClassification", "LayoutLMForQuestionAnswering", "LayoutLMModel", "LayoutLMPreTrainedModel", ] ) _import_structure["models.layoutlmv2"].extend( [ "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMv2ForQuestionAnswering", "LayoutLMv2ForSequenceClassification", "LayoutLMv2ForTokenClassification", "LayoutLMv2Model", "LayoutLMv2PreTrainedModel", ] ) _import_structure["models.layoutlmv3"].extend( [ "LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMv3ForQuestionAnswering", "LayoutLMv3ForSequenceClassification", "LayoutLMv3ForTokenClassification", "LayoutLMv3Model", "LayoutLMv3PreTrainedModel", ] ) _import_structure["models.led"].extend( [ "LED_PRETRAINED_MODEL_ARCHIVE_LIST", "LEDForConditionalGeneration", "LEDForQuestionAnswering", "LEDForSequenceClassification", "LEDModel", "LEDPreTrainedModel", ] ) _import_structure["models.levit"].extend( [ "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "LevitForImageClassification", "LevitForImageClassificationWithTeacher", "LevitModel", "LevitPreTrainedModel", ] ) _import_structure["models.longformer"].extend( [ "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "LongformerForMaskedLM", "LongformerForMultipleChoice", "LongformerForQuestionAnswering", "LongformerForSequenceClassification", "LongformerForTokenClassification", "LongformerModel", "LongformerPreTrainedModel", "LongformerSelfAttention", ] ) _import_structure["models.longt5"].extend( [ "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST", "LongT5EncoderModel", "LongT5ForConditionalGeneration", "LongT5Model", "LongT5PreTrainedModel", ] ) _import_structure["models.luke"].extend( [ "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "LukeForMultipleChoice", "LukeForQuestionAnswering", "LukeForSequenceClassification", "LukeForTokenClassification", "LukeForMaskedLM", "LukeModel", "LukePreTrainedModel", ] ) _import_structure["models.lxmert"].extend( [ "LxmertEncoder", "LxmertForPreTraining", "LxmertForQuestionAnswering", "LxmertModel", "LxmertPreTrainedModel", "LxmertVisualFeatureEncoder", "LxmertXLayer", ] ) _import_structure["models.m2m_100"].extend( [ "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST", "M2M100ForConditionalGeneration", "M2M100Model", "M2M100PreTrainedModel", ] ) _import_structure["models.marian"].extend(["MarianForCausalLM", "MarianModel", "MarianMTModel"]) _import_structure["models.maskformer"].extend( [ "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel", ] ) _import_structure["models.markuplm"].extend( [ "MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST", "MarkupLMForQuestionAnswering", "MarkupLMForSequenceClassification", "MarkupLMForTokenClassification", "MarkupLMModel", "MarkupLMPreTrainedModel", ] ) _import_structure["models.mbart"].extend( [ "MBartForCausalLM", "MBartForConditionalGeneration", "MBartForQuestionAnswering", "MBartForSequenceClassification", "MBartModel", "MBartPreTrainedModel", ] ) _import_structure["models.mctct"].extend( [ "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST", "MCTCTForCTC", "MCTCTModel", "MCTCTPreTrainedModel", ] ) _import_structure["models.megatron_bert"].extend( [ "MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "MegatronBertForCausalLM", "MegatronBertForMaskedLM", "MegatronBertForMultipleChoice", "MegatronBertForNextSentencePrediction", "MegatronBertForPreTraining", "MegatronBertForQuestionAnswering", "MegatronBertForSequenceClassification", "MegatronBertForTokenClassification", "MegatronBertModel", "MegatronBertPreTrainedModel", ] ) _import_structure["models.mmbt"].extend(["MMBTForClassification", "MMBTModel", "ModalEmbeddings"]) _import_structure["models.mobilebert"].extend( [ "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileBertForMaskedLM", "MobileBertForMultipleChoice", "MobileBertForNextSentencePrediction", "MobileBertForPreTraining", "MobileBertForQuestionAnswering", "MobileBertForSequenceClassification", "MobileBertForTokenClassification", "MobileBertLayer", "MobileBertModel", "MobileBertPreTrainedModel", "load_tf_weights_in_mobilebert", ] ) _import_structure["models.mobilenet_v2"].extend( [ "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileNetV2ForImageClassification", "MobileNetV2ForSemanticSegmentation", "MobileNetV2Model", "MobileNetV2PreTrainedModel", "load_tf_weights_in_mobilenet_v2", ] ) _import_structure["models.mobilevit"].extend( [ "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "MobileViTForImageClassification", "MobileViTForSemanticSegmentation", "MobileViTModel", "MobileViTPreTrainedModel", ] ) _import_structure["models.mpnet"].extend( [ "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", "MPNetForMaskedLM", "MPNetForMultipleChoice", "MPNetForQuestionAnswering", "MPNetForSequenceClassification", "MPNetForTokenClassification", "MPNetLayer", "MPNetModel", "MPNetPreTrainedModel", ] ) _import_structure["models.mt5"].extend(["MT5EncoderModel", "MT5ForConditionalGeneration", "MT5Model"]) _import_structure["models.nezha"].extend( [ "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST", "NezhaForMaskedLM", "NezhaForPreTraining", "NezhaForNextSentencePrediction", "NezhaForMultipleChoice", "NezhaForQuestionAnswering", "NezhaForSequenceClassification", "NezhaForTokenClassification", "NezhaModel", "NezhaPreTrainedModel", ] ) _import_structure["models.nystromformer"].extend( [ "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "NystromformerForMaskedLM", "NystromformerForMultipleChoice", "NystromformerForQuestionAnswering", "NystromformerForSequenceClassification", "NystromformerForTokenClassification", "NystromformerLayer", "NystromformerModel", "NystromformerPreTrainedModel", ] ) _import_structure["models.openai"].extend( [ "OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OpenAIGPTDoubleHeadsModel", "OpenAIGPTForSequenceClassification", "OpenAIGPTLMHeadModel", "OpenAIGPTModel", "OpenAIGPTPreTrainedModel", "load_tf_weights_in_openai_gpt", ] ) _import_structure["models.opt"].extend( [ "OPT_PRETRAINED_MODEL_ARCHIVE_LIST", "OPTForCausalLM", "OPTModel", "OPTPreTrainedModel", "OPTForSequenceClassification", "OPTForQuestionAnswering", ] ) _import_structure["models.owlvit"].extend( [ "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "OwlViTModel", "OwlViTPreTrainedModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", ] ) _import_structure["models.pegasus"].extend( ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"] ) _import_structure["models.pegasus_x"].extend( [ "PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST", "PegasusXForConditionalGeneration", "PegasusXModel", "PegasusXPreTrainedModel", ] ) _import_structure["models.perceiver"].extend( [ "PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST", "PerceiverForImageClassificationConvProcessing", "PerceiverForImageClassificationFourier", "PerceiverForImageClassificationLearned", "PerceiverForMaskedLM", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "PerceiverForSequenceClassification", "PerceiverLayer", "PerceiverModel", "PerceiverPreTrainedModel", ] ) _import_structure["models.plbart"].extend( [ "PLBART_PRETRAINED_MODEL_ARCHIVE_LIST", "PLBartForCausalLM", "PLBartForConditionalGeneration", "PLBartForSequenceClassification", "PLBartModel", "PLBartPreTrainedModel", ] ) _import_structure["models.poolformer"].extend( [ "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "PoolFormerForImageClassification", "PoolFormerModel", "PoolFormerPreTrainedModel", ] ) _import_structure["models.prophetnet"].extend( [ "PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ProphetNetDecoder", "ProphetNetEncoder", "ProphetNetForCausalLM", "ProphetNetForConditionalGeneration", "ProphetNetModel", "ProphetNetPreTrainedModel", ] ) _import_structure["models.qdqbert"].extend( [ "QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "QDQBertForMaskedLM", "QDQBertForMultipleChoice", "QDQBertForNextSentencePrediction", "QDQBertForQuestionAnswering", "QDQBertForSequenceClassification", "QDQBertForTokenClassification", "QDQBertLayer", "QDQBertLMHeadModel", "QDQBertModel", "QDQBertPreTrainedModel", "load_tf_weights_in_qdqbert", ] ) _import_structure["models.rag"].extend( ["RagModel", "RagPreTrainedModel", "RagSequenceForGeneration", "RagTokenForGeneration"] ) _import_structure["models.realm"].extend( [ "REALM_PRETRAINED_MODEL_ARCHIVE_LIST", "RealmEmbedder", "RealmForOpenQA", "RealmKnowledgeAugEncoder", "RealmPreTrainedModel", "RealmReader", "RealmRetriever", "RealmScorer", "load_tf_weights_in_realm", ] ) _import_structure["models.reformer"].extend( [ "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "ReformerAttention", "ReformerForMaskedLM", "ReformerForQuestionAnswering", "ReformerForSequenceClassification", "ReformerLayer", "ReformerModel", "ReformerModelWithLMHead", "ReformerPreTrainedModel", ] ) _import_structure["models.regnet"].extend( [ "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "RegNetForImageClassification", "RegNetModel", "RegNetPreTrainedModel", ] ) _import_structure["models.rembert"].extend( [ "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RemBertForCausalLM", "RemBertForMaskedLM", "RemBertForMultipleChoice", "RemBertForQuestionAnswering", "RemBertForSequenceClassification", "RemBertForTokenClassification", "RemBertLayer", "RemBertModel", "RemBertPreTrainedModel", "load_tf_weights_in_rembert", ] ) _import_structure["models.resnet"].extend( [ "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ResNetForImageClassification", "ResNetModel", "ResNetPreTrainedModel", "ResNetBackbone", ] ) _import_structure["models.retribert"].extend( ["RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "RetriBertModel", "RetriBertPreTrainedModel"] ) _import_structure["models.roberta"].extend( [ "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaForCausalLM", "RobertaForMaskedLM", "RobertaForMultipleChoice", "RobertaForQuestionAnswering", "RobertaForSequenceClassification", "RobertaForTokenClassification", "RobertaModel", "RobertaPreTrainedModel", ] ) _import_structure["models.lilt"].extend( [ "LILT_PRETRAINED_MODEL_ARCHIVE_LIST", "LiltForQuestionAnswering", "LiltForSequenceClassification", "LiltForTokenClassification", "LiltModel", "LiltPreTrainedModel", ] ) _import_structure["models.roformer"].extend( [ "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "RoFormerForCausalLM", "RoFormerForMaskedLM", "RoFormerForMultipleChoice", "RoFormerForQuestionAnswering", "RoFormerForSequenceClassification", "RoFormerForTokenClassification", "RoFormerLayer", "RoFormerModel", "RoFormerPreTrainedModel", "load_tf_weights_in_roformer", ] ) _import_structure["models.segformer"].extend( [ "SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "SegformerDecodeHead", "SegformerForImageClassification", "SegformerForSemanticSegmentation", "SegformerLayer", "SegformerModel", "SegformerPreTrainedModel", ] ) _import_structure["models.sew"].extend( [ "SEW_PRETRAINED_MODEL_ARCHIVE_LIST", "SEWForCTC", "SEWForSequenceClassification", "SEWModel", "SEWPreTrainedModel", ] ) _import_structure["models.sew_d"].extend( [ "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST", "SEWDForCTC", "SEWDForSequenceClassification", "SEWDModel", "SEWDPreTrainedModel", ] ) _import_structure["models.speech_encoder_decoder"].extend(["SpeechEncoderDecoderModel"]) _import_structure["models.speech_to_text"].extend( [ "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "Speech2TextForConditionalGeneration", "Speech2TextModel", "Speech2TextPreTrainedModel", ] ) _import_structure["models.whisper"].extend( [ "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "WhisperForConditionalGeneration", "WhisperModel", "WhisperPreTrainedModel", ] ) _import_structure["models.speech_to_text_2"].extend(["Speech2Text2ForCausalLM", "Speech2Text2PreTrainedModel"]) _import_structure["models.splinter"].extend( [ "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST", "SplinterForPreTraining", "SplinterForQuestionAnswering", "SplinterLayer", "SplinterModel", "SplinterPreTrainedModel", ] ) _import_structure["models.squeezebert"].extend( [ "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "SqueezeBertForMaskedLM", "SqueezeBertForMultipleChoice", "SqueezeBertForQuestionAnswering", "SqueezeBertForSequenceClassification", "SqueezeBertForTokenClassification", "SqueezeBertModel", "SqueezeBertModule", "SqueezeBertPreTrainedModel", ] ) _import_structure["models.swin"].extend( [ "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "SwinForImageClassification", "SwinForMaskedImageModeling", "SwinModel", "SwinPreTrainedModel", ] ) _import_structure["models.swinv2"].extend( [ "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST", "Swinv2ForImageClassification", "Swinv2ForMaskedImageModeling", "Swinv2Model", "Swinv2PreTrainedModel", ] ) _import_structure["models.tapas"].extend( [ "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TapasForMaskedLM", "TapasForQuestionAnswering", "TapasForSequenceClassification", "TapasModel", "TapasPreTrainedModel", "load_tf_weights_in_tapas", ] ) _import_structure["models.t5"].extend( [ "T5_PRETRAINED_MODEL_ARCHIVE_LIST", "T5EncoderModel", "T5ForConditionalGeneration", "T5Model", "T5PreTrainedModel", "load_tf_weights_in_t5", ] ) _import_structure["models.switch_transformers"].extend( [ "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST", "SwitchTransformersEncoderModel", "SwitchTransformersForConditionalGeneration", "SwitchTransformersModel", "SwitchTransformersPreTrainedModel", "SwitchTransformersTop1Router", "SwitchTransformersSparseMLP", ] ) _import_structure["models.trajectory_transformer"].extend( [ "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TrajectoryTransformerModel", "TrajectoryTransformerPreTrainedModel", ] ) _import_structure["models.transfo_xl"].extend( [ "TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "AdaptiveEmbedding", "TransfoXLForSequenceClassification", "TransfoXLLMHeadModel", "TransfoXLModel", "TransfoXLPreTrainedModel", "load_tf_weights_in_transfo_xl", ] ) _import_structure["models.trocr"].extend( ["TROCR_PRETRAINED_MODEL_ARCHIVE_LIST", "TrOCRForCausalLM", "TrOCRPreTrainedModel"] ) _import_structure["models.unispeech"].extend( [ "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechForCTC", "UniSpeechForPreTraining", "UniSpeechForSequenceClassification", "UniSpeechModel", "UniSpeechPreTrainedModel", ] ) _import_structure["models.unispeech_sat"].extend( [ "UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST", "UniSpeechSatForAudioFrameClassification", "UniSpeechSatForCTC", "UniSpeechSatForPreTraining", "UniSpeechSatForSequenceClassification", "UniSpeechSatForXVector", "UniSpeechSatModel", "UniSpeechSatPreTrainedModel", ] ) _import_structure["models.van"].extend( [ "VAN_PRETRAINED_MODEL_ARCHIVE_LIST", "VanForImageClassification", "VanModel", "VanPreTrainedModel", ] ) _import_structure["models.vilt"].extend( [ "VILT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViltForImageAndTextRetrieval", "ViltForImagesAndTextClassification", "ViltForTokenClassification", "ViltForMaskedLM", "ViltForQuestionAnswering", "ViltLayer", "ViltModel", "ViltPreTrainedModel", ] ) _import_structure["models.vision_encoder_decoder"].extend(["VisionEncoderDecoderModel"]) _import_structure["models.vision_text_dual_encoder"].extend(["VisionTextDualEncoderModel"]) _import_structure["models.visual_bert"].extend( [ "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "VisualBertForMultipleChoice", "VisualBertForPreTraining", "VisualBertForQuestionAnswering", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertLayer", "VisualBertModel", "VisualBertPreTrainedModel", ] ) _import_structure["models.vit"].extend( [ "VIT_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTForImageClassification", "ViTForMaskedImageModeling", "ViTModel", "ViTPreTrainedModel", ] ) _import_structure["models.vit_mae"].extend( [ "VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTMAEForPreTraining", "ViTMAELayer", "ViTMAEModel", "ViTMAEPreTrainedModel", ] ) _import_structure["models.vit_msn"].extend( [ "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST", "ViTMSNModel", "ViTMSNForImageClassification", "ViTMSNPreTrainedModel", ] ) _import_structure["models.videomae"].extend( [ "VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST", "VideoMAEForPreTraining", "VideoMAEModel", "VideoMAEPreTrainedModel", "VideoMAEForVideoClassification", ] ) _import_structure["models.wav2vec2"].extend( [ "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", "Wav2Vec2ForAudioFrameClassification", "Wav2Vec2ForCTC", "Wav2Vec2ForMaskedLM", "Wav2Vec2ForPreTraining", "Wav2Vec2ForSequenceClassification", "Wav2Vec2ForXVector", "Wav2Vec2Model", "Wav2Vec2PreTrainedModel", ] ) _import_structure["models.wav2vec2_conformer"].extend( [ "WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "Wav2Vec2ConformerForAudioFrameClassification", "Wav2Vec2ConformerForCTC", "Wav2Vec2ConformerForPreTraining", "Wav2Vec2ConformerForSequenceClassification", "Wav2Vec2ConformerForXVector", "Wav2Vec2ConformerModel", "Wav2Vec2ConformerPreTrainedModel", ] ) _import_structure["models.wavlm"].extend( [ "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST", "WavLMForAudioFrameClassification", "WavLMForCTC", "WavLMForSequenceClassification", "WavLMForXVector", "WavLMModel", "WavLMPreTrainedModel", ] ) _import_structure["models.xglm"].extend( [ "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XGLMForCausalLM", "XGLMModel", "XGLMPreTrainedModel", ] ) _import_structure["models.xlm"].extend( [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] ) _import_structure["models.xlm_prophetnet"].extend( [ "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMProphetNetDecoder", "XLMProphetNetEncoder", "XLMProphetNetForCausalLM", "XLMProphetNetForConditionalGeneration", "XLMProphetNetModel", "XLMProphetNetPreTrainedModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMRobertaForCausalLM", "XLMRobertaForMaskedLM", "XLMRobertaForMultipleChoice", "XLMRobertaForQuestionAnswering", "XLMRobertaForSequenceClassification", "XLMRobertaForTokenClassification", "XLMRobertaModel", "XLMRobertaPreTrainedModel", ] ) _import_structure["models.xlm_roberta_xl"].extend( [ "XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMRobertaXLForCausalLM", "XLMRobertaXLForMaskedLM", "XLMRobertaXLForMultipleChoice", "XLMRobertaXLForQuestionAnswering", "XLMRobertaXLForSequenceClassification", "XLMRobertaXLForTokenClassification", "XLMRobertaXLModel", "XLMRobertaXLPreTrainedModel", ] ) _import_structure["models.xlnet"].extend( [ "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", "XLNetForMultipleChoice", "XLNetForQuestionAnswering", "XLNetForQuestionAnsweringSimple", "XLNetForSequenceClassification", "XLNetForTokenClassification", "XLNetLMHeadModel", "XLNetModel", "XLNetPreTrainedModel", "load_tf_weights_in_xlnet", ] ) _import_structure["models.yolos"].extend( [ "YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST", "YolosForObjectDetection", "YolosModel", "YolosPreTrainedModel", ] ) _import_structure["models.yoso"].extend( [ "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST", "YosoForMaskedLM", "YosoForMultipleChoice", "YosoForQuestionAnswering", "YosoForSequenceClassification", "YosoForTokenClassification", "YosoLayer", "YosoModel", "YosoPreTrainedModel", ] ) _import_structure["optimization"] = [ "Adafactor", "AdamW", "get_constant_schedule", "get_constant_schedule_with_warmup", "get_cosine_schedule_with_warmup", "get_cosine_with_hard_restarts_schedule_with_warmup", "get_linear_schedule_with_warmup", "get_polynomial_decay_schedule_with_warmup", "get_scheduler", ] _import_structure["pytorch_utils"] = ["Conv1D", "apply_chunking_to_forward", "prune_layer"] _import_structure["sagemaker"] = [] _import_structure["trainer"] = ["Trainer"] _import_structure["trainer_pt_utils"] = ["torch_distributed_zero_first"] _import_structure["trainer_seq2seq"] = ["Seq2SeqTrainer"] # TensorFlow-backed objects try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_tf_objects _import_structure["utils.dummy_tf_objects"] = [name for name in dir(dummy_tf_objects) if not name.startswith("_")] else: _import_structure["activations_tf"] = [] _import_structure["benchmark.benchmark_args_tf"] = ["TensorFlowBenchmarkArguments"] _import_structure["benchmark.benchmark_tf"] = ["TensorFlowBenchmark"] _import_structure["generation_tf_utils"] = [] _import_structure["generation"].extend( [ "TFForcedBOSTokenLogitsProcessor", "TFForcedEOSTokenLogitsProcessor", "TFLogitsProcessor", "TFLogitsProcessorList", "TFLogitsWarper", "TFMinLengthLogitsProcessor", "TFNoBadWordsLogitsProcessor", "TFNoRepeatNGramLogitsProcessor", "TFRepetitionPenaltyLogitsProcessor", "TFTemperatureLogitsWarper", "TFTopKLogitsWarper", "TFTopPLogitsWarper", "TFGenerationMixin", "tf_top_k_top_p_filtering", ] ) _import_structure["keras_callbacks"] = ["KerasMetricCallback", "PushToHubCallback"] _import_structure["modeling_tf_outputs"] = [] _import_structure["modeling_tf_utils"] = [ "TFPreTrainedModel", "TFSequenceSummary", "TFSharedEmbeddings", "shape_list", ] # TensorFlow models structure _import_structure["models.albert"].extend( [ "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAlbertForMaskedLM", "TFAlbertForMultipleChoice", "TFAlbertForPreTraining", "TFAlbertForQuestionAnswering", "TFAlbertForSequenceClassification", "TFAlbertForTokenClassification", "TFAlbertMainLayer", "TFAlbertModel", "TFAlbertPreTrainedModel", ] ) _import_structure["models.auto"].extend( [ "TF_MODEL_FOR_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "TF_MODEL_FOR_MASKED_LM_MAPPING", "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "TF_MODEL_FOR_PRETRAINING_MAPPING", "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", "TF_MODEL_MAPPING", "TF_MODEL_WITH_LM_HEAD_MAPPING", "TFAutoModel", "TFAutoModelForCausalLM", "TFAutoModelForImageClassification", "TFAutoModelForMaskedLM", "TFAutoModelForMultipleChoice", "TFAutoModelForNextSentencePrediction", "TFAutoModelForPreTraining", "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForQuestionAnswering", "TFAutoModelForSemanticSegmentation", "TFAutoModelForSeq2SeqLM", "TFAutoModelForSequenceClassification", "TFAutoModelForSpeechSeq2Seq", "TFAutoModelForTableQuestionAnswering", "TFAutoModelForTokenClassification", "TFAutoModelForVision2Seq", "TFAutoModelWithLMHead", ] ) _import_structure["models.bart"].extend(["TFBartForConditionalGeneration", "TFBartModel", "TFBartPretrainedModel"]) _import_structure["models.bert"].extend( [ "TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFBertEmbeddings", "TFBertForMaskedLM", "TFBertForMultipleChoice", "TFBertForNextSentencePrediction", "TFBertForPreTraining", "TFBertForQuestionAnswering", "TFBertForSequenceClassification", "TFBertForTokenClassification", "TFBertLMHeadModel", "TFBertMainLayer", "TFBertModel", "TFBertPreTrainedModel", ] ) _import_structure["models.blenderbot"].extend( ["TFBlenderbotForConditionalGeneration", "TFBlenderbotModel", "TFBlenderbotPreTrainedModel"] ) _import_structure["models.blenderbot_small"].extend( ["TFBlenderbotSmallForConditionalGeneration", "TFBlenderbotSmallModel", "TFBlenderbotSmallPreTrainedModel"] ) _import_structure["models.camembert"].extend( [ "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCamembertForCausalLM", "TFCamembertForMaskedLM", "TFCamembertForMultipleChoice", "TFCamembertForQuestionAnswering", "TFCamembertForSequenceClassification", "TFCamembertForTokenClassification", "TFCamembertModel", "TFCamembertPreTrainedModel", ] ) _import_structure["models.clip"].extend( [ "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCLIPModel", "TFCLIPPreTrainedModel", "TFCLIPTextModel", "TFCLIPVisionModel", ] ) _import_structure["models.convbert"].extend( [ "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFConvBertForMaskedLM", "TFConvBertForMultipleChoice", "TFConvBertForQuestionAnswering", "TFConvBertForSequenceClassification", "TFConvBertForTokenClassification", "TFConvBertLayer", "TFConvBertModel", "TFConvBertPreTrainedModel", ] ) _import_structure["models.convnext"].extend( [ "TFConvNextForImageClassification", "TFConvNextModel", "TFConvNextPreTrainedModel", ] ) _import_structure["models.ctrl"].extend( [ "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCTRLForSequenceClassification", "TFCTRLLMHeadModel", "TFCTRLModel", "TFCTRLPreTrainedModel", ] ) _import_structure["models.cvt"].extend( [ "TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFCvtForImageClassification", "TFCvtModel", "TFCvtPreTrainedModel", ] ) _import_structure["models.data2vec"].extend( [ "TFData2VecVisionForImageClassification", "TFData2VecVisionForSemanticSegmentation", "TFData2VecVisionModel", "TFData2VecVisionPreTrainedModel", ] ) _import_structure["models.deberta"].extend( [ "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDebertaForMaskedLM", "TFDebertaForQuestionAnswering", "TFDebertaForSequenceClassification", "TFDebertaForTokenClassification", "TFDebertaModel", "TFDebertaPreTrainedModel", ] ) _import_structure["models.deberta_v2"].extend( [ "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDebertaV2ForMaskedLM", "TFDebertaV2ForQuestionAnswering", "TFDebertaV2ForSequenceClassification", "TFDebertaV2ForTokenClassification", "TFDebertaV2Model", "TFDebertaV2PreTrainedModel", ] ) _import_structure["models.deit"].extend( [ "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDeiTForImageClassification", "TFDeiTForImageClassificationWithTeacher", "TFDeiTForMaskedImageModeling", "TFDeiTModel", "TFDeiTPreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDistilBertForMaskedLM", "TFDistilBertForMultipleChoice", "TFDistilBertForQuestionAnswering", "TFDistilBertForSequenceClassification", "TFDistilBertForTokenClassification", "TFDistilBertMainLayer", "TFDistilBertModel", "TFDistilBertPreTrainedModel", ] ) _import_structure["models.dpr"].extend( [ "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST", "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDPRContextEncoder", "TFDPRPretrainedContextEncoder", "TFDPRPretrainedQuestionEncoder", "TFDPRPretrainedReader", "TFDPRQuestionEncoder", "TFDPRReader", ] ) _import_structure["models.electra"].extend( [ "TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFElectraForMaskedLM", "TFElectraForMultipleChoice", "TFElectraForPreTraining", "TFElectraForQuestionAnswering", "TFElectraForSequenceClassification", "TFElectraForTokenClassification", "TFElectraModel", "TFElectraPreTrainedModel", ] ) _import_structure["models.encoder_decoder"].append("TFEncoderDecoderModel") _import_structure["models.esm"].extend( [ "ESM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFEsmForMaskedLM", "TFEsmForSequenceClassification", "TFEsmForTokenClassification", "TFEsmModel", "TFEsmPreTrainedModel", ] ) _import_structure["models.flaubert"].extend( [ "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFFlaubertForMultipleChoice", "TFFlaubertForQuestionAnsweringSimple", "TFFlaubertForSequenceClassification", "TFFlaubertForTokenClassification", "TFFlaubertModel", "TFFlaubertPreTrainedModel", "TFFlaubertWithLMHeadModel", ] ) _import_structure["models.funnel"].extend( [ "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFFunnelBaseModel", "TFFunnelForMaskedLM", "TFFunnelForMultipleChoice", "TFFunnelForPreTraining", "TFFunnelForQuestionAnswering", "TFFunnelForSequenceClassification", "TFFunnelForTokenClassification", "TFFunnelModel", "TFFunnelPreTrainedModel", ] ) _import_structure["models.gpt2"].extend( [ "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFGPT2DoubleHeadsModel", "TFGPT2ForSequenceClassification", "TFGPT2LMHeadModel", "TFGPT2MainLayer", "TFGPT2Model", "TFGPT2PreTrainedModel", ] ) _import_structure["models.gptj"].extend( [ "TFGPTJForCausalLM", "TFGPTJForQuestionAnswering", "TFGPTJForSequenceClassification", "TFGPTJModel", "TFGPTJPreTrainedModel", ] ) _import_structure["models.groupvit"].extend( [ "TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFGroupViTModel", "TFGroupViTPreTrainedModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", ] ) _import_structure["models.hubert"].extend( [ "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFHubertForCTC", "TFHubertModel", "TFHubertPreTrainedModel", ] ) _import_structure["models.layoutlm"].extend( [ "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLayoutLMForMaskedLM", "TFLayoutLMForSequenceClassification", "TFLayoutLMForQuestionAnswering", "TFLayoutLMForTokenClassification", "TFLayoutLMMainLayer", "TFLayoutLMModel", "TFLayoutLMPreTrainedModel", ] ) _import_structure["models.layoutlmv3"].extend( [ "TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLayoutLMv3ForQuestionAnswering", "TFLayoutLMv3ForSequenceClassification", "TFLayoutLMv3ForTokenClassification", "TFLayoutLMv3Model", "TFLayoutLMv3PreTrainedModel", ] ) _import_structure["models.led"].extend(["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]) _import_structure["models.longformer"].extend( [ "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLongformerForMaskedLM", "TFLongformerForMultipleChoice", "TFLongformerForQuestionAnswering", "TFLongformerForSequenceClassification", "TFLongformerForTokenClassification", "TFLongformerModel", "TFLongformerPreTrainedModel", "TFLongformerSelfAttention", ] ) _import_structure["models.lxmert"].extend( [ "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFLxmertForPreTraining", "TFLxmertMainLayer", "TFLxmertModel", "TFLxmertPreTrainedModel", "TFLxmertVisualFeatureEncoder", ] ) _import_structure["models.marian"].extend(["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]) _import_structure["models.mbart"].extend( ["TFMBartForConditionalGeneration", "TFMBartModel", "TFMBartPreTrainedModel"] ) _import_structure["models.mobilebert"].extend( [ "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMobileBertForMaskedLM", "TFMobileBertForMultipleChoice", "TFMobileBertForNextSentencePrediction", "TFMobileBertForPreTraining", "TFMobileBertForQuestionAnswering", "TFMobileBertForSequenceClassification", "TFMobileBertForTokenClassification", "TFMobileBertMainLayer", "TFMobileBertModel", "TFMobileBertPreTrainedModel", ] ) _import_structure["models.mobilevit"].extend( [ "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMobileViTPreTrainedModel", "TFMobileViTModel", "TFMobileViTForImageClassification", "TFMobileViTForSemanticSegmentation", ] ) _import_structure["models.mpnet"].extend( [ "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFMPNetForMaskedLM", "TFMPNetForMultipleChoice", "TFMPNetForQuestionAnswering", "TFMPNetForSequenceClassification", "TFMPNetForTokenClassification", "TFMPNetMainLayer", "TFMPNetModel", "TFMPNetPreTrainedModel", ] ) _import_structure["models.mt5"].extend(["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]) _import_structure["models.openai"].extend( [ "TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFOpenAIGPTDoubleHeadsModel", "TFOpenAIGPTForSequenceClassification", "TFOpenAIGPTLMHeadModel", "TFOpenAIGPTMainLayer", "TFOpenAIGPTModel", "TFOpenAIGPTPreTrainedModel", ] ) _import_structure["models.opt"].extend( [ "TFOPTForCausalLM", "TFOPTModel", "TFOPTPreTrainedModel", ] ) _import_structure["models.pegasus"].extend( ["TFPegasusForConditionalGeneration", "TFPegasusModel", "TFPegasusPreTrainedModel"] ) _import_structure["models.rag"].extend( [ "TFRagModel", "TFRagPreTrainedModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", ] ) _import_structure["models.regnet"].extend( [ "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRegNetForImageClassification", "TFRegNetModel", "TFRegNetPreTrainedModel", ] ) _import_structure["models.rembert"].extend( [ "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRemBertForCausalLM", "TFRemBertForMaskedLM", "TFRemBertForMultipleChoice", "TFRemBertForQuestionAnswering", "TFRemBertForSequenceClassification", "TFRemBertForTokenClassification", "TFRemBertLayer", "TFRemBertModel", "TFRemBertPreTrainedModel", ] ) _import_structure["models.resnet"].extend( [ "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFResNetForImageClassification", "TFResNetModel", "TFResNetPreTrainedModel", ] ) _import_structure["models.roberta"].extend( [ "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaForCausalLM", "TFRobertaForMaskedLM", "TFRobertaForMultipleChoice", "TFRobertaForQuestionAnswering", "TFRobertaForSequenceClassification", "TFRobertaForTokenClassification", "TFRobertaMainLayer", "TFRobertaModel", "TFRobertaPreTrainedModel", ] ) _import_structure["models.roformer"].extend( [ "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRoFormerForCausalLM", "TFRoFormerForMaskedLM", "TFRoFormerForMultipleChoice", "TFRoFormerForQuestionAnswering", "TFRoFormerForSequenceClassification", "TFRoFormerForTokenClassification", "TFRoFormerLayer", "TFRoFormerModel", "TFRoFormerPreTrainedModel", ] ) _import_structure["models.segformer"].extend( [ "TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSegformerDecodeHead", "TFSegformerForImageClassification", "TFSegformerForSemanticSegmentation", "TFSegformerModel", "TFSegformerPreTrainedModel", ] ) _import_structure["models.speech_to_text"].extend( [ "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSpeech2TextForConditionalGeneration", "TFSpeech2TextModel", "TFSpeech2TextPreTrainedModel", ] ) _import_structure["models.swin"].extend( [ "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST", "TFSwinForImageClassification", "TFSwinForMaskedImageModeling", "TFSwinModel", "TFSwinPreTrainedModel", ] ) _import_structure["models.t5"].extend( [ "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST", "TFT5EncoderModel", "TFT5ForConditionalGeneration", "TFT5Model", "TFT5PreTrainedModel", ] ) _import_structure["models.tapas"].extend( [ "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST", "TFTapasForMaskedLM", "TFTapasForQuestionAnswering", "TFTapasForSequenceClassification", "TFTapasModel", "TFTapasPreTrainedModel", ] ) _import_structure["models.transfo_xl"].extend( [ "TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST", "TFAdaptiveEmbedding", "TFTransfoXLForSequenceClassification", "TFTransfoXLLMHeadModel", "TFTransfoXLMainLayer", "TFTransfoXLModel", "TFTransfoXLPreTrainedModel", ] ) _import_structure["models.vision_encoder_decoder"].extend(["TFVisionEncoderDecoderModel"]) _import_structure["models.vit"].extend( [ "TFViTForImageClassification", "TFViTModel", "TFViTPreTrainedModel", ] ) _import_structure["models.vit_mae"].extend( [ "TFViTMAEForPreTraining", "TFViTMAEModel", "TFViTMAEPreTrainedModel", ] ) _import_structure["models.wav2vec2"].extend( [ "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST", "TFWav2Vec2ForCTC", "TFWav2Vec2Model", "TFWav2Vec2PreTrainedModel", ] ) _import_structure["models.whisper"].extend( [ "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFWhisperForConditionalGeneration", "TFWhisperModel", "TFWhisperPreTrainedModel", ] ) _import_structure["models.xglm"].extend( [ "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXGLMForCausalLM", "TFXGLMModel", "TFXGLMPreTrainedModel", ] ) _import_structure["models.xlm"].extend( [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMRobertaForMaskedLM", "TFXLMRobertaForMultipleChoice", "TFXLMRobertaForQuestionAnswering", "TFXLMRobertaForSequenceClassification", "TFXLMRobertaForTokenClassification", "TFXLMRobertaModel", ] ) _import_structure["models.xlnet"].extend( [ "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLNetForMultipleChoice", "TFXLNetForQuestionAnsweringSimple", "TFXLNetForSequenceClassification", "TFXLNetForTokenClassification", "TFXLNetLMHeadModel", "TFXLNetMainLayer", "TFXLNetModel", "TFXLNetPreTrainedModel", ] ) _import_structure["optimization_tf"] = ["AdamWeightDecay", "GradientAccumulator", "WarmUp", "create_optimizer"] _import_structure["tf_utils"] = [] _import_structure["trainer_tf"] = ["TFTrainer"] # FLAX-backed objects try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils import dummy_flax_objects _import_structure["utils.dummy_flax_objects"] = [ name for name in dir(dummy_flax_objects) if not name.startswith("_") ] else: _import_structure["generation_flax_utils"] = [] _import_structure["generation"].extend( [ "FlaxForcedBOSTokenLogitsProcessor", "FlaxForcedEOSTokenLogitsProcessor", "FlaxLogitsProcessor", "FlaxLogitsProcessorList", "FlaxLogitsWarper", "FlaxMinLengthLogitsProcessor", "FlaxTemperatureLogitsWarper", "FlaxTopKLogitsWarper", "FlaxTopPLogitsWarper", "FlaxGenerationMixin", ] ) _import_structure["modeling_flax_outputs"] = [] _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"] _import_structure["models.albert"].extend( [ "FlaxAlbertForMaskedLM", "FlaxAlbertForMultipleChoice", "FlaxAlbertForPreTraining", "FlaxAlbertForQuestionAnswering", "FlaxAlbertForSequenceClassification", "FlaxAlbertForTokenClassification", "FlaxAlbertModel", "FlaxAlbertPreTrainedModel", ] ) _import_structure["models.auto"].extend( [ "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_MASKED_LM_MAPPING", "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "FLAX_MODEL_FOR_PRETRAINING_MAPPING", "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", "FLAX_MODEL_MAPPING", "FlaxAutoModel", "FlaxAutoModelForCausalLM", "FlaxAutoModelForImageClassification", "FlaxAutoModelForMaskedLM", "FlaxAutoModelForMultipleChoice", "FlaxAutoModelForNextSentencePrediction", "FlaxAutoModelForPreTraining", "FlaxAutoModelForQuestionAnswering", "FlaxAutoModelForSeq2SeqLM", "FlaxAutoModelForSequenceClassification", "FlaxAutoModelForTokenClassification", "FlaxAutoModelForVision2Seq", ] ) # Flax models structure _import_structure["models.bart"].extend( [ "FlaxBartDecoderPreTrainedModel", "FlaxBartForCausalLM", "FlaxBartForConditionalGeneration", "FlaxBartForQuestionAnswering", "FlaxBartForSequenceClassification", "FlaxBartModel", "FlaxBartPreTrainedModel", ] ) _import_structure["models.beit"].extend( [ "FlaxBeitForImageClassification", "FlaxBeitForMaskedImageModeling", "FlaxBeitModel", "FlaxBeitPreTrainedModel", ] ) _import_structure["models.bert"].extend( [ "FlaxBertForCausalLM", "FlaxBertForMaskedLM", "FlaxBertForMultipleChoice", "FlaxBertForNextSentencePrediction", "FlaxBertForPreTraining", "FlaxBertForQuestionAnswering", "FlaxBertForSequenceClassification", "FlaxBertForTokenClassification", "FlaxBertModel", "FlaxBertPreTrainedModel", ] ) _import_structure["models.big_bird"].extend( [ "FlaxBigBirdForCausalLM", "FlaxBigBirdForMaskedLM", "FlaxBigBirdForMultipleChoice", "FlaxBigBirdForPreTraining", "FlaxBigBirdForQuestionAnswering", "FlaxBigBirdForSequenceClassification", "FlaxBigBirdForTokenClassification", "FlaxBigBirdModel", "FlaxBigBirdPreTrainedModel", ] ) _import_structure["models.blenderbot"].extend( ["FlaxBlenderbotForConditionalGeneration", "FlaxBlenderbotModel", "FlaxBlenderbotPreTrainedModel"] ) _import_structure["models.blenderbot_small"].extend( [ "FlaxBlenderbotSmallForConditionalGeneration", "FlaxBlenderbotSmallModel", "FlaxBlenderbotSmallPreTrainedModel", ] ) _import_structure["models.clip"].extend( [ "FlaxCLIPModel", "FlaxCLIPPreTrainedModel", "FlaxCLIPTextModel", "FlaxCLIPTextPreTrainedModel", "FlaxCLIPVisionModel", "FlaxCLIPVisionPreTrainedModel", ] ) _import_structure["models.distilbert"].extend( [ "FlaxDistilBertForMaskedLM", "FlaxDistilBertForMultipleChoice", "FlaxDistilBertForQuestionAnswering", "FlaxDistilBertForSequenceClassification", "FlaxDistilBertForTokenClassification", "FlaxDistilBertModel", "FlaxDistilBertPreTrainedModel", ] ) _import_structure["models.electra"].extend( [ "FlaxElectraForCausalLM", "FlaxElectraForMaskedLM", "FlaxElectraForMultipleChoice", "FlaxElectraForPreTraining", "FlaxElectraForQuestionAnswering", "FlaxElectraForSequenceClassification", "FlaxElectraForTokenClassification", "FlaxElectraModel", "FlaxElectraPreTrainedModel", ] ) _import_structure["models.encoder_decoder"].append("FlaxEncoderDecoderModel") _import_structure["models.gpt2"].extend(["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]) _import_structure["models.gpt_neo"].extend( ["FlaxGPTNeoForCausalLM", "FlaxGPTNeoModel", "FlaxGPTNeoPreTrainedModel"] ) _import_structure["models.gptj"].extend(["FlaxGPTJForCausalLM", "FlaxGPTJModel", "FlaxGPTJPreTrainedModel"]) _import_structure["models.longt5"].extend( ["FlaxLongT5ForConditionalGeneration", "FlaxLongT5Model", "FlaxLongT5PreTrainedModel"] ) _import_structure["models.marian"].extend( [ "FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel", ] ) _import_structure["models.mbart"].extend( [ "FlaxMBartForConditionalGeneration", "FlaxMBartForQuestionAnswering", "FlaxMBartForSequenceClassification", "FlaxMBartModel", "FlaxMBartPreTrainedModel", ] ) _import_structure["models.mt5"].extend(["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]) _import_structure["models.opt"].extend( [ "FlaxOPTForCausalLM", "FlaxOPTModel", "FlaxOPTPreTrainedModel", ] ) _import_structure["models.pegasus"].extend( [ "FlaxPegasusForConditionalGeneration", "FlaxPegasusModel", "FlaxPegasusPreTrainedModel", ] ) _import_structure["models.roberta"].extend( [ "FlaxRobertaForCausalLM", "FlaxRobertaForMaskedLM", "FlaxRobertaForMultipleChoice", "FlaxRobertaForQuestionAnswering", "FlaxRobertaForSequenceClassification", "FlaxRobertaForTokenClassification", "FlaxRobertaModel", "FlaxRobertaPreTrainedModel", ] ) _import_structure["models.roformer"].extend( [ "FlaxRoFormerForMaskedLM", "FlaxRoFormerForMultipleChoice", "FlaxRoFormerForQuestionAnswering", "FlaxRoFormerForSequenceClassification", "FlaxRoFormerForTokenClassification", "FlaxRoFormerModel", "FlaxRoFormerPreTrainedModel", ] ) _import_structure["models.speech_encoder_decoder"].append("FlaxSpeechEncoderDecoderModel") _import_structure["models.t5"].extend( ["FlaxT5EncoderModel", "FlaxT5ForConditionalGeneration", "FlaxT5Model", "FlaxT5PreTrainedModel"] ) _import_structure["models.vision_encoder_decoder"].append("FlaxVisionEncoderDecoderModel") _import_structure["models.vision_text_dual_encoder"].extend(["FlaxVisionTextDualEncoderModel"]) _import_structure["models.vit"].extend(["FlaxViTForImageClassification", "FlaxViTModel", "FlaxViTPreTrainedModel"]) _import_structure["models.wav2vec2"].extend( ["FlaxWav2Vec2ForCTC", "FlaxWav2Vec2ForPreTraining", "FlaxWav2Vec2Model", "FlaxWav2Vec2PreTrainedModel"] ) _import_structure["models.xglm"].extend( [ "FlaxXGLMForCausalLM", "FlaxXGLMModel", "FlaxXGLMPreTrainedModel", ] ) _import_structure["models.xlm_roberta"].extend( [ "FlaxXLMRobertaForMaskedLM", "FlaxXLMRobertaForMultipleChoice", "FlaxXLMRobertaForQuestionAnswering", "FlaxXLMRobertaForSequenceClassification", "FlaxXLMRobertaForTokenClassification", "FlaxXLMRobertaModel", ] ) # Direct imports for type-checking if TYPE_CHECKING: # Configuration from .configuration_utils import PretrainedConfig # Data from .data import ( DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor, SquadExample, SquadFeatures, SquadV1Processor, SquadV2Processor, glue_compute_metrics, glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels, squad_convert_examples_to_features, xnli_compute_metrics, xnli_output_modes, xnli_processors, xnli_tasks_num_labels, ) from .data.data_collator import ( DataCollator, DataCollatorForLanguageModeling, DataCollatorForPermutationLanguageModeling, DataCollatorForSeq2Seq, DataCollatorForSOP, DataCollatorForTokenClassification, DataCollatorForWholeWordMask, DataCollatorWithPadding, DefaultDataCollator, default_data_collator, ) from .feature_extraction_sequence_utils import SequenceFeatureExtractor # Feature Extractor from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin from .hf_argparser import HfArgumentParser # Integrations from .integrations import ( is_clearml_available, is_comet_available, is_neptune_available, is_optuna_available, is_ray_available, is_ray_tune_available, is_sigopt_available, is_tensorboard_available, is_wandb_available, ) # Model Cards from .modelcard import ModelCard # TF 2.0 <=> PyTorch conversion utilities from .modeling_tf_pytorch_utils import ( convert_tf_weight_name_to_pt_weight_name, load_pytorch_checkpoint_in_tf2_model, load_pytorch_model_in_tf2_model, load_pytorch_weights_in_tf2_model, load_tf2_checkpoint_in_pytorch_model, load_tf2_model_in_pytorch_model, load_tf2_weights_in_pytorch_model, ) from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig from .models.auto import ( ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, MODEL_NAMES_MAPPING, PROCESSOR_MAPPING, TOKENIZER_MAPPING, AutoConfig, AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer, ) from .models.bart import BartConfig, BartTokenizer from .models.beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig from .models.bert import ( BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BasicTokenizer, BertConfig, BertTokenizer, WordpieceTokenizer, ) from .models.bert_generation import BertGenerationConfig from .models.bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer from .models.bertweet import BertweetTokenizer from .models.big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig from .models.bigbird_pegasus import BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdPegasusConfig from .models.blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotTokenizer from .models.blenderbot_small import ( BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotSmallConfig, BlenderbotSmallTokenizer, ) from .models.bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig from .models.byt5 import ByT5Tokenizer from .models.camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig from .models.canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig, CanineTokenizer from .models.clip import ( CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPConfig, CLIPProcessor, CLIPTextConfig, CLIPTokenizer, CLIPVisionConfig, ) from .models.clipseg import ( CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig, ) from .models.codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenTokenizer from .models.conditional_detr import CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, ConditionalDetrConfig from .models.convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertTokenizer from .models.convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig from .models.ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, CTRLTokenizer from .models.cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig from .models.data2vec import ( DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP, Data2VecAudioConfig, Data2VecTextConfig, Data2VecVisionConfig, ) from .models.deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaTokenizer from .models.deberta_v2 import DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaV2Config from .models.decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, DecisionTransformerConfig, ) from .models.deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig from .models.deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig from .models.detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer from .models.donut import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutProcessor, DonutSwinConfig from .models.dpr import ( DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig, DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderOutput, DPRReaderTokenizer, ) from .models.dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig from .models.electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraTokenizer from .models.encoder_decoder import EncoderDecoderConfig from .models.ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig from .models.esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig, EsmTokenizer from .models.flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertTokenizer from .models.flava import ( FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) from .models.fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig from .models.fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig, FSMTTokenizer from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer from .models.glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer from .models.gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig from .models.gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig from .models.gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig from .models.gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig from .models.groupvit import ( GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig, ) from .models.herbert import HerbertTokenizer from .models.hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig from .models.ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig from .models.imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig from .models.jukebox import ( JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP, JukeboxConfig, JukeboxPriorConfig, JukeboxTokenizer, JukeboxVQVAEConfig, ) from .models.layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMTokenizer from .models.layoutlmv2 import ( LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv2Config, LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor, LayoutLMv2Processor, LayoutLMv2Tokenizer, ) from .models.layoutlmv3 import ( LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMv3Config, LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor, LayoutLMv3Processor, LayoutLMv3Tokenizer, ) from .models.layoutxlm import LayoutXLMProcessor from .models.led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig, LEDTokenizer from .models.levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig from .models.lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer from .models.longt5 import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongT5Config from .models.luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig, LukeTokenizer from .models.lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig, LxmertTokenizer from .models.m2m_100 import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, M2M100Config from .models.marian import MarianConfig from .models.markuplm import ( MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig, MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer, ) from .models.maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig from .models.mbart import MBartConfig from .models.mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig, MCTCTProcessor from .models.megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig from .models.mmbt import MMBTConfig from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer from .models.mobilenet_v2 import MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileNetV2Config from .models.mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig from .models.mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig, MPNetTokenizer from .models.mt5 import MT5Config from .models.mvp import MvpConfig, MvpTokenizer from .models.nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig from .models.nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer from .models.opt import OPTConfig from .models.owlvit import ( OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, OwlViTConfig, OwlViTProcessor, OwlViTTextConfig, OwlViTVisionConfig, ) from .models.pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig, PegasusTokenizer from .models.pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig from .models.perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverTokenizer from .models.phobert import PhobertTokenizer from .models.plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig from .models.poolformer import POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, PoolFormerConfig from .models.prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig, ProphetNetTokenizer from .models.qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig from .models.rag import RagConfig, RagRetriever, RagTokenizer from .models.realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig, RealmTokenizer from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig from .models.regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig from .models.rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig from .models.resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer from .models.roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig, RoCBertTokenizer from .models.roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerTokenizer from .models.segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig from .models.sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig from .models.sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig from .models.speech_encoder_decoder import SpeechEncoderDecoderConfig from .models.speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig from .models.speech_to_text_2 import ( SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2Text2Config, Speech2Text2Processor, Speech2Text2Tokenizer, ) from .models.splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig, SplinterTokenizer from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer from .models.swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig from .models.swinv2 import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, Swinv2Config from .models.switch_transformers import SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP, SwitchTransformersConfig from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config from .models.table_transformer import TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TableTransformerConfig from .models.tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig, TapasTokenizer from .models.tapex import TapexTokenizer from .models.time_series_transformer import ( TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimeSeriesTransformerConfig, ) from .models.trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TrajectoryTransformerConfig, ) from .models.transfo_xl import ( TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig, TransfoXLCorpus, TransfoXLTokenizer, ) from .models.trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig, TrOCRProcessor from .models.unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig from .models.unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig from .models.van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig from .models.videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig from .models.vilt import ( VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig, ViltFeatureExtractor, ViltImageProcessor, ViltProcessor, ) from .models.vision_encoder_decoder import VisionEncoderDecoderConfig from .models.vision_text_dual_encoder import VisionTextDualEncoderConfig, VisionTextDualEncoderProcessor from .models.visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig from .models.vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig from .models.vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig from .models.vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig from .models.wav2vec2 import ( WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2Config, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, Wav2Vec2Tokenizer, ) from .models.wav2vec2_conformer import WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, Wav2Vec2ConformerConfig from .models.wav2vec2_phoneme import Wav2Vec2PhonemeCTCTokenizer from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM from .models.wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig from .models.whisper import ( WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperFeatureExtractor, WhisperProcessor, WhisperTokenizer, ) from .models.x_clip import ( XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, XCLIPConfig, XCLIPProcessor, XCLIPTextConfig, XCLIPVisionConfig, ) from .models.xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig from .models.xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMTokenizer from .models.xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig from .models.xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig from .models.xlm_roberta_xl import XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaXLConfig from .models.xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig from .models.yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig from .models.yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig # Pipelines from .pipelines import ( AudioClassificationPipeline, AutomaticSpeechRecognitionPipeline, Conversation, ConversationalPipeline, CsvPipelineDataFormat, DepthEstimationPipeline, DocumentQuestionAnsweringPipeline, FeatureExtractionPipeline, FillMaskPipeline, ImageClassificationPipeline, ImageSegmentationPipeline, ImageToTextPipeline, JsonPipelineDataFormat, NerPipeline, ObjectDetectionPipeline, PipedPipelineDataFormat, Pipeline, PipelineDataFormat, QuestionAnsweringPipeline, SummarizationPipeline, TableQuestionAnsweringPipeline, Text2TextGenerationPipeline, TextClassificationPipeline, TextGenerationPipeline, TokenClassificationPipeline, TranslationPipeline, VisualQuestionAnsweringPipeline, ZeroShotClassificationPipeline, ZeroShotImageClassificationPipeline, ZeroShotObjectDetectionPipeline, pipeline, ) from .processing_utils import ProcessorMixin # Tokenization from .tokenization_utils import PreTrainedTokenizer from .tokenization_utils_base import ( AddedToken, BatchEncoding, CharSpan, PreTrainedTokenizerBase, SpecialTokensMixin, TokenSpan, ) # Trainer from .trainer_callback import ( DefaultFlowCallback, EarlyStoppingCallback, PrinterCallback, ProgressCallback, TrainerCallback, TrainerControl, TrainerState, ) from .trainer_utils import EvalPrediction, IntervalStrategy, SchedulerType, enable_full_determinism, set_seed from .training_args import TrainingArguments from .training_args_seq2seq import Seq2SeqTrainingArguments from .training_args_tf import TFTrainingArguments # Files and general utilities from .utils import ( CONFIG_NAME, MODEL_CARD_NAME, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TRANSFORMERS_CACHE, WEIGHTS_NAME, TensorType, add_end_docstrings, add_start_docstrings, is_apex_available, is_datasets_available, is_faiss_available, is_flax_available, is_phonemizer_available, is_psutil_available, is_py3nvml_available, is_pyctcdecode_available, is_safetensors_available, is_scipy_available, is_sentencepiece_available, is_sklearn_available, is_speech_available, is_tensorflow_text_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_tpu_available, is_vision_available, logging, ) try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_sentencepiece_objects import * else: from .models.albert import AlbertTokenizer from .models.barthez import BarthezTokenizer from .models.bartpho import BartphoTokenizer from .models.bert_generation import BertGenerationTokenizer from .models.big_bird import BigBirdTokenizer from .models.camembert import CamembertTokenizer from .models.cpm import CpmTokenizer from .models.deberta_v2 import DebertaV2Tokenizer from .models.fnet import FNetTokenizer from .models.layoutxlm import LayoutXLMTokenizer from .models.m2m_100 import M2M100Tokenizer from .models.marian import MarianTokenizer from .models.mbart import MBart50Tokenizer, MBartTokenizer from .models.mluke import MLukeTokenizer from .models.mt5 import MT5Tokenizer from .models.nllb import NllbTokenizer from .models.pegasus import PegasusTokenizer from .models.plbart import PLBartTokenizer from .models.reformer import ReformerTokenizer from .models.rembert import RemBertTokenizer from .models.speech_to_text import Speech2TextTokenizer from .models.t5 import T5Tokenizer from .models.xglm import XGLMTokenizer from .models.xlm_prophetnet import XLMProphetNetTokenizer from .models.xlm_roberta import XLMRobertaTokenizer from .models.xlnet import XLNetTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_tokenizers_objects import * else: # Fast tokenizers imports from .models.albert import AlbertTokenizerFast from .models.bart import BartTokenizerFast from .models.barthez import BarthezTokenizerFast from .models.bert import BertTokenizerFast from .models.big_bird import BigBirdTokenizerFast from .models.blenderbot import BlenderbotTokenizerFast from .models.blenderbot_small import BlenderbotSmallTokenizerFast from .models.bloom import BloomTokenizerFast from .models.camembert import CamembertTokenizerFast from .models.clip import CLIPTokenizerFast from .models.codegen import CodeGenTokenizerFast from .models.convbert import ConvBertTokenizerFast from .models.cpm import CpmTokenizerFast from .models.deberta import DebertaTokenizerFast from .models.deberta_v2 import DebertaV2TokenizerFast from .models.distilbert import DistilBertTokenizerFast from .models.dpr import DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast, DPRReaderTokenizerFast from .models.electra import ElectraTokenizerFast from .models.fnet import FNetTokenizerFast from .models.funnel import FunnelTokenizerFast from .models.gpt2 import GPT2TokenizerFast from .models.gpt_neox import GPTNeoXTokenizerFast from .models.gpt_neox_japanese import GPTNeoXJapaneseTokenizer from .models.herbert import HerbertTokenizerFast from .models.layoutlm import LayoutLMTokenizerFast from .models.layoutlmv2 import LayoutLMv2TokenizerFast from .models.layoutlmv3 import LayoutLMv3TokenizerFast from .models.layoutxlm import LayoutXLMTokenizerFast from .models.led import LEDTokenizerFast from .models.longformer import LongformerTokenizerFast from .models.lxmert import LxmertTokenizerFast from .models.markuplm import MarkupLMTokenizerFast from .models.mbart import MBartTokenizerFast from .models.mbart50 import MBart50TokenizerFast from .models.mobilebert import MobileBertTokenizerFast from .models.mpnet import MPNetTokenizerFast from .models.mt5 import MT5TokenizerFast from .models.mvp import MvpTokenizerFast from .models.nllb import NllbTokenizerFast from .models.openai import OpenAIGPTTokenizerFast from .models.pegasus import PegasusTokenizerFast from .models.realm import RealmTokenizerFast from .models.reformer import ReformerTokenizerFast from .models.rembert import RemBertTokenizerFast from .models.retribert import RetriBertTokenizerFast from .models.roberta import RobertaTokenizerFast from .models.roformer import RoFormerTokenizerFast from .models.splinter import SplinterTokenizerFast from .models.squeezebert import SqueezeBertTokenizerFast from .models.t5 import T5TokenizerFast from .models.xglm import XGLMTokenizerFast from .models.xlm_roberta import XLMRobertaTokenizerFast from .models.xlnet import XLNetTokenizerFast from .tokenization_utils_fast import PreTrainedTokenizerFast try: if not (is_sentencepiece_available() and is_tokenizers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummies_sentencepiece_and_tokenizers_objects import * else: from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, convert_slow_tokenizer try: if not is_speech_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_speech_objects import * else: from .models.mctct import MCTCTFeatureExtractor from .models.speech_to_text import Speech2TextFeatureExtractor try: if not is_tensorflow_text_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_tensorflow_text_objects import * else: from .models.bert import TFBertTokenizer try: if not (is_speech_available() and is_sentencepiece_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_sentencepiece_and_speech_objects import * else: from .models.speech_to_text import Speech2TextProcessor try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_vision_objects import * else: from .image_processing_utils import ImageProcessingMixin from .image_transforms import rescale, resize, to_pil_image from .image_utils import ImageFeatureExtractionMixin from .models.beit import BeitFeatureExtractor, BeitImageProcessor from .models.clip import CLIPFeatureExtractor, CLIPImageProcessor from .models.conditional_detr import ConditionalDetrFeatureExtractor from .models.convnext import ConvNextFeatureExtractor, ConvNextImageProcessor from .models.deformable_detr import DeformableDetrFeatureExtractor from .models.deit import DeiTFeatureExtractor, DeiTImageProcessor from .models.detr import DetrFeatureExtractor from .models.donut import DonutFeatureExtractor from .models.dpt import DPTFeatureExtractor, DPTImageProcessor from .models.flava import FlavaFeatureExtractor, FlavaImageProcessor, FlavaProcessor from .models.glpn import GLPNFeatureExtractor, GLPNImageProcessor from .models.imagegpt import ImageGPTFeatureExtractor, ImageGPTImageProcessor from .models.layoutlmv2 import LayoutLMv2FeatureExtractor, LayoutLMv2ImageProcessor from .models.layoutlmv3 import LayoutLMv3FeatureExtractor, LayoutLMv3ImageProcessor from .models.levit import LevitFeatureExtractor, LevitImageProcessor from .models.maskformer import MaskFormerFeatureExtractor from .models.mobilenet_v2 import MobileNetV2FeatureExtractor, MobileNetV2ImageProcessor from .models.mobilevit import MobileViTFeatureExtractor, MobileViTImageProcessor from .models.owlvit import OwlViTFeatureExtractor from .models.perceiver import PerceiverFeatureExtractor, PerceiverImageProcessor from .models.poolformer import PoolFormerFeatureExtractor, PoolFormerImageProcessor from .models.segformer import SegformerFeatureExtractor, SegformerImageProcessor from .models.videomae import VideoMAEFeatureExtractor, VideoMAEImageProcessor from .models.vilt import ViltFeatureExtractor, ViltImageProcessor, ViltProcessor from .models.vit import ViTFeatureExtractor, ViTImageProcessor from .models.yolos import YolosFeatureExtractor # Modeling try: if not (is_timm_available() and is_vision_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_timm_and_vision_objects import * else: from .models.conditional_detr import ( CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, ConditionalDetrForObjectDetection, ConditionalDetrForSegmentation, ConditionalDetrModel, ConditionalDetrPreTrainedModel, ) from .models.deformable_detr import ( DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DeformableDetrForObjectDetection, DeformableDetrModel, DeformableDetrPreTrainedModel, ) from .models.detr import ( DETR_PRETRAINED_MODEL_ARCHIVE_LIST, DetrForObjectDetection, DetrForSegmentation, DetrModel, DetrPreTrainedModel, ) from .models.table_transformer import ( TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TableTransformerForObjectDetection, TableTransformerModel, TableTransformerPreTrainedModel, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * else: # Benchmarks from .benchmark.benchmark import PyTorchBenchmark from .benchmark.benchmark_args import PyTorchBenchmarkArguments from .data.datasets import ( GlueDataset, GlueDataTrainingArguments, LineByLineTextDataset, LineByLineWithRefDataset, LineByLineWithSOPTextDataset, SquadDataset, SquadDataTrainingArguments, TextDataset, TextDatasetForNextSentencePrediction, ) from .generation import ( BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer, Constraint, ConstraintListState, DisjunctiveConstraint, ForcedBOSTokenLogitsProcessor, ForcedEOSTokenLogitsProcessor, GenerationMixin, HammingDiversityLogitsProcessor, InfNanRemoveLogitsProcessor, LogitsProcessor, LogitsProcessorList, LogitsWarper, MaxLengthCriteria, MaxTimeCriteria, MinLengthLogitsProcessor, NoBadWordsLogitsProcessor, NoRepeatNGramLogitsProcessor, PhrasalConstraint, PrefixConstrainedLogitsProcessor, RepetitionPenaltyLogitsProcessor, StoppingCriteria, StoppingCriteriaList, TemperatureLogitsWarper, TopKLogitsWarper, TopPLogitsWarper, TypicalLogitsWarper, top_k_top_p_filtering, ) from .modeling_utils import PreTrainedModel # PyTorch model imports from .models.albert import ( ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, AlbertForMaskedLM, AlbertForMultipleChoice, AlbertForPreTraining, AlbertForQuestionAnswering, AlbertForSequenceClassification, AlbertForTokenClassification, AlbertModel, AlbertPreTrainedModel, load_tf_weights_in_albert, ) from .models.auto import ( MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_CTC_MAPPING, MODEL_FOR_DEPTH_ESTIMATION_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_FOR_VISION_2_SEQ_MAPPING, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, AutoBackbone, AutoModel, AutoModelForAudioClassification, AutoModelForAudioFrameClassification, AutoModelForAudioXVector, AutoModelForCausalLM, AutoModelForCTC, AutoModelForDepthEstimation, AutoModelForDocumentQuestionAnswering, AutoModelForImageClassification, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, AutoModelForMaskedImageModeling, AutoModelForMaskedLM, AutoModelForMultipleChoice, AutoModelForNextSentencePrediction, AutoModelForObjectDetection, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSemanticSegmentation, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForSpeechSeq2Seq, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelForVideoClassification, AutoModelForVision2Seq, AutoModelForVisualQuestionAnswering, AutoModelForZeroShotObjectDetection, AutoModelWithLMHead, ) from .models.bart import ( BART_PRETRAINED_MODEL_ARCHIVE_LIST, BartForCausalLM, BartForConditionalGeneration, BartForQuestionAnswering, BartForSequenceClassification, BartModel, BartPretrainedModel, PretrainedBartModel, ) from .models.beit import ( BEIT_PRETRAINED_MODEL_ARCHIVE_LIST, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitModel, BeitPreTrainedModel, ) from .models.bert import ( BERT_PRETRAINED_MODEL_ARCHIVE_LIST, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLayer, BertLMHeadModel, BertModel, BertPreTrainedModel, load_tf_weights_in_bert, ) from .models.bert_generation import ( BertGenerationDecoder, BertGenerationEncoder, BertGenerationPreTrainedModel, load_tf_weights_in_bert_generation, ) from .models.big_bird import ( BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdForCausalLM, BigBirdForMaskedLM, BigBirdForMultipleChoice, BigBirdForPreTraining, BigBirdForQuestionAnswering, BigBirdForSequenceClassification, BigBirdForTokenClassification, BigBirdLayer, BigBirdModel, BigBirdPreTrainedModel, load_tf_weights_in_big_bird, ) from .models.bigbird_pegasus import ( BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST, BigBirdPegasusForCausalLM, BigBirdPegasusForConditionalGeneration, BigBirdPegasusForQuestionAnswering, BigBirdPegasusForSequenceClassification, BigBirdPegasusModel, BigBirdPegasusPreTrainedModel, ) from .models.blenderbot import ( BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotForCausalLM, BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotPreTrainedModel, ) from .models.blenderbot_small import ( BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotSmallForCausalLM, BlenderbotSmallForConditionalGeneration, BlenderbotSmallModel, BlenderbotSmallPreTrainedModel, ) from .models.bloom import ( BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel, BloomPreTrainedModel, ) from .models.camembert import ( CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, CamembertForCausalLM, CamembertForMaskedLM, CamembertForMultipleChoice, CamembertForQuestionAnswering, CamembertForSequenceClassification, CamembertForTokenClassification, CamembertModel, CamembertPreTrainedModel, ) from .models.canine import ( CANINE_PRETRAINED_MODEL_ARCHIVE_LIST, CanineForMultipleChoice, CanineForQuestionAnswering, CanineForSequenceClassification, CanineForTokenClassification, CanineLayer, CanineModel, CaninePreTrainedModel, load_tf_weights_in_canine, ) from .models.clip import ( CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPModel, CLIPPreTrainedModel, CLIPTextModel, CLIPTextModelWithProjection, CLIPVisionModel, CLIPVisionModelWithProjection, ) from .models.clipseg import ( CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegPreTrainedModel, CLIPSegTextModel, CLIPSegVisionModel, ) from .models.codegen import ( CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST, CodeGenForCausalLM, CodeGenModel, CodeGenPreTrainedModel, ) from .models.convbert import ( CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, ConvBertForMaskedLM, ConvBertForMultipleChoice, ConvBertForQuestionAnswering, ConvBertForSequenceClassification, ConvBertForTokenClassification, ConvBertLayer, ConvBertModel, ConvBertPreTrainedModel, load_tf_weights_in_convbert, ) from .models.convnext import ( CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST, ConvNextForImageClassification, ConvNextModel, ConvNextPreTrainedModel, ) from .models.ctrl import ( CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, CTRLPreTrainedModel, ) from .models.cvt import ( CVT_PRETRAINED_MODEL_ARCHIVE_LIST, CvtForImageClassification, CvtModel, CvtPreTrainedModel, ) from .models.data2vec import ( DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST, DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST, Data2VecAudioForAudioFrameClassification, Data2VecAudioForCTC, Data2VecAudioForSequenceClassification, Data2VecAudioForXVector, Data2VecAudioModel, Data2VecAudioPreTrainedModel, Data2VecTextForCausalLM, Data2VecTextForMaskedLM, Data2VecTextForMultipleChoice, Data2VecTextForQuestionAnswering, Data2VecTextForSequenceClassification, Data2VecTextForTokenClassification, Data2VecTextModel, Data2VecTextPreTrainedModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation, Data2VecVisionModel, Data2VecVisionPreTrainedModel, ) from .models.deberta import ( DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, DebertaForMaskedLM, DebertaForQuestionAnswering, DebertaForSequenceClassification, DebertaForTokenClassification, DebertaModel, DebertaPreTrainedModel, ) from .models.deberta_v2 import ( DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST, DebertaV2ForMaskedLM, DebertaV2ForMultipleChoice, DebertaV2ForQuestionAnswering, DebertaV2ForSequenceClassification, DebertaV2ForTokenClassification, DebertaV2Model, DebertaV2PreTrainedModel, ) from .models.decision_transformer import ( DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, DecisionTransformerGPT2Model, DecisionTransformerGPT2PreTrainedModel, DecisionTransformerModel, DecisionTransformerPreTrainedModel, ) from .models.deit import ( DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, DeiTForImageClassification, DeiTForImageClassificationWithTeacher, DeiTForMaskedImageModeling, DeiTModel, DeiTPreTrainedModel, ) from .models.distilbert import ( DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, DistilBertModel, DistilBertPreTrainedModel, ) from .models.donut import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, DonutSwinModel, DonutSwinPreTrainedModel from .models.dpr import ( DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, DPRContextEncoder, DPRPretrainedContextEncoder, DPRPreTrainedModel, DPRPretrainedQuestionEncoder, DPRPretrainedReader, DPRQuestionEncoder, DPRReader, ) from .models.dpt import ( DPT_PRETRAINED_MODEL_ARCHIVE_LIST, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel, DPTPreTrainedModel, ) from .models.electra import ( ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, ElectraForCausalLM, ElectraForMaskedLM, ElectraForMultipleChoice, ElectraForPreTraining, ElectraForQuestionAnswering, ElectraForSequenceClassification, ElectraForTokenClassification, ElectraModel, ElectraPreTrainedModel, load_tf_weights_in_electra, ) from .models.encoder_decoder import EncoderDecoderModel from .models.ernie import ( ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST, ErnieForCausalLM, ErnieForMaskedLM, ErnieForMultipleChoice, ErnieForNextSentencePrediction, ErnieForPreTraining, ErnieForQuestionAnswering, ErnieForSequenceClassification, ErnieForTokenClassification, ErnieModel, ErniePreTrainedModel, ) from .models.esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, EsmFoldPreTrainedModel, EsmForMaskedLM, EsmForProteinFolding, EsmForSequenceClassification, EsmForTokenClassification, EsmModel, EsmPreTrainedModel, ) from .models.flaubert import ( FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, FlaubertForMultipleChoice, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertModel, FlaubertPreTrainedModel, FlaubertWithLMHeadModel, ) from .models.flava import ( FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST, FlavaForPreTraining, FlavaImageCodebook, FlavaImageModel, FlavaModel, FlavaMultimodalModel, FlavaPreTrainedModel, FlavaTextModel, ) from .models.fnet import ( FNET_PRETRAINED_MODEL_ARCHIVE_LIST, FNetForMaskedLM, FNetForMultipleChoice, FNetForNextSentencePrediction, FNetForPreTraining, FNetForQuestionAnswering, FNetForSequenceClassification, FNetForTokenClassification, FNetLayer, FNetModel, FNetPreTrainedModel, ) from .models.fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel from .models.funnel import ( FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, FunnelBaseModel, FunnelForMaskedLM, FunnelForMultipleChoice, FunnelForPreTraining, FunnelForQuestionAnswering, FunnelForSequenceClassification, FunnelForTokenClassification, FunnelModel, FunnelPreTrainedModel, load_tf_weights_in_funnel, ) from .models.glpn import ( GLPN_PRETRAINED_MODEL_ARCHIVE_LIST, GLPNForDepthEstimation, GLPNModel, GLPNPreTrainedModel, ) from .models.gpt2 import ( GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, GPT2DoubleHeadsModel, GPT2ForSequenceClassification, GPT2ForTokenClassification, GPT2LMHeadModel, GPT2Model, GPT2PreTrainedModel, load_tf_weights_in_gpt2, ) from .models.gpt_neo import ( GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoForCausalLM, GPTNeoForSequenceClassification, GPTNeoModel, GPTNeoPreTrainedModel, load_tf_weights_in_gpt_neo, ) from .models.gpt_neox import ( GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXForCausalLM, GPTNeoXLayer, GPTNeoXModel, GPTNeoXPreTrainedModel, ) from .models.gpt_neox_japanese import ( GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseLayer, GPTNeoXJapaneseModel, GPTNeoXJapanesePreTrainedModel, ) from .models.gptj import ( GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST, GPTJForCausalLM, GPTJForQuestionAnswering, GPTJForSequenceClassification, GPTJModel, GPTJPreTrainedModel, ) from .models.groupvit import ( GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, GroupViTModel, GroupViTPreTrainedModel, GroupViTTextModel, GroupViTVisionModel, ) from .models.hubert import ( HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, HubertForCTC, HubertForSequenceClassification, HubertModel, HubertPreTrainedModel, ) from .models.ibert import ( IBERT_PRETRAINED_MODEL_ARCHIVE_LIST, IBertForMaskedLM, IBertForMultipleChoice, IBertForQuestionAnswering, IBertForSequenceClassification, IBertForTokenClassification, IBertModel, IBertPreTrainedModel, ) from .models.imagegpt import ( IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST, ImageGPTForCausalImageModeling, ImageGPTForImageClassification, ImageGPTModel, ImageGPTPreTrainedModel, load_tf_weights_in_imagegpt, ) from .models.jukebox import ( JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST, JukeboxModel, JukeboxPreTrainedModel, JukeboxPrior, JukeboxVQVAE, ) from .models.layoutlm import ( LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMForMaskedLM, LayoutLMForQuestionAnswering, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMModel, LayoutLMPreTrainedModel, ) from .models.layoutlmv2 import ( LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMv2ForQuestionAnswering, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2Model, LayoutLMv2PreTrainedModel, ) from .models.layoutlmv3 import ( LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMv3ForQuestionAnswering, LayoutLMv3ForSequenceClassification, LayoutLMv3ForTokenClassification, LayoutLMv3Model, LayoutLMv3PreTrainedModel, ) from .models.led import ( LED_PRETRAINED_MODEL_ARCHIVE_LIST, LEDForConditionalGeneration, LEDForQuestionAnswering, LEDForSequenceClassification, LEDModel, LEDPreTrainedModel, ) from .models.levit import ( LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, LevitForImageClassification, LevitForImageClassificationWithTeacher, LevitModel, LevitPreTrainedModel, ) from .models.lilt import ( LILT_PRETRAINED_MODEL_ARCHIVE_LIST, LiltForQuestionAnswering, LiltForSequenceClassification, LiltForTokenClassification, LiltModel, LiltPreTrainedModel, ) from .models.longformer import ( LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, LongformerForMaskedLM, LongformerForMultipleChoice, LongformerForQuestionAnswering, LongformerForSequenceClassification, LongformerForTokenClassification, LongformerModel, LongformerPreTrainedModel, LongformerSelfAttention, ) from .models.longt5 import ( LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST, LongT5EncoderModel, LongT5ForConditionalGeneration, LongT5Model, LongT5PreTrainedModel, ) from .models.luke import ( LUKE_PRETRAINED_MODEL_ARCHIVE_LIST, LukeForEntityClassification, LukeForEntityPairClassification, LukeForEntitySpanClassification, LukeForMaskedLM, LukeForMultipleChoice, LukeForQuestionAnswering, LukeForSequenceClassification, LukeForTokenClassification, LukeModel, LukePreTrainedModel, ) from .models.lxmert import ( LxmertEncoder, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, LxmertPreTrainedModel, LxmertVisualFeatureEncoder, LxmertXLayer, ) from .models.m2m_100 import ( M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST, M2M100ForConditionalGeneration, M2M100Model, M2M100PreTrainedModel, ) from .models.marian import MarianForCausalLM, MarianModel, MarianMTModel from .models.markuplm import ( MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST, MarkupLMForQuestionAnswering, MarkupLMForSequenceClassification, MarkupLMForTokenClassification, MarkupLMModel, MarkupLMPreTrainedModel, ) from .models.maskformer import ( MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskFormerForInstanceSegmentation, MaskFormerModel, MaskFormerPreTrainedModel, ) from .models.mbart import ( MBartForCausalLM, MBartForConditionalGeneration, MBartForQuestionAnswering, MBartForSequenceClassification, MBartModel, MBartPreTrainedModel, ) from .models.mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel from .models.megatron_bert import ( MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, MegatronBertForCausalLM, MegatronBertForMaskedLM, MegatronBertForMultipleChoice, MegatronBertForNextSentencePrediction, MegatronBertForPreTraining, MegatronBertForQuestionAnswering, MegatronBertForSequenceClassification, MegatronBertForTokenClassification, MegatronBertModel, MegatronBertPreTrainedModel, ) from .models.mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings from .models.mobilebert import ( MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileBertForMaskedLM, MobileBertForMultipleChoice, MobileBertForNextSentencePrediction, MobileBertForPreTraining, MobileBertForQuestionAnswering, MobileBertForSequenceClassification, MobileBertForTokenClassification, MobileBertLayer, MobileBertModel, MobileBertPreTrainedModel, load_tf_weights_in_mobilebert, ) from .models.mobilenet_v2 import ( MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST, MobileNetV2ForImageClassification, MobileNetV2ForSemanticSegmentation, MobileNetV2Model, MobileNetV2PreTrainedModel, load_tf_weights_in_mobilenet_v2, ) from .models.mobilevit import ( MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel, MobileViTPreTrainedModel, ) from .models.mpnet import ( MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, MPNetForMaskedLM, MPNetForMultipleChoice, MPNetForQuestionAnswering, MPNetForSequenceClassification, MPNetForTokenClassification, MPNetLayer, MPNetModel, MPNetPreTrainedModel, ) from .models.mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model from .models.mvp import ( MVP_PRETRAINED_MODEL_ARCHIVE_LIST, MvpForCausalLM, MvpForConditionalGeneration, MvpForQuestionAnswering, MvpForSequenceClassification, MvpModel, MvpPreTrainedModel, ) from .models.nezha import ( NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST, NezhaForMaskedLM, NezhaForMultipleChoice, NezhaForNextSentencePrediction, NezhaForPreTraining, NezhaForQuestionAnswering, NezhaForSequenceClassification, NezhaForTokenClassification, NezhaModel, NezhaPreTrainedModel, ) from .models.nystromformer import ( NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, NystromformerForMaskedLM, NystromformerForMultipleChoice, NystromformerForQuestionAnswering, NystromformerForSequenceClassification, NystromformerForTokenClassification, NystromformerLayer, NystromformerModel, NystromformerPreTrainedModel, ) from .models.openai import ( OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification, OpenAIGPTLMHeadModel, OpenAIGPTModel, OpenAIGPTPreTrainedModel, load_tf_weights_in_openai_gpt, ) from .models.opt import ( OPT_PRETRAINED_MODEL_ARCHIVE_LIST, OPTForCausalLM, OPTForQuestionAnswering, OPTForSequenceClassification, OPTModel, OPTPreTrainedModel, ) from .models.owlvit import ( OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST, OwlViTForObjectDetection, OwlViTModel, OwlViTPreTrainedModel, OwlViTTextModel, OwlViTVisionModel, ) from .models.pegasus import ( PegasusForCausalLM, PegasusForConditionalGeneration, PegasusModel, PegasusPreTrainedModel, ) from .models.pegasus_x import ( PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST, PegasusXForConditionalGeneration, PegasusXModel, PegasusXPreTrainedModel, ) from .models.perceiver import ( PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST, PerceiverForImageClassificationConvProcessing, PerceiverForImageClassificationFourier, PerceiverForImageClassificationLearned, PerceiverForMaskedLM, PerceiverForMultimodalAutoencoding, PerceiverForOpticalFlow, PerceiverForSequenceClassification, PerceiverLayer, PerceiverModel, PerceiverPreTrainedModel, ) from .models.plbart import ( PLBART_PRETRAINED_MODEL_ARCHIVE_LIST, PLBartForCausalLM, PLBartForConditionalGeneration, PLBartForSequenceClassification, PLBartModel, PLBartPreTrainedModel, ) from .models.poolformer import ( POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, PoolFormerForImageClassification, PoolFormerModel, PoolFormerPreTrainedModel, ) from .models.prophetnet import ( PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, ProphetNetDecoder, ProphetNetEncoder, ProphetNetForCausalLM, ProphetNetForConditionalGeneration, ProphetNetModel, ProphetNetPreTrainedModel, ) from .models.qdqbert import ( QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST, QDQBertForMaskedLM, QDQBertForMultipleChoice, QDQBertForNextSentencePrediction, QDQBertForQuestionAnswering, QDQBertForSequenceClassification, QDQBertForTokenClassification, QDQBertLayer, QDQBertLMHeadModel, QDQBertModel, QDQBertPreTrainedModel, load_tf_weights_in_qdqbert, ) from .models.rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration from .models.realm import ( REALM_PRETRAINED_MODEL_ARCHIVE_LIST, RealmEmbedder, RealmForOpenQA, RealmKnowledgeAugEncoder, RealmPreTrainedModel, RealmReader, RealmRetriever, RealmScorer, load_tf_weights_in_realm, ) from .models.reformer import ( REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ReformerAttention, ReformerForMaskedLM, ReformerForQuestionAnswering, ReformerForSequenceClassification, ReformerLayer, ReformerModel, ReformerModelWithLMHead, ReformerPreTrainedModel, ) from .models.regnet import ( REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, RegNetForImageClassification, RegNetModel, RegNetPreTrainedModel, ) from .models.rembert import ( REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RemBertForCausalLM, RemBertForMaskedLM, RemBertForMultipleChoice, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForTokenClassification, RemBertLayer, RemBertModel, RemBertPreTrainedModel, load_tf_weights_in_rembert, ) from .models.resnet import ( RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, ResNetBackbone, ResNetForImageClassification, ResNetModel, ResNetPreTrainedModel, ) from .models.retribert import RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RetriBertModel, RetriBertPreTrainedModel from .models.roberta import ( ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaForCausalLM, RobertaForMaskedLM, RobertaForMultipleChoice, RobertaForQuestionAnswering, RobertaForSequenceClassification, RobertaForTokenClassification, RobertaModel, RobertaPreTrainedModel, ) from .models.roc_bert import ( ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, RoCBertForCausalLM, RoCBertForMaskedLM, RoCBertForMultipleChoice, RoCBertForPreTraining, RoCBertForQuestionAnswering, RoCBertForSequenceClassification, RoCBertForTokenClassification, RoCBertLayer, RoCBertModel, RoCBertPreTrainedModel, load_tf_weights_in_roc_bert, ) from .models.roformer import ( ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, RoFormerForCausalLM, RoFormerForMaskedLM, RoFormerForMultipleChoice, RoFormerForQuestionAnswering, RoFormerForSequenceClassification, RoFormerForTokenClassification, RoFormerLayer, RoFormerModel, RoFormerPreTrainedModel, load_tf_weights_in_roformer, ) from .models.segformer import ( SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, SegformerDecodeHead, SegformerForImageClassification, SegformerForSemanticSegmentation, SegformerLayer, SegformerModel, SegformerPreTrainedModel, ) from .models.sew import ( SEW_PRETRAINED_MODEL_ARCHIVE_LIST, SEWForCTC, SEWForSequenceClassification, SEWModel, SEWPreTrainedModel, ) from .models.sew_d import ( SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST, SEWDForCTC, SEWDForSequenceClassification, SEWDModel, SEWDPreTrainedModel, ) from .models.speech_encoder_decoder import SpeechEncoderDecoderModel from .models.speech_to_text import ( SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, Speech2TextForConditionalGeneration, Speech2TextModel, Speech2TextPreTrainedModel, ) from .models.speech_to_text_2 import Speech2Text2ForCausalLM, Speech2Text2PreTrainedModel from .models.splinter import ( SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST, SplinterForPreTraining, SplinterForQuestionAnswering, SplinterLayer, SplinterModel, SplinterPreTrainedModel, ) from .models.squeezebert import ( SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, SqueezeBertForMaskedLM, SqueezeBertForMultipleChoice, SqueezeBertForQuestionAnswering, SqueezeBertForSequenceClassification, SqueezeBertForTokenClassification, SqueezeBertModel, SqueezeBertModule, SqueezeBertPreTrainedModel, ) from .models.swin import ( SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel, SwinPreTrainedModel, ) from .models.swinv2 import ( SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST, Swinv2ForImageClassification, Swinv2ForMaskedImageModeling, Swinv2Model, Swinv2PreTrainedModel, ) from .models.switch_transformers import ( SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST, SwitchTransformersEncoderModel, SwitchTransformersForConditionalGeneration, SwitchTransformersModel, SwitchTransformersPreTrainedModel, SwitchTransformersSparseMLP, SwitchTransformersTop1Router, ) from .models.t5 import ( T5_PRETRAINED_MODEL_ARCHIVE_LIST, T5EncoderModel, T5ForConditionalGeneration, T5Model, T5PreTrainedModel, load_tf_weights_in_t5, ) from .models.tapas import ( TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasPreTrainedModel, load_tf_weights_in_tapas, ) from .models.time_series_transformer import ( TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TimeSeriesTransformerForPrediction, TimeSeriesTransformerModel, TimeSeriesTransformerPreTrainedModel, ) from .models.trajectory_transformer import ( TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TrajectoryTransformerModel, TrajectoryTransformerPreTrainedModel, ) from .models.transfo_xl import ( TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, AdaptiveEmbedding, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel, TransfoXLPreTrainedModel, load_tf_weights_in_transfo_xl, ) from .models.trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel from .models.unispeech import ( UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechForCTC, UniSpeechForPreTraining, UniSpeechForSequenceClassification, UniSpeechModel, UniSpeechPreTrainedModel, ) from .models.unispeech_sat import ( UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, UniSpeechSatPreTrainedModel, ) from .models.van import ( VAN_PRETRAINED_MODEL_ARCHIVE_LIST, VanForImageClassification, VanModel, VanPreTrainedModel, ) from .models.videomae import ( VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST, VideoMAEForPreTraining, VideoMAEForVideoClassification, VideoMAEModel, VideoMAEPreTrainedModel, ) from .models.vilt import ( VILT_PRETRAINED_MODEL_ARCHIVE_LIST, ViltForImageAndTextRetrieval, ViltForImagesAndTextClassification, ViltForMaskedLM, ViltForQuestionAnswering, ViltForTokenClassification, ViltLayer, ViltModel, ViltPreTrainedModel, ) from .models.vision_encoder_decoder import VisionEncoderDecoderModel from .models.vision_text_dual_encoder import VisionTextDualEncoderModel from .models.visual_bert import ( VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, VisualBertForMultipleChoice, VisualBertForPreTraining, VisualBertForQuestionAnswering, VisualBertForRegionToPhraseAlignment, VisualBertForVisualReasoning, VisualBertLayer, VisualBertModel, VisualBertPreTrainedModel, ) from .models.vit import ( VIT_PRETRAINED_MODEL_ARCHIVE_LIST, ViTForImageClassification, ViTForMaskedImageModeling, ViTModel, ViTPreTrainedModel, ) from .models.vit_mae import ( VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST, ViTMAEForPreTraining, ViTMAELayer, ViTMAEModel, ViTMAEPreTrainedModel, ) from .models.vit_msn import ( VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST, ViTMSNForImageClassification, ViTMSNModel, ViTMSNPreTrainedModel, ) from .models.wav2vec2 import ( WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2ForAudioFrameClassification, Wav2Vec2ForCTC, Wav2Vec2ForMaskedLM, Wav2Vec2ForPreTraining, Wav2Vec2ForSequenceClassification, Wav2Vec2ForXVector, Wav2Vec2Model, Wav2Vec2PreTrainedModel, ) from .models.wav2vec2_conformer import ( WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, Wav2Vec2ConformerForAudioFrameClassification, Wav2Vec2ConformerForCTC, Wav2Vec2ConformerForPreTraining, Wav2Vec2ConformerForSequenceClassification, Wav2Vec2ConformerForXVector, Wav2Vec2ConformerModel, Wav2Vec2ConformerPreTrainedModel, ) from .models.wavlm import ( WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST, WavLMForAudioFrameClassification, WavLMForCTC, WavLMForSequenceClassification, WavLMForXVector, WavLMModel, WavLMPreTrainedModel, ) from .models.whisper import ( WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, WhisperForConditionalGeneration, WhisperModel, WhisperPreTrainedModel, ) from .models.x_clip import ( XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST, XCLIPModel, XCLIPPreTrainedModel, XCLIPTextModel, XCLIPVisionModel, ) from .models.xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel from .models.xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) from .models.xlm_prophetnet import ( XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST, XLMProphetNetDecoder, XLMProphetNetEncoder, XLMProphetNetForCausalLM, XLMProphetNetForConditionalGeneration, XLMProphetNetModel, XLMProphetNetPreTrainedModel, ) from .models.xlm_roberta import ( XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, XLMRobertaForCausalLM, XLMRobertaForMaskedLM, XLMRobertaForMultipleChoice, XLMRobertaForQuestionAnswering, XLMRobertaForSequenceClassification, XLMRobertaForTokenClassification, XLMRobertaModel, XLMRobertaPreTrainedModel, ) from .models.xlm_roberta_xl import ( XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST, XLMRobertaXLForCausalLM, XLMRobertaXLForMaskedLM, XLMRobertaXLForMultipleChoice, XLMRobertaXLForQuestionAnswering, XLMRobertaXLForSequenceClassification, XLMRobertaXLForTokenClassification, XLMRobertaXLModel, XLMRobertaXLPreTrainedModel, ) from .models.xlnet import ( XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, XLNetForMultipleChoice, XLNetForQuestionAnswering, XLNetForQuestionAnsweringSimple, XLNetForSequenceClassification, XLNetForTokenClassification, XLNetLMHeadModel, XLNetModel, XLNetPreTrainedModel, load_tf_weights_in_xlnet, ) from .models.yolos import ( YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST, YolosForObjectDetection, YolosModel, YolosPreTrainedModel, ) from .models.yoso import ( YOSO_PRETRAINED_MODEL_ARCHIVE_LIST, YosoForMaskedLM, YosoForMultipleChoice, YosoForQuestionAnswering, YosoForSequenceClassification, YosoForTokenClassification, YosoLayer, YosoModel, YosoPreTrainedModel, ) # Optimization from .optimization import ( Adafactor, AdamW, get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, ) from .pytorch_utils import Conv1D, apply_chunking_to_forward, prune_layer # Trainer from .trainer import Trainer from .trainer_pt_utils import torch_distributed_zero_first from .trainer_seq2seq import Seq2SeqTrainer # TensorFlow try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # Import the same objects as dummies to get them in the namespace. # They will raise an import error if the user tries to instantiate / use them. from .utils.dummy_tf_objects import * else: from .benchmark.benchmark_args_tf import TensorFlowBenchmarkArguments # Benchmarks from .benchmark.benchmark_tf import TensorFlowBenchmark from .generation import ( TFForcedBOSTokenLogitsProcessor, TFForcedEOSTokenLogitsProcessor, TFGenerationMixin, TFLogitsProcessor, TFLogitsProcessorList, TFLogitsWarper, TFMinLengthLogitsProcessor, TFNoBadWordsLogitsProcessor, TFNoRepeatNGramLogitsProcessor, TFRepetitionPenaltyLogitsProcessor, TFTemperatureLogitsWarper, TFTopKLogitsWarper, TFTopPLogitsWarper, tf_top_k_top_p_filtering, ) from .keras_callbacks import KerasMetricCallback, PushToHubCallback from .modeling_tf_layoutlm import ( TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFLayoutLMForMaskedLM, TFLayoutLMForQuestionAnswering, TFLayoutLMForSequenceClassification, TFLayoutLMForTokenClassification, TFLayoutLMMainLayer, TFLayoutLMModel, TFLayoutLMPreTrainedModel, ) from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list # TensorFlow model imports from .models.albert import ( TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFAlbertForMaskedLM, TFAlbertForMultipleChoice, TFAlbertForPreTraining, TFAlbertForQuestionAnswering, TFAlbertForSequenceClassification, TFAlbertForTokenClassification, TFAlbertMainLayer, TFAlbertModel, TFAlbertPreTrainedModel, ) from .models.auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForDocumentQuestionAnswering, TFAutoModelForImageClassification, TFAutoModelForMaskedLM, TFAutoModelForMultipleChoice, TFAutoModelForNextSentencePrediction, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSemanticSegmentation, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForSpeechSeq2Seq, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelForVision2Seq, TFAutoModelWithLMHead, ) from .models.bart import TFBartForConditionalGeneration, TFBartModel, TFBartPretrainedModel from .models.bert import ( TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFBertEmbeddings, TFBertForMaskedLM, TFBertForMultipleChoice, TFBertForNextSentencePrediction, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertForTokenClassification, TFBertLMHeadModel, TFBertMainLayer, TFBertModel, TFBertPreTrainedModel, ) from .models.blenderbot import ( TFBlenderbotForConditionalGeneration, TFBlenderbotModel, TFBlenderbotPreTrainedModel, ) from .models.blenderbot_small import ( TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel, TFBlenderbotSmallPreTrainedModel, ) from .models.camembert import ( TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFCamembertForCausalLM, TFCamembertForMaskedLM, TFCamembertForMultipleChoice, TFCamembertForQuestionAnswering, TFCamembertForSequenceClassification, TFCamembertForTokenClassification, TFCamembertModel, TFCamembertPreTrainedModel, ) from .models.clip import ( TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST, TFCLIPModel, TFCLIPPreTrainedModel, TFCLIPTextModel, TFCLIPVisionModel, ) from .models.convbert import ( TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFConvBertForMaskedLM, TFConvBertForMultipleChoice, TFConvBertForQuestionAnswering, TFConvBertForSequenceClassification, TFConvBertForTokenClassification, TFConvBertLayer, TFConvBertModel, TFConvBertPreTrainedModel, ) from .models.convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel from .models.ctrl import ( TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, TFCTRLForSequenceClassification, TFCTRLLMHeadModel, TFCTRLModel, TFCTRLPreTrainedModel, ) from .models.cvt import ( TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST, TFCvtForImageClassification, TFCvtModel, TFCvtPreTrainedModel, ) from .models.data2vec import ( TFData2VecVisionForImageClassification, TFData2VecVisionForSemanticSegmentation, TFData2VecVisionModel, TFData2VecVisionPreTrainedModel, ) from .models.deberta import ( TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFDebertaForMaskedLM, TFDebertaForQuestionAnswering, TFDebertaForSequenceClassification, TFDebertaForTokenClassification, TFDebertaModel, TFDebertaPreTrainedModel, ) from .models.deberta_v2 import ( TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST, TFDebertaV2ForMaskedLM, TFDebertaV2ForQuestionAnswering, TFDebertaV2ForSequenceClassification, TFDebertaV2ForTokenClassification, TFDebertaV2Model, TFDebertaV2PreTrainedModel, ) from .models.deit import ( TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, TFDeiTModel, TFDeiTPreTrainedModel, ) from .models.distilbert import ( TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDistilBertForMaskedLM, TFDistilBertForMultipleChoice, TFDistilBertForQuestionAnswering, TFDistilBertForSequenceClassification, TFDistilBertForTokenClassification, TFDistilBertMainLayer, TFDistilBertModel, TFDistilBertPreTrainedModel, ) from .models.dpr import ( TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, TFDPRContextEncoder, TFDPRPretrainedContextEncoder, TFDPRPretrainedQuestionEncoder, TFDPRPretrainedReader, TFDPRQuestionEncoder, TFDPRReader, ) from .models.electra import ( TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST, TFElectraForMaskedLM, TFElectraForMultipleChoice, TFElectraForPreTraining, TFElectraForQuestionAnswering, TFElectraForSequenceClassification, TFElectraForTokenClassification, TFElectraModel, TFElectraPreTrainedModel, ) from .models.encoder_decoder import TFEncoderDecoderModel from .models.esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, TFEsmForMaskedLM, TFEsmForSequenceClassification, TFEsmForTokenClassification, TFEsmModel, TFEsmPreTrainedModel, ) from .models.flaubert import ( TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFFlaubertForMultipleChoice, TFFlaubertForQuestionAnsweringSimple, TFFlaubertForSequenceClassification, TFFlaubertForTokenClassification, TFFlaubertModel, TFFlaubertPreTrainedModel, TFFlaubertWithLMHeadModel, ) from .models.funnel import ( TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST, TFFunnelBaseModel, TFFunnelForMaskedLM, TFFunnelForMultipleChoice, TFFunnelForPreTraining, TFFunnelForQuestionAnswering, TFFunnelForSequenceClassification, TFFunnelForTokenClassification, TFFunnelModel, TFFunnelPreTrainedModel, ) from .models.gpt2 import ( TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST, TFGPT2DoubleHeadsModel, TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2MainLayer, TFGPT2Model, TFGPT2PreTrainedModel, ) from .models.gptj import ( TFGPTJForCausalLM, TFGPTJForQuestionAnswering, TFGPTJForSequenceClassification, TFGPTJModel, TFGPTJPreTrainedModel, ) from .models.groupvit import ( TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFGroupViTModel, TFGroupViTPreTrainedModel, TFGroupViTTextModel, TFGroupViTVisionModel, ) from .models.hubert import ( TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFHubertForCTC, TFHubertModel, TFHubertPreTrainedModel, ) from .models.layoutlmv3 import ( TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, TFLayoutLMv3ForQuestionAnswering, TFLayoutLMv3ForSequenceClassification, TFLayoutLMv3ForTokenClassification, TFLayoutLMv3Model, TFLayoutLMv3PreTrainedModel, ) from .models.led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel from .models.longformer import ( TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFLongformerForMaskedLM, TFLongformerForMultipleChoice, TFLongformerForQuestionAnswering, TFLongformerForSequenceClassification, TFLongformerForTokenClassification, TFLongformerModel, TFLongformerPreTrainedModel, TFLongformerSelfAttention, ) from .models.lxmert import ( TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFLxmertForPreTraining, TFLxmertMainLayer, TFLxmertModel, TFLxmertPreTrainedModel, TFLxmertVisualFeatureEncoder, ) from .models.marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel from .models.mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel from .models.mobilebert import ( TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFMobileBertForMaskedLM, TFMobileBertForMultipleChoice, TFMobileBertForNextSentencePrediction, TFMobileBertForPreTraining, TFMobileBertForQuestionAnswering, TFMobileBertForSequenceClassification, TFMobileBertForTokenClassification, TFMobileBertMainLayer, TFMobileBertModel, TFMobileBertPreTrainedModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel, TFMobileViTPreTrainedModel, ) from .models.mpnet import ( TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification, TFMPNetForTokenClassification, TFMPNetMainLayer, TFMPNetModel, TFMPNetPreTrainedModel, ) from .models.mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model from .models.openai import ( TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification, TFOpenAIGPTLMHeadModel, TFOpenAIGPTMainLayer, TFOpenAIGPTModel, TFOpenAIGPTPreTrainedModel, ) from .models.opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel from .models.pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel from .models.rag import TFRagModel, TFRagPreTrainedModel, TFRagSequenceForGeneration, TFRagTokenForGeneration from .models.regnet import ( TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel, TFRegNetPreTrainedModel, ) from .models.rembert import ( TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForMultipleChoice, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertLayer, TFRemBertModel, TFRemBertPreTrainedModel, ) from .models.resnet import ( TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFResNetForImageClassification, TFResNetModel, TFResNetPreTrainedModel, ) from .models.roberta import ( TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForMultipleChoice, TFRobertaForQuestionAnswering, TFRobertaForSequenceClassification, TFRobertaForTokenClassification, TFRobertaMainLayer, TFRobertaModel, TFRobertaPreTrainedModel, ) from .models.roformer import ( TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFRoFormerForCausalLM, TFRoFormerForMaskedLM, TFRoFormerForMultipleChoice, TFRoFormerForQuestionAnswering, TFRoFormerForSequenceClassification, TFRoFormerForTokenClassification, TFRoFormerLayer, TFRoFormerModel, TFRoFormerPreTrainedModel, ) from .models.segformer import ( TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFSegformerDecodeHead, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel, TFSegformerPreTrainedModel, ) from .models.speech_to_text import ( TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel, TFSpeech2TextPreTrainedModel, ) from .models.swin import ( TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, TFSwinForImageClassification, TFSwinForMaskedImageModeling, TFSwinModel, TFSwinPreTrainedModel, ) from .models.t5 import ( TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST, TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model, TFT5PreTrainedModel, ) from .models.tapas import ( TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TFTapasForMaskedLM, TFTapasForQuestionAnswering, TFTapasForSequenceClassification, TFTapasModel, TFTapasPreTrainedModel, ) from .models.transfo_xl import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFAdaptiveEmbedding, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLMainLayer, TFTransfoXLModel, TFTransfoXLPreTrainedModel, ) from .models.vision_encoder_decoder import TFVisionEncoderDecoderModel from .models.vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel from .models.vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel from .models.wav2vec2 import ( TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, TFWav2Vec2ForCTC, TFWav2Vec2Model, TFWav2Vec2PreTrainedModel, ) from .models.whisper import ( TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, TFWhisperForConditionalGeneration, TFWhisperModel, TFWhisperPreTrainedModel, ) from .models.xglm import ( TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXGLMForCausalLM, TFXGLMModel, TFXGLMPreTrainedModel, ) from .models.xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) from .models.xlm_roberta import ( TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMRobertaForMaskedLM, TFXLMRobertaForMultipleChoice, TFXLMRobertaForQuestionAnswering, TFXLMRobertaForSequenceClassification, TFXLMRobertaForTokenClassification, TFXLMRobertaModel, ) from .models.xlnet import ( TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLNetForMultipleChoice, TFXLNetForQuestionAnsweringSimple, TFXLNetForSequenceClassification, TFXLNetForTokenClassification, TFXLNetLMHeadModel, TFXLNetMainLayer, TFXLNetModel, TFXLNetPreTrainedModel, ) # Optimization from .optimization_tf import AdamWeightDecay, GradientAccumulator, WarmUp, create_optimizer # Trainer from .trainer_tf import TFTrainer try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: # Import the same objects as dummies to get them in the namespace. # They will raise an import error if the user tries to instantiate / use them. from .utils.dummy_flax_objects import * else: from .generation import ( FlaxForcedBOSTokenLogitsProcessor, FlaxForcedEOSTokenLogitsProcessor, FlaxGenerationMixin, FlaxLogitsProcessor, FlaxLogitsProcessorList, FlaxLogitsWarper, FlaxMinLengthLogitsProcessor, FlaxTemperatureLogitsWarper, FlaxTopKLogitsWarper, FlaxTopPLogitsWarper, ) from .modeling_flax_utils import FlaxPreTrainedModel # Flax model imports from .models.albert import ( FlaxAlbertForMaskedLM, FlaxAlbertForMultipleChoice, FlaxAlbertForPreTraining, FlaxAlbertForQuestionAnswering, FlaxAlbertForSequenceClassification, FlaxAlbertForTokenClassification, FlaxAlbertModel, FlaxAlbertPreTrainedModel, ) from .models.auto import ( FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, FLAX_MODEL_FOR_PRETRAINING_MAPPING, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, FLAX_MODEL_MAPPING, FlaxAutoModel, FlaxAutoModelForCausalLM, FlaxAutoModelForImageClassification, FlaxAutoModelForMaskedLM, FlaxAutoModelForMultipleChoice, FlaxAutoModelForNextSentencePrediction, FlaxAutoModelForPreTraining, FlaxAutoModelForQuestionAnswering, FlaxAutoModelForSeq2SeqLM, FlaxAutoModelForSequenceClassification, FlaxAutoModelForTokenClassification, FlaxAutoModelForVision2Seq, ) from .models.bart import ( FlaxBartDecoderPreTrainedModel, FlaxBartForCausalLM, FlaxBartForConditionalGeneration, FlaxBartForQuestionAnswering, FlaxBartForSequenceClassification, FlaxBartModel, FlaxBartPreTrainedModel, ) from .models.beit import ( FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel, FlaxBeitPreTrainedModel, ) from .models.bert import ( FlaxBertForCausalLM, FlaxBertForMaskedLM, FlaxBertForMultipleChoice, FlaxBertForNextSentencePrediction, FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification, FlaxBertForTokenClassification, FlaxBertModel, FlaxBertPreTrainedModel, ) from .models.big_bird import ( FlaxBigBirdForCausalLM, FlaxBigBirdForMaskedLM, FlaxBigBirdForMultipleChoice, FlaxBigBirdForPreTraining, FlaxBigBirdForQuestionAnswering, FlaxBigBirdForSequenceClassification, FlaxBigBirdForTokenClassification, FlaxBigBirdModel, FlaxBigBirdPreTrainedModel, ) from .models.blenderbot import ( FlaxBlenderbotForConditionalGeneration, FlaxBlenderbotModel, FlaxBlenderbotPreTrainedModel, ) from .models.blenderbot_small import ( FlaxBlenderbotSmallForConditionalGeneration, FlaxBlenderbotSmallModel, FlaxBlenderbotSmallPreTrainedModel, ) from .models.clip import ( FlaxCLIPModel, FlaxCLIPPreTrainedModel, FlaxCLIPTextModel, FlaxCLIPTextPreTrainedModel, FlaxCLIPVisionModel, FlaxCLIPVisionPreTrainedModel, ) from .models.distilbert import ( FlaxDistilBertForMaskedLM, FlaxDistilBertForMultipleChoice, FlaxDistilBertForQuestionAnswering, FlaxDistilBertForSequenceClassification, FlaxDistilBertForTokenClassification, FlaxDistilBertModel, FlaxDistilBertPreTrainedModel, ) from .models.electra import ( FlaxElectraForCausalLM, FlaxElectraForMaskedLM, FlaxElectraForMultipleChoice, FlaxElectraForPreTraining, FlaxElectraForQuestionAnswering, FlaxElectraForSequenceClassification, FlaxElectraForTokenClassification, FlaxElectraModel, FlaxElectraPreTrainedModel, ) from .models.encoder_decoder import FlaxEncoderDecoderModel from .models.gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model, FlaxGPT2PreTrainedModel from .models.gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel from .models.gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel from .models.longt5 import FlaxLongT5ForConditionalGeneration, FlaxLongT5Model, FlaxLongT5PreTrainedModel from .models.marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel from .models.mbart import ( FlaxMBartForConditionalGeneration, FlaxMBartForQuestionAnswering, FlaxMBartForSequenceClassification, FlaxMBartModel, FlaxMBartPreTrainedModel, ) from .models.mt5 import FlaxMT5EncoderModel, FlaxMT5ForConditionalGeneration, FlaxMT5Model from .models.opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel from .models.pegasus import FlaxPegasusForConditionalGeneration, FlaxPegasusModel, FlaxPegasusPreTrainedModel from .models.roberta import ( FlaxRobertaForCausalLM, FlaxRobertaForMaskedLM, FlaxRobertaForMultipleChoice, FlaxRobertaForQuestionAnswering, FlaxRobertaForSequenceClassification, FlaxRobertaForTokenClassification, FlaxRobertaModel, FlaxRobertaPreTrainedModel, ) from .models.roformer import ( FlaxRoFormerForMaskedLM, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerModel, FlaxRoFormerPreTrainedModel, ) from .models.speech_encoder_decoder import FlaxSpeechEncoderDecoderModel from .models.t5 import FlaxT5EncoderModel, FlaxT5ForConditionalGeneration, FlaxT5Model, FlaxT5PreTrainedModel from .models.vision_encoder_decoder import FlaxVisionEncoderDecoderModel from .models.vision_text_dual_encoder import FlaxVisionTextDualEncoderModel from .models.vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel from .models.wav2vec2 import ( FlaxWav2Vec2ForCTC, FlaxWav2Vec2ForPreTraining, FlaxWav2Vec2Model, FlaxWav2Vec2PreTrainedModel, ) from .models.xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel from .models.xlm_roberta import ( FlaxXLMRobertaForMaskedLM, FlaxXLMRobertaForMultipleChoice, FlaxXLMRobertaForQuestionAnswering, FlaxXLMRobertaForSequenceClassification, FlaxXLMRobertaForTokenClassification, FlaxXLMRobertaModel, ) else: import sys sys.modules[__name__] = _LazyModule( __name__, globals()["__file__"], _import_structure, module_spec=__spec__, extra_objects={"__version__": __version__}, ) if not is_tf_available() and not is_torch_available() and not is_flax_available(): logger.warning( "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. " "Models won't be available and only tokenizers, configuration " "and file/data utilities can be used." )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/modeling_outputs.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass from typing import Optional, Tuple import torch from .utils import ModelOutput @dataclass class BaseModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithNoAttention(ModelOutput): """ Base class for model's outputs, with potential hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPooling(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPoolingAndNoAttention(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state after a pooling operation on the spatial dimensions. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPast(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithCrossAttentions(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPastAndCrossAttentions(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MoEModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary loss and the z_loss for Mixture of Experts models. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None router_probs: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MoEModelOutputWithPastAndCrossAttentions(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding) as well as Mixture of Expert's router hidden states terms, to train a MoE model. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary loss and the z_loss for Mixture of Experts models. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None router_probs: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqMoEModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse modules. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CausalLMOutput(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CausalLMOutputWithPast(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CausalLMOutputWithCrossAttentions(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if `config.is_decoder = True`. Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class SequenceClassifierOutputWithPast(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MaskedLMOutput(ModelOutput): """ Base class for masked language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Masked language modeling (MLM) loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqLMOutput(ModelOutput): """ Base class for sequence-to-sequence language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqMoEOutput(ModelOutput): """ Base class for sequence-to-sequence language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the encoder model, useful to compute the auxiliary loss and z_loss for Mixture of Experts models. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None encoder_z_loss: torch.FloatTensor = None decoder_z_loss: torch.FloatTensor = None encoder_aux_loss: torch.FloatTensor = None decoder_aux_loss: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None @dataclass class NextSentencePredictorOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided): Next sequence prediction (classification) loss. logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class SequenceClassifierOutput(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MultipleChoiceModelOutput(ModelOutput): """ Base class for outputs of multiple choice models. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class TokenClassifierOutput(ModelOutput): """ Base class for outputs of token classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class QuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None start_logits: torch.FloatTensor = None end_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None start_logits: torch.FloatTensor = None end_logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class SemanticSegmenterOutput(ModelOutput): """ Base class for outputs of semantic segmentation models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`): Classification scores for each pixel. <Tip warning={true}> The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the original image size as post-processing. You should always check your logits shape and resize as needed. </Tip> hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class ImageClassifierOutput(ModelOutput): """ Base class for outputs of image classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class ImageClassifierOutputWithNoAttention(ModelOutput): """ Base class for outputs of image classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class DepthEstimatorOutput(ModelOutput): """ Base class for outputs of depth estimation models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. predicted_depth (`torch.FloatTensor` of shape `(batch_size, height, width)`): Predicted depth for each pixel. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None predicted_depth: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Wav2Vec2BaseModelOutput(ModelOutput): """ Base class for models that have been trained with the Wav2Vec2 loss objective. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. extract_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`): Sequence of extracted feature vectors of the last convolutional layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None extract_features: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class XVectorOutput(ModelOutput): """ Output type of [`Wav2Vec2ForXVector`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`): Classification hidden states before AMSoftmax. embeddings (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`): Utterance embeddings used for vector similarity-based retrieval. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None embeddings: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass from typing import Optional, Tuple import torch from .utils import ModelOutput @dataclass class BaseModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithNoAttention(ModelOutput): """ Base class for model's outputs, with potential hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPooling(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPoolingAndNoAttention(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state after a pooling operation on the spatial dimensions. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPast(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithCrossAttentions(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPoolingAndCrossAttentions(ModelOutput): """ Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ last_hidden_state: torch.FloatTensor = None pooler_output: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BaseModelOutputWithPastAndCrossAttentions(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MoEModelOutput(ModelOutput): """ Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary loss and the z_loss for Mixture of Experts models. """ last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None router_probs: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MoEModelOutputWithPastAndCrossAttentions(ModelOutput): """ Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding) as well as Mixture of Expert's router hidden states terms, to train a MoE model. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and optionally if `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. router_probs (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_probs=True` and `config.add_router_probs=True` is passed or when `config.output_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Raw router probabilities that are computed by MoE routers, these terms are used to compute the auxiliary loss and the z_loss for Mixture of Experts models. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None router_probs: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqMoEModelOutput(ModelOutput): """ Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential decoding. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the decoder of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse modules. """ last_hidden_state: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CausalLMOutput(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CausalLMOutputWithPast(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class CausalLMOutputWithCrossAttentions(ModelOutput): """ Base class for causal language model (or autoregressive) outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `torch.FloatTensor` tuples of length `config.n_layers`, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if `config.is_decoder = True`. Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class SequenceClassifierOutputWithPast(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MaskedLMOutput(ModelOutput): """ Base class for masked language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Masked language modeling (MLM) loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqLMOutput(ModelOutput): """ Base class for sequence-to-sequence language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqMoEOutput(ModelOutput): """ Base class for sequence-to-sequence language models outputs. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. decoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the decoder model, useful to compute the auxiliary loss for Mixture of Experts models. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. encoder_router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`. Router logits of the encoder model, useful to compute the auxiliary loss and z_loss for Mixture of Experts models. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None encoder_z_loss: torch.FloatTensor = None decoder_z_loss: torch.FloatTensor = None encoder_aux_loss: torch.FloatTensor = None decoder_aux_loss: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None decoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_router_logits: Optional[Tuple[torch.FloatTensor]] = None @dataclass class NextSentencePredictorOutput(ModelOutput): """ Base class for outputs of models predicting if two sentences are consecutive or not. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `next_sentence_label` is provided): Next sequence prediction (classification) loss. logits (`torch.FloatTensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class SequenceClassifierOutput(ModelOutput): """ Base class for outputs of sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqSequenceClassifierOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence sentence classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class MultipleChoiceModelOutput(ModelOutput): """ Base class for outputs of multiple choice models. Args: loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): *num_choices* is the second dimension of the input tensors. (see *input_ids* above). Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class TokenClassifierOutput(ModelOutput): """ Base class for outputs of token classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) : Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class QuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None start_logits: torch.FloatTensor = None end_logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Seq2SeqQuestionAnsweringModelOutput(ModelOutput): """ Base class for outputs of sequence-to-sequence question answering models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): Span-end scores (before SoftMax). past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads. cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder of the model. encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None start_logits: torch.FloatTensor = None end_logits: torch.FloatTensor = None past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None cross_attentions: Optional[Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: Optional[torch.FloatTensor] = None encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class SemanticSegmenterOutput(ModelOutput): """ Base class for outputs of semantic segmentation models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels, logits_height, logits_width)`): Classification scores for each pixel. <Tip warning={true}> The logits returned do not necessarily have the same size as the `pixel_values` passed as inputs. This is to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the original image size as post-processing. You should always check your logits shape and resize as needed. </Tip> hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, patch_size, hidden_size)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class ImageClassifierOutput(ModelOutput): """ Base class for outputs of image classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class ImageClassifierOutputWithNoAttention(ModelOutput): """ Base class for outputs of image classification models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the model at the output of each stage. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None @dataclass class DepthEstimatorOutput(ModelOutput): """ Base class for outputs of depth estimation models. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. predicted_depth (`torch.FloatTensor` of shape `(batch_size, height, width)`): Predicted depth for each pixel. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None predicted_depth: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class Wav2Vec2BaseModelOutput(ModelOutput): """ Base class for models that have been trained with the Wav2Vec2 loss objective. Args: last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. extract_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, conv_dim[-1])`): Sequence of extracted feature vectors of the last convolutional layer of the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: torch.FloatTensor = None extract_features: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class XVectorOutput(ModelOutput): """ Output type of [`Wav2Vec2ForXVector`]. Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. logits (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`): Classification hidden states before AMSoftmax. embeddings (`torch.FloatTensor` of shape `(batch_size, config.xvector_output_dim)`): Utterance embeddings used for vector similarity-based retrieval. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ loss: Optional[torch.FloatTensor] = None logits: torch.FloatTensor = None embeddings: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass class BackboneOutput(ModelOutput): """ Base class for outputs of backbones. Args: feature_maps (`tuple(torch.FloatTensor)` of shape `(batch_size, num_channels, height, width)`): Feature maps of the stages. """ feature_maps: Tuple[torch.FloatTensor] = None

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/auto/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = { "auto_factory": ["get_values"], "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"], "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"], "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"], "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"], "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_auto"] = [ "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", "MODEL_FOR_AUDIO_XVECTOR_MAPPING", "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", "MODEL_FOR_CAUSAL_LM_MAPPING", "MODEL_FOR_CTC_MAPPING", "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "MODEL_FOR_MASKED_LM_MAPPING", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "MODEL_FOR_OBJECT_DETECTION_MAPPING", "MODEL_FOR_PRETRAINING_MAPPING", "MODEL_FOR_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", "MODEL_FOR_VISION_2_SEQ_MAPPING", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", "MODEL_MAPPING", "MODEL_WITH_LM_HEAD_MAPPING", "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", "AutoModel", "AutoModelForAudioClassification", "AutoModelForAudioFrameClassification", "AutoModelForAudioXVector", "AutoModelForCausalLM", "AutoModelForCTC", "AutoModelForDepthEstimation", "AutoModelForImageClassification", "AutoModelForImageSegmentation", "AutoModelForInstanceSegmentation", "AutoModelForMaskedImageModeling", "AutoModelForMaskedLM", "AutoModelForMultipleChoice", "AutoModelForNextSentencePrediction", "AutoModelForObjectDetection", "AutoModelForPreTraining", "AutoModelForQuestionAnswering", "AutoModelForSemanticSegmentation", "AutoModelForSeq2SeqLM", "AutoModelForSequenceClassification", "AutoModelForSpeechSeq2Seq", "AutoModelForTableQuestionAnswering", "AutoModelForTokenClassification", "AutoModelForVideoClassification", "AutoModelForVision2Seq", "AutoModelForVisualQuestionAnswering", "AutoModelForDocumentQuestionAnswering", "AutoModelWithLMHead", "AutoModelForZeroShotObjectDetection", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_auto"] = [ "TF_MODEL_FOR_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "TF_MODEL_FOR_MASKED_LM_MAPPING", "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "TF_MODEL_FOR_PRETRAINING_MAPPING", "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", "TF_MODEL_MAPPING", "TF_MODEL_WITH_LM_HEAD_MAPPING", "TFAutoModel", "TFAutoModelForCausalLM", "TFAutoModelForImageClassification", "TFAutoModelForMaskedLM", "TFAutoModelForMultipleChoice", "TFAutoModelForNextSentencePrediction", "TFAutoModelForPreTraining", "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForQuestionAnswering", "TFAutoModelForSemanticSegmentation", "TFAutoModelForSeq2SeqLM", "TFAutoModelForSequenceClassification", "TFAutoModelForSpeechSeq2Seq", "TFAutoModelForTableQuestionAnswering", "TFAutoModelForTokenClassification", "TFAutoModelForVision2Seq", "TFAutoModelWithLMHead", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_auto"] = [ "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_MASKED_LM_MAPPING", "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "FLAX_MODEL_FOR_PRETRAINING_MAPPING", "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", "FLAX_MODEL_MAPPING", "FlaxAutoModel", "FlaxAutoModelForCausalLM", "FlaxAutoModelForImageClassification", "FlaxAutoModelForMaskedLM", "FlaxAutoModelForMultipleChoice", "FlaxAutoModelForNextSentencePrediction", "FlaxAutoModelForPreTraining", "FlaxAutoModelForQuestionAnswering", "FlaxAutoModelForSeq2SeqLM", "FlaxAutoModelForSequenceClassification", "FlaxAutoModelForTokenClassification", "FlaxAutoModelForVision2Seq", ] if TYPE_CHECKING: from .auto_factory import get_values from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor from .processing_auto import PROCESSOR_MAPPING, AutoProcessor from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_auto import ( MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_CTC_MAPPING, MODEL_FOR_DEPTH_ESTIMATION_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_FOR_VISION_2_SEQ_MAPPING, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, AutoModel, AutoModelForAudioClassification, AutoModelForAudioFrameClassification, AutoModelForAudioXVector, AutoModelForCausalLM, AutoModelForCTC, AutoModelForDepthEstimation, AutoModelForDocumentQuestionAnswering, AutoModelForImageClassification, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, AutoModelForMaskedImageModeling, AutoModelForMaskedLM, AutoModelForMultipleChoice, AutoModelForNextSentencePrediction, AutoModelForObjectDetection, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSemanticSegmentation, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForSpeechSeq2Seq, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelForVideoClassification, AutoModelForVision2Seq, AutoModelForVisualQuestionAnswering, AutoModelForZeroShotObjectDetection, AutoModelWithLMHead, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForDocumentQuestionAnswering, TFAutoModelForImageClassification, TFAutoModelForMaskedLM, TFAutoModelForMultipleChoice, TFAutoModelForNextSentencePrediction, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSemanticSegmentation, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForSpeechSeq2Seq, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelForVision2Seq, TFAutoModelWithLMHead, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_auto import ( FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, FLAX_MODEL_FOR_PRETRAINING_MAPPING, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, FLAX_MODEL_MAPPING, FlaxAutoModel, FlaxAutoModelForCausalLM, FlaxAutoModelForImageClassification, FlaxAutoModelForMaskedLM, FlaxAutoModelForMultipleChoice, FlaxAutoModelForNextSentencePrediction, FlaxAutoModelForPreTraining, FlaxAutoModelForQuestionAnswering, FlaxAutoModelForSeq2SeqLM, FlaxAutoModelForSequenceClassification, FlaxAutoModelForTokenClassification, FlaxAutoModelForVision2Seq, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _import_structure = { "auto_factory": ["get_values"], "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"], "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"], "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"], "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"], "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_auto"] = [ "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING", "MODEL_FOR_AUDIO_XVECTOR_MAPPING", "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING", "MODEL_FOR_CAUSAL_LM_MAPPING", "MODEL_FOR_CTC_MAPPING", "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING", "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING", "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "MODEL_FOR_MASKED_LM_MAPPING", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "MODEL_FOR_OBJECT_DETECTION_MAPPING", "MODEL_FOR_PRETRAINING_MAPPING", "MODEL_FOR_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING", "MODEL_FOR_VISION_2_SEQ_MAPPING", "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING", "MODEL_MAPPING", "MODEL_WITH_LM_HEAD_MAPPING", "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING", "AutoModel", "AutoBackbone", "AutoModelForAudioClassification", "AutoModelForAudioFrameClassification", "AutoModelForAudioXVector", "AutoModelForCausalLM", "AutoModelForCTC", "AutoModelForDepthEstimation", "AutoModelForImageClassification", "AutoModelForImageSegmentation", "AutoModelForInstanceSegmentation", "AutoModelForMaskedImageModeling", "AutoModelForMaskedLM", "AutoModelForMultipleChoice", "AutoModelForNextSentencePrediction", "AutoModelForObjectDetection", "AutoModelForPreTraining", "AutoModelForQuestionAnswering", "AutoModelForSemanticSegmentation", "AutoModelForSeq2SeqLM", "AutoModelForSequenceClassification", "AutoModelForSpeechSeq2Seq", "AutoModelForTableQuestionAnswering", "AutoModelForTokenClassification", "AutoModelForVideoClassification", "AutoModelForVision2Seq", "AutoModelForVisualQuestionAnswering", "AutoModelForDocumentQuestionAnswering", "AutoModelWithLMHead", "AutoModelForZeroShotObjectDetection", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_auto"] = [ "TF_MODEL_FOR_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING", "TF_MODEL_FOR_MASKED_LM_MAPPING", "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "TF_MODEL_FOR_PRETRAINING_MAPPING", "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING", "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING", "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING", "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "TF_MODEL_FOR_VISION_2_SEQ_MAPPING", "TF_MODEL_MAPPING", "TF_MODEL_WITH_LM_HEAD_MAPPING", "TFAutoModel", "TFAutoModelForCausalLM", "TFAutoModelForImageClassification", "TFAutoModelForMaskedLM", "TFAutoModelForMultipleChoice", "TFAutoModelForNextSentencePrediction", "TFAutoModelForPreTraining", "TFAutoModelForDocumentQuestionAnswering", "TFAutoModelForQuestionAnswering", "TFAutoModelForSemanticSegmentation", "TFAutoModelForSeq2SeqLM", "TFAutoModelForSequenceClassification", "TFAutoModelForSpeechSeq2Seq", "TFAutoModelForTableQuestionAnswering", "TFAutoModelForTokenClassification", "TFAutoModelForVision2Seq", "TFAutoModelWithLMHead", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flax_auto"] = [ "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_MASKED_LM_MAPPING", "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING", "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING", "FLAX_MODEL_FOR_PRETRAINING_MAPPING", "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING", "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING", "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING", "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING", "FLAX_MODEL_MAPPING", "FlaxAutoModel", "FlaxAutoModelForCausalLM", "FlaxAutoModelForImageClassification", "FlaxAutoModelForMaskedLM", "FlaxAutoModelForMultipleChoice", "FlaxAutoModelForNextSentencePrediction", "FlaxAutoModelForPreTraining", "FlaxAutoModelForQuestionAnswering", "FlaxAutoModelForSeq2SeqLM", "FlaxAutoModelForSequenceClassification", "FlaxAutoModelForTokenClassification", "FlaxAutoModelForVision2Seq", ] if TYPE_CHECKING: from .auto_factory import get_values from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor from .processing_auto import PROCESSOR_MAPPING, AutoProcessor from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_auto import ( MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, MODEL_FOR_AUDIO_XVECTOR_MAPPING, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_CTC_MAPPING, MODEL_FOR_DEPTH_ESTIMATION_MAPPING, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, MODEL_FOR_VISION_2_SEQ_MAPPING, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, AutoBackbone, AutoModel, AutoModelForAudioClassification, AutoModelForAudioFrameClassification, AutoModelForAudioXVector, AutoModelForCausalLM, AutoModelForCTC, AutoModelForDepthEstimation, AutoModelForDocumentQuestionAnswering, AutoModelForImageClassification, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, AutoModelForMaskedImageModeling, AutoModelForMaskedLM, AutoModelForMultipleChoice, AutoModelForNextSentencePrediction, AutoModelForObjectDetection, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSemanticSegmentation, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForSpeechSeq2Seq, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelForVideoClassification, AutoModelForVision2Seq, AutoModelForVisualQuestionAnswering, AutoModelForZeroShotObjectDetection, AutoModelWithLMHead, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING, TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForDocumentQuestionAnswering, TFAutoModelForImageClassification, TFAutoModelForMaskedLM, TFAutoModelForMultipleChoice, TFAutoModelForNextSentencePrediction, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSemanticSegmentation, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForSpeechSeq2Seq, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelForVision2Seq, TFAutoModelWithLMHead, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_auto import ( FLAX_MODEL_FOR_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING, FLAX_MODEL_FOR_PRETRAINING_MAPPING, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING, FLAX_MODEL_MAPPING, FlaxAutoModel, FlaxAutoModelForCausalLM, FlaxAutoModelForImageClassification, FlaxAutoModelForMaskedLM, FlaxAutoModelForMultipleChoice, FlaxAutoModelForNextSentencePrediction, FlaxAutoModelForPreTraining, FlaxAutoModelForQuestionAnswering, FlaxAutoModelForSeq2SeqLM, FlaxAutoModelForSequenceClassification, FlaxAutoModelForTokenClassification, FlaxAutoModelForVision2Seq, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/auto/modeling_auto.py

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Model class.""" import warnings from collections import OrderedDict from ...utils import logging from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update from .configuration_auto import CONFIG_MAPPING_NAMES logger = logging.get_logger(__name__) MODEL_MAPPING_NAMES = OrderedDict( [ # Base model mapping ("albert", "AlbertModel"), ("bart", "BartModel"), ("beit", "BeitModel"), ("bert", "BertModel"), ("bert-generation", "BertGenerationEncoder"), ("big_bird", "BigBirdModel"), ("bigbird_pegasus", "BigBirdPegasusModel"), ("blenderbot", "BlenderbotModel"), ("blenderbot-small", "BlenderbotSmallModel"), ("bloom", "BloomModel"), ("camembert", "CamembertModel"), ("canine", "CanineModel"), ("clip", "CLIPModel"), ("clipseg", "CLIPSegModel"), ("codegen", "CodeGenModel"), ("conditional_detr", "ConditionalDetrModel"), ("convbert", "ConvBertModel"), ("convnext", "ConvNextModel"), ("ctrl", "CTRLModel"), ("cvt", "CvtModel"), ("data2vec-audio", "Data2VecAudioModel"), ("data2vec-text", "Data2VecTextModel"), ("data2vec-vision", "Data2VecVisionModel"), ("deberta", "DebertaModel"), ("deberta-v2", "DebertaV2Model"), ("decision_transformer", "DecisionTransformerModel"), ("decision_transformer_gpt2", "DecisionTransformerGPT2Model"), ("deformable_detr", "DeformableDetrModel"), ("deit", "DeiTModel"), ("detr", "DetrModel"), ("distilbert", "DistilBertModel"), ("donut-swin", "DonutSwinModel"), ("dpr", "DPRQuestionEncoder"), ("dpt", "DPTModel"), ("electra", "ElectraModel"), ("ernie", "ErnieModel"), ("esm", "EsmModel"), ("flaubert", "FlaubertModel"), ("flava", "FlavaModel"), ("fnet", "FNetModel"), ("fsmt", "FSMTModel"), ("funnel", ("FunnelModel", "FunnelBaseModel")), ("glpn", "GLPNModel"), ("gpt2", "GPT2Model"), ("gpt_neo", "GPTNeoModel"), ("gpt_neox", "GPTNeoXModel"), ("gpt_neox_japanese", "GPTNeoXJapaneseModel"), ("gptj", "GPTJModel"), ("groupvit", "GroupViTModel"), ("hubert", "HubertModel"), ("ibert", "IBertModel"), ("imagegpt", "ImageGPTModel"), ("jukebox", "JukeboxModel"), ("layoutlm", "LayoutLMModel"), ("layoutlmv2", "LayoutLMv2Model"), ("layoutlmv3", "LayoutLMv3Model"), ("led", "LEDModel"), ("levit", "LevitModel"), ("lilt", "LiltModel"), ("longformer", "LongformerModel"), ("longt5", "LongT5Model"), ("luke", "LukeModel"), ("lxmert", "LxmertModel"), ("m2m_100", "M2M100Model"), ("marian", "MarianModel"), ("markuplm", "MarkupLMModel"), ("maskformer", "MaskFormerModel"), ("mbart", "MBartModel"), ("mctct", "MCTCTModel"), ("megatron-bert", "MegatronBertModel"), ("mobilebert", "MobileBertModel"), ("mobilenet_v2", "MobileNetV2Model"), ("mobilevit", "MobileViTModel"), ("mpnet", "MPNetModel"), ("mt5", "MT5Model"), ("mvp", "MvpModel"), ("nezha", "NezhaModel"), ("nllb", "M2M100Model"), ("nystromformer", "NystromformerModel"), ("openai-gpt", "OpenAIGPTModel"), ("opt", "OPTModel"), ("owlvit", "OwlViTModel"), ("pegasus", "PegasusModel"), ("pegasus_x", "PegasusXModel"), ("perceiver", "PerceiverModel"), ("plbart", "PLBartModel"), ("poolformer", "PoolFormerModel"), ("prophetnet", "ProphetNetModel"), ("qdqbert", "QDQBertModel"), ("reformer", "ReformerModel"), ("regnet", "RegNetModel"), ("rembert", "RemBertModel"), ("resnet", "ResNetModel"), ("retribert", "RetriBertModel"), ("roberta", "RobertaModel"), ("roc_bert", "RoCBertModel"), ("roformer", "RoFormerModel"), ("segformer", "SegformerModel"), ("sew", "SEWModel"), ("sew-d", "SEWDModel"), ("speech_to_text", "Speech2TextModel"), ("splinter", "SplinterModel"), ("squeezebert", "SqueezeBertModel"), ("swin", "SwinModel"), ("swinv2", "Swinv2Model"), ("switch_transformers", "SwitchTransformersModel"), ("t5", "T5Model"), ("table-transformer", "TableTransformerModel"), ("tapas", "TapasModel"), ("time_series_transformer", "TimeSeriesTransformerModel"), ("trajectory_transformer", "TrajectoryTransformerModel"), ("transfo-xl", "TransfoXLModel"), ("unispeech", "UniSpeechModel"), ("unispeech-sat", "UniSpeechSatModel"), ("van", "VanModel"), ("videomae", "VideoMAEModel"), ("vilt", "ViltModel"), ("vision-text-dual-encoder", "VisionTextDualEncoderModel"), ("visual_bert", "VisualBertModel"), ("vit", "ViTModel"), ("vit_mae", "ViTMAEModel"), ("vit_msn", "ViTMSNModel"), ("wav2vec2", "Wav2Vec2Model"), ("wav2vec2-conformer", "Wav2Vec2ConformerModel"), ("wavlm", "WavLMModel"), ("whisper", "WhisperModel"), ("xclip", "XCLIPModel"), ("xglm", "XGLMModel"), ("xlm", "XLMModel"), ("xlm-prophetnet", "XLMProphetNetModel"), ("xlm-roberta", "XLMRobertaModel"), ("xlm-roberta-xl", "XLMRobertaXLModel"), ("xlnet", "XLNetModel"), ("yolos", "YolosModel"), ("yoso", "YosoModel"), ] ) MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict( [ # Model for pre-training mapping ("albert", "AlbertForPreTraining"), ("bart", "BartForConditionalGeneration"), ("bert", "BertForPreTraining"), ("big_bird", "BigBirdForPreTraining"), ("bloom", "BloomForCausalLM"), ("camembert", "CamembertForMaskedLM"), ("ctrl", "CTRLLMHeadModel"), ("data2vec-text", "Data2VecTextForMaskedLM"), ("deberta", "DebertaForMaskedLM"), ("deberta-v2", "DebertaV2ForMaskedLM"), ("distilbert", "DistilBertForMaskedLM"), ("electra", "ElectraForPreTraining"), ("ernie", "ErnieForPreTraining"), ("flaubert", "FlaubertWithLMHeadModel"), ("flava", "FlavaForPreTraining"), ("fnet", "FNetForPreTraining"), ("fsmt", "FSMTForConditionalGeneration"), ("funnel", "FunnelForPreTraining"), ("gpt2", "GPT2LMHeadModel"), ("ibert", "IBertForMaskedLM"), ("layoutlm", "LayoutLMForMaskedLM"), ("longformer", "LongformerForMaskedLM"), ("luke", "LukeForMaskedLM"), ("lxmert", "LxmertForPreTraining"), ("megatron-bert", "MegatronBertForPreTraining"), ("mobilebert", "MobileBertForPreTraining"), ("mpnet", "MPNetForMaskedLM"), ("mvp", "MvpForConditionalGeneration"), ("nezha", "NezhaForPreTraining"), ("openai-gpt", "OpenAIGPTLMHeadModel"), ("retribert", "RetriBertModel"), ("roberta", "RobertaForMaskedLM"), ("roc_bert", "RoCBertForPreTraining"), ("splinter", "SplinterForPreTraining"), ("squeezebert", "SqueezeBertForMaskedLM"), ("switch_transformers", "SwitchTransformersForConditionalGeneration"), ("t5", "T5ForConditionalGeneration"), ("tapas", "TapasForMaskedLM"), ("transfo-xl", "TransfoXLLMHeadModel"), ("unispeech", "UniSpeechForPreTraining"), ("unispeech-sat", "UniSpeechSatForPreTraining"), ("videomae", "VideoMAEForPreTraining"), ("visual_bert", "VisualBertForPreTraining"), ("vit_mae", "ViTMAEForPreTraining"), ("wav2vec2", "Wav2Vec2ForPreTraining"), ("wav2vec2-conformer", "Wav2Vec2ConformerForPreTraining"), ("xlm", "XLMWithLMHeadModel"), ("xlm-roberta", "XLMRobertaForMaskedLM"), ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"), ("xlnet", "XLNetLMHeadModel"), ] ) MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict( [ # Model with LM heads mapping ("albert", "AlbertForMaskedLM"), ("bart", "BartForConditionalGeneration"), ("bert", "BertForMaskedLM"), ("big_bird", "BigBirdForMaskedLM"), ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"), ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"), ("bloom", "BloomForCausalLM"), ("camembert", "CamembertForMaskedLM"), ("codegen", "CodeGenForCausalLM"), ("convbert", "ConvBertForMaskedLM"), ("ctrl", "CTRLLMHeadModel"), ("data2vec-text", "Data2VecTextForMaskedLM"), ("deberta", "DebertaForMaskedLM"), ("deberta-v2", "DebertaV2ForMaskedLM"), ("distilbert", "DistilBertForMaskedLM"), ("electra", "ElectraForMaskedLM"), ("encoder-decoder", "EncoderDecoderModel"), ("ernie", "ErnieForMaskedLM"), ("esm", "EsmForMaskedLM"), ("flaubert", "FlaubertWithLMHeadModel"), ("fnet", "FNetForMaskedLM"), ("fsmt", "FSMTForConditionalGeneration"), ("funnel", "FunnelForMaskedLM"), ("gpt2", "GPT2LMHeadModel"), ("gpt_neo", "GPTNeoForCausalLM"), ("gpt_neox", "GPTNeoXForCausalLM"), ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"), ("gptj", "GPTJForCausalLM"), ("ibert", "IBertForMaskedLM"), ("layoutlm", "LayoutLMForMaskedLM"), ("led", "LEDForConditionalGeneration"), ("longformer", "LongformerForMaskedLM"), ("longt5", "LongT5ForConditionalGeneration"), ("luke", "LukeForMaskedLM"), ("m2m_100", "M2M100ForConditionalGeneration"), ("marian", "MarianMTModel"), ("megatron-bert", "MegatronBertForCausalLM"), ("mobilebert", "MobileBertForMaskedLM"), ("mpnet", "MPNetForMaskedLM"), ("mvp", "MvpForConditionalGeneration"), ("nezha", "NezhaForMaskedLM"), ("nllb", "M2M100ForConditionalGeneration"), ("nystromformer", "NystromformerForMaskedLM"), ("openai-gpt", "OpenAIGPTLMHeadModel"), ("pegasus_x", "PegasusXForConditionalGeneration"), ("plbart", "PLBartForConditionalGeneration"), ("qdqbert", "QDQBertForMaskedLM"), ("reformer", "ReformerModelWithLMHead"), ("rembert", "RemBertForMaskedLM"), ("roberta", "RobertaForMaskedLM"), ("roc_bert", "RoCBertForMaskedLM"), ("roformer", "RoFormerForMaskedLM"), ("speech_to_text", "Speech2TextForConditionalGeneration"), ("squeezebert", "SqueezeBertForMaskedLM"), ("switch_transformers", "SwitchTransformersForConditionalGeneration"), ("t5", "T5ForConditionalGeneration"), ("tapas", "TapasForMaskedLM"), ("transfo-xl", "TransfoXLLMHeadModel"), ("wav2vec2", "Wav2Vec2ForMaskedLM"), ("whisper", "WhisperForConditionalGeneration"), ("xlm", "XLMWithLMHeadModel"), ("xlm-roberta", "XLMRobertaForMaskedLM"), ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"), ("xlnet", "XLNetLMHeadModel"), ("yoso", "YosoForMaskedLM"), ] ) MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Causal LM mapping ("bart", "BartForCausalLM"), ("bert", "BertLMHeadModel"), ("bert-generation", "BertGenerationDecoder"), ("big_bird", "BigBirdForCausalLM"), ("bigbird_pegasus", "BigBirdPegasusForCausalLM"), ("blenderbot", "BlenderbotForCausalLM"), ("blenderbot-small", "BlenderbotSmallForCausalLM"), ("bloom", "BloomForCausalLM"), ("camembert", "CamembertForCausalLM"), ("codegen", "CodeGenForCausalLM"), ("ctrl", "CTRLLMHeadModel"), ("data2vec-text", "Data2VecTextForCausalLM"), ("electra", "ElectraForCausalLM"), ("ernie", "ErnieForCausalLM"), ("gpt2", "GPT2LMHeadModel"), ("gpt_neo", "GPTNeoForCausalLM"), ("gpt_neox", "GPTNeoXForCausalLM"), ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"), ("gptj", "GPTJForCausalLM"), ("marian", "MarianForCausalLM"), ("mbart", "MBartForCausalLM"), ("megatron-bert", "MegatronBertForCausalLM"), ("mvp", "MvpForCausalLM"), ("openai-gpt", "OpenAIGPTLMHeadModel"), ("opt", "OPTForCausalLM"), ("pegasus", "PegasusForCausalLM"), ("plbart", "PLBartForCausalLM"), ("prophetnet", "ProphetNetForCausalLM"), ("qdqbert", "QDQBertLMHeadModel"), ("reformer", "ReformerModelWithLMHead"), ("rembert", "RemBertForCausalLM"), ("roberta", "RobertaForCausalLM"), ("roc_bert", "RoCBertForCausalLM"), ("roformer", "RoFormerForCausalLM"), ("speech_to_text_2", "Speech2Text2ForCausalLM"), ("transfo-xl", "TransfoXLLMHeadModel"), ("trocr", "TrOCRForCausalLM"), ("xglm", "XGLMForCausalLM"), ("xlm", "XLMWithLMHeadModel"), ("xlm-prophetnet", "XLMProphetNetForCausalLM"), ("xlm-roberta", "XLMRobertaForCausalLM"), ("xlm-roberta-xl", "XLMRobertaXLForCausalLM"), ("xlnet", "XLNetLMHeadModel"), ] ) MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES = OrderedDict( [ ("deit", "DeiTForMaskedImageModeling"), ("swin", "SwinForMaskedImageModeling"), ("swinv2", "Swinv2ForMaskedImageModeling"), ("vit", "ViTForMaskedImageModeling"), ] ) MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES = OrderedDict( # Model for Causal Image Modeling mapping [ ("imagegpt", "ImageGPTForCausalImageModeling"), ] ) MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Image Classification mapping ("beit", "BeitForImageClassification"), ("convnext", "ConvNextForImageClassification"), ("cvt", "CvtForImageClassification"), ("data2vec-vision", "Data2VecVisionForImageClassification"), ("deit", ("DeiTForImageClassification", "DeiTForImageClassificationWithTeacher")), ("imagegpt", "ImageGPTForImageClassification"), ("levit", ("LevitForImageClassification", "LevitForImageClassificationWithTeacher")), ("mobilenet_v2", "MobileNetV2ForImageClassification"), ("mobilevit", "MobileViTForImageClassification"), ( "perceiver", ( "PerceiverForImageClassificationLearned", "PerceiverForImageClassificationFourier", "PerceiverForImageClassificationConvProcessing", ), ), ("poolformer", "PoolFormerForImageClassification"), ("regnet", "RegNetForImageClassification"), ("resnet", "ResNetForImageClassification"), ("segformer", "SegformerForImageClassification"), ("swin", "SwinForImageClassification"), ("swinv2", "Swinv2ForImageClassification"), ("van", "VanForImageClassification"), ("vit", "ViTForImageClassification"), ("vit_msn", "ViTMSNForImageClassification"), ] ) MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = OrderedDict( [ # Do not add new models here, this class will be deprecated in the future. # Model for Image Segmentation mapping ("detr", "DetrForSegmentation"), ] ) MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = OrderedDict( [ # Model for Semantic Segmentation mapping ("beit", "BeitForSemanticSegmentation"), ("data2vec-vision", "Data2VecVisionForSemanticSegmentation"), ("dpt", "DPTForSemanticSegmentation"), ("mobilenet_v2", "MobileNetV2ForSemanticSegmentation"), ("mobilevit", "MobileViTForSemanticSegmentation"), ("segformer", "SegformerForSemanticSegmentation"), ] ) MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES = OrderedDict( [ # Model for Instance Segmentation mapping ("maskformer", "MaskFormerForInstanceSegmentation"), ] ) MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ ("videomae", "VideoMAEForVideoClassification"), ] ) MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("vision-encoder-decoder", "VisionEncoderDecoderModel"), ] ) MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict( [ # Model for Masked LM mapping ("albert", "AlbertForMaskedLM"), ("bart", "BartForConditionalGeneration"), ("bert", "BertForMaskedLM"), ("big_bird", "BigBirdForMaskedLM"), ("camembert", "CamembertForMaskedLM"), ("convbert", "ConvBertForMaskedLM"), ("data2vec-text", "Data2VecTextForMaskedLM"), ("deberta", "DebertaForMaskedLM"), ("deberta-v2", "DebertaV2ForMaskedLM"), ("distilbert", "DistilBertForMaskedLM"), ("electra", "ElectraForMaskedLM"), ("ernie", "ErnieForMaskedLM"), ("esm", "EsmForMaskedLM"), ("flaubert", "FlaubertWithLMHeadModel"), ("fnet", "FNetForMaskedLM"), ("funnel", "FunnelForMaskedLM"), ("ibert", "IBertForMaskedLM"), ("layoutlm", "LayoutLMForMaskedLM"), ("longformer", "LongformerForMaskedLM"), ("luke", "LukeForMaskedLM"), ("mbart", "MBartForConditionalGeneration"), ("megatron-bert", "MegatronBertForMaskedLM"), ("mobilebert", "MobileBertForMaskedLM"), ("mpnet", "MPNetForMaskedLM"), ("mvp", "MvpForConditionalGeneration"), ("nezha", "NezhaForMaskedLM"), ("nystromformer", "NystromformerForMaskedLM"), ("perceiver", "PerceiverForMaskedLM"), ("qdqbert", "QDQBertForMaskedLM"), ("reformer", "ReformerForMaskedLM"), ("rembert", "RemBertForMaskedLM"), ("roberta", "RobertaForMaskedLM"), ("roc_bert", "RoCBertForMaskedLM"), ("roformer", "RoFormerForMaskedLM"), ("squeezebert", "SqueezeBertForMaskedLM"), ("tapas", "TapasForMaskedLM"), ("wav2vec2", "Wav2Vec2ForMaskedLM"), ("xlm", "XLMWithLMHeadModel"), ("xlm-roberta", "XLMRobertaForMaskedLM"), ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"), ("yoso", "YosoForMaskedLM"), ] ) MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict( [ # Model for Object Detection mapping ("conditional_detr", "ConditionalDetrForObjectDetection"), ("deformable_detr", "DeformableDetrForObjectDetection"), ("detr", "DetrForObjectDetection"), ("table-transformer", "TableTransformerForObjectDetection"), ("yolos", "YolosForObjectDetection"), ] ) MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict( [ # Model for Zero Shot Object Detection mapping ("owlvit", "OwlViTForObjectDetection") ] ) MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = OrderedDict( [ # Model for depth estimation mapping ("dpt", "DPTForDepthEstimation"), ("glpn", "GLPNForDepthEstimation"), ] ) MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Seq2Seq Causal LM mapping ("bart", "BartForConditionalGeneration"), ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"), ("blenderbot", "BlenderbotForConditionalGeneration"), ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"), ("encoder-decoder", "EncoderDecoderModel"), ("fsmt", "FSMTForConditionalGeneration"), ("led", "LEDForConditionalGeneration"), ("longt5", "LongT5ForConditionalGeneration"), ("m2m_100", "M2M100ForConditionalGeneration"), ("marian", "MarianMTModel"), ("mbart", "MBartForConditionalGeneration"), ("mt5", "MT5ForConditionalGeneration"), ("mvp", "MvpForConditionalGeneration"), ("nllb", "M2M100ForConditionalGeneration"), ("pegasus", "PegasusForConditionalGeneration"), ("pegasus_x", "PegasusXForConditionalGeneration"), ("plbart", "PLBartForConditionalGeneration"), ("prophetnet", "ProphetNetForConditionalGeneration"), ("switch_transformers", "SwitchTransformersForConditionalGeneration"), ("t5", "T5ForConditionalGeneration"), ("xlm-prophetnet", "XLMProphetNetForConditionalGeneration"), ] ) MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("speech-encoder-decoder", "SpeechEncoderDecoderModel"), ("speech_to_text", "Speech2TextForConditionalGeneration"), ("whisper", "WhisperForConditionalGeneration"), ] ) MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Sequence Classification mapping ("albert", "AlbertForSequenceClassification"), ("bart", "BartForSequenceClassification"), ("bert", "BertForSequenceClassification"), ("big_bird", "BigBirdForSequenceClassification"), ("bigbird_pegasus", "BigBirdPegasusForSequenceClassification"), ("bloom", "BloomForSequenceClassification"), ("camembert", "CamembertForSequenceClassification"), ("canine", "CanineForSequenceClassification"), ("convbert", "ConvBertForSequenceClassification"), ("ctrl", "CTRLForSequenceClassification"), ("data2vec-text", "Data2VecTextForSequenceClassification"), ("deberta", "DebertaForSequenceClassification"), ("deberta-v2", "DebertaV2ForSequenceClassification"), ("distilbert", "DistilBertForSequenceClassification"), ("electra", "ElectraForSequenceClassification"), ("ernie", "ErnieForSequenceClassification"), ("esm", "EsmForSequenceClassification"), ("flaubert", "FlaubertForSequenceClassification"), ("fnet", "FNetForSequenceClassification"), ("funnel", "FunnelForSequenceClassification"), ("gpt2", "GPT2ForSequenceClassification"), ("gpt_neo", "GPTNeoForSequenceClassification"), ("gptj", "GPTJForSequenceClassification"), ("ibert", "IBertForSequenceClassification"), ("layoutlm", "LayoutLMForSequenceClassification"), ("layoutlmv2", "LayoutLMv2ForSequenceClassification"), ("layoutlmv3", "LayoutLMv3ForSequenceClassification"), ("led", "LEDForSequenceClassification"), ("lilt", "LiltForSequenceClassification"), ("longformer", "LongformerForSequenceClassification"), ("luke", "LukeForSequenceClassification"), ("markuplm", "MarkupLMForSequenceClassification"), ("mbart", "MBartForSequenceClassification"), ("megatron-bert", "MegatronBertForSequenceClassification"), ("mobilebert", "MobileBertForSequenceClassification"), ("mpnet", "MPNetForSequenceClassification"), ("mvp", "MvpForSequenceClassification"), ("nezha", "NezhaForSequenceClassification"), ("nystromformer", "NystromformerForSequenceClassification"), ("openai-gpt", "OpenAIGPTForSequenceClassification"), ("opt", "OPTForSequenceClassification"), ("perceiver", "PerceiverForSequenceClassification"), ("plbart", "PLBartForSequenceClassification"), ("qdqbert", "QDQBertForSequenceClassification"), ("reformer", "ReformerForSequenceClassification"), ("rembert", "RemBertForSequenceClassification"), ("roberta", "RobertaForSequenceClassification"), ("roc_bert", "RoCBertForSequenceClassification"), ("roformer", "RoFormerForSequenceClassification"), ("squeezebert", "SqueezeBertForSequenceClassification"), ("tapas", "TapasForSequenceClassification"), ("transfo-xl", "TransfoXLForSequenceClassification"), ("xlm", "XLMForSequenceClassification"), ("xlm-roberta", "XLMRobertaForSequenceClassification"), ("xlm-roberta-xl", "XLMRobertaXLForSequenceClassification"), ("xlnet", "XLNetForSequenceClassification"), ("yoso", "YosoForSequenceClassification"), ] ) MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Question Answering mapping ("albert", "AlbertForQuestionAnswering"), ("bart", "BartForQuestionAnswering"), ("bert", "BertForQuestionAnswering"), ("big_bird", "BigBirdForQuestionAnswering"), ("bigbird_pegasus", "BigBirdPegasusForQuestionAnswering"), ("bloom", "BloomForQuestionAnswering"), ("camembert", "CamembertForQuestionAnswering"), ("canine", "CanineForQuestionAnswering"), ("convbert", "ConvBertForQuestionAnswering"), ("data2vec-text", "Data2VecTextForQuestionAnswering"), ("deberta", "DebertaForQuestionAnswering"), ("deberta-v2", "DebertaV2ForQuestionAnswering"), ("distilbert", "DistilBertForQuestionAnswering"), ("electra", "ElectraForQuestionAnswering"), ("ernie", "ErnieForQuestionAnswering"), ("flaubert", "FlaubertForQuestionAnsweringSimple"), ("fnet", "FNetForQuestionAnswering"), ("funnel", "FunnelForQuestionAnswering"), ("gptj", "GPTJForQuestionAnswering"), ("ibert", "IBertForQuestionAnswering"), ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"), ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"), ("led", "LEDForQuestionAnswering"), ("lilt", "LiltForQuestionAnswering"), ("longformer", "LongformerForQuestionAnswering"), ("luke", "LukeForQuestionAnswering"), ("lxmert", "LxmertForQuestionAnswering"), ("markuplm", "MarkupLMForQuestionAnswering"), ("mbart", "MBartForQuestionAnswering"), ("megatron-bert", "MegatronBertForQuestionAnswering"), ("mobilebert", "MobileBertForQuestionAnswering"), ("mpnet", "MPNetForQuestionAnswering"), ("mvp", "MvpForQuestionAnswering"), ("nezha", "NezhaForQuestionAnswering"), ("nystromformer", "NystromformerForQuestionAnswering"), ("opt", "OPTForQuestionAnswering"), ("qdqbert", "QDQBertForQuestionAnswering"), ("reformer", "ReformerForQuestionAnswering"), ("rembert", "RemBertForQuestionAnswering"), ("roberta", "RobertaForQuestionAnswering"), ("roc_bert", "RoCBertForQuestionAnswering"), ("roformer", "RoFormerForQuestionAnswering"), ("splinter", "SplinterForQuestionAnswering"), ("squeezebert", "SqueezeBertForQuestionAnswering"), ("xlm", "XLMForQuestionAnsweringSimple"), ("xlm-roberta", "XLMRobertaForQuestionAnswering"), ("xlm-roberta-xl", "XLMRobertaXLForQuestionAnswering"), ("xlnet", "XLNetForQuestionAnsweringSimple"), ("yoso", "YosoForQuestionAnswering"), ] ) MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Table Question Answering mapping ("tapas", "TapasForQuestionAnswering"), ] ) MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ ("vilt", "ViltForQuestionAnswering"), ] ) MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ ("layoutlm", "LayoutLMForQuestionAnswering"), ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"), ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"), ] ) MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Token Classification mapping ("albert", "AlbertForTokenClassification"), ("bert", "BertForTokenClassification"), ("big_bird", "BigBirdForTokenClassification"), ("bloom", "BloomForTokenClassification"), ("camembert", "CamembertForTokenClassification"), ("canine", "CanineForTokenClassification"), ("convbert", "ConvBertForTokenClassification"), ("data2vec-text", "Data2VecTextForTokenClassification"), ("deberta", "DebertaForTokenClassification"), ("deberta-v2", "DebertaV2ForTokenClassification"), ("distilbert", "DistilBertForTokenClassification"), ("electra", "ElectraForTokenClassification"), ("ernie", "ErnieForTokenClassification"), ("esm", "EsmForTokenClassification"), ("flaubert", "FlaubertForTokenClassification"), ("fnet", "FNetForTokenClassification"), ("funnel", "FunnelForTokenClassification"), ("gpt2", "GPT2ForTokenClassification"), ("ibert", "IBertForTokenClassification"), ("layoutlm", "LayoutLMForTokenClassification"), ("layoutlmv2", "LayoutLMv2ForTokenClassification"), ("layoutlmv3", "LayoutLMv3ForTokenClassification"), ("lilt", "LiltForTokenClassification"), ("longformer", "LongformerForTokenClassification"), ("luke", "LukeForTokenClassification"), ("markuplm", "MarkupLMForTokenClassification"), ("megatron-bert", "MegatronBertForTokenClassification"), ("mobilebert", "MobileBertForTokenClassification"), ("mpnet", "MPNetForTokenClassification"), ("nezha", "NezhaForTokenClassification"), ("nystromformer", "NystromformerForTokenClassification"), ("qdqbert", "QDQBertForTokenClassification"), ("rembert", "RemBertForTokenClassification"), ("roberta", "RobertaForTokenClassification"), ("roc_bert", "RoCBertForTokenClassification"), ("roformer", "RoFormerForTokenClassification"), ("squeezebert", "SqueezeBertForTokenClassification"), ("xlm", "XLMForTokenClassification"), ("xlm-roberta", "XLMRobertaForTokenClassification"), ("xlm-roberta-xl", "XLMRobertaXLForTokenClassification"), ("xlnet", "XLNetForTokenClassification"), ("yoso", "YosoForTokenClassification"), ] ) MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict( [ # Model for Multiple Choice mapping ("albert", "AlbertForMultipleChoice"), ("bert", "BertForMultipleChoice"), ("big_bird", "BigBirdForMultipleChoice"), ("camembert", "CamembertForMultipleChoice"), ("canine", "CanineForMultipleChoice"), ("convbert", "ConvBertForMultipleChoice"), ("data2vec-text", "Data2VecTextForMultipleChoice"), ("deberta-v2", "DebertaV2ForMultipleChoice"), ("distilbert", "DistilBertForMultipleChoice"), ("electra", "ElectraForMultipleChoice"), ("ernie", "ErnieForMultipleChoice"), ("flaubert", "FlaubertForMultipleChoice"), ("fnet", "FNetForMultipleChoice"), ("funnel", "FunnelForMultipleChoice"), ("ibert", "IBertForMultipleChoice"), ("longformer", "LongformerForMultipleChoice"), ("luke", "LukeForMultipleChoice"), ("megatron-bert", "MegatronBertForMultipleChoice"), ("mobilebert", "MobileBertForMultipleChoice"), ("mpnet", "MPNetForMultipleChoice"), ("nezha", "NezhaForMultipleChoice"), ("nystromformer", "NystromformerForMultipleChoice"), ("qdqbert", "QDQBertForMultipleChoice"), ("rembert", "RemBertForMultipleChoice"), ("roberta", "RobertaForMultipleChoice"), ("roc_bert", "RoCBertForMultipleChoice"), ("roformer", "RoFormerForMultipleChoice"), ("squeezebert", "SqueezeBertForMultipleChoice"), ("xlm", "XLMForMultipleChoice"), ("xlm-roberta", "XLMRobertaForMultipleChoice"), ("xlm-roberta-xl", "XLMRobertaXLForMultipleChoice"), ("xlnet", "XLNetForMultipleChoice"), ("yoso", "YosoForMultipleChoice"), ] ) MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict( [ ("bert", "BertForNextSentencePrediction"), ("ernie", "ErnieForNextSentencePrediction"), ("fnet", "FNetForNextSentencePrediction"), ("megatron-bert", "MegatronBertForNextSentencePrediction"), ("mobilebert", "MobileBertForNextSentencePrediction"), ("nezha", "NezhaForNextSentencePrediction"), ("qdqbert", "QDQBertForNextSentencePrediction"), ] ) MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Audio Classification mapping ("data2vec-audio", "Data2VecAudioForSequenceClassification"), ("hubert", "HubertForSequenceClassification"), ("sew", "SEWForSequenceClassification"), ("sew-d", "SEWDForSequenceClassification"), ("unispeech", "UniSpeechForSequenceClassification"), ("unispeech-sat", "UniSpeechSatForSequenceClassification"), ("wav2vec2", "Wav2Vec2ForSequenceClassification"), ("wav2vec2-conformer", "Wav2Vec2ConformerForSequenceClassification"), ("wavlm", "WavLMForSequenceClassification"), ] ) MODEL_FOR_CTC_MAPPING_NAMES = OrderedDict( [ # Model for Connectionist temporal classification (CTC) mapping ("data2vec-audio", "Data2VecAudioForCTC"), ("hubert", "HubertForCTC"), ("mctct", "MCTCTForCTC"), ("sew", "SEWForCTC"), ("sew-d", "SEWDForCTC"), ("unispeech", "UniSpeechForCTC"), ("unispeech-sat", "UniSpeechSatForCTC"), ("wav2vec2", "Wav2Vec2ForCTC"), ("wav2vec2-conformer", "Wav2Vec2ConformerForCTC"), ("wavlm", "WavLMForCTC"), ] ) MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Audio Classification mapping ("data2vec-audio", "Data2VecAudioForAudioFrameClassification"), ("unispeech-sat", "UniSpeechSatForAudioFrameClassification"), ("wav2vec2", "Wav2Vec2ForAudioFrameClassification"), ("wav2vec2-conformer", "Wav2Vec2ConformerForAudioFrameClassification"), ("wavlm", "WavLMForAudioFrameClassification"), ] ) MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = OrderedDict( [ # Model for Audio Classification mapping ("data2vec-audio", "Data2VecAudioForXVector"), ("unispeech-sat", "UniSpeechSatForXVector"), ("wav2vec2", "Wav2Vec2ForXVector"), ("wav2vec2-conformer", "Wav2Vec2ConformerForXVector"), ("wavlm", "WavLMForXVector"), ] ) _MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Zero Shot Image Classification mapping ("clip", "CLIPModel"), ("clipseg", "CLIPSegModel"), ] ) MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES) MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_PRETRAINING_MAPPING_NAMES) MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_WITH_LM_HEAD_MAPPING_NAMES) MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES) MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES ) MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES ) MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES ) MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES ) MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES) MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_LM_MAPPING_NAMES) MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES ) MODEL_FOR_OBJECT_DETECTION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES) MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES ) MODEL_FOR_DEPTH_ESTIMATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES) MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES ) MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES) MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES ) MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_CTC_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CTC_MAPPING_NAMES) MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES) MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_AUDIO_XVECTOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES) class AutoModel(_BaseAutoModelClass): _model_mapping = MODEL_MAPPING AutoModel = auto_class_update(AutoModel) class AutoModelForPreTraining(_BaseAutoModelClass): _model_mapping = MODEL_FOR_PRETRAINING_MAPPING AutoModelForPreTraining = auto_class_update(AutoModelForPreTraining, head_doc="pretraining") # Private on purpose, the public class will add the deprecation warnings. class _AutoModelWithLMHead(_BaseAutoModelClass): _model_mapping = MODEL_WITH_LM_HEAD_MAPPING _AutoModelWithLMHead = auto_class_update(_AutoModelWithLMHead, head_doc="language modeling") class AutoModelForCausalLM(_BaseAutoModelClass): _model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING AutoModelForCausalLM = auto_class_update(AutoModelForCausalLM, head_doc="causal language modeling") class AutoModelForMaskedLM(_BaseAutoModelClass): _model_mapping = MODEL_FOR_MASKED_LM_MAPPING AutoModelForMaskedLM = auto_class_update(AutoModelForMaskedLM, head_doc="masked language modeling") class AutoModelForSeq2SeqLM(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING AutoModelForSeq2SeqLM = auto_class_update( AutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base" ) class AutoModelForSequenceClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING AutoModelForSequenceClassification = auto_class_update( AutoModelForSequenceClassification, head_doc="sequence classification" ) class AutoModelForQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING AutoModelForQuestionAnswering = auto_class_update(AutoModelForQuestionAnswering, head_doc="question answering") class AutoModelForTableQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING AutoModelForTableQuestionAnswering = auto_class_update( AutoModelForTableQuestionAnswering, head_doc="table question answering", checkpoint_for_example="google/tapas-base-finetuned-wtq", ) class AutoModelForVisualQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING AutoModelForVisualQuestionAnswering = auto_class_update( AutoModelForVisualQuestionAnswering, head_doc="visual question answering", checkpoint_for_example="dandelin/vilt-b32-finetuned-vqa", ) class AutoModelForDocumentQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING AutoModelForDocumentQuestionAnswering = auto_class_update( AutoModelForDocumentQuestionAnswering, head_doc="document question answering", checkpoint_for_example='impira/layoutlm-document-qa", revision="52e01b3', ) class AutoModelForTokenClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING AutoModelForTokenClassification = auto_class_update(AutoModelForTokenClassification, head_doc="token classification") class AutoModelForMultipleChoice(_BaseAutoModelClass): _model_mapping = MODEL_FOR_MULTIPLE_CHOICE_MAPPING AutoModelForMultipleChoice = auto_class_update(AutoModelForMultipleChoice, head_doc="multiple choice") class AutoModelForNextSentencePrediction(_BaseAutoModelClass): _model_mapping = MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING AutoModelForNextSentencePrediction = auto_class_update( AutoModelForNextSentencePrediction, head_doc="next sentence prediction" ) class AutoModelForImageClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING AutoModelForImageClassification = auto_class_update(AutoModelForImageClassification, head_doc="image classification") class AutoModelForImageSegmentation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING AutoModelForImageSegmentation = auto_class_update(AutoModelForImageSegmentation, head_doc="image segmentation") class AutoModelForSemanticSegmentation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING AutoModelForSemanticSegmentation = auto_class_update( AutoModelForSemanticSegmentation, head_doc="semantic segmentation" ) class AutoModelForInstanceSegmentation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING AutoModelForInstanceSegmentation = auto_class_update( AutoModelForInstanceSegmentation, head_doc="instance segmentation" ) class AutoModelForObjectDetection(_BaseAutoModelClass): _model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING AutoModelForObjectDetection = auto_class_update(AutoModelForObjectDetection, head_doc="object detection") class AutoModelForZeroShotObjectDetection(_BaseAutoModelClass): _model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING AutoModelForZeroShotObjectDetection = auto_class_update( AutoModelForZeroShotObjectDetection, head_doc="zero-shot object detection" ) class AutoModelForDepthEstimation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING AutoModelForDepthEstimation = auto_class_update(AutoModelForDepthEstimation, head_doc="depth estimation") class AutoModelForVideoClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING AutoModelForVideoClassification = auto_class_update(AutoModelForVideoClassification, head_doc="video classification") class AutoModelForVision2Seq(_BaseAutoModelClass): _model_mapping = MODEL_FOR_VISION_2_SEQ_MAPPING AutoModelForVision2Seq = auto_class_update(AutoModelForVision2Seq, head_doc="vision-to-text modeling") class AutoModelForAudioClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING AutoModelForAudioClassification = auto_class_update(AutoModelForAudioClassification, head_doc="audio classification") class AutoModelForCTC(_BaseAutoModelClass): _model_mapping = MODEL_FOR_CTC_MAPPING AutoModelForCTC = auto_class_update(AutoModelForCTC, head_doc="connectionist temporal classification") class AutoModelForSpeechSeq2Seq(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING AutoModelForSpeechSeq2Seq = auto_class_update( AutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling" ) class AutoModelForAudioFrameClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING AutoModelForAudioFrameClassification = auto_class_update( AutoModelForAudioFrameClassification, head_doc="audio frame (token) classification" ) class AutoModelForAudioXVector(_BaseAutoModelClass): _model_mapping = MODEL_FOR_AUDIO_XVECTOR_MAPPING AutoModelForAudioXVector = auto_class_update(AutoModelForAudioXVector, head_doc="audio retrieval via x-vector") class AutoModelForMaskedImageModeling(_BaseAutoModelClass): _model_mapping = MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING AutoModelForMaskedImageModeling = auto_class_update(AutoModelForMaskedImageModeling, head_doc="masked image modeling") class AutoModelWithLMHead(_AutoModelWithLMHead): @classmethod def from_config(cls, config): warnings.warn( "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use " "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and " "`AutoModelForSeq2SeqLM` for encoder-decoder models.", FutureWarning, ) return super().from_config(config) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): warnings.warn( "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use " "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and " "`AutoModelForSeq2SeqLM` for encoder-decoder models.", FutureWarning, ) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Auto Model class.""" import warnings from collections import OrderedDict from ...utils import logging from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update from .configuration_auto import CONFIG_MAPPING_NAMES logger = logging.get_logger(__name__) MODEL_MAPPING_NAMES = OrderedDict( [ # Base model mapping ("albert", "AlbertModel"), ("bart", "BartModel"), ("beit", "BeitModel"), ("bert", "BertModel"), ("bert-generation", "BertGenerationEncoder"), ("big_bird", "BigBirdModel"), ("bigbird_pegasus", "BigBirdPegasusModel"), ("blenderbot", "BlenderbotModel"), ("blenderbot-small", "BlenderbotSmallModel"), ("bloom", "BloomModel"), ("camembert", "CamembertModel"), ("canine", "CanineModel"), ("clip", "CLIPModel"), ("clipseg", "CLIPSegModel"), ("codegen", "CodeGenModel"), ("conditional_detr", "ConditionalDetrModel"), ("convbert", "ConvBertModel"), ("convnext", "ConvNextModel"), ("ctrl", "CTRLModel"), ("cvt", "CvtModel"), ("data2vec-audio", "Data2VecAudioModel"), ("data2vec-text", "Data2VecTextModel"), ("data2vec-vision", "Data2VecVisionModel"), ("deberta", "DebertaModel"), ("deberta-v2", "DebertaV2Model"), ("decision_transformer", "DecisionTransformerModel"), ("decision_transformer_gpt2", "DecisionTransformerGPT2Model"), ("deformable_detr", "DeformableDetrModel"), ("deit", "DeiTModel"), ("detr", "DetrModel"), ("distilbert", "DistilBertModel"), ("donut-swin", "DonutSwinModel"), ("dpr", "DPRQuestionEncoder"), ("dpt", "DPTModel"), ("electra", "ElectraModel"), ("ernie", "ErnieModel"), ("esm", "EsmModel"), ("flaubert", "FlaubertModel"), ("flava", "FlavaModel"), ("fnet", "FNetModel"), ("fsmt", "FSMTModel"), ("funnel", ("FunnelModel", "FunnelBaseModel")), ("glpn", "GLPNModel"), ("gpt2", "GPT2Model"), ("gpt_neo", "GPTNeoModel"), ("gpt_neox", "GPTNeoXModel"), ("gpt_neox_japanese", "GPTNeoXJapaneseModel"), ("gptj", "GPTJModel"), ("groupvit", "GroupViTModel"), ("hubert", "HubertModel"), ("ibert", "IBertModel"), ("imagegpt", "ImageGPTModel"), ("jukebox", "JukeboxModel"), ("layoutlm", "LayoutLMModel"), ("layoutlmv2", "LayoutLMv2Model"), ("layoutlmv3", "LayoutLMv3Model"), ("led", "LEDModel"), ("levit", "LevitModel"), ("lilt", "LiltModel"), ("longformer", "LongformerModel"), ("longt5", "LongT5Model"), ("luke", "LukeModel"), ("lxmert", "LxmertModel"), ("m2m_100", "M2M100Model"), ("marian", "MarianModel"), ("markuplm", "MarkupLMModel"), ("maskformer", "MaskFormerModel"), ("mbart", "MBartModel"), ("mctct", "MCTCTModel"), ("megatron-bert", "MegatronBertModel"), ("mobilebert", "MobileBertModel"), ("mobilenet_v2", "MobileNetV2Model"), ("mobilevit", "MobileViTModel"), ("mpnet", "MPNetModel"), ("mt5", "MT5Model"), ("mvp", "MvpModel"), ("nezha", "NezhaModel"), ("nllb", "M2M100Model"), ("nystromformer", "NystromformerModel"), ("openai-gpt", "OpenAIGPTModel"), ("opt", "OPTModel"), ("owlvit", "OwlViTModel"), ("pegasus", "PegasusModel"), ("pegasus_x", "PegasusXModel"), ("perceiver", "PerceiverModel"), ("plbart", "PLBartModel"), ("poolformer", "PoolFormerModel"), ("prophetnet", "ProphetNetModel"), ("qdqbert", "QDQBertModel"), ("reformer", "ReformerModel"), ("regnet", "RegNetModel"), ("rembert", "RemBertModel"), ("resnet", "ResNetModel"), ("retribert", "RetriBertModel"), ("roberta", "RobertaModel"), ("roc_bert", "RoCBertModel"), ("roformer", "RoFormerModel"), ("segformer", "SegformerModel"), ("sew", "SEWModel"), ("sew-d", "SEWDModel"), ("speech_to_text", "Speech2TextModel"), ("splinter", "SplinterModel"), ("squeezebert", "SqueezeBertModel"), ("swin", "SwinModel"), ("swinv2", "Swinv2Model"), ("switch_transformers", "SwitchTransformersModel"), ("t5", "T5Model"), ("table-transformer", "TableTransformerModel"), ("tapas", "TapasModel"), ("time_series_transformer", "TimeSeriesTransformerModel"), ("trajectory_transformer", "TrajectoryTransformerModel"), ("transfo-xl", "TransfoXLModel"), ("unispeech", "UniSpeechModel"), ("unispeech-sat", "UniSpeechSatModel"), ("van", "VanModel"), ("videomae", "VideoMAEModel"), ("vilt", "ViltModel"), ("vision-text-dual-encoder", "VisionTextDualEncoderModel"), ("visual_bert", "VisualBertModel"), ("vit", "ViTModel"), ("vit_mae", "ViTMAEModel"), ("vit_msn", "ViTMSNModel"), ("wav2vec2", "Wav2Vec2Model"), ("wav2vec2-conformer", "Wav2Vec2ConformerModel"), ("wavlm", "WavLMModel"), ("whisper", "WhisperModel"), ("xclip", "XCLIPModel"), ("xglm", "XGLMModel"), ("xlm", "XLMModel"), ("xlm-prophetnet", "XLMProphetNetModel"), ("xlm-roberta", "XLMRobertaModel"), ("xlm-roberta-xl", "XLMRobertaXLModel"), ("xlnet", "XLNetModel"), ("yolos", "YolosModel"), ("yoso", "YosoModel"), ] ) MODEL_FOR_PRETRAINING_MAPPING_NAMES = OrderedDict( [ # Model for pre-training mapping ("albert", "AlbertForPreTraining"), ("bart", "BartForConditionalGeneration"), ("bert", "BertForPreTraining"), ("big_bird", "BigBirdForPreTraining"), ("bloom", "BloomForCausalLM"), ("camembert", "CamembertForMaskedLM"), ("ctrl", "CTRLLMHeadModel"), ("data2vec-text", "Data2VecTextForMaskedLM"), ("deberta", "DebertaForMaskedLM"), ("deberta-v2", "DebertaV2ForMaskedLM"), ("distilbert", "DistilBertForMaskedLM"), ("electra", "ElectraForPreTraining"), ("ernie", "ErnieForPreTraining"), ("flaubert", "FlaubertWithLMHeadModel"), ("flava", "FlavaForPreTraining"), ("fnet", "FNetForPreTraining"), ("fsmt", "FSMTForConditionalGeneration"), ("funnel", "FunnelForPreTraining"), ("gpt2", "GPT2LMHeadModel"), ("ibert", "IBertForMaskedLM"), ("layoutlm", "LayoutLMForMaskedLM"), ("longformer", "LongformerForMaskedLM"), ("luke", "LukeForMaskedLM"), ("lxmert", "LxmertForPreTraining"), ("megatron-bert", "MegatronBertForPreTraining"), ("mobilebert", "MobileBertForPreTraining"), ("mpnet", "MPNetForMaskedLM"), ("mvp", "MvpForConditionalGeneration"), ("nezha", "NezhaForPreTraining"), ("openai-gpt", "OpenAIGPTLMHeadModel"), ("retribert", "RetriBertModel"), ("roberta", "RobertaForMaskedLM"), ("roc_bert", "RoCBertForPreTraining"), ("splinter", "SplinterForPreTraining"), ("squeezebert", "SqueezeBertForMaskedLM"), ("switch_transformers", "SwitchTransformersForConditionalGeneration"), ("t5", "T5ForConditionalGeneration"), ("tapas", "TapasForMaskedLM"), ("transfo-xl", "TransfoXLLMHeadModel"), ("unispeech", "UniSpeechForPreTraining"), ("unispeech-sat", "UniSpeechSatForPreTraining"), ("videomae", "VideoMAEForPreTraining"), ("visual_bert", "VisualBertForPreTraining"), ("vit_mae", "ViTMAEForPreTraining"), ("wav2vec2", "Wav2Vec2ForPreTraining"), ("wav2vec2-conformer", "Wav2Vec2ConformerForPreTraining"), ("xlm", "XLMWithLMHeadModel"), ("xlm-roberta", "XLMRobertaForMaskedLM"), ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"), ("xlnet", "XLNetLMHeadModel"), ] ) MODEL_WITH_LM_HEAD_MAPPING_NAMES = OrderedDict( [ # Model with LM heads mapping ("albert", "AlbertForMaskedLM"), ("bart", "BartForConditionalGeneration"), ("bert", "BertForMaskedLM"), ("big_bird", "BigBirdForMaskedLM"), ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"), ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"), ("bloom", "BloomForCausalLM"), ("camembert", "CamembertForMaskedLM"), ("codegen", "CodeGenForCausalLM"), ("convbert", "ConvBertForMaskedLM"), ("ctrl", "CTRLLMHeadModel"), ("data2vec-text", "Data2VecTextForMaskedLM"), ("deberta", "DebertaForMaskedLM"), ("deberta-v2", "DebertaV2ForMaskedLM"), ("distilbert", "DistilBertForMaskedLM"), ("electra", "ElectraForMaskedLM"), ("encoder-decoder", "EncoderDecoderModel"), ("ernie", "ErnieForMaskedLM"), ("esm", "EsmForMaskedLM"), ("flaubert", "FlaubertWithLMHeadModel"), ("fnet", "FNetForMaskedLM"), ("fsmt", "FSMTForConditionalGeneration"), ("funnel", "FunnelForMaskedLM"), ("gpt2", "GPT2LMHeadModel"), ("gpt_neo", "GPTNeoForCausalLM"), ("gpt_neox", "GPTNeoXForCausalLM"), ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"), ("gptj", "GPTJForCausalLM"), ("ibert", "IBertForMaskedLM"), ("layoutlm", "LayoutLMForMaskedLM"), ("led", "LEDForConditionalGeneration"), ("longformer", "LongformerForMaskedLM"), ("longt5", "LongT5ForConditionalGeneration"), ("luke", "LukeForMaskedLM"), ("m2m_100", "M2M100ForConditionalGeneration"), ("marian", "MarianMTModel"), ("megatron-bert", "MegatronBertForCausalLM"), ("mobilebert", "MobileBertForMaskedLM"), ("mpnet", "MPNetForMaskedLM"), ("mvp", "MvpForConditionalGeneration"), ("nezha", "NezhaForMaskedLM"), ("nllb", "M2M100ForConditionalGeneration"), ("nystromformer", "NystromformerForMaskedLM"), ("openai-gpt", "OpenAIGPTLMHeadModel"), ("pegasus_x", "PegasusXForConditionalGeneration"), ("plbart", "PLBartForConditionalGeneration"), ("qdqbert", "QDQBertForMaskedLM"), ("reformer", "ReformerModelWithLMHead"), ("rembert", "RemBertForMaskedLM"), ("roberta", "RobertaForMaskedLM"), ("roc_bert", "RoCBertForMaskedLM"), ("roformer", "RoFormerForMaskedLM"), ("speech_to_text", "Speech2TextForConditionalGeneration"), ("squeezebert", "SqueezeBertForMaskedLM"), ("switch_transformers", "SwitchTransformersForConditionalGeneration"), ("t5", "T5ForConditionalGeneration"), ("tapas", "TapasForMaskedLM"), ("transfo-xl", "TransfoXLLMHeadModel"), ("wav2vec2", "Wav2Vec2ForMaskedLM"), ("whisper", "WhisperForConditionalGeneration"), ("xlm", "XLMWithLMHeadModel"), ("xlm-roberta", "XLMRobertaForMaskedLM"), ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"), ("xlnet", "XLNetLMHeadModel"), ("yoso", "YosoForMaskedLM"), ] ) MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Causal LM mapping ("bart", "BartForCausalLM"), ("bert", "BertLMHeadModel"), ("bert-generation", "BertGenerationDecoder"), ("big_bird", "BigBirdForCausalLM"), ("bigbird_pegasus", "BigBirdPegasusForCausalLM"), ("blenderbot", "BlenderbotForCausalLM"), ("blenderbot-small", "BlenderbotSmallForCausalLM"), ("bloom", "BloomForCausalLM"), ("camembert", "CamembertForCausalLM"), ("codegen", "CodeGenForCausalLM"), ("ctrl", "CTRLLMHeadModel"), ("data2vec-text", "Data2VecTextForCausalLM"), ("electra", "ElectraForCausalLM"), ("ernie", "ErnieForCausalLM"), ("gpt2", "GPT2LMHeadModel"), ("gpt_neo", "GPTNeoForCausalLM"), ("gpt_neox", "GPTNeoXForCausalLM"), ("gpt_neox_japanese", "GPTNeoXJapaneseForCausalLM"), ("gptj", "GPTJForCausalLM"), ("marian", "MarianForCausalLM"), ("mbart", "MBartForCausalLM"), ("megatron-bert", "MegatronBertForCausalLM"), ("mvp", "MvpForCausalLM"), ("openai-gpt", "OpenAIGPTLMHeadModel"), ("opt", "OPTForCausalLM"), ("pegasus", "PegasusForCausalLM"), ("plbart", "PLBartForCausalLM"), ("prophetnet", "ProphetNetForCausalLM"), ("qdqbert", "QDQBertLMHeadModel"), ("reformer", "ReformerModelWithLMHead"), ("rembert", "RemBertForCausalLM"), ("roberta", "RobertaForCausalLM"), ("roc_bert", "RoCBertForCausalLM"), ("roformer", "RoFormerForCausalLM"), ("speech_to_text_2", "Speech2Text2ForCausalLM"), ("transfo-xl", "TransfoXLLMHeadModel"), ("trocr", "TrOCRForCausalLM"), ("xglm", "XGLMForCausalLM"), ("xlm", "XLMWithLMHeadModel"), ("xlm-prophetnet", "XLMProphetNetForCausalLM"), ("xlm-roberta", "XLMRobertaForCausalLM"), ("xlm-roberta-xl", "XLMRobertaXLForCausalLM"), ("xlnet", "XLNetLMHeadModel"), ] ) MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES = OrderedDict( [ ("deit", "DeiTForMaskedImageModeling"), ("swin", "SwinForMaskedImageModeling"), ("swinv2", "Swinv2ForMaskedImageModeling"), ("vit", "ViTForMaskedImageModeling"), ] ) MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES = OrderedDict( # Model for Causal Image Modeling mapping [ ("imagegpt", "ImageGPTForCausalImageModeling"), ] ) MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Image Classification mapping ("beit", "BeitForImageClassification"), ("convnext", "ConvNextForImageClassification"), ("cvt", "CvtForImageClassification"), ("data2vec-vision", "Data2VecVisionForImageClassification"), ("deit", ("DeiTForImageClassification", "DeiTForImageClassificationWithTeacher")), ("imagegpt", "ImageGPTForImageClassification"), ("levit", ("LevitForImageClassification", "LevitForImageClassificationWithTeacher")), ("mobilenet_v2", "MobileNetV2ForImageClassification"), ("mobilevit", "MobileViTForImageClassification"), ( "perceiver", ( "PerceiverForImageClassificationLearned", "PerceiverForImageClassificationFourier", "PerceiverForImageClassificationConvProcessing", ), ), ("poolformer", "PoolFormerForImageClassification"), ("regnet", "RegNetForImageClassification"), ("resnet", "ResNetForImageClassification"), ("segformer", "SegformerForImageClassification"), ("swin", "SwinForImageClassification"), ("swinv2", "Swinv2ForImageClassification"), ("van", "VanForImageClassification"), ("vit", "ViTForImageClassification"), ("vit_msn", "ViTMSNForImageClassification"), ] ) MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = OrderedDict( [ # Do not add new models here, this class will be deprecated in the future. # Model for Image Segmentation mapping ("detr", "DetrForSegmentation"), ] ) MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = OrderedDict( [ # Model for Semantic Segmentation mapping ("beit", "BeitForSemanticSegmentation"), ("data2vec-vision", "Data2VecVisionForSemanticSegmentation"), ("dpt", "DPTForSemanticSegmentation"), ("mobilenet_v2", "MobileNetV2ForSemanticSegmentation"), ("mobilevit", "MobileViTForSemanticSegmentation"), ("segformer", "SegformerForSemanticSegmentation"), ] ) MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES = OrderedDict( [ # Model for Instance Segmentation mapping ("maskformer", "MaskFormerForInstanceSegmentation"), ] ) MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ ("videomae", "VideoMAEForVideoClassification"), ] ) MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("vision-encoder-decoder", "VisionEncoderDecoderModel"), ] ) MODEL_FOR_MASKED_LM_MAPPING_NAMES = OrderedDict( [ # Model for Masked LM mapping ("albert", "AlbertForMaskedLM"), ("bart", "BartForConditionalGeneration"), ("bert", "BertForMaskedLM"), ("big_bird", "BigBirdForMaskedLM"), ("camembert", "CamembertForMaskedLM"), ("convbert", "ConvBertForMaskedLM"), ("data2vec-text", "Data2VecTextForMaskedLM"), ("deberta", "DebertaForMaskedLM"), ("deberta-v2", "DebertaV2ForMaskedLM"), ("distilbert", "DistilBertForMaskedLM"), ("electra", "ElectraForMaskedLM"), ("ernie", "ErnieForMaskedLM"), ("esm", "EsmForMaskedLM"), ("flaubert", "FlaubertWithLMHeadModel"), ("fnet", "FNetForMaskedLM"), ("funnel", "FunnelForMaskedLM"), ("ibert", "IBertForMaskedLM"), ("layoutlm", "LayoutLMForMaskedLM"), ("longformer", "LongformerForMaskedLM"), ("luke", "LukeForMaskedLM"), ("mbart", "MBartForConditionalGeneration"), ("megatron-bert", "MegatronBertForMaskedLM"), ("mobilebert", "MobileBertForMaskedLM"), ("mpnet", "MPNetForMaskedLM"), ("mvp", "MvpForConditionalGeneration"), ("nezha", "NezhaForMaskedLM"), ("nystromformer", "NystromformerForMaskedLM"), ("perceiver", "PerceiverForMaskedLM"), ("qdqbert", "QDQBertForMaskedLM"), ("reformer", "ReformerForMaskedLM"), ("rembert", "RemBertForMaskedLM"), ("roberta", "RobertaForMaskedLM"), ("roc_bert", "RoCBertForMaskedLM"), ("roformer", "RoFormerForMaskedLM"), ("squeezebert", "SqueezeBertForMaskedLM"), ("tapas", "TapasForMaskedLM"), ("wav2vec2", "Wav2Vec2ForMaskedLM"), ("xlm", "XLMWithLMHeadModel"), ("xlm-roberta", "XLMRobertaForMaskedLM"), ("xlm-roberta-xl", "XLMRobertaXLForMaskedLM"), ("yoso", "YosoForMaskedLM"), ] ) MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict( [ # Model for Object Detection mapping ("conditional_detr", "ConditionalDetrForObjectDetection"), ("deformable_detr", "DeformableDetrForObjectDetection"), ("detr", "DetrForObjectDetection"), ("table-transformer", "TableTransformerForObjectDetection"), ("yolos", "YolosForObjectDetection"), ] ) MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = OrderedDict( [ # Model for Zero Shot Object Detection mapping ("owlvit", "OwlViTForObjectDetection") ] ) MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = OrderedDict( [ # Model for depth estimation mapping ("dpt", "DPTForDepthEstimation"), ("glpn", "GLPNForDepthEstimation"), ] ) MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = OrderedDict( [ # Model for Seq2Seq Causal LM mapping ("bart", "BartForConditionalGeneration"), ("bigbird_pegasus", "BigBirdPegasusForConditionalGeneration"), ("blenderbot", "BlenderbotForConditionalGeneration"), ("blenderbot-small", "BlenderbotSmallForConditionalGeneration"), ("encoder-decoder", "EncoderDecoderModel"), ("fsmt", "FSMTForConditionalGeneration"), ("led", "LEDForConditionalGeneration"), ("longt5", "LongT5ForConditionalGeneration"), ("m2m_100", "M2M100ForConditionalGeneration"), ("marian", "MarianMTModel"), ("mbart", "MBartForConditionalGeneration"), ("mt5", "MT5ForConditionalGeneration"), ("mvp", "MvpForConditionalGeneration"), ("nllb", "M2M100ForConditionalGeneration"), ("pegasus", "PegasusForConditionalGeneration"), ("pegasus_x", "PegasusXForConditionalGeneration"), ("plbart", "PLBartForConditionalGeneration"), ("prophetnet", "ProphetNetForConditionalGeneration"), ("switch_transformers", "SwitchTransformersForConditionalGeneration"), ("t5", "T5ForConditionalGeneration"), ("xlm-prophetnet", "XLMProphetNetForConditionalGeneration"), ] ) MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = OrderedDict( [ ("speech-encoder-decoder", "SpeechEncoderDecoderModel"), ("speech_to_text", "Speech2TextForConditionalGeneration"), ("whisper", "WhisperForConditionalGeneration"), ] ) MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Sequence Classification mapping ("albert", "AlbertForSequenceClassification"), ("bart", "BartForSequenceClassification"), ("bert", "BertForSequenceClassification"), ("big_bird", "BigBirdForSequenceClassification"), ("bigbird_pegasus", "BigBirdPegasusForSequenceClassification"), ("bloom", "BloomForSequenceClassification"), ("camembert", "CamembertForSequenceClassification"), ("canine", "CanineForSequenceClassification"), ("convbert", "ConvBertForSequenceClassification"), ("ctrl", "CTRLForSequenceClassification"), ("data2vec-text", "Data2VecTextForSequenceClassification"), ("deberta", "DebertaForSequenceClassification"), ("deberta-v2", "DebertaV2ForSequenceClassification"), ("distilbert", "DistilBertForSequenceClassification"), ("electra", "ElectraForSequenceClassification"), ("ernie", "ErnieForSequenceClassification"), ("esm", "EsmForSequenceClassification"), ("flaubert", "FlaubertForSequenceClassification"), ("fnet", "FNetForSequenceClassification"), ("funnel", "FunnelForSequenceClassification"), ("gpt2", "GPT2ForSequenceClassification"), ("gpt_neo", "GPTNeoForSequenceClassification"), ("gptj", "GPTJForSequenceClassification"), ("ibert", "IBertForSequenceClassification"), ("layoutlm", "LayoutLMForSequenceClassification"), ("layoutlmv2", "LayoutLMv2ForSequenceClassification"), ("layoutlmv3", "LayoutLMv3ForSequenceClassification"), ("led", "LEDForSequenceClassification"), ("lilt", "LiltForSequenceClassification"), ("longformer", "LongformerForSequenceClassification"), ("luke", "LukeForSequenceClassification"), ("markuplm", "MarkupLMForSequenceClassification"), ("mbart", "MBartForSequenceClassification"), ("megatron-bert", "MegatronBertForSequenceClassification"), ("mobilebert", "MobileBertForSequenceClassification"), ("mpnet", "MPNetForSequenceClassification"), ("mvp", "MvpForSequenceClassification"), ("nezha", "NezhaForSequenceClassification"), ("nystromformer", "NystromformerForSequenceClassification"), ("openai-gpt", "OpenAIGPTForSequenceClassification"), ("opt", "OPTForSequenceClassification"), ("perceiver", "PerceiverForSequenceClassification"), ("plbart", "PLBartForSequenceClassification"), ("qdqbert", "QDQBertForSequenceClassification"), ("reformer", "ReformerForSequenceClassification"), ("rembert", "RemBertForSequenceClassification"), ("roberta", "RobertaForSequenceClassification"), ("roc_bert", "RoCBertForSequenceClassification"), ("roformer", "RoFormerForSequenceClassification"), ("squeezebert", "SqueezeBertForSequenceClassification"), ("tapas", "TapasForSequenceClassification"), ("transfo-xl", "TransfoXLForSequenceClassification"), ("xlm", "XLMForSequenceClassification"), ("xlm-roberta", "XLMRobertaForSequenceClassification"), ("xlm-roberta-xl", "XLMRobertaXLForSequenceClassification"), ("xlnet", "XLNetForSequenceClassification"), ("yoso", "YosoForSequenceClassification"), ] ) MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Question Answering mapping ("albert", "AlbertForQuestionAnswering"), ("bart", "BartForQuestionAnswering"), ("bert", "BertForQuestionAnswering"), ("big_bird", "BigBirdForQuestionAnswering"), ("bigbird_pegasus", "BigBirdPegasusForQuestionAnswering"), ("bloom", "BloomForQuestionAnswering"), ("camembert", "CamembertForQuestionAnswering"), ("canine", "CanineForQuestionAnswering"), ("convbert", "ConvBertForQuestionAnswering"), ("data2vec-text", "Data2VecTextForQuestionAnswering"), ("deberta", "DebertaForQuestionAnswering"), ("deberta-v2", "DebertaV2ForQuestionAnswering"), ("distilbert", "DistilBertForQuestionAnswering"), ("electra", "ElectraForQuestionAnswering"), ("ernie", "ErnieForQuestionAnswering"), ("flaubert", "FlaubertForQuestionAnsweringSimple"), ("fnet", "FNetForQuestionAnswering"), ("funnel", "FunnelForQuestionAnswering"), ("gptj", "GPTJForQuestionAnswering"), ("ibert", "IBertForQuestionAnswering"), ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"), ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"), ("led", "LEDForQuestionAnswering"), ("lilt", "LiltForQuestionAnswering"), ("longformer", "LongformerForQuestionAnswering"), ("luke", "LukeForQuestionAnswering"), ("lxmert", "LxmertForQuestionAnswering"), ("markuplm", "MarkupLMForQuestionAnswering"), ("mbart", "MBartForQuestionAnswering"), ("megatron-bert", "MegatronBertForQuestionAnswering"), ("mobilebert", "MobileBertForQuestionAnswering"), ("mpnet", "MPNetForQuestionAnswering"), ("mvp", "MvpForQuestionAnswering"), ("nezha", "NezhaForQuestionAnswering"), ("nystromformer", "NystromformerForQuestionAnswering"), ("opt", "OPTForQuestionAnswering"), ("qdqbert", "QDQBertForQuestionAnswering"), ("reformer", "ReformerForQuestionAnswering"), ("rembert", "RemBertForQuestionAnswering"), ("roberta", "RobertaForQuestionAnswering"), ("roc_bert", "RoCBertForQuestionAnswering"), ("roformer", "RoFormerForQuestionAnswering"), ("splinter", "SplinterForQuestionAnswering"), ("squeezebert", "SqueezeBertForQuestionAnswering"), ("xlm", "XLMForQuestionAnsweringSimple"), ("xlm-roberta", "XLMRobertaForQuestionAnswering"), ("xlm-roberta-xl", "XLMRobertaXLForQuestionAnswering"), ("xlnet", "XLNetForQuestionAnsweringSimple"), ("yoso", "YosoForQuestionAnswering"), ] ) MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ # Model for Table Question Answering mapping ("tapas", "TapasForQuestionAnswering"), ] ) MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ ("vilt", "ViltForQuestionAnswering"), ] ) MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( [ ("layoutlm", "LayoutLMForQuestionAnswering"), ("layoutlmv2", "LayoutLMv2ForQuestionAnswering"), ("layoutlmv3", "LayoutLMv3ForQuestionAnswering"), ] ) MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Token Classification mapping ("albert", "AlbertForTokenClassification"), ("bert", "BertForTokenClassification"), ("big_bird", "BigBirdForTokenClassification"), ("bloom", "BloomForTokenClassification"), ("camembert", "CamembertForTokenClassification"), ("canine", "CanineForTokenClassification"), ("convbert", "ConvBertForTokenClassification"), ("data2vec-text", "Data2VecTextForTokenClassification"), ("deberta", "DebertaForTokenClassification"), ("deberta-v2", "DebertaV2ForTokenClassification"), ("distilbert", "DistilBertForTokenClassification"), ("electra", "ElectraForTokenClassification"), ("ernie", "ErnieForTokenClassification"), ("esm", "EsmForTokenClassification"), ("flaubert", "FlaubertForTokenClassification"), ("fnet", "FNetForTokenClassification"), ("funnel", "FunnelForTokenClassification"), ("gpt2", "GPT2ForTokenClassification"), ("ibert", "IBertForTokenClassification"), ("layoutlm", "LayoutLMForTokenClassification"), ("layoutlmv2", "LayoutLMv2ForTokenClassification"), ("layoutlmv3", "LayoutLMv3ForTokenClassification"), ("lilt", "LiltForTokenClassification"), ("longformer", "LongformerForTokenClassification"), ("luke", "LukeForTokenClassification"), ("markuplm", "MarkupLMForTokenClassification"), ("megatron-bert", "MegatronBertForTokenClassification"), ("mobilebert", "MobileBertForTokenClassification"), ("mpnet", "MPNetForTokenClassification"), ("nezha", "NezhaForTokenClassification"), ("nystromformer", "NystromformerForTokenClassification"), ("qdqbert", "QDQBertForTokenClassification"), ("rembert", "RemBertForTokenClassification"), ("roberta", "RobertaForTokenClassification"), ("roc_bert", "RoCBertForTokenClassification"), ("roformer", "RoFormerForTokenClassification"), ("squeezebert", "SqueezeBertForTokenClassification"), ("xlm", "XLMForTokenClassification"), ("xlm-roberta", "XLMRobertaForTokenClassification"), ("xlm-roberta-xl", "XLMRobertaXLForTokenClassification"), ("xlnet", "XLNetForTokenClassification"), ("yoso", "YosoForTokenClassification"), ] ) MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES = OrderedDict( [ # Model for Multiple Choice mapping ("albert", "AlbertForMultipleChoice"), ("bert", "BertForMultipleChoice"), ("big_bird", "BigBirdForMultipleChoice"), ("camembert", "CamembertForMultipleChoice"), ("canine", "CanineForMultipleChoice"), ("convbert", "ConvBertForMultipleChoice"), ("data2vec-text", "Data2VecTextForMultipleChoice"), ("deberta-v2", "DebertaV2ForMultipleChoice"), ("distilbert", "DistilBertForMultipleChoice"), ("electra", "ElectraForMultipleChoice"), ("ernie", "ErnieForMultipleChoice"), ("flaubert", "FlaubertForMultipleChoice"), ("fnet", "FNetForMultipleChoice"), ("funnel", "FunnelForMultipleChoice"), ("ibert", "IBertForMultipleChoice"), ("longformer", "LongformerForMultipleChoice"), ("luke", "LukeForMultipleChoice"), ("megatron-bert", "MegatronBertForMultipleChoice"), ("mobilebert", "MobileBertForMultipleChoice"), ("mpnet", "MPNetForMultipleChoice"), ("nezha", "NezhaForMultipleChoice"), ("nystromformer", "NystromformerForMultipleChoice"), ("qdqbert", "QDQBertForMultipleChoice"), ("rembert", "RemBertForMultipleChoice"), ("roberta", "RobertaForMultipleChoice"), ("roc_bert", "RoCBertForMultipleChoice"), ("roformer", "RoFormerForMultipleChoice"), ("squeezebert", "SqueezeBertForMultipleChoice"), ("xlm", "XLMForMultipleChoice"), ("xlm-roberta", "XLMRobertaForMultipleChoice"), ("xlm-roberta-xl", "XLMRobertaXLForMultipleChoice"), ("xlnet", "XLNetForMultipleChoice"), ("yoso", "YosoForMultipleChoice"), ] ) MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES = OrderedDict( [ ("bert", "BertForNextSentencePrediction"), ("ernie", "ErnieForNextSentencePrediction"), ("fnet", "FNetForNextSentencePrediction"), ("megatron-bert", "MegatronBertForNextSentencePrediction"), ("mobilebert", "MobileBertForNextSentencePrediction"), ("nezha", "NezhaForNextSentencePrediction"), ("qdqbert", "QDQBertForNextSentencePrediction"), ] ) MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Audio Classification mapping ("data2vec-audio", "Data2VecAudioForSequenceClassification"), ("hubert", "HubertForSequenceClassification"), ("sew", "SEWForSequenceClassification"), ("sew-d", "SEWDForSequenceClassification"), ("unispeech", "UniSpeechForSequenceClassification"), ("unispeech-sat", "UniSpeechSatForSequenceClassification"), ("wav2vec2", "Wav2Vec2ForSequenceClassification"), ("wav2vec2-conformer", "Wav2Vec2ConformerForSequenceClassification"), ("wavlm", "WavLMForSequenceClassification"), ] ) MODEL_FOR_CTC_MAPPING_NAMES = OrderedDict( [ # Model for Connectionist temporal classification (CTC) mapping ("data2vec-audio", "Data2VecAudioForCTC"), ("hubert", "HubertForCTC"), ("mctct", "MCTCTForCTC"), ("sew", "SEWForCTC"), ("sew-d", "SEWDForCTC"), ("unispeech", "UniSpeechForCTC"), ("unispeech-sat", "UniSpeechSatForCTC"), ("wav2vec2", "Wav2Vec2ForCTC"), ("wav2vec2-conformer", "Wav2Vec2ConformerForCTC"), ("wavlm", "WavLMForCTC"), ] ) MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Audio Classification mapping ("data2vec-audio", "Data2VecAudioForAudioFrameClassification"), ("unispeech-sat", "UniSpeechSatForAudioFrameClassification"), ("wav2vec2", "Wav2Vec2ForAudioFrameClassification"), ("wav2vec2-conformer", "Wav2Vec2ConformerForAudioFrameClassification"), ("wavlm", "WavLMForAudioFrameClassification"), ] ) MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = OrderedDict( [ # Model for Audio Classification mapping ("data2vec-audio", "Data2VecAudioForXVector"), ("unispeech-sat", "UniSpeechSatForXVector"), ("wav2vec2", "Wav2Vec2ForXVector"), ("wav2vec2-conformer", "Wav2Vec2ConformerForXVector"), ("wavlm", "WavLMForXVector"), ] ) _MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict( [ # Model for Zero Shot Image Classification mapping ("clip", "CLIPModel"), ("clipseg", "CLIPSegModel"), ] ) MODEL_FOR_BACKBONE_MAPPING_NAMES = OrderedDict( [ # Backbone mapping ("resnet", "ResNetBackbone"), ] ) MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES) MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_PRETRAINING_MAPPING_NAMES) MODEL_WITH_LM_HEAD_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_WITH_LM_HEAD_MAPPING_NAMES) MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES) MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING_NAMES ) MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES ) MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES ) MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES ) MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_VISION_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES) MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_MASKED_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_LM_MAPPING_NAMES) MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING_NAMES ) MODEL_FOR_OBJECT_DETECTION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES) MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES ) MODEL_FOR_DEPTH_ESTIMATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES) MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES ) MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES ) MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_MULTIPLE_CHOICE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES) MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES ) MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_CTC_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CTC_MAPPING_NAMES) MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES) MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING = _LazyAutoMapping( CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES ) MODEL_FOR_AUDIO_XVECTOR_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES) MODEL_FOR_BACKBONE_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_BACKBONE_MAPPING_NAMES) class AutoModel(_BaseAutoModelClass): _model_mapping = MODEL_MAPPING AutoModel = auto_class_update(AutoModel) class AutoModelForPreTraining(_BaseAutoModelClass): _model_mapping = MODEL_FOR_PRETRAINING_MAPPING AutoModelForPreTraining = auto_class_update(AutoModelForPreTraining, head_doc="pretraining") # Private on purpose, the public class will add the deprecation warnings. class _AutoModelWithLMHead(_BaseAutoModelClass): _model_mapping = MODEL_WITH_LM_HEAD_MAPPING _AutoModelWithLMHead = auto_class_update(_AutoModelWithLMHead, head_doc="language modeling") class AutoModelForCausalLM(_BaseAutoModelClass): _model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING AutoModelForCausalLM = auto_class_update(AutoModelForCausalLM, head_doc="causal language modeling") class AutoModelForMaskedLM(_BaseAutoModelClass): _model_mapping = MODEL_FOR_MASKED_LM_MAPPING AutoModelForMaskedLM = auto_class_update(AutoModelForMaskedLM, head_doc="masked language modeling") class AutoModelForSeq2SeqLM(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING AutoModelForSeq2SeqLM = auto_class_update( AutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base" ) class AutoModelForSequenceClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING AutoModelForSequenceClassification = auto_class_update( AutoModelForSequenceClassification, head_doc="sequence classification" ) class AutoModelForQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING AutoModelForQuestionAnswering = auto_class_update(AutoModelForQuestionAnswering, head_doc="question answering") class AutoModelForTableQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING AutoModelForTableQuestionAnswering = auto_class_update( AutoModelForTableQuestionAnswering, head_doc="table question answering", checkpoint_for_example="google/tapas-base-finetuned-wtq", ) class AutoModelForVisualQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING AutoModelForVisualQuestionAnswering = auto_class_update( AutoModelForVisualQuestionAnswering, head_doc="visual question answering", checkpoint_for_example="dandelin/vilt-b32-finetuned-vqa", ) class AutoModelForDocumentQuestionAnswering(_BaseAutoModelClass): _model_mapping = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING AutoModelForDocumentQuestionAnswering = auto_class_update( AutoModelForDocumentQuestionAnswering, head_doc="document question answering", checkpoint_for_example='impira/layoutlm-document-qa", revision="52e01b3', ) class AutoModelForTokenClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING AutoModelForTokenClassification = auto_class_update(AutoModelForTokenClassification, head_doc="token classification") class AutoModelForMultipleChoice(_BaseAutoModelClass): _model_mapping = MODEL_FOR_MULTIPLE_CHOICE_MAPPING AutoModelForMultipleChoice = auto_class_update(AutoModelForMultipleChoice, head_doc="multiple choice") class AutoModelForNextSentencePrediction(_BaseAutoModelClass): _model_mapping = MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING AutoModelForNextSentencePrediction = auto_class_update( AutoModelForNextSentencePrediction, head_doc="next sentence prediction" ) class AutoModelForImageClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING AutoModelForImageClassification = auto_class_update(AutoModelForImageClassification, head_doc="image classification") class AutoModelForImageSegmentation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING AutoModelForImageSegmentation = auto_class_update(AutoModelForImageSegmentation, head_doc="image segmentation") class AutoModelForSemanticSegmentation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING AutoModelForSemanticSegmentation = auto_class_update( AutoModelForSemanticSegmentation, head_doc="semantic segmentation" ) class AutoModelForInstanceSegmentation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING AutoModelForInstanceSegmentation = auto_class_update( AutoModelForInstanceSegmentation, head_doc="instance segmentation" ) class AutoModelForObjectDetection(_BaseAutoModelClass): _model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING AutoModelForObjectDetection = auto_class_update(AutoModelForObjectDetection, head_doc="object detection") class AutoModelForZeroShotObjectDetection(_BaseAutoModelClass): _model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING AutoModelForZeroShotObjectDetection = auto_class_update( AutoModelForZeroShotObjectDetection, head_doc="zero-shot object detection" ) class AutoModelForDepthEstimation(_BaseAutoModelClass): _model_mapping = MODEL_FOR_DEPTH_ESTIMATION_MAPPING AutoModelForDepthEstimation = auto_class_update(AutoModelForDepthEstimation, head_doc="depth estimation") class AutoModelForVideoClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING AutoModelForVideoClassification = auto_class_update(AutoModelForVideoClassification, head_doc="video classification") class AutoModelForVision2Seq(_BaseAutoModelClass): _model_mapping = MODEL_FOR_VISION_2_SEQ_MAPPING AutoModelForVision2Seq = auto_class_update(AutoModelForVision2Seq, head_doc="vision-to-text modeling") class AutoModelForAudioClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING AutoModelForAudioClassification = auto_class_update(AutoModelForAudioClassification, head_doc="audio classification") class AutoModelForCTC(_BaseAutoModelClass): _model_mapping = MODEL_FOR_CTC_MAPPING AutoModelForCTC = auto_class_update(AutoModelForCTC, head_doc="connectionist temporal classification") class AutoModelForSpeechSeq2Seq(_BaseAutoModelClass): _model_mapping = MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING AutoModelForSpeechSeq2Seq = auto_class_update( AutoModelForSpeechSeq2Seq, head_doc="sequence-to-sequence speech-to-text modeling" ) class AutoModelForAudioFrameClassification(_BaseAutoModelClass): _model_mapping = MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING AutoModelForAudioFrameClassification = auto_class_update( AutoModelForAudioFrameClassification, head_doc="audio frame (token) classification" ) class AutoModelForAudioXVector(_BaseAutoModelClass): _model_mapping = MODEL_FOR_AUDIO_XVECTOR_MAPPING class AutoBackbone(_BaseAutoModelClass): _model_mapping = MODEL_FOR_BACKBONE_MAPPING AutoModelForAudioXVector = auto_class_update(AutoModelForAudioXVector, head_doc="audio retrieval via x-vector") class AutoModelForMaskedImageModeling(_BaseAutoModelClass): _model_mapping = MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING AutoModelForMaskedImageModeling = auto_class_update(AutoModelForMaskedImageModeling, head_doc="masked image modeling") class AutoModelWithLMHead(_AutoModelWithLMHead): @classmethod def from_config(cls, config): warnings.warn( "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use " "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and " "`AutoModelForSeq2SeqLM` for encoder-decoder models.", FutureWarning, ) return super().from_config(config) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): warnings.warn( "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use " "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and " "`AutoModelForSeq2SeqLM` for encoder-decoder models.", FutureWarning, ) return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/resnet/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_resnet"] = [ "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ResNetForImageClassification", "ResNetModel", "ResNetPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_resnet"] = [ "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFResNetForImageClassification", "TFResNetModel", "TFResNetPreTrainedModel", ] if TYPE_CHECKING: from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_resnet import ( RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, ResNetForImageClassification, ResNetModel, ResNetPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_resnet import ( TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFResNetForImageClassification, TFResNetModel, TFResNetPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _import_structure = { "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"] } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_resnet"] = [ "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "ResNetForImageClassification", "ResNetModel", "ResNetPreTrainedModel", "ResNetBackbone", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_tf_resnet"] = [ "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST", "TFResNetForImageClassification", "TFResNetModel", "TFResNetPreTrainedModel", ] if TYPE_CHECKING: from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_resnet import ( RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, ResNetBackbone, ResNetForImageClassification, ResNetModel, ResNetPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_resnet import ( TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFResNetForImageClassification, TFResNetModel, TFResNetPreTrainedModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/resnet/configuration_resnet.py

# coding=utf-8 # Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ ResNet model configuration""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json", } class ResNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ResNetModel`]. It is used to instantiate an ResNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the ResNet [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. embedding_size (`int`, *optional*, defaults to 64): Dimensionality (hidden size) for the embedding layer. hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`): Dimensionality (hidden size) at each stage. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`): Depth (number of layers) for each stage. layer_type (`str`, *optional*, defaults to `"bottleneck"`): The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or `"bottleneck"` (used for larger models like resnet-50 and above). hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. downsample_in_first_stage (`bool`, *optional*, defaults to `False`): If `True`, the first stage will downsample the inputs using a `stride` of 2. Example: ```python >>> from transformers import ResNetConfig, ResNetModel >>> # Initializing a ResNet resnet-50 style configuration >>> configuration = ResNetConfig() >>> # Initializing a model (with random weights) from the resnet-50 style configuration >>> model = ResNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "resnet" layer_types = ["basic", "bottleneck"] def __init__( self, num_channels=3, embedding_size=64, hidden_sizes=[256, 512, 1024, 2048], depths=[3, 4, 6, 3], layer_type="bottleneck", hidden_act="relu", downsample_in_first_stage=False, **kwargs ): super().__init__(**kwargs) if layer_type not in self.layer_types: raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}") self.num_channels = num_channels self.embedding_size = embedding_size self.hidden_sizes = hidden_sizes self.depths = depths self.layer_type = layer_type self.hidden_act = hidden_act self.downsample_in_first_stage = downsample_in_first_stage class ResNetOnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.11") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def atol_for_validation(self) -> float: return 1e-3

# coding=utf-8 # Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ ResNet model configuration""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP = { "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json", } class ResNetConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ResNetModel`]. It is used to instantiate an ResNet model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the ResNet [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: num_channels (`int`, *optional*, defaults to 3): The number of input channels. embedding_size (`int`, *optional*, defaults to 64): Dimensionality (hidden size) for the embedding layer. hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`): Dimensionality (hidden size) at each stage. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`): Depth (number of layers) for each stage. layer_type (`str`, *optional*, defaults to `"bottleneck"`): The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or `"bottleneck"` (used for larger models like resnet-50 and above). hidden_act (`str`, *optional*, defaults to `"relu"`): The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. downsample_in_first_stage (`bool`, *optional*, defaults to `False`): If `True`, the first stage will downsample the inputs using a `stride` of 2. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, `"stage3"`, `"stage4"`. Example: ```python >>> from transformers import ResNetConfig, ResNetModel >>> # Initializing a ResNet resnet-50 style configuration >>> configuration = ResNetConfig() >>> # Initializing a model (with random weights) from the resnet-50 style configuration >>> model = ResNetModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` """ model_type = "resnet" layer_types = ["basic", "bottleneck"] def __init__( self, num_channels=3, embedding_size=64, hidden_sizes=[256, 512, 1024, 2048], depths=[3, 4, 6, 3], layer_type="bottleneck", hidden_act="relu", downsample_in_first_stage=False, out_features=None, **kwargs ): super().__init__(**kwargs) if layer_type not in self.layer_types: raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}") self.num_channels = num_channels self.embedding_size = embedding_size self.hidden_sizes = hidden_sizes self.depths = depths self.layer_type = layer_type self.hidden_act = hidden_act self.downsample_in_first_stage = downsample_in_first_stage self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)] if out_features is not None: if not isinstance(out_features, list): raise ValueError("out_features should be a list") for feature in out_features: if feature not in self.stage_names: raise ValueError( f"Feature {feature} is not a valid feature name. Valid names are {self.stage_names}" ) self.out_features = out_features class ResNetOnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.11") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def atol_for_validation(self) -> float: return 1e-3

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/resnet/modeling_resnet.py

# coding=utf-8 # Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ResNet model.""" from typing import Optional import torch import torch.utils.checkpoint from torch import Tensor, nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, ) from ...modeling_utils import PreTrainedModel from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_resnet import ResNetConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "ResNetConfig" _FEAT_EXTRACTOR_FOR_DOC = "AutoFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "microsoft/resnet-50" _EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "microsoft/resnet-50" _IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat" RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/resnet-50", # See all resnet models at https://huggingface.co/models?filter=resnet ] class ResNetConvLayer(nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu" ): super().__init__() self.convolution = nn.Conv2d( in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False ) self.normalization = nn.BatchNorm2d(out_channels) self.activation = ACT2FN[activation] if activation is not None else nn.Identity() def forward(self, input: Tensor) -> Tensor: hidden_state = self.convolution(input) hidden_state = self.normalization(hidden_state) hidden_state = self.activation(hidden_state) return hidden_state class ResNetEmbeddings(nn.Module): """ ResNet Embeddings (stem) composed of a single aggressive convolution. """ def __init__(self, config: ResNetConfig): super().__init__() self.embedder = ResNetConvLayer( config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act ) self.pooler = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.num_channels = config.num_channels def forward(self, pixel_values: Tensor) -> Tensor: num_channels = pixel_values.shape[1] if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embedding = self.embedder(pixel_values) embedding = self.pooler(embedding) return embedding class ResNetShortCut(nn.Module): """ ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to downsample the input using `stride=2`. """ def __init__(self, in_channels: int, out_channels: int, stride: int = 2): super().__init__() self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) self.normalization = nn.BatchNorm2d(out_channels) def forward(self, input: Tensor) -> Tensor: hidden_state = self.convolution(input) hidden_state = self.normalization(hidden_state) return hidden_state class ResNetBasicLayer(nn.Module): """ A classic ResNet's residual layer composed by two `3x3` convolutions. """ def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu"): super().__init__() should_apply_shortcut = in_channels != out_channels or stride != 1 self.shortcut = ( ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() ) self.layer = nn.Sequential( ResNetConvLayer(in_channels, out_channels, stride=stride), ResNetConvLayer(out_channels, out_channels, activation=None), ) self.activation = ACT2FN[activation] def forward(self, hidden_state): residual = hidden_state hidden_state = self.layer(hidden_state) residual = self.shortcut(residual) hidden_state += residual hidden_state = self.activation(hidden_state) return hidden_state class ResNetBottleNeckLayer(nn.Module): """ A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3` convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. """ def __init__( self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu", reduction: int = 4 ): super().__init__() should_apply_shortcut = in_channels != out_channels or stride != 1 reduces_channels = out_channels // reduction self.shortcut = ( ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() ) self.layer = nn.Sequential( ResNetConvLayer(in_channels, reduces_channels, kernel_size=1), ResNetConvLayer(reduces_channels, reduces_channels, stride=stride), ResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None), ) self.activation = ACT2FN[activation] def forward(self, hidden_state): residual = hidden_state hidden_state = self.layer(hidden_state) residual = self.shortcut(residual) hidden_state += residual hidden_state = self.activation(hidden_state) return hidden_state class ResNetStage(nn.Module): """ A ResNet stage composed by stacked layers. """ def __init__( self, config: ResNetConfig, in_channels: int, out_channels: int, stride: int = 2, depth: int = 2, ): super().__init__() layer = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer self.layers = nn.Sequential( # downsampling is done in the first layer with stride of 2 layer(in_channels, out_channels, stride=stride, activation=config.hidden_act), *[layer(out_channels, out_channels, activation=config.hidden_act) for _ in range(depth - 1)], ) def forward(self, input: Tensor) -> Tensor: hidden_state = input for layer in self.layers: hidden_state = layer(hidden_state) return hidden_state class ResNetEncoder(nn.Module): def __init__(self, config: ResNetConfig): super().__init__() self.stages = nn.ModuleList([]) # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input self.stages.append( ResNetStage( config, config.embedding_size, config.hidden_sizes[0], stride=2 if config.downsample_in_first_stage else 1, depth=config.depths[0], ) ) in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:]) for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]): self.stages.append(ResNetStage(config, in_channels, out_channels, depth=depth)) def forward( self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True ) -> BaseModelOutputWithNoAttention: hidden_states = () if output_hidden_states else None for stage_module in self.stages: if output_hidden_states: hidden_states = hidden_states + (hidden_state,) hidden_state = stage_module(hidden_state) if output_hidden_states: hidden_states = hidden_states + (hidden_state,) if not return_dict: return tuple(v for v in [hidden_state, hidden_states] if v is not None) return BaseModelOutputWithNoAttention( last_hidden_state=hidden_state, hidden_states=hidden_states, ) class ResNetPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ResNetConfig base_model_prefix = "resnet" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): if isinstance(module, nn.Conv2d): nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, ResNetModel): module.gradient_checkpointing = value RESNET_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ RESNET_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See [`AutoFeatureExtractor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ResNet model outputting raw features without any specific head on top.", RESNET_START_DOCSTRING, ) class ResNetModel(ResNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embedder = ResNetEmbeddings(config) self.encoder = ResNetEncoder(config) self.pooler = nn.AdaptiveAvgPool2d((1, 1)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict embedding_output = self.embedder(pixel_values) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict ) last_hidden_state = encoder_outputs[0] pooled_output = self.pooler(last_hidden_state) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, ) @add_start_docstrings( """ ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, RESNET_START_DOCSTRING, ) class ResNetForImageClassification(ResNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.resnet = ResNetModel(config) # classification head self.classifier = nn.Sequential( nn.Flatten(), nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(), ) # initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> ImageClassifierOutputWithNoAttention: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return (loss,) + output if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)

# coding=utf-8 # Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ResNet model.""" from typing import Optional import torch import torch.utils.checkpoint from torch import Tensor, nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import ( BackboneOutput, BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention, ) from ...modeling_utils import PreTrainedModel from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_resnet import ResNetConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "ResNetConfig" _FEAT_EXTRACTOR_FOR_DOC = "AutoFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "microsoft/resnet-50" _EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "microsoft/resnet-50" _IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat" RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = [ "microsoft/resnet-50", # See all resnet models at https://huggingface.co/models?filter=resnet ] class ResNetConvLayer(nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu" ): super().__init__() self.convolution = nn.Conv2d( in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False ) self.normalization = nn.BatchNorm2d(out_channels) self.activation = ACT2FN[activation] if activation is not None else nn.Identity() def forward(self, input: Tensor) -> Tensor: hidden_state = self.convolution(input) hidden_state = self.normalization(hidden_state) hidden_state = self.activation(hidden_state) return hidden_state class ResNetEmbeddings(nn.Module): """ ResNet Embeddings (stem) composed of a single aggressive convolution. """ def __init__(self, config: ResNetConfig): super().__init__() self.embedder = ResNetConvLayer( config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act ) self.pooler = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.num_channels = config.num_channels def forward(self, pixel_values: Tensor) -> Tensor: num_channels = pixel_values.shape[1] if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) embedding = self.embedder(pixel_values) embedding = self.pooler(embedding) return embedding class ResNetShortCut(nn.Module): """ ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to downsample the input using `stride=2`. """ def __init__(self, in_channels: int, out_channels: int, stride: int = 2): super().__init__() self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) self.normalization = nn.BatchNorm2d(out_channels) def forward(self, input: Tensor) -> Tensor: hidden_state = self.convolution(input) hidden_state = self.normalization(hidden_state) return hidden_state class ResNetBasicLayer(nn.Module): """ A classic ResNet's residual layer composed by two `3x3` convolutions. """ def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu"): super().__init__() should_apply_shortcut = in_channels != out_channels or stride != 1 self.shortcut = ( ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() ) self.layer = nn.Sequential( ResNetConvLayer(in_channels, out_channels, stride=stride), ResNetConvLayer(out_channels, out_channels, activation=None), ) self.activation = ACT2FN[activation] def forward(self, hidden_state): residual = hidden_state hidden_state = self.layer(hidden_state) residual = self.shortcut(residual) hidden_state += residual hidden_state = self.activation(hidden_state) return hidden_state class ResNetBottleNeckLayer(nn.Module): """ A classic ResNet's bottleneck layer composed by three `3x3` convolutions. The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3` convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. """ def __init__( self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu", reduction: int = 4 ): super().__init__() should_apply_shortcut = in_channels != out_channels or stride != 1 reduces_channels = out_channels // reduction self.shortcut = ( ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() ) self.layer = nn.Sequential( ResNetConvLayer(in_channels, reduces_channels, kernel_size=1), ResNetConvLayer(reduces_channels, reduces_channels, stride=stride), ResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None), ) self.activation = ACT2FN[activation] def forward(self, hidden_state): residual = hidden_state hidden_state = self.layer(hidden_state) residual = self.shortcut(residual) hidden_state += residual hidden_state = self.activation(hidden_state) return hidden_state class ResNetStage(nn.Module): """ A ResNet stage composed by stacked layers. """ def __init__( self, config: ResNetConfig, in_channels: int, out_channels: int, stride: int = 2, depth: int = 2, ): super().__init__() layer = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer self.layers = nn.Sequential( # downsampling is done in the first layer with stride of 2 layer(in_channels, out_channels, stride=stride, activation=config.hidden_act), *[layer(out_channels, out_channels, activation=config.hidden_act) for _ in range(depth - 1)], ) def forward(self, input: Tensor) -> Tensor: hidden_state = input for layer in self.layers: hidden_state = layer(hidden_state) return hidden_state class ResNetEncoder(nn.Module): def __init__(self, config: ResNetConfig): super().__init__() self.stages = nn.ModuleList([]) # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input self.stages.append( ResNetStage( config, config.embedding_size, config.hidden_sizes[0], stride=2 if config.downsample_in_first_stage else 1, depth=config.depths[0], ) ) in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:]) for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]): self.stages.append(ResNetStage(config, in_channels, out_channels, depth=depth)) def forward( self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True ) -> BaseModelOutputWithNoAttention: hidden_states = () if output_hidden_states else None for stage_module in self.stages: if output_hidden_states: hidden_states = hidden_states + (hidden_state,) hidden_state = stage_module(hidden_state) if output_hidden_states: hidden_states = hidden_states + (hidden_state,) if not return_dict: return tuple(v for v in [hidden_state, hidden_states] if v is not None) return BaseModelOutputWithNoAttention( last_hidden_state=hidden_state, hidden_states=hidden_states, ) class ResNetPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ResNetConfig base_model_prefix = "resnet" main_input_name = "pixel_values" supports_gradient_checkpointing = True def _init_weights(self, module): if isinstance(module, nn.Conv2d): nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(module.weight, 1) nn.init.constant_(module.bias, 0) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, ResNetModel): module.gradient_checkpointing = value RESNET_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ResNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ RESNET_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoFeatureExtractor`]. See [`AutoFeatureExtractor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ResNet model outputting raw features without any specific head on top.", RESNET_START_DOCSTRING, ) class ResNetModel(ResNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embedder = ResNetEmbeddings(config) self.encoder = ResNetEncoder(config) self.pooler = nn.AdaptiveAvgPool2d((1, 1)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None ) -> BaseModelOutputWithPoolingAndNoAttention: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict embedding_output = self.embedder(pixel_values) encoder_outputs = self.encoder( embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict ) last_hidden_state = encoder_outputs[0] pooled_output = self.pooler(last_hidden_state) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndNoAttention( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, ) @add_start_docstrings( """ ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. """, RESNET_START_DOCSTRING, ) class ResNetForImageClassification(ResNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.resnet = ResNetModel(config) # classification head self.classifier = nn.Sequential( nn.Flatten(), nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(), ) # initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutputWithNoAttention, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> ImageClassifierOutputWithNoAttention: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[2:] return (loss,) + output if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) @add_start_docstrings( """ ResNet backbone, to be used with frameworks like DETR and MaskFormer. """, RESNET_START_DOCSTRING, ) class ResNetBackbone(ResNetPreTrainedModel): def __init__(self, config): super().__init__(config) self.stage_names = config.stage_names self.resnet = ResNetModel(config) self.out_features = config.out_features self.out_feature_channels = { "stem": config.embedding_size, "stage1": config.hidden_sizes[0], "stage2": config.hidden_sizes[1], "stage3": config.hidden_sizes[2], "stage4": config.hidden_sizes[3], } # initialize weights and apply final processing self.post_init() @property def channels(self): return [self.out_feature_channels[name] for name in self.out_features] @add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC) def forward(self, pixel_values: Optional[torch.FloatTensor] = None) -> BackboneOutput: """ Returns: Examples: ```python >>> from transformers import AutoImageProcessor, AutoBackbone >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") >>> model = AutoBackbone.from_pretrained("microsoft/resnet-50") >>> inputs = processor(image, return_tensors="pt") >>> outputs = model(**inputs) ```""" outputs = self.resnet(pixel_values, output_hidden_states=True, return_dict=True) hidden_states = outputs.hidden_states feature_maps = () for idx, stage in enumerate(self.stage_names): if stage in self.out_features: feature_maps += (hidden_states[idx],) return BackboneOutput(feature_maps=feature_maps)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/utils/dummy_pt_objects.py

# This file is autogenerated by the command `make fix-copies`, do not edit. # flake8: noqa from ..utils import DummyObject, requires_backends class PyTorchBenchmark(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PyTorchBenchmarkArguments(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GlueDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GlueDataTrainingArguments(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LineByLineTextDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LineByLineWithRefDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LineByLineWithSOPTextDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SquadDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SquadDataTrainingArguments(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TextDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TextDatasetForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeamScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeamSearchScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConstrainedBeamSearchScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Constraint(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConstraintListState(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DisjunctiveConstraint(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ForcedBOSTokenLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ForcedEOSTokenLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GenerationMixin(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HammingDiversityLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class InfNanRemoveLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LogitsProcessorList(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaxLengthCriteria(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaxTimeCriteria(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MinLengthLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NoBadWordsLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NoRepeatNGramLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PhrasalConstraint(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PrefixConstrainedLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RepetitionPenaltyLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class StoppingCriteria(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class StoppingCriteriaList(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TemperatureLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TopKLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TopPLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TypicalLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def top_k_top_p_filtering(*args, **kwargs): requires_backends(top_k_top_p_filtering, ["torch"]) class PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class AlbertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_albert(*args, **kwargs): requires_backends(load_tf_weights_in_albert, ["torch"]) MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None MODEL_FOR_AUDIO_XVECTOR_MAPPING = None MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = None MODEL_FOR_CAUSAL_LM_MAPPING = None MODEL_FOR_CTC_MAPPING = None MODEL_FOR_DEPTH_ESTIMATION_MAPPING = None MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = None MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = None MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = None MODEL_FOR_MASKED_LM_MAPPING = None MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None MODEL_FOR_OBJECT_DETECTION_MAPPING = None MODEL_FOR_PRETRAINING_MAPPING = None MODEL_FOR_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = None MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = None MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING = None MODEL_FOR_VISION_2_SEQ_MAPPING = None MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING = None MODEL_MAPPING = None MODEL_WITH_LM_HEAD_MAPPING = None class AutoModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForAudioClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForAudioXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForDepthEstimation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForDocumentQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForImageSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForInstanceSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSeq2SeqLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSpeechSeq2Seq(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForTableQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForVideoClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForVision2Seq(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForVisualQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForZeroShotObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelWithLMHead(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BART_PRETRAINED_MODEL_ARCHIVE_LIST = None class BartForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartPretrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PretrainedBartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class BeitForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class BertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_bert(*args, **kwargs): requires_backends(load_tf_weights_in_bert, ["torch"]) class BertGenerationDecoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertGenerationEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertGenerationPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_bert_generation(*args, **kwargs): requires_backends(load_tf_weights_in_bert_generation, ["torch"]) BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST = None class BigBirdForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_big_bird(*args, **kwargs): requires_backends(load_tf_weights_in_big_bird, ["torch"]) BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = None class BigBirdPegasusForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None class BlenderbotForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = None class BlenderbotSmallForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotSmallForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotSmallModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotSmallPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST = None class BloomForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class CamembertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = None class CanineForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CaninePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_canine(*args, **kwargs): requires_backends(load_tf_weights_in_canine, ["torch"]) CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None class CLIPModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPTextModelWithProjection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPVisionModelWithProjection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST = None class CLIPSegForImageSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST = None class CodeGenForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CodeGenModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CodeGenPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ConvBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_convbert(*args, **kwargs): requires_backends(load_tf_weights_in_convbert, ["torch"]) CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ConvNextForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvNextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvNextPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None class CTRLForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CTRLLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CTRLModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CTRLPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CVT_PRETRAINED_MODEL_ARCHIVE_LIST = None class CvtForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CvtModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CvtPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST = None DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST = None class Data2VecAudioForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None class DebertaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None class DebertaV2ForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class DecisionTransformerGPT2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DecisionTransformerGPT2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DecisionTransformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DecisionTransformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class DeiTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTForImageClassificationWithTeacher(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class DistilBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST = None class DonutSwinModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DonutSwinPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = None class DPRContextEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPretrainedContextEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPretrainedQuestionEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPretrainedReader(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRQuestionEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRReader(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class DPTForDepthEstimation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPTForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None class ElectraForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_electra(*args, **kwargs): requires_backends(load_tf_weights_in_electra, ["torch"]) class EncoderDecoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST = None class ErnieForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErniePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ESM_PRETRAINED_MODEL_ARCHIVE_LIST = None class EsmFoldPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForProteinFolding(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class FlaubertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForQuestionAnsweringSimple(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertWithLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = None class FlavaForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaImageCodebook(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaImageModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaMultimodalModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class FNetForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FSMTForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FSMTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PretrainedFSMTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = None class FunnelBaseModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_funnel(*args, **kwargs): requires_backends(load_tf_weights_in_funnel, ["torch"]) GLPN_PRETRAINED_MODEL_ARCHIVE_LIST = None class GLPNForDepthEstimation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GLPNModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GLPNPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPT2DoubleHeadsModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2LMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_gpt2(*args, **kwargs): requires_backends(load_tf_weights_in_gpt2, ["torch"]) GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTNeoForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_gpt_neo(*args, **kwargs): requires_backends(load_tf_weights_in_gpt_neo, ["torch"]) GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTNeoXForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTNeoXJapaneseForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXJapaneseLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXJapaneseModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXJapanesePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTJForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class GroupViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GroupViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GroupViTTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GroupViTVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class HubertForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HubertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HubertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HubertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class IBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ImageGPTForCausalImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ImageGPTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ImageGPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ImageGPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_imagegpt(*args, **kwargs): requires_backends(load_tf_weights_in_imagegpt, ["torch"]) JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST = None class JukeboxModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class JukeboxPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class JukeboxPrior(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class JukeboxVQVAE(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMv2ForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMv3ForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LED_PRETRAINED_MODEL_ARCHIVE_LIST = None class LEDForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class LevitForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LevitForImageClassificationWithTeacher(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LevitModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LevitPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LILT_PRETRAINED_MODEL_ARCHIVE_LIST = None class LiltForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class LongformerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerSelfAttention(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST = None class LongT5EncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongT5ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongT5Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongT5PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LUKE_PRETRAINED_MODEL_ARCHIVE_LIST = None class LukeForEntityClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForEntityPairClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForEntitySpanClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertVisualFeatureEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertXLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST = None class M2M100ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class M2M100Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class M2M100PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarianForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarianModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarianMTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class MarkupLMForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class MaskFormerForInstanceSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaskFormerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaskFormerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MCTCTForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MCTCTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MCTCTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MegatronBertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MMBTForClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MMBTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ModalEmbeddings(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MobileBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_mobilebert(*args, **kwargs): requires_backends(load_tf_weights_in_mobilebert, ["torch"]) MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None class MobileNetV2ForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileNetV2ForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileNetV2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileNetV2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_mobilenet_v2(*args, **kwargs): requires_backends(load_tf_weights_in_mobilenet_v2, ["torch"]) MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MobileViTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileViTForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class MPNetForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MT5EncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MT5ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MT5Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MVP_PRETRAINED_MODEL_ARCHIVE_LIST = None class MvpForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST = None class NezhaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class NystromformerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class OpenAIGPTDoubleHeadsModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_openai_gpt(*args, **kwargs): requires_backends(load_tf_weights_in_openai_gpt, ["torch"]) OPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class OPTForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class OwlViTForObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST = None class PegasusXForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusXModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusXPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST = None class PerceiverForImageClassificationConvProcessing(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForImageClassificationFourier(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForImageClassificationLearned(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForMultimodalAutoencoding(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForOpticalFlow(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PLBART_PRETRAINED_MODEL_ARCHIVE_LIST = None class PLBartForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class PoolFormerForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PoolFormerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PoolFormerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class ProphetNetDecoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class QDQBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_qdqbert(*args, **kwargs): requires_backends(load_tf_weights_in_qdqbert, ["torch"]) class RagModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RagPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RagSequenceForGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RagTokenForGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) REALM_PRETRAINED_MODEL_ARCHIVE_LIST = None class RealmEmbedder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmForOpenQA(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmKnowledgeAugEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmReader(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmRetriever(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_realm(*args, **kwargs): requires_backends(load_tf_weights_in_realm, ["torch"]) REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class ReformerAttention(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerModelWithLMHead(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) REGNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class RegNetForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RegNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RegNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class RemBertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_rembert(*args, **kwargs): requires_backends(load_tf_weights_in_rembert, ["torch"]) RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class ResNetForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ResNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ResNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class RetriBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RetriBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None class RobertaForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class RoCBertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_roc_bert(*args, **kwargs): requires_backends(load_tf_weights_in_roc_bert, ["torch"]) ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class RoFormerForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_roformer(*args, **kwargs): requires_backends(load_tf_weights_in_roformer, ["torch"]) SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class SegformerDecodeHead(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SEW_PRETRAINED_MODEL_ARCHIVE_LIST = None class SEWForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST = None class SEWDForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWDForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWDModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWDPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SpeechEncoderDecoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None class Speech2TextForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2TextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2TextPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2Text2ForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2Text2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST = None class SplinterForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class SqueezeBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertModule(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SWIN_PRETRAINED_MODEL_ARCHIVE_LIST = None class SwinForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwinForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwinModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwinPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST = None class Swinv2ForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Swinv2ForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Swinv2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Swinv2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST = None class SwitchTransformersEncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersSparseMLP(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersTop1Router(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) T5_PRETRAINED_MODEL_ARCHIVE_LIST = None class T5EncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class T5ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class T5Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class T5PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_t5(*args, **kwargs): requires_backends(load_tf_weights_in_t5, ["torch"]) TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None class TapasForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_tapas(*args, **kwargs): requires_backends(load_tf_weights_in_tapas, ["torch"]) TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class TimeSeriesTransformerForPrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TimeSeriesTransformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TimeSeriesTransformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class TrajectoryTransformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TrajectoryTransformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None class AdaptiveEmbedding(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_transfo_xl(*args, **kwargs): requires_backends(load_tf_weights_in_transfo_xl, ["torch"]) TROCR_PRETRAINED_MODEL_ARCHIVE_LIST = None class TrOCRForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TrOCRPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST = None class UniSpeechForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST = None class UniSpeechSatForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VAN_PRETRAINED_MODEL_ARCHIVE_LIST = None class VanForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VanModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VanPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST = None class VideoMAEForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VideoMAEForVideoClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VideoMAEModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VideoMAEPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VILT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViltForImageAndTextRetrieval(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForImagesAndTextClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisionEncoderDecoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisionTextDualEncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class VisualBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForRegionToPhraseAlignment(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForVisualReasoning(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViTMAEForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMAELayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMAEModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMAEPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViTMSNForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMSNModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMSNPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None class Wav2Vec2ForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class Wav2Vec2ConformerForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class WavLMForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST = None class WhisperForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WhisperModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WhisperPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None class XCLIPModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XCLIPPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XCLIPTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XCLIPVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XGLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class XGLMForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XGLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XGLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForQuestionAnsweringSimple(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMWithLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMProphetNetDecoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMRobertaForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMRobertaXLForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLNetForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForQuestionAnsweringSimple(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_xlnet(*args, **kwargs): requires_backends(load_tf_weights_in_xlnet, ["torch"]) YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST = None class YolosForObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YolosModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YolosPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) YOSO_PRETRAINED_MODEL_ARCHIVE_LIST = None class YosoForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Adafactor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AdamW(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def get_constant_schedule(*args, **kwargs): requires_backends(get_constant_schedule, ["torch"]) def get_constant_schedule_with_warmup(*args, **kwargs): requires_backends(get_constant_schedule_with_warmup, ["torch"]) def get_cosine_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_schedule_with_warmup, ["torch"]) def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"]) def get_linear_schedule_with_warmup(*args, **kwargs): requires_backends(get_linear_schedule_with_warmup, ["torch"]) def get_polynomial_decay_schedule_with_warmup(*args, **kwargs): requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"]) def get_scheduler(*args, **kwargs): requires_backends(get_scheduler, ["torch"]) class Conv1D(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def apply_chunking_to_forward(*args, **kwargs): requires_backends(apply_chunking_to_forward, ["torch"]) def prune_layer(*args, **kwargs): requires_backends(prune_layer, ["torch"]) class Trainer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def torch_distributed_zero_first(*args, **kwargs): requires_backends(torch_distributed_zero_first, ["torch"]) class Seq2SeqTrainer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"])

# This file is autogenerated by the command `make fix-copies`, do not edit. # flake8: noqa from ..utils import DummyObject, requires_backends class PyTorchBenchmark(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PyTorchBenchmarkArguments(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GlueDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GlueDataTrainingArguments(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LineByLineTextDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LineByLineWithRefDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LineByLineWithSOPTextDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SquadDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SquadDataTrainingArguments(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TextDataset(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TextDatasetForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeamScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeamSearchScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConstrainedBeamSearchScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Constraint(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConstraintListState(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DisjunctiveConstraint(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ForcedBOSTokenLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ForcedEOSTokenLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GenerationMixin(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HammingDiversityLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class InfNanRemoveLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LogitsProcessorList(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaxLengthCriteria(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaxTimeCriteria(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MinLengthLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NoBadWordsLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NoRepeatNGramLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PhrasalConstraint(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PrefixConstrainedLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RepetitionPenaltyLogitsProcessor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class StoppingCriteria(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class StoppingCriteriaList(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TemperatureLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TopKLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TopPLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TypicalLogitsWarper(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def top_k_top_p_filtering(*args, **kwargs): requires_backends(top_k_top_p_filtering, ["torch"]) class PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class AlbertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AlbertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_albert(*args, **kwargs): requires_backends(load_tf_weights_in_albert, ["torch"]) MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING = None MODEL_FOR_AUDIO_XVECTOR_MAPPING = None MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING = None MODEL_FOR_CAUSAL_LM_MAPPING = None MODEL_FOR_CTC_MAPPING = None MODEL_FOR_DEPTH_ESTIMATION_MAPPING = None MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = None MODEL_FOR_IMAGE_SEGMENTATION_MAPPING = None MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING = None MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING = None MODEL_FOR_MASKED_LM_MAPPING = None MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None MODEL_FOR_OBJECT_DETECTION_MAPPING = None MODEL_FOR_PRETRAINING_MAPPING = None MODEL_FOR_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING = None MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = None MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = None MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING = None MODEL_FOR_VISION_2_SEQ_MAPPING = None MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING = None MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING = None MODEL_MAPPING = None MODEL_WITH_LM_HEAD_MAPPING = None class AutoBackbone(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForAudioClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForAudioXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForDepthEstimation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForDocumentQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForImageSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForInstanceSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSeq2SeqLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForSpeechSeq2Seq(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForTableQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForVideoClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForVision2Seq(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForVisualQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelForZeroShotObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AutoModelWithLMHead(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BART_PRETRAINED_MODEL_ARCHIVE_LIST = None class BartForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BartPretrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PretrainedBartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class BeitForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BeitPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class BertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_bert(*args, **kwargs): requires_backends(load_tf_weights_in_bert, ["torch"]) class BertGenerationDecoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertGenerationEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BertGenerationPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_bert_generation(*args, **kwargs): requires_backends(load_tf_weights_in_bert_generation, ["torch"]) BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST = None class BigBirdForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_big_bird(*args, **kwargs): requires_backends(load_tf_weights_in_big_bird, ["torch"]) BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = None class BigBirdPegasusForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BigBirdPegasusPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST = None class BlenderbotForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST = None class BlenderbotSmallForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotSmallForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotSmallModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BlenderbotSmallPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST = None class BloomForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class BloomPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class CamembertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CamembertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = None class CanineForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CanineModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CaninePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_canine(*args, **kwargs): requires_backends(load_tf_weights_in_canine, ["torch"]) CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None class CLIPModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPTextModelWithProjection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPVisionModelWithProjection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST = None class CLIPSegForImageSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CLIPSegVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST = None class CodeGenForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CodeGenModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CodeGenPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ConvBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_convbert(*args, **kwargs): requires_backends(load_tf_weights_in_convbert, ["torch"]) CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ConvNextForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvNextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ConvNextPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None class CTRLForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CTRLLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CTRLModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CTRLPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) CVT_PRETRAINED_MODEL_ARCHIVE_LIST = None class CvtForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CvtModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class CvtPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST = None DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST = None class Data2VecAudioForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecAudioPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecTextPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Data2VecVisionPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None class DebertaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None class DebertaV2ForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DebertaV2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class DecisionTransformerGPT2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DecisionTransformerGPT2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DecisionTransformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DecisionTransformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class DeiTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTForImageClassificationWithTeacher(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DeiTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class DistilBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DistilBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST = None class DonutSwinModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DonutSwinPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST = None DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST = None class DPRContextEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPretrainedContextEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPretrainedQuestionEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRPretrainedReader(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRQuestionEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPRReader(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) DPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class DPTForDepthEstimation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPTForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class DPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = None class ElectraForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ElectraPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_electra(*args, **kwargs): requires_backends(load_tf_weights_in_electra, ["torch"]) class EncoderDecoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST = None class ErnieForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErnieModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ErniePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ESM_PRETRAINED_MODEL_ARCHIVE_LIST = None class EsmFoldPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForProteinFolding(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class EsmPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class FlaubertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForQuestionAnsweringSimple(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlaubertWithLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = None class FlavaForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaImageCodebook(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaImageModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaMultimodalModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FlavaTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class FNetForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FSMTForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FSMTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PretrainedFSMTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = None class FunnelBaseModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class FunnelPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_funnel(*args, **kwargs): requires_backends(load_tf_weights_in_funnel, ["torch"]) GLPN_PRETRAINED_MODEL_ARCHIVE_LIST = None class GLPNForDepthEstimation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GLPNModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GLPNPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GPT2_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPT2DoubleHeadsModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2LMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPT2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_gpt2(*args, **kwargs): requires_backends(load_tf_weights_in_gpt2, ["torch"]) GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTNeoForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_gpt_neo(*args, **kwargs): requires_backends(load_tf_weights_in_gpt_neo, ["torch"]) GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTNeoXForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTNeoXJapaneseForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXJapaneseLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXJapaneseModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTNeoXJapanesePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = None class GPTJForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GPTJPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class GroupViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GroupViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GroupViTTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class GroupViTVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class HubertForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HubertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HubertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class HubertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) IBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class IBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class IBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ImageGPTForCausalImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ImageGPTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ImageGPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ImageGPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_imagegpt(*args, **kwargs): requires_backends(load_tf_weights_in_imagegpt, ["torch"]) JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST = None class JukeboxModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class JukeboxPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class JukeboxPrior(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class JukeboxVQVAE(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMv2ForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST = None class LayoutLMv3ForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3ForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LayoutLMv3PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LED_PRETRAINED_MODEL_ARCHIVE_LIST = None class LEDForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LEDPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class LevitForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LevitForImageClassificationWithTeacher(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LevitModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LevitPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LILT_PRETRAINED_MODEL_ARCHIVE_LIST = None class LiltForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LiltPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class LongformerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongformerSelfAttention(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST = None class LongT5EncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongT5ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongT5Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LongT5PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) LUKE_PRETRAINED_MODEL_ARCHIVE_LIST = None class LukeForEntityClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForEntityPairClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForEntitySpanClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukeModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LukePreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertVisualFeatureEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class LxmertXLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST = None class M2M100ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class M2M100Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class M2M100PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarianForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarianModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarianMTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class MarkupLMForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MarkupLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class MaskFormerForInstanceSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaskFormerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MaskFormerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MBartPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MCTCTForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MCTCTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MCTCTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MegatronBertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MegatronBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MMBTForClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MMBTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ModalEmbeddings(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MobileBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_mobilebert(*args, **kwargs): requires_backends(load_tf_weights_in_mobilebert, ["torch"]) MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST = None class MobileNetV2ForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileNetV2ForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileNetV2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileNetV2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_mobilenet_v2(*args, **kwargs): requires_backends(load_tf_weights_in_mobilenet_v2, ["torch"]) MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class MobileViTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileViTForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MobileViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MPNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class MPNetForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MPNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MT5EncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MT5ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MT5Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) MVP_PRETRAINED_MODEL_ARCHIVE_LIST = None class MvpForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class MvpPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST = None class NezhaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NezhaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class NystromformerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class NystromformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class OpenAIGPTDoubleHeadsModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OpenAIGPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_openai_gpt(*args, **kwargs): requires_backends(load_tf_weights_in_openai_gpt, ["torch"]) OPT_PRETRAINED_MODEL_ARCHIVE_LIST = None class OPTForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OPTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class OwlViTForObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class OwlViTVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST = None class PegasusXForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusXModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PegasusXPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST = None class PerceiverForImageClassificationConvProcessing(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForImageClassificationFourier(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForImageClassificationLearned(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForMultimodalAutoencoding(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForOpticalFlow(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PerceiverPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PLBART_PRETRAINED_MODEL_ARCHIVE_LIST = None class PLBartForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PLBartPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class PoolFormerForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PoolFormerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class PoolFormerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class ProphetNetDecoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ProphetNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class QDQBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForNextSentencePrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class QDQBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_qdqbert(*args, **kwargs): requires_backends(load_tf_weights_in_qdqbert, ["torch"]) class RagModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RagPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RagSequenceForGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RagTokenForGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) REALM_PRETRAINED_MODEL_ARCHIVE_LIST = None class RealmEmbedder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmForOpenQA(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmKnowledgeAugEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmReader(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmRetriever(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RealmScorer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_realm(*args, **kwargs): requires_backends(load_tf_weights_in_realm, ["torch"]) REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class ReformerAttention(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerModelWithLMHead(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ReformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) REGNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class RegNetForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RegNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RegNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class RemBertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RemBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_rembert(*args, **kwargs): requires_backends(load_tf_weights_in_rembert, ["torch"]) RESNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class ResNetBackbone(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ResNetForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ResNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ResNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class RetriBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RetriBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None class RobertaForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RobertaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class RoCBertForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoCBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_roc_bert(*args, **kwargs): requires_backends(load_tf_weights_in_roc_bert, ["torch"]) ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class RoFormerForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class RoFormerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_roformer(*args, **kwargs): requires_backends(load_tf_weights_in_roformer, ["torch"]) SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class SegformerDecodeHead(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerForSemanticSegmentation(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SegformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SEW_PRETRAINED_MODEL_ARCHIVE_LIST = None class SEWForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST = None class SEWDForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWDForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWDModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SEWDPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SpeechEncoderDecoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None class Speech2TextForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2TextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2TextPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2Text2ForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Speech2Text2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST = None class SplinterForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SplinterPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class SqueezeBertForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertModule(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SqueezeBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SWIN_PRETRAINED_MODEL_ARCHIVE_LIST = None class SwinForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwinForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwinModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwinPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST = None class Swinv2ForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Swinv2ForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Swinv2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Swinv2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST = None class SwitchTransformersEncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersSparseMLP(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class SwitchTransformersTop1Router(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) T5_PRETRAINED_MODEL_ARCHIVE_LIST = None class T5EncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class T5ForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class T5Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class T5PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_t5(*args, **kwargs): requires_backends(load_tf_weights_in_t5, ["torch"]) TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST = None class TapasForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TapasPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_tapas(*args, **kwargs): requires_backends(load_tf_weights_in_tapas, ["torch"]) TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class TimeSeriesTransformerForPrediction(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TimeSeriesTransformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TimeSeriesTransformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class TrajectoryTransformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TrajectoryTransformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None class AdaptiveEmbedding(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TransfoXLPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_transfo_xl(*args, **kwargs): requires_backends(load_tf_weights_in_transfo_xl, ["torch"]) TROCR_PRETRAINED_MODEL_ARCHIVE_LIST = None class TrOCRForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class TrOCRPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST = None class UniSpeechForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST = None class UniSpeechSatForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class UniSpeechSatPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VAN_PRETRAINED_MODEL_ARCHIVE_LIST = None class VanForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VanModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VanPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST = None class VideoMAEForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VideoMAEForVideoClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VideoMAEModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VideoMAEPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VILT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViltForImageAndTextRetrieval(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForImagesAndTextClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViltPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisionEncoderDecoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisionTextDualEncoderModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = None class VisualBertForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForRegionToPhraseAlignment(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertForVisualReasoning(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class VisualBertPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIT_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViTForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTForMaskedImageModeling(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViTMAEForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMAELayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMAEModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMAEPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST = None class ViTMSNForImageClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMSNModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class ViTMSNPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST = None class Wav2Vec2ForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2Model(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2PreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None class Wav2Vec2ConformerForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForPreTraining(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Wav2Vec2ConformerPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class WavLMForAudioFrameClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMForCTC(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMForXVector(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WavLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST = None class WhisperForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WhisperModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class WhisperPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None class XCLIPModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XCLIPPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XCLIPTextModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XCLIPVisionModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XGLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class XGLMForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XGLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XGLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForQuestionAnsweringSimple(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMWithLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMProphetNetDecoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetEncoder(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetForConditionalGeneration(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMProphetNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMRobertaForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLMRobertaXLForCausalLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLMRobertaXLPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) XLNET_PRETRAINED_MODEL_ARCHIVE_LIST = None class XLNetForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForQuestionAnsweringSimple(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetLMHeadModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class XLNetPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def load_tf_weights_in_xlnet(*args, **kwargs): requires_backends(load_tf_weights_in_xlnet, ["torch"]) YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST = None class YolosForObjectDetection(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YolosModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YolosPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) YOSO_PRETRAINED_MODEL_ARCHIVE_LIST = None class YosoForMaskedLM(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForMultipleChoice(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForQuestionAnswering(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoForTokenClassification(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoLayer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class YosoPreTrainedModel(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class Adafactor(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) class AdamW(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def get_constant_schedule(*args, **kwargs): requires_backends(get_constant_schedule, ["torch"]) def get_constant_schedule_with_warmup(*args, **kwargs): requires_backends(get_constant_schedule_with_warmup, ["torch"]) def get_cosine_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_schedule_with_warmup, ["torch"]) def get_cosine_with_hard_restarts_schedule_with_warmup(*args, **kwargs): requires_backends(get_cosine_with_hard_restarts_schedule_with_warmup, ["torch"]) def get_linear_schedule_with_warmup(*args, **kwargs): requires_backends(get_linear_schedule_with_warmup, ["torch"]) def get_polynomial_decay_schedule_with_warmup(*args, **kwargs): requires_backends(get_polynomial_decay_schedule_with_warmup, ["torch"]) def get_scheduler(*args, **kwargs): requires_backends(get_scheduler, ["torch"]) class Conv1D(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def apply_chunking_to_forward(*args, **kwargs): requires_backends(apply_chunking_to_forward, ["torch"]) def prune_layer(*args, **kwargs): requires_backends(prune_layer, ["torch"]) class Trainer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"]) def torch_distributed_zero_first(*args, **kwargs): requires_backends(torch_distributed_zero_first, ["torch"]) class Seq2SeqTrainer(metaclass=DummyObject): _backends = ["torch"] def __init__(self, *args, **kwargs): requires_backends(self, ["torch"])

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/resnet/test_modeling_resnet.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ResNet model. """ import inspect import unittest from transformers import ResNetConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import ResNetForImageClassification, ResNetModel from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoFeatureExtractor class ResNetModelTester: def __init__( self, parent, batch_size=3, image_size=32, num_channels=3, embeddings_size=10, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 2, 1], is_training=True, use_labels=True, hidden_act="relu", num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.embeddings_size = embeddings_size self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.num_labels = num_labels self.scope = scope self.num_stages = len(hidden_sizes) def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return ResNetConfig( num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = ResNetModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = ResNetForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ResNetModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ResNet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ResNetModel, ResNetForImageClassification) if is_torch_available() else () fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = ResNetModelTester(self) self.config_tester = ConfigTester(self, config_class=ResNetConfig, has_text_modality=False) def test_config(self): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return @unittest.skip(reason="ResNet does not output attentions") def test_attention_outputs(self): pass @unittest.skip(reason="ResNet does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ResNet does not support input and output embeddings") def test_model_common_attributes(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) for name, module in model.named_modules(): if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)): self.assertTrue( torch.all(module.weight == 1), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) self.assertTrue( torch.all(module.bias == 0), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ResNet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() layers_type = ["basic", "bottleneck"] for model_class in self.all_model_classes: for layer_type in layers_type: config.layer_type = layer_type inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ResNetModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class ResNetModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ( AutoFeatureExtractor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-11.1069, -9.7877, -8.3777]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ResNet model. """ import inspect import unittest from transformers import ResNetConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from torch import nn from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoFeatureExtractor class ResNetModelTester: def __init__( self, parent, batch_size=3, image_size=32, num_channels=3, embeddings_size=10, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 2, 1], is_training=True, use_labels=True, hidden_act="relu", num_labels=3, scope=None, out_features=["stage1", "stage2", "stage3", "stage4"], ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.embeddings_size = embeddings_size self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.num_labels = num_labels self.scope = scope self.num_stages = len(hidden_sizes) self.out_features = out_features def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return ResNetConfig( num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, out_features=self.out_features, ) def create_and_check_model(self, config, pixel_values, labels): model = ResNetModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = ResNetForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_backbone(self, config, pixel_values, labels): model = ResNetBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify hidden states self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) self.parent.assertListEqual(list(result.feature_maps[0].shape), [3, 10, 8, 8]) # verify channels self.parent.assertListEqual(model.channels, config.hidden_sizes) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ResNetModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ResNet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ResNetModel, ResNetForImageClassification) if is_torch_available() else () fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = ResNetModelTester(self) self.config_tester = ConfigTester(self, config_class=ResNetConfig, has_text_modality=False) def test_config(self): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return @unittest.skip(reason="ResNet does not output attentions") def test_attention_outputs(self): pass @unittest.skip(reason="ResNet does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ResNet does not support input and output embeddings") def test_model_common_attributes(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*config_and_inputs) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) for name, module in model.named_modules(): if isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)): self.assertTrue( torch.all(module.weight == 1), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) self.assertTrue( torch.all(module.bias == 0), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ResNet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() layers_type = ["basic", "bottleneck"] for model_class in self.all_model_classes: for layer_type in layers_type: config.layer_type = layer_type inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ResNetModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class ResNetModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ( AutoFeatureExtractor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-11.1069, -9.7877, -8.3777]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./utils/check_repo.py

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import inspect import os import re import warnings from collections import OrderedDict from difflib import get_close_matches from pathlib import Path from transformers import is_flax_available, is_tf_available, is_torch_available from transformers.models.auto import get_values from transformers.utils import ENV_VARS_TRUE_VALUES # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_repo.py PATH_TO_TRANSFORMERS = "src/transformers" PATH_TO_TESTS = "tests" PATH_TO_DOC = "docs/source/en" # Update this list with models that are supposed to be private. PRIVATE_MODELS = [ "DPRSpanPredictor", "LongT5Stack", "RealmBertModel", "T5Stack", "SwitchTransformersStack", "TFDPRSpanPredictor", ] # Update this list for models that are not tested with a comment explaining the reason it should not be. # Being in this list is an exception and should **not** be the rule. IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ # models to ignore for not tested "CLIPSegDecoder", # Building part of bigger (tested) model. "TableTransformerEncoder", # Building part of bigger (tested) model. "TableTransformerDecoder", # Building part of bigger (tested) model. "TimeSeriesTransformerEncoder", # Building part of bigger (tested) model. "TimeSeriesTransformerDecoder", # Building part of bigger (tested) model. "JukeboxVQVAE", # Building part of bigger (tested) model. "JukeboxPrior", # Building part of bigger (tested) model. "DeformableDetrEncoder", # Building part of bigger (tested) model. "DeformableDetrDecoder", # Building part of bigger (tested) model. "OPTDecoder", # Building part of bigger (tested) model. "WhisperDecoder", # Building part of bigger (tested) model. "WhisperEncoder", # Building part of bigger (tested) model. "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. "SegformerDecodeHead", # Building part of bigger (tested) model. "PLBartEncoder", # Building part of bigger (tested) model. "PLBartDecoder", # Building part of bigger (tested) model. "PLBartDecoderWrapper", # Building part of bigger (tested) model. "BigBirdPegasusEncoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model. "DetrEncoder", # Building part of bigger (tested) model. "DetrDecoder", # Building part of bigger (tested) model. "DetrDecoderWrapper", # Building part of bigger (tested) model. "ConditionalDetrEncoder", # Building part of bigger (tested) model. "ConditionalDetrDecoder", # Building part of bigger (tested) model. "M2M100Encoder", # Building part of bigger (tested) model. "M2M100Decoder", # Building part of bigger (tested) model. "MCTCTEncoder", # Building part of bigger (tested) model. "Speech2TextEncoder", # Building part of bigger (tested) model. "Speech2TextDecoder", # Building part of bigger (tested) model. "LEDEncoder", # Building part of bigger (tested) model. "LEDDecoder", # Building part of bigger (tested) model. "BartDecoderWrapper", # Building part of bigger (tested) model. "BartEncoder", # Building part of bigger (tested) model. "BertLMHeadModel", # Needs to be setup as decoder. "BlenderbotSmallEncoder", # Building part of bigger (tested) model. "BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model. "BlenderbotEncoder", # Building part of bigger (tested) model. "BlenderbotDecoderWrapper", # Building part of bigger (tested) model. "MBartEncoder", # Building part of bigger (tested) model. "MBartDecoderWrapper", # Building part of bigger (tested) model. "MegatronBertLMHeadModel", # Building part of bigger (tested) model. "MegatronBertEncoder", # Building part of bigger (tested) model. "MegatronBertDecoder", # Building part of bigger (tested) model. "MegatronBertDecoderWrapper", # Building part of bigger (tested) model. "MvpDecoderWrapper", # Building part of bigger (tested) model. "MvpEncoder", # Building part of bigger (tested) model. "PegasusEncoder", # Building part of bigger (tested) model. "PegasusDecoderWrapper", # Building part of bigger (tested) model. "PegasusXEncoder", # Building part of bigger (tested) model. "PegasusXDecoder", # Building part of bigger (tested) model. "PegasusXDecoderWrapper", # Building part of bigger (tested) model. "DPREncoder", # Building part of bigger (tested) model. "ProphetNetDecoderWrapper", # Building part of bigger (tested) model. "RealmBertModel", # Building part of bigger (tested) model. "RealmReader", # Not regular model. "RealmScorer", # Not regular model. "RealmForOpenQA", # Not regular model. "ReformerForMaskedLM", # Needs to be setup as decoder. "Speech2Text2DecoderWrapper", # Building part of bigger (tested) model. "TFDPREncoder", # Building part of bigger (tested) model. "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?) "TFRobertaForMultipleChoice", # TODO: fix "TrOCRDecoderWrapper", # Building part of bigger (tested) model. "TFWhisperEncoder", # Building part of bigger (tested) model. "TFWhisperDecoder", # Building part of bigger (tested) model. "SeparableConv1D", # Building part of bigger (tested) model. "FlaxBartForCausalLM", # Building part of bigger (tested) model. "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. "OPTDecoderWrapper", "TFSegformerDecodeHead", # Not a regular model. ] # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't # trigger the common tests. TEST_FILES_WITH_NO_COMMON_TESTS = [ "models/decision_transformer/test_modeling_decision_transformer.py", "models/camembert/test_modeling_camembert.py", "models/mt5/test_modeling_flax_mt5.py", "models/mbart/test_modeling_mbart.py", "models/mt5/test_modeling_mt5.py", "models/pegasus/test_modeling_pegasus.py", "models/camembert/test_modeling_tf_camembert.py", "models/mt5/test_modeling_tf_mt5.py", "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", "models/xlm_roberta/test_modeling_xlm_roberta.py", "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", "models/decision_transformer/test_modeling_decision_transformer.py", ] # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and # should **not** be the rule. IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ # models to ignore for model xxx mapping "CLIPSegForImageSegmentation", "CLIPSegVisionModel", "CLIPSegTextModel", "EsmForProteinFolding", "TimeSeriesTransformerForPrediction", "JukeboxVQVAE", "JukeboxPrior", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "DPTForDepthEstimation", "DecisionTransformerGPT2Model", "GLPNForDepthEstimation", "ViltForImagesAndTextClassification", "ViltForImageAndTextRetrieval", "ViltForTokenClassification", "ViltForMaskedLM", "XGLMEncoder", "XGLMDecoder", "XGLMDecoderWrapper", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "SegformerDecodeHead", "TFSegformerDecodeHead", "FlaxBeitForMaskedImageModeling", "PLBartEncoder", "PLBartDecoder", "PLBartDecoderWrapper", "BeitForMaskedImageModeling", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", "GroupViTTextModel", "GroupViTVisionModel", "TFCLIPTextModel", "TFCLIPVisionModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", "FlaxCLIPTextModel", "FlaxCLIPVisionModel", "FlaxWav2Vec2ForCTC", "DetrForSegmentation", "ConditionalDetrForSegmentation", "DPRReader", "FlaubertForQuestionAnswering", "FlavaImageCodebook", "FlavaTextModel", "FlavaImageModel", "FlavaMultimodalModel", "GPT2DoubleHeadsModel", "LayoutLMForQuestionAnswering", "LukeForMaskedLM", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "OpenAIGPTDoubleHeadsModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", "RagModel", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmEmbedder", "RealmForOpenQA", "RealmScorer", "RealmReader", "TFDPRReader", "TFGPT2DoubleHeadsModel", "TFLayoutLMForQuestionAnswering", "TFOpenAIGPTDoubleHeadsModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "Wav2Vec2ForCTC", "HubertForCTC", "SEWForCTC", "SEWDForCTC", "XLMForQuestionAnswering", "XLNetForQuestionAnswering", "SeparableConv1D", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertForQuestionAnswering", "VisualBertForMultipleChoice", "TFWav2Vec2ForCTC", "TFHubertForCTC", "MaskFormerForInstanceSegmentation", "XCLIPVisionModel", "XCLIPTextModel", ] # Update this list for models that have multiple model types for the same # model doc MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( [ ("data2vec-text", "data2vec"), ("data2vec-audio", "data2vec"), ("data2vec-vision", "data2vec"), ("donut-swin", "donut"), ] ) # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), submodule_search_locations=[PATH_TO_TRANSFORMERS], ) transformers = spec.loader.load_module() def check_model_list(): """Check the model list inside the transformers library.""" # Get the models from the directory structure of `src/transformers/models/` models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): _models.append(model) # Get the models from the directory structure of `src/transformers/models/` models = [model for model in dir(transformers.models) if not model.startswith("__")] missing_models = sorted(list(set(_models).difference(models))) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." ) # If some modeling modules should be ignored for all checks, they should be added in the nested list # _ignore_modules of this function. def get_model_modules(): """Get the model modules inside the transformers library.""" _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_transfo_xl_utilities", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_transfo_xl_utilities", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if not model.startswith("__"): model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules def get_models(module, include_pretrained=False): """Get the objects in module that are models.""" models = [] model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel) for attr_name in dir(module): if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): continue attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: models.append((attr_name, attr)) return models def is_a_private_model(model): """Returns True if the model should not be in the main init.""" if model in PRIVATE_MODELS: return True # Wrapper, Encoder and Decoder are all privates if model.endswith("Wrapper"): return True if model.endswith("Encoder"): return True if model.endswith("Decoder"): return True return False def check_models_are_in_init(): """Checks all models defined in the library are in the main init.""" models_not_in_init = [] dir_transformers = dir(transformers) for module in get_model_modules(): models_not_in_init += [ model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers ] # Remove private models models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] if len(models_not_in_init) > 0: raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") # If some test_modeling files should be ignored when checking models are all tested, they should be added in the # nested list _ignore_files of this function. def get_model_test_files(): """Get the model test files. The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. """ _ignore_files = [ "test_modeling_common", "test_modeling_encoder_decoder", "test_modeling_flax_encoder_decoder", "test_modeling_flax_speech_encoder_decoder", "test_modeling_marian", "test_modeling_tf_common", "test_modeling_tf_encoder_decoder", ] test_files = [] # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models` model_test_root = os.path.join(PATH_TO_TESTS, "models") model_test_dirs = [] for x in os.listdir(model_test_root): x = os.path.join(model_test_root, x) if os.path.isdir(x): model_test_dirs.append(x) for target_dir in [PATH_TO_TESTS] + model_test_dirs: for file_or_dir in os.listdir(target_dir): path = os.path.join(target_dir, file_or_dir) if os.path.isfile(path): filename = os.path.split(path)[-1] if "test_modeling" in filename and not os.path.splitext(filename)[0] in _ignore_files: file = os.path.join(*path.split(os.sep)[1:]) test_files.append(file) return test_files # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class # for the all_model_classes variable. def find_tested_models(test_file): """Parse the content of test_file to detect what's in all_model_classes""" # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: content = f.read() all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) # Check with one less parenthesis as well all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) if len(all_models) > 0: model_tested = [] for entry in all_models: for line in entry.split(","): name = line.strip() if len(name) > 0: model_tested.append(name) return model_tested def check_models_are_tested(module, test_file): """Check models defined in module are tested in test_file.""" # XxxPreTrainedModel are not tested defined_models = get_models(module) tested_models = find_tested_models(test_file) if tested_models is None: if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: return return [ f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + "`utils/check_repo.py`." ] failures = [] for model_name, _ in defined_models: if model_name not in tested_models and model_name not in IGNORE_NON_TESTED: failures.append( f"{model_name} is defined in {module.__name__} but is not tested in " + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + "in the file `utils/check_repo.py`." ) return failures def check_all_models_are_tested(): """Check all models are properly tested.""" modules = get_model_modules() test_files = get_model_test_files() failures = [] for module in modules: test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] if len(test_file) == 0: failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") elif len(test_file) > 1: failures.append(f"{module.__name__} has several test files: {test_file}.") else: test_file = test_file[0] new_failures = check_models_are_tested(module, test_file) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def get_all_auto_configured_models(): """Return the list of all models in at least one auto class.""" result = set() # To avoid duplicates we concatenate all model classes in a set. if is_torch_available(): for attr_name in dir(transformers.models.auto.modeling_auto): if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name))) if is_tf_available(): for attr_name in dir(transformers.models.auto.modeling_tf_auto): if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name))) if is_flax_available(): for attr_name in dir(transformers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name))) return [cls for cls in result] def ignore_unautoclassed(model_name): """Rules to determine if `name` should be in an auto class.""" # Special white list if model_name in IGNORE_NON_AUTO_CONFIGURED: return True # Encoder and Decoder should be ignored if "Encoder" in model_name or "Decoder" in model_name: return True return False def check_models_are_auto_configured(module, all_auto_models): """Check models defined in module are each in an auto class.""" defined_models = get_models(module) failures = [] for model_name, _ in defined_models: if model_name not in all_auto_models and not ignore_unautoclassed(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " "`utils/check_repo.py`." ) return failures def check_all_models_are_auto_configured(): """Check all models are each in an auto class.""" missing_backends = [] if not is_torch_available(): missing_backends.append("PyTorch") if not is_tf_available(): missing_backends.append("TensorFlow") if not is_flax_available(): missing_backends.append("Flax") if len(missing_backends) > 0: missing = ", ".join(missing_backends) if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: raise Exception( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}." ) else: warnings.warn( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " "didn't make any change in one of those backends modeling files, you should probably execute the " "command above to be on the safe side." ) modules = get_model_modules() all_auto_models = get_all_auto_configured_models() failures = [] for module in modules: new_failures = check_models_are_auto_configured(module, all_auto_models) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) _re_decorator = re.compile(r"^\s*@(\S+)\s+$") def check_decorator_order(filename): """Check that in the test file `filename` the slow decorator is always last.""" with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() decorator_before = None errors = [] for i, line in enumerate(lines): search = _re_decorator.search(line) if search is not None: decorator_name = search.groups()[0] if decorator_before is not None and decorator_name.startswith("parameterized"): errors.append(i) decorator_before = decorator_name elif decorator_before is not None: decorator_before = None return errors def check_all_decorator_order(): """Check that in all test files, the slow decorator is always last.""" errors = [] for fname in os.listdir(PATH_TO_TESTS): if fname.endswith(".py"): filename = os.path.join(PATH_TO_TESTS, fname) new_errors = check_decorator_order(filename) errors += [f"- {filename}, line {i}" for i in new_errors] if len(errors) > 0: msg = "\n".join(errors) raise ValueError( "The parameterized decorator (and its variants) should always be first, but this is not the case in the" f" following files:\n{msg}" ) def find_all_documented_objects(): """Parse the content of all doc files to detect which classes and functions it documents""" documented_obj = [] for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] return documented_obj # One good reason for not being documented is to be deprecated. Put in this list deprecated objects. DEPRECATED_OBJECTS = [ "AutoModelWithLMHead", "BartPretrainedModel", "DataCollator", "DataCollatorForSOP", "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "PretrainedBartModel", "PretrainedFSMTModel", "SingleSentenceClassificationProcessor", "SquadDataTrainingArguments", "SquadDataset", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "TFAutoModelWithLMHead", "TFBartPretrainedModel", "TextDataset", "TextDatasetForNextSentencePrediction", "Wav2Vec2ForMaskedLM", "Wav2Vec2Tokenizer", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", "TFTrainer", "TFTrainingArguments", ] # Exceptionally, some objects should not be documented after all rules passed. # ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! UNDOCUMENTED_OBJECTS = [ "AddedToken", # This is a tokenizers class. "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) "TFDPRPretrainedReader", # Like an Encoder. "TransfoXLCorpus", # Internal type. "WordpieceTokenizer", # Internal, should never have been in the main init. "absl", # External module "add_end_docstrings", # Internal, should never have been in the main init. "add_start_docstrings", # Internal, should never have been in the main init. "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights "logger", # Internal logger "logging", # External module "requires_backends", # Internal function ] # This list should be empty. Objects in it should get their own doc page. SHOULD_HAVE_THEIR_OWN_PAGE = [ # Benchmarks "PyTorchBenchmark", "PyTorchBenchmarkArguments", "TensorFlowBenchmark", "TensorFlowBenchmarkArguments", ] def ignore_undocumented(name): """Rules to determine if `name` should be undocumented.""" # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("PreTrainedModel") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile( os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False def check_all_objects_are_documented(): """Check all models are properly documented.""" documented_objs = find_all_documented_objects() modules = transformers._modules objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")] undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] if len(undocumented_objs) > 0: raise Exception( "The following objects are in the public init so should be documented:\n - " + "\n - ".join(undocumented_objs) ) check_docstrings_are_in_md() check_model_type_doc_match() def check_model_type_doc_match(): """Check all doc pages have a corresponding model type.""" model_doc_folder = Path(PATH_TO_DOC) / "model_doc" model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")] model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] errors = [] for m in model_docs: if m not in model_types and m != "auto": close_matches = get_close_matches(m, model_types) error_message = f"{m} is not a proper model identifier." if len(close_matches) > 0: close_matches = "/".join(close_matches) error_message += f" Did you mean {close_matches}?" errors.append(error_message) if len(errors) > 0: raise ValueError( "Some model doc pages do not match any existing model type:\n" + "\n".join(errors) + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " "models/auto/configuration_auto.py." ) # Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. _re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") # Re pattern to catch things between double backquotes. _re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") # Re pattern to catch example introduction. _re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) def is_rst_docstring(docstring): """ Returns `True` if `docstring` is written in rst. """ if _re_rst_special_words.search(docstring) is not None: return True if _re_double_backquotes.search(docstring) is not None: return True if _re_rst_example.search(docstring) is not None: return True return False def check_docstrings_are_in_md(): """Check all docstrings are in md""" files_with_rst = [] for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"): with open(file, encoding="utf-8") as f: code = f.read() docstrings = code.split('"""') for idx, docstring in enumerate(docstrings): if idx % 2 == 0 or not is_rst_docstring(docstring): continue files_with_rst.append(file) break if len(files_with_rst) > 0: raise ValueError( "The following files have docstrings written in rst:\n" + "\n".join([f"- {f}" for f in files_with_rst]) + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" "(`pip install git+https://github.com/huggingface/doc-builder`)" ) def check_repo_quality(): """Check all models are properly tested and documented.""" print("Checking all models are included.") check_model_list() print("Checking all models are public.") check_models_are_in_init() print("Checking all models are properly tested.") check_all_decorator_order() check_all_models_are_tested() print("Checking all objects are properly documented.") check_all_objects_are_documented() print("Checking all models are in at least one auto class.") check_all_models_are_auto_configured() if __name__ == "__main__": check_repo_quality()

# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import inspect import os import re import warnings from collections import OrderedDict from difflib import get_close_matches from pathlib import Path from transformers import is_flax_available, is_tf_available, is_torch_available from transformers.models.auto import get_values from transformers.utils import ENV_VARS_TRUE_VALUES # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_repo.py PATH_TO_TRANSFORMERS = "src/transformers" PATH_TO_TESTS = "tests" PATH_TO_DOC = "docs/source/en" # Update this list with models that are supposed to be private. PRIVATE_MODELS = [ "DPRSpanPredictor", "LongT5Stack", "RealmBertModel", "T5Stack", "SwitchTransformersStack", "TFDPRSpanPredictor", ] # Update this list for models that are not tested with a comment explaining the reason it should not be. # Being in this list is an exception and should **not** be the rule. IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [ # models to ignore for not tested "ResNetBackbone", # Backbones have their own tests. "CLIPSegDecoder", # Building part of bigger (tested) model. "TableTransformerEncoder", # Building part of bigger (tested) model. "TableTransformerDecoder", # Building part of bigger (tested) model. "TimeSeriesTransformerEncoder", # Building part of bigger (tested) model. "TimeSeriesTransformerDecoder", # Building part of bigger (tested) model. "JukeboxVQVAE", # Building part of bigger (tested) model. "JukeboxPrior", # Building part of bigger (tested) model. "DeformableDetrEncoder", # Building part of bigger (tested) model. "DeformableDetrDecoder", # Building part of bigger (tested) model. "OPTDecoder", # Building part of bigger (tested) model. "WhisperDecoder", # Building part of bigger (tested) model. "WhisperEncoder", # Building part of bigger (tested) model. "DecisionTransformerGPT2Model", # Building part of bigger (tested) model. "SegformerDecodeHead", # Building part of bigger (tested) model. "PLBartEncoder", # Building part of bigger (tested) model. "PLBartDecoder", # Building part of bigger (tested) model. "PLBartDecoderWrapper", # Building part of bigger (tested) model. "BigBirdPegasusEncoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoder", # Building part of bigger (tested) model. "BigBirdPegasusDecoderWrapper", # Building part of bigger (tested) model. "DetrEncoder", # Building part of bigger (tested) model. "DetrDecoder", # Building part of bigger (tested) model. "DetrDecoderWrapper", # Building part of bigger (tested) model. "ConditionalDetrEncoder", # Building part of bigger (tested) model. "ConditionalDetrDecoder", # Building part of bigger (tested) model. "M2M100Encoder", # Building part of bigger (tested) model. "M2M100Decoder", # Building part of bigger (tested) model. "MCTCTEncoder", # Building part of bigger (tested) model. "Speech2TextEncoder", # Building part of bigger (tested) model. "Speech2TextDecoder", # Building part of bigger (tested) model. "LEDEncoder", # Building part of bigger (tested) model. "LEDDecoder", # Building part of bigger (tested) model. "BartDecoderWrapper", # Building part of bigger (tested) model. "BartEncoder", # Building part of bigger (tested) model. "BertLMHeadModel", # Needs to be setup as decoder. "BlenderbotSmallEncoder", # Building part of bigger (tested) model. "BlenderbotSmallDecoderWrapper", # Building part of bigger (tested) model. "BlenderbotEncoder", # Building part of bigger (tested) model. "BlenderbotDecoderWrapper", # Building part of bigger (tested) model. "MBartEncoder", # Building part of bigger (tested) model. "MBartDecoderWrapper", # Building part of bigger (tested) model. "MegatronBertLMHeadModel", # Building part of bigger (tested) model. "MegatronBertEncoder", # Building part of bigger (tested) model. "MegatronBertDecoder", # Building part of bigger (tested) model. "MegatronBertDecoderWrapper", # Building part of bigger (tested) model. "MvpDecoderWrapper", # Building part of bigger (tested) model. "MvpEncoder", # Building part of bigger (tested) model. "PegasusEncoder", # Building part of bigger (tested) model. "PegasusDecoderWrapper", # Building part of bigger (tested) model. "PegasusXEncoder", # Building part of bigger (tested) model. "PegasusXDecoder", # Building part of bigger (tested) model. "PegasusXDecoderWrapper", # Building part of bigger (tested) model. "DPREncoder", # Building part of bigger (tested) model. "ProphetNetDecoderWrapper", # Building part of bigger (tested) model. "RealmBertModel", # Building part of bigger (tested) model. "RealmReader", # Not regular model. "RealmScorer", # Not regular model. "RealmForOpenQA", # Not regular model. "ReformerForMaskedLM", # Needs to be setup as decoder. "Speech2Text2DecoderWrapper", # Building part of bigger (tested) model. "TFDPREncoder", # Building part of bigger (tested) model. "TFElectraMainLayer", # Building part of bigger (tested) model (should it be a TFPreTrainedModel ?) "TFRobertaForMultipleChoice", # TODO: fix "TrOCRDecoderWrapper", # Building part of bigger (tested) model. "TFWhisperEncoder", # Building part of bigger (tested) model. "TFWhisperDecoder", # Building part of bigger (tested) model. "SeparableConv1D", # Building part of bigger (tested) model. "FlaxBartForCausalLM", # Building part of bigger (tested) model. "FlaxBertForCausalLM", # Building part of bigger (tested) model. Tested implicitly through FlaxRobertaForCausalLM. "OPTDecoderWrapper", "TFSegformerDecodeHead", # Not a regular model. ] # Update this list with test files that don't have a tester with a `all_model_classes` variable and which don't # trigger the common tests. TEST_FILES_WITH_NO_COMMON_TESTS = [ "models/decision_transformer/test_modeling_decision_transformer.py", "models/camembert/test_modeling_camembert.py", "models/mt5/test_modeling_flax_mt5.py", "models/mbart/test_modeling_mbart.py", "models/mt5/test_modeling_mt5.py", "models/pegasus/test_modeling_pegasus.py", "models/camembert/test_modeling_tf_camembert.py", "models/mt5/test_modeling_tf_mt5.py", "models/xlm_roberta/test_modeling_tf_xlm_roberta.py", "models/xlm_roberta/test_modeling_flax_xlm_roberta.py", "models/xlm_prophetnet/test_modeling_xlm_prophetnet.py", "models/xlm_roberta/test_modeling_xlm_roberta.py", "models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.py", "models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.py", "models/decision_transformer/test_modeling_decision_transformer.py", ] # Update this list for models that are not in any of the auto MODEL_XXX_MAPPING. Being in this list is an exception and # should **not** be the rule. IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [ # models to ignore for model xxx mapping "CLIPSegForImageSegmentation", "CLIPSegVisionModel", "CLIPSegTextModel", "EsmForProteinFolding", "TimeSeriesTransformerForPrediction", "JukeboxVQVAE", "JukeboxPrior", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "PegasusXEncoder", "PegasusXDecoder", "PegasusXDecoderWrapper", "DPTForDepthEstimation", "DecisionTransformerGPT2Model", "GLPNForDepthEstimation", "ViltForImagesAndTextClassification", "ViltForImageAndTextRetrieval", "ViltForTokenClassification", "ViltForMaskedLM", "XGLMEncoder", "XGLMDecoder", "XGLMDecoderWrapper", "PerceiverForMultimodalAutoencoding", "PerceiverForOpticalFlow", "SegformerDecodeHead", "TFSegformerDecodeHead", "FlaxBeitForMaskedImageModeling", "PLBartEncoder", "PLBartDecoder", "PLBartDecoderWrapper", "BeitForMaskedImageModeling", "CLIPTextModel", "CLIPTextModelWithProjection", "CLIPVisionModel", "CLIPVisionModelWithProjection", "GroupViTTextModel", "GroupViTVisionModel", "TFCLIPTextModel", "TFCLIPVisionModel", "TFGroupViTTextModel", "TFGroupViTVisionModel", "FlaxCLIPTextModel", "FlaxCLIPVisionModel", "FlaxWav2Vec2ForCTC", "DetrForSegmentation", "ConditionalDetrForSegmentation", "DPRReader", "FlaubertForQuestionAnswering", "FlavaImageCodebook", "FlavaTextModel", "FlavaImageModel", "FlavaMultimodalModel", "GPT2DoubleHeadsModel", "LayoutLMForQuestionAnswering", "LukeForMaskedLM", "LukeForEntityClassification", "LukeForEntityPairClassification", "LukeForEntitySpanClassification", "OpenAIGPTDoubleHeadsModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection", "RagModel", "RagSequenceForGeneration", "RagTokenForGeneration", "RealmEmbedder", "RealmForOpenQA", "RealmScorer", "RealmReader", "TFDPRReader", "TFGPT2DoubleHeadsModel", "TFLayoutLMForQuestionAnswering", "TFOpenAIGPTDoubleHeadsModel", "TFRagModel", "TFRagSequenceForGeneration", "TFRagTokenForGeneration", "Wav2Vec2ForCTC", "HubertForCTC", "SEWForCTC", "SEWDForCTC", "XLMForQuestionAnswering", "XLNetForQuestionAnswering", "SeparableConv1D", "VisualBertForRegionToPhraseAlignment", "VisualBertForVisualReasoning", "VisualBertForQuestionAnswering", "VisualBertForMultipleChoice", "TFWav2Vec2ForCTC", "TFHubertForCTC", "MaskFormerForInstanceSegmentation", "XCLIPVisionModel", "XCLIPTextModel", ] # Update this list for models that have multiple model types for the same # model doc MODEL_TYPE_TO_DOC_MAPPING = OrderedDict( [ ("data2vec-text", "data2vec"), ("data2vec-audio", "data2vec"), ("data2vec-vision", "data2vec"), ("donut-swin", "donut"), ] ) # This is to make sure the transformers module imported is the one in the repo. spec = importlib.util.spec_from_file_location( "transformers", os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), submodule_search_locations=[PATH_TO_TRANSFORMERS], ) transformers = spec.loader.load_module() def check_model_list(): """Check the model list inside the transformers library.""" # Get the models from the directory structure of `src/transformers/models/` models_dir = os.path.join(PATH_TO_TRANSFORMERS, "models") _models = [] for model in os.listdir(models_dir): model_dir = os.path.join(models_dir, model) if os.path.isdir(model_dir) and "__init__.py" in os.listdir(model_dir): _models.append(model) # Get the models from the directory structure of `src/transformers/models/` models = [model for model in dir(transformers.models) if not model.startswith("__")] missing_models = sorted(list(set(_models).difference(models))) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." ) # If some modeling modules should be ignored for all checks, they should be added in the nested list # _ignore_modules of this function. def get_model_modules(): """Get the model modules inside the transformers library.""" _ignore_modules = [ "modeling_auto", "modeling_encoder_decoder", "modeling_marian", "modeling_mmbt", "modeling_outputs", "modeling_retribert", "modeling_utils", "modeling_flax_auto", "modeling_flax_encoder_decoder", "modeling_flax_utils", "modeling_speech_encoder_decoder", "modeling_flax_speech_encoder_decoder", "modeling_flax_vision_encoder_decoder", "modeling_transfo_xl_utilities", "modeling_tf_auto", "modeling_tf_encoder_decoder", "modeling_tf_outputs", "modeling_tf_pytorch_utils", "modeling_tf_utils", "modeling_tf_transfo_xl_utilities", "modeling_tf_vision_encoder_decoder", "modeling_vision_encoder_decoder", ] modules = [] for model in dir(transformers.models): # There are some magic dunder attributes in the dir, we ignore them if not model.startswith("__"): model_module = getattr(transformers.models, model) for submodule in dir(model_module): if submodule.startswith("modeling") and submodule not in _ignore_modules: modeling_module = getattr(model_module, submodule) if inspect.ismodule(modeling_module): modules.append(modeling_module) return modules def get_models(module, include_pretrained=False): """Get the objects in module that are models.""" models = [] model_classes = (transformers.PreTrainedModel, transformers.TFPreTrainedModel, transformers.FlaxPreTrainedModel) for attr_name in dir(module): if not include_pretrained and ("Pretrained" in attr_name or "PreTrained" in attr_name): continue attr = getattr(module, attr_name) if isinstance(attr, type) and issubclass(attr, model_classes) and attr.__module__ == module.__name__: models.append((attr_name, attr)) return models def is_a_private_model(model): """Returns True if the model should not be in the main init.""" if model in PRIVATE_MODELS: return True # Wrapper, Encoder and Decoder are all privates if model.endswith("Wrapper"): return True if model.endswith("Encoder"): return True if model.endswith("Decoder"): return True return False def check_models_are_in_init(): """Checks all models defined in the library are in the main init.""" models_not_in_init = [] dir_transformers = dir(transformers) for module in get_model_modules(): models_not_in_init += [ model[0] for model in get_models(module, include_pretrained=True) if model[0] not in dir_transformers ] # Remove private models models_not_in_init = [model for model in models_not_in_init if not is_a_private_model(model)] if len(models_not_in_init) > 0: raise Exception(f"The following models should be in the main init: {','.join(models_not_in_init)}.") # If some test_modeling files should be ignored when checking models are all tested, they should be added in the # nested list _ignore_files of this function. def get_model_test_files(): """Get the model test files. The returned files should NOT contain the `tests` (i.e. `PATH_TO_TESTS` defined in this script). They will be considered as paths relative to `tests`. A caller has to use `os.path.join(PATH_TO_TESTS, ...)` to access the files. """ _ignore_files = [ "test_modeling_common", "test_modeling_encoder_decoder", "test_modeling_flax_encoder_decoder", "test_modeling_flax_speech_encoder_decoder", "test_modeling_marian", "test_modeling_tf_common", "test_modeling_tf_encoder_decoder", ] test_files = [] # Check both `PATH_TO_TESTS` and `PATH_TO_TESTS/models` model_test_root = os.path.join(PATH_TO_TESTS, "models") model_test_dirs = [] for x in os.listdir(model_test_root): x = os.path.join(model_test_root, x) if os.path.isdir(x): model_test_dirs.append(x) for target_dir in [PATH_TO_TESTS] + model_test_dirs: for file_or_dir in os.listdir(target_dir): path = os.path.join(target_dir, file_or_dir) if os.path.isfile(path): filename = os.path.split(path)[-1] if "test_modeling" in filename and not os.path.splitext(filename)[0] in _ignore_files: file = os.path.join(*path.split(os.sep)[1:]) test_files.append(file) return test_files # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the tester class # for the all_model_classes variable. def find_tested_models(test_file): """Parse the content of test_file to detect what's in all_model_classes""" # This is a bit hacky but I didn't find a way to import the test_file as a module and read inside the class with open(os.path.join(PATH_TO_TESTS, test_file), "r", encoding="utf-8", newline="\n") as f: content = f.read() all_models = re.findall(r"all_model_classes\s+=\s+\(\s*\(([^\)]*)\)", content) # Check with one less parenthesis as well all_models += re.findall(r"all_model_classes\s+=\s+\(([^\)]*)\)", content) if len(all_models) > 0: model_tested = [] for entry in all_models: for line in entry.split(","): name = line.strip() if len(name) > 0: model_tested.append(name) return model_tested def check_models_are_tested(module, test_file): """Check models defined in module are tested in test_file.""" # XxxPreTrainedModel are not tested defined_models = get_models(module) tested_models = find_tested_models(test_file) if tested_models is None: if test_file.replace(os.path.sep, "/") in TEST_FILES_WITH_NO_COMMON_TESTS: return return [ f"{test_file} should define `all_model_classes` to apply common tests to the models it tests. " + "If this intentional, add the test filename to `TEST_FILES_WITH_NO_COMMON_TESTS` in the file " + "`utils/check_repo.py`." ] failures = [] for model_name, _ in defined_models: if model_name not in tested_models and model_name not in IGNORE_NON_TESTED: failures.append( f"{model_name} is defined in {module.__name__} but is not tested in " + f"{os.path.join(PATH_TO_TESTS, test_file)}. Add it to the all_model_classes in that file." + "If common tests should not applied to that model, add its name to `IGNORE_NON_TESTED`" + "in the file `utils/check_repo.py`." ) return failures def check_all_models_are_tested(): """Check all models are properly tested.""" modules = get_model_modules() test_files = get_model_test_files() failures = [] for module in modules: test_file = [file for file in test_files if f"test_{module.__name__.split('.')[-1]}.py" in file] if len(test_file) == 0: failures.append(f"{module.__name__} does not have its corresponding test file {test_file}.") elif len(test_file) > 1: failures.append(f"{module.__name__} has several test files: {test_file}.") else: test_file = test_file[0] new_failures = check_models_are_tested(module, test_file) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) def get_all_auto_configured_models(): """Return the list of all models in at least one auto class.""" result = set() # To avoid duplicates we concatenate all model classes in a set. if is_torch_available(): for attr_name in dir(transformers.models.auto.modeling_auto): if attr_name.startswith("MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_auto, attr_name))) if is_tf_available(): for attr_name in dir(transformers.models.auto.modeling_tf_auto): if attr_name.startswith("TF_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_tf_auto, attr_name))) if is_flax_available(): for attr_name in dir(transformers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(transformers.models.auto.modeling_flax_auto, attr_name))) return [cls for cls in result] def ignore_unautoclassed(model_name): """Rules to determine if `name` should be in an auto class.""" # Special white list if model_name in IGNORE_NON_AUTO_CONFIGURED: return True # Encoder and Decoder should be ignored if "Encoder" in model_name or "Decoder" in model_name: return True return False def check_models_are_auto_configured(module, all_auto_models): """Check models defined in module are each in an auto class.""" defined_models = get_models(module) failures = [] for model_name, _ in defined_models: if model_name not in all_auto_models and not ignore_unautoclassed(model_name): failures.append( f"{model_name} is defined in {module.__name__} but is not present in any of the auto mapping. " "If that is intended behavior, add its name to `IGNORE_NON_AUTO_CONFIGURED` in the file " "`utils/check_repo.py`." ) return failures def check_all_models_are_auto_configured(): """Check all models are each in an auto class.""" missing_backends = [] if not is_torch_available(): missing_backends.append("PyTorch") if not is_tf_available(): missing_backends.append("TensorFlow") if not is_flax_available(): missing_backends.append("Flax") if len(missing_backends) > 0: missing = ", ".join(missing_backends) if os.getenv("TRANSFORMERS_IS_CI", "").upper() in ENV_VARS_TRUE_VALUES: raise Exception( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}." ) else: warnings.warn( "Full quality checks require all backends to be installed (with `pip install -e .[dev]` in the " f"Transformers repo, the following are missing: {missing}. While it's probably fine as long as you " "didn't make any change in one of those backends modeling files, you should probably execute the " "command above to be on the safe side." ) modules = get_model_modules() all_auto_models = get_all_auto_configured_models() failures = [] for module in modules: new_failures = check_models_are_auto_configured(module, all_auto_models) if new_failures is not None: failures += new_failures if len(failures) > 0: raise Exception(f"There were {len(failures)} failures:\n" + "\n".join(failures)) _re_decorator = re.compile(r"^\s*@(\S+)\s+$") def check_decorator_order(filename): """Check that in the test file `filename` the slow decorator is always last.""" with open(filename, "r", encoding="utf-8", newline="\n") as f: lines = f.readlines() decorator_before = None errors = [] for i, line in enumerate(lines): search = _re_decorator.search(line) if search is not None: decorator_name = search.groups()[0] if decorator_before is not None and decorator_name.startswith("parameterized"): errors.append(i) decorator_before = decorator_name elif decorator_before is not None: decorator_before = None return errors def check_all_decorator_order(): """Check that in all test files, the slow decorator is always last.""" errors = [] for fname in os.listdir(PATH_TO_TESTS): if fname.endswith(".py"): filename = os.path.join(PATH_TO_TESTS, fname) new_errors = check_decorator_order(filename) errors += [f"- {filename}, line {i}" for i in new_errors] if len(errors) > 0: msg = "\n".join(errors) raise ValueError( "The parameterized decorator (and its variants) should always be first, but this is not the case in the" f" following files:\n{msg}" ) def find_all_documented_objects(): """Parse the content of all doc files to detect which classes and functions it documents""" documented_obj = [] for doc_file in Path(PATH_TO_DOC).glob("**/*.rst"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall(r"(?:autoclass|autofunction):: transformers.(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] for doc_file in Path(PATH_TO_DOC).glob("**/*.mdx"): with open(doc_file, "r", encoding="utf-8", newline="\n") as f: content = f.read() raw_doc_objs = re.findall("\[\[autodoc\]\]\s+(\S+)\s+", content) documented_obj += [obj.split(".")[-1] for obj in raw_doc_objs] return documented_obj # One good reason for not being documented is to be deprecated. Put in this list deprecated objects. DEPRECATED_OBJECTS = [ "AutoModelWithLMHead", "BartPretrainedModel", "DataCollator", "DataCollatorForSOP", "GlueDataset", "GlueDataTrainingArguments", "LineByLineTextDataset", "LineByLineWithRefDataset", "LineByLineWithSOPTextDataset", "PretrainedBartModel", "PretrainedFSMTModel", "SingleSentenceClassificationProcessor", "SquadDataTrainingArguments", "SquadDataset", "SquadExample", "SquadFeatures", "SquadV1Processor", "SquadV2Processor", "TFAutoModelWithLMHead", "TFBartPretrainedModel", "TextDataset", "TextDatasetForNextSentencePrediction", "Wav2Vec2ForMaskedLM", "Wav2Vec2Tokenizer", "glue_compute_metrics", "glue_convert_examples_to_features", "glue_output_modes", "glue_processors", "glue_tasks_num_labels", "squad_convert_examples_to_features", "xnli_compute_metrics", "xnli_output_modes", "xnli_processors", "xnli_tasks_num_labels", "TFTrainer", "TFTrainingArguments", ] # Exceptionally, some objects should not be documented after all rules passed. # ONLY PUT SOMETHING IN THIS LIST AS A LAST RESORT! UNDOCUMENTED_OBJECTS = [ "AddedToken", # This is a tokenizers class. "BasicTokenizer", # Internal, should never have been in the main init. "CharacterTokenizer", # Internal, should never have been in the main init. "DPRPretrainedReader", # Like an Encoder. "DummyObject", # Just picked by mistake sometimes. "MecabTokenizer", # Internal, should never have been in the main init. "ModelCard", # Internal type. "SqueezeBertModule", # Internal building block (should have been called SqueezeBertLayer) "TFDPRPretrainedReader", # Like an Encoder. "TransfoXLCorpus", # Internal type. "WordpieceTokenizer", # Internal, should never have been in the main init. "absl", # External module "add_end_docstrings", # Internal, should never have been in the main init. "add_start_docstrings", # Internal, should never have been in the main init. "convert_tf_weight_name_to_pt_weight_name", # Internal used to convert model weights "logger", # Internal logger "logging", # External module "requires_backends", # Internal function ] # This list should be empty. Objects in it should get their own doc page. SHOULD_HAVE_THEIR_OWN_PAGE = [ # Benchmarks "PyTorchBenchmark", "PyTorchBenchmarkArguments", "TensorFlowBenchmark", "TensorFlowBenchmarkArguments", "ResNetBackbone", "AutoBackbone", ] def ignore_undocumented(name): """Rules to determine if `name` should be undocumented.""" # NOT DOCUMENTED ON PURPOSE. # Constants uppercase are not documented. if name.isupper(): return True # PreTrainedModels / Encoders / Decoders / Layers / Embeddings / Attention are not documented. if ( name.endswith("PreTrainedModel") or name.endswith("Decoder") or name.endswith("Encoder") or name.endswith("Layer") or name.endswith("Embeddings") or name.endswith("Attention") ): return True # Submodules are not documented. if os.path.isdir(os.path.join(PATH_TO_TRANSFORMERS, name)) or os.path.isfile( os.path.join(PATH_TO_TRANSFORMERS, f"{name}.py") ): return True # All load functions are not documented. if name.startswith("load_tf") or name.startswith("load_pytorch"): return True # is_xxx_available functions are not documented. if name.startswith("is_") and name.endswith("_available"): return True # Deprecated objects are not documented. if name in DEPRECATED_OBJECTS or name in UNDOCUMENTED_OBJECTS: return True # MMBT model does not really work. if name.startswith("MMBT"): return True if name in SHOULD_HAVE_THEIR_OWN_PAGE: return True return False def check_all_objects_are_documented(): """Check all models are properly documented.""" documented_objs = find_all_documented_objects() modules = transformers._modules objects = [c for c in dir(transformers) if c not in modules and not c.startswith("_")] undocumented_objs = [c for c in objects if c not in documented_objs and not ignore_undocumented(c)] if len(undocumented_objs) > 0: raise Exception( "The following objects are in the public init so should be documented:\n - " + "\n - ".join(undocumented_objs) ) check_docstrings_are_in_md() check_model_type_doc_match() def check_model_type_doc_match(): """Check all doc pages have a corresponding model type.""" model_doc_folder = Path(PATH_TO_DOC) / "model_doc" model_docs = [m.stem for m in model_doc_folder.glob("*.mdx")] model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys()) model_types = [MODEL_TYPE_TO_DOC_MAPPING[m] if m in MODEL_TYPE_TO_DOC_MAPPING else m for m in model_types] errors = [] for m in model_docs: if m not in model_types and m != "auto": close_matches = get_close_matches(m, model_types) error_message = f"{m} is not a proper model identifier." if len(close_matches) > 0: close_matches = "/".join(close_matches) error_message += f" Did you mean {close_matches}?" errors.append(error_message) if len(errors) > 0: raise ValueError( "Some model doc pages do not match any existing model type:\n" + "\n".join(errors) + "\nYou can add any missing model type to the `MODEL_NAMES_MAPPING` constant in " "models/auto/configuration_auto.py." ) # Re pattern to catch :obj:`xx`, :class:`xx`, :func:`xx` or :meth:`xx`. _re_rst_special_words = re.compile(r":(?:obj|func|class|meth):`([^`]+)`") # Re pattern to catch things between double backquotes. _re_double_backquotes = re.compile(r"(^|[^`])``([^`]+)``([^`]|$)") # Re pattern to catch example introduction. _re_rst_example = re.compile(r"^\s*Example.*::\s*$", flags=re.MULTILINE) def is_rst_docstring(docstring): """ Returns `True` if `docstring` is written in rst. """ if _re_rst_special_words.search(docstring) is not None: return True if _re_double_backquotes.search(docstring) is not None: return True if _re_rst_example.search(docstring) is not None: return True return False def check_docstrings_are_in_md(): """Check all docstrings are in md""" files_with_rst = [] for file in Path(PATH_TO_TRANSFORMERS).glob("**/*.py"): with open(file, encoding="utf-8") as f: code = f.read() docstrings = code.split('"""') for idx, docstring in enumerate(docstrings): if idx % 2 == 0 or not is_rst_docstring(docstring): continue files_with_rst.append(file) break if len(files_with_rst) > 0: raise ValueError( "The following files have docstrings written in rst:\n" + "\n".join([f"- {f}" for f in files_with_rst]) + "\nTo fix this run `doc-builder convert path_to_py_file` after installing `doc-builder`\n" "(`pip install git+https://github.com/huggingface/doc-builder`)" ) def check_repo_quality(): """Check all models are properly tested and documented.""" print("Checking all models are included.") check_model_list() print("Checking all models are public.") check_models_are_in_init() print("Checking all models are properly tested.") check_all_decorator_order() check_all_models_are_tested() print("Checking all objects are properly documented.") check_all_objects_are_documented() print("Checking all models are in at least one auto class.") check_all_models_are_auto_configured() if __name__ == "__main__": check_repo_quality()

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/vit/modeling_vit.py

# coding=utf-8 # Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ViT model.""" import collections.abc import math from typing import Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_vit import ViTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "ViTConfig" _FEAT_EXTRACTOR_FOR_DOC = "ViTFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k" _EXPECTED_OUTPUT_SHAPE = [1, 197, 768] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224" _IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat" VIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/vit-base-patch16-224", # See all ViT models at https://huggingface.co/models?filter=vit ] class ViTEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None: super().__init__() self.cls_token = nn.Parameter( nn.init.trunc_normal_( torch.zeros(1, 1, config.hidden_size, dtype=torch.float32), mean=0.0, std=config.initializer_range ) ) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = ViTPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter( nn.init.trunc_normal_( torch.zeros(1, num_patches + 1, config.hidden_size, dtype=torch.float32), mean=0.0, std=config.initializer_range, ) ) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 """ num_patches = embeddings.shape[1] - 1 num_positions = self.position_embeddings.shape[1] - 1 if num_patches == num_positions and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] h0 = height // self.config.patch_size w0 = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 h0, w0 = h0 + 0.1, w0 + 0.1 patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)), mode="bicubic", align_corners=False, ) assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1] patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) if bool_masked_pos is not None: seq_length = embeddings.shape[1] mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings class ViTPatchEmbeddings(nn.Module): """ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. """ def __init__(self, config): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings class ViTSelfAttention(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class ViTSelfOutput(nn.Module): """ The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class ViTAttention(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.attention = ViTSelfAttention(config) self.output = ViTSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class ViTIntermediate(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class ViTOutput(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class ViTLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: ViTConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = ViTAttention(config) self.intermediate = ViTIntermediate(config) self.output = ViTOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class ViTEncoder(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class ViTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ViTConfig base_model_prefix = "vit" main_input_name = "pixel_values" supports_gradient_checkpointing = True _no_split_modules = [] def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid # `trunc_normal_cpu` not implemented in `half` issues module.weight.data = nn.init.trunc_normal_( module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range ).to(module.weight.dtype) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module: ViTEncoder, value: bool = False) -> None: if isinstance(module, ViTEncoder): module.gradient_checkpointing = value VIT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.", VIT_START_DOCSTRING, ) class ViTModel(ViTPreTrainedModel): def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False): super().__init__(config) self.config = config self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token) self.encoder = ViTEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = ViTPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> ViTPatchEmbeddings: return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype if pixel_values.dtype != expected_dtype: pixel_values = pixel_values.to(expected_dtype) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class ViTPooler(nn.Module): def __init__(self, config: ViTConfig): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output @add_start_docstrings( """ViT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://arxiv.org/abs/2111.09886). <Tip> Note that we provide a script to pre-train this model on custom data in our [examples directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining). </Tip> """, VIT_START_DOCSTRING, ) class ViTForMaskedImageModeling(ViTPreTrainedModel): def __init__(self, config: ViTConfig) -> None: super().__init__(config) self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True) self.decoder = nn.Sequential( nn.Conv2d( in_channels=config.hidden_size, out_channels=config.encoder_stride**2 * config.num_channels, kernel_size=1, ), nn.PixelShuffle(config.encoder_stride), ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, MaskedLMOutput]: r""" bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Returns: Examples: ```python >>> from transformers import ViTFeatureExtractor, ViTForMaskedImageModeling >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k") >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 >>> pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values >>> # create random boolean mask of shape (batch_size, num_patches) >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss, reconstructed_pixel_values = outputs.loss, outputs.logits >>> list(reconstructed_pixel_values.shape) [1, 3, 224, 224] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.vit( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) sequence_output = outputs[0] # Reshape to (batch_size, num_channels, height, width) sequence_output = sequence_output[:, 1:] batch_size, sequence_length, num_channels = sequence_output.shape height = width = math.floor(sequence_length**0.5) sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) # Reconstruct pixel values reconstructed_pixel_values = self.decoder(sequence_output) masked_im_loss = None if bool_masked_pos is not None: size = self.config.image_size // self.config.patch_size bool_masked_pos = bool_masked_pos.reshape(-1, size, size) mask = ( bool_masked_pos.repeat_interleave(self.config.patch_size, 1) .repeat_interleave(self.config.patch_size, 2) .unsqueeze(1) .contiguous() ) reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none") masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels if not return_dict: output = (reconstructed_pixel_values,) + outputs[1:] return ((masked_im_loss,) + output) if masked_im_loss is not None else output return MaskedLMOutput( loss=masked_im_loss, logits=reconstructed_pixel_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. <Tip> Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained position embeddings to the higher resolution. </Tip> """, VIT_START_DOCSTRING, ) class ViTForImageClassification(ViTPreTrainedModel): def __init__(self, config: ViTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.vit = ViTModel(config, add_pooling_layer=False) # Classifier head self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, ImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.vit( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output[:, 0, :]) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

# coding=utf-8 # Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch ViT model.""" import collections.abc import math from typing import Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, MaskedLMOutput from ...modeling_utils import PreTrainedModel from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_vit import ViTConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "ViTConfig" _FEAT_EXTRACTOR_FOR_DOC = "ViTFeatureExtractor" # Base docstring _CHECKPOINT_FOR_DOC = "google/vit-base-patch16-224-in21k" _EXPECTED_OUTPUT_SHAPE = [1, 197, 768] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "google/vit-base-patch16-224" _IMAGE_CLASS_EXPECTED_OUTPUT = "Egyptian cat" VIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/vit-base-patch16-224", # See all ViT models at https://huggingface.co/models?filter=vit ] class ViTEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None: super().__init__() self.cls_token = nn.Parameter( nn.init.trunc_normal_( torch.zeros(1, 1, config.hidden_size, dtype=torch.float32), mean=0.0, std=config.initializer_range ) ) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = ViTPatchEmbeddings(config) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter( nn.init.trunc_normal_( torch.zeros(1, num_patches + 1, config.hidden_size, dtype=torch.float32), mean=0.0, std=config.initializer_range, ) ) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174 """ num_patches = embeddings.shape[1] - 1 num_positions = self.position_embeddings.shape[1] - 1 if num_patches == num_positions and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] h0 = height // self.config.patch_size w0 = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 h0, w0 = h0 + 0.1, w0 + 0.1 patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim) patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)), mode="bicubic", align_corners=False, ) assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1] patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) if bool_masked_pos is not None: seq_length = embeddings.shape[1] mask_tokens = self.mask_token.expand(batch_size, seq_length, -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings class ViTPatchEmbeddings(nn.Module): """ This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. """ def __init__(self, config): super().__init__() image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the configuration." ) if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) return embeddings class ViTSelfAttention(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class ViTSelfOutput(nn.Module): """ The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class ViTAttention(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.attention = ViTSelfAttention(config) self.output = ViTSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention(hidden_states, head_mask, output_attentions) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class ViTIntermediate(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class ViTOutput(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class ViTLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: ViTConfig) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = ViTAttention(config) self.intermediate = ViTIntermediate(config) self.output = ViTOutput(config) self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class ViTEncoder(nn.Module): def __init__(self, config: ViTConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) class ViTPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = ViTConfig base_model_prefix = "vit" main_input_name = "pixel_values" supports_gradient_checkpointing = True _no_split_modules = [] def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid # `trunc_normal_cpu` not implemented in `half` issues module.weight.data = nn.init.trunc_normal_( module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range ).to(module.weight.dtype) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module: ViTEncoder, value: bool = False) -> None: if isinstance(module, ViTEncoder): module.gradient_checkpointing = value VIT_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`ViTConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ VIT_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`ViTFeatureExtractor`]. See [`ViTFeatureExtractor.__call__`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ @add_start_docstrings( "The bare ViT Model transformer outputting raw hidden-states without any specific head on top.", VIT_START_DOCSTRING, ) class ViTModel(ViTPreTrainedModel): def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False): super().__init__(config) self.config = config self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token) self.encoder = ViTEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = ViTPooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> ViTPatchEmbeddings: return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?) expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype if pixel_values.dtype != expected_dtype: pixel_values = pixel_values.to(expected_dtype) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class ViTPooler(nn.Module): def __init__(self, config: ViTConfig): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output @add_start_docstrings( """ViT Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://arxiv.org/abs/2111.09886). <Tip> Note that we provide a script to pre-train this model on custom data in our [examples directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining). </Tip> """, VIT_START_DOCSTRING, ) class ViTForMaskedImageModeling(ViTPreTrainedModel): def __init__(self, config: ViTConfig) -> None: super().__init__(config) self.vit = ViTModel(config, add_pooling_layer=False, use_mask_token=True) self.decoder = nn.Sequential( nn.Conv2d( in_channels=config.hidden_size, out_channels=config.encoder_stride**2 * config.num_channels, kernel_size=1, ), nn.PixelShuffle(config.encoder_stride), ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, MaskedLMOutput]: r""" bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Returns: Examples: ```python >>> from transformers import ViTFeatureExtractor, ViTForMaskedImageModeling >>> import torch >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k") >>> model = ViTForMaskedImageModeling.from_pretrained("google/vit-base-patch16-224-in21k") >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2 >>> pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values >>> # create random boolean mask of shape (batch_size, num_patches) >>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss, reconstructed_pixel_values = outputs.loss, outputs.logits >>> list(reconstructed_pixel_values.shape) [1, 3, 224, 224] ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.vit( pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) sequence_output = outputs[0] # Reshape to (batch_size, num_channels, height, width) sequence_output = sequence_output[:, 1:] batch_size, sequence_length, num_channels = sequence_output.shape height = width = math.floor(sequence_length**0.5) sequence_output = sequence_output.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) # Reconstruct pixel values reconstructed_pixel_values = self.decoder(sequence_output) masked_im_loss = None if bool_masked_pos is not None: size = self.config.image_size // self.config.patch_size bool_masked_pos = bool_masked_pos.reshape(-1, size, size) mask = ( bool_masked_pos.repeat_interleave(self.config.patch_size, 1) .repeat_interleave(self.config.patch_size, 2) .unsqueeze(1) .contiguous() ) reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none") masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels if not return_dict: output = (reconstructed_pixel_values,) + outputs[1:] return ((masked_im_loss,) + output) if masked_im_loss is not None else output return MaskedLMOutput( loss=masked_im_loss, logits=reconstructed_pixel_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) @add_start_docstrings( """ ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for ImageNet. <Tip> Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained position embeddings to the higher resolution. </Tip> """, VIT_START_DOCSTRING, ) class ViTForImageClassification(ViTPreTrainedModel): def __init__(self, config: ViTConfig) -> None: super().__init__(config) self.num_labels = config.num_labels self.vit = ViTModel(config, add_pooling_layer=False) # Classifier head self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, ImageClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.vit( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) sequence_output = outputs[0] logits = self.classifier(sequence_output[:, 0, :]) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits,) + outputs[1:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ import logging import os import sys import time from collections import defaultdict from dataclasses import dataclass, field # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. from pathlib import Path from typing import Dict, List, Optional, Tuple import datasets import numpy as np from datasets import load_dataset from tqdm import tqdm import flax import jax import jax.numpy as jnp import optax from flax import jax_utils, traverse_util from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoTokenizer, FlaxAutoModelForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed, ) if datasets.__version__ <= "1.8.0": raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming") MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the column to retrieve the training text."} ) shuffle_buffer_size: int = field( default=10000, metadata={"help": "The number of examples to pre-load for shuffling."} ) num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."}) num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"}) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." @flax.struct.dataclass class FlaxDataCollatorForLanguageModeling: """ Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same length. Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data. mlm_probability (:obj:`float`, `optional`, defaults to 0.15): The probability with which to (randomly) mask tokens in the input. .. note:: For best performance, this data collator should be used with a dataset having items that are dictionaries or BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the argument :obj:`return_special_tokens_mask=True`. """ tokenizer: PreTrainedTokenizerBase mlm_probability: float = 0.15 def __post_init__(self): if self.tokenizer.mask_token is None: raise ValueError( "This tokenizer does not have a mask token which is necessary for masked language modeling. " "You should pass `mlm=False` to train on causal language modeling instead." ) def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]: # Handle dict or lists with proper padding and conversion to tensor. batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY) # If special token mask has been preprocessed, pop it from the dict. special_tokens_mask = batch.pop("special_tokens_mask", None) batch["input_ids"], batch["labels"] = self.mask_tokens( batch["input_ids"], special_tokens_mask=special_tokens_mask ) return batch def mask_tokens( self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray] ) -> Tuple[jnp.ndarray, jnp.ndarray]: """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ labels = inputs.copy() # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`) probability_matrix = np.full(labels.shape, self.mlm_probability) special_tokens_mask = special_tokens_mask.astype("bool") probability_matrix[special_tokens_mask] = 0.0 masked_indices = np.random.binomial(1, probability_matrix).astype("bool") labels[~masked_indices] = -100 # We only compute loss on masked tokens # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token) # 10% of the time, we replace masked input tokens with random word indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool") indices_random &= masked_indices & ~indices_replaced random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4") inputs[indices_random] = random_words[indices_random] # The rest of the time (10% of the time) we keep the masked input tokens unchanged return inputs, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray: num_samples = len(samples_idx) samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size batch_idx = np.split(samples_idx, sections_split) return batch_idx def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length): """ The training iterator is advanced so that after groupifying the samples, `num_samples` of length `max_seq_length` are returned. """ num_total_tokens = max_seq_length * num_samples samples = defaultdict(list) i = 0 while i < num_total_tokens: tokenized_samples = next(train_iterator) i += len(tokenized_samples["input_ids"]) # concatenate tokenized samples to list (excluding "id" and "text") samples = { k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"] } # Concatenated tokens are split to lists of length `max_seq_length`. # Note that remainedr of % max_seq_length are thrown away. def group_texts(examples): result = { k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)] for k, t in examples.items() } return result grouped_samples = group_texts(samples) return grouped_samples def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) if __name__ == "__main__": # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty." "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level="INFO", datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, streaming=True, split="train", ) if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more # efficient when it receives the `special_tokens_mask`. def tokenize_function(examples): return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True) tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys())) shuffle_seed = training_args.seed tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed) has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) # Data collator # This one will take care of randomly masking the tokens. data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxAutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: model = FlaxAutoModelForMaskedLM.from_config( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count() # define number steps per stream epoch num_train_steps = data_args.num_train_steps # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. # Note that this mask is specifically adapted for FlaxBERT-like models. # For other models, one should correct the layer norm parameter naming # accordingly. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer adamw = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() / label_mask.sum() return loss grad_fn = jax.value_and_grad(loss_fn) loss, grad = grad_fn(state.params) grad = jax.lax.pmean(grad, "batch") new_state = state.apply_gradients(grads=grad) metrics = jax.lax.pmean( {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch" ) return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 train_start = time.time() train_metrics = [] eval_metrics = [] training_iter = iter(tokenized_datasets) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) steps = tqdm(range(num_train_steps), desc="Training...", position=0) for step in range(num_train_steps): # ======================== Training ================================ try: samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) except StopIteration: # Once the end of the dataset stream is reached, the training iterator # is reinitialized and reshuffled and a new eval dataset is randomely chosen. shuffle_seed += 1 tokenized_datasets.set_epoch(shuffle_seed) training_iter = iter(tokenized_datasets) eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) # process input samples model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) if step % training_args.logging_steps == 0 and step > 0: steps.write( f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:" f" {train_metric['learning_rate'].mean()})" ) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, step) train_metrics = [] # ======================== Evaluating ============================== if step % training_args.eval_steps == 0 and step > 0: # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(data_args.num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)): # process input samples batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()} model_inputs = data_collator(batch_eval_samples) # Model forward model_inputs = shard(model_inputs.data) metrics = p_eval_step(state.params, model_inputs) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar steps.desc = ( f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:" f" {eval_metrics['accuracy']})" ) if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, step) eval_metrics = [] # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained( training_args.output_dir, params=params, push_to_hub=training_args.push_to_hub, commit_message=f"Saving weights and logs of step {step+1}", ) # update tqdm bar steps.update(1)

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Team All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a text file or a dataset. Here is the full list of checkpoints on the hub that can be fine-tuned by this script: https://huggingface.co/models?filter=fill-mask """ import logging import os import sys import time from collections import defaultdict from dataclasses import dataclass, field # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. from pathlib import Path from typing import Dict, List, Optional, Tuple import datasets import numpy as np from datasets import load_dataset from tqdm import tqdm import flax import jax import jax.numpy as jnp import optax from flax import jax_utils, traverse_util from flax.training import train_state from flax.training.common_utils import get_metrics, onehot, shard from transformers import ( CONFIG_MAPPING, FLAX_MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoTokenizer, FlaxAutoModelForMaskedLM, HfArgumentParser, PreTrainedTokenizerBase, TensorType, TrainingArguments, is_tensorboard_available, set_seed, ) if datasets.__version__ <= "1.8.0": raise ValueError("Make sure to upgrade `datasets` to a version >= 1.9.0 to use dataset streaming") MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. """ model_name_or_path: Optional[str] = field( default=None, metadata={ "help": ( "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." ) }, ) model_type: Optional[str] = field( default=None, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, ) config_name: Optional[str] = field( default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} ) tokenizer_name: Optional[str] = field( default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} ) use_fast_tokenizer: bool = field( default=True, metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, ) dtype: Optional[str] = field( default="float32", metadata={ "help": ( "Floating-point format in which the model weights should be initialized and trained. Choose one of" " `[float32, float16, bfloat16]`." ) }, ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. """ dataset_name: Optional[str] = field( default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} ) dataset_config_name: Optional[str] = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) validation_file: Optional[str] = field( default=None, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, ) train_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, ) validation_ref_file: Optional[str] = field( default=None, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} ) validation_split_percentage: Optional[int] = field( default=5, metadata={ "help": "The percentage of the train set used as validation set in case there's no validation split" }, ) max_seq_length: Optional[int] = field( default=None, metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated. Default to the max input length of the model." ) }, ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) mlm_probability: float = field( default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} ) pad_to_max_length: bool = field( default=False, metadata={ "help": ( "Whether to pad all samples to `max_seq_length`. " "If False, will pad the samples dynamically when batching to the maximum length in the batch." ) }, ) line_by_line: bool = field( default=False, metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the column to retrieve the training text."} ) shuffle_buffer_size: int = field( default=10000, metadata={"help": "The number of examples to pre-load for shuffling."} ) num_train_steps: int = field(default=50000, metadata={"help": "The number of training steps."}) num_eval_samples: int = field(default=50000, metadata={"help": "The number of samples to be used for evaluation"}) def __post_init__(self): if self.dataset_name is None and self.train_file is None and self.validation_file is None: raise ValueError("Need either a dataset name or a training/validation file.") else: if self.train_file is not None: extension = self.train_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." if self.validation_file is not None: extension = self.validation_file.split(".")[-1] assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." @flax.struct.dataclass class FlaxDataCollatorForLanguageModeling: """ Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same length. Args: tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): The tokenizer used for encoding the data. mlm_probability (:obj:`float`, `optional`, defaults to 0.15): The probability with which to (randomly) mask tokens in the input. .. note:: For best performance, this data collator should be used with a dataset having items that are dictionaries or BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a :class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the argument :obj:`return_special_tokens_mask=True`. """ tokenizer: PreTrainedTokenizerBase mlm_probability: float = 0.15 def __post_init__(self): if self.tokenizer.mask_token is None: raise ValueError( "This tokenizer does not have a mask token which is necessary for masked language modeling. " "You should pass `mlm=False` to train on causal language modeling instead." ) def __call__(self, examples: List[Dict[str, np.ndarray]]) -> Dict[str, np.ndarray]: # Handle dict or lists with proper padding and conversion to tensor. batch = self.tokenizer.pad(examples, return_tensors=TensorType.NUMPY) # If special token mask has been preprocessed, pop it from the dict. special_tokens_mask = batch.pop("special_tokens_mask", None) batch["input_ids"], batch["labels"] = self.mask_tokens( batch["input_ids"], special_tokens_mask=special_tokens_mask ) return batch def mask_tokens( self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray] ) -> Tuple[jnp.ndarray, jnp.ndarray]: """ Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. """ labels = inputs.copy() # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`) probability_matrix = np.full(labels.shape, self.mlm_probability) special_tokens_mask = special_tokens_mask.astype("bool") probability_matrix[special_tokens_mask] = 0.0 masked_indices = np.random.binomial(1, probability_matrix).astype("bool") labels[~masked_indices] = -100 # We only compute loss on masked tokens # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK]) indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token) # 10% of the time, we replace masked input tokens with random word indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool") indices_random &= masked_indices & ~indices_replaced random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4") inputs[indices_random] = random_words[indices_random] # The rest of the time (10% of the time) we keep the masked input tokens unchanged return inputs, labels def generate_batch_splits(samples_idx: np.ndarray, batch_size: int) -> np.ndarray: num_samples = len(samples_idx) samples_to_remove = num_samples % batch_size if samples_to_remove != 0: samples_idx = samples_idx[:-samples_to_remove] sections_split = num_samples // batch_size batch_idx = np.split(samples_idx, sections_split) return batch_idx def advance_iter_and_group_samples(train_iterator, num_samples, max_seq_length): """ The training iterator is advanced so that after groupifying the samples, `num_samples` of length `max_seq_length` are returned. """ num_total_tokens = max_seq_length * num_samples samples = defaultdict(list) i = 0 while i < num_total_tokens: tokenized_samples = next(train_iterator) i += len(tokenized_samples["input_ids"]) # concatenate tokenized samples to list (excluding "id" and "text") samples = { k: samples[k] + tokenized_samples[k] for k in ["input_ids", "attention_mask", "special_tokens_mask"] } # Concatenated tokens are split to lists of length `max_seq_length`. # Note that remainedr of % max_seq_length are thrown away. def group_texts(examples): result = { k: [t[i : i + max_seq_length] for i in range(0, num_total_tokens, max_seq_length)] for k, t in examples.items() } return result grouped_samples = group_texts(samples) return grouped_samples def write_train_metric(summary_writer, train_metrics, train_time, step): summary_writer.scalar("train_time", train_time, step) train_metrics = get_metrics(train_metrics) for key, vals in train_metrics.items(): tag = f"train_{key}" for i, val in enumerate(vals): summary_writer.scalar(tag, val, step - len(vals) + i + 1) def write_eval_metric(summary_writer, eval_metrics, step): for metric_name, value in eval_metrics.items(): summary_writer.scalar(f"eval_{metric_name}", value, step) if __name__ == "__main__": # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir) and os.listdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty." "Use --overwrite_output_dir to overcome." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level="INFO", datefmt="[%X]", ) # Log on each process the small summary: logger = logging.getLogger(__name__) logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): logger.info(f"Training/evaluation parameters {training_args}") # Set seed before initializing model. set_seed(training_args.seed) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. dataset = load_dataset( data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, streaming=True, split="train", ) if model_args.config_name: config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir) elif model_args.model_name_or_path: config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir) else: config = CONFIG_MAPPING[model_args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if model_args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained( model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) elif model_args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer ) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) # Otherwise, we tokenize every text, then concatenate them together before splitting them in smaller parts. # We use `return_special_tokens_mask=True` because DataCollatorForLanguageModeling (see below) is more # efficient when it receives the `special_tokens_mask`. def tokenize_function(examples): return tokenizer(examples[data_args.text_column_name], return_special_tokens_mask=True) tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=list(dataset.features.keys())) shuffle_seed = training_args.seed tokenized_datasets = tokenized_datasets.shuffle(buffer_size=data_args.shuffle_buffer_size, seed=shuffle_seed) has_tensorboard = is_tensorboard_available() if has_tensorboard and jax.process_index() == 0: try: from flax.metrics.tensorboard import SummaryWriter except ImportError as ie: has_tensorboard = False logger.warning( f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" ) summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) # Data collator # This one will take care of randomly masking the tokens. data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) # Initialize our training rng = jax.random.PRNGKey(training_args.seed) dropout_rngs = jax.random.split(rng, jax.local_device_count()) if model_args.model_name_or_path: model = FlaxAutoModelForMaskedLM.from_pretrained( model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) else: model = FlaxAutoModelForMaskedLM.from_config( config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype) ) # Store some constant num_epochs = int(training_args.num_train_epochs) train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count() # define number steps per stream epoch num_train_steps = data_args.num_train_steps # Create learning rate schedule warmup_fn = optax.linear_schedule( init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps ) decay_fn = optax.linear_schedule( init_value=training_args.learning_rate, end_value=0, transition_steps=num_train_steps - training_args.warmup_steps, ) linear_decay_lr_schedule_fn = optax.join_schedules( schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] ) # We use Optax's "masking" functionality to not apply weight decay # to bias and LayerNorm scale parameters. decay_mask_fn returns a # mask boolean with the same structure as the parameters. # The mask is True for parameters that should be decayed. # Note that this mask is specifically adapted for FlaxBERT-like models. # For other models, one should correct the layer norm parameter naming # accordingly. def decay_mask_fn(params): flat_params = traverse_util.flatten_dict(params) flat_mask = {path: (path[-1] != "bias" and path[-2:] != ("LayerNorm", "scale")) for path in flat_params} return traverse_util.unflatten_dict(flat_mask) # create adam optimizer adamw = optax.adamw( learning_rate=linear_decay_lr_schedule_fn, b1=training_args.adam_beta1, b2=training_args.adam_beta2, eps=training_args.adam_epsilon, weight_decay=training_args.weight_decay, mask=decay_mask_fn, ) # Setup train state state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw) # Define gradient update step fn def train_step(state, batch, dropout_rng): dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) def loss_fn(params): labels = batch.pop("labels") logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # take average loss = loss.sum() / label_mask.sum() return loss grad_fn = jax.value_and_grad(loss_fn) loss, grad = grad_fn(state.params) grad = jax.lax.pmean(grad, "batch") new_state = state.apply_gradients(grads=grad) metrics = jax.lax.pmean( {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch" ) return new_state, metrics, new_dropout_rng # Create parallel version of the train step p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) # Define eval fn def eval_step(params, batch): labels = batch.pop("labels") logits = model(**batch, params=params, train=False)[0] # compute loss, ignore padded input tokens label_mask = jnp.where(labels > 0, 1.0, 0.0) loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask # compute accuracy accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask # summarize metrics metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} metrics = jax.lax.psum(metrics, axis_name="batch") return metrics p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) # Replicate the train state on each device state = jax_utils.replicate(state) train_time = 0 train_start = time.time() train_metrics = [] eval_metrics = [] training_iter = iter(tokenized_datasets) max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) eval_samples = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) steps = tqdm(range(num_train_steps), desc="Training...", position=0) for step in range(num_train_steps): # ======================== Training ================================ try: samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) except StopIteration: # Once the end of the dataset stream is reached, the training iterator # is reinitialized and reshuffled and a new eval dataset is randomely chosen. shuffle_seed += 1 tokenized_datasets.set_epoch(shuffle_seed) training_iter = iter(tokenized_datasets) eval_dataset = advance_iter_and_group_samples(training_iter, data_args.num_eval_samples, max_seq_length) samples = advance_iter_and_group_samples(training_iter, train_batch_size, max_seq_length) # process input samples model_inputs = data_collator(samples) # Model forward model_inputs = shard(model_inputs.data) state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) train_metrics.append(train_metric) if step % training_args.logging_steps == 0 and step > 0: steps.write( f"Step... ({step} | Loss: {train_metric['loss'].mean()}, Learning Rate:" f" {train_metric['learning_rate'].mean()})" ) train_time += time.time() - train_start if has_tensorboard and jax.process_index() == 0: write_train_metric(summary_writer, train_metrics, train_time, step) train_metrics = [] # ======================== Evaluating ============================== if step % training_args.eval_steps == 0 and step > 0: # Avoid using jax.numpy here in case of TPU training eval_samples_idx = np.arange(data_args.num_eval_samples) eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=1)): # process input samples batch_eval_samples = {k: [v[idx] for idx in batch_idx] for k, v in eval_samples.items()} model_inputs = data_collator(batch_eval_samples) # Model forward model_inputs = shard(model_inputs.data) metrics = p_eval_step(state.params, model_inputs) eval_metrics.append(metrics) # normalize eval metrics eval_metrics = get_metrics(eval_metrics) eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) eval_normalizer = eval_metrics.pop("normalizer") eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) # Update progress bar steps.desc = ( f"Step... ({step + 1}/{num_train_steps} | Loss: {eval_metrics['loss']}, Acc:" f" {eval_metrics['accuracy']})" ) if has_tensorboard and jax.process_index() == 0: write_eval_metric(summary_writer, eval_metrics, step) eval_metrics = [] # save checkpoint after each epoch and push checkpoint to the hub if jax.process_index() == 0: params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) model.save_pretrained( training_args.output_dir, params=params, push_to_hub=training_args.push_to_hub, commit_message=f"Saving weights and logs of step {step+1}", ) # update tqdm bar steps.update(1)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./examples/research_projects/rag/__init__.py

import os import sys sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))

import os import sys sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./examples/research_projects/jax-projects/big_bird/prepare_natural_questions.py

import os import numpy as np from tqdm import tqdm import jsonlines DOC_STRIDE = 2048 MAX_LENGTH = 4096 SEED = 42 PROCESS_TRAIN = os.environ.pop("PROCESS_TRAIN", "false") CATEGORY_MAPPING = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4} def _get_single_answer(example): def choose_first(answer, is_long_answer=False): assert isinstance(answer, list) if len(answer) == 1: answer = answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: a = {k: [a[k]] for k in a} if len(a["start_token"]) > 0: break return a answer = {"id": example["id"]} annotation = example["annotations"] yes_no_answer = annotation["yes_no_answer"] if 0 in yes_no_answer or 1 in yes_no_answer: answer["category"] = ["yes"] if 1 in yes_no_answer else ["no"] answer["start_token"] = answer["end_token"] = [] answer["start_byte"] = answer["end_byte"] = [] answer["text"] = ["<cls>"] else: answer["category"] = ["short"] out = choose_first(annotation["short_answers"]) if len(out["start_token"]) == 0: # answer will be long if short is not available answer["category"] = ["long"] out = choose_first(annotation["long_answer"], is_long_answer=True) out["text"] = [] answer.update(out) # disregard some samples if len(answer["start_token"]) > 1 or answer["start_token"] == answer["end_token"]: answer["remove_it"] = True else: answer["remove_it"] = False cols = ["start_token", "end_token", "start_byte", "end_byte", "text"] if not all([isinstance(answer[k], list) for k in cols]): raise ValueError("Issue in ID", example["id"]) return answer def get_context_and_ans(example, assertion=False): """Gives new context after removing <html> & new answer tokens as per new context""" answer = _get_single_answer(example) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element doc = example["document"]["tokens"] context = [] for i in range(len(doc["token"])): if not doc["is_html"][i]: context.append(doc["token"][i]) return { "context": " ".join(context), "answer": { "start_token": -100, # ignore index in cross-entropy "end_token": -100, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples cols = ["start_token", "end_token"] answer.update({k: answer[k][0] if len(answer[k]) > 0 else answer[k] for k in cols}) # e.g. [10] == 10 doc = example["document"]["tokens"] start_token = answer["start_token"] end_token = answer["end_token"] context = [] for i in range(len(doc["token"])): if not doc["is_html"][i]: context.append(doc["token"][i]) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 new = " ".join(context[start_token:end_token]) # checking above code if assertion: """checking if above code is working as expected for all the samples""" is_html = doc["is_html"][answer["start_token"] : answer["end_token"]] old = doc["token"][answer["start_token"] : answer["end_token"]] old = " ".join([old[i] for i in range(len(old)) if not is_html[i]]) if new != old: print("ID:", example["id"]) print("New:", new, end="\n") print("Old:", old, end="\n\n") return { "context": " ".join(context), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def get_strided_contexts_and_ans(example, tokenizer, doc_stride=2048, max_length=4096, assertion=True): # overlap will be of doc_stride - q_len out = get_context_and_ans(example, assertion=assertion) answer = out["answer"] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } input_ids = tokenizer(example["question"]["text"], out["context"]).input_ids q_len = input_ids.index(tokenizer.sep_token_id) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element inputs = [] category = [] q_indices = input_ids[:q_len] doc_start_indices = range(q_len, len(input_ids), max_length - doc_stride) for i in doc_start_indices: end_index = i + max_length - q_len slice = input_ids[i:end_index] inputs.append(q_indices + slice) category.append(answer["category"][0]) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-100] * len(category), "end_token": [-100] * len(category), "category": category, }, } splitted_context = out["context"].split() complete_end_token = splitted_context[answer["end_token"]] answer["start_token"] = len( tokenizer( " ".join(splitted_context[: answer["start_token"]]), add_special_tokens=False, ).input_ids ) answer["end_token"] = len( tokenizer(" ".join(splitted_context[: answer["end_token"]]), add_special_tokens=False).input_ids ) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token num_sub_tokens = len(tokenizer(complete_end_token, add_special_tokens=False).input_ids) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 old = input_ids[answer["start_token"] : answer["end_token"] + 1] # right & left are inclusive start_token = answer["start_token"] end_token = answer["end_token"] if assertion: """This won't match exactly because of extra gaps => visaully inspect everything""" new = tokenizer.decode(old) if answer["span"] != new: print("ISSUE IN TOKENIZATION") print("OLD:", answer["span"]) print("NEW:", new, end="\n\n") if len(input_ids) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } q_indices = input_ids[:q_len] doc_start_indices = range(q_len, len(input_ids), max_length - doc_stride) inputs = [] answers_start_token = [] answers_end_token = [] answers_category = [] # null, yes, no, long, short for i in doc_start_indices: end_index = i + max_length - q_len slice = input_ids[i:end_index] inputs.append(q_indices + slice) assert len(inputs[-1]) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: start_token = start_token - i + q_len end_token = end_token - i + q_len answers_category.append(answer["category"][0]) # ["short"] -> "short" else: start_token = -100 end_token = -100 answers_category.append("null") new = inputs[-1][start_token : end_token + 1] answers_start_token.append(start_token) answers_end_token.append(end_token) if assertion: """checking if above code is working as expected for all the samples""" if new != old and new != [tokenizer.cls_token_id]: print("ISSUE in strided for ID:", example["id"]) print("New:", tokenizer.decode(new)) print("Old:", tokenizer.decode(old), end="\n\n") if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def prepare_inputs(example, tokenizer, doc_stride=2048, max_length=4096, assertion=False): example = get_strided_contexts_and_ans( example, tokenizer, doc_stride=doc_stride, max_length=max_length, assertion=assertion, ) return example def save_to_disk(hf_data, file_name): with jsonlines.open(file_name, "a") as writer: for example in tqdm(hf_data, total=len(hf_data), desc="Saving samples ... "): labels = example["labels"] for ids, start, end, cat in zip( example["input_ids"], labels["start_token"], labels["end_token"], labels["category"], ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { "input_ids": ids, "start_token": start, "end_token": end, "category": CATEGORY_MAPPING[cat], } ) if __name__ == "__main__": """Running area""" from datasets import load_dataset from transformers import BigBirdTokenizer data = load_dataset("natural_questions") tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") data = data["train" if PROCESS_TRAIN == "true" else "validation"] fn_kwargs = dict( tokenizer=tokenizer, doc_stride=DOC_STRIDE, max_length=MAX_LENGTH, assertion=False, ) data = data.map(prepare_inputs, fn_kwargs=fn_kwargs) data = data.remove_columns(["annotations", "document", "id", "question"]) print(data) np.random.seed(SEED) cache_file_name = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl" save_to_disk(data, file_name=cache_file_name)

import os import numpy as np from tqdm import tqdm import jsonlines DOC_STRIDE = 2048 MAX_LENGTH = 4096 SEED = 42 PROCESS_TRAIN = os.environ.pop("PROCESS_TRAIN", "false") CATEGORY_MAPPING = {"null": 0, "short": 1, "long": 2, "yes": 3, "no": 4} def _get_single_answer(example): def choose_first(answer, is_long_answer=False): assert isinstance(answer, list) if len(answer) == 1: answer = answer[0] return {k: [answer[k]] for k in answer} if is_long_answer else answer for a in answer: if is_long_answer: a = {k: [a[k]] for k in a} if len(a["start_token"]) > 0: break return a answer = {"id": example["id"]} annotation = example["annotations"] yes_no_answer = annotation["yes_no_answer"] if 0 in yes_no_answer or 1 in yes_no_answer: answer["category"] = ["yes"] if 1 in yes_no_answer else ["no"] answer["start_token"] = answer["end_token"] = [] answer["start_byte"] = answer["end_byte"] = [] answer["text"] = ["<cls>"] else: answer["category"] = ["short"] out = choose_first(annotation["short_answers"]) if len(out["start_token"]) == 0: # answer will be long if short is not available answer["category"] = ["long"] out = choose_first(annotation["long_answer"], is_long_answer=True) out["text"] = [] answer.update(out) # disregard some samples if len(answer["start_token"]) > 1 or answer["start_token"] == answer["end_token"]: answer["remove_it"] = True else: answer["remove_it"] = False cols = ["start_token", "end_token", "start_byte", "end_byte", "text"] if not all([isinstance(answer[k], list) for k in cols]): raise ValueError("Issue in ID", example["id"]) return answer def get_context_and_ans(example, assertion=False): """Gives new context after removing <html> & new answer tokens as per new context""" answer = _get_single_answer(example) # bytes are of no use del answer["start_byte"] del answer["end_byte"] # handle yes_no answers explicitly if answer["category"][0] in ["yes", "no"]: # category is list with one element doc = example["document"]["tokens"] context = [] for i in range(len(doc["token"])): if not doc["is_html"][i]: context.append(doc["token"][i]) return { "context": " ".join(context), "answer": { "start_token": -100, # ignore index in cross-entropy "end_token": -100, # ignore index in cross-entropy "category": answer["category"], "span": answer["category"], # extra }, } # later, help in removing all no answers if answer["start_token"] == [-1]: return { "context": "None", "answer": { "start_token": -1, "end_token": -1, "category": "null", "span": "None", # extra }, } # handling normal samples cols = ["start_token", "end_token"] answer.update({k: answer[k][0] if len(answer[k]) > 0 else answer[k] for k in cols}) # e.g. [10] == 10 doc = example["document"]["tokens"] start_token = answer["start_token"] end_token = answer["end_token"] context = [] for i in range(len(doc["token"])): if not doc["is_html"][i]: context.append(doc["token"][i]) else: if answer["start_token"] > i: start_token -= 1 if answer["end_token"] > i: end_token -= 1 new = " ".join(context[start_token:end_token]) # checking above code if assertion: """checking if above code is working as expected for all the samples""" is_html = doc["is_html"][answer["start_token"] : answer["end_token"]] old = doc["token"][answer["start_token"] : answer["end_token"]] old = " ".join([old[i] for i in range(len(old)) if not is_html[i]]) if new != old: print("ID:", example["id"]) print("New:", new, end="\n") print("Old:", old, end="\n\n") return { "context": " ".join(context), "answer": { "start_token": start_token, "end_token": end_token - 1, # this makes it inclusive "category": answer["category"], # either long or short "span": new, # extra }, } def get_strided_contexts_and_ans(example, tokenizer, doc_stride=2048, max_length=4096, assertion=True): # overlap will be of doc_stride - q_len out = get_context_and_ans(example, assertion=assertion) answer = out["answer"] # later, removing these samples if answer["start_token"] == -1: return { "example_id": example["id"], "input_ids": [[-1]], "labels": { "start_token": [-1], "end_token": [-1], "category": ["null"], }, } input_ids = tokenizer(example["question"]["text"], out["context"]).input_ids q_len = input_ids.index(tokenizer.sep_token_id) + 1 # return yes/no if answer["category"][0] in ["yes", "no"]: # category is list with one element inputs = [] category = [] q_indices = input_ids[:q_len] doc_start_indices = range(q_len, len(input_ids), max_length - doc_stride) for i in doc_start_indices: end_index = i + max_length - q_len slice = input_ids[i:end_index] inputs.append(q_indices + slice) category.append(answer["category"][0]) if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": [-100] * len(category), "end_token": [-100] * len(category), "category": category, }, } splitted_context = out["context"].split() complete_end_token = splitted_context[answer["end_token"]] answer["start_token"] = len( tokenizer( " ".join(splitted_context[: answer["start_token"]]), add_special_tokens=False, ).input_ids ) answer["end_token"] = len( tokenizer(" ".join(splitted_context[: answer["end_token"]]), add_special_tokens=False).input_ids ) answer["start_token"] += q_len answer["end_token"] += q_len # fixing end token num_sub_tokens = len(tokenizer(complete_end_token, add_special_tokens=False).input_ids) if num_sub_tokens > 1: answer["end_token"] += num_sub_tokens - 1 old = input_ids[answer["start_token"] : answer["end_token"] + 1] # right & left are inclusive start_token = answer["start_token"] end_token = answer["end_token"] if assertion: """This won't match exactly because of extra gaps => visaully inspect everything""" new = tokenizer.decode(old) if answer["span"] != new: print("ISSUE IN TOKENIZATION") print("OLD:", answer["span"]) print("NEW:", new, end="\n\n") if len(input_ids) <= max_length: return { "example_id": example["id"], "input_ids": [input_ids], "labels": { "start_token": [answer["start_token"]], "end_token": [answer["end_token"]], "category": answer["category"], }, } q_indices = input_ids[:q_len] doc_start_indices = range(q_len, len(input_ids), max_length - doc_stride) inputs = [] answers_start_token = [] answers_end_token = [] answers_category = [] # null, yes, no, long, short for i in doc_start_indices: end_index = i + max_length - q_len slice = input_ids[i:end_index] inputs.append(q_indices + slice) assert len(inputs[-1]) <= max_length, "Issue in truncating length" if start_token >= i and end_token <= end_index - 1: start_token = start_token - i + q_len end_token = end_token - i + q_len answers_category.append(answer["category"][0]) # ["short"] -> "short" else: start_token = -100 end_token = -100 answers_category.append("null") new = inputs[-1][start_token : end_token + 1] answers_start_token.append(start_token) answers_end_token.append(end_token) if assertion: """checking if above code is working as expected for all the samples""" if new != old and new != [tokenizer.cls_token_id]: print("ISSUE in strided for ID:", example["id"]) print("New:", tokenizer.decode(new)) print("Old:", tokenizer.decode(old), end="\n\n") if slice[-1] == tokenizer.sep_token_id: break return { "example_id": example["id"], "input_ids": inputs, "labels": { "start_token": answers_start_token, "end_token": answers_end_token, "category": answers_category, }, } def prepare_inputs(example, tokenizer, doc_stride=2048, max_length=4096, assertion=False): example = get_strided_contexts_and_ans( example, tokenizer, doc_stride=doc_stride, max_length=max_length, assertion=assertion, ) return example def save_to_disk(hf_data, file_name): with jsonlines.open(file_name, "a") as writer: for example in tqdm(hf_data, total=len(hf_data), desc="Saving samples ... "): labels = example["labels"] for ids, start, end, cat in zip( example["input_ids"], labels["start_token"], labels["end_token"], labels["category"], ): if start == -1 and end == -1: continue # leave waste samples with no answer if cat == "null" and np.random.rand() < 0.6: continue # removing 50 % samples writer.write( { "input_ids": ids, "start_token": start, "end_token": end, "category": CATEGORY_MAPPING[cat], } ) if __name__ == "__main__": """Running area""" from datasets import load_dataset from transformers import BigBirdTokenizer data = load_dataset("natural_questions") tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base") data = data["train" if PROCESS_TRAIN == "true" else "validation"] fn_kwargs = dict( tokenizer=tokenizer, doc_stride=DOC_STRIDE, max_length=MAX_LENGTH, assertion=False, ) data = data.map(prepare_inputs, fn_kwargs=fn_kwargs) data = data.remove_columns(["annotations", "document", "id", "question"]) print(data) np.random.seed(SEED) cache_file_name = "nq-training.jsonl" if PROCESS_TRAIN == "true" else "nq-validation.jsonl" save_to_disk(data, file_name=cache_file_name)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/vit_mae/convert_vit_mae_to_pytorch.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert ViT MAE checkpoints from the original repository: https://github.com/facebookresearch/mae""" import argparse import torch from PIL import Image import requests from transformers import ViTMAEConfig, ViTMAEFeatureExtractor, ViTMAEForPreTraining def rename_key(name): if "cls_token" in name: name = name.replace("cls_token", "vit.embeddings.cls_token") if "mask_token" in name: name = name.replace("mask_token", "decoder.mask_token") if "decoder_pos_embed" in name: name = name.replace("decoder_pos_embed", "decoder.decoder_pos_embed") if "pos_embed" in name and "decoder" not in name: name = name.replace("pos_embed", "vit.embeddings.position_embeddings") if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "vit.embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "vit.embeddings.norm") if "decoder_blocks" in name: name = name.replace("decoder_blocks", "decoder.decoder_layers") if "blocks" in name: name = name.replace("blocks", "vit.encoder.layer") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if "decoder_embed" in name: name = name.replace("decoder_embed", "decoder.decoder_embed") if "decoder_norm" in name: name = name.replace("decoder_norm", "decoder.decoder_norm") if "decoder_pred" in name: name = name.replace("decoder_pred", "decoder.decoder_pred") if "norm.weight" in name and "decoder" not in name: name = name.replace("norm.weight", "vit.layernorm.weight") if "norm.bias" in name and "decoder" not in name: name = name.replace("norm.bias", "vit.layernorm.bias") return name def convert_state_dict(orig_state_dict, config): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "qkv" in key: key_split = key.split(".") layer_num = int(key_split[1]) if "decoder_blocks" in key: dim = config.decoder_hidden_size prefix = "decoder.decoder_layers." if "weight" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] elif "bias" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.bias"] = val[:dim] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.bias"] = val[-dim:] else: dim = config.hidden_size prefix = "vit.encoder.layer." if "weight" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] elif "bias" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.bias"] = val[:dim] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.bias"] = val[-dim:] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_vit_mae_checkpoint(checkpoint_url, pytorch_dump_folder_path): config = ViTMAEConfig() if "large" in checkpoint_url: config.hidden_size = 1024 config.intermediate_size = 4096 config.num_hidden_layers = 24 config.num_attention_heads = 16 elif "huge" in checkpoint_url: config.patch_size = 14 config.hidden_size = 1280 config.intermediate_size = 5120 config.num_hidden_layers = 32 config.num_attention_heads = 16 model = ViTMAEForPreTraining(config) state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"] feature_extractor = ViTMAEFeatureExtractor(size=config.image_size) new_state_dict = convert_state_dict(state_dict, config) model.load_state_dict(new_state_dict) model.eval() url = "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTMAEFeatureExtractor(size=config.image_size) inputs = feature_extractor(images=image, return_tensors="pt") # forward pass torch.manual_seed(2) outputs = model(**inputs) logits = outputs.logits if "large" in checkpoint_url: expected_slice = torch.tensor( [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]] ) elif "huge" in checkpoint_url: expected_slice = torch.tensor( [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]] ) else: expected_slice = torch.tensor( [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]] ) # verify logits assert torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-4) print(f"Saving model to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth", type=str, help="URL of the checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert ViT MAE checkpoints from the original repository: https://github.com/facebookresearch/mae""" import argparse import torch from PIL import Image import requests from transformers import ViTMAEConfig, ViTMAEFeatureExtractor, ViTMAEForPreTraining def rename_key(name): if "cls_token" in name: name = name.replace("cls_token", "vit.embeddings.cls_token") if "mask_token" in name: name = name.replace("mask_token", "decoder.mask_token") if "decoder_pos_embed" in name: name = name.replace("decoder_pos_embed", "decoder.decoder_pos_embed") if "pos_embed" in name and "decoder" not in name: name = name.replace("pos_embed", "vit.embeddings.position_embeddings") if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "vit.embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "vit.embeddings.norm") if "decoder_blocks" in name: name = name.replace("decoder_blocks", "decoder.decoder_layers") if "blocks" in name: name = name.replace("blocks", "vit.encoder.layer") if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if "decoder_embed" in name: name = name.replace("decoder_embed", "decoder.decoder_embed") if "decoder_norm" in name: name = name.replace("decoder_norm", "decoder.decoder_norm") if "decoder_pred" in name: name = name.replace("decoder_pred", "decoder.decoder_pred") if "norm.weight" in name and "decoder" not in name: name = name.replace("norm.weight", "vit.layernorm.weight") if "norm.bias" in name and "decoder" not in name: name = name.replace("norm.bias", "vit.layernorm.bias") return name def convert_state_dict(orig_state_dict, config): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "qkv" in key: key_split = key.split(".") layer_num = int(key_split[1]) if "decoder_blocks" in key: dim = config.decoder_hidden_size prefix = "decoder.decoder_layers." if "weight" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] elif "bias" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.bias"] = val[:dim] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.bias"] = val[-dim:] else: dim = config.hidden_size prefix = "vit.encoder.layer." if "weight" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.weight"] = val[:dim, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.weight"] = val[dim : dim * 2, :] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.weight"] = val[-dim:, :] elif "bias" in key: orig_state_dict[f"{prefix}{layer_num}.attention.attention.query.bias"] = val[:dim] orig_state_dict[f"{prefix}{layer_num}.attention.attention.key.bias"] = val[dim : dim * 2] orig_state_dict[f"{prefix}{layer_num}.attention.attention.value.bias"] = val[-dim:] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_vit_mae_checkpoint(checkpoint_url, pytorch_dump_folder_path): config = ViTMAEConfig() if "large" in checkpoint_url: config.hidden_size = 1024 config.intermediate_size = 4096 config.num_hidden_layers = 24 config.num_attention_heads = 16 elif "huge" in checkpoint_url: config.patch_size = 14 config.hidden_size = 1280 config.intermediate_size = 5120 config.num_hidden_layers = 32 config.num_attention_heads = 16 model = ViTMAEForPreTraining(config) state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")["model"] feature_extractor = ViTMAEFeatureExtractor(size=config.image_size) new_state_dict = convert_state_dict(state_dict, config) model.load_state_dict(new_state_dict) model.eval() url = "https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg" image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTMAEFeatureExtractor(size=config.image_size) inputs = feature_extractor(images=image, return_tensors="pt") # forward pass torch.manual_seed(2) outputs = model(**inputs) logits = outputs.logits if "large" in checkpoint_url: expected_slice = torch.tensor( [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]] ) elif "huge" in checkpoint_url: expected_slice = torch.tensor( [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]] ) else: expected_slice = torch.tensor( [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]] ) # verify logits assert torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-4) print(f"Saving model to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth", type=str, help="URL of the checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/mctct/test_feature_extraction_mctct.py

# coding=utf-8 # Copyright 2022 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import random import unittest import numpy as np from transformers import is_speech_available from transformers.testing_utils import require_torch, require_torchaudio from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_speech_available(): from transformers import MCTCTFeatureExtractor global_rng = random.Random() def floats_list(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng values = [] for _batch_idx in range(shape[0]): values.append([]) for _ in range(shape[1]): values[-1].append(rng.random() * scale) return values @require_torch @require_torchaudio class MCTCTFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=24, num_mel_bins=24, padding_value=0.0, sampling_rate=16_000, return_attention_mask=True, do_normalize=True, ): self.parent = parent self.batch_size = batch_size self.min_seq_length = min_seq_length self.max_seq_length = max_seq_length self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) self.feature_size = feature_size self.num_mel_bins = num_mel_bins self.padding_value = padding_value self.sampling_rate = sampling_rate self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "num_mel_bins": self.num_mel_bins, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def prepare_inputs_for_common(self, equal_length=False, numpify=False): def _flatten(list_of_lists): return list(itertools.chain(*list_of_lists)) if equal_length: speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)] else: # make sure that inputs increase in size speech_inputs = [ floats_list((x, self.feature_size)) for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff) ] if numpify: speech_inputs = [np.asarray(x) for x in speech_inputs] return speech_inputs @require_torch @require_torchaudio class MCTCTFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = MCTCTFeatureExtractor if is_speech_available() else None def setUp(self): self.feat_extract_tester = MCTCTFeatureExtractionTester(self) def _check_zero_mean_unit_variance(self, input_vector): self.assertTrue(np.all(np.mean(input_vector) < 1e-3)) self.assertTrue(np.all(np.abs(np.var(input_vector) - 1) < 1e-3)) def test_call(self): # Tests that all call wrap to encode_plus and batch_encode_plus feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) # create three inputs of length 800, 1000, and 12000 speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] # Test feature size input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features self.assertTrue(input_features.ndim == 3) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size) # Test not batched input encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3)) # Test batched encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3)) def test_cepstral_mean_and_variance_normalization(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 16, None] for max_length, padding in zip(max_lengths, paddings): inputs = feature_extractor( speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True, truncation=max_length is not None, # reference to #16419 ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = [np.sum(x) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]) def test_cepstral_mean_and_variance_normalization_np(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 16, None] for max_length, padding in zip(max_lengths, paddings): inputs = feature_extractor( speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True, truncation=max_length is not None, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = [np.sum(x) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]) self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]) self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]) def test_cepstral_mean_and_variance_normalization_trunc_max_length(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] inputs = feature_extractor( speech_inputs, padding="max_length", max_length=4, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1]) self._check_zero_mean_unit_variance(input_features[2]) def test_cepstral_mean_and_variance_normalization_trunc_longest(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] inputs = feature_extractor( speech_inputs, padding="longest", max_length=4, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2]) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape, (3, 4, 24)) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] inputs = feature_extractor( speech_inputs, padding="longest", max_length=16, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2]) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape, (3, 16, 24)) def test_double_precision_pad(self): import torch feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) np_speech_inputs = np.random.rand(100, 32).astype(np.float64) py_speech_inputs = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np") self.assertTrue(np_processed.input_features.dtype == np.float32) pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt") self.assertTrue(pt_processed.input_features.dtype == torch.float32) def test_different_window(self): import torch init_dict = self.feat_extract_tester.prepare_feat_extract_dict() init_dict["win_function"] = "hann_window" feature_extractor = self.feature_extraction_class(**init_dict) np_speech_inputs = np.random.rand(100, 32).astype(np.float64) py_speech_inputs = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np") self.assertTrue(np_processed.input_features.dtype == np.float32) pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt") self.assertTrue(pt_processed.input_features.dtype == torch.float32)

# coding=utf-8 # Copyright 2022 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import random import unittest import numpy as np from transformers import is_speech_available from transformers.testing_utils import require_torch, require_torchaudio from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_speech_available(): from transformers import MCTCTFeatureExtractor global_rng = random.Random() def floats_list(shape, scale=1.0, rng=None, name=None): """Creates a random float32 tensor""" if rng is None: rng = global_rng values = [] for _batch_idx in range(shape[0]): values.append([]) for _ in range(shape[1]): values[-1].append(rng.random() * scale) return values @require_torch @require_torchaudio class MCTCTFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=24, num_mel_bins=24, padding_value=0.0, sampling_rate=16_000, return_attention_mask=True, do_normalize=True, ): self.parent = parent self.batch_size = batch_size self.min_seq_length = min_seq_length self.max_seq_length = max_seq_length self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1) self.feature_size = feature_size self.num_mel_bins = num_mel_bins self.padding_value = padding_value self.sampling_rate = sampling_rate self.return_attention_mask = return_attention_mask self.do_normalize = do_normalize def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "num_mel_bins": self.num_mel_bins, "padding_value": self.padding_value, "sampling_rate": self.sampling_rate, "return_attention_mask": self.return_attention_mask, "do_normalize": self.do_normalize, } def prepare_inputs_for_common(self, equal_length=False, numpify=False): def _flatten(list_of_lists): return list(itertools.chain(*list_of_lists)) if equal_length: speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)] else: # make sure that inputs increase in size speech_inputs = [ floats_list((x, self.feature_size)) for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff) ] if numpify: speech_inputs = [np.asarray(x) for x in speech_inputs] return speech_inputs @require_torch @require_torchaudio class MCTCTFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = MCTCTFeatureExtractor if is_speech_available() else None def setUp(self): self.feat_extract_tester = MCTCTFeatureExtractionTester(self) def _check_zero_mean_unit_variance(self, input_vector): self.assertTrue(np.all(np.mean(input_vector) < 1e-3)) self.assertTrue(np.all(np.abs(np.var(input_vector) - 1) < 1e-3)) def test_call(self): # Tests that all call wrap to encode_plus and batch_encode_plus feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) # create three inputs of length 800, 1000, and 12000 speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] # Test feature size input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features self.assertTrue(input_features.ndim == 3) self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size) # Test not batched input encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3)) # Test batched encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2): self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3)) def test_cepstral_mean_and_variance_normalization(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 16, None] for max_length, padding in zip(max_lengths, paddings): inputs = feature_extractor( speech_inputs, padding=padding, max_length=max_length, return_attention_mask=True, truncation=max_length is not None, # reference to #16419 ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = [np.sum(x) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]) def test_cepstral_mean_and_variance_normalization_np(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] paddings = ["longest", "max_length", "do_not_pad"] max_lengths = [None, 16, None] for max_length, padding in zip(max_lengths, paddings): inputs = feature_extractor( speech_inputs, max_length=max_length, padding=padding, return_tensors="np", return_attention_mask=True, truncation=max_length is not None, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = [np.sum(x) for x in attention_mask] self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]) self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6) self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]) self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6) self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]) def test_cepstral_mean_and_variance_normalization_trunc_max_length(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] inputs = feature_extractor( speech_inputs, padding="max_length", max_length=4, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1]) self._check_zero_mean_unit_variance(input_features[2]) def test_cepstral_mean_and_variance_normalization_trunc_longest(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] inputs = feature_extractor( speech_inputs, padding="longest", max_length=4, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2]) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape, (3, 4, 24)) speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)] inputs = feature_extractor( speech_inputs, padding="longest", max_length=16, truncation=True, return_tensors="np", return_attention_mask=True, ) input_features = inputs.input_features attention_mask = inputs.attention_mask fbank_feat_lengths = np.sum(attention_mask == 1, axis=1) self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]) self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]) self._check_zero_mean_unit_variance(input_features[2]) # make sure that if max_length < longest -> then pad to max_length self.assertEqual(input_features.shape, (3, 16, 24)) def test_double_precision_pad(self): import torch feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) np_speech_inputs = np.random.rand(100, 32).astype(np.float64) py_speech_inputs = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np") self.assertTrue(np_processed.input_features.dtype == np.float32) pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt") self.assertTrue(pt_processed.input_features.dtype == torch.float32) def test_different_window(self): import torch init_dict = self.feat_extract_tester.prepare_feat_extract_dict() init_dict["win_function"] = "hann_window" feature_extractor = self.feature_extraction_class(**init_dict) np_speech_inputs = np.random.rand(100, 32).astype(np.float64) py_speech_inputs = np_speech_inputs.tolist() for inputs in [py_speech_inputs, np_speech_inputs]: np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np") self.assertTrue(np_processed.input_features.dtype == np.float32) pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt") self.assertTrue(pt_processed.input_features.dtype == torch.float32)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/sagemaker/conftest.py

# we define a fixture function below and it will be "used" by # referencing its name from tests import os import pytest from attr import dataclass os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # defaults region @dataclass class SageMakerTestEnvironment: framework: str role = "arn:aws:iam::558105141721:role/sagemaker_execution_role" hyperparameters = { "task_name": "mnli", "per_device_train_batch_size": 16, "per_device_eval_batch_size": 16, "do_train": True, "do_eval": True, "do_predict": True, "output_dir": "/opt/ml/model", "overwrite_output_dir": True, "max_steps": 500, "save_steps": 5500, } distributed_hyperparameters = {**hyperparameters, "max_steps": 1000} @property def metric_definitions(self) -> str: if self.framework == "pytorch": return [ {"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": "eval_accuracy.*=\D*(.*?)$"}, {"Name": "eval_loss", "Regex": "eval_loss.*=\D*(.*?)$"}, ] else: return [ {"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": "loss.*=\D*(.*?)]?$"}, {"Name": "eval_loss", "Regex": "sparse_categorical_accuracy.*=\D*(.*?)]?$"}, ] @property def base_job_name(self) -> str: return f"{self.framework}-transfromers-test" @property def test_path(self) -> str: return f"./tests/sagemaker/scripts/{self.framework}" @property def image_uri(self) -> str: if self.framework == "pytorch": return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04" else: return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04" @pytest.fixture(scope="class") def sm_env(request): request.cls.env = SageMakerTestEnvironment(framework=request.cls.framework)

# we define a fixture function below and it will be "used" by # referencing its name from tests import os import pytest from attr import dataclass os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # defaults region @dataclass class SageMakerTestEnvironment: framework: str role = "arn:aws:iam::558105141721:role/sagemaker_execution_role" hyperparameters = { "task_name": "mnli", "per_device_train_batch_size": 16, "per_device_eval_batch_size": 16, "do_train": True, "do_eval": True, "do_predict": True, "output_dir": "/opt/ml/model", "overwrite_output_dir": True, "max_steps": 500, "save_steps": 5500, } distributed_hyperparameters = {**hyperparameters, "max_steps": 1000} @property def metric_definitions(self) -> str: if self.framework == "pytorch": return [ {"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": "eval_accuracy.*=\D*(.*?)$"}, {"Name": "eval_loss", "Regex": "eval_loss.*=\D*(.*?)$"}, ] else: return [ {"Name": "train_runtime", "Regex": "train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": "loss.*=\D*(.*?)]?$"}, {"Name": "eval_loss", "Regex": "sparse_categorical_accuracy.*=\D*(.*?)]?$"}, ] @property def base_job_name(self) -> str: return f"{self.framework}-transfromers-test" @property def test_path(self) -> str: return f"./tests/sagemaker/scripts/{self.framework}" @property def image_uri(self) -> str: if self.framework == "pytorch": return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04" else: return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04" @pytest.fixture(scope="class") def sm_env(request): request.cls.env = SageMakerTestEnvironment(framework=request.cls.framework)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/convert_pytorch_checkpoint_to_tf2.py

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert pytorch checkpoints to TensorFlow""" import argparse import os from . import ( ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BART_PRETRAINED_MODEL_ARCHIVE_LIST, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, BartConfig, BertConfig, CamembertConfig, CTRLConfig, DistilBertConfig, DPRConfig, ElectraConfig, FlaubertConfig, GPT2Config, LayoutLMConfig, LxmertConfig, OpenAIGPTConfig, RobertaConfig, T5Config, TFAlbertForPreTraining, TFBartForConditionalGeneration, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFCamembertForMaskedLM, TFCTRLLMHeadModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, TFElectraForPreTraining, TFFlaubertWithLMHeadModel, TFGPT2LMHeadModel, TFLayoutLMForMaskedLM, TFLxmertForPreTraining, TFLxmertVisualFeatureEncoder, TFOpenAIGPTLMHeadModel, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, TFT5ForConditionalGeneration, TFTransfoXLLMHeadModel, TFWav2Vec2Model, TFXLMRobertaForMaskedLM, TFXLMWithLMHeadModel, TFXLNetLMHeadModel, TransfoXLConfig, Wav2Vec2Config, Wav2Vec2Model, XLMConfig, XLMRobertaConfig, XLNetConfig, is_torch_available, load_pytorch_checkpoint_in_tf2_model, ) from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging if is_torch_available(): import numpy as np import torch from . import ( AlbertForPreTraining, BartForConditionalGeneration, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, CamembertForMaskedLM, CTRLLMHeadModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DPRContextEncoder, DPRQuestionEncoder, DPRReader, ElectraForPreTraining, FlaubertWithLMHeadModel, GPT2LMHeadModel, LayoutLMForMaskedLM, LxmertForPreTraining, LxmertVisualFeatureEncoder, OpenAIGPTLMHeadModel, RobertaForMaskedLM, RobertaForSequenceClassification, T5ForConditionalGeneration, TransfoXLLMHeadModel, XLMRobertaForMaskedLM, XLMWithLMHeadModel, XLNetLMHeadModel, ) logging.set_verbosity_info() MODEL_CLASSES = { "bart": ( BartConfig, TFBartForConditionalGeneration, BartForConditionalGeneration, BART_PRETRAINED_MODEL_ARCHIVE_LIST, ), "bert": ( BertConfig, TFBertForPreTraining, BertForPreTraining, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "bert-large-uncased-whole-word-masking-finetuned-squad": ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "bert-large-cased-whole-word-masking-finetuned-squad": ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "bert-base-cased-finetuned-mrpc": ( BertConfig, TFBertForSequenceClassification, BertForSequenceClassification, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "dpr": ( DPRConfig, TFDPRQuestionEncoder, TFDPRContextEncoder, TFDPRReader, DPRQuestionEncoder, DPRContextEncoder, DPRReader, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ), "gpt2": ( GPT2Config, TFGPT2LMHeadModel, GPT2LMHeadModel, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "xlnet": ( XLNetConfig, TFXLNetLMHeadModel, XLNetLMHeadModel, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "xlm": ( XLMConfig, TFXLMWithLMHeadModel, XLMWithLMHeadModel, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "xlm-roberta": ( XLMRobertaConfig, TFXLMRobertaForMaskedLM, XLMRobertaForMaskedLM, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "transfo-xl": ( TransfoXLConfig, TFTransfoXLLMHeadModel, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "openai-gpt": ( OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "roberta": ( RobertaConfig, TFRobertaForCausalLM, TFRobertaForMaskedLM, RobertaForMaskedLM, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "layoutlm": ( LayoutLMConfig, TFLayoutLMForMaskedLM, LayoutLMForMaskedLM, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, ), "roberta-large-mnli": ( RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "camembert": ( CamembertConfig, TFCamembertForMaskedLM, CamembertForMaskedLM, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "flaubert": ( FlaubertConfig, TFFlaubertWithLMHeadModel, FlaubertWithLMHeadModel, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "distilbert": ( DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "distilbert-base-distilled-squad": ( DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "lxmert": ( LxmertConfig, TFLxmertForPreTraining, LxmertForPreTraining, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "lxmert-visual-feature-encoder": ( LxmertConfig, TFLxmertVisualFeatureEncoder, LxmertVisualFeatureEncoder, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "ctrl": ( CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "albert": ( AlbertConfig, TFAlbertForPreTraining, AlbertForPreTraining, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "t5": ( T5Config, TFT5ForConditionalGeneration, T5ForConditionalGeneration, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "electra": ( ElectraConfig, TFElectraForPreTraining, ElectraForPreTraining, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "wav2vec2": ( Wav2Vec2Config, TFWav2Vec2Model, Wav2Vec2Model, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), } def convert_pt_checkpoint_to_tf( model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True ): if model_type not in MODEL_CLASSES: raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.") config_class, model_class, pt_model_class, aws_config_map = MODEL_CLASSES[model_type] # Initialise TF model if config_file in aws_config_map: config_file = cached_file(config_file, CONFIG_NAME, force_download=not use_cached_models) config = config_class.from_json_file(config_file) config.output_hidden_states = True config.output_attentions = True print(f"Building TensorFlow model from configuration: {config}") tf_model = model_class(config) # Load weights from tf checkpoint if pytorch_checkpoint_path in aws_config_map.keys(): pytorch_checkpoint_path = cached_file( pytorch_checkpoint_path, WEIGHTS_NAME, force_download=not use_cached_models ) # Load PyTorch checkpoint in tf2 model: tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path) if compare_with_pt_model: tfo = tf_model(tf_model.dummy_inputs, training=False) # build the network state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu") pt_model = pt_model_class.from_pretrained( pretrained_model_name_or_path=None, config=config, state_dict=state_dict ) with torch.no_grad(): pto = pt_model(**pt_model.dummy_inputs) np_pt = pto[0].numpy() np_tf = tfo[0].numpy() diff = np.amax(np.abs(np_pt - np_tf)) print(f"Max absolute difference between models outputs {diff}") assert diff <= 2e-2, f"Error, model absolute difference is >2e-2: {diff}" # Save pytorch-model print(f"Save TensorFlow model to {tf_dump_path}") tf_model.save_weights(tf_dump_path, save_format="h5") def convert_all_pt_checkpoints_to_tf( args_model_type, tf_dump_path, model_shortcut_names_or_path=None, config_shortcut_names_or_path=None, compare_with_pt_model=False, use_cached_models=False, remove_cached_files=False, only_convert_finetuned_models=False, ): if args_model_type is None: model_types = list(MODEL_CLASSES.keys()) else: model_types = [args_model_type] for j, model_type in enumerate(model_types, start=1): print("=" * 100) print(f" Converting model type {j}/{len(model_types)}: {model_type}") print("=" * 100) if model_type not in MODEL_CLASSES: raise ValueError(f"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys())}.") config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type] if model_shortcut_names_or_path is None: model_shortcut_names_or_path = list(aws_model_maps.keys()) if config_shortcut_names_or_path is None: config_shortcut_names_or_path = model_shortcut_names_or_path for i, (model_shortcut_name, config_shortcut_name) in enumerate( zip(model_shortcut_names_or_path, config_shortcut_names_or_path), start=1 ): print("-" * 100) if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name: if not only_convert_finetuned_models: print(f" Skipping finetuned checkpoint {model_shortcut_name}") continue model_type = model_shortcut_name elif only_convert_finetuned_models: print(f" Skipping not finetuned checkpoint {model_shortcut_name}") continue print( f" Converting checkpoint {i}/{len(aws_config_map)}: {model_shortcut_name} - model_type {model_type}" ) print("-" * 100) if config_shortcut_name in aws_config_map: config_file = cached_file(config_shortcut_name, CONFIG_NAME, force_download=not use_cached_models) else: config_file = config_shortcut_name if model_shortcut_name in aws_model_maps: model_file = cached_file(model_shortcut_name, WEIGHTS_NAME, force_download=not use_cached_models) else: model_file = model_shortcut_name if os.path.isfile(model_shortcut_name): model_shortcut_name = "converted_model" convert_pt_checkpoint_to_tf( model_type=model_type, pytorch_checkpoint_path=model_file, config_file=config_file, tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + "-tf_model.h5"), compare_with_pt_model=compare_with_pt_model, ) if remove_cached_files: os.remove(config_file) os.remove(model_file) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file." ) parser.add_argument( "--model_type", default=None, type=str, help=( f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and " "convert all the models from AWS." ), ) parser.add_argument( "--pytorch_checkpoint_path", default=None, type=str, help=( "Path to the PyTorch checkpoint path or shortcut name to download from AWS. " "If not given, will download and convert all the checkpoints from AWS." ), ) parser.add_argument( "--config_file", default=None, type=str, help=( "The config json file corresponding to the pre-trained model. \n" "This specifies the model architecture. If not given and " "--pytorch_checkpoint_path is not given or is a shortcut name " "use the configuration associated to the shortcut name on the AWS" ), ) parser.add_argument( "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions." ) parser.add_argument( "--use_cached_models", action="store_true", help="Use cached models if possible instead of updating to latest checkpoint versions.", ) parser.add_argument( "--remove_cached_files", action="store_true", help="Remove pytorch models after conversion (save memory when converting in batches).", ) parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.") args = parser.parse_args() # if args.pytorch_checkpoint_path is not None: # convert_pt_checkpoint_to_tf(args.model_type.lower(), # args.pytorch_checkpoint_path, # args.config_file if args.config_file is not None else args.pytorch_checkpoint_path, # args.tf_dump_path, # compare_with_pt_model=args.compare_with_pt_model, # use_cached_models=args.use_cached_models) # else: convert_all_pt_checkpoints_to_tf( args.model_type.lower() if args.model_type is not None else None, args.tf_dump_path, model_shortcut_names_or_path=[args.pytorch_checkpoint_path] if args.pytorch_checkpoint_path is not None else None, config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None, compare_with_pt_model=args.compare_with_pt_model, use_cached_models=args.use_cached_models, remove_cached_files=args.remove_cached_files, only_convert_finetuned_models=args.only_convert_finetuned_models, )

# coding=utf-8 # Copyright 2018 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert pytorch checkpoints to TensorFlow""" import argparse import os from . import ( ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BART_PRETRAINED_MODEL_ARCHIVE_LIST, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, BartConfig, BertConfig, CamembertConfig, CTRLConfig, DistilBertConfig, DPRConfig, ElectraConfig, FlaubertConfig, GPT2Config, LayoutLMConfig, LxmertConfig, OpenAIGPTConfig, RobertaConfig, T5Config, TFAlbertForPreTraining, TFBartForConditionalGeneration, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFCamembertForMaskedLM, TFCTRLLMHeadModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, TFElectraForPreTraining, TFFlaubertWithLMHeadModel, TFGPT2LMHeadModel, TFLayoutLMForMaskedLM, TFLxmertForPreTraining, TFLxmertVisualFeatureEncoder, TFOpenAIGPTLMHeadModel, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, TFT5ForConditionalGeneration, TFTransfoXLLMHeadModel, TFWav2Vec2Model, TFXLMRobertaForMaskedLM, TFXLMWithLMHeadModel, TFXLNetLMHeadModel, TransfoXLConfig, Wav2Vec2Config, Wav2Vec2Model, XLMConfig, XLMRobertaConfig, XLNetConfig, is_torch_available, load_pytorch_checkpoint_in_tf2_model, ) from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging if is_torch_available(): import numpy as np import torch from . import ( AlbertForPreTraining, BartForConditionalGeneration, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, CamembertForMaskedLM, CTRLLMHeadModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DPRContextEncoder, DPRQuestionEncoder, DPRReader, ElectraForPreTraining, FlaubertWithLMHeadModel, GPT2LMHeadModel, LayoutLMForMaskedLM, LxmertForPreTraining, LxmertVisualFeatureEncoder, OpenAIGPTLMHeadModel, RobertaForMaskedLM, RobertaForSequenceClassification, T5ForConditionalGeneration, TransfoXLLMHeadModel, XLMRobertaForMaskedLM, XLMWithLMHeadModel, XLNetLMHeadModel, ) logging.set_verbosity_info() MODEL_CLASSES = { "bart": ( BartConfig, TFBartForConditionalGeneration, BartForConditionalGeneration, BART_PRETRAINED_MODEL_ARCHIVE_LIST, ), "bert": ( BertConfig, TFBertForPreTraining, BertForPreTraining, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "bert-large-uncased-whole-word-masking-finetuned-squad": ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "bert-large-cased-whole-word-masking-finetuned-squad": ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "bert-base-cased-finetuned-mrpc": ( BertConfig, TFBertForSequenceClassification, BertForSequenceClassification, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "dpr": ( DPRConfig, TFDPRQuestionEncoder, TFDPRContextEncoder, TFDPRReader, DPRQuestionEncoder, DPRContextEncoder, DPRReader, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ), "gpt2": ( GPT2Config, TFGPT2LMHeadModel, GPT2LMHeadModel, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "xlnet": ( XLNetConfig, TFXLNetLMHeadModel, XLNetLMHeadModel, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "xlm": ( XLMConfig, TFXLMWithLMHeadModel, XLMWithLMHeadModel, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "xlm-roberta": ( XLMRobertaConfig, TFXLMRobertaForMaskedLM, XLMRobertaForMaskedLM, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "transfo-xl": ( TransfoXLConfig, TFTransfoXLLMHeadModel, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "openai-gpt": ( OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "roberta": ( RobertaConfig, TFRobertaForCausalLM, TFRobertaForMaskedLM, RobertaForMaskedLM, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "layoutlm": ( LayoutLMConfig, TFLayoutLMForMaskedLM, LayoutLMForMaskedLM, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, ), "roberta-large-mnli": ( RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "camembert": ( CamembertConfig, TFCamembertForMaskedLM, CamembertForMaskedLM, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "flaubert": ( FlaubertConfig, TFFlaubertWithLMHeadModel, FlaubertWithLMHeadModel, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "distilbert": ( DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "distilbert-base-distilled-squad": ( DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "lxmert": ( LxmertConfig, TFLxmertForPreTraining, LxmertForPreTraining, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "lxmert-visual-feature-encoder": ( LxmertConfig, TFLxmertVisualFeatureEncoder, LxmertVisualFeatureEncoder, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "ctrl": ( CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "albert": ( AlbertConfig, TFAlbertForPreTraining, AlbertForPreTraining, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "t5": ( T5Config, TFT5ForConditionalGeneration, T5ForConditionalGeneration, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "electra": ( ElectraConfig, TFElectraForPreTraining, ElectraForPreTraining, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), "wav2vec2": ( Wav2Vec2Config, TFWav2Vec2Model, Wav2Vec2Model, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), } def convert_pt_checkpoint_to_tf( model_type, pytorch_checkpoint_path, config_file, tf_dump_path, compare_with_pt_model=False, use_cached_models=True ): if model_type not in MODEL_CLASSES: raise ValueError(f"Unrecognized model type, should be one of {list(MODEL_CLASSES.keys())}.") config_class, model_class, pt_model_class, aws_config_map = MODEL_CLASSES[model_type] # Initialise TF model if config_file in aws_config_map: config_file = cached_file(config_file, CONFIG_NAME, force_download=not use_cached_models) config = config_class.from_json_file(config_file) config.output_hidden_states = True config.output_attentions = True print(f"Building TensorFlow model from configuration: {config}") tf_model = model_class(config) # Load weights from tf checkpoint if pytorch_checkpoint_path in aws_config_map.keys(): pytorch_checkpoint_path = cached_file( pytorch_checkpoint_path, WEIGHTS_NAME, force_download=not use_cached_models ) # Load PyTorch checkpoint in tf2 model: tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path) if compare_with_pt_model: tfo = tf_model(tf_model.dummy_inputs, training=False) # build the network state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu") pt_model = pt_model_class.from_pretrained( pretrained_model_name_or_path=None, config=config, state_dict=state_dict ) with torch.no_grad(): pto = pt_model(**pt_model.dummy_inputs) np_pt = pto[0].numpy() np_tf = tfo[0].numpy() diff = np.amax(np.abs(np_pt - np_tf)) print(f"Max absolute difference between models outputs {diff}") assert diff <= 2e-2, f"Error, model absolute difference is >2e-2: {diff}" # Save pytorch-model print(f"Save TensorFlow model to {tf_dump_path}") tf_model.save_weights(tf_dump_path, save_format="h5") def convert_all_pt_checkpoints_to_tf( args_model_type, tf_dump_path, model_shortcut_names_or_path=None, config_shortcut_names_or_path=None, compare_with_pt_model=False, use_cached_models=False, remove_cached_files=False, only_convert_finetuned_models=False, ): if args_model_type is None: model_types = list(MODEL_CLASSES.keys()) else: model_types = [args_model_type] for j, model_type in enumerate(model_types, start=1): print("=" * 100) print(f" Converting model type {j}/{len(model_types)}: {model_type}") print("=" * 100) if model_type not in MODEL_CLASSES: raise ValueError(f"Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys())}.") config_class, model_class, pt_model_class, aws_model_maps, aws_config_map = MODEL_CLASSES[model_type] if model_shortcut_names_or_path is None: model_shortcut_names_or_path = list(aws_model_maps.keys()) if config_shortcut_names_or_path is None: config_shortcut_names_or_path = model_shortcut_names_or_path for i, (model_shortcut_name, config_shortcut_name) in enumerate( zip(model_shortcut_names_or_path, config_shortcut_names_or_path), start=1 ): print("-" * 100) if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name: if not only_convert_finetuned_models: print(f" Skipping finetuned checkpoint {model_shortcut_name}") continue model_type = model_shortcut_name elif only_convert_finetuned_models: print(f" Skipping not finetuned checkpoint {model_shortcut_name}") continue print( f" Converting checkpoint {i}/{len(aws_config_map)}: {model_shortcut_name} - model_type {model_type}" ) print("-" * 100) if config_shortcut_name in aws_config_map: config_file = cached_file(config_shortcut_name, CONFIG_NAME, force_download=not use_cached_models) else: config_file = config_shortcut_name if model_shortcut_name in aws_model_maps: model_file = cached_file(model_shortcut_name, WEIGHTS_NAME, force_download=not use_cached_models) else: model_file = model_shortcut_name if os.path.isfile(model_shortcut_name): model_shortcut_name = "converted_model" convert_pt_checkpoint_to_tf( model_type=model_type, pytorch_checkpoint_path=model_file, config_file=config_file, tf_dump_path=os.path.join(tf_dump_path, model_shortcut_name + "-tf_model.h5"), compare_with_pt_model=compare_with_pt_model, ) if remove_cached_files: os.remove(config_file) os.remove(model_file) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file." ) parser.add_argument( "--model_type", default=None, type=str, help=( f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and " "convert all the models from AWS." ), ) parser.add_argument( "--pytorch_checkpoint_path", default=None, type=str, help=( "Path to the PyTorch checkpoint path or shortcut name to download from AWS. " "If not given, will download and convert all the checkpoints from AWS." ), ) parser.add_argument( "--config_file", default=None, type=str, help=( "The config json file corresponding to the pre-trained model. \n" "This specifies the model architecture. If not given and " "--pytorch_checkpoint_path is not given or is a shortcut name " "use the configuration associated to the shortcut name on the AWS" ), ) parser.add_argument( "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions." ) parser.add_argument( "--use_cached_models", action="store_true", help="Use cached models if possible instead of updating to latest checkpoint versions.", ) parser.add_argument( "--remove_cached_files", action="store_true", help="Remove pytorch models after conversion (save memory when converting in batches).", ) parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.") args = parser.parse_args() # if args.pytorch_checkpoint_path is not None: # convert_pt_checkpoint_to_tf(args.model_type.lower(), # args.pytorch_checkpoint_path, # args.config_file if args.config_file is not None else args.pytorch_checkpoint_path, # args.tf_dump_path, # compare_with_pt_model=args.compare_with_pt_model, # use_cached_models=args.use_cached_models) # else: convert_all_pt_checkpoints_to_tf( args.model_type.lower() if args.model_type is not None else None, args.tf_dump_path, model_shortcut_names_or_path=[args.pytorch_checkpoint_path] if args.pytorch_checkpoint_path is not None else None, config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None, compare_with_pt_model=args.compare_with_pt_model, use_cached_models=args.use_cached_models, remove_cached_files=args.remove_cached_files, only_convert_finetuned_models=args.only_convert_finetuned_models, )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/big_bird/test_modeling_flax_big_bird.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers import BigBirdConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax from transformers.models.big_bird.modeling_flax_big_bird import ( FlaxBigBirdForCausalLM, FlaxBigBirdForMaskedLM, FlaxBigBirdForMultipleChoice, FlaxBigBirdForPreTraining, FlaxBigBirdForQuestionAnswering, FlaxBigBirdForSequenceClassification, FlaxBigBirdForTokenClassification, FlaxBigBirdModel, ) class FlaxBigBirdModelTester(unittest.TestCase): def __init__( self, parent, batch_size=2, seq_length=56, is_training=True, use_attention_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=2, intermediate_size=7, hidden_act="gelu_new", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_choices=4, attention_type="block_sparse", use_bias=True, rescale_embeddings=False, block_size=2, num_random_blocks=3, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_choices = num_choices self.rescale_embeddings = rescale_embeddings self.attention_type = attention_type self.use_bias = use_bias self.block_size = block_size self.num_random_blocks = num_random_blocks def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) config = BigBirdConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, attention_type=self.attention_type, block_size=self.block_size, num_random_blocks=self.num_random_blocks, use_bias=self.use_bias, rescale_embeddings=self.rescale_embeddings, ) return config, input_ids, token_type_ids, attention_mask def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, token_type_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask} return config, inputs_dict @require_flax class FlaxBigBirdModelTest(FlaxModelTesterMixin, unittest.TestCase): all_model_classes = ( ( FlaxBigBirdForCausalLM, FlaxBigBirdModel, FlaxBigBirdForPreTraining, FlaxBigBirdForMaskedLM, FlaxBigBirdForMultipleChoice, FlaxBigBirdForQuestionAnswering, FlaxBigBirdForSequenceClassification, FlaxBigBirdForTokenClassification, ) if is_flax_available() else () ) test_attn_probs = False test_mismatched_shapes = False def setUp(self): self.model_tester = FlaxBigBirdModelTester(self) @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_from_pretrained_save_pretrained(self): super().test_from_pretrained_save_pretrained() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_from_pretrained_with_no_automatic_init(self): super().test_from_pretrained_with_no_automatic_init() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_no_automatic_init(self): super().test_no_automatic_init() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_hidden_states_output(self): super().test_hidden_states_output() @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("google/bigbird-roberta-base") outputs = model(np.ones((1, 1))) self.assertIsNotNone(outputs) def test_attention_outputs(self): if self.test_attn_probs: super().test_attention_outputs() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_jit_compilation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) @jax.jit def model_jitted(input_ids, attention_mask=None, **kwargs): return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) with self.subTest("JIT Enabled"): jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = model_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) # overwrite from common in order to skip the check on `attentions` def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version, # an effort was done to return `attention_probs` (yet to be verified). if name.startswith("outputs.attentions"): return else: super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers import BigBirdConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax from transformers.models.big_bird.modeling_flax_big_bird import ( FlaxBigBirdForCausalLM, FlaxBigBirdForMaskedLM, FlaxBigBirdForMultipleChoice, FlaxBigBirdForPreTraining, FlaxBigBirdForQuestionAnswering, FlaxBigBirdForSequenceClassification, FlaxBigBirdForTokenClassification, FlaxBigBirdModel, ) class FlaxBigBirdModelTester(unittest.TestCase): def __init__( self, parent, batch_size=2, seq_length=56, is_training=True, use_attention_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=2, intermediate_size=7, hidden_act="gelu_new", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_choices=4, attention_type="block_sparse", use_bias=True, rescale_embeddings=False, block_size=2, num_random_blocks=3, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_choices = num_choices self.rescale_embeddings = rescale_embeddings self.attention_type = attention_type self.use_bias = use_bias self.block_size = block_size self.num_random_blocks = num_random_blocks def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) config = BigBirdConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, attention_type=self.attention_type, block_size=self.block_size, num_random_blocks=self.num_random_blocks, use_bias=self.use_bias, rescale_embeddings=self.rescale_embeddings, ) return config, input_ids, token_type_ids, attention_mask def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, token_type_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask} return config, inputs_dict @require_flax class FlaxBigBirdModelTest(FlaxModelTesterMixin, unittest.TestCase): all_model_classes = ( ( FlaxBigBirdForCausalLM, FlaxBigBirdModel, FlaxBigBirdForPreTraining, FlaxBigBirdForMaskedLM, FlaxBigBirdForMultipleChoice, FlaxBigBirdForQuestionAnswering, FlaxBigBirdForSequenceClassification, FlaxBigBirdForTokenClassification, ) if is_flax_available() else () ) test_attn_probs = False test_mismatched_shapes = False def setUp(self): self.model_tester = FlaxBigBirdModelTester(self) @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_from_pretrained_save_pretrained(self): super().test_from_pretrained_save_pretrained() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_from_pretrained_with_no_automatic_init(self): super().test_from_pretrained_with_no_automatic_init() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_no_automatic_init(self): super().test_no_automatic_init() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_hidden_states_output(self): super().test_hidden_states_output() @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("google/bigbird-roberta-base") outputs = model(np.ones((1, 1))) self.assertIsNotNone(outputs) def test_attention_outputs(self): if self.test_attn_probs: super().test_attention_outputs() @slow # copied from `test_modeling_flax_common` because it takes much longer than other models def test_jit_compilation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__): prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) @jax.jit def model_jitted(input_ids, attention_mask=None, **kwargs): return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) with self.subTest("JIT Enabled"): jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = model_jitted(**prepared_inputs_dict).to_tuple() self.assertEqual(len(outputs), len(jitted_outputs)) for jitted_output, output in zip(jitted_outputs, outputs): self.assertEqual(jitted_output.shape, output.shape) # overwrite from common in order to skip the check on `attentions` def check_pt_flax_outputs(self, fx_outputs, pt_outputs, model_class, tol=1e-5, name="outputs", attributes=None): # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version, # an effort was done to return `attention_probs` (yet to be verified). if name.startswith("outputs.attentions"): return else: super().check_pt_flax_outputs(fx_outputs, pt_outputs, model_class, tol, name, attributes)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/xglm/configuration_xglm.py

# coding=utf-8 # Copyright The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ XGLM model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json", # See all XGLM models at https://huggingface.co/models?filter=xglm } class XGLMConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`XGLMModel`]. It is used to instantiate an XGLM model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the XGLM [facebook/xglm-564M](https://huggingface.co/facebook/xglm-564M) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 256008): Vocabulary size of the XGLM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XGLMModel`] or [`FlaxXGLMModel`]. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). d_model (`int`, *optional*, defaults to 1024): Dimension of the layers and the pooler layer. ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. num_layers (`int`, *optional*, defaults to 24): Number of hidden layers Transformer decoder. attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, dencoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_embedding (`bool`, *optional*, defaults to `True`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Example: ```python >>> from transformers import XGLMModel, XGLMConfig >>> # Initializing a XGLM facebook/xglm-564M style configuration >>> configuration = XGLMConfig() >>> # Initializing a model from the facebook/xglm-564M style configuration >>> model = XGLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xglm" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "attention_heads", "hidden_size": "d_model", "num_hidden_layers": "num_layers", } def __init__( self, vocab_size=256008, max_position_embeddings=2048, d_model=1024, ffn_dim=4096, num_layers=24, attention_heads=16, activation_function="gelu", dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, layerdrop=0.0, init_std=0.02, scale_embedding=True, use_cache=True, decoder_start_token_id=2, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.ffn_dim = ffn_dim self.num_layers = num_layers self.attention_heads = attention_heads self.activation_function = activation_function self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.layerdrop = layerdrop self.init_std = init_std self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.use_cache = use_cache super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, **kwargs, )

# coding=utf-8 # Copyright The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ XGLM model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json", # See all XGLM models at https://huggingface.co/models?filter=xglm } class XGLMConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`XGLMModel`]. It is used to instantiate an XGLM model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the XGLM [facebook/xglm-564M](https://huggingface.co/facebook/xglm-564M) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 256008): Vocabulary size of the XGLM model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`XGLMModel`] or [`FlaxXGLMModel`]. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). d_model (`int`, *optional*, defaults to 1024): Dimension of the layers and the pooler layer. ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. num_layers (`int`, *optional*, defaults to 24): Number of hidden layers Transformer decoder. attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, dencoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_embedding (`bool`, *optional*, defaults to `True`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Example: ```python >>> from transformers import XGLMModel, XGLMConfig >>> # Initializing a XGLM facebook/xglm-564M style configuration >>> configuration = XGLMConfig() >>> # Initializing a model from the facebook/xglm-564M style configuration >>> model = XGLMModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "xglm" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_attention_heads": "attention_heads", "hidden_size": "d_model", "num_hidden_layers": "num_layers", } def __init__( self, vocab_size=256008, max_position_embeddings=2048, d_model=1024, ffn_dim=4096, num_layers=24, attention_heads=16, activation_function="gelu", dropout=0.1, attention_dropout=0.1, activation_dropout=0.0, layerdrop=0.0, init_std=0.02, scale_embedding=True, use_cache=True, decoder_start_token_id=2, pad_token_id=1, bos_token_id=0, eos_token_id=2, **kwargs ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.ffn_dim = ffn_dim self.num_layers = num_layers self.attention_heads = attention_heads self.activation_function = activation_function self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.layerdrop = layerdrop self.init_std = init_std self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.use_cache = use_cache super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, **kwargs, )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/esm/test_modeling_esm.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ESM model. """ import unittest from transformers import EsmConfig, is_torch_available from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel from transformers.models.esm.modeling_esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, EsmEmbeddings, create_position_ids_from_input_ids, ) # copied from tests.test_modeling_roberta class EsmModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=33, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return EsmConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, pad_token_id=1, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = EsmModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = EsmForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = EsmForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class EsmModelTest(ModelTesterMixin, unittest.TestCase): test_mismatched_shapes = False all_model_classes = ( ( EsmForMaskedLM, EsmModel, EsmForSequenceClassification, EsmForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = () test_sequence_classification_problem_types = True def setUp(self): self.model_tester = EsmModelTester(self) self.config_tester = ConfigTester(self, config_class=EsmConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = EsmModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_create_position_ids_respects_padding_index(self): """Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is EsmEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = EsmEmbeddings(config=config) input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]]) expected_positions = torch.as_tensor( [ [ 0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx, ] ] ) position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_create_position_ids_from_inputs_embeds(self): """Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is EsmEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = EsmEmbeddings(config=config) inputs_embeds = torch.empty(2, 4, 30) expected_single_positions = [ 0 + embeddings.padding_idx + 1, 1 + embeddings.padding_idx + 1, 2 + embeddings.padding_idx + 1, 3 + embeddings.padding_idx + 1, ] expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions]) position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) @unittest.skip("Esm does not support embedding resizing") def test_resize_embeddings_untied(self): pass @unittest.skip("Esm does not support embedding resizing") def test_resize_tokens_embeddings(self): pass @require_torch class EsmModelIntegrationTest(TestCasePlus): @slow def test_inference_masked_lm(self): with torch.no_grad(): model = EsmForMaskedLM.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D") model.eval() input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] vocab_size = 33 expected_shape = torch.Size((1, 6, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_inference_no_head(self): with torch.no_grad(): model = EsmModel.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D") model.eval() input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. expected_slice = torch.tensor( [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ESM model. """ import unittest from transformers import EsmConfig, is_torch_available from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel from transformers.models.esm.modeling_esm import ( ESM_PRETRAINED_MODEL_ARCHIVE_LIST, EsmEmbeddings, create_position_ids_from_input_ids, ) # copied from tests.test_modeling_roberta class EsmModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=33, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return EsmConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, pad_token_id=1, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = EsmModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = EsmForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = EsmForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class EsmModelTest(ModelTesterMixin, unittest.TestCase): test_mismatched_shapes = False all_model_classes = ( ( EsmForMaskedLM, EsmModel, EsmForSequenceClassification, EsmForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = () test_sequence_classification_problem_types = True def setUp(self): self.model_tester = EsmModelTester(self) self.config_tester = ConfigTester(self, config_class=EsmConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = EsmModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_create_position_ids_respects_padding_index(self): """Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is EsmEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = EsmEmbeddings(config=config) input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]]) expected_positions = torch.as_tensor( [ [ 0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx, ] ] ) position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_create_position_ids_from_inputs_embeds(self): """Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is EsmEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = EsmEmbeddings(config=config) inputs_embeds = torch.empty(2, 4, 30) expected_single_positions = [ 0 + embeddings.padding_idx + 1, 1 + embeddings.padding_idx + 1, 2 + embeddings.padding_idx + 1, 3 + embeddings.padding_idx + 1, ] expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions]) position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) @unittest.skip("Esm does not support embedding resizing") def test_resize_embeddings_untied(self): pass @unittest.skip("Esm does not support embedding resizing") def test_resize_tokens_embeddings(self): pass @require_torch class EsmModelIntegrationTest(TestCasePlus): @slow def test_inference_masked_lm(self): with torch.no_grad(): model = EsmForMaskedLM.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D") model.eval() input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] vocab_size = 33 expected_shape = torch.Size((1, 6, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_inference_no_head(self): with torch.no_grad(): model = EsmModel.from_pretrained("Rocketknight1/esm2_t6_8M_UR50D") model.eval() input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. expected_slice = torch.tensor( [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./examples/pytorch/question-answering/run_qa_no_trainer.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model for question answering using 🤗 Accelerate. """ # You can also adapt this script on your own question answering task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from pathlib import Path import datasets import numpy as np import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version from utils_qa import postprocess_qa_predictions # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt") logger = get_logger(__name__) # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def save_prefixed_metrics(results, output_dir, file_name: str = "all_results.json", metric_key_prefix: str = "eval"): """ Save results while prefixing metric names. Args: results: (:obj:`dict`): A dictionary of results. output_dir: (:obj:`str`): An output directory. file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`): An output file name. metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`): A metric name prefix. """ # Prefix all keys with metric_key_prefix + '_' for key in list(results.keys()): if not key.startswith(f"{metric_key_prefix}_"): results[f"{metric_key_prefix}_{key}"] = results.pop(key) with open(os.path.join(output_dir, file_name), "w") as f: json.dump(results, f, indent=4) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--preprocessing_num_workers", type=int, default=1, help="A csv or a json file containing the training data." ) parser.add_argument("--do_predict", action="store_true", help="To do prediction on the question answering model") parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--test_file", type=str, default=None, help="A csv or a json file containing the Prediction data." ) parser.add_argument( "--max_seq_length", type=int, default=384, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--doc_stride", type=int, default=128, help="When splitting up a long document into chunks how much stride to take between chunks.", ) parser.add_argument( "--n_best_size", type=int, default=20, help="The total number of n-best predictions to generate when looking for an answer.", ) parser.add_argument( "--null_score_diff_threshold", type=float, default=0.0, help=( "The threshold used to select the null answer: if the best answer has a score that is less than " "the score of the null answer minus this threshold, the null answer is selected for this example. " "Only useful when `version_2_with_negative=True`." ), ) parser.add_argument( "--version_2_with_negative", action="store_true", help="If true, some of the examples do not have an answer.", ) parser.add_argument( "--max_answer_length", type=int, default=30, help=( "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another." ), ) parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--max_eval_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ), ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--max_predict_samples", type=int, default=None, help="For debugging purposes or quicker training, truncate the number of prediction examples to this", ) parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if ( args.dataset_name is None and args.train_file is None and args.validation_file is None and args.test_file is None ): raise ValueError("Need either a dataset name or a training/validation/test file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.test_file is not None: extension = args.test_file.split(".")[-1] assert extension in ["csv", "json"], "`test_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_qa_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file if args.test_file is not None: data_files["test"] = args.test_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files, field="data") # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForQuestionAnswering.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForQuestionAnswering.from_config(config) # Preprocessing the datasets. # Preprocessing is slighlty different for training and evaluation. column_names = raw_datasets["train"].column_names question_column_name = "question" if "question" in column_names else column_names[0] context_column_name = "context" if "context" in column_names else column_names[1] answer_column_name = "answers" if "answers" in column_names else column_names[2] # Padding side determines if we do (question|context) or (context|question). pad_on_right = tokenizer.padding_side == "right" if args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(args.max_seq_length, tokenizer.model_max_length) # Training preprocessing def prepare_train_features(examples): # Some of the questions have lots of whitespace on the left, which is not useful and will make the # truncation of the context fail (the tokenized question will take a lots of space). So we remove that # left whitespace examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples[question_column_name if pad_on_right else context_column_name], examples[context_column_name if pad_on_right else question_column_name], truncation="only_second" if pad_on_right else "only_first", max_length=max_seq_length, stride=args.doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length" if args.pad_to_max_length else False, ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # The offset mappings will give us a map from token to character position in the original context. This will # help us compute the start_positions and end_positions. offset_mapping = tokenized_examples.pop("offset_mapping") # Let's label those examples! tokenized_examples["start_positions"] = [] tokenized_examples["end_positions"] = [] for i, offsets in enumerate(offset_mapping): # We will label impossible answers with the index of the CLS token. input_ids = tokenized_examples["input_ids"][i] cls_index = input_ids.index(tokenizer.cls_token_id) # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] answers = examples[answer_column_name][sample_index] # If no answers are given, set the cls_index as answer. if len(answers["answer_start"]) == 0: tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Start/end character index of the answer in the text. start_char = answers["answer_start"][0] end_char = start_char + len(answers["text"][0]) # Start token index of the current span in the text. token_start_index = 0 while sequence_ids[token_start_index] != (1 if pad_on_right else 0): token_start_index += 1 # End token index of the current span in the text. token_end_index = len(input_ids) - 1 while sequence_ids[token_end_index] != (1 if pad_on_right else 0): token_end_index -= 1 # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index). if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char): tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Otherwise move the token_start_index and token_end_index to the two ends of the answer. # Note: we could go after the last offset if the answer is the last word (edge case). while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char: token_start_index += 1 tokenized_examples["start_positions"].append(token_start_index - 1) while offsets[token_end_index][1] >= end_char: token_end_index -= 1 tokenized_examples["end_positions"].append(token_end_index + 1) return tokenized_examples if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if args.max_train_samples is not None: # We will select sample from whole data if agument is specified train_dataset = train_dataset.select(range(args.max_train_samples)) # Create train feature from dataset with accelerator.main_process_first(): train_dataset = train_dataset.map( prepare_train_features, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on train dataset", ) if args.max_train_samples is not None: # Number of samples might increase during Feature Creation, We select only specified max samples train_dataset = train_dataset.select(range(args.max_train_samples)) # Validation preprocessing def prepare_validation_features(examples): # Some of the questions have lots of whitespace on the left, which is not useful and will make the # truncation of the context fail (the tokenized question will take a lots of space). So we remove that # left whitespace examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples[question_column_name if pad_on_right else context_column_name], examples[context_column_name if pad_on_right else question_column_name], truncation="only_second" if pad_on_right else "only_first", max_length=max_seq_length, stride=args.doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length" if args.pad_to_max_length else False, ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the # corresponding example_id and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_examples = raw_datasets["validation"] if args.max_eval_samples is not None: # We will select sample from whole data eval_examples = eval_examples.select(range(args.max_eval_samples)) # Validation Feature Creation with accelerator.main_process_first(): eval_dataset = eval_examples.map( prepare_validation_features, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on validation dataset", ) if args.max_eval_samples is not None: # During Feature creation dataset samples might increase, we will select required samples again eval_dataset = eval_dataset.select(range(args.max_eval_samples)) if args.do_predict: if "test" not in raw_datasets: raise ValueError("--do_predict requires a test dataset") predict_examples = raw_datasets["test"] if args.max_predict_samples is not None: # We will select sample from whole data predict_examples = predict_examples.select(range(args.max_predict_samples)) # Predict Feature Creation with accelerator.main_process_first(): predict_dataset = predict_examples.map( prepare_validation_features, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on prediction dataset", ) if args.max_predict_samples is not None: # During Feature creation dataset samples might increase, we will select required samples again predict_dataset = predict_dataset.select(range(args.max_predict_samples)) # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataset_for_model = eval_dataset.remove_columns(["example_id", "offset_mapping"]) eval_dataloader = DataLoader( eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) if args.do_predict: predict_dataset_for_model = predict_dataset.remove_columns(["example_id", "offset_mapping"]) predict_dataloader = DataLoader( predict_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) # Post-processing: def post_processing_function(examples, features, predictions, stage="eval"): # Post-processing: we match the start logits and end logits to answers in the original context. predictions = postprocess_qa_predictions( examples=examples, features=features, predictions=predictions, version_2_with_negative=args.version_2_with_negative, n_best_size=args.n_best_size, max_answer_length=args.max_answer_length, null_score_diff_threshold=args.null_score_diff_threshold, output_dir=args.output_dir, prefix=stage, ) # Format the result to the format the metric expects. if args.version_2_with_negative: formatted_predictions = [ {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items() ] else: formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()] references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples] return EvalPrediction(predictions=formatted_predictions, label_ids=references) metric = evaluate.load("squad_v2" if args.version_2_with_negative else "squad") # Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor def create_and_fill_np_array(start_or_end_logits, dataset, max_len): """ Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor Args: start_or_end_logits(:obj:`tensor`): This is the output predictions of the model. We can only enter either start or end logits. eval_dataset: Evaluation dataset max_len(:obj:`int`): The maximum length of the output tensor. ( See the model.eval() part for more details ) """ step = 0 # create a numpy array and fill it with -100. logits_concat = np.full((len(dataset), max_len), -100, dtype=np.float64) # Now since we have create an array now we will populate it with the outputs gathered using accelerator.gather_for_metrics for i, output_logit in enumerate(start_or_end_logits): # populate columns # We have to fill it such that we have to take the whole tensor and replace it on the newly created array # And after every iteration we have to change the step batch_size = output_logit.shape[0] cols = output_logit.shape[1] if step + batch_size < len(dataset): logits_concat[step : step + batch_size, :cols] = output_logit else: logits_concat[step:, :cols] = output_logit[: len(dataset) - step] step += batch_size return logits_concat # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("qa_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) # Evaluation logger.info("***** Running Evaluation *****") logger.info(f" Num examples = {len(eval_dataset)}") logger.info(f" Batch size = {args.per_device_eval_batch_size}") all_start_logits = [] all_end_logits = [] model.eval() for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) start_logits = outputs.start_logits end_logits = outputs.end_logits if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered start_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100) end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100) all_start_logits.append(accelerator.gather_for_metrics(start_logits).cpu().numpy()) all_end_logits.append(accelerator.gather_for_metrics(end_logits).cpu().numpy()) max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor # concatenate the numpy array start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len) end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len) # delete the list of numpy arrays del all_start_logits del all_end_logits outputs_numpy = (start_logits_concat, end_logits_concat) prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy) eval_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids) logger.info(f"Evaluation metrics: {eval_metric}") # Prediction if args.do_predict: logger.info("***** Running Prediction *****") logger.info(f" Num examples = {len(predict_dataset)}") logger.info(f" Batch size = {args.per_device_eval_batch_size}") all_start_logits = [] all_end_logits = [] model.eval() for step, batch in enumerate(predict_dataloader): with torch.no_grad(): outputs = model(**batch) start_logits = outputs.start_logits end_logits = outputs.end_logits if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered start_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100) end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100) all_start_logits.append(accelerator.gather_for_metrics(start_logits).cpu().numpy()) all_end_logits.append(accelerator.gather_for_metrics(end_logits).cpu().numpy()) max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor # concatenate the numpy array start_logits_concat = create_and_fill_np_array(all_start_logits, predict_dataset, max_len) end_logits_concat = create_and_fill_np_array(all_end_logits, predict_dataset, max_len) # delete the list of numpy arrays del all_start_logits del all_end_logits outputs_numpy = (start_logits_concat, end_logits_concat) prediction = post_processing_function(predict_examples, predict_dataset, outputs_numpy) predict_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids) logger.info(f"Predict metrics: {predict_metric}") if args.with_tracking: log = { "squad_v2" if args.version_2_with_negative else "squad": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, } if args.do_predict: log["squad_v2_predict" if args.version_2_with_negative else "squad_predict"] = predict_metric accelerator.log(log, step=completed_steps) if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) logger.info(json.dumps(eval_metric, indent=4)) save_prefixed_metrics(eval_metric, args.output_dir) if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Fine-tuning a 🤗 Transformers model for question answering using 🤗 Accelerate. """ # You can also adapt this script on your own question answering task. Pointers for this are left as comments. import argparse import json import logging import math import os import random from pathlib import Path import datasets import numpy as np import torch from datasets import load_dataset from torch.utils.data import DataLoader from tqdm.auto import tqdm import evaluate import transformers from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import set_seed from huggingface_hub import Repository from transformers import ( CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, SchedulerType, default_data_collator, get_scheduler, ) from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry from transformers.utils.versions import require_version from utils_qa import postprocess_qa_predictions # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.25.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt") logger = get_logger(__name__) # You should update this to your particular problem to have better documentation of `model_type` MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def save_prefixed_metrics(results, output_dir, file_name: str = "all_results.json", metric_key_prefix: str = "eval"): """ Save results while prefixing metric names. Args: results: (:obj:`dict`): A dictionary of results. output_dir: (:obj:`str`): An output directory. file_name: (:obj:`str`, `optional`, defaults to :obj:`all_results.json`): An output file name. metric_key_prefix: (:obj:`str`, `optional`, defaults to :obj:`eval`): A metric name prefix. """ # Prefix all keys with metric_key_prefix + '_' for key in list(results.keys()): if not key.startswith(f"{metric_key_prefix}_"): results[f"{metric_key_prefix}_{key}"] = results.pop(key) with open(os.path.join(output_dir, file_name), "w") as f: json.dump(results, f, indent=4) def parse_args(): parser = argparse.ArgumentParser(description="Finetune a transformers model on a Question Answering task") parser.add_argument( "--dataset_name", type=str, default=None, help="The name of the dataset to use (via the datasets library).", ) parser.add_argument( "--dataset_config_name", type=str, default=None, help="The configuration name of the dataset to use (via the datasets library).", ) parser.add_argument( "--train_file", type=str, default=None, help="A csv or a json file containing the training data." ) parser.add_argument( "--preprocessing_num_workers", type=int, default=1, help="A csv or a json file containing the training data." ) parser.add_argument("--do_predict", action="store_true", help="To do prediction on the question answering model") parser.add_argument( "--validation_file", type=str, default=None, help="A csv or a json file containing the validation data." ) parser.add_argument( "--test_file", type=str, default=None, help="A csv or a json file containing the Prediction data." ) parser.add_argument( "--max_seq_length", type=int, default=384, help=( "The maximum total input sequence length after tokenization. Sequences longer than this will be truncated," " sequences shorter will be padded if `--pad_to_max_lengh` is passed." ), ) parser.add_argument( "--pad_to_max_length", action="store_true", help="If passed, pad all samples to `max_seq_length`. Otherwise, dynamic padding is used.", ) parser.add_argument( "--model_name_or_path", type=str, help="Path to pretrained model or model identifier from huggingface.co/models.", required=False, ) parser.add_argument( "--config_name", type=str, default=None, help="Pretrained config name or path if not the same as model_name", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--use_slow_tokenizer", action="store_true", help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", ) parser.add_argument( "--per_device_train_batch_size", type=int, default=8, help="Batch size (per device) for the training dataloader.", ) parser.add_argument( "--per_device_eval_batch_size", type=int, default=8, help="Batch size (per device) for the evaluation dataloader.", ) parser.add_argument( "--learning_rate", type=float, default=5e-5, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.") parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--lr_scheduler_type", type=SchedulerType, default="linear", help="The scheduler type to use.", choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"], ) parser.add_argument( "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.") parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") parser.add_argument( "--doc_stride", type=int, default=128, help="When splitting up a long document into chunks how much stride to take between chunks.", ) parser.add_argument( "--n_best_size", type=int, default=20, help="The total number of n-best predictions to generate when looking for an answer.", ) parser.add_argument( "--null_score_diff_threshold", type=float, default=0.0, help=( "The threshold used to select the null answer: if the best answer has a score that is less than " "the score of the null answer minus this threshold, the null answer is selected for this example. " "Only useful when `version_2_with_negative=True`." ), ) parser.add_argument( "--version_2_with_negative", action="store_true", help="If true, some of the examples do not have an answer.", ) parser.add_argument( "--max_answer_length", type=int, default=30, help=( "The maximum length of an answer that can be generated. This is needed because the start " "and end predictions are not conditioned on one another." ), ) parser.add_argument( "--max_train_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ), ) parser.add_argument( "--max_eval_samples", type=int, default=None, help=( "For debugging purposes or quicker training, truncate the number of evaluation examples to this " "value if set." ), ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument( "--max_predict_samples", type=int, default=None, help="For debugging purposes or quicker training, truncate the number of prediction examples to this", ) parser.add_argument( "--model_type", type=str, default=None, help="Model type to use if training from scratch.", choices=MODEL_TYPES, ) parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") parser.add_argument( "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`." ) parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") parser.add_argument( "--checkpointing_steps", type=str, default=None, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", ) parser.add_argument( "--resume_from_checkpoint", type=str, default=None, help="If the training should continue from a checkpoint folder.", ) parser.add_argument( "--with_tracking", action="store_true", help="Whether to enable experiment trackers for logging.", ) parser.add_argument( "--report_to", type=str, default="all", help=( 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' "Only applicable when `--with_tracking` is passed." ), ) args = parser.parse_args() # Sanity checks if ( args.dataset_name is None and args.train_file is None and args.validation_file is None and args.test_file is None ): raise ValueError("Need either a dataset name or a training/validation/test file.") else: if args.train_file is not None: extension = args.train_file.split(".")[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if args.validation_file is not None: extension = args.validation_file.split(".")[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." if args.test_file is not None: extension = args.test_file.split(".")[-1] assert extension in ["csv", "json"], "`test_file` should be a csv or a json file." if args.push_to_hub: assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." return args def main(): args = parse_args() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_qa_no_trainer", args) # Initialize the accelerator. We will let the accelerator handle device placement for us in this example. # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers # in the environment accelerator_log_kwargs = {} if args.with_tracking: accelerator_log_kwargs["log_with"] = args.report_to accelerator_log_kwargs["logging_dir"] = args.output_dir accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs) # Make one log on every process with the configuration for debugging. logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO, ) logger.info(accelerator.state, main_process_only=False) if accelerator.is_local_main_process: datasets.utils.logging.set_verbosity_warning() transformers.utils.logging.set_verbosity_info() else: datasets.utils.logging.set_verbosity_error() transformers.utils.logging.set_verbosity_error() # If passed along, set the training seed now. if args.seed is not None: set_seed(args.seed) # Handle the repository creation if accelerator.is_main_process: if args.push_to_hub: if args.hub_model_id is None: repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) else: repo_name = args.hub_model_id repo = Repository(args.output_dir, clone_from=repo_name) with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: if "step_*" not in gitignore: gitignore.write("step_*\n") if "epoch_*" not in gitignore: gitignore.write("epoch_*\n") elif args.output_dir is not None: os.makedirs(args.output_dir, exist_ok=True) accelerator.wait_for_everyone() # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if args.dataset_name is not None: # Downloading and loading a dataset from the hub. raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name) else: data_files = {} if args.train_file is not None: data_files["train"] = args.train_file if args.validation_file is not None: data_files["validation"] = args.validation_file if args.test_file is not None: data_files["test"] = args.test_file extension = args.train_file.split(".")[-1] raw_datasets = load_dataset(extension, data_files=data_files, field="data") # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. if args.config_name: config = AutoConfig.from_pretrained(args.config_name) elif args.model_name_or_path: config = AutoConfig.from_pretrained(args.model_name_or_path) else: config = CONFIG_MAPPING[args.model_type]() logger.warning("You are instantiating a new config instance from scratch.") if args.tokenizer_name: tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True) elif args.model_name_or_path: tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True) else: raise ValueError( "You are instantiating a new tokenizer from scratch. This is not supported by this script." "You can do it from another script, save it, and load it from here, using --tokenizer_name." ) if args.model_name_or_path: model = AutoModelForQuestionAnswering.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, ) else: logger.info("Training new model from scratch") model = AutoModelForQuestionAnswering.from_config(config) # Preprocessing the datasets. # Preprocessing is slighlty different for training and evaluation. column_names = raw_datasets["train"].column_names question_column_name = "question" if "question" in column_names else column_names[0] context_column_name = "context" if "context" in column_names else column_names[1] answer_column_name = "answers" if "answers" in column_names else column_names[2] # Padding side determines if we do (question|context) or (context|question). pad_on_right = tokenizer.padding_side == "right" if args.max_seq_length > tokenizer.model_max_length: logger.warning( f"The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the" f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." ) max_seq_length = min(args.max_seq_length, tokenizer.model_max_length) # Training preprocessing def prepare_train_features(examples): # Some of the questions have lots of whitespace on the left, which is not useful and will make the # truncation of the context fail (the tokenized question will take a lots of space). So we remove that # left whitespace examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples[question_column_name if pad_on_right else context_column_name], examples[context_column_name if pad_on_right else question_column_name], truncation="only_second" if pad_on_right else "only_first", max_length=max_seq_length, stride=args.doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length" if args.pad_to_max_length else False, ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # The offset mappings will give us a map from token to character position in the original context. This will # help us compute the start_positions and end_positions. offset_mapping = tokenized_examples.pop("offset_mapping") # Let's label those examples! tokenized_examples["start_positions"] = [] tokenized_examples["end_positions"] = [] for i, offsets in enumerate(offset_mapping): # We will label impossible answers with the index of the CLS token. input_ids = tokenized_examples["input_ids"][i] cls_index = input_ids.index(tokenizer.cls_token_id) # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] answers = examples[answer_column_name][sample_index] # If no answers are given, set the cls_index as answer. if len(answers["answer_start"]) == 0: tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Start/end character index of the answer in the text. start_char = answers["answer_start"][0] end_char = start_char + len(answers["text"][0]) # Start token index of the current span in the text. token_start_index = 0 while sequence_ids[token_start_index] != (1 if pad_on_right else 0): token_start_index += 1 # End token index of the current span in the text. token_end_index = len(input_ids) - 1 while sequence_ids[token_end_index] != (1 if pad_on_right else 0): token_end_index -= 1 # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index). if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char): tokenized_examples["start_positions"].append(cls_index) tokenized_examples["end_positions"].append(cls_index) else: # Otherwise move the token_start_index and token_end_index to the two ends of the answer. # Note: we could go after the last offset if the answer is the last word (edge case). while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char: token_start_index += 1 tokenized_examples["start_positions"].append(token_start_index - 1) while offsets[token_end_index][1] >= end_char: token_end_index -= 1 tokenized_examples["end_positions"].append(token_end_index + 1) return tokenized_examples if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset") train_dataset = raw_datasets["train"] if args.max_train_samples is not None: # We will select sample from whole data if agument is specified train_dataset = train_dataset.select(range(args.max_train_samples)) # Create train feature from dataset with accelerator.main_process_first(): train_dataset = train_dataset.map( prepare_train_features, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on train dataset", ) if args.max_train_samples is not None: # Number of samples might increase during Feature Creation, We select only specified max samples train_dataset = train_dataset.select(range(args.max_train_samples)) # Validation preprocessing def prepare_validation_features(examples): # Some of the questions have lots of whitespace on the left, which is not useful and will make the # truncation of the context fail (the tokenized question will take a lots of space). So we remove that # left whitespace examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]] # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results # in one example possible giving several features when a context is long, each of those features having a # context that overlaps a bit the context of the previous feature. tokenized_examples = tokenizer( examples[question_column_name if pad_on_right else context_column_name], examples[context_column_name if pad_on_right else question_column_name], truncation="only_second" if pad_on_right else "only_first", max_length=max_seq_length, stride=args.doc_stride, return_overflowing_tokens=True, return_offsets_mapping=True, padding="max_length" if args.pad_to_max_length else False, ) # Since one example might give us several features if it has a long context, we need a map from a feature to # its corresponding example. This key gives us just that. sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping") # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the # corresponding example_id and we will store the offset mappings. tokenized_examples["example_id"] = [] for i in range(len(tokenized_examples["input_ids"])): # Grab the sequence corresponding to that example (to know what is the context and what is the question). sequence_ids = tokenized_examples.sequence_ids(i) context_index = 1 if pad_on_right else 0 # One example can give several spans, this is the index of the example containing this span of text. sample_index = sample_mapping[i] tokenized_examples["example_id"].append(examples["id"][sample_index]) # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token # position is part of the context or not. tokenized_examples["offset_mapping"][i] = [ (o if sequence_ids[k] == context_index else None) for k, o in enumerate(tokenized_examples["offset_mapping"][i]) ] return tokenized_examples if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset") eval_examples = raw_datasets["validation"] if args.max_eval_samples is not None: # We will select sample from whole data eval_examples = eval_examples.select(range(args.max_eval_samples)) # Validation Feature Creation with accelerator.main_process_first(): eval_dataset = eval_examples.map( prepare_validation_features, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on validation dataset", ) if args.max_eval_samples is not None: # During Feature creation dataset samples might increase, we will select required samples again eval_dataset = eval_dataset.select(range(args.max_eval_samples)) if args.do_predict: if "test" not in raw_datasets: raise ValueError("--do_predict requires a test dataset") predict_examples = raw_datasets["test"] if args.max_predict_samples is not None: # We will select sample from whole data predict_examples = predict_examples.select(range(args.max_predict_samples)) # Predict Feature Creation with accelerator.main_process_first(): predict_dataset = predict_examples.map( prepare_validation_features, batched=True, num_proc=args.preprocessing_num_workers, remove_columns=column_names, load_from_cache_file=not args.overwrite_cache, desc="Running tokenizer on prediction dataset", ) if args.max_predict_samples is not None: # During Feature creation dataset samples might increase, we will select required samples again predict_dataset = predict_dataset.select(range(args.max_predict_samples)) # Log a few random samples from the training set: for index in random.sample(range(len(train_dataset)), 3): logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") # DataLoaders creation: if args.pad_to_max_length: # If padding was already done ot max length, we use the default data collator that will just convert everything # to tensors. data_collator = default_data_collator else: # Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of # the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple # of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=(8 if accelerator.use_fp16 else None)) train_dataloader = DataLoader( train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size ) eval_dataset_for_model = eval_dataset.remove_columns(["example_id", "offset_mapping"]) eval_dataloader = DataLoader( eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) if args.do_predict: predict_dataset_for_model = predict_dataset.remove_columns(["example_id", "offset_mapping"]) predict_dataloader = DataLoader( predict_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size ) # Post-processing: def post_processing_function(examples, features, predictions, stage="eval"): # Post-processing: we match the start logits and end logits to answers in the original context. predictions = postprocess_qa_predictions( examples=examples, features=features, predictions=predictions, version_2_with_negative=args.version_2_with_negative, n_best_size=args.n_best_size, max_answer_length=args.max_answer_length, null_score_diff_threshold=args.null_score_diff_threshold, output_dir=args.output_dir, prefix=stage, ) # Format the result to the format the metric expects. if args.version_2_with_negative: formatted_predictions = [ {"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items() ] else: formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()] references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples] return EvalPrediction(predictions=formatted_predictions, label_ids=references) metric = evaluate.load("squad_v2" if args.version_2_with_negative else "squad") # Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor def create_and_fill_np_array(start_or_end_logits, dataset, max_len): """ Create and fill numpy array of size len_of_validation_data * max_length_of_output_tensor Args: start_or_end_logits(:obj:`tensor`): This is the output predictions of the model. We can only enter either start or end logits. eval_dataset: Evaluation dataset max_len(:obj:`int`): The maximum length of the output tensor. ( See the model.eval() part for more details ) """ step = 0 # create a numpy array and fill it with -100. logits_concat = np.full((len(dataset), max_len), -100, dtype=np.float64) # Now since we have create an array now we will populate it with the outputs gathered using accelerator.gather_for_metrics for i, output_logit in enumerate(start_or_end_logits): # populate columns # We have to fill it such that we have to take the whole tensor and replace it on the newly created array # And after every iteration we have to change the step batch_size = output_logit.shape[0] cols = output_logit.shape[1] if step + batch_size < len(dataset): logits_concat[step : step + batch_size, :cols] = output_logit else: logits_concat[step:, :cols] = output_logit[: len(dataset) - step] step += batch_size return logits_concat # Optimizer # Split weights in two groups, one with weight decay and the other not. no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, { "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0, }, ] optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True lr_scheduler = get_scheduler( name=args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) # Prepare everything with our `accelerator`. model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare( model, optimizer, train_dataloader, eval_dataloader, lr_scheduler ) # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch # Afterwards we recalculate our number of training epochs args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) # Figure out how many steps we should save the Accelerator states checkpointing_steps = args.checkpointing_steps if checkpointing_steps is not None and checkpointing_steps.isdigit(): checkpointing_steps = int(checkpointing_steps) # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if args.with_tracking: experiment_config = vars(args) # TensorBoard cannot log Enums, need the raw value experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value accelerator.init_trackers("qa_no_trainer", experiment_config) # Train! total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num examples = {len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process) completed_steps = 0 starting_epoch = 0 # Potentially load in the weights and states from a previous save if args.resume_from_checkpoint: if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "": accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}") accelerator.load_state(args.resume_from_checkpoint) path = os.path.basename(args.resume_from_checkpoint) else: # Get the most recent checkpoint dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()] dirs.sort(key=os.path.getctime) path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last # Extract `epoch_{i}` or `step_{i}` training_difference = os.path.splitext(path)[0] if "epoch" in training_difference: starting_epoch = int(training_difference.replace("epoch_", "")) + 1 resume_step = None else: resume_step = int(training_difference.replace("step_", "")) starting_epoch = resume_step // len(train_dataloader) resume_step -= starting_epoch * len(train_dataloader) for epoch in range(starting_epoch, args.num_train_epochs): model.train() if args.with_tracking: total_loss = 0 for step, batch in enumerate(train_dataloader): # We need to skip steps until we reach the resumed step if args.resume_from_checkpoint and epoch == starting_epoch: if resume_step is not None and step < resume_step: completed_steps += 1 continue with accelerator.accumulate(model): outputs = model(**batch) loss = outputs.loss # We keep track of the loss at each epoch if args.with_tracking: total_loss += loss.detach().float() accelerator.backward(loss) optimizer.step() lr_scheduler.step() optimizer.zero_grad() # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: progress_bar.update(1) completed_steps += 1 if isinstance(checkpointing_steps, int): if completed_steps % checkpointing_steps == 0: output_dir = f"step_{completed_steps }" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if completed_steps >= args.max_train_steps: break if args.checkpointing_steps == "epoch": output_dir = f"epoch_{epoch}" if args.output_dir is not None: output_dir = os.path.join(args.output_dir, output_dir) accelerator.save_state(output_dir) if args.push_to_hub and epoch < args.num_train_epochs - 1: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) repo.push_to_hub( commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True ) # Evaluation logger.info("***** Running Evaluation *****") logger.info(f" Num examples = {len(eval_dataset)}") logger.info(f" Batch size = {args.per_device_eval_batch_size}") all_start_logits = [] all_end_logits = [] model.eval() for step, batch in enumerate(eval_dataloader): with torch.no_grad(): outputs = model(**batch) start_logits = outputs.start_logits end_logits = outputs.end_logits if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered start_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100) end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100) all_start_logits.append(accelerator.gather_for_metrics(start_logits).cpu().numpy()) all_end_logits.append(accelerator.gather_for_metrics(end_logits).cpu().numpy()) max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor # concatenate the numpy array start_logits_concat = create_and_fill_np_array(all_start_logits, eval_dataset, max_len) end_logits_concat = create_and_fill_np_array(all_end_logits, eval_dataset, max_len) # delete the list of numpy arrays del all_start_logits del all_end_logits outputs_numpy = (start_logits_concat, end_logits_concat) prediction = post_processing_function(eval_examples, eval_dataset, outputs_numpy) eval_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids) logger.info(f"Evaluation metrics: {eval_metric}") # Prediction if args.do_predict: logger.info("***** Running Prediction *****") logger.info(f" Num examples = {len(predict_dataset)}") logger.info(f" Batch size = {args.per_device_eval_batch_size}") all_start_logits = [] all_end_logits = [] model.eval() for step, batch in enumerate(predict_dataloader): with torch.no_grad(): outputs = model(**batch) start_logits = outputs.start_logits end_logits = outputs.end_logits if not args.pad_to_max_length: # necessary to pad predictions and labels for being gathered start_logits = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100) end_logits = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100) all_start_logits.append(accelerator.gather_for_metrics(start_logits).cpu().numpy()) all_end_logits.append(accelerator.gather_for_metrics(end_logits).cpu().numpy()) max_len = max([x.shape[1] for x in all_start_logits]) # Get the max_length of the tensor # concatenate the numpy array start_logits_concat = create_and_fill_np_array(all_start_logits, predict_dataset, max_len) end_logits_concat = create_and_fill_np_array(all_end_logits, predict_dataset, max_len) # delete the list of numpy arrays del all_start_logits del all_end_logits outputs_numpy = (start_logits_concat, end_logits_concat) prediction = post_processing_function(predict_examples, predict_dataset, outputs_numpy) predict_metric = metric.compute(predictions=prediction.predictions, references=prediction.label_ids) logger.info(f"Predict metrics: {predict_metric}") if args.with_tracking: log = { "squad_v2" if args.version_2_with_negative else "squad": eval_metric, "train_loss": total_loss.item() / len(train_dataloader), "epoch": epoch, "step": completed_steps, } if args.do_predict: log["squad_v2_predict" if args.version_2_with_negative else "squad_predict"] = predict_metric accelerator.log(log, step=completed_steps) if args.output_dir is not None: accelerator.wait_for_everyone() unwrapped_model = accelerator.unwrap_model(model) unwrapped_model.save_pretrained( args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save ) if accelerator.is_main_process: tokenizer.save_pretrained(args.output_dir) if args.push_to_hub: repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True) logger.info(json.dumps(eval_metric, indent=4)) save_prefixed_metrics(eval_metric, args.output_dir) if __name__ == "__main__": main()

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/squeezebert/test_tokenization_squeezebert.py

# coding=utf-8 # Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast from transformers.testing_utils import require_tokenizers, slow from ..bert.test_tokenization_bert import BertTokenizationTest @require_tokenizers class SqueezeBertTokenizationTest(BertTokenizationTest): tokenizer_class = SqueezeBertTokenizer rust_tokenizer_class = SqueezeBertTokenizerFast test_rust_tokenizer = True def get_rust_tokenizer(self, **kwargs): return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) @slow def test_sequence_builders(self): tokenizer = SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [ tokenizer.sep_token_id ]

# coding=utf-8 # Copyright 2020 The SqueezeBert authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast from transformers.testing_utils import require_tokenizers, slow from ..bert.test_tokenization_bert import BertTokenizationTest @require_tokenizers class SqueezeBertTokenizationTest(BertTokenizationTest): tokenizer_class = SqueezeBertTokenizer rust_tokenizer_class = SqueezeBertTokenizerFast test_rust_tokenizer = True def get_rust_tokenizer(self, **kwargs): return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) @slow def test_sequence_builders(self): tokenizer = SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless") text = tokenizer.encode("sequence builders", add_special_tokens=False) text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_2 + [ tokenizer.sep_token_id ]

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./examples/research_projects/codeparrot/scripts/tests/__init__.py

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/speech_to_text/test_modeling_tf_speech_to_text.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow Speech2Text model. """ import inspect import unittest from transformers import Speech2TextConfig from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property, is_tf_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel def prepare_speech_to_text_inputs_dict( config, input_features, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.math.not_equal(input_features, 0) if decoder_attention_mask is None: decoder_attention_mask = tf.math.not_equal(decoder_input_ids, config.pad_token_id) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_features": input_features, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFSpeech2TextModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=32, input_feat_per_channel=24, input_channels=1, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=20, max_target_positions=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, scale_embedding=False, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.num_conv_layers = num_conv_layers self.conv_kernel_sizes = conv_kernel_sizes self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.scale_embedding = scale_embedding def prepare_config_and_inputs(self): input_features = floats_tensor( [self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size ) attention_mask = tf.ones([self.batch_size, self.seq_length], dtype=tf.int64) decoder_input_ids = tf.math.maximum(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 2) config = self.get_config() inputs_dict = prepare_speech_to_text_inputs_dict( config, input_features=input_features, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, ) return config, inputs_dict def get_config(self): return Speech2TextConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, num_conv_layers=self.num_conv_layers, conv_kernel_sizes=self.conv_kernel_sizes, conv_channels=self.conv_channels, input_feat_per_channel=self.input_feat_per_channel, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, max_target_positions=self.max_target_positions, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, scale_embedding=self.scale_embedding, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for _ in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFSpeech2TextModel(config=config).get_decoder() input_ids = inputs_dict["decoder_input_ids"] attention_mask = inputs_dict["decoder_attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) _, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = tf.math.maximum(ids_tensor((self.batch_size, 3), config.vocab_size), 2) next_attn_mask = ids_tensor((self.batch_size, 3), 2, dtype=tf.int64) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, atol=1e-2) @require_tf class TFSpeech2TextModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFSpeech2TextModel, TFSpeech2TextForConditionalGeneration) if is_tf_available() else () all_generative_model_classes = (TFSpeech2TextForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False test_onnx = False input_name = "input_ids" def setUp(self): self.model_tester = TFSpeech2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Speech2TextConfig) self.maxDiff = 3000 def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) # not implemented currently def test_inputs_embeds(self): pass # training is not supported yet def test_training(self): pass def test_training_gradient_checkpointing(self): pass def test_generate_fp16(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length) subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_resize_token_embeddings(self): # Overwritten method from parent; see `test_resize_embeddings_untied` pass def test_resize_tokens_embeddings(self): # see `test_resize_embeddings_untied` pass def test_resize_embeddings_untied(self): # TODO: copy test from PT. Not working at the moment because the test relies on `model.resize_token_embeddings`, # whose TF implementation assumes the use of `TFWrappedEmbeddings`. But with a `TFWrappedEmbeddings` we can't # load the weights from PT (also, it induces TF1 behavior, so we might want to rework how # `model.resize_token_embeddings` operates). pass def test_generate_without_input_ids(self): pass @staticmethod def _get_encoder_outputs( model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1 ): encoder = model.get_encoder() encoder_outputs = encoder( input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, num_interleave, axis=0) input_ids = input_ids[:, :, 0] input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + model._get_decoder_start_token_id() attention_mask = None return encoder_outputs, input_ids, attention_mask def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1): batch_size, seq_length = input_ids.shape[:2] subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length) num_sequences_in_output = batch_size * num_return_sequences gen_len = ( output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length ) # scores self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config) # Attentions # encoder self._check_encoder_attention_for_generate( output.encoder_attentions, batch_size, config, subsampled_seq_length ) # decoder self._check_attentions_for_generate( num_sequences_in_output, output.decoder_attentions, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # Hidden States # encoder self._check_encoder_hidden_states_for_generate( output.encoder_hidden_states, batch_size, config, subsampled_seq_length ) # decoder self._check_hidden_states_for_generate( num_sequences_in_output, output.decoder_hidden_states, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_features with self.assertRaises(AssertionError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_features, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2)) with self.assertRaises(ValueError): # generating more sequences than having beams leads is not possible model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_features, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids( model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2) ) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent -- the input is `input_features`, not `input_ids` def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_features", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @require_tf @require_sentencepiece @require_tokenizers @slow class TFSpeech2TextModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_generation_librispeech(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features) generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel" ] self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS) def test_generation_librispeech_batched(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(4) inputs = processor(input_speech, return_tensors="tf", padding=True) generated_ids = model.generate(inputs.input_features, attention_mask=inputs.attention_mask) generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister cultar's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and roast beef looming before us" " similes drawn from eating and its results occur most readily to the mind", "he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it" " but little of rocky ithaca", ] self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the TensorFlow Speech2Text model. """ import inspect import unittest from transformers import Speech2TextConfig from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property, is_tf_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration, TFSpeech2TextModel def prepare_speech_to_text_inputs_dict( config, input_features, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.math.not_equal(input_features, 0) if decoder_attention_mask is None: decoder_attention_mask = tf.math.not_equal(decoder_input_ids, config.pad_token_id) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_features": input_features, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFSpeech2TextModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, num_conv_layers=2, conv_kernel_sizes=(5, 5), conv_channels=32, input_feat_per_channel=24, input_channels=1, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, max_source_positions=20, max_target_positions=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, scale_embedding=False, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.num_conv_layers = num_conv_layers self.conv_kernel_sizes = conv_kernel_sizes self.conv_channels = conv_channels self.input_feat_per_channel = input_feat_per_channel self.input_channels = input_channels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.max_source_positions = max_source_positions self.max_target_positions = max_target_positions self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.scale_embedding = scale_embedding def prepare_config_and_inputs(self): input_features = floats_tensor( [self.batch_size, self.seq_length, self.input_feat_per_channel], self.vocab_size ) attention_mask = tf.ones([self.batch_size, self.seq_length], dtype=tf.int64) decoder_input_ids = tf.math.maximum(ids_tensor([self.batch_size, self.seq_length], self.vocab_size), 2) config = self.get_config() inputs_dict = prepare_speech_to_text_inputs_dict( config, input_features=input_features, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, ) return config, inputs_dict def get_config(self): return Speech2TextConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, num_conv_layers=self.num_conv_layers, conv_kernel_sizes=self.conv_kernel_sizes, conv_channels=self.conv_channels, input_feat_per_channel=self.input_feat_per_channel, input_channels=self.input_channels, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, max_source_positions=self.max_source_positions, max_target_positions=self.max_target_positions, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, scale_embedding=self.scale_embedding, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def get_subsampled_output_lengths(self, input_lengths): """ Computes the output length of the convolutional layers """ for _ in range(self.num_conv_layers): input_lengths = (input_lengths - 1) // 2 + 1 return input_lengths def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFSpeech2TextModel(config=config).get_decoder() input_ids = inputs_dict["decoder_input_ids"] attention_mask = inputs_dict["decoder_attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, use_cache=True) _, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = tf.math.maximum(ids_tensor((self.batch_size, 3), config.vocab_size), 2) next_attn_mask = ids_tensor((self.batch_size, 3), 2, dtype=tf.int64) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, atol=1e-2) @require_tf class TFSpeech2TextModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFSpeech2TextModel, TFSpeech2TextForConditionalGeneration) if is_tf_available() else () all_generative_model_classes = (TFSpeech2TextForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False test_onnx = False input_name = "input_ids" def setUp(self): self.model_tester = TFSpeech2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=Speech2TextConfig) self.maxDiff = 3000 def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) # not implemented currently def test_inputs_embeds(self): pass # training is not supported yet def test_training(self): pass def test_training_gradient_checkpointing(self): pass def test_generate_fp16(self): pass def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length else: seq_length = self.model_tester.seq_length subsampled_seq_length = model._get_feat_extract_output_lengths(seq_length) self.assertListEqual( list(hidden_states[0].shape[-2:]), [subsampled_seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[0].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) subsampled_encoder_seq_length = model._get_feat_extract_output_lengths(encoder_seq_length) subsampled_encoder_key_length = model._get_feat_extract_output_lengths(encoder_key_length) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) out_len = len(outputs) correct_outlen = 5 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, decoder_seq_length, subsampled_encoder_key_length, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, subsampled_encoder_seq_length, subsampled_encoder_key_length], ) def test_resize_token_embeddings(self): # Overwritten method from parent; see `test_resize_embeddings_untied` pass def test_resize_tokens_embeddings(self): # see `test_resize_embeddings_untied` pass def test_resize_embeddings_untied(self): # TODO: copy test from PT. Not working at the moment because the test relies on `model.resize_token_embeddings`, # whose TF implementation assumes the use of `TFWrappedEmbeddings`. But with a `TFWrappedEmbeddings` we can't # load the weights from PT (also, it induces TF1 behavior, so we might want to rework how # `model.resize_token_embeddings` operates). pass def test_generate_without_input_ids(self): pass @staticmethod def _get_encoder_outputs( model, input_ids, attention_mask, output_attentions=None, output_hidden_states=None, num_interleave=1 ): encoder = model.get_encoder() encoder_outputs = encoder( input_ids, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) encoder_outputs["last_hidden_state"] = tf.repeat(encoder_outputs.last_hidden_state, num_interleave, axis=0) input_ids = input_ids[:, :, 0] input_ids = tf.zeros_like(input_ids[:, :1], dtype=tf.int64) + model._get_decoder_start_token_id() attention_mask = None return encoder_outputs, input_ids, attention_mask def _check_outputs(self, output, input_ids, config, use_cache=False, num_return_sequences=1): batch_size, seq_length = input_ids.shape[:2] subsampled_seq_length = self.model_tester.get_subsampled_output_lengths(seq_length) num_sequences_in_output = batch_size * num_return_sequences gen_len = ( output.sequences.shape[-1] - 1 if config.is_encoder_decoder else output.sequences.shape[-1] - seq_length ) # scores self._check_scores(num_sequences_in_output, output.scores, length=gen_len, config=config) # Attentions # encoder self._check_encoder_attention_for_generate( output.encoder_attentions, batch_size, config, subsampled_seq_length ) # decoder self._check_attentions_for_generate( num_sequences_in_output, output.decoder_attentions, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # Hidden States # encoder self._check_encoder_hidden_states_for_generate( output.encoder_hidden_states, batch_size, config, subsampled_seq_length ) # decoder self._check_hidden_states_for_generate( num_sequences_in_output, output.decoder_hidden_states, min_length=1, max_length=output.sequences.shape[-1], config=config, use_cache=use_cache, ) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_no_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) # iterate over all generative models for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_features with self.assertRaises(AssertionError): model.generate(do_sample=True, max_length=5) # num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True)) with self.assertRaises(ValueError): # generating multiple sequences when no beam search generation # is not allowed as it would always generate the same sequences model.generate(input_features, do_sample=False, num_return_sequences=2) # num_return_sequences > 1, sample self._check_generated_ids(model.generate(input_features, do_sample=True, num_return_sequences=2)) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent due to the inability to work when non-text inputs are not passed AND because the input is # `input_features` def test_lm_head_model_random_beam_search_generate(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() input_features = inputs_dict.get("input_features", None) for model_class in self.all_generative_model_classes: model = model_class(config) if config.bos_token_id is None: # if bos token id is not defined model needs input_ids, num_return_sequences = 1 self._check_generated_ids(model.generate(input_features, do_sample=True, num_beams=2)) with self.assertRaises(ValueError): # generating more sequences than having beams leads is not possible model.generate(input_features, do_sample=False, num_return_sequences=3, num_beams=2) # num_return_sequences > 1, sample self._check_generated_ids( model.generate( input_features, do_sample=True, num_beams=2, num_return_sequences=2, ) ) # num_return_sequences > 1, greedy self._check_generated_ids( model.generate(input_features, do_sample=False, num_beams=2, num_return_sequences=2) ) # check bad words tokens language generation # create list of 1-seq bad token and list of 2-seq of bad tokens bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)] output_tokens = model.generate( input_features, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2 ) # only count generated tokens generated_ids = output_tokens[:, input_features.shape[-1] :] self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids)) # overwritten from parent -- the input is `input_features`, not `input_ids` def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.call) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = [ "input_features", "attention_mask", "decoder_input_ids", "decoder_attention_mask", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @require_tf @require_sentencepiece @require_tokenizers @slow class TFSpeech2TextModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr") def _load_datasamples(self, num_samples): from datasets import load_dataset ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_generation_librispeech(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(1) input_features = processor(input_speech, return_tensors="tf").input_features generated_ids = model.generate(input_features) generated_transcript = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel" ] self.assertListEqual(generated_transcript, EXPECTED_TRANSCRIPTIONS) def test_generation_librispeech_batched(self): model = TFSpeech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-librispeech-asr") processor = self.default_processor input_speech = self._load_datasamples(4) inputs = processor(input_speech, return_tensors="tf", padding=True) generated_ids = model.generate(inputs.input_features, attention_mask=inputs.attention_mask) generated_transcripts = processor.batch_decode(generated_ids, skip_special_tokens=True) EXPECTED_TRANSCRIPTIONS = [ "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel", "nor is mister cultar's manner less interesting than his matter", "he tells us that at this festive season of the year with christmas and roast beef looming before us" " similes drawn from eating and its results occur most readily to the mind", "he has grave doubts whether sir frederick leyton's work is really greek after all and can discover in it" " but little of rocky ithaca", ] self.assertListEqual(generated_transcripts, EXPECTED_TRANSCRIPTIONS)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/training_args_seq2seq.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from dataclasses import dataclass, field from typing import Optional from .training_args import TrainingArguments from .utils import add_start_docstrings logger = logging.getLogger(__name__) @dataclass @add_start_docstrings(TrainingArguments.__doc__) class Seq2SeqTrainingArguments(TrainingArguments): """ Args: sortish_sampler (`bool`, *optional*, defaults to `False`): Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset* for now but will become generally available in the near future. It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for the training set. predict_with_generate (`bool`, *optional*, defaults to `False`): Whether to use generate to calculate generative metrics (ROUGE, BLEU). generation_max_length (`int`, *optional*): The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. generation_num_beams (`int`, *optional*): The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. """ sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."}) predict_with_generate: bool = field( default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} ) generation_max_length: Optional[int] = field( default=None, metadata={ "help": ( "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default " "to the `max_length` value of the model configuration." ) }, ) generation_num_beams: Optional[int] = field( default=None, metadata={ "help": ( "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default " "to the `num_beams` value of the model configuration." ) }, )

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging from dataclasses import dataclass, field from typing import Optional from .training_args import TrainingArguments from .utils import add_start_docstrings logger = logging.getLogger(__name__) @dataclass @add_start_docstrings(TrainingArguments.__doc__) class Seq2SeqTrainingArguments(TrainingArguments): """ Args: sortish_sampler (`bool`, *optional*, defaults to `False`): Whether to use a *sortish sampler* or not. Only possible if the underlying datasets are *Seq2SeqDataset* for now but will become generally available in the near future. It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for the training set. predict_with_generate (`bool`, *optional*, defaults to `False`): Whether to use generate to calculate generative metrics (ROUGE, BLEU). generation_max_length (`int`, *optional*): The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration. generation_num_beams (`int`, *optional*): The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration. """ sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."}) predict_with_generate: bool = field( default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} ) generation_max_length: Optional[int] = field( default=None, metadata={ "help": ( "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default " "to the `max_length` value of the model configuration." ) }, ) generation_num_beams: Optional[int] = field( default=None, metadata={ "help": ( "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default " "to the `num_beams` value of the model configuration." ) }, )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/led/tokenization_led_fast.py

# coding=utf-8 # Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for LED.""" import json from typing import Dict, List, Optional, Tuple, Union from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, logging from .tokenization_led import LEDTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json", }, "merges_file": { "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt", }, "tokenizer_file": { "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "allenai/led-base-16384": 16384, } class LEDTokenizerFast(PreTrainedTokenizerFast): r""" Construct a "fast" LED tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import LEDTokenizerFast >>> tokenizer = LEDTokenizerFast.from_pretrained("allenai/led-base-16384") >>> tokenizer("Hello world")['input_ids'] [0, 31414, 232, 2] >>> tokenizer(" Hello world")['input_ids'] [0, 20920, 232, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (LED tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = LEDTokenizer model_input_names = ["input_ids", "attention_mask"] # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.__init__ def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, trim_offsets=True, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__` tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if state.get("trim_offsets", trim_offsets) != trim_offsets: state["trim_offsets"] = trim_offsets changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) @property # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. LED tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily comprise the space before the *<mask>*. """ if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @mask_token.setter def mask_token(self, value): """ Overriding the default behavior of the mask token to have it eat the space before it. This is needed to preserve backward compatibility with all the previously used models based on LED. """ # Mask token behave like a normal word, i.e. include the space before it # So we set lstrip to True value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value self._mask_token = value # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast._batch_encode_plus def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words and not self.add_prefix_space: raise ValueError( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast._encode_plus def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words and not self.add_prefix_space: raise ValueError( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.save_vocabulary def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.build_inputs_with_special_tokens def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] if token_ids_1 is None: return output return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.create_token_type_ids_from_sequences with BART->LED def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. LED does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] # Copied from transformers.models.led.tokenization_led.LEDTokenizer._pad def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: encoded_inputs = super()._pad( encoded_inputs=encoded_inputs, max_length=max_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names if return_attention_mask and "global_attention_mask" in encoded_inputs: required_input = encoded_inputs[self.model_input_names[0]] # `global_attention_mask` need to have the same length as other (sequential) inputs. needs_to_be_padded = len(encoded_inputs["global_attention_mask"]) != len(required_input) if needs_to_be_padded: difference = len(required_input) - len(encoded_inputs["global_attention_mask"]) if self.padding_side == "right": # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend` encoded_inputs["global_attention_mask"] = ( encoded_inputs["global_attention_mask"] + [-1] * difference ) elif self.padding_side == "left": encoded_inputs["global_attention_mask"] = [-1] * difference + encoded_inputs[ "global_attention_mask" ] else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs

# coding=utf-8 # Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for LED.""" import json from typing import Dict, List, Optional, Tuple, Union from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import PaddingStrategy, logging from .tokenization_led import LEDTokenizer logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json", }, "merges_file": { "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt", }, "tokenizer_file": { "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { "allenai/led-base-16384": 16384, } class LEDTokenizerFast(PreTrainedTokenizerFast): r""" Construct a "fast" LED tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not: ``` >>> from transformers import LEDTokenizerFast >>> tokenizer = LEDTokenizerFast.from_pretrained("allenai/led-base-16384") >>> tokenizer("Hello world")['input_ids'] [0, 31414, 232, 2] >>> tokenizer(" Hello world")['input_ids'] [0, 20920, 232, 2] ``` You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. <Tip> When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. </Tip> This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (`str`): Path to the vocabulary file. merges_file (`str`): Path to the merges file. errors (`str`, *optional*, defaults to `"replace"`): Paradigm to follow when decoding bytes to UTF-8. See [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. bos_token (`str`, *optional*, defaults to `"<s>"`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. <Tip> When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`. </Tip> eos_token (`str`, *optional*, defaults to `"</s>"`): The end of sequence token. <Tip> When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`. </Tip> sep_token (`str`, *optional*, defaults to `"</s>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. cls_token (`str`, *optional*, defaults to `"<s>"`): The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. unk_token (`str`, *optional*, defaults to `"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (`str`, *optional*, defaults to `"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `"<mask>"`): The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. add_prefix_space (`bool`, *optional*, defaults to `False`): Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (LED tokenizer detect beginning of words by the preceding space). trim_offsets (`bool`, *optional*, defaults to `True`): Whether the post processing step should trim offsets to avoid including whitespaces. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES slow_tokenizer_class = LEDTokenizer model_input_names = ["input_ids", "attention_mask"] # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.__init__ def __init__( self, vocab_file=None, merges_file=None, tokenizer_file=None, errors="replace", bos_token="<s>", eos_token="</s>", sep_token="</s>", cls_token="<s>", unk_token="<unk>", pad_token="<pad>", mask_token="<mask>", add_prefix_space=False, trim_offsets=True, **kwargs ): super().__init__( vocab_file, merges_file, tokenizer_file=tokenizer_file, errors=errors, bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets, **kwargs, ) pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) pre_tok_state["add_prefix_space"] = add_prefix_space self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) self.add_prefix_space = add_prefix_space # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__` tokenizer_component = "post_processor" tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) if tokenizer_component_instance: state = json.loads(tokenizer_component_instance.__getstate__()) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: state["sep"] = tuple(state["sep"]) if "cls" in state: state["cls"] = tuple(state["cls"]) changes_to_apply = False if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: state["add_prefix_space"] = add_prefix_space changes_to_apply = True if state.get("trim_offsets", trim_offsets) != trim_offsets: state["trim_offsets"] = trim_offsets changes_to_apply = True if changes_to_apply: component_class = getattr(processors, state.pop("type")) new_value = component_class(**state) setattr(self.backend_tokenizer, tokenizer_component, new_value) @property # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED def mask_token(self) -> str: """ `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not having been set. LED tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily comprise the space before the *<mask>*. """ if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet.") return None return str(self._mask_token) @mask_token.setter def mask_token(self, value): """ Overriding the default behavior of the mask token to have it eat the space before it. This is needed to preserve backward compatibility with all the previously used models based on LED. """ # Mask token behave like a normal word, i.e. include the space before it # So we set lstrip to True value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value self._mask_token = value # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast._batch_encode_plus def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words and not self.add_prefix_space: raise ValueError( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*args, **kwargs) # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast._encode_plus def _encode_plus(self, *args, **kwargs) -> BatchEncoding: is_split_into_words = kwargs.get("is_split_into_words", False) if is_split_into_words and not self.add_prefix_space: raise ValueError( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*args, **kwargs) # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.save_vocabulary def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: files = self._tokenizer.model.save(save_directory, name=filename_prefix) return tuple(files) # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.build_inputs_with_special_tokens def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] if token_ids_1 is None: return output return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.create_token_type_ids_from_sequences with BART->LED def create_token_type_ids_from_sequences( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. LED does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (`List[int]`): List of IDs. token_ids_1 (`List[int]`, *optional*): Optional second list of IDs for sequence pairs. Returns: `List[int]`: List of zeros. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] # Copied from transformers.models.led.tokenization_led.LEDTokenizer._pad def _pad( self, encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], max_length: Optional[int] = None, padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, pad_to_multiple_of: Optional[int] = None, return_attention_mask: Optional[bool] = None, ) -> dict: encoded_inputs = super()._pad( encoded_inputs=encoded_inputs, max_length=max_length, padding_strategy=padding_strategy, pad_to_multiple_of=pad_to_multiple_of, return_attention_mask=return_attention_mask, ) # Load from model defaults if return_attention_mask is None: return_attention_mask = "attention_mask" in self.model_input_names if return_attention_mask and "global_attention_mask" in encoded_inputs: required_input = encoded_inputs[self.model_input_names[0]] # `global_attention_mask` need to have the same length as other (sequential) inputs. needs_to_be_padded = len(encoded_inputs["global_attention_mask"]) != len(required_input) if needs_to_be_padded: difference = len(required_input) - len(encoded_inputs["global_attention_mask"]) if self.padding_side == "right": # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend` encoded_inputs["global_attention_mask"] = ( encoded_inputs["global_attention_mask"] + [-1] * difference ) elif self.padding_side == "left": encoded_inputs["global_attention_mask"] = [-1] * difference + encoded_inputs[ "global_attention_mask" ] else: raise ValueError("Invalid padding strategy:" + str(self.padding_side)) return encoded_inputs

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py

#!/usr/bin/env python # coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and """ Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition in streaming mode""" import logging import os import re import sys import warnings from dataclasses import dataclass, field from typing import Dict, List, Optional, Union import datasets import numpy as np import torch from datasets import IterableDatasetDict, interleave_datasets, load_dataset, load_metric from torch.utils.data import IterableDataset import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForCTC, AutoProcessor, AutoTokenizer, HfArgumentParser, Trainer, TrainerCallback, TrainingArguments, Wav2Vec2Processor, set_seed, ) from transformers.trainer_pt_utils import IterableDatasetShard from transformers.trainer_utils import get_last_checkpoint, is_main_process from transformers.utils import check_min_version from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risk. check_min_version("4.17.0.dev0") require_version("datasets>=1.18.2", "To fix: pip install 'datasets>=1.18.2'") logger = logging.getLogger(__name__) def list_field(default=None, metadata=None): return field(default_factory=lambda: default, metadata=metadata) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) tokenizer_name_or_path: Optional[str] = field( default=None, metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"}, ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) attention_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."} ) activation_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} ) feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."}) hidden_dropout: float = field( default=0.0, metadata={ "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler." }, ) final_dropout: float = field( default=0.0, metadata={"help": "The dropout probability for the final projection layer."}, ) mask_time_prob: float = field( default=0.05, metadata={ "help": ( "Probability of each feature vector along the time axis to be chosen as the start of the vector" "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature" "vectors will be masked along the time axis." ) }, ) mask_time_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the time axis."}, ) mask_feature_prob: float = field( default=0.0, metadata={ "help": ( "Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan" " to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature" " bins will be masked along the time axis." ) }, ) mask_feature_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the feature axis."}, ) layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."}) ctc_loss_reduction: Optional[str] = field( default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: str = field( metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) dataset_config_name: str = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_split_name: str = field( default="train+validation", metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to " "'train+validation'" ) }, ) eval_split_name: str = field( default="test", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'test'" }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of validation examples to this " "value if set." ) }, ) shuffle_buffer_size: Optional[int] = field( default=500, metadata={ "help": ( "The number of streamed examples to download before shuffling them. The large the buffer, " "the closer it is to real offline shuffling." ) }, ) chars_to_ignore: Optional[List[str]] = list_field( default=None, metadata={"help": "A list of characters to remove from the transcripts."}, ) eval_metrics: List[str] = list_field( default=["wer"], metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"}, ) max_duration_in_seconds: float = field( default=20.0, metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds."}, ) preprocessing_only: bool = field( default=False, metadata={ "help": ( "Whether to only do data preprocessing and skip training. This is especially useful when data" " preprocessing errors out in distributed training due to timeout. In this case, one should run the" " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets" " can consequently be loaded in distributed training" ) }, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "If :obj:`True`, will use the token generated when running" ":obj:`huggingface-cli login` as HTTP bearer authorization for remote files." ) }, ) phoneme_language: Optional[str] = field( default=None, metadata={ "help": ( "The target language that should be used be" " passed to the tokenizer for tokenization. Note that" " this is only relevant if the model classifies the" " input audio to a sequence of phoneme sequences." ) }, ) @dataclass class DataCollatorCTCWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor (:class:`~transformers.AutoProcessor`) The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). max_length_labels (:obj:`int`, `optional`): Maximum length of the ``labels`` returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ processor: AutoProcessor padding: Union[bool, str] = "longest" max_length: Optional[int] = None pad_to_multiple_of: Optional[int] = None pad_to_multiple_of_labels: Optional[int] = None def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lenghts and need # different padding methods input_features = [] label_features = [] for feature in features: if self.max_length and feature["input_values"].shape[-1] > self.max_length: continue input_features.append({"input_values": feature["input_values"]}) label_features.append({"input_ids": feature["labels"]}) batch = self.processor.pad( input_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) labels_batch = self.processor.pad( labels=label_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of_labels, return_tensors="pt", ) # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels return batch def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # 1. First, let's load the dataset raw_datasets = IterableDatasetDict() raw_column_names = {} def load_streaming_dataset(split, sampling_rate, **kwargs): if "+" in split: dataset_splits = [load_dataset(split=split_name, **kwargs) for split_name in split.split("+")] # `features` and `cast_column` won't be available after interleaving, so we'll use them here features = dataset_splits[0].features # make sure that the dataset decodes audio with a correct sampling rate dataset_splits = [ dataset.cast_column(data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)) for dataset in dataset_splits ] interleaved_dataset = interleave_datasets(dataset_splits) return interleaved_dataset, features else: dataset = load_dataset(split=split, **kwargs) features = dataset.features # make sure that the dataset decodes audio with a correct sampling rate dataset = dataset.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate) ) return dataset, features # `datasets` takes care of automatically loading and resampling the audio, # so we just need to set the correct target sampling rate and normalize the input # via the `feature_extractor` feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) if training_args.do_train: raw_datasets["train"], train_features = load_streaming_dataset( path=data_args.dataset_name, name=data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=data_args.use_auth_token, streaming=True, sampling_rate=feature_extractor.sampling_rate, ) raw_column_names["train"] = list(train_features.keys()) if data_args.audio_column_name not in raw_column_names["train"]: raise ValueError( f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'." " Make sure to set `--audio_column_name` to the correct audio column - one of" f" {', '.join(raw_column_names['train'])}." ) if data_args.text_column_name not in raw_column_names["train"]: raise ValueError( f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--text_column_name` to the correct text column - one of " f"{', '.join(raw_column_names['train'])}." ) if data_args.max_train_samples is not None: raw_datasets["train"] = raw_datasets["train"].take(range(data_args.max_train_samples)) if training_args.do_eval: raw_datasets["eval"], eval_features = load_streaming_dataset( path=data_args.dataset_name, name=data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=data_args.use_auth_token, streaming=True, sampling_rate=feature_extractor.sampling_rate, ) raw_column_names["eval"] = list(eval_features.keys()) if data_args.max_eval_samples is not None: raw_datasets["eval"] = raw_datasets["eval"].take(range(data_args.max_eval_samples)) # 2. We remove some special characters from the datasets # that make training complicated and do not help in transcribing the speech # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic # that could be easily picked up by the model chars_to_ignore_regex = ( f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None ) text_column_name = data_args.text_column_name def remove_special_characters(batch): if chars_to_ignore_regex is not None: batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " " else: batch["target_text"] = batch[text_column_name].lower() + " " return batch with training_args.main_process_first(desc="dataset map special characters removal"): for split, dataset in raw_datasets.items(): raw_datasets[split] = dataset.map( remove_special_characters, ).remove_columns([text_column_name]) # 3. Next, let's load the config as we might need it to create # the tokenizer config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) # 4. Now we can instantiate the tokenizer and model # Note for distributed training, the .from_pretrained methods guarantee that only # one local process can concurrently download model & vocab. tokenizer_name_or_path = model_args.tokenizer_name_or_path if tokenizer_name_or_path is None: raise ValueError( "Tokenizer has to be created before training in streaming mode. Please specify --tokenizer_name_or_path" ) # load feature_extractor and tokenizer tokenizer = AutoTokenizer.from_pretrained( tokenizer_name_or_path, config=config, use_auth_token=data_args.use_auth_token, ) # adapt config config.update( { "feat_proj_dropout": model_args.feat_proj_dropout, "attention_dropout": model_args.attention_dropout, "hidden_dropout": model_args.hidden_dropout, "final_dropout": model_args.final_dropout, "mask_time_prob": model_args.mask_time_prob, "mask_time_length": model_args.mask_time_length, "mask_feature_prob": model_args.mask_feature_prob, "mask_feature_length": model_args.mask_feature_length, "gradient_checkpointing": training_args.gradient_checkpointing, "layerdrop": model_args.layerdrop, "ctc_loss_reduction": model_args.ctc_loss_reduction, "pad_token_id": tokenizer.pad_token_id, "vocab_size": len(tokenizer), "activation_dropout": model_args.activation_dropout, } ) # create model model = AutoModelForCTC.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, config=config, use_auth_token=data_args.use_auth_token, ) # freeze encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() # 5. Now we preprocess the datasets including loading the audio, resampling and normalization audio_column_name = data_args.audio_column_name # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification phoneme_language = data_args.phoneme_language # Preprocessing the datasets. # We need to read the audio files as arrays and tokenize the targets. def prepare_dataset(batch): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch vectorized_datasets = IterableDatasetDict() with training_args.main_process_first(desc="dataset map preprocessing"): for split, dataset in raw_datasets.items(): vectorized_datasets[split] = ( dataset.map(prepare_dataset) .remove_columns(raw_column_names[split] + ["target_text"]) .with_format("torch") ) if split == "train": vectorized_datasets[split] = vectorized_datasets[split].shuffle( buffer_size=data_args.shuffle_buffer_size, seed=training_args.seed, ) # 6. Next, we can prepare the training. # Let's use word error rate (WER) as our evaluation metric, # instantiate a data collator and the trainer # Define evaluation metrics during training, *i.e.* word error rate, character error rate eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics} def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id pred_str = tokenizer.batch_decode(pred_ids) # we do not want to group tokens when computing the metrics label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False) metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} return metrics # Now save everything to be able to create a single processor later if is_main_process(training_args.local_rank): # save feature extractor, tokenizer and config feature_extractor.save_pretrained(training_args.output_dir) tokenizer.save_pretrained(training_args.output_dir) config.save_pretrained(training_args.output_dir) try: processor = AutoProcessor.from_pretrained(training_args.output_dir) except (OSError, KeyError): warnings.warn( "Loading a processor from a feature extractor config that does not" " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following " " attribute to your `preprocessor_config.json` file to suppress this warning: " " `'processor_class': 'Wav2Vec2Processor'`", FutureWarning, ) processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir) # Instantiate custom data collator max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate data_collator = DataCollatorCTCWithPadding(processor=processor, max_length=max_input_length) # trainer callback to reinitialize and reshuffle the streamable datasets at the beginning of each epoch class ShuffleCallback(TrainerCallback): def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs): if isinstance(train_dataloader.dataset, IterableDatasetShard): pass # set_epoch() is handled by the Trainer elif isinstance(train_dataloader.dataset, IterableDataset): train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1) # Initialize Trainer trainer = Trainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=vectorized_datasets["train"] if training_args.do_train else None, eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None, tokenizer=processor, callbacks=[ShuffleCallback()], ) # 7. Finally, we can start training # Training if training_args.do_train: # use last checkpoint if exist if last_checkpoint is not None: checkpoint = last_checkpoint elif os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() metrics = train_result.metrics if data_args.max_train_samples: metrics["train_samples"] = data_args.max_train_samples trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate() if data_args.max_eval_samples: metrics["eval_samples"] = data_args.max_eval_samples trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na" kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition", "tags": ["automatic-speech-recognition", data_args.dataset_name], "dataset_args": ( f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:" f" {data_args.eval_split_name}" ), "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}", } if "common_voice" in data_args.dataset_name: kwargs["language"] = config_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) return results if __name__ == "__main__": main()

#!/usr/bin/env python # coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and """ Fine-tuning a 🤗 Transformers CTC model for automatic speech recognition in streaming mode""" import logging import os import re import sys import warnings from dataclasses import dataclass, field from typing import Dict, List, Optional, Union import datasets import numpy as np import torch from datasets import IterableDatasetDict, interleave_datasets, load_dataset, load_metric from torch.utils.data import IterableDataset import transformers from transformers import ( AutoConfig, AutoFeatureExtractor, AutoModelForCTC, AutoProcessor, AutoTokenizer, HfArgumentParser, Trainer, TrainerCallback, TrainingArguments, Wav2Vec2Processor, set_seed, ) from transformers.trainer_pt_utils import IterableDatasetShard from transformers.trainer_utils import get_last_checkpoint, is_main_process from transformers.utils import check_min_version from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risk. check_min_version("4.17.0.dev0") require_version("datasets>=1.18.2", "To fix: pip install 'datasets>=1.18.2'") logger = logging.getLogger(__name__) def list_field(default=None, metadata=None): return field(default_factory=lambda: default, metadata=metadata) @dataclass class ModelArguments: """ Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. """ model_name_or_path: str = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) tokenizer_name_or_path: Optional[str] = field( default=None, metadata={"help": "Path to pretrained tokenizer or tokenizer identifier from huggingface.co/models"}, ) cache_dir: Optional[str] = field( default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, ) freeze_feature_encoder: bool = field( default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."} ) attention_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for the attention probabilities."} ) activation_dropout: float = field( default=0.0, metadata={"help": "The dropout ratio for activations inside the fully connected layer."} ) feat_proj_dropout: float = field(default=0.0, metadata={"help": "The dropout ratio for the projected features."}) hidden_dropout: float = field( default=0.0, metadata={ "help": "The dropout probability for all fully connected layers in the embeddings, encoder, and pooler." }, ) final_dropout: float = field( default=0.0, metadata={"help": "The dropout probability for the final projection layer."}, ) mask_time_prob: float = field( default=0.05, metadata={ "help": ( "Probability of each feature vector along the time axis to be chosen as the start of the vector" "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature" "vectors will be masked along the time axis." ) }, ) mask_time_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the time axis."}, ) mask_feature_prob: float = field( default=0.0, metadata={ "help": ( "Probability of each feature vector along the feature axis to be chosen as the start of the vectorspan" " to be masked. Approximately ``mask_feature_prob * sequence_length // mask_feature_length`` feature" " bins will be masked along the time axis." ) }, ) mask_feature_length: int = field( default=10, metadata={"help": "Length of vector span to mask along the feature axis."}, ) layerdrop: float = field(default=0.0, metadata={"help": "The LayerDrop probability."}) ctc_loss_reduction: Optional[str] = field( default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."} ) @dataclass class DataTrainingArguments: """ Arguments pertaining to what data we are going to input our model for training and eval. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. """ dataset_name: str = field( metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) dataset_config_name: str = field( default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} ) train_split_name: str = field( default="train+validation", metadata={ "help": ( "The name of the training data set split to use (via the datasets library). Defaults to " "'train+validation'" ) }, ) eval_split_name: str = field( default="test", metadata={ "help": "The name of the training data set split to use (via the datasets library). Defaults to 'test'" }, ) audio_column_name: str = field( default="audio", metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"}, ) text_column_name: str = field( default="text", metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"}, ) overwrite_cache: bool = field( default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} ) preprocessing_num_workers: Optional[int] = field( default=None, metadata={"help": "The number of processes to use for the preprocessing."}, ) max_train_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of training examples to this " "value if set." ) }, ) max_eval_samples: Optional[int] = field( default=None, metadata={ "help": ( "For debugging purposes or quicker training, truncate the number of validation examples to this " "value if set." ) }, ) shuffle_buffer_size: Optional[int] = field( default=500, metadata={ "help": ( "The number of streamed examples to download before shuffling them. The large the buffer, " "the closer it is to real offline shuffling." ) }, ) chars_to_ignore: Optional[List[str]] = list_field( default=None, metadata={"help": "A list of characters to remove from the transcripts."}, ) eval_metrics: List[str] = list_field( default=["wer"], metadata={"help": "A list of metrics the model should be evaluated on. E.g. `'wer cer'`"}, ) max_duration_in_seconds: float = field( default=20.0, metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds."}, ) preprocessing_only: bool = field( default=False, metadata={ "help": ( "Whether to only do data preprocessing and skip training. This is especially useful when data" " preprocessing errors out in distributed training due to timeout. In this case, one should run the" " preprocessing in a non-distributed setup with `preprocessing_only=True` so that the cached datasets" " can consequently be loaded in distributed training" ) }, ) use_auth_token: bool = field( default=False, metadata={ "help": ( "If :obj:`True`, will use the token generated when running" ":obj:`huggingface-cli login` as HTTP bearer authorization for remote files." ) }, ) phoneme_language: Optional[str] = field( default=None, metadata={ "help": ( "The target language that should be used be" " passed to the tokenizer for tokenization. Note that" " this is only relevant if the model classifies the" " input audio to a sequence of phoneme sequences." ) }, ) @dataclass class DataCollatorCTCWithPadding: """ Data collator that will dynamically pad the inputs received. Args: processor (:class:`~transformers.AutoProcessor`) The processor used for proccessing the data. padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not provided. * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (:obj:`int`, `optional`): Maximum length of the ``input_values`` of the returned list and optionally padding length (see above). max_length_labels (:obj:`int`, `optional`): Maximum length of the ``labels`` returned list and optionally padding length (see above). pad_to_multiple_of (:obj:`int`, `optional`): If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). """ processor: AutoProcessor padding: Union[bool, str] = "longest" max_length: Optional[int] = None pad_to_multiple_of: Optional[int] = None pad_to_multiple_of_labels: Optional[int] = None def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]: # split inputs and labels since they have to be of different lenghts and need # different padding methods input_features = [] label_features = [] for feature in features: if self.max_length and feature["input_values"].shape[-1] > self.max_length: continue input_features.append({"input_values": feature["input_values"]}) label_features.append({"input_ids": feature["labels"]}) batch = self.processor.pad( input_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of, return_tensors="pt", ) labels_batch = self.processor.pad( labels=label_features, padding=self.padding, pad_to_multiple_of=self.pad_to_multiple_of_labels, return_tensors="pt", ) # replace padding with -100 to ignore loss correctly labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100) batch["labels"] = labels return batch def main(): # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) else: model_args, data_args, training_args = parser.parse_args_into_dataclasses() # Detecting last checkpoint. last_checkpoint = None if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: last_checkpoint = get_last_checkpoint(training_args.output_dir) if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: raise ValueError( f"Output directory ({training_args.output_dir}) already exists and is not empty. " "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None: logger.info( f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", handlers=[logging.StreamHandler(sys.stdout)], ) logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) # Log on each process the small summary: logger.warning( f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank): transformers.utils.logging.set_verbosity_info() logger.info("Training/evaluation parameters %s", training_args) # Set seed before initializing model. set_seed(training_args.seed) # 1. First, let's load the dataset raw_datasets = IterableDatasetDict() raw_column_names = {} def load_streaming_dataset(split, sampling_rate, **kwargs): if "+" in split: dataset_splits = [load_dataset(split=split_name, **kwargs) for split_name in split.split("+")] # `features` and `cast_column` won't be available after interleaving, so we'll use them here features = dataset_splits[0].features # make sure that the dataset decodes audio with a correct sampling rate dataset_splits = [ dataset.cast_column(data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate)) for dataset in dataset_splits ] interleaved_dataset = interleave_datasets(dataset_splits) return interleaved_dataset, features else: dataset = load_dataset(split=split, **kwargs) features = dataset.features # make sure that the dataset decodes audio with a correct sampling rate dataset = dataset.cast_column( data_args.audio_column_name, datasets.features.Audio(sampling_rate=sampling_rate) ) return dataset, features # `datasets` takes care of automatically loading and resampling the audio, # so we just need to set the correct target sampling rate and normalize the input # via the `feature_extractor` feature_extractor = AutoFeatureExtractor.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) if training_args.do_train: raw_datasets["train"], train_features = load_streaming_dataset( path=data_args.dataset_name, name=data_args.dataset_config_name, split=data_args.train_split_name, use_auth_token=data_args.use_auth_token, streaming=True, sampling_rate=feature_extractor.sampling_rate, ) raw_column_names["train"] = list(train_features.keys()) if data_args.audio_column_name not in raw_column_names["train"]: raise ValueError( f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'." " Make sure to set `--audio_column_name` to the correct audio column - one of" f" {', '.join(raw_column_names['train'])}." ) if data_args.text_column_name not in raw_column_names["train"]: raise ValueError( f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. " "Make sure to set `--text_column_name` to the correct text column - one of " f"{', '.join(raw_column_names['train'])}." ) if data_args.max_train_samples is not None: raw_datasets["train"] = raw_datasets["train"].take(range(data_args.max_train_samples)) if training_args.do_eval: raw_datasets["eval"], eval_features = load_streaming_dataset( path=data_args.dataset_name, name=data_args.dataset_config_name, split=data_args.eval_split_name, use_auth_token=data_args.use_auth_token, streaming=True, sampling_rate=feature_extractor.sampling_rate, ) raw_column_names["eval"] = list(eval_features.keys()) if data_args.max_eval_samples is not None: raw_datasets["eval"] = raw_datasets["eval"].take(range(data_args.max_eval_samples)) # 2. We remove some special characters from the datasets # that make training complicated and do not help in transcribing the speech # E.g. characters, such as `,` and `.` do not really have an acoustic characteristic # that could be easily picked up by the model chars_to_ignore_regex = ( f'[{"".join(data_args.chars_to_ignore)}]' if data_args.chars_to_ignore is not None else None ) text_column_name = data_args.text_column_name def remove_special_characters(batch): if chars_to_ignore_regex is not None: batch["target_text"] = re.sub(chars_to_ignore_regex, "", batch[text_column_name]).lower() + " " else: batch["target_text"] = batch[text_column_name].lower() + " " return batch with training_args.main_process_first(desc="dataset map special characters removal"): for split, dataset in raw_datasets.items(): raw_datasets[split] = dataset.map( remove_special_characters, ).remove_columns([text_column_name]) # 3. Next, let's load the config as we might need it to create # the tokenizer config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_auth_token=data_args.use_auth_token ) # 4. Now we can instantiate the tokenizer and model # Note for distributed training, the .from_pretrained methods guarantee that only # one local process can concurrently download model & vocab. tokenizer_name_or_path = model_args.tokenizer_name_or_path if tokenizer_name_or_path is None: raise ValueError( "Tokenizer has to be created before training in streaming mode. Please specify --tokenizer_name_or_path" ) # load feature_extractor and tokenizer tokenizer = AutoTokenizer.from_pretrained( tokenizer_name_or_path, config=config, use_auth_token=data_args.use_auth_token, ) # adapt config config.update( { "feat_proj_dropout": model_args.feat_proj_dropout, "attention_dropout": model_args.attention_dropout, "hidden_dropout": model_args.hidden_dropout, "final_dropout": model_args.final_dropout, "mask_time_prob": model_args.mask_time_prob, "mask_time_length": model_args.mask_time_length, "mask_feature_prob": model_args.mask_feature_prob, "mask_feature_length": model_args.mask_feature_length, "gradient_checkpointing": training_args.gradient_checkpointing, "layerdrop": model_args.layerdrop, "ctc_loss_reduction": model_args.ctc_loss_reduction, "pad_token_id": tokenizer.pad_token_id, "vocab_size": len(tokenizer), "activation_dropout": model_args.activation_dropout, } ) # create model model = AutoModelForCTC.from_pretrained( model_args.model_name_or_path, cache_dir=model_args.cache_dir, config=config, use_auth_token=data_args.use_auth_token, ) # freeze encoder if model_args.freeze_feature_encoder: model.freeze_feature_encoder() # 5. Now we preprocess the datasets including loading the audio, resampling and normalization audio_column_name = data_args.audio_column_name # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification phoneme_language = data_args.phoneme_language # Preprocessing the datasets. # We need to read the audio files as arrays and tokenize the targets. def prepare_dataset(batch): # load audio sample = batch[audio_column_name] inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"]) batch["input_values"] = inputs.input_values[0] batch["input_length"] = len(batch["input_values"]) # encode targets additional_kwargs = {} if phoneme_language is not None: additional_kwargs["phonemizer_lang"] = phoneme_language batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids return batch vectorized_datasets = IterableDatasetDict() with training_args.main_process_first(desc="dataset map preprocessing"): for split, dataset in raw_datasets.items(): vectorized_datasets[split] = ( dataset.map(prepare_dataset) .remove_columns(raw_column_names[split] + ["target_text"]) .with_format("torch") ) if split == "train": vectorized_datasets[split] = vectorized_datasets[split].shuffle( buffer_size=data_args.shuffle_buffer_size, seed=training_args.seed, ) # 6. Next, we can prepare the training. # Let's use word error rate (WER) as our evaluation metric, # instantiate a data collator and the trainer # Define evaluation metrics during training, *i.e.* word error rate, character error rate eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics} def compute_metrics(pred): pred_logits = pred.predictions pred_ids = np.argmax(pred_logits, axis=-1) pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id pred_str = tokenizer.batch_decode(pred_ids) # we do not want to group tokens when computing the metrics label_str = tokenizer.batch_decode(pred.label_ids, group_tokens=False) metrics = {k: v.compute(predictions=pred_str, references=label_str) for k, v in eval_metrics.items()} return metrics # Now save everything to be able to create a single processor later if is_main_process(training_args.local_rank): # save feature extractor, tokenizer and config feature_extractor.save_pretrained(training_args.output_dir) tokenizer.save_pretrained(training_args.output_dir) config.save_pretrained(training_args.output_dir) try: processor = AutoProcessor.from_pretrained(training_args.output_dir) except (OSError, KeyError): warnings.warn( "Loading a processor from a feature extractor config that does not" " include a `processor_class` attribute is deprecated and will be removed in v5. Please add the following " " attribute to your `preprocessor_config.json` file to suppress this warning: " " `'processor_class': 'Wav2Vec2Processor'`", FutureWarning, ) processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir) # Instantiate custom data collator max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate data_collator = DataCollatorCTCWithPadding(processor=processor, max_length=max_input_length) # trainer callback to reinitialize and reshuffle the streamable datasets at the beginning of each epoch class ShuffleCallback(TrainerCallback): def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs): if isinstance(train_dataloader.dataset, IterableDatasetShard): pass # set_epoch() is handled by the Trainer elif isinstance(train_dataloader.dataset, IterableDataset): train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1) # Initialize Trainer trainer = Trainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=vectorized_datasets["train"] if training_args.do_train else None, eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None, tokenizer=processor, callbacks=[ShuffleCallback()], ) # 7. Finally, we can start training # Training if training_args.do_train: # use last checkpoint if exist if last_checkpoint is not None: checkpoint = last_checkpoint elif os.path.isdir(model_args.model_name_or_path): checkpoint = model_args.model_name_or_path else: checkpoint = None train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() metrics = train_result.metrics if data_args.max_train_samples: metrics["train_samples"] = data_args.max_train_samples trainer.log_metrics("train", metrics) trainer.save_metrics("train", metrics) trainer.save_state() # Evaluation results = {} if training_args.do_eval: logger.info("*** Evaluate ***") metrics = trainer.evaluate() if data_args.max_eval_samples: metrics["eval_samples"] = data_args.max_eval_samples trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics) # Write model card and (optionally) push to hub config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na" kwargs = { "finetuned_from": model_args.model_name_or_path, "tasks": "automatic-speech-recognition", "tags": ["automatic-speech-recognition", data_args.dataset_name], "dataset_args": ( f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:" f" {data_args.eval_split_name}" ), "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}", } if "common_voice" in data_args.dataset_name: kwargs["language"] = config_name if training_args.push_to_hub: trainer.push_to_hub(**kwargs) else: trainer.create_model_card(**kwargs) return results if __name__ == "__main__": main()

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/bert/test_modeling_bert.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import tempfile import unittest from transformers import BertConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLMHeadModel, BertModel, ) from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST class BertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): """ Returns a tiny configuration by default. """ return BertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = BertModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = BertLMHeadModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_model_for_causal_lm_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = BertLMHeadModel(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, encoder_hidden_states=encoder_hidden_states, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = BertLMHeadModel(config=config).to(torch_device).eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_for_next_sequence_prediction( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForNextSentencePrediction(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, 2)) def create_and_check_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForPreTraining(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, next_sentence_label=sequence_labels, ) self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = BertForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class BertModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( ( BertModel, BertLMHeadModel, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (BertLMHeadModel,) if is_torch_available() else () fx_compatible = True # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["next_sentence_label"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = BertModelTester(self) self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_model_as_decoder_with_default_input_mask(self): # This regression test was failing with PyTorch < 1.3 ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_causal_lm_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs_relative_pos_emb(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() config_and_inputs[0].position_embedding_type = "relative_key" self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_next_sequence_prediction(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BertModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow @require_torch_gpu def test_torchscript_device_change(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # BertForMultipleChoice behaves incorrectly in JIT environments. if model_class == BertForMultipleChoice: return config.torchscript = True model = model_class(config=config) inputs_dict = self._prepare_for_class(inputs_dict, model_class) traced_model = torch.jit.trace( model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu")) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(traced_model, os.path.join(tmp, "bert.pt")) loaded = torch.jit.load(os.path.join(tmp, "bert.pt"), map_location=torch_device) loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device)) @require_torch class BertModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = BertModel.from_pretrained("bert-base-uncased") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]]) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)) @slow def test_inference_no_head_relative_embedding_key(self): model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.0756, 0.3142, -0.5128], [0.3761, 0.3462, -0.5477], [0.2052, 0.3760, -0.1240]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)) @slow def test_inference_no_head_relative_embedding_key_query(self): model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key-query") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.6496, 0.3784, 0.8203], [0.8148, 0.5656, 0.2636], [-0.0681, 0.5597, 0.7045]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import tempfile import unittest from transformers import BertConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLMHeadModel, BertModel, ) from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST class BertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): """ Returns a tiny configuration by default. """ return BertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]) encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, token_type_ids=token_type_ids) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = BertModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = BertLMHeadModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_model_for_causal_lm_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = BertLMHeadModel(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, encoder_hidden_states=encoder_hidden_states, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = BertLMHeadModel(config=config).to(torch_device).eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) def create_and_check_for_next_sequence_prediction( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForNextSentencePrediction(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, 2)) def create_and_check_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForPreTraining(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, next_sentence_label=sequence_labels, ) self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = BertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = BertForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = BertForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class BertModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( ( BertModel, BertLMHeadModel, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (BertLMHeadModel,) if is_torch_available() else () fx_compatible = True # special case for ForPreTraining model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) inputs_dict["next_sentence_label"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = BertModelTester(self) self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_model_various_embeddings(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config_and_inputs[0].position_embedding_type = type self.model_tester.create_and_check_model(*config_and_inputs) def test_model_as_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*config_and_inputs) def test_model_as_decoder_with_default_input_mask(self): # This regression test was failing with PyTorch < 1.3 ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) = self.model_tester.prepare_config_and_inputs_for_decoder() input_mask = None self.model_tester.create_and_check_model_as_decoder( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def test_for_causal_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_for_causal_lm(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) def test_for_causal_lm_decoder(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs_relative_pos_emb(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() config_and_inputs[0].position_embedding_type = "relative_key" self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_for_next_sequence_prediction(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs) def test_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BertModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow @require_torch_gpu def test_torchscript_device_change(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # BertForMultipleChoice behaves incorrectly in JIT environments. if model_class == BertForMultipleChoice: return config.torchscript = True model = model_class(config=config) inputs_dict = self._prepare_for_class(inputs_dict, model_class) traced_model = torch.jit.trace( model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu")) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(traced_model, os.path.join(tmp, "bert.pt")) loaded = torch.jit.load(os.path.join(tmp, "bert.pt"), map_location=torch_device) loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device)) @require_torch class BertModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = BertModel.from_pretrained("bert-base-uncased") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor([[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]]) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)) @slow def test_inference_no_head_relative_embedding_key(self): model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.0756, 0.3142, -0.5128], [0.3761, 0.3462, -0.5477], [0.2052, 0.3760, -0.1240]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4)) @slow def test_inference_no_head_relative_embedding_key_query(self): model = BertModel.from_pretrained("zhiheng-huang/bert-base-uncased-embedding-relative-key-query") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[0.6496, 0.3784, 0.8203], [0.8148, 0.5656, 0.2636], [-0.0681, 0.5597, 0.7045]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/dpr/configuration_dpr.py

# coding=utf-8 # Copyright 2010, DPR authors, The Hugging Face Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ DPR model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DPR_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/dpr-ctx_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-reader-single-nq-base": ( "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json" ), "facebook/dpr-ctx_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-reader-multiset-base": ( "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json" ), } class DPRConfig(PretrainedConfig): r""" [`DPRConfig`] is the configuration class to store the configuration of a *DPRModel*. This is the configuration class to store the configuration of a [`DPRContextEncoder`], [`DPRQuestionEncoder`], or a [`DPRReader`]. It is used to instantiate the components of the DPR model according to the specified arguments, defining the model component architectures. Instantiating a configuration with the defaults will yield a similar configuration to that of the DPRContextEncoder [facebook/dpr-ctx_encoder-single-nq-base](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base) architecture. This class is a subclass of [`BertConfig`]. Please check the superclass for the documentation of all kwargs. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the DPR model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`BertModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the *token_type_ids* passed into [`BertModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). projection_dim (`int`, *optional*, defaults to 0): Dimension of the projection for the context and question encoders. If it is set to zero (default), then no projection is done. Example: ```python >>> from transformers import DPRConfig, DPRContextEncoder >>> # Initializing a DPR facebook/dpr-ctx_encoder-single-nq-base style configuration >>> configuration = DPRConfig() >>> # Initializing a model (with random weights) from the facebook/dpr-ctx_encoder-single-nq-base style configuration >>> model = DPRContextEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "dpr" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", projection_dim: int = 0, **kwargs ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.projection_dim = projection_dim self.position_embedding_type = position_embedding_type

# coding=utf-8 # Copyright 2010, DPR authors, The Hugging Face Team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ DPR model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) DPR_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/dpr-ctx_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-reader-single-nq-base": ( "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json" ), "facebook/dpr-ctx_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-reader-multiset-base": ( "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json" ), } class DPRConfig(PretrainedConfig): r""" [`DPRConfig`] is the configuration class to store the configuration of a *DPRModel*. This is the configuration class to store the configuration of a [`DPRContextEncoder`], [`DPRQuestionEncoder`], or a [`DPRReader`]. It is used to instantiate the components of the DPR model according to the specified arguments, defining the model component architectures. Instantiating a configuration with the defaults will yield a similar configuration to that of the DPRContextEncoder [facebook/dpr-ctx_encoder-single-nq-base](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base) architecture. This class is a subclass of [`BertConfig`]. Please check the superclass for the documentation of all kwargs. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the DPR model. Defines the different tokens that can be represented by the *inputs_ids* passed to the forward method of [`BertModel`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the *token_type_ids* passed into [`BertModel`]. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). projection_dim (`int`, *optional*, defaults to 0): Dimension of the projection for the context and question encoders. If it is set to zero (default), then no projection is done. Example: ```python >>> from transformers import DPRConfig, DPRContextEncoder >>> # Initializing a DPR facebook/dpr-ctx_encoder-single-nq-base style configuration >>> configuration = DPRConfig() >>> # Initializing a model (with random weights) from the facebook/dpr-ctx_encoder-single-nq-base style configuration >>> model = DPRContextEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "dpr" def __init__( self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", projection_dim: int = 0, **kwargs ): super().__init__(pad_token_id=pad_token_id, **kwargs) self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.projection_dim = projection_dim self.position_embedding_type = position_embedding_type

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/xglm/modeling_flax_xglm.py

# coding=utf-8 # Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax XGLM model.""" import math import random from functools import partial from typing import Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import ( FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, ) from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_xglm import XGLMConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/xglm-564M" _CONFIG_FOR_DOC = "XGLMConfig" _TOKENIZER_FOR_DOC = "XGLMTokenizer" XGLM_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`XGLMConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ XGLM_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`~XGLMTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ def create_sinusoidal_positions(n_pos, dim, padding_idx=1): half_dim = dim // 2 emb = math.log(10000) / (half_dim - 1) emb = np.exp(np.arange(half_dim) * -emb) emb = np.expand_dims(np.arange(n_pos), 1) * np.expand_dims(emb, 0) emb = np.concatenate([np.sin(emb), np.cos(emb)], 1) emb = np.reshape(emb, (n_pos, dim)) if padding_idx is not None: emb[padding_idx, :] = 0 return jnp.array(emb) def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray: """ Shift input ids one token to the right. """ shifted_input_ids = jnp.roll(input_ids, 1, axis=-1) shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) return shifted_input_ids class FlaxXGLMAttention(nn.Module): config: XGLMConfig embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} " f"and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend # to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxXGLMDecoderLayer(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxXGLMAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) if self.config.add_cross_attention: self.encoder_attn = FlaxXGLMAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.fc1 = nn.Dense( self.config.ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) # Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartDecoderLayer.__call__ def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class FlaxXGLMDecoderLayerCollection(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxXGLMDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_layers) ] self.layerdrop = self.config.layerdrop def __call__( self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_self_attns, all_cross_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) class FlaxXGLMModule(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 self.embed_tokens = nn.Embed( self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) # XGLM is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = create_sinusoidal_positions( self.config.max_position_embeddings + self.offset, embed_dim ) self.layers = FlaxXGLMDecoderLayerCollection(self.config, self.dtype) self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale # embed positions position_ids = position_ids + self.offset positions = jnp.take(self.embed_positions, position_ids, axis=0) hidden_states = inputs_embeds + positions hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_states = outputs[0] last_hidden_states = self.layer_norm(last_hidden_states) hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_states,) if not return_dict: outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=last_hidden_states, hidden_states=hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) class FlaxXGLMPreTrainedModel(FlaxPreTrainedModel): config_class = XGLMConfig base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: XGLMConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.n_embd,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length), dtype="i4") attention_mask = jnp.ones_like(input_ids, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING) def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, past_key_values: dict = None, dropout_rng: PRNGKey = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if encoder_hidden_states is not None and encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxXGLMAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] return outputs @add_start_docstrings( "The bare XGLM Model transformer outputting raw hidden-states without any specific head on top.", XGLM_START_DOCSTRING, ) class FlaxXGLMModel(FlaxXGLMPreTrainedModel): module_class = FlaxXGLMModule append_call_sample_docstring( FlaxXGLMModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPastAndCrossAttentions, _CONFIG_FOR_DOC, ) class FlaxXGLMForCausalLMModule(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.model = FlaxXGLMModule(self.config, self.dtype) self.lm_head = nn.Dense( self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["embed_tokens"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + outputs[1:] return FlaxCausalLMOutputWithCrossAttentions( logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( """ The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, XGLM_START_DOCSTRING, ) class FlaxXGLMForCausalLM(FlaxXGLMPreTrainedModel): module_class = FlaxXGLMForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since GPT2 uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxXGLMForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, )

# coding=utf-8 # Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax XGLM model.""" import math import random from functools import partial from typing import Optional, Tuple import numpy as np import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import ( FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, ) from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_xglm import XGLMConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/xglm-564M" _CONFIG_FOR_DOC = "XGLMConfig" _TOKENIZER_FOR_DOC = "XGLMTokenizer" XGLM_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`XGLMConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ XGLM_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`~XGLMTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ def create_sinusoidal_positions(n_pos, dim, padding_idx=1): half_dim = dim // 2 emb = math.log(10000) / (half_dim - 1) emb = np.exp(np.arange(half_dim) * -emb) emb = np.expand_dims(np.arange(n_pos), 1) * np.expand_dims(emb, 0) emb = np.concatenate([np.sin(emb), np.cos(emb)], 1) emb = np.reshape(emb, (n_pos, dim)) if padding_idx is not None: emb[padding_idx, :] = 0 return jnp.array(emb) def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray: """ Shift input ids one token to the right. """ shifted_input_ids = jnp.roll(input_ids, 1, axis=-1) shifted_input_ids = shifted_input_ids.at[(..., 0)].set(decoder_start_token_id) # replace possible -100 values in labels by `pad_token_id` shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) return shifted_input_ids class FlaxXGLMAttention(nn.Module): config: XGLMConfig embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} " f"and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend # to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxXGLMDecoderLayer(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxXGLMAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) if self.config.add_cross_attention: self.encoder_attn = FlaxXGLMAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.fc1 = nn.Dense( self.config.ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) # Copied from transformers.models.mbart.modeling_flax_mbart.FlaxMBartDecoderLayer.__call__ def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layer_norm(hidden_states) hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs class FlaxXGLMDecoderLayerCollection(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxXGLMDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_layers) ] self.layerdrop = self.config.layerdrop def __call__( self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_self_attns, all_cross_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) class FlaxXGLMModule(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 self.embed_tokens = nn.Embed( self.config.vocab_size, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) # XGLM is set up so that if padding_idx is specified then offset the embedding ids by 2 # and adjust num_embeddings appropriately. Other models don't have this hack self.offset = 2 self.embed_positions = create_sinusoidal_positions( self.config.max_position_embeddings + self.offset, embed_dim ) self.layers = FlaxXGLMDecoderLayerCollection(self.config, self.dtype) self.layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale # embed positions position_ids = position_ids + self.offset positions = jnp.take(self.embed_positions, position_ids, axis=0) hidden_states = inputs_embeds + positions hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_states = outputs[0] last_hidden_states = self.layer_norm(last_hidden_states) hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_states,) if not return_dict: outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=last_hidden_states, hidden_states=hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) class FlaxXGLMPreTrainedModel(FlaxPreTrainedModel): config_class = XGLMConfig base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: XGLMConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_ids) position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} if self.config.add_cross_attention: encoder_hidden_states = jnp.zeros(input_shape + (self.config.n_embd,)) encoder_attention_mask = attention_mask module_init_outputs = self.module.init( rngs, input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, return_dict=False, ) else: module_init_outputs = self.module.init(rngs, input_ids, attention_mask, position_ids, return_dict=False) random_params = module_init_outputs["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params def init_cache(self, batch_size, max_length): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. """ # init input variables to retrieve cache input_ids = jnp.ones((batch_size, max_length), dtype="i4") attention_mask = jnp.ones_like(input_ids, dtype="i4") position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape) init_variables = self.module.init( jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True ) return unfreeze(init_variables["cache"]) @add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING) def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, past_key_values: dict = None, dropout_rng: PRNGKey = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict if encoder_hidden_states is not None and encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be # changed by FlaxXGLMAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False outputs = self.module.apply( inputs, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past_key_values = outputs outputs["past_key_values"] = unfreeze(past_key_values["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past_key_values = outputs outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:] return outputs @add_start_docstrings( "The bare XGLM Model transformer outputting raw hidden-states without any specific head on top.", XGLM_START_DOCSTRING, ) class FlaxXGLMModel(FlaxXGLMPreTrainedModel): module_class = FlaxXGLMModule append_call_sample_docstring( FlaxXGLMModel, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPastAndCrossAttentions, _CONFIG_FOR_DOC, ) class FlaxXGLMForCausalLMModule(nn.Module): config: XGLMConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.model = FlaxXGLMModule(self.config, self.dtype) self.lm_head = nn.Dense( self.config.vocab_size, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["embed_tokens"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) if not return_dict: return (lm_logits,) + outputs[1:] return FlaxCausalLMOutputWithCrossAttentions( logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) @add_start_docstrings( """ The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input embeddings). """, XGLM_START_DOCSTRING, ) class FlaxXGLMForCausalLM(FlaxXGLMPreTrainedModel): module_class = FlaxXGLMForCausalLMModule def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jnp.DeviceArray] = None): # initializing the cache batch_size, seq_length = input_ids.shape past_key_values = self.init_cache(batch_size, max_length) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since GPT2 uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if attention_mask is not None: position_ids = attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0)) else: position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)) return { "past_key_values": past_key_values, "attention_mask": extended_attention_mask, "position_ids": position_ids, } def update_inputs_for_generation(self, model_outputs, model_kwargs): model_kwargs["past_key_values"] = model_outputs.past_key_values model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1 return model_kwargs append_call_sample_docstring( FlaxXGLMForCausalLM, _TOKENIZER_FOR_DOC, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutputWithCrossAttentions, _CONFIG_FOR_DOC, )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./scripts/fsmt/gen-card-facebook-wmt19.py

#!/usr/bin/env python # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Usage: # ./gen-card-facebook-wmt19.py import os from pathlib import Path def write_model_card(model_card_dir, src_lang, tgt_lang): texts = { "en": "Machine learning is great, isn't it?", "ru": "Машинное обучение - это здорово, не так ли?", "de": "Maschinelles Lernen ist großartig, oder?", } # BLUE scores as follows: # "pair": [fairseq, transformers] scores = { "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"], "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"], "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"], "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"], } pair = f"{src_lang}-{tgt_lang}" readme = f""" --- language: - {src_lang} - {tgt_lang} thumbnail: tags: - translation - wmt19 - facebook license: apache-2.0 datasets: - wmt19 metrics: - bleu --- # FSMT ## Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for FairSeqMachineTranslation All four models are available: * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de) * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-{src_lang}-{tgt_lang}" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "{texts[src_lang]}" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # {texts[tgt_lang]} ``` #### Limitations and bias - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981) ## Training data Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616). ## Eval results pair | fairseq | transformers -------|---------|---------- {pair} | {scores[pair][0]} | {scores[pair][1]} The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``). - re-ranking The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR={pair} export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=15 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`. ## Data Sources - [training, etc.](http://www.statmt.org/wmt19/) - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561) ### BibTeX entry and citation info ```bibtex @inproceedings{{..., year={{2020}}, title={{Facebook FAIR's WMT19 News Translation Task Submission}}, author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}}, booktitle={{Proc. of WMT}}, }} ``` ## TODO - port model ensemble (fairseq uses 4 model checkpoints) """ os.makedirs(model_card_dir, exist_ok=True) path = os.path.join(model_card_dir, "README.md") print(f"Generating {path}") with open(path, "w", encoding="utf-8") as f: f.write(readme) # make sure we are under the root of the project repo_dir = Path(__file__).resolve().parent.parent.parent model_cards_dir = repo_dir / "model_cards" for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]: base, src_lang, tgt_lang = model_name.split("-") model_card_dir = model_cards_dir / "facebook" / model_name write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)

#!/usr/bin/env python # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Usage: # ./gen-card-facebook-wmt19.py import os from pathlib import Path def write_model_card(model_card_dir, src_lang, tgt_lang): texts = { "en": "Machine learning is great, isn't it?", "ru": "Машинное обучение - это здорово, не так ли?", "de": "Maschinelles Lernen ist großartig, oder?", } # BLUE scores as follows: # "pair": [fairseq, transformers] scores = { "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"], "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"], "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"], "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"], } pair = f"{src_lang}-{tgt_lang}" readme = f""" --- language: - {src_lang} - {tgt_lang} thumbnail: tags: - translation - wmt19 - facebook license: apache-2.0 datasets: - wmt19 metrics: - bleu --- # FSMT ## Model description This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}. For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616). The abbreviation FSMT stands for FairSeqMachineTranslation All four models are available: * [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru) * [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en) * [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de) * [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en) ## Intended uses & limitations #### How to use ```python from transformers import FSMTForConditionalGeneration, FSMTTokenizer mname = "facebook/wmt19-{src_lang}-{tgt_lang}" tokenizer = FSMTTokenizer.from_pretrained(mname) model = FSMTForConditionalGeneration.from_pretrained(mname) input = "{texts[src_lang]}" input_ids = tokenizer.encode(input, return_tensors="pt") outputs = model.generate(input_ids) decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) print(decoded) # {texts[tgt_lang]} ``` #### Limitations and bias - The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981) ## Training data Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616). ## Eval results pair | fairseq | transformers -------|---------|---------- {pair} | {scores[pair][0]} | {scores[pair][1]} The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support: - model ensemble, therefore the best performing checkpoint was ported (``model4.pt``). - re-ranking The score was calculated using this code: ```bash git clone https://github.com/huggingface/transformers cd transformers export PAIR={pair} export DATA_DIR=data/$PAIR export SAVE_DIR=data/$PAIR export BS=8 export NUM_BEAMS=15 mkdir -p $DATA_DIR sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target echo $PAIR PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS ``` note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`. ## Data Sources - [training, etc.](http://www.statmt.org/wmt19/) - [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561) ### BibTeX entry and citation info ```bibtex @inproceedings{{..., year={{2020}}, title={{Facebook FAIR's WMT19 News Translation Task Submission}}, author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}}, booktitle={{Proc. of WMT}}, }} ``` ## TODO - port model ensemble (fairseq uses 4 model checkpoints) """ os.makedirs(model_card_dir, exist_ok=True) path = os.path.join(model_card_dir, "README.md") print(f"Generating {path}") with open(path, "w", encoding="utf-8") as f: f.write(readme) # make sure we are under the root of the project repo_dir = Path(__file__).resolve().parent.parent.parent model_cards_dir = repo_dir / "model_cards" for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]: base, src_lang, tgt_lang = model_name.split("-") model_card_dir = model_cards_dir / "facebook" / model_name write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/swin/__init__.py

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/tf_utils.py

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Optional, Union import numpy as np import tensorflow as tf from .utils import logging logger = logging.get_logger(__name__) def shape_list(tensor: Union[tf.Tensor, np.ndarray]) -> List[int]: """ Deal with dynamic shape in tensorflow cleanly. Args: tensor (`tf.Tensor` or `np.ndarray`): The tensor we want the shape of. Returns: `List[int]`: The shape of the tensor as a list. """ if isinstance(tensor, np.ndarray): return list(tensor.shape) dynamic = tf.shape(tensor) if tensor.shape == tf.TensorShape(None): return dynamic static = tensor.shape.as_list() return [dynamic[i] if s is None else s for i, s in enumerate(static)] def stable_softmax(logits: tf.Tensor, axis: Optional[int] = None, name: Optional[str] = None) -> tf.Tensor: """ Stable wrapper that returns the same output as `tf.nn.softmax`, but that works reliably with XLA on CPU. It is meant as a workaround for the [following issue](https://github.com/tensorflow/tensorflow/issues/55682), and will be removed after it gets fixed. The arguments and outputs are the same as `tf.nn.softmax`, and relies on the fact that `softmax(x) = softmax(x + c)` (see https://ogunlao.github.io/2020/04/26/you_dont_really_know_softmax.html). Args: logits (`tf.Tensor`): Must be one of the following types: half, float32, float64. axis (`int`, *optional*): The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name (`str`, *optional*): A name for the operation. Returns: `tf.Tensor`: A Tensor. Has the same type and shape as logits. """ # TODO: When the issue linked above gets sorted, add a check on TF version here and use the original function if # it has the fix. After we drop the support for unfixed versions, remove this function. return tf.nn.softmax(logits=logits + 1e-9, axis=axis, name=name)

# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import List, Optional, Union import numpy as np import tensorflow as tf from .utils import logging logger = logging.get_logger(__name__) def shape_list(tensor: Union[tf.Tensor, np.ndarray]) -> List[int]: """ Deal with dynamic shape in tensorflow cleanly. Args: tensor (`tf.Tensor` or `np.ndarray`): The tensor we want the shape of. Returns: `List[int]`: The shape of the tensor as a list. """ if isinstance(tensor, np.ndarray): return list(tensor.shape) dynamic = tf.shape(tensor) if tensor.shape == tf.TensorShape(None): return dynamic static = tensor.shape.as_list() return [dynamic[i] if s is None else s for i, s in enumerate(static)] def stable_softmax(logits: tf.Tensor, axis: Optional[int] = None, name: Optional[str] = None) -> tf.Tensor: """ Stable wrapper that returns the same output as `tf.nn.softmax`, but that works reliably with XLA on CPU. It is meant as a workaround for the [following issue](https://github.com/tensorflow/tensorflow/issues/55682), and will be removed after it gets fixed. The arguments and outputs are the same as `tf.nn.softmax`, and relies on the fact that `softmax(x) = softmax(x + c)` (see https://ogunlao.github.io/2020/04/26/you_dont_really_know_softmax.html). Args: logits (`tf.Tensor`): Must be one of the following types: half, float32, float64. axis (`int`, *optional*): The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name (`str`, *optional*): A name for the operation. Returns: `tf.Tensor`: A Tensor. Has the same type and shape as logits. """ # TODO: When the issue linked above gets sorted, add a check on TF version here and use the original function if # it has the fix. After we drop the support for unfixed versions, remove this function. return tf.nn.softmax(logits=logits + 1e-9, axis=axis, name=name)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/clipseg/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _import_structure = { "configuration_clipseg": [ "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPSegConfig", "CLIPSegTextConfig", "CLIPSegVisionConfig", ], "processing_clipseg": ["CLIPSegProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_clipseg"] = [ "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPSegModel", "CLIPSegPreTrainedModel", "CLIPSegTextModel", "CLIPSegVisionModel", "CLIPSegForImageSegmentation", ] if TYPE_CHECKING: from .configuration_clipseg import ( CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig, ) from .processing_clipseg import CLIPSegProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_clipseg import ( CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegPreTrainedModel, CLIPSegTextModel, CLIPSegVisionModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _import_structure = { "configuration_clipseg": [ "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPSegConfig", "CLIPSegTextConfig", "CLIPSegVisionConfig", ], "processing_clipseg": ["CLIPSegProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_clipseg"] = [ "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPSegModel", "CLIPSegPreTrainedModel", "CLIPSegTextModel", "CLIPSegVisionModel", "CLIPSegForImageSegmentation", ] if TYPE_CHECKING: from .configuration_clipseg import ( CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig, ) from .processing_clipseg import CLIPSegProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_clipseg import ( CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegPreTrainedModel, CLIPSegTextModel, CLIPSegVisionModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/gpt2/test_tokenization_gpt2.py

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import unittest from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast from transformers.models.gpt2.tokenization_gpt2 import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = GPT2Tokenizer rust_tokenizer_class = GPT2TokenizerFast test_rust_tokenizer = True from_pretrained_kwargs = {"add_prefix_space": True} test_seq2seq = False def setUp(self): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", "<|endoftext|>", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": "<unk>"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return GPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return GPT2TokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text def test_full_tokenizer(self): tokenizer = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) text = "lower newer" bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text, add_prefix_space=True) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: return tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) sequence = "lower newer" # Testing tokenization tokens = tokenizer.tokenize(sequence, add_prefix_space=True) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) # Testing conversion to ids without special tokens ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) # Testing conversion to ids with special tokens rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) ids = tokenizer.encode(sequence, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) # Testing the unknown token input_tokens = tokens + [rust_tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_pretokenized_inputs(self, *args, **kwargs): # It's very difficult to mix/test pretokenization with byte-level # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string) pass def test_padding(self, max_length=15): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) # Simple input s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, padding="max_length", ) # Pair input self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, padding="max_length", ) def test_padding_if_pad_token_set_slow(self): tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, pad_token="<pad>") # Simple input s = "This is a simple input" s2 = ["This is a simple input looooooooong", "This is a simple input"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] pad_token_id = tokenizer.pad_token_id out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np") out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np") out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np") out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np") # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1], 30) self.assertTrue(pad_token_id in out_s["input_ids"]) self.assertTrue(0 in out_s["attention_mask"]) # s2 # test automatic padding self.assertEqual(out_s2["input_ids"].shape[-1], 33) # long slice doesn't have padding self.assertFalse(pad_token_id in out_s2["input_ids"][0]) self.assertFalse(0 in out_s2["attention_mask"][0]) # short slice does have padding self.assertTrue(pad_token_id in out_s2["input_ids"][1]) self.assertTrue(0 in out_s2["attention_mask"][1]) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1], 60) self.assertTrue(pad_token_id in out_p["input_ids"]) self.assertTrue(0 in out_p["attention_mask"]) # p2 # test automatic padding pair self.assertEqual(out_p2["input_ids"].shape[-1], 52) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_p2["input_ids"][0]) self.assertFalse(0 in out_p2["attention_mask"][0]) # short slice pair does have padding self.assertTrue(pad_token_id in out_p2["input_ids"][1]) self.assertTrue(0 in out_p2["attention_mask"][1]) def test_add_bos_token_slow(self): bos_token = "$$$" tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, bos_token=bos_token, add_bos_token=True) s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] bos_token_id = tokenizer.bos_token_id out_s = tokenizer(s) out_s2 = tokenizer(s2) self.assertEqual(out_s.input_ids[0], bos_token_id) self.assertTrue(all(o[0] == bos_token_id for o in out_s2.input_ids)) decode_s = tokenizer.decode(out_s.input_ids) decode_s2 = tokenizer.batch_decode(out_s2.input_ids) self.assertEqual(decode_s.split()[0], bos_token) self.assertTrue(all(d.split()[0] == bos_token for d in decode_s2)) # tokenizer has no padding token def test_padding_different_model_input_name(self): pass def test_special_tokens_mask_input_pairs_and_bos_token(self): # TODO: change to self.get_tokenizers() when the fast version is implemented tokenizers = [self.get_tokenizer(do_lower_case=False, add_bos_token=True)] for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): sequence_0 = "Encode this." sequence_1 = "This one too please." encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( sequence_0, sequence_1, add_special_tokens=True, return_special_tokens_mask=True, ) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) filtered_sequence = [ (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special) ] filtered_sequence = [x for x in filtered_sequence if x is not None] self.assertEqual(encoded_sequence, filtered_sequence) @require_tokenizers class OPTTokenizationTest(unittest.TestCase): def test_serialize_deserialize_fast_opt(self): # More context: # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1 # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519 # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439 tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True) text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) tokenizer.save_pretrained("test_opt") tokenizer = AutoTokenizer.from_pretrained("./test_opt") tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) def test_fast_slow_equivalence(self): tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", use_slow=True) text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) # Same as above self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) def test_users_can_modify_bos(self): tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True) tokenizer.bos_token = "bos" tokenizer.bos_token_id = tokenizer.get_vocab()["bos"] text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) # We changed the bos token self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758]) tokenizer.save_pretrained("./tok") tokenizer = AutoTokenizer.from_pretrained("./tok") self.assertTrue(tokenizer.is_fast) tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758])

# coding=utf-8 # Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import os import unittest from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast from transformers.models.gpt2.tokenization_gpt2 import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = GPT2Tokenizer rust_tokenizer_class = GPT2TokenizerFast test_rust_tokenizer = True from_pretrained_kwargs = {"add_prefix_space": True} test_seq2seq = False def setUp(self): super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt vocab = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "\u0120", "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "<unk>", "<|endoftext|>", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.special_tokens_map = {"unk_token": "<unk>"} self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(self.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(self.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) def get_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return GPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return GPT2TokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "lower newer" output_text = "lower newer" return input_text, output_text def test_full_tokenizer(self): tokenizer = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) text = "lower newer" bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"] tokens = tokenizer.tokenize(text, add_prefix_space=True) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: return tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) sequence = "lower newer" # Testing tokenization tokens = tokenizer.tokenize(sequence, add_prefix_space=True) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) # Testing conversion to ids without special tokens ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) # Testing conversion to ids with special tokens rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) ids = tokenizer.encode(sequence, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) # Testing the unknown token input_tokens = tokens + [rust_tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_pretokenized_inputs(self, *args, **kwargs): # It's very difficult to mix/test pretokenization with byte-level # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string) pass def test_padding(self, max_length=15): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) # Simple input s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, padding="max_length", ) # Pair input self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, padding="max_length", ) def test_padding_if_pad_token_set_slow(self): tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, pad_token="<pad>") # Simple input s = "This is a simple input" s2 = ["This is a simple input looooooooong", "This is a simple input"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] pad_token_id = tokenizer.pad_token_id out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np") out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np") out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np") out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np") # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1], 30) self.assertTrue(pad_token_id in out_s["input_ids"]) self.assertTrue(0 in out_s["attention_mask"]) # s2 # test automatic padding self.assertEqual(out_s2["input_ids"].shape[-1], 33) # long slice doesn't have padding self.assertFalse(pad_token_id in out_s2["input_ids"][0]) self.assertFalse(0 in out_s2["attention_mask"][0]) # short slice does have padding self.assertTrue(pad_token_id in out_s2["input_ids"][1]) self.assertTrue(0 in out_s2["attention_mask"][1]) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1], 60) self.assertTrue(pad_token_id in out_p["input_ids"]) self.assertTrue(0 in out_p["attention_mask"]) # p2 # test automatic padding pair self.assertEqual(out_p2["input_ids"].shape[-1], 52) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_p2["input_ids"][0]) self.assertFalse(0 in out_p2["attention_mask"][0]) # short slice pair does have padding self.assertTrue(pad_token_id in out_p2["input_ids"][1]) self.assertTrue(0 in out_p2["attention_mask"][1]) def test_add_bos_token_slow(self): bos_token = "$$$" tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, bos_token=bos_token, add_bos_token=True) s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] bos_token_id = tokenizer.bos_token_id out_s = tokenizer(s) out_s2 = tokenizer(s2) self.assertEqual(out_s.input_ids[0], bos_token_id) self.assertTrue(all(o[0] == bos_token_id for o in out_s2.input_ids)) decode_s = tokenizer.decode(out_s.input_ids) decode_s2 = tokenizer.batch_decode(out_s2.input_ids) self.assertEqual(decode_s.split()[0], bos_token) self.assertTrue(all(d.split()[0] == bos_token for d in decode_s2)) # tokenizer has no padding token def test_padding_different_model_input_name(self): pass def test_special_tokens_mask_input_pairs_and_bos_token(self): # TODO: change to self.get_tokenizers() when the fast version is implemented tokenizers = [self.get_tokenizer(do_lower_case=False, add_bos_token=True)] for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): sequence_0 = "Encode this." sequence_1 = "This one too please." encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( sequence_0, sequence_1, add_special_tokens=True, return_special_tokens_mask=True, ) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) filtered_sequence = [ (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special) ] filtered_sequence = [x for x in filtered_sequence if x is not None] self.assertEqual(encoded_sequence, filtered_sequence) @require_tokenizers class OPTTokenizationTest(unittest.TestCase): def test_serialize_deserialize_fast_opt(self): # More context: # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1 # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519 # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439 tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True) text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) tokenizer.save_pretrained("test_opt") tokenizer = AutoTokenizer.from_pretrained("./test_opt") tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) def test_fast_slow_equivalence(self): tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", use_slow=True) text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) # Same as above self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) def test_users_can_modify_bos(self): tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True) tokenizer.bos_token = "bos" tokenizer.bos_token_id = tokenizer.get_vocab()["bos"] text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) # We changed the bos token self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758]) tokenizer.save_pretrained("./tok") tokenizer = AutoTokenizer.from_pretrained("./tok") self.assertTrue(tokenizer.is_fast) tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758])

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/mt5/configuration_mt5.py

# coding=utf-8 # Copyright 2020, The T5 Authors and HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ mT5 model configuration""" from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxSeq2SeqConfigWithPast from ...utils import logging logger = logging.get_logger(__name__) class MT5Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MT5Model`] or a [`TFMT5Model`]. It is used to instantiate a mT5 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the mT5 [google/mt5-small](https://huggingface.co/google/mt5-small) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: vocab_size (`int`, *optional*, defaults to 250112): Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`]. d_model (`int`, *optional*, defaults to 512): Size of the encoder layers and the pooler layer. d_kv (`int`, *optional*, defaults to 64): Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model // num_heads`. d_ff (`int`, *optional*, defaults to 1024): Size of the intermediate feed forward layer in each `T5Block`. num_layers (`int`, *optional*, defaults to 8): Number of hidden layers in the Transformer encoder. num_decoder_layers (`int`, *optional*): Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. num_heads (`int`, *optional*, defaults to 6): Number of attention heads for each attention layer in the Transformer encoder. relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. relative_attention_max_distance (`int`, *optional*, defaults to 128): The maximum distance of the longer sequences for the bucket separation. dropout_rate (`float`, *optional*, defaults to 0.1): The ratio for all dropout layers. layer_norm_eps (`float`, *optional*, defaults to 1e-6): The epsilon used by the layer normalization layers. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). feed_forward_proj (`string`, *optional*, defaults to `"gated-gelu"`): Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ model_type = "mt5" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=250112, d_model=512, d_kv=64, d_ff=1024, num_layers=8, num_decoder_layers=None, num_heads=6, relative_attention_num_buckets=32, relative_attention_max_distance=128, dropout_rate=0.1, layer_norm_epsilon=1e-6, initializer_factor=1.0, feed_forward_proj="gated-gelu", is_encoder_decoder=True, use_cache=True, tokenizer_class="T5Tokenizer", tie_word_embeddings=False, pad_token_id=0, eos_token_id=1, decoder_start_token_id=0, **kwargs ): super().__init__( is_encoder_decoder=is_encoder_decoder, tokenizer_class=tokenizer_class, tie_word_embeddings=tie_word_embeddings, pad_token_id=pad_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, **kwargs, ) self.vocab_size = vocab_size self.d_model = d_model self.d_kv = d_kv self.d_ff = d_ff self.num_layers = num_layers self.num_decoder_layers = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry self.num_heads = num_heads self.relative_attention_num_buckets = relative_attention_num_buckets self.relative_attention_max_distance = relative_attention_max_distance self.dropout_rate = dropout_rate self.layer_norm_epsilon = layer_norm_epsilon self.initializer_factor = initializer_factor self.feed_forward_proj = feed_forward_proj self.use_cache = use_cache act_info = self.feed_forward_proj.split("-") self.dense_act_fn = act_info[-1] self.is_gated_act = act_info[0] == "gated" if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2: raise ValueError( f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer." "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. " "'gated-gelu' or 'relu'" ) # for backwards compatibility if feed_forward_proj == "gated-gelu": self.dense_act_fn = "gelu_new" @property def hidden_size(self): return self.d_model @property def num_attention_heads(self): return self.num_heads @property def num_hidden_layers(self): return self.num_layers class MT5OnnxConfig(OnnxSeq2SeqConfigWithPast): @property # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = { "input_ids": {0: "batch", 1: "encoder_sequence"}, "attention_mask": {0: "batch", 1: "encoder_sequence"}, } if self.use_past: common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence" common_inputs["decoder_input_ids"] = {0: "batch"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") return common_inputs @property # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset def default_onnx_opset(self) -> int: return 13 @property def atol_for_validation(self) -> float: return 5e-4

# coding=utf-8 # Copyright 2020, The T5 Authors and HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ mT5 model configuration""" from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxSeq2SeqConfigWithPast from ...utils import logging logger = logging.get_logger(__name__) class MT5Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MT5Model`] or a [`TFMT5Model`]. It is used to instantiate a mT5 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the mT5 [google/mt5-small](https://huggingface.co/google/mt5-small) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Arguments: vocab_size (`int`, *optional*, defaults to 250112): Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`]. d_model (`int`, *optional*, defaults to 512): Size of the encoder layers and the pooler layer. d_kv (`int`, *optional*, defaults to 64): Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model // num_heads`. d_ff (`int`, *optional*, defaults to 1024): Size of the intermediate feed forward layer in each `T5Block`. num_layers (`int`, *optional*, defaults to 8): Number of hidden layers in the Transformer encoder. num_decoder_layers (`int`, *optional*): Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. num_heads (`int`, *optional*, defaults to 6): Number of attention heads for each attention layer in the Transformer encoder. relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. relative_attention_max_distance (`int`, *optional*, defaults to 128): The maximum distance of the longer sequences for the bucket separation. dropout_rate (`float`, *optional*, defaults to 0.1): The ratio for all dropout layers. layer_norm_eps (`float`, *optional*, defaults to 1e-6): The epsilon used by the layer normalization layers. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). feed_forward_proj (`string`, *optional*, defaults to `"gated-gelu"`): Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ model_type = "mt5" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=250112, d_model=512, d_kv=64, d_ff=1024, num_layers=8, num_decoder_layers=None, num_heads=6, relative_attention_num_buckets=32, relative_attention_max_distance=128, dropout_rate=0.1, layer_norm_epsilon=1e-6, initializer_factor=1.0, feed_forward_proj="gated-gelu", is_encoder_decoder=True, use_cache=True, tokenizer_class="T5Tokenizer", tie_word_embeddings=False, pad_token_id=0, eos_token_id=1, decoder_start_token_id=0, **kwargs ): super().__init__( is_encoder_decoder=is_encoder_decoder, tokenizer_class=tokenizer_class, tie_word_embeddings=tie_word_embeddings, pad_token_id=pad_token_id, eos_token_id=eos_token_id, decoder_start_token_id=decoder_start_token_id, **kwargs, ) self.vocab_size = vocab_size self.d_model = d_model self.d_kv = d_kv self.d_ff = d_ff self.num_layers = num_layers self.num_decoder_layers = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry self.num_heads = num_heads self.relative_attention_num_buckets = relative_attention_num_buckets self.relative_attention_max_distance = relative_attention_max_distance self.dropout_rate = dropout_rate self.layer_norm_epsilon = layer_norm_epsilon self.initializer_factor = initializer_factor self.feed_forward_proj = feed_forward_proj self.use_cache = use_cache act_info = self.feed_forward_proj.split("-") self.dense_act_fn = act_info[-1] self.is_gated_act = act_info[0] == "gated" if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2: raise ValueError( f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer." "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. " "'gated-gelu' or 'relu'" ) # for backwards compatibility if feed_forward_proj == "gated-gelu": self.dense_act_fn = "gelu_new" @property def hidden_size(self): return self.d_model @property def num_attention_heads(self): return self.num_heads @property def num_hidden_layers(self): return self.num_layers class MT5OnnxConfig(OnnxSeq2SeqConfigWithPast): @property # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = { "input_ids": {0: "batch", 1: "encoder_sequence"}, "attention_mask": {0: "batch", 1: "encoder_sequence"}, } if self.use_past: common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence" common_inputs["decoder_input_ids"] = {0: "batch"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"} else: common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"} common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"} if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") return common_inputs @property # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset def default_onnx_opset(self) -> int: return 13 @property def atol_for_validation(self) -> float: return 5e-4

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/convnext/test_modeling_convnext.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ConvNext model. """ import inspect import unittest from transformers import ConvNextConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import ConvNextForImageClassification, ConvNextModel from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoFeatureExtractor class ConvNextModelTester: def __init__( self, parent, batch_size=13, image_size=32, num_channels=3, num_stages=4, hidden_sizes=[10, 20, 30, 40], depths=[2, 2, 3, 2], is_training=True, use_labels=True, intermediate_size=37, hidden_act="gelu", type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_stages = num_stages self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return ConvNextConfig( num_channels=self.num_channels, hidden_sizes=self.hidden_sizes, depths=self.depths, num_stages=self.num_stages, hidden_act=self.hidden_act, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels): model = ConvNextModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = ConvNextForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ConvNextModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( ConvNextModel, ConvNextForImageClassification, ) if is_torch_available() else () ) fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = ConvNextModelTester(self) self.config_tester = ConfigTester(self, config_class=ConvNextConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return @unittest.skip(reason="ConvNext does not output attentions") def test_attention_outputs(self): pass @unittest.skip(reason="ConvNext does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ConvNext does not support input and output embeddings") def test_model_common_attributes(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ConvNextModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class ConvNextModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return AutoFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224").to(torch_device) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-0.0260, -0.4739, 0.1911]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch ConvNext model. """ import inspect import unittest from transformers import ConvNextConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import ConvNextForImageClassification, ConvNextModel from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoFeatureExtractor class ConvNextModelTester: def __init__( self, parent, batch_size=13, image_size=32, num_channels=3, num_stages=4, hidden_sizes=[10, 20, 30, 40], depths=[2, 2, 3, 2], is_training=True, use_labels=True, intermediate_size=37, hidden_act="gelu", type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_stages = num_stages self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return ConvNextConfig( num_channels=self.num_channels, hidden_sizes=self.hidden_sizes, depths=self.depths, num_stages=self.num_stages, hidden_act=self.hidden_act, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels): model = ConvNextModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = ConvNextForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ConvNextModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( ConvNextModel, ConvNextForImageClassification, ) if is_torch_available() else () ) fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = ConvNextModelTester(self) self.config_tester = ConfigTester(self, config_class=ConvNextConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return @unittest.skip(reason="ConvNext does not output attentions") def test_attention_outputs(self): pass @unittest.skip(reason="ConvNext does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ConvNext does not support input and output embeddings") def test_model_common_attributes(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ConvNextModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class ConvNextModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return AutoFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224").to(torch_device) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-0.0260, -0.4739, 0.1911]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py

# coding=utf-8 # Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax Wav2Vec2 model.""" from functools import partial from typing import Optional, Tuple, Union import numpy as np import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_wav2vec2 import Wav2Vec2Config logger = logging.get_logger(__name__) @flax.struct.dataclass class FlaxWav2Vec2BaseModelOutput(ModelOutput): """ Output type of [`FlaxWav2Vec2BaseModelOutput`], with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. extract_features (`jnp.ndarray` of shape `(batch_size, sequence_length, last_conv_dim)`): Sequence of extracted feature vectors of the last convolutional layer of the model with `last_conv_dim` being the dimension of the last convolutional layer. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None extract_features: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput): """ Output type of [`FlaxWav2Vec2ForPreTrainingOutput`], with potential hidden states and attentions. Args: loss (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`): Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss. projected_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked projected quantized states. projected_quantized_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive target vectors for contrastive loss. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ projected_states: jnp.ndarray = None projected_quantized_states: jnp.ndarray = None codevector_perplexity: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None def _compute_mask_indices( shape: Tuple[int, int], mask_prob: float, mask_length: int, attention_mask: Optional[np.ndarray] = None, min_masks: int = 0, ) -> np.ndarray: """ Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: the shape for which to compute masks. should be of size 2 where first element is batch size and 2nd is timesteps mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by number of timesteps divided by length of mask span to mask approximately this percentage of all elements. however due to overlaps, the actual number will be smaller (unless no_overlap is True) mask_length: size of the mask min_masks: minimum number of masked spans """ batch_size, sequence_length = shape if mask_length < 1: raise ValueError("`mask_length` has to be bigger than 0.") if mask_length > sequence_length: raise ValueError( f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and" f" `sequence_length`: {sequence_length}`" ) # compute number of masked spans in batch num_masked_spans = int(mask_prob * sequence_length / mask_length + np.random.rand(1).item()) num_masked_spans = max(num_masked_spans, min_masks) # make sure num masked indices <= sequence_length if num_masked_spans * mask_length > sequence_length: num_masked_spans = sequence_length // mask_length # SpecAugment mask to fill spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) # get random indices to mask spec_aug_mask_idxs = np.array( [ np.random.choice(np.arange(sequence_length - (mask_length - 1)), num_masked_spans, replace=False) for _ in range(batch_size) ] ) # expand masked indices to masked spans spec_aug_mask_idxs = np.broadcast_to(spec_aug_mask_idxs[:, :, None], (batch_size, num_masked_spans, mask_length)) spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, num_masked_spans * mask_length) offsets = np.arange(mask_length)[None, None, :] offsets = np.broadcast_to(offsets, (batch_size, num_masked_spans, mask_length)).reshape( batch_size, num_masked_spans * mask_length ) spec_aug_mask_idxs = spec_aug_mask_idxs + offsets # scatter indices to mask np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) if attention_mask is not None: # make sure padded input ids cannot be masked spec_aug_mask = np.where(attention_mask, spec_aug_mask, False) return spec_aug_mask def _sample_negative_indices(features_shape: Tuple, num_negatives: int, attention_mask: Optional[np.ndarray] = None): """ Sample `num_negatives` vectors from feature vectors. """ batch_size, sequence_length, hidden_size = features_shape if sequence_length <= 1: raise ValueError( "`features should have `sequence_length` > 1, but are of shape " f"(batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})." ) # get `num_negatives` random vector indices from the same utterance sampled_negative_indices = [] for batch_idx in range(batch_size): high = attention_mask[batch_idx].sum() - 1 if attention_mask is not None else sequence_length - 1 sampled_indices_slice = np.random.randint(0, high, size=(num_negatives * sequence_length,)) sampled_negative_indices.append(sampled_indices_slice) sampled_negative_indices = np.asarray(sampled_negative_indices, dtype=np.int32) # generate indices of the positive vectors themselves, repeat them `num_negatives` times feature_indices = np.broadcast_to(np.arange(sequence_length)[:, None], (sequence_length, num_negatives)).flatten() # avoid sampling the same positive vector, but keep the distribution uniform sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1 # correct for batch size for batch_idx in range(1, batch_size): sampled_negative_indices[batch_idx] += batch_idx * sequence_length return sampled_negative_indices WAV_2_VEC_2_START_DOCSTRING = r""" Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli. This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`Wav2Vec2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ WAV_2_VEC_2_INPUTS_DOCSTRING = r""" Args: input_values (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install soundfile*). To prepare the array into *input_values*, the [`Wav2Vec2Processor`] should be used for padding and conversion into a tensor of type *jnp.ndarray*. See [`Wav2Vec2Processor.__call__`] for details. attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor has `config.return_attention_mask == False`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be passed to avoid degraded performance when doing batched inference. For such models `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly different results depending on whether `input_values` is padded or not. mask_time_indices (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict masked extracted features in *config.proj_codevector_dim* space. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxWav2Vec2LayerNormConvLayer(nn.Module): config: Wav2Vec2Config layer_id: int = 0 dtype: jnp.dtype = jnp.float32 def setup(self): self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1 self.out_conv_dim = self.config.conv_dim[self.layer_id] self.conv = nn.Conv( features=self.config.conv_dim[self.layer_id], kernel_size=(self.config.conv_kernel[self.layer_id],), strides=(self.config.conv_stride[self.layer_id],), use_bias=self.config.conv_bias, kernel_init=jax.nn.initializers.he_normal(), padding="VALID", dtype=self.dtype, ) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.activation = ACT2FN[self.config.feat_extract_activation] def __call__(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = self.layer_norm(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states class FlaxConvWithWeightNorm(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( features=self.config.hidden_size, kernel_size=(self.config.num_conv_pos_embeddings,), kernel_init=jax.nn.initializers.he_normal(), padding="VALID", feature_group_count=self.config.num_conv_pos_embedding_groups, dtype=self.dtype, ) weight_shape = ( self.conv.features, self.conv.features // self.conv.feature_group_count, self.conv.kernel_size[0], ) self.weight_v = self.param("weight_v", jax.nn.initializers.he_normal(), weight_shape) self.weight_g = self.param("weight_g", lambda _: jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :]) self.bias = self.param("bias", jax.nn.initializers.zeros, (self.conv.features,)) self.prev_padding = self.conv.kernel_size[0] // 2 def _get_normed_weights(self): weight_v_norm = jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :] normed_weight_v = jnp.divide(self.weight_v, weight_v_norm) normed_kernel = jnp.multiply(normed_weight_v, self.weight_g) return normed_kernel def __call__(self, hidden_states): kernel = self._get_normed_weights() hidden_states = jnp.pad(hidden_states, ((0, 0), (self.prev_padding, self.prev_padding), (0, 0))) hidden_states = self.conv.apply({"params": {"kernel": kernel.T, "bias": self.bias}}, hidden_states) return hidden_states class FlaxWav2Vec2PositionalConvEmbedding(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype) self.activation = ACT2FN[self.config.feat_extract_activation] self.num_pad_remove = 1 if self.config.num_conv_pos_embeddings % 2 == 0 else 0 def __call__(self, hidden_states): hidden_states = hidden_states.transpose((0, 1, 2)) hidden_states = self.conv(hidden_states) if self.num_pad_remove > 0: hidden_states = hidden_states[:, : -self.num_pad_remove, :] hidden_states = self.activation(hidden_states) hidden_states = hidden_states.transpose((0, 1, 2)) return hidden_states class FlaxConvLayersCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): if self.config.feat_extract_norm == "layer": self.layers = [ FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype) for i in range(self.config.num_feat_extract_layers) ] elif self.config.feat_extract_norm == "group": raise NotImplementedError("At the moment only ``config.feat_extact_norm == 'layer'`` is supported") else: raise ValueError( f"`config.feat_extract_norm` is {self.config.feat_extract_norm}, but has to be one of ['group'," " 'layer']" ) def __call__(self, hidden_states): for i, conv_layer in enumerate(self.layers): hidden_states = conv_layer(hidden_states) return hidden_states class FlaxWav2Vec2FeatureEncoder(nn.Module): """Construct the features from raw audio waveform""" config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype) def __call__(self, input_values, freeze_feature_encoder=False): hidden_states = input_values[:, :, None] hidden_states = self.conv_layers(hidden_states) if freeze_feature_encoder: hidden_states = jax.lax.stop_gradient(hidden_states) return hidden_states class FlaxWav2Vec2FeatureProjection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.projection = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.feat_proj_dropout) def __call__(self, hidden_states, deterministic=True): norm_hidden_states = self.layer_norm(hidden_states) hidden_states = self.projection(norm_hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states, norm_hidden_states class FlaxWav2Vec2Attention(nn.Module): config: Wav2Vec2Config embed_dim: int num_heads: int dropout: float = 0.0 bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # get query proj query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) if attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxWav2Vec2FeedForward(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout) self.intermediate_dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) if isinstance(self.config.hidden_act, str): self.intermediate_act_fn = ACT2FN[self.config.hidden_act] else: self.intermediate_act_fn = self.config.hidden_act self.output_dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.output_dropout = nn.Dropout(rate=self.config.hidden_dropout) def __call__(self, hidden_states, deterministic=True): hidden_states = self.intermediate_dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) hidden_states = self.intermediate_dropout(hidden_states, deterministic=deterministic) hidden_states = self.output_dense(hidden_states) hidden_states = self.output_dropout(hidden_states, deterministic=deterministic) return hidden_states class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.attention = FlaxWav2Vec2Attention( config=self.config, embed_dim=self.config.hidden_size, num_heads=self.config.num_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.feed_forward = FlaxWav2Vec2FeedForward(self.config, dtype=self.dtype) self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False): attn_residual = hidden_states hidden_states = self.layer_norm(hidden_states) hidden_states, attn_weights = self.attention( hidden_states, attention_mask=attention_mask, deterministic=deterministic ) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = attn_residual + hidden_states hidden_states = hidden_states + self.feed_forward( self.final_layer_norm(hidden_states), deterministic=deterministic ) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = [ FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxWav2Vec2StableLayerNormEncoder(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout) self.layers = FlaxWav2Vec2EncoderLayerStableLayerNormCollection(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False, output_hidden_states=False, return_dict=True, ): if attention_mask is not None: # make sure padded tokens are not attended to hidden_states = jnp.where( jnp.broadcast_to(attention_mask[:, :, None], hidden_states.shape), hidden_states, 0 ) position_embeddings = self.pos_conv_embed(hidden_states) hidden_states = hidden_states + position_embeddings hidden_states = self.dropout(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = self.layer_norm(outputs[0]) # update the last element in `hidden_states` after applying `layernorm` above hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_state,) if not return_dict: outputs = (last_hidden_state, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=outputs.attentions ) class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module): """ Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information. """ config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.num_groups = self.config.num_codevector_groups self.num_vars = self.config.num_codevectors_per_group if self.config.codevector_dim % self.num_groups != 0: raise ValueError( f"`config.codevector_dim {self.config.codevector_dim} must be divisible by" f" `config.num_codevector_groups` {self.num_groups} for concatenation" ) # storage for codebook variables (codewords) self.codevectors = self.param( "codevectors", jax.nn.initializers.uniform(), (1, self.num_groups * self.num_vars, self.config.codevector_dim // self.num_groups), ) self.weight_proj = nn.Dense( self.num_groups * self.num_vars, kernel_init=jax.nn.initializers.normal(1.0), dtype=self.dtype, ) @staticmethod def _compute_perplexity(probs, mask=None): if mask is not None: mask_extended = jnp.broadcast_to(mask.flatten()[:, None, None], probs.shape) probs = jnp.where(mask_extended, probs, jnp.zeros_like(probs)) marginal_probs = probs.sum(axis=0) / mask.sum() else: marginal_probs = probs.mean(axis=0) perplexity = jnp.exp(-jnp.sum(marginal_probs * jnp.log(marginal_probs + 1e-7), axis=-1)).sum() return perplexity def __call__(self, hidden_states, mask_time_indices=None, deterministic=True, temperature=1): batch_size, sequence_length, hidden_size = hidden_states.shape # project to codevector dim hidden_states = self.weight_proj(hidden_states) hidden_states = hidden_states.reshape(batch_size * sequence_length * self.num_groups, -1) if not deterministic: # sample code vector probs via gumbel in differentiateable way gumbel_rng = self.make_rng("gumbel") gumbels = jax.random.gumbel(gumbel_rng, hidden_states.shape) codevector_probs = nn.softmax((hidden_states + gumbels) / temperature) # compute perplexity codevector_soft_dist = nn.softmax( hidden_states.reshape(batch_size * sequence_length, self.num_groups, -1), axis=-1 ) perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices) else: # take argmax in non-differentiable way # comptute hard codevector distribution (one hot) codevector_idx = hidden_states.argmax(axis=-1) codevector_probs = jax.nn.one_hot(codevector_idx, hidden_states.shape[-1]) * 1.0 codevector_probs = codevector_probs.reshape(batch_size * sequence_length, self.num_groups, -1) perplexity = self._compute_perplexity(codevector_probs, mask_time_indices) codevector_probs = codevector_probs.reshape(batch_size * sequence_length, -1) # use probs to retrieve codevectors codevectors_per_group = jnp.expand_dims(codevector_probs, axis=-1) * self.codevectors codevectors = codevectors_per_group.reshape(batch_size * sequence_length, self.num_groups, self.num_vars, -1) codevectors = codevectors.sum(-2).reshape(batch_size, sequence_length, -1) return codevectors, perplexity class FlaxWav2Vec2Adapter(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): # hidden_states require down-projection if feature dims don't match if self.config.output_hidden_size != self.config.hidden_size: self.proj = nn.Dense( self.config.output_hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.proj_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) else: self.proj = self.proj_layer_norm = None self.layers = FlaxWav2Vec2AdapterLayersCollection(self.config, dtype=self.dtype) def __call__(self, hidden_states, deterministic=True): # down-project hidden_states if required if self.proj is not None and self.proj_layer_norm is not None: hidden_states = self.proj(hidden_states) hidden_states = self.proj_layer_norm(hidden_states) hidden_states = self.layers(hidden_states) return hidden_states class FlaxWav2Vec2AdapterLayer(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( features=2 * self.config.output_hidden_size, kernel_size=(self.config.adapter_kernel_size,), strides=(self.config.adapter_stride,), padding=((1, 1),), kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = nn.glu(hidden_states, axis=2) return hidden_states class FlaxWav2Vec2AdapterLayersCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = [ FlaxWav2Vec2AdapterLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_adapter_layers) ] def __call__(self, hidden_states): for conv_layer in self.layers: hidden_states = conv_layer(hidden_states) return hidden_states class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Wav2Vec2Config base_model_prefix: str = "wav2vec2" main_input_name = "input_values" module_class: nn.Module = None def __init__( self, config: Wav2Vec2Config, input_shape: Tuple = (1, 1024), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_values = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_values) params_rng, dropout_rng = jax.random.split(rng, 2) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_values, attention_mask, return_dict=False)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, freeze_feature_encoder: bool = False, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_values.shape if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} return self.module.apply( inputs, jnp.array(input_values, dtype="f4"), jnp.array(attention_mask, dtype="i4"), mask_time_indices, not train, output_attentions, output_hidden_states, freeze_feature_encoder, return_dict, rngs=rngs, ) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None ): return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter) class FlaxWav2Vec2Module(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.feature_extractor = FlaxWav2Vec2FeatureEncoder(self.config, dtype=self.dtype) self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype) self.masked_spec_embed = self.param( "masked_spec_embed", jax.nn.initializers.uniform(), (self.config.hidden_size,) ) if self.config.do_stable_layer_norm: self.encoder = FlaxWav2Vec2StableLayerNormEncoder(self.config, dtype=self.dtype) else: raise NotImplementedError("``config.do_stable_layer_norm is False`` is currently not supported.") self.adapter = FlaxWav2Vec2Adapter(self.config, dtype=self.dtype) if self.config.add_adapter else None def __call__( self, input_values, attention_mask=None, mask_time_indices=None, deterministic=True, output_attentions=None, output_hidden_states=None, freeze_feature_encoder=False, return_dict=None, ): extract_features = self.feature_extractor(input_values, freeze_feature_encoder=freeze_feature_encoder) # make sure that no loss is computed on padded inputs if attention_mask is not None: # compute reduced attention_mask corresponding to feature vectors attention_mask = self._get_feature_vector_attention_mask( extract_features.shape[1], attention_mask, add_adapter=False ) hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic) if mask_time_indices is not None: # apply SpecAugment along time axis with given indices hidden_states = jnp.where( jnp.broadcast_to(mask_time_indices[:, :, None], hidden_states.shape), jnp.broadcast_to(self.masked_spec_embed[None, None, :], hidden_states.shape), hidden_states, ) encoder_outputs = self.encoder( hidden_states, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = encoder_outputs[0] if self.adapter is not None: hidden_states = self.adapter(hidden_states) if not return_dict: return (hidden_states, extract_features) + encoder_outputs[1:] return FlaxWav2Vec2BaseModelOutput( last_hidden_state=hidden_states, extract_features=extract_features, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None ): """ Computes the output length of the convolutional layers """ add_adapter = self.config.add_adapter if add_adapter is None else add_adapter def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) if add_adapter: for _ in range(self.config.num_adapter_layers): input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) return input_lengths def _get_feature_vector_attention_mask( self, feature_vector_length: int, attention_mask: jnp.ndarray, add_adapter=None ): # Effectively attention_mask.sum(-1), but not inplace to be able to run # on inference mode. non_padded_lengths = attention_mask.cumsum(axis=-1)[:, -1] output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter) batch_size = attention_mask.shape[0] attention_mask = jnp.zeros((batch_size, feature_vector_length), dtype=attention_mask.dtype) # these two operations makes sure that all values # before the output lengths indices are attended to attention_mask = attention_mask.at[jnp.arange(attention_mask.shape[0]), output_lengths - 1].set(1) attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool") return attention_mask @add_start_docstrings( "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.", WAV_2_VEC_2_START_DOCSTRING, ) class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2Module FLAX_WAV2VEC2_MODEL_DOCSTRING = """ Returns: Example: ```python >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2Model >>> from datasets import load_dataset >>> import soundfile as sf >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-lv60") >>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = processor( ... ds["speech"][0], sampling_rate=16_000, return_tensors="np" ... ).input_values # Batch size 1 >>> hidden_states = model(input_values).last_hidden_state ``` """ overwrite_call_docstring( FlaxWav2Vec2Model, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxWav2Vec2Model, output_type=FlaxWav2Vec2BaseModelOutput, config_class=Wav2Vec2Config ) class FlaxWav2Vec2ForCTCModule(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.final_dropout) self.lm_head = nn.Dense( self.config.vocab_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, deterministic=True, output_attentions=None, output_hidden_states=None, freeze_feature_encoder=False, return_dict=None, ): outputs = self.wav2vec2( input_values, attention_mask=attention_mask, mask_time_indices=mask_time_indices, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, freeze_feature_encoder=freeze_feature_encoder, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.lm_head(hidden_states) if not return_dict: return (logits,) + outputs[2:] return FlaxCausalLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None, ): """ Computes the output length of the convolutional layers """ add_adapter = self.config.add_adapter if add_adapter is None else add_adapter def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) if add_adapter: for _ in range(self.config.num_adapter_layers): input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) return input_lengths @add_start_docstrings( "Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).", WAV_2_VEC_2_START_DOCSTRING, ) class FlaxWav2Vec2ForCTC(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2ForCTCModule FLAX_WAV2VEC2_FOR_CTC_DOCSTRING = """ Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2ForCTC >>> from datasets import load_dataset >>> import soundfile as sf >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60") >>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = processor( ... ds["speech"][0], sampling_rate=16_000, return_tensors="np" ... ).input_values # Batch size 1 >>> logits = model(input_values).logits >>> predicted_ids = jnp.argmax(logits, axis=-1) >>> transcription = processor.decode(predicted_ids[0]) >>> # should give: "A MAN SAID TO THE UNIVERSE SIR I EXIST" ``` """ overwrite_call_docstring( FlaxWav2Vec2ForCTC, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_CTC_DOCSTRING, ) append_replace_return_docstrings(FlaxWav2Vec2ForCTC, output_type=FlaxCausalLMOutput, config_class=Wav2Vec2Config) class FlaxWav2Vec2ForPreTrainingModule(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) self.dropout_features = nn.Dropout(self.config.feat_quantizer_dropout) self.quantizer = FlaxWav2Vec2GumbelVectorQuantizer(self.config, dtype=self.dtype) self.project_q = nn.Dense( self.config.proj_codevector_dim, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.project_hid = nn.Dense( self.config.proj_codevector_dim, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, gumbel_temperature: int = 1, deterministic: bool = True, output_attentions=None, output_hidden_states=None, freeze_feature_encoder=False, return_dict=None, ): r""" Returns: Example: ```python ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.wav2vec2( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, mask_time_indices=mask_time_indices, deterministic=deterministic, freeze_feature_encoder=freeze_feature_encoder, return_dict=return_dict, ) # project all transformed features (including masked) to final vq dim transformer_features = self.project_hid(outputs[0]) # quantize all (unmasked) extracted features and project to final vq dim extract_features = self.dropout_features(outputs[1], deterministic=deterministic) quantized_features, codevector_perplexity = self.quantizer( extract_features, mask_time_indices, deterministic=deterministic, temperature=gumbel_temperature ) quantized_features = self.project_q(quantized_features) if not return_dict: return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:] return FlaxWav2Vec2ForPreTrainingOutput( projected_states=transformer_features, projected_quantized_states=quantized_features, codevector_perplexity=codevector_perplexity, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None ): """ Computes the output length of the convolutional layers """ add_adapter = self.config.add_adapter if add_adapter is None else add_adapter def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) if add_adapter: for _ in range(self.config.num_adapter_layers): input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) return input_lengths @add_start_docstrings("""Wav2Vec2 Model with a quantizer and `VQ` head on top.""", WAV_2_VEC_2_START_DOCSTRING) class FlaxWav2Vec2ForPreTraining(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2ForPreTrainingModule @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) # overwrite since has `gumbel_temperature` input def __call__( self, input_values, attention_mask=None, mask_time_indices=None, gumbel_temperature: int = 1, params: dict = None, dropout_rng: jax.random.PRNGKey = None, gumbel_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, freeze_feature_encoder: bool = False, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_values.shape if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng if gumbel_rng is not None: rngs["gumbel"] = gumbel_rng inputs = {"params": params or self.params} return self.module.apply( inputs, jnp.array(input_values, dtype="f4"), jnp.array(attention_mask, dtype="i4"), mask_time_indices, gumbel_temperature, not train, output_attentions, output_hidden_states, freeze_feature_encoder, return_dict, rngs=rngs, ) FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING = """ Returns: Example: ```python >>> import optax >>> import numpy as np >>> import jax.numpy as jnp >>> from transformers import Wav2Vec2FeatureExtractor, FlaxWav2Vec2ForPreTraining >>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices >>> from datasets import load_dataset >>> import soundfile as sf >>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60") >>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values # Batch size 1 >>> # compute masked indices >>> batch_size, raw_sequence_length = input_values.shape >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length) >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2) >>> outputs = model(input_values, mask_time_indices=mask_time_indices) >>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states) >>> cosine_sim = optax.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states) >>> # show that cosine similarity is much higher than random >>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.5 ``` """ overwrite_call_docstring( FlaxWav2Vec2ForPreTraining, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING, ) append_replace_return_docstrings( FlaxWav2Vec2ForPreTraining, output_type=FlaxWav2Vec2ForPreTrainingOutput, config_class=Wav2Vec2Config )

# coding=utf-8 # Copyright 2021 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax Wav2Vec2 model.""" from functools import partial from typing import Optional, Tuple, Union import numpy as np import flax import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_wav2vec2 import Wav2Vec2Config logger = logging.get_logger(__name__) @flax.struct.dataclass class FlaxWav2Vec2BaseModelOutput(ModelOutput): """ Output type of [`FlaxWav2Vec2BaseModelOutput`], with potential hidden states and attentions. Args: last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. extract_features (`jnp.ndarray` of shape `(batch_size, sequence_length, last_conv_dim)`): Sequence of extracted feature vectors of the last convolutional layer of the model with `last_conv_dim` being the dimension of the last convolutional layer. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: jnp.ndarray = None extract_features: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None @flax.struct.dataclass class FlaxWav2Vec2ForPreTrainingOutput(ModelOutput): """ Output type of [`FlaxWav2Vec2ForPreTrainingOutput`], with potential hidden states and attentions. Args: loss (*optional*, returned when model is in train mode, `jnp.ndarray` of shape `(1,)`): Total loss as the sum of the contrastive loss (L_m) and the diversity loss (L_d) as stated in the [official paper](https://arxiv.org/pdf/2006.11477.pdf) . (classification) loss. projected_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): Hidden-states of the model projected to *config.proj_codevector_dim* that can be used to predict the masked projected quantized states. projected_quantized_states (`jnp.ndarray` of shape `(batch_size, sequence_length, config.proj_codevector_dim)`): Quantized extracted feature vectors projected to *config.proj_codevector_dim* representing the positive target vectors for contrastive loss. hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ projected_states: jnp.ndarray = None projected_quantized_states: jnp.ndarray = None codevector_perplexity: jnp.ndarray = None hidden_states: Optional[Tuple[jnp.ndarray]] = None attentions: Optional[Tuple[jnp.ndarray]] = None def _compute_mask_indices( shape: Tuple[int, int], mask_prob: float, mask_length: int, attention_mask: Optional[np.ndarray] = None, min_masks: int = 0, ) -> np.ndarray: """ Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: the shape for which to compute masks. should be of size 2 where first element is batch size and 2nd is timesteps mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by number of timesteps divided by length of mask span to mask approximately this percentage of all elements. however due to overlaps, the actual number will be smaller (unless no_overlap is True) mask_length: size of the mask min_masks: minimum number of masked spans """ batch_size, sequence_length = shape if mask_length < 1: raise ValueError("`mask_length` has to be bigger than 0.") if mask_length > sequence_length: raise ValueError( f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and" f" `sequence_length`: {sequence_length}`" ) # compute number of masked spans in batch num_masked_spans = int(mask_prob * sequence_length / mask_length + np.random.rand(1).item()) num_masked_spans = max(num_masked_spans, min_masks) # make sure num masked indices <= sequence_length if num_masked_spans * mask_length > sequence_length: num_masked_spans = sequence_length // mask_length # SpecAugment mask to fill spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) # get random indices to mask spec_aug_mask_idxs = np.array( [ np.random.choice(np.arange(sequence_length - (mask_length - 1)), num_masked_spans, replace=False) for _ in range(batch_size) ] ) # expand masked indices to masked spans spec_aug_mask_idxs = np.broadcast_to(spec_aug_mask_idxs[:, :, None], (batch_size, num_masked_spans, mask_length)) spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, num_masked_spans * mask_length) offsets = np.arange(mask_length)[None, None, :] offsets = np.broadcast_to(offsets, (batch_size, num_masked_spans, mask_length)).reshape( batch_size, num_masked_spans * mask_length ) spec_aug_mask_idxs = spec_aug_mask_idxs + offsets # scatter indices to mask np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) if attention_mask is not None: # make sure padded input ids cannot be masked spec_aug_mask = np.where(attention_mask, spec_aug_mask, False) return spec_aug_mask def _sample_negative_indices(features_shape: Tuple, num_negatives: int, attention_mask: Optional[np.ndarray] = None): """ Sample `num_negatives` vectors from feature vectors. """ batch_size, sequence_length, hidden_size = features_shape if sequence_length <= 1: raise ValueError( "`features should have `sequence_length` > 1, but are of shape " f"(batch_size, sequence_length, hidden_size) = ({batch_size, sequence_length, hidden_size})." ) # get `num_negatives` random vector indices from the same utterance sampled_negative_indices = [] for batch_idx in range(batch_size): high = attention_mask[batch_idx].sum() - 1 if attention_mask is not None else sequence_length - 1 sampled_indices_slice = np.random.randint(0, high, size=(num_negatives * sequence_length,)) sampled_negative_indices.append(sampled_indices_slice) sampled_negative_indices = np.asarray(sampled_negative_indices, dtype=np.int32) # generate indices of the positive vectors themselves, repeat them `num_negatives` times feature_indices = np.broadcast_to(np.arange(sequence_length)[:, None], (sequence_length, num_negatives)).flatten() # avoid sampling the same positive vector, but keep the distribution uniform sampled_negative_indices[sampled_negative_indices >= feature_indices] += 1 # correct for batch size for batch_idx in range(1, batch_size): sampled_negative_indices[batch_idx] += batch_idx * sequence_length return sampled_negative_indices WAV_2_VEC_2_START_DOCSTRING = r""" Wav2Vec2 was proposed in [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli. This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`Wav2Vec2Config`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ WAV_2_VEC_2_INPUTS_DOCSTRING = r""" Args: input_values (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Float values of input raw speech waveform. Values can be obtained by loading a *.flac* or *.wav* audio file into an array of type *List[float]* or a *numpy.ndarray*, *e.g.* via the soundfile library (*pip install soundfile*). To prepare the array into *input_values*, the [`Wav2Vec2Processor`] should be used for padding and conversion into a tensor of type *jnp.ndarray*. See [`Wav2Vec2Processor.__call__`] for details. attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing convolution and attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) .. warning:: `attention_mask` should only be passed if the corresponding processor has `config.return_attention_mask == True`. For all models whose processor has `config.return_attention_mask == False`, such as [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base-960h), `attention_mask` should **not** be passed to avoid degraded performance when doing batched inference. For such models `input_values` should simply be padded with 0 and passed without `attention_mask`. Be aware that these models also yield slightly different results depending on whether `input_values` is padded or not. mask_time_indices (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict masked extracted features in *config.proj_codevector_dim* space. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class FlaxWav2Vec2LayerNormConvLayer(nn.Module): config: Wav2Vec2Config layer_id: int = 0 dtype: jnp.dtype = jnp.float32 def setup(self): self.in_conv_dim = self.config.conv_dim[self.layer_id] if self.layer_id > 0 else 1 self.out_conv_dim = self.config.conv_dim[self.layer_id] self.conv = nn.Conv( features=self.config.conv_dim[self.layer_id], kernel_size=(self.config.conv_kernel[self.layer_id],), strides=(self.config.conv_stride[self.layer_id],), use_bias=self.config.conv_bias, kernel_init=jax.nn.initializers.he_normal(), padding="VALID", dtype=self.dtype, ) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.activation = ACT2FN[self.config.feat_extract_activation] def __call__(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = self.layer_norm(hidden_states) hidden_states = self.activation(hidden_states) return hidden_states class FlaxConvWithWeightNorm(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( features=self.config.hidden_size, kernel_size=(self.config.num_conv_pos_embeddings,), kernel_init=jax.nn.initializers.he_normal(), padding="VALID", feature_group_count=self.config.num_conv_pos_embedding_groups, dtype=self.dtype, ) weight_shape = ( self.conv.features, self.conv.features // self.conv.feature_group_count, self.conv.kernel_size[0], ) self.weight_v = self.param("weight_v", jax.nn.initializers.he_normal(), weight_shape) self.weight_g = self.param("weight_g", lambda _: jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :]) self.bias = self.param("bias", jax.nn.initializers.zeros, (self.conv.features,)) self.prev_padding = self.conv.kernel_size[0] // 2 def _get_normed_weights(self): weight_v_norm = jnp.linalg.norm(self.weight_v, axis=(0, 1))[None, None, :] normed_weight_v = jnp.divide(self.weight_v, weight_v_norm) normed_kernel = jnp.multiply(normed_weight_v, self.weight_g) return normed_kernel def __call__(self, hidden_states): kernel = self._get_normed_weights() hidden_states = jnp.pad(hidden_states, ((0, 0), (self.prev_padding, self.prev_padding), (0, 0))) hidden_states = self.conv.apply({"params": {"kernel": kernel.T, "bias": self.bias}}, hidden_states) return hidden_states class FlaxWav2Vec2PositionalConvEmbedding(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = FlaxConvWithWeightNorm(self.config, dtype=self.dtype) self.activation = ACT2FN[self.config.feat_extract_activation] self.num_pad_remove = 1 if self.config.num_conv_pos_embeddings % 2 == 0 else 0 def __call__(self, hidden_states): hidden_states = hidden_states.transpose((0, 1, 2)) hidden_states = self.conv(hidden_states) if self.num_pad_remove > 0: hidden_states = hidden_states[:, : -self.num_pad_remove, :] hidden_states = self.activation(hidden_states) hidden_states = hidden_states.transpose((0, 1, 2)) return hidden_states class FlaxConvLayersCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): if self.config.feat_extract_norm == "layer": self.layers = [ FlaxWav2Vec2LayerNormConvLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype) for i in range(self.config.num_feat_extract_layers) ] elif self.config.feat_extract_norm == "group": raise NotImplementedError("At the moment only ``config.feat_extact_norm == 'layer'`` is supported") else: raise ValueError( f"`config.feat_extract_norm` is {self.config.feat_extract_norm}, but has to be one of ['group'," " 'layer']" ) def __call__(self, hidden_states): for i, conv_layer in enumerate(self.layers): hidden_states = conv_layer(hidden_states) return hidden_states class FlaxWav2Vec2FeatureEncoder(nn.Module): """Construct the features from raw audio waveform""" config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv_layers = FlaxConvLayersCollection(self.config, dtype=self.dtype) def __call__(self, input_values, freeze_feature_encoder=False): hidden_states = input_values[:, :, None] hidden_states = self.conv_layers(hidden_states) if freeze_feature_encoder: hidden_states = jax.lax.stop_gradient(hidden_states) return hidden_states class FlaxWav2Vec2FeatureProjection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.projection = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.feat_proj_dropout) def __call__(self, hidden_states, deterministic=True): norm_hidden_states = self.layer_norm(hidden_states) hidden_states = self.projection(norm_hidden_states) hidden_states = self.dropout(hidden_states, deterministic=deterministic) return hidden_states, norm_hidden_states class FlaxWav2Vec2Attention(nn.Module): config: Wav2Vec2Config embed_dim: int num_heads: int dropout: float = 0.0 bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # get query proj query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) if attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, float("-inf")).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class FlaxWav2Vec2FeedForward(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.intermediate_dropout = nn.Dropout(rate=self.config.activation_dropout) self.intermediate_dense = nn.Dense( self.config.intermediate_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) if isinstance(self.config.hidden_act, str): self.intermediate_act_fn = ACT2FN[self.config.hidden_act] else: self.intermediate_act_fn = self.config.hidden_act self.output_dense = nn.Dense( self.config.hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.output_dropout = nn.Dropout(rate=self.config.hidden_dropout) def __call__(self, hidden_states, deterministic=True): hidden_states = self.intermediate_dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) hidden_states = self.intermediate_dropout(hidden_states, deterministic=deterministic) hidden_states = self.output_dense(hidden_states) hidden_states = self.output_dropout(hidden_states, deterministic=deterministic) return hidden_states class FlaxWav2Vec2EncoderLayerStableLayerNorm(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.attention = FlaxWav2Vec2Attention( config=self.config, embed_dim=self.config.hidden_size, num_heads=self.config.num_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.dropout = nn.Dropout(rate=self.config.hidden_dropout) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.feed_forward = FlaxWav2Vec2FeedForward(self.config, dtype=self.dtype) self.final_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) def __call__(self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False): attn_residual = hidden_states hidden_states = self.layer_norm(hidden_states) hidden_states, attn_weights = self.attention( hidden_states, attention_mask=attention_mask, deterministic=deterministic ) hidden_states = self.dropout(hidden_states, deterministic=deterministic) hidden_states = attn_residual + hidden_states hidden_states = hidden_states + self.feed_forward( self.final_layer_norm(hidden_states), deterministic=deterministic ) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class FlaxWav2Vec2EncoderLayerStableLayerNormCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = [ FlaxWav2Vec2EncoderLayerStableLayerNorm(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers) ] def __call__( self, hidden_states, attention_mask=None, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, layer in enumerate(self.layers): if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = layer( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions ) hidden_states = layer_outputs[0] if output_attentions: all_attentions += (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) class FlaxWav2Vec2StableLayerNormEncoder(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.pos_conv_embed = FlaxWav2Vec2PositionalConvEmbedding(self.config, dtype=self.dtype) self.layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.hidden_dropout) self.layers = FlaxWav2Vec2EncoderLayerStableLayerNormCollection(self.config, dtype=self.dtype) def __call__( self, hidden_states, attention_mask=None, deterministic=True, output_attentions=False, output_hidden_states=False, return_dict=True, ): if attention_mask is not None: # make sure padded tokens are not attended to hidden_states = jnp.where( jnp.broadcast_to(attention_mask[:, :, None], hidden_states.shape), hidden_states, 0 ) position_embeddings = self.pos_conv_embed(hidden_states) hidden_states = hidden_states + position_embeddings hidden_states = self.dropout(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = self.layer_norm(outputs[0]) # update the last element in `hidden_states` after applying `layernorm` above hidden_states = None if output_hidden_states: hidden_states = outputs[1] hidden_states = hidden_states[:-1] + (last_hidden_state,) if not return_dict: outputs = (last_hidden_state, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:]) return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=last_hidden_state, hidden_states=hidden_states, attentions=outputs.attentions ) class FlaxWav2Vec2GumbelVectorQuantizer(nn.Module): """ Vector quantization using gumbel softmax. See [CATEGORICAL REPARAMETERIZATION WITH GUMBEL-SOFTMAX](https://arxiv.org/pdf/1611.01144.pdf) for more information. """ config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.num_groups = self.config.num_codevector_groups self.num_vars = self.config.num_codevectors_per_group if self.config.codevector_dim % self.num_groups != 0: raise ValueError( f"`config.codevector_dim {self.config.codevector_dim} must be divisible by" f" `config.num_codevector_groups` {self.num_groups} for concatenation" ) # storage for codebook variables (codewords) self.codevectors = self.param( "codevectors", jax.nn.initializers.uniform(), (1, self.num_groups * self.num_vars, self.config.codevector_dim // self.num_groups), ) self.weight_proj = nn.Dense( self.num_groups * self.num_vars, kernel_init=jax.nn.initializers.normal(1.0), dtype=self.dtype, ) @staticmethod def _compute_perplexity(probs, mask=None): if mask is not None: mask_extended = jnp.broadcast_to(mask.flatten()[:, None, None], probs.shape) probs = jnp.where(mask_extended, probs, jnp.zeros_like(probs)) marginal_probs = probs.sum(axis=0) / mask.sum() else: marginal_probs = probs.mean(axis=0) perplexity = jnp.exp(-jnp.sum(marginal_probs * jnp.log(marginal_probs + 1e-7), axis=-1)).sum() return perplexity def __call__(self, hidden_states, mask_time_indices=None, deterministic=True, temperature=1): batch_size, sequence_length, hidden_size = hidden_states.shape # project to codevector dim hidden_states = self.weight_proj(hidden_states) hidden_states = hidden_states.reshape(batch_size * sequence_length * self.num_groups, -1) if not deterministic: # sample code vector probs via gumbel in differentiateable way gumbel_rng = self.make_rng("gumbel") gumbels = jax.random.gumbel(gumbel_rng, hidden_states.shape) codevector_probs = nn.softmax((hidden_states + gumbels) / temperature) # compute perplexity codevector_soft_dist = nn.softmax( hidden_states.reshape(batch_size * sequence_length, self.num_groups, -1), axis=-1 ) perplexity = self._compute_perplexity(codevector_soft_dist, mask_time_indices) else: # take argmax in non-differentiable way # comptute hard codevector distribution (one hot) codevector_idx = hidden_states.argmax(axis=-1) codevector_probs = jax.nn.one_hot(codevector_idx, hidden_states.shape[-1]) * 1.0 codevector_probs = codevector_probs.reshape(batch_size * sequence_length, self.num_groups, -1) perplexity = self._compute_perplexity(codevector_probs, mask_time_indices) codevector_probs = codevector_probs.reshape(batch_size * sequence_length, -1) # use probs to retrieve codevectors codevectors_per_group = jnp.expand_dims(codevector_probs, axis=-1) * self.codevectors codevectors = codevectors_per_group.reshape(batch_size * sequence_length, self.num_groups, self.num_vars, -1) codevectors = codevectors.sum(-2).reshape(batch_size, sequence_length, -1) return codevectors, perplexity class FlaxWav2Vec2Adapter(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): # hidden_states require down-projection if feature dims don't match if self.config.output_hidden_size != self.config.hidden_size: self.proj = nn.Dense( self.config.output_hidden_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.proj_layer_norm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype) else: self.proj = self.proj_layer_norm = None self.layers = FlaxWav2Vec2AdapterLayersCollection(self.config, dtype=self.dtype) def __call__(self, hidden_states, deterministic=True): # down-project hidden_states if required if self.proj is not None and self.proj_layer_norm is not None: hidden_states = self.proj(hidden_states) hidden_states = self.proj_layer_norm(hidden_states) hidden_states = self.layers(hidden_states) return hidden_states class FlaxWav2Vec2AdapterLayer(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.conv = nn.Conv( features=2 * self.config.output_hidden_size, kernel_size=(self.config.adapter_kernel_size,), strides=(self.config.adapter_stride,), padding=((1, 1),), kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__(self, hidden_states): hidden_states = self.conv(hidden_states) hidden_states = nn.glu(hidden_states, axis=2) return hidden_states class FlaxWav2Vec2AdapterLayersCollection(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.layers = [ FlaxWav2Vec2AdapterLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_adapter_layers) ] def __call__(self, hidden_states): for conv_layer in self.layers: hidden_states = conv_layer(hidden_states) return hidden_states class FlaxWav2Vec2PreTrainedModel(FlaxPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Wav2Vec2Config base_model_prefix: str = "wav2vec2" main_input_name = "input_values" module_class: nn.Module = None def __init__( self, config: Wav2Vec2Config, input_shape: Tuple = (1, 1024), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_values = jnp.zeros(input_shape, dtype="i4") attention_mask = jnp.ones_like(input_values) params_rng, dropout_rng = jax.random.split(rng, 2) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init(rngs, input_values, attention_mask, return_dict=False)["params"] if params is not None: random_params = flatten_dict(unfreeze(random_params)) params = flatten_dict(unfreeze(params)) for missing_key in self._missing_keys: params[missing_key] = random_params[missing_key] self._missing_keys = set() return freeze(unflatten_dict(params)) else: return random_params @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, freeze_feature_encoder: bool = False, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_values.shape if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} return self.module.apply( inputs, jnp.array(input_values, dtype="f4"), jnp.array(attention_mask, dtype="i4"), mask_time_indices, not train, output_attentions, output_hidden_states, freeze_feature_encoder, return_dict, rngs=rngs, ) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None ): return self.module._get_feat_extract_output_lengths(input_lengths, add_adapter=add_adapter) class FlaxWav2Vec2Module(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.feature_extractor = FlaxWav2Vec2FeatureEncoder(self.config, dtype=self.dtype) self.feature_projection = FlaxWav2Vec2FeatureProjection(self.config, dtype=self.dtype) self.masked_spec_embed = self.param( "masked_spec_embed", jax.nn.initializers.uniform(), (self.config.hidden_size,) ) if self.config.do_stable_layer_norm: self.encoder = FlaxWav2Vec2StableLayerNormEncoder(self.config, dtype=self.dtype) else: raise NotImplementedError("``config.do_stable_layer_norm is False`` is currently not supported.") self.adapter = FlaxWav2Vec2Adapter(self.config, dtype=self.dtype) if self.config.add_adapter else None def __call__( self, input_values, attention_mask=None, mask_time_indices=None, deterministic=True, output_attentions=None, output_hidden_states=None, freeze_feature_encoder=False, return_dict=None, ): extract_features = self.feature_extractor(input_values, freeze_feature_encoder=freeze_feature_encoder) # make sure that no loss is computed on padded inputs if attention_mask is not None: # compute reduced attention_mask corresponding to feature vectors attention_mask = self._get_feature_vector_attention_mask( extract_features.shape[1], attention_mask, add_adapter=False ) hidden_states, extract_features = self.feature_projection(extract_features, deterministic=deterministic) if mask_time_indices is not None: # apply SpecAugment along time axis with given indices hidden_states = jnp.where( jnp.broadcast_to(mask_time_indices[:, :, None], hidden_states.shape), jnp.broadcast_to(self.masked_spec_embed[None, None, :], hidden_states.shape), hidden_states, ) encoder_outputs = self.encoder( hidden_states, attention_mask=attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = encoder_outputs[0] if self.adapter is not None: hidden_states = self.adapter(hidden_states) if not return_dict: return (hidden_states, extract_features) + encoder_outputs[1:] return FlaxWav2Vec2BaseModelOutput( last_hidden_state=hidden_states, extract_features=extract_features, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None ): """ Computes the output length of the convolutional layers """ add_adapter = self.config.add_adapter if add_adapter is None else add_adapter def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) if add_adapter: for _ in range(self.config.num_adapter_layers): input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) return input_lengths def _get_feature_vector_attention_mask( self, feature_vector_length: int, attention_mask: jnp.ndarray, add_adapter=None ): # Effectively attention_mask.sum(-1), but not inplace to be able to run # on inference mode. non_padded_lengths = attention_mask.cumsum(axis=-1)[:, -1] output_lengths = self._get_feat_extract_output_lengths(non_padded_lengths, add_adapter=add_adapter) batch_size = attention_mask.shape[0] attention_mask = jnp.zeros((batch_size, feature_vector_length), dtype=attention_mask.dtype) # these two operations makes sure that all values # before the output lengths indices are attended to attention_mask = attention_mask.at[jnp.arange(attention_mask.shape[0]), output_lengths - 1].set(1) attention_mask = jnp.flip(jnp.flip(attention_mask, -1).cumsum(-1), -1).astype("bool") return attention_mask @add_start_docstrings( "The bare Wav2Vec2 Model transformer outputting raw hidden-states without any specific head on top.", WAV_2_VEC_2_START_DOCSTRING, ) class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2Module FLAX_WAV2VEC2_MODEL_DOCSTRING = """ Returns: Example: ```python >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2Model >>> from datasets import load_dataset >>> import soundfile as sf >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-lv60") >>> model = FlaxWav2Vec2Model.from_pretrained("facebook/wav2vec2-large-lv60") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = processor( ... ds["speech"][0], sampling_rate=16_000, return_tensors="np" ... ).input_values # Batch size 1 >>> hidden_states = model(input_values).last_hidden_state ``` """ overwrite_call_docstring( FlaxWav2Vec2Model, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_MODEL_DOCSTRING, ) append_replace_return_docstrings( FlaxWav2Vec2Model, output_type=FlaxWav2Vec2BaseModelOutput, config_class=Wav2Vec2Config ) class FlaxWav2Vec2ForCTCModule(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) self.dropout = nn.Dropout(rate=self.config.final_dropout) self.lm_head = nn.Dense( self.config.vocab_size, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, deterministic=True, output_attentions=None, output_hidden_states=None, freeze_feature_encoder=False, return_dict=None, ): outputs = self.wav2vec2( input_values, attention_mask=attention_mask, mask_time_indices=mask_time_indices, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, freeze_feature_encoder=freeze_feature_encoder, return_dict=return_dict, ) hidden_states = outputs[0] hidden_states = self.dropout(hidden_states, deterministic=deterministic) logits = self.lm_head(hidden_states) if not return_dict: return (logits,) + outputs[2:] return FlaxCausalLMOutput(logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None, ): """ Computes the output length of the convolutional layers """ add_adapter = self.config.add_adapter if add_adapter is None else add_adapter def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) if add_adapter: for _ in range(self.config.num_adapter_layers): input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) return input_lengths @add_start_docstrings( "Wav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).", WAV_2_VEC_2_START_DOCSTRING, ) class FlaxWav2Vec2ForCTC(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2ForCTCModule FLAX_WAV2VEC2_FOR_CTC_DOCSTRING = """ Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import Wav2Vec2Processor, FlaxWav2Vec2ForCTC >>> from datasets import load_dataset >>> import soundfile as sf >>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60") >>> model = FlaxWav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = processor( ... ds["speech"][0], sampling_rate=16_000, return_tensors="np" ... ).input_values # Batch size 1 >>> logits = model(input_values).logits >>> predicted_ids = jnp.argmax(logits, axis=-1) >>> transcription = processor.decode(predicted_ids[0]) >>> # should give: "A MAN SAID TO THE UNIVERSE SIR I EXIST" ``` """ overwrite_call_docstring( FlaxWav2Vec2ForCTC, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_CTC_DOCSTRING, ) append_replace_return_docstrings(FlaxWav2Vec2ForCTC, output_type=FlaxCausalLMOutput, config_class=Wav2Vec2Config) class FlaxWav2Vec2ForPreTrainingModule(nn.Module): config: Wav2Vec2Config dtype: jnp.dtype = jnp.float32 def setup(self): self.wav2vec2 = FlaxWav2Vec2Module(self.config, dtype=self.dtype) self.dropout_features = nn.Dropout(self.config.feat_quantizer_dropout) self.quantizer = FlaxWav2Vec2GumbelVectorQuantizer(self.config, dtype=self.dtype) self.project_q = nn.Dense( self.config.proj_codevector_dim, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) self.project_hid = nn.Dense( self.config.proj_codevector_dim, kernel_init=jax.nn.initializers.normal(self.config.initializer_range), dtype=self.dtype, ) def __call__( self, input_values, attention_mask=None, mask_time_indices=None, gumbel_temperature: int = 1, deterministic: bool = True, output_attentions=None, output_hidden_states=None, freeze_feature_encoder=False, return_dict=None, ): r""" Returns: Example: ```python ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.wav2vec2( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, mask_time_indices=mask_time_indices, deterministic=deterministic, freeze_feature_encoder=freeze_feature_encoder, return_dict=return_dict, ) # project all transformed features (including masked) to final vq dim transformer_features = self.project_hid(outputs[0]) # quantize all (unmasked) extracted features and project to final vq dim extract_features = self.dropout_features(outputs[1], deterministic=deterministic) quantized_features, codevector_perplexity = self.quantizer( extract_features, mask_time_indices, deterministic=deterministic, temperature=gumbel_temperature ) quantized_features = self.project_q(quantized_features) if not return_dict: return (transformer_features, quantized_features, codevector_perplexity) + outputs[2:] return FlaxWav2Vec2ForPreTrainingOutput( projected_states=transformer_features, projected_quantized_states=quantized_features, codevector_perplexity=codevector_perplexity, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def _get_feat_extract_output_lengths( self, input_lengths: Union[jnp.ndarray, int], add_adapter: Optional[bool] = None ): """ Computes the output length of the convolutional layers """ add_adapter = self.config.add_adapter if add_adapter is None else add_adapter def _conv_out_length(input_length, kernel_size, stride): # 1D convolutional layer output length formula taken # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html return (input_length - kernel_size) // stride + 1 for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): input_lengths = _conv_out_length(input_lengths, kernel_size, stride) if add_adapter: for _ in range(self.config.num_adapter_layers): input_lengths = _conv_out_length(input_lengths, 1, self.config.adapter_stride) return input_lengths @add_start_docstrings("""Wav2Vec2 Model with a quantizer and `VQ` head on top.""", WAV_2_VEC_2_START_DOCSTRING) class FlaxWav2Vec2ForPreTraining(FlaxWav2Vec2PreTrainedModel): module_class = FlaxWav2Vec2ForPreTrainingModule @add_start_docstrings_to_model_forward(WAV_2_VEC_2_INPUTS_DOCSTRING) # overwrite since has `gumbel_temperature` input def __call__( self, input_values, attention_mask=None, mask_time_indices=None, gumbel_temperature: int = 1, params: dict = None, dropout_rng: jax.random.PRNGKey = None, gumbel_rng: jax.random.PRNGKey = None, train: bool = False, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, freeze_feature_encoder: bool = False, return_dict: Optional[bool] = None, ): output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.return_dict batch_size, sequence_length = input_values.shape if attention_mask is None: attention_mask = jnp.ones((batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng if gumbel_rng is not None: rngs["gumbel"] = gumbel_rng inputs = {"params": params or self.params} return self.module.apply( inputs, jnp.array(input_values, dtype="f4"), jnp.array(attention_mask, dtype="i4"), mask_time_indices, gumbel_temperature, not train, output_attentions, output_hidden_states, freeze_feature_encoder, return_dict, rngs=rngs, ) FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING = """ Returns: Example: ```python >>> import optax >>> import numpy as np >>> import jax.numpy as jnp >>> from transformers import Wav2Vec2FeatureExtractor, FlaxWav2Vec2ForPreTraining >>> from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices >>> from datasets import load_dataset >>> import soundfile as sf >>> feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-lv60") >>> model = FlaxWav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-large-lv60") >>> def map_to_array(batch): ... speech, _ = sf.read(batch["file"]) ... batch["speech"] = speech ... return batch >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> ds = ds.map(map_to_array) >>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values # Batch size 1 >>> # compute masked indices >>> batch_size, raw_sequence_length = input_values.shape >>> sequence_length = model._get_feat_extract_output_lengths(raw_sequence_length) >>> mask_time_indices = _compute_mask_indices((batch_size, sequence_length), mask_prob=0.2, mask_length=2) >>> outputs = model(input_values, mask_time_indices=mask_time_indices) >>> # compute cosine similarity between predicted (=projected_states) and target (=projected_quantized_states) >>> cosine_sim = optax.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states) >>> # show that cosine similarity is much higher than random >>> assert np.asarray(cosine_sim)[mask_time_indices].mean() > 0.5 ``` """ overwrite_call_docstring( FlaxWav2Vec2ForPreTraining, WAV_2_VEC_2_INPUTS_DOCSTRING + FLAX_WAV2VEC2_FOR_PRETRAINING_DOCSTRING, ) append_replace_return_docstrings( FlaxWav2Vec2ForPreTraining, output_type=FlaxWav2Vec2ForPreTrainingOutput, config_class=Wav2Vec2Config )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/xlm_prophetnet/__init__.py

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./examples/research_projects/distillation/scripts/extract_distilbert.py

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocessing script before training DistilBERT. Specific to BERT -> DistilBERT. """ import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": parser = argparse.ArgumentParser( description=( "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned" " Distillation" ) ) parser.add_argument("--model_type", default="bert", choices=["bert"]) parser.add_argument("--model_name", default="bert-base-uncased", type=str) parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str) parser.add_argument("--vocab_transform", action="store_true") args = parser.parse_args() if args.model_type == "bert": model = BertForMaskedLM.from_pretrained(args.model_name) prefix = "bert" else: raise ValueError('args.model_type should be "bert".') state_dict = model.state_dict() compressed_sd = {} for w in ["word_embeddings", "position_embeddings"]: compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] std_idx = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"] compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"] if args.vocab_transform: for w in ["weight", "bias"]: compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"] compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Preprocessing script before training DistilBERT. Specific to BERT -> DistilBERT. """ import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": parser = argparse.ArgumentParser( description=( "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned" " Distillation" ) ) parser.add_argument("--model_type", default="bert", choices=["bert"]) parser.add_argument("--model_name", default="bert-base-uncased", type=str) parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str) parser.add_argument("--vocab_transform", action="store_true") args = parser.parse_args() if args.model_type == "bert": model = BertForMaskedLM.from_pretrained(args.model_name) prefix = "bert" else: raise ValueError('args.model_type should be "bert".') state_dict = model.state_dict() compressed_sd = {} for w in ["word_embeddings", "position_embeddings"]: compressed_sd[f"distilbert.embeddings.{w}.weight"] = state_dict[f"{prefix}.embeddings.{w}.weight"] for w in ["weight", "bias"]: compressed_sd[f"distilbert.embeddings.LayerNorm.{w}"] = state_dict[f"{prefix}.embeddings.LayerNorm.{w}"] std_idx = 0 for teacher_idx in [0, 2, 4, 7, 9, 11]: for w in ["weight", "bias"]: compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.q_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.k_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.v_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.attention.out_lin.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.sa_layer_norm.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin1.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.ffn.lin2.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}" ] compressed_sd[f"distilbert.transformer.layer.{std_idx}.output_layer_norm.{w}"] = state_dict[ f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}" ] std_idx += 1 compressed_sd["vocab_projector.weight"] = state_dict["cls.predictions.decoder.weight"] compressed_sd["vocab_projector.bias"] = state_dict["cls.predictions.bias"] if args.vocab_transform: for w in ["weight", "bias"]: compressed_sd[f"vocab_transform.{w}"] = state_dict[f"cls.predictions.transform.dense.{w}"] compressed_sd[f"vocab_layer_norm.{w}"] = state_dict[f"cls.predictions.transform.LayerNorm.{w}"] print(f"N layers selected for distillation: {std_idx}") print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}") print(f"Save transferred checkpoint to {args.dump_checkpoint}.") torch.save(compressed_sd, args.dump_checkpoint)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/flava/__init__.py

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/opt/convert_opt_original_pytorch_checkpoint_to_pytorch.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert OPT checkpoint.""" import argparse from pathlib import Path import torch from transformers import OPTConfig, OPTModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def load_checkpoint(checkpoint_path): """Checkpoint path should end in model.pt""" sd = torch.load(checkpoint_path, map_location="cpu") if "model" in sd.keys(): sd = torch.load(checkpoint_path, map_location="cpu")["model"] # pop unnecessary weights keys_to_delete = [ "decoder.version", "decoder.output_projection.weight", ] for key in keys_to_delete: if key in sd: sd.pop(key) keys_to_rename = { "decoder.project_in_dim.weight": "decoder.project_in.weight", "decoder.project_out_dim.weight": "decoder.project_out.weight", "decoder.layer_norm.weight": "decoder.final_layer_norm.weight", "decoder.layer_norm.bias": "decoder.final_layer_norm.bias", } for old_key, new_key in keys_to_rename.items(): if old_key in sd: sd[new_key] = sd.pop(old_key) return sd @torch.no_grad() def convert_opt_checkpoint(checkpoint_path, pytorch_dump_folder_path, config=None): """ Copy/paste/tweak model's weights to our BERT structure. """ state_dict = load_checkpoint(checkpoint_path) if config is not None: config = OPTConfig.from_pretrained(config) else: config = OPTConfig() model = OPTModel(config).half().eval() model.load_state_dict(state_dict) # Check results Path(pytorch_dump_folder_path).mkdir(exist_ok=True) model.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fairseq_path", type=str, help=( "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:" " https://huggingface.co/models?other=opt_metasq" ), ) parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.") args = parser.parse_args() convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert OPT checkpoint.""" import argparse from pathlib import Path import torch from transformers import OPTConfig, OPTModel from transformers.utils import logging logging.set_verbosity_info() logger = logging.get_logger(__name__) def load_checkpoint(checkpoint_path): """Checkpoint path should end in model.pt""" sd = torch.load(checkpoint_path, map_location="cpu") if "model" in sd.keys(): sd = torch.load(checkpoint_path, map_location="cpu")["model"] # pop unnecessary weights keys_to_delete = [ "decoder.version", "decoder.output_projection.weight", ] for key in keys_to_delete: if key in sd: sd.pop(key) keys_to_rename = { "decoder.project_in_dim.weight": "decoder.project_in.weight", "decoder.project_out_dim.weight": "decoder.project_out.weight", "decoder.layer_norm.weight": "decoder.final_layer_norm.weight", "decoder.layer_norm.bias": "decoder.final_layer_norm.bias", } for old_key, new_key in keys_to_rename.items(): if old_key in sd: sd[new_key] = sd.pop(old_key) return sd @torch.no_grad() def convert_opt_checkpoint(checkpoint_path, pytorch_dump_folder_path, config=None): """ Copy/paste/tweak model's weights to our BERT structure. """ state_dict = load_checkpoint(checkpoint_path) if config is not None: config = OPTConfig.from_pretrained(config) else: config = OPTConfig() model = OPTModel(config).half().eval() model.load_state_dict(state_dict) # Check results Path(pytorch_dump_folder_path).mkdir(exist_ok=True) model.save_pretrained(pytorch_dump_folder_path) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fairseq_path", type=str, help=( "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:" " https://huggingface.co/models?other=opt_metasq" ), ) parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.") args = parser.parse_args() convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/blenderbot/test_modeling_tf_blenderbot.py

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available from transformers.testing_utils import require_tf, require_tokenizers, slow, tooslow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import TFAutoModelForSeq2SeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel @require_tf class TFBlenderbotModelTester: config_cls = BlenderbotConfig config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) input_ids = tf.concat([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, **self.config_updates, ) inputs_dict = prepare_blenderbot_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFBlenderbotModel(config=config).get_decoder() input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] head_mask = inputs_dict["head_mask"] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def prepare_blenderbot_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8) if decoder_attention_mask is None: decoder_attention_mask = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8), ], axis=-1, ) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFBlenderbotModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else () all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_onnx = False def setUp(self): self.model_tester = TFBlenderbotModelTester(self) self.config_tester = ConfigTester(self, config_class=BlenderbotConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @tooslow def test_saved_model_creation(self): pass @require_tokenizers @require_tf class TFBlenderbot400MIntegrationTests(unittest.TestCase): src_text = ["My friends are cool but they eat too many carbs."] model_name = "facebook/blenderbot-400M-distill" @cached_property def tokenizer(self): return BlenderbotTokenizer.from_pretrained(self.model_name) @cached_property def model(self): model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name) return model @slow def test_generation_from_long_input(self): model_inputs = self.tokenizer(self.src_text, return_tensors="tf") generated_ids = self.model.generate( model_inputs.input_ids, ) generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)[0] assert ( generated_words == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?" )

# coding=utf-8 # Copyright 2021 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available from transformers.testing_utils import require_tf, require_tokenizers, slow, tooslow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import TFAutoModelForSeq2SeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel @require_tf class TFBlenderbotModelTester: config_cls = BlenderbotConfig config_updates = {} hidden_act = "gelu" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size) eos_tensor = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size), 1) input_ids = tf.concat([input_ids, eos_tensor], axis=1) decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.config_cls( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_ids=[2], bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.pad_token_id, **self.config_updates, ) inputs_dict = prepare_blenderbot_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFBlenderbotModel(config=config).get_decoder() input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] head_mask = inputs_dict["head_mask"] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = tf.cast(ids_tensor((self.batch_size, 3), 2), tf.int8) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([attention_mask, next_attn_mask], axis=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)[0] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[0] self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx] output_from_past_slice = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(output_from_past_slice, output_from_no_past_slice, rtol=1e-3) def prepare_blenderbot_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8) if decoder_attention_mask is None: decoder_attention_mask = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape, dtype=tf.int8), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:], config.pad_token_id), tf.int8), ], axis=-1, ) if head_mask is None: head_mask = tf.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = tf.ones((config.decoder_layers, config.decoder_attention_heads)) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class TFBlenderbotModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else () all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else () is_encoder_decoder = True test_pruning = False test_onnx = False def setUp(self): self.model_tester = TFBlenderbotModelTester(self) self.config_tester = ConfigTester(self, config_class=BlenderbotConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*config_and_inputs) def test_model_common_attributes(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer) if model_class in self.all_generative_model_classes: x = model.get_output_embeddings() assert isinstance(x, tf.keras.layers.Layer) name = model.get_bias() assert isinstance(name, dict) for k, v in name.items(): assert isinstance(v, tf.Variable) else: x = model.get_output_embeddings() assert x is None name = model.get_bias() assert name is None @tooslow def test_saved_model_creation(self): pass @require_tokenizers @require_tf class TFBlenderbot400MIntegrationTests(unittest.TestCase): src_text = ["My friends are cool but they eat too many carbs."] model_name = "facebook/blenderbot-400M-distill" @cached_property def tokenizer(self): return BlenderbotTokenizer.from_pretrained(self.model_name) @cached_property def model(self): model = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name) return model @slow def test_generation_from_long_input(self): model_inputs = self.tokenizer(self.src_text, return_tensors="tf") generated_ids = self.model.generate( model_inputs.input_ids, ) generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True)[0] assert ( generated_words == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?" )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/flava/modeling_flava.py

# coding=utf-8 # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch FLAVA model.""" import collections import math from collections import OrderedDict from dataclasses import dataclass from typing import Any, Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from transformers.utils.doc import add_code_sample_docstrings from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_flava import ( FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/flava-full" # Codebook docstring _CHECKPOINT_FOR_CODEBOOK_DOC = "facebook/flava-image-codebook" _FEAT_EXTRACTOR_FOR_DOC = "FlavaFeatureExtractor" _CONFIG_CLASS_FOR_IMAGE_MODEL_DOC = "FlavaImageConfig" _CONFIG_CLASS_FOR_TEXT_MODEL_DOC = "FlavaTextConfig" _CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC = "FlavaMultimodalConfig" _TOKENIZER_FOR_DOC = "BertTokenizer" _EXPECTED_IMAGE_OUTPUT_SHAPE = [1, 197, 768] FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/flava-full", # See all flava models at https://huggingface.co/models?filter=flava ] FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST = ["facebook/flava-image-codebook"] LOGIT_SCALE_CLAMP_MIN = 0 LOGIT_SCALE_CLAMP_MAX = 4.6052 FlavaPossibleConfigs = Union[FlavaTextConfig, FlavaImageConfig, FlavaMultimodalConfig] @dataclass class FlavaModelOutput(ModelOutput): """ Output from FlavaModel containing embeddings and outputs from individual encoders. Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. """ image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_output", "image_output", "multimodal_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass class FlavaLosses(ModelOutput): """Class representing pretraining losses from FLAVA model Args: mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.: Masked Image Modeling loss as used in BeIT calculated only for unimodal image data. mlm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels` and `input_ids_masked` are present, `pixel_values` is absent and `mlm_weight` > 0.: Masked Language Modeling loss as used in BERT calculated only for unimodal text data. itm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `itm_labels`, `input_ids_masked`, `pixel_values` are present and `itm_weight` > 0.: Image Text Matching (ITM) loss calculated for paired image-text data. Note that ITM loss is calculated on masked pairs in FLAVA. global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.: Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text data. This is calculated on unmasked images and texts. mmm_image (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_image_weight` > 0.: Masked Multimodal Modeling loss's image component calculated on paired image-text data. mmm_text (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_text_weight` > 0.: Masked Multimodal Modeling loss's text component calculated on paired image-text data. """ mim: Optional[torch.FloatTensor] = None mlm: Optional[torch.FloatTensor] = None itm: Optional[torch.FloatTensor] = None global_contrastive: Optional[torch.FloatTensor] = None mmm_image: Optional[torch.FloatTensor] = None mmm_text: Optional[torch.FloatTensor] = None def all_none(self) -> bool: all_none = True for v in self.values(): if v is not None: all_none = False break return all_none @dataclass class FlavaForPreTrainingOutput(ModelOutput): """ Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders. Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True): Total loss calculated for this model. loss_info (`FlavaLosses`): Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on the keys. image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. image_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. text_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present): The output of the [`FlavaTextModel`]. multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_masked_output (`BaseModelOutputWithPooling`, returned when `input_ids_masked` and `pixel_values` are present): The output of the [`FlavaMultimodalModel`]. mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not): The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mlm_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not): The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. itm_logits (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present): The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA. mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mmm_text_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total_masked_seq_length, text_vocab_size)`), *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM text multimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's `image_projection` and `text_projection` layers respectively. This represents the image-text similarity scores. This is calculated on unmasked images and texts. contrastive_logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA's `text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and texts. """ loss: Optional[torch.FloatTensor] = None loss_info: FlavaLosses = None image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None image_masked_embeddings: Optional[torch.FloatTensor] = None image_masked_output: Optional[BaseModelOutputWithPooling] = None text_masked_embeddings: Optional[torch.FloatTensor] = None text_masked_output: Optional[BaseModelOutputWithPooling] = None multimodal_masked_embeddings: Optional[torch.FloatTensor] = None multimodal_masked_output: Optional[BaseModelOutputWithPooling] = None mim_logits: Optional[torch.FloatTensor] = None mlm_logits: Optional[torch.FloatTensor] = None itm_logits: Optional[torch.FloatTensor] = None contrastive_logits_per_image: Optional[torch.FloatTensor] = None contrastive_logits_per_text: Optional[torch.FloatTensor] = None mmm_image_logits: Optional[torch.FloatTensor] = None mmm_text_logits: Optional[torch.FloatTensor] = None def to_tuple(self) -> Tuple[Any]: transformer_outputs = [ "text_output", "image_output", "multimodal_output", "text_masked_output", "image_masked_output", "multimodal_masked_output", ] return tuple(self[k] if k not in transformer_outputs else getattr(self, k).to_tuple() for k in self.keys()) # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class FlavaImageEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None: super().__init__() use_mask_token = use_mask_token or config.mask_token self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = PatchEmbeddings( image_size=config.image_size, patch_size=config.patch_size, num_channels=config.num_channels, embed_dim=config.hidden_size, ) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/image_transformer.py#L174 """ npatch = embeddings.shape[1] - 1 num_pos = self.position_embeddings.shape[1] - 1 if npatch == num_pos and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] num_h_patches = height // self.config.patch_size num_w_patches = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1 patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, int(math.sqrt(num_pos)), int(math.sqrt(num_pos)), dim).permute(0, 3, 1, 2), scale_factor=(num_h_patches / math.sqrt(num_pos), num_w_patches / math.sqrt(num_pos)), mode="bicubic", align_corners=False, ) if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]: raise ValueError( f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the " f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})" ) patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) batch_size, seq_len, _ = embeddings.size() if bool_masked_pos is not None: mask_tokens = self.mask_token.expand(batch_size, seq_len, -1) # B X H X W = B X HW if bool_masked_pos.dim() == 3: bool_masked_pos = bool_masked_pos.view(bool_masked_pos.size(0), -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class PatchEmbeddings(nn.Module): """ Image to Patch Embedding. """ def __init__( self, image_size: int = 224, patch_size: Union[int, Tuple[int, int]] = 16, num_channels: int = 3, embed_dim: int = 768, ): super().__init__() if not isinstance(image_size, collections.abc.Iterable): image_size = (image_size, image_size) if not isinstance(patch_size, collections.abc.Iterable): patch_size = (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) x = self.projection(pixel_values).flatten(2).transpose(1, 2) return x class FlavaTextEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, ): input_shape = input_ids.size() seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class FlavaSelfAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class FlavaSelfOutput(nn.Module): """ The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class FlavaAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.attention = FlavaSelfAttention(config) self.output = FlavaSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention( hidden_states, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class FlavaIntermediate(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act # Copied from transformers.models.vit.modeling_vit.ViTIntermediate.forward def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class FlavaOutput(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) # Copied from transformers.models.vit.modeling_vit.ViTOutput.forward def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class FlavaLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = FlavaAttention(config) self.intermediate = FlavaIntermediate(config) self.output = FlavaOutput(config) # TODO: Check fp32 layer norm possiblity self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class FlavaEncoder(nn.Module): def __init__(self, config: FlavaConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions ) class FlavaPooler(nn.Module): def __init__(self, config: FlavaPossibleConfigs): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output FLAVA_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`{config}`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ FLAVA_INPUTS_DOCSTRING_COMMON = r""" attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ FLAVA_IMAGE_INPUTS_DOCSTRING_BASE = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`FlavaFeatureExtractor`]. See [`FlavaFeatureExtractor.__call__`] for details. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. """ FLAVA_IMAGE_INPUTS_DOCSTRING = FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_TEXT_INPUTS_DOCSTRING_BASE = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) """ FLAVA_TEXT_INPUTS_DOCSTRING = FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_MULTIMODAL_INPUTS_DOCSTRING = ( r""" Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`): The concatenated hidden states of unimodal encoders. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_MODEL_INPUTS_DOCSTRING_BASE = r""" Args: skip_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used. """ FLAVA_MODEL_INPUTS_DOCSTRING = ( FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON + FLAVA_MODEL_INPUTS_DOCSTRING_BASE ) FLAVA_PRETRAINING_INPUTS_DOCSTRING = ( r""" Args: input_ids_masked (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task to be used with MLM. Indices can be obtained using [`BertTokenizer`] along with [`DataCollatorForMaskedLanguageModeling`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) """ + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + r""" image_attention_mask (`torch.FloatTensor` of shape `({1})`, *optional*): Mask to avoid performing attention on padding token indices specifically for images. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) skip_unmasked_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn't need unmasked multimodal embeddings or outputs as of now. mlm_labels (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*): Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction). Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., text_config.vocab_size - 1]`. mim_labels (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*): Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ..., image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are generated automatically using the image codebook assigned to the model. By default, it uses [`FlavaImageCodebook`]. See [`FlavaImageCodebook`] to understand how to generate mim_labels. itm_labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*): Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match. The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well. return_loss (`bool`, *optional*, default to None): Whether to return calculated loss or not. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_PRETRAINING_START_DOCSTRING_EXTRA = r""" Parameters: image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will be initialized using the image_codebook_config defined in the config first as the first parameter. """ class FlavaPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FlavaConfig base_model_prefix = "flava" supports_gradient_checkpointing = True def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module: FlavaEncoder, value: bool = False) -> None: if isinstance(module, FlavaEncoder): module.gradient_checkpointing = value @add_start_docstrings( "The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaImageConfig"), ) class FlavaImageModel(FlavaPreTrainedModel): config_class = FlavaImageConfig # This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.image_model" main_input_name = "pixel_values" def __init__(self, config: FlavaImageConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaImageEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> nn.Module: return self.embeddings.patch_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.patch_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC, modality="vision", expected_output=_EXPECTED_IMAGE_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaTextConfig"), ) class FlavaTextModel(FlavaPreTrainedModel): config_class = FlavaTextConfig # This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.text_model" def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaTextEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> PatchEmbeddings: return self.embeddings.word_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_TEXT_MODEL_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify input_ids") input_shape = input_ids.size() if attention_mask is None: attention_mask = torch.ones(input_shape, device=input_ids.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, input_shape, input_ids.device ) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaMultimodalConfig"), ) class FlavaMultimodalModel(FlavaPreTrainedModel): config_class = FlavaMultimodalConfig # This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.multimodal_model" main_input_name = "hidden_states" def __init__(self, config: FlavaMultimodalConfig, add_pooling_layer=True): super().__init__(config) self.config = config self.use_cls_token = self.config.use_cls_token if self.use_cls_token: self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward( FLAVA_MULTIMODAL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC, ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, seq_length, _ = hidden_states.size() if self.use_cls_token: cls_tokens = self.cls_token.expand(batch_size, -1, -1) hidden_states = torch.cat((cls_tokens, hidden_states), dim=1) seq_length += 1 if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, (batch_size, seq_length), hidden_states.device ) encoder_outputs = self.encoder( hidden_states, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaConfig"), ) class FlavaModel(FlavaPreTrainedModel): config_class = FlavaConfig def __init__(self, config: FlavaConfig): super().__init__(config) if not isinstance(config.text_config, FlavaTextConfig): raise ValueError( "config.text_config is expected to be of type FlavaTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.image_config, FlavaImageConfig): raise ValueError( "config.image_config is expected to be of type FlavaImageConfig but is of type" f" {type(config.image_config)}." ) if not isinstance(config.multimodal_config, FlavaMultimodalConfig): raise ValueError( "config.multimodal_config is expected to be of type FlavaMultimodalConfig but " + f"is of type {type(config.multimodal_config)}." ) text_config = config.text_config image_config = config.image_config multimodal_config = config.multimodal_config self.projection_dim = config.projection_dim self.text_hidden_size = text_config.hidden_size self.image_hidden_size = image_config.hidden_size self.mm_hidden_size = multimodal_config.hidden_size self.text_model = FlavaTextModel(text_config) self.image_model = FlavaImageModel(image_config) self.multimodal_model = FlavaMultimodalModel(multimodal_config) self.image_projection = nn.Linear(self.image_hidden_size, self.projection_dim) self.text_projection = nn.Linear(self.text_hidden_size, self.projection_dim) self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value) self.image_to_mm_projection = nn.Linear(self.image_hidden_size, self.mm_hidden_size) self.text_to_mm_projection = nn.Linear(self.text_hidden_size, self.mm_hidden_size) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlavaTextModel`]. Examples: ```python >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt" ... ) >>> text_features = model.get_text_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[0] # last_hidden_state text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) def get_image_features( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlavaImageModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) image_outputs = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) pooled_output = image_outputs[0] # last_hidden_state image_features = self.image_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward( FLAVA_MODEL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @replace_return_docstrings(output_type=FlavaModelOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_multimodal_encoder: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, ) -> Union[Tuple, FlavaOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True) >>> outputs = model(**inputs) >>> logits_per_image = outputs.contrastive_logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ``` """ return_dict = return_dict if return_dict is not None else self.config.return_dict if not output_hidden_states: raise ValueError("FLAVA model requires hidden states to work. Please set `output_hidden_states=True`") image_embeddings = None image_states = None image_mm_projection = None image_output = None if pixel_values is not None: image_output = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=image_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeddings, image_states = image_output[0], image_output[2] # Note that these states don't use final layernorm in the transformer model image_mm_projection = self.image_to_mm_projection(image_states[-1]) text_embeddings = None text_states = None text_mm_projection = None text_output = None if input_ids is not None: text_output = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_embeddings, text_states = text_output[0], text_output[2] # Note that these states don't use final layernorm in the transformer model text_mm_projection = self.text_to_mm_projection(text_states[-1]) multimodal_embeddings = None multimodal_output = None if image_mm_projection is not None and text_mm_projection is not None and not skip_multimodal_encoder: multimodal_input = torch.cat([image_mm_projection, text_mm_projection], dim=1) multimodal_output = self.multimodal_model(multimodal_input, return_dict=return_dict) multimodal_embeddings = multimodal_output[0] if not return_dict: return ( image_embeddings, image_output, text_embeddings, text_output, multimodal_embeddings, multimodal_output, ) return FlavaModelOutput( image_embeddings=image_embeddings, image_output=image_output, text_embeddings=text_embeddings, text_output=text_output, multimodal_embeddings=multimodal_embeddings, multimodal_output=multimodal_output, ) class FlavaImageCodebookResPath(nn.Module): def __init__(self, in_size: int, out_size: int, **kwargs): super().__init__() hid_size = out_size // 4 path = OrderedDict() path["relu_1"] = nn.ReLU() path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1) path["relu_2"] = nn.ReLU() path["conv_2"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_3"] = nn.ReLU() path["conv_3"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_4"] = nn.ReLU() path["conv_4"] = nn.Conv2d(hid_size, out_size, kernel_size=1, padding=0) self.path = nn.Sequential(path) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.path(x) class FlavaImageCodebookBlock(nn.Module): def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs): super().__init__() self.post_gain = 1 / (num_layers**2) if in_size != out_size: self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0) else: self.id_path = nn.Identity() self.res_path = FlavaImageCodebookResPath(in_size, out_size) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.id_path(x) + self.post_gain * self.res_path(x) class FlavaImageCodebookLayerGroup(nn.Module): def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True): super().__init__() blocks = OrderedDict() for i in range(num_blocks): if i == 0: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(in_size, out_size, num_layers) else: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(out_size, out_size, num_layers) if use_pool: blocks["pool"] = nn.MaxPool2d(kernel_size=2) self.group = nn.Sequential(blocks) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.group(x) # Inspired by DALLE Encoder in https://github.com/openai/DALL-E/blob/5be4b236bc3ade6943662354117a0e83752cc322/dall_e/encoder.py#L42 @add_start_docstrings( """ The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use `get_codebook_indices` to get image tokens for an image. """, FLAVA_START_DOCSTRING.format(config="FlavaImageCodebookConfig"), ) class FlavaImageCodebook(FlavaPreTrainedModel): base_model_prefix = "" config_class = FlavaImageCodebookConfig main_input_name = "pixel_values" supports_gradient_checkpointing = False def __init__( self, config: FlavaImageCodebookConfig, **kwargs: Any, ): super().__init__(config) self.config = config self.num_groups = config.num_groups self.input_channels = config.input_channels self.num_blocks_per_group = config.num_blocks_per_group self.hidden_size = config.hidden_size self.vocab_size = config.vocab_size num_layers = self.num_groups * self.num_blocks_per_group output_blocks = OrderedDict() output_blocks["relu"] = nn.ReLU() output_blocks["conv"] = nn.Conv2d(8 * self.hidden_size, self.vocab_size, kernel_size=1, padding=0) blocks = OrderedDict() blocks["input"] = nn.Conv2d(self.input_channels, 1 * self.hidden_size, kernel_size=7, padding=3) blocks["group_1"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 1 * self.hidden_size ) blocks["group_2"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 2 * self.hidden_size ) blocks["group_3"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 2 * self.hidden_size, 4 * self.hidden_size ) blocks["group_4"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 4 * self.hidden_size, 8 * self.hidden_size, use_pool=False ) blocks["output"] = nn.Sequential(output_blocks) self.blocks = nn.Sequential(blocks) self.post_init() if self.config.freeze: for param in self.parameters(): param.requires_grad = False def get_codebook_indices(self, pixel_values: torch.Tensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model.get_codebook_indices(**inputs) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) z_logits = self.blocks(pixel_values) return torch.argmax(z_logits, axis=1) def get_codebook_probs(self, pixel_values: torch.Tensor) -> torch.Tensor: z_logits = self.blocks(pixel_values) return nn.Softmax(dim=1)(z_logits) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model(**inputs) >>> print(outputs.shape) (1, 196) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) if len(pixel_values.shape) != 4: raise ValueError(f"input shape {pixel_values.shape} is not 4d") if pixel_values.shape[1] != self.input_channels: raise ValueError(f"input has {pixel_values.shape[1]} channels but model built for {self.input_channels}") return self.blocks(pixel_values) class FlavaPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class FlavaMaskedPredictionHead(nn.Module): def __init__(self, config, weight=None): super().__init__() self.config = config self.transform = FlavaPredictionHeadTransform(config) self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) if weight is not None: self.decoder.weight = weight # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, x): x = self.transform(x) x = self.decoder(x) return x class FlavaITMHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.pooler = FlavaPooler(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, x): x = self.pooler(x) x = self.seq_relationship(x) return x class FlavaGlobalContrastiveHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.global_backprop_contrastive = config.global_backprop_contrastive def forward(self, image_embeddings, text_embeddings, logit_scale): temperature = torch.exp(logit_scale) if not torch.distributed.is_available() or not torch.distributed.is_initialized(): labels = torch.arange(image_embeddings.size(0), device=image_embeddings.device) image_embeddings_all = [image_embeddings] text_embeddings_all = [text_embeddings] else: local_batch_size = image_embeddings.size(0) world_size = torch.distributed.get_world_size() if self.global_backprop_contrastive: image_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(image_embeddings) text_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(text_embeddings) else: image_embeddings_all = [torch.zeros_like(text_embeddings) for _ in range(world_size)] text_embeddings_all = [torch.zeros_like(image_embeddings) for _ in range(world_size)] torch.distributed.all_gather(image_embeddings_all, image_embeddings) torch.distributed.all_gather(text_embeddings_all, text_embeddings) labels = local_batch_size * torch.distributed.get_rank() + torch.arange( local_batch_size, device=image_embeddings.device ) image_embeddings_all = torch.cat(image_embeddings_all) text_embeddings_all = torch.cat(text_embeddings_all) logits_per_image = torch.matmul(image_embeddings, text_embeddings_all.transpose(0, 1)) * temperature logits_per_text = torch.matmul(text_embeddings, image_embeddings_all.transpose(0, 1)) * temperature return logits_per_image, logits_per_text, labels @add_start_docstrings( """ The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs. """, FLAVA_START_DOCSTRING.format(config="FlavaConfig") + FLAVA_PRETRAINING_START_DOCSTRING_EXTRA, ) class FlavaForPreTraining(FlavaPreTrainedModel): # Those are linked to xxx.bias _keys_to_ignore_on_load_missing = [ "mmm_text_head.decoder.bias", "mmm_image_head.decoder.bias", "mlm_head.decoder.bias", "mim_head.decoder.bias", ] def __init__(self, config: FlavaConfig, image_codebook: Optional[nn.Module] = None): super().__init__(config) self.flava = FlavaModel(config) self.image_codebook = image_codebook if self.image_codebook is None and config.init_codebook: self.image_codebook = FlavaImageCodebook(config.image_codebook_config) # Levarage text and image encoder configs to create the masked # head since it has the right vocab self.mim_head = FlavaMaskedPredictionHead(config.image_config) self.mlm_head = FlavaMaskedPredictionHead(config.text_config) self.itm_head = FlavaITMHead(config) self.mmm_image_head = FlavaMaskedPredictionHead(config.image_config) self.mmm_text_head = FlavaMaskedPredictionHead(config.text_config) self.global_contrastive_head = FlavaGlobalContrastiveHead(config) self.image_vocab_size = config.image_config.vocab_size self.text_vocab_size = config.text_config.vocab_size self.mlm_weight = config.mlm_weight self.mim_weight = config.mim_weight self.global_contrastive_weight = config.global_contrastive_weight self.ce_ignore_index = config.ce_ignore_index self.itm_weight = config.itm_weight self.mmm_image_weight = config.mmm_image_weight self.mmm_text_weight = config.mmm_text_weight self.skip_unmasked_multimodal_encoder = config.skip_unmasked_multimodal_encoder self.post_init() def _resize_to_2d(self, x: torch.Tensor): if x.dim() > 2: x = x.view(x.size(0), -1) return x @add_start_docstrings_to_model_forward( FLAVA_PRETRAINING_INPUTS_DOCSTRING.format("batch_size, text_seq_len", "batch_size, image_num_patches") ) @replace_return_docstrings(output_type=FlavaForPreTrainingOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, input_ids_masked: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, codebook_pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_unmasked_multimodal_encoder: bool = None, mlm_labels: Optional[torch.Tensor] = None, mim_labels: Optional[torch.Tensor] = None, itm_labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, return_loss: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], FlavaForPreTrainingOutput]: """ Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaForPreTraining, FlavaProcessor >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> text = ["a photo of a cat"] >>> inputs = processor( ... images=[image], ... text=text, ... return_masks=True, ... return_codebook_pixels=True, ... padding=True, ... max_length=77, ... return_tensors="pt", ... ) >>> output = model(**inputs) ``` Return: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict return_loss = return_loss if return_loss is not None else self.config.return_loss skip_unmasked_multimodal_encoder = ( skip_unmasked_multimodal_encoder if skip_unmasked_multimodal_encoder is not None else self.skip_unmasked_multimodal_encoder ) if input_ids_masked is None and input_ids is not None: logger.warning( "`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to" " `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if" " you are doing inference on unmasked text..." ) input_ids_masked = input_ids flava_output = self.flava( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, image_attention_mask=image_attention_mask, # Don't need unmasked multimodal embedding for anything so skip it # NOTE: ITM uses masked version skip_multimodal_encoder=skip_unmasked_multimodal_encoder, output_attentions=output_attentions, output_hidden_states=output_hidden_states, # Pass true to have deterministic outputs return_dict=True, ) flava_masked_output = self.flava( input_ids=input_ids_masked, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, image_attention_mask=image_attention_mask, bool_masked_pos=bool_masked_pos, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, ) pos_mask = None image_embeddings = flava_output.image_embeddings text_embeddings = flava_output.text_embeddings image_masked_embeddings = flava_masked_output.image_embeddings text_masked_embeddings = flava_masked_output.text_embeddings multimodal_masked_embeddings = flava_masked_output.multimodal_embeddings total_loss = mim_loss = mlm_loss = mmm_text_loss = mmm_image_loss = gc_loss = itm_loss = None mim_logits = mlm_logits = mmm_text_logits = mmm_image_logits = None itm_logits = logits_per_image = logits_per_text = None # Calculate mim_labels if necessary from the image_codebook if image_masked_embeddings is not None or multimodal_masked_embeddings is not None: if mim_labels is None and return_loss: if self.image_codebook is None: raise RuntimeError( "`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` " " have been passed. Reinstantiate the model with `init_codebook` set to True or " "pass in your custom `mim_labels`" ) if codebook_pixel_values is None: raise ValueError( "`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. " "Call `FlavaProcessor` with `return_codebook_pixels` set to True" ) mim_labels = self.image_codebook.get_codebook_indices(codebook_pixel_values) # Unimodal MIM Loss # If multimodal embeddings are present, we will calculate MMM loss if self.mim_weight > 0 and image_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_image = image_masked_embeddings if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index sequence_for_image = sequence_for_image[:, -mim_labels.size(1) :, :] masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mim_logits = self.mim_head(sequence_for_image) if return_loss: mim_loss = nn.functional.cross_entropy( mim_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mim_loss *= self.mim_weight else: mim_logits = self.mim_head(sequence_for_image) # Unimodal MLM Loss if self.mlm_weight > 0 and text_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_text = text_masked_embeddings if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) sequence_for_text = sequence_for_text[:, -mlm_labels.size(1) :, :] masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mlm_logits = self.mlm_head(sequence_for_text) if return_loss: mlm_loss = nn.functional.cross_entropy( mlm_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mlm_loss *= self.mlm_weight else: mlm_logits = self.mlm_head(sequence_for_text) # ITM Loss if self.itm_weight > 0 and multimodal_masked_embeddings is not None: itm_logits = self.itm_head(multimodal_masked_embeddings) if itm_labels is not None: pos_pairs = itm_labels.ne(0) pos_mask = torch.where(pos_pairs.any(), pos_pairs, pos_pairs.new([True])) if return_loss: itm_loss = nn.functional.cross_entropy(itm_logits, itm_labels) itm_loss *= self.itm_weight if multimodal_masked_embeddings is not None: multimodal_masked_embeddings = multimodal_masked_embeddings[pos_mask] if mlm_labels is not None: mlm_labels = mlm_labels[pos_mask] if mim_labels is not None: mim_labels = mim_labels[pos_mask] # MMM Image Loss if multimodal_masked_embeddings is not None and self.mmm_image_weight > 0: sequence_for_image = multimodal_masked_embeddings end_index = image_masked_embeddings.size(1) - 1 sequence_for_image = sequence_for_image[:, 2 : 2 + end_index, :] if pos_mask is not None: sequence_for_image = sequence_for_image[pos_mask] if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mmm_image_logits = self.mmm_image_head(sequence_for_image) if return_loss: mmm_image_loss = nn.functional.cross_entropy( mmm_image_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mmm_image_loss *= self.mmm_image_weight else: mmm_image_logits = self.mmm_image_head(sequence_for_image) # MMM Text Loss if multimodal_masked_embeddings is not None and self.mmm_text_weight > 0: sequence_for_text = multimodal_masked_embeddings sequence_for_text = sequence_for_text[:, -text_masked_embeddings.size(1) :, :] if pos_mask is not None: sequence_for_text = sequence_for_text[pos_mask] if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mmm_text_logits = self.mmm_text_head(sequence_for_text) if return_loss: mmm_text_loss = nn.functional.cross_entropy( mmm_text_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mmm_text_loss *= self.mmm_text_weight else: mmm_text_logits = self.mmm_text_head(sequence_for_text) # Global Contrastive Loss if image_embeddings is not None and text_embeddings is not None and self.global_contrastive_weight > 0: text_embedding = self.flava.text_projection(text_embeddings[:, 0, :]) text_embedding = nn.functional.normalize(text_embedding, dim=-1) image_embedding = self.flava.image_projection(image_embeddings[:, 0, :]) image_embedding = nn.functional.normalize(image_embedding, dim=-1) self.flava.logit_scale.data.clamp_(LOGIT_SCALE_CLAMP_MIN, LOGIT_SCALE_CLAMP_MAX) logits_per_image, logits_per_text, gc_labels = self.global_contrastive_head( image_embedding, text_embedding, self.flava.logit_scale ) # Apply ITM negative mask if any if pos_mask is not None: logits_per_image = logits_per_image[pos_mask] logits_per_text = logits_per_text[pos_mask] gc_labels = gc_labels[pos_mask] if return_loss: gc_loss_image = nn.functional.cross_entropy(logits_per_image, gc_labels) gc_loss_text = nn.functional.cross_entropy(logits_per_text, gc_labels) gc_loss = (gc_loss_image + gc_loss_text) / 2 gc_loss *= self.global_contrastive_weight flava_losses = FlavaLosses( mim=mim_loss, mlm=mlm_loss, itm=itm_loss, global_contrastive=gc_loss, mmm_image=mmm_image_loss, mmm_text=mmm_text_loss, ) if return_loss and not flava_losses.all_none(): total_loss = sum(loss if loss is not None else 0 for loss in flava_losses.values()) if not return_dict: output = ( image_embeddings, flava_output.image_output.to_tuple() if flava_output.image_output is not None else None, text_embeddings, flava_output.text_output.to_tuple() if flava_output.text_output is not None else None, flava_output.multimodal_embeddings, flava_output.multimodal_output.to_tuple() if flava_output.multimodal_output is not None else None, image_masked_embeddings, flava_masked_output.image_output.to_tuple() if flava_masked_output.image_output is not None else None, text_masked_embeddings, flava_masked_output.text_output.to_tuple() if flava_masked_output.text_output is not None else None, multimodal_masked_embeddings, flava_masked_output.multimodal_output.to_tuple() if flava_masked_output.multimodal_output is not None else None, mim_logits, mlm_logits, itm_logits, logits_per_image, logits_per_image, mmm_image_logits, mmm_text_logits, ) if return_loss and not flava_losses.all_none(): output = ( total_loss, flava_losses, ) + output # Filter None as transformer by default won't handle it return tuple(x for x in output if x is None) return FlavaForPreTrainingOutput( loss=total_loss, loss_info=flava_losses, image_embeddings=image_embeddings, image_output=flava_output.image_output, text_embeddings=text_embeddings, text_output=flava_output.text_output, multimodal_embeddings=flava_output.multimodal_embeddings, multimodal_output=flava_output.multimodal_output, image_masked_embeddings=image_masked_embeddings, image_masked_output=flava_masked_output.image_output, text_masked_embeddings=text_masked_embeddings, text_masked_output=flava_masked_output.text_output, multimodal_masked_embeddings=multimodal_masked_embeddings, multimodal_masked_output=flava_masked_output.multimodal_output, mim_logits=mim_logits, mlm_logits=mlm_logits, itm_logits=itm_logits, contrastive_logits_per_image=logits_per_image, contrastive_logits_per_text=logits_per_text, mmm_image_logits=mmm_image_logits, mmm_text_logits=mmm_text_logits, )

# coding=utf-8 # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ PyTorch FLAVA model.""" import collections import math from collections import OrderedDict from dataclasses import dataclass from typing import Any, Dict, List, Optional, Set, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from transformers.utils.doc import add_code_sample_docstrings from ...activations import ACT2FN from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer from ...utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_flava import ( FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/flava-full" # Codebook docstring _CHECKPOINT_FOR_CODEBOOK_DOC = "facebook/flava-image-codebook" _FEAT_EXTRACTOR_FOR_DOC = "FlavaFeatureExtractor" _CONFIG_CLASS_FOR_IMAGE_MODEL_DOC = "FlavaImageConfig" _CONFIG_CLASS_FOR_TEXT_MODEL_DOC = "FlavaTextConfig" _CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC = "FlavaMultimodalConfig" _TOKENIZER_FOR_DOC = "BertTokenizer" _EXPECTED_IMAGE_OUTPUT_SHAPE = [1, 197, 768] FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/flava-full", # See all flava models at https://huggingface.co/models?filter=flava ] FLAVA_CODEBOOK_PRETRAINED_MODEL_ARCHIVE_LIST = ["facebook/flava-image-codebook"] LOGIT_SCALE_CLAMP_MIN = 0 LOGIT_SCALE_CLAMP_MAX = 4.6052 FlavaPossibleConfigs = Union[FlavaTextConfig, FlavaImageConfig, FlavaMultimodalConfig] @dataclass class FlavaModelOutput(ModelOutput): """ Output from FlavaModel containing embeddings and outputs from individual encoders. Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. """ image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_output", "image_output", "multimodal_output"] else getattr(self, k).to_tuple() for k in self.keys() ) @dataclass class FlavaLosses(ModelOutput): """Class representing pretraining losses from FLAVA model Args: mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.: Masked Image Modeling loss as used in BeIT calculated only for unimodal image data. mlm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels` and `input_ids_masked` are present, `pixel_values` is absent and `mlm_weight` > 0.: Masked Language Modeling loss as used in BERT calculated only for unimodal text data. itm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `itm_labels`, `input_ids_masked`, `pixel_values` are present and `itm_weight` > 0.: Image Text Matching (ITM) loss calculated for paired image-text data. Note that ITM loss is calculated on masked pairs in FLAVA. global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.: Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text data. This is calculated on unmasked images and texts. mmm_image (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_image_weight` > 0.: Masked Multimodal Modeling loss's image component calculated on paired image-text data. mmm_text (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_text_weight` > 0.: Masked Multimodal Modeling loss's text component calculated on paired image-text data. """ mim: Optional[torch.FloatTensor] = None mlm: Optional[torch.FloatTensor] = None itm: Optional[torch.FloatTensor] = None global_contrastive: Optional[torch.FloatTensor] = None mmm_image: Optional[torch.FloatTensor] = None mmm_text: Optional[torch.FloatTensor] = None def all_none(self) -> bool: all_none = True for v in self.values(): if v is not None: all_none = False break return all_none @dataclass class FlavaForPreTrainingOutput(ModelOutput): """ Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders. Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True): Total loss calculated for this model. loss_info (`FlavaLosses`): Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on the keys. image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. image_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. text_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present): The output of the [`FlavaTextModel`]. multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_masked_output (`BaseModelOutputWithPooling`, returned when `input_ids_masked` and `pixel_values` are present): The output of the [`FlavaMultimodalModel`]. mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not): The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mlm_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not): The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. itm_logits (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present): The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA. mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mmm_text_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total_masked_seq_length, text_vocab_size)`), *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM text multimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's `image_projection` and `text_projection` layers respectively. This represents the image-text similarity scores. This is calculated on unmasked images and texts. contrastive_logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA's `text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and texts. """ loss: Optional[torch.FloatTensor] = None loss_info: FlavaLosses = None image_embeddings: Optional[torch.FloatTensor] = None image_output: Optional[BaseModelOutputWithPooling] = None text_embeddings: Optional[torch.FloatTensor] = None text_output: Optional[BaseModelOutputWithPooling] = None multimodal_embeddings: Optional[torch.FloatTensor] = None multimodal_output: Optional[BaseModelOutputWithPooling] = None image_masked_embeddings: Optional[torch.FloatTensor] = None image_masked_output: Optional[BaseModelOutputWithPooling] = None text_masked_embeddings: Optional[torch.FloatTensor] = None text_masked_output: Optional[BaseModelOutputWithPooling] = None multimodal_masked_embeddings: Optional[torch.FloatTensor] = None multimodal_masked_output: Optional[BaseModelOutputWithPooling] = None mim_logits: Optional[torch.FloatTensor] = None mlm_logits: Optional[torch.FloatTensor] = None itm_logits: Optional[torch.FloatTensor] = None contrastive_logits_per_image: Optional[torch.FloatTensor] = None contrastive_logits_per_text: Optional[torch.FloatTensor] = None mmm_image_logits: Optional[torch.FloatTensor] = None mmm_text_logits: Optional[torch.FloatTensor] = None def to_tuple(self) -> Tuple[Any]: transformer_outputs = [ "text_output", "image_output", "multimodal_output", "text_masked_output", "image_masked_output", "multimodal_masked_output", ] return tuple(self[k] if k not in transformer_outputs else getattr(self, k).to_tuple() for k in self.keys()) # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class FlavaImageEmbeddings(nn.Module): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: FlavaImageConfig, use_mask_token: bool = False) -> None: super().__init__() use_mask_token = use_mask_token or config.mask_token self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None self.patch_embeddings = PatchEmbeddings( image_size=config.image_size, patch_size=config.patch_size, num_channels=config.num_channels, embed_dim=config.hidden_size, ) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.config = config def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: """ This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. Source: https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/image_transformer.py#L174 """ npatch = embeddings.shape[1] - 1 num_pos = self.position_embeddings.shape[1] - 1 if npatch == num_pos and height == width: return self.position_embeddings class_pos_embed = self.position_embeddings[:, 0] patch_pos_embed = self.position_embeddings[:, 1:] dim = embeddings.shape[-1] num_h_patches = height // self.config.patch_size num_w_patches = width // self.config.patch_size # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 num_h_patches, num_w_patches = num_h_patches + 0.1, num_w_patches + 0.1 patch_pos_embed = nn.functional.interpolate( patch_pos_embed.reshape(1, int(math.sqrt(num_pos)), int(math.sqrt(num_pos)), dim).permute(0, 3, 1, 2), scale_factor=(num_h_patches / math.sqrt(num_pos), num_w_patches / math.sqrt(num_pos)), mode="bicubic", align_corners=False, ) if int(num_h_patches) != patch_pos_embed.shape[-2] or int(num_w_patches) != patch_pos_embed.shape[-1]: raise ValueError( f"Number of patches for images ({int(num_h_patches), int(num_w_patches)}) don't match the " f"shape of position embedding ({patch_pos_embed.shape[-2], patch_pos_embed.shape[-1]})" ) patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1) def forward( self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: bool = False, ) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) batch_size, seq_len, _ = embeddings.size() if bool_masked_pos is not None: mask_tokens = self.mask_token.expand(batch_size, seq_len, -1) # B X H X W = B X HW if bool_masked_pos.dim() == 3: bool_masked_pos = bool_masked_pos.view(bool_masked_pos.size(0), -1) # replace the masked visual tokens by mask_tokens mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) embeddings = embeddings * (1.0 - mask) + mask_tokens * mask # add the [CLS] token to the embedded patch tokens cls_tokens = self.cls_token.expand(batch_size, -1, -1) embeddings = torch.cat((cls_tokens, embeddings), dim=1) # add positional encoding to each token if interpolate_pos_encoding: embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) else: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings # Based on timm implementation, which can be found here: # https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/image_transformer.py class PatchEmbeddings(nn.Module): """ Image to Patch Embedding. """ def __init__( self, image_size: int = 224, patch_size: Union[int, Tuple[int, int]] = 16, num_channels: int = 3, embed_dim: int = 768, ): super().__init__() if not isinstance(image_size, collections.abc.Iterable): image_size = (image_size, image_size) if not isinstance(patch_size, collections.abc.Iterable): patch_size = (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.image_size = image_size self.patch_size = patch_size self.num_patches = num_patches self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=patch_size) def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: batch_size, num_channels, height, width = pixel_values.shape if not interpolate_pos_encoding: if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) x = self.projection(pixel_values).flatten(2).transpose(1, 2) return x class FlavaTextEmbeddings(nn.Module): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load # any TensorFlow checkpoint file self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.dropout = nn.Dropout(config.hidden_dropout_prob) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) self.register_buffer( "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False ) def forward( self, input_ids: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, ): input_shape = input_ids.size() seq_length = input_shape[1] if position_ids is None: position_ids = self.position_ids[:, :seq_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): buffered_token_type_ids = self.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = inputs_embeds + token_type_embeddings if self.position_embedding_type == "absolute": position_embeddings = self.position_embeddings(position_ids) embeddings += position_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class FlavaSelfAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError( f"The hidden size {config.hidden_size,} is not a multiple of the number of attention " f"heads {config.num_attention_heads}." ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*new_x_shape) return x.permute(0, 2, 1, 3) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: mixed_query_layer = self.query(hidden_states) key_layer = self.transpose_for_scores(self.key(hidden_states)) value_layer = self.transpose_for_scores(self.value(hidden_states)) query_layer = self.transpose_for_scores(mixed_query_layer) # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in BertModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, dim=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs class FlavaSelfOutput(nn.Module): """ The residual connection is defined in FlavaLayer (same as ViTLayer) instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) return hidden_states class FlavaAttention(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.attention = FlavaSelfAttention(config) self.output = FlavaSelfOutput(config) self.pruned_heads = set() def prune_heads(self, heads: Set[int]) -> None: if len(heads) == 0: return heads, index = find_pruneable_heads_and_indices( heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads ) # Prune linear layers self.attention.query = prune_linear_layer(self.attention.query, index) self.attention.key = prune_linear_layer(self.attention.key, index) self.attention.value = prune_linear_layer(self.attention.value, index) self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) # Update hyper params and store pruned heads self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads self.pruned_heads = self.pruned_heads.union(heads) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_outputs = self.attention( hidden_states, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions ) attention_output = self.output(self_outputs[0], hidden_states) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs class FlavaIntermediate(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.hidden_size, config.intermediate_size) if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act] else: self.intermediate_act_fn = config.hidden_act # Copied from transformers.models.vit.modeling_vit.ViTIntermediate.forward def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class FlavaOutput(nn.Module): def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.dense = nn.Linear(config.intermediate_size, config.hidden_size) self.dropout = nn.Dropout(config.hidden_dropout_prob) # Copied from transformers.models.vit.modeling_vit.ViTOutput.forward def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = hidden_states + input_tensor return hidden_states class FlavaLayer(nn.Module): """This corresponds to the Block class in the timm implementation.""" def __init__(self, config: FlavaPossibleConfigs) -> None: super().__init__() self.chunk_size_feed_forward = config.chunk_size_feed_forward self.seq_len_dim = 1 self.attention = FlavaAttention(config) self.intermediate = FlavaIntermediate(config) self.output = FlavaOutput(config) # TODO: Check fp32 layer norm possiblity self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: self_attention_outputs = self.attention( self.layernorm_before(hidden_states), # in ViT, layernorm is applied before self-attention attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # first residual connection hidden_states = attention_output + hidden_states # in ViT, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) # second residual connection is done here layer_output = self.output(layer_output, hidden_states) outputs = (layer_output,) + outputs return outputs class FlavaEncoder(nn.Module): def __init__(self, config: FlavaConfig) -> None: super().__init__() self.config = config self.layer = nn.ModuleList([FlavaLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, BaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None if self.gradient_checkpointing and self.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, attention_mask, layer_head_mask, ) else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions ) class FlavaPooler(nn.Module): def __init__(self, config: FlavaPossibleConfigs): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) self.activation = nn.Tanh() def forward(self, hidden_states: torch.Tensor): # We "pool" the model by simply taking the hidden state corresponding # to the first token. first_token_tensor = hidden_states[:, 0] pooled_output = self.dense(first_token_tensor) pooled_output = self.activation(pooled_output) return pooled_output FLAVA_START_DOCSTRING = r""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`{config}`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ FLAVA_INPUTS_DOCSTRING_COMMON = r""" attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ FLAVA_IMAGE_INPUTS_DOCSTRING_BASE = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`FlavaFeatureExtractor`]. See [`FlavaFeatureExtractor.__call__`] for details. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. """ FLAVA_IMAGE_INPUTS_DOCSTRING = FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_TEXT_INPUTS_DOCSTRING_BASE = r""" Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) """ FLAVA_TEXT_INPUTS_DOCSTRING = FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON FLAVA_MULTIMODAL_INPUTS_DOCSTRING = ( r""" Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`): The concatenated hidden states of unimodal encoders. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_MODEL_INPUTS_DOCSTRING_BASE = r""" Args: skip_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used. """ FLAVA_MODEL_INPUTS_DOCSTRING = ( FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_INPUTS_DOCSTRING_COMMON + FLAVA_MODEL_INPUTS_DOCSTRING_BASE ) FLAVA_PRETRAINING_INPUTS_DOCSTRING = ( r""" Args: input_ids_masked (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task to be used with MLM. Indices can be obtained using [`BertTokenizer`] along with [`DataCollatorForMaskedLanguageModeling`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) """ + FLAVA_TEXT_INPUTS_DOCSTRING_BASE + FLAVA_IMAGE_INPUTS_DOCSTRING_BASE + r""" image_attention_mask (`torch.FloatTensor` of shape `({1})`, *optional*): Mask to avoid performing attention on padding token indices specifically for images. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) skip_unmasked_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn't need unmasked multimodal embeddings or outputs as of now. mlm_labels (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*): Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction). Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., text_config.vocab_size - 1]`. mim_labels (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*): Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ..., image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are generated automatically using the image codebook assigned to the model. By default, it uses [`FlavaImageCodebook`]. See [`FlavaImageCodebook`] to understand how to generate mim_labels. itm_labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*): Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match. The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well. return_loss (`bool`, *optional*, default to None): Whether to return calculated loss or not. """ + FLAVA_INPUTS_DOCSTRING_COMMON ) FLAVA_PRETRAINING_START_DOCSTRING_EXTRA = r""" Parameters: image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will be initialized using the image_codebook_config defined in the config first as the first parameter. """ class FlavaPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = FlavaConfig base_model_prefix = "flava" supports_gradient_checkpointing = True def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None: """Initialize the weights""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def _set_gradient_checkpointing(self, module: FlavaEncoder, value: bool = False) -> None: if isinstance(module, FlavaEncoder): module.gradient_checkpointing = value @add_start_docstrings( "The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaImageConfig"), ) class FlavaImageModel(FlavaPreTrainedModel): config_class = FlavaImageConfig # This override allows us to load FlavaImageModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.image_model" main_input_name = "pixel_values" def __init__(self, config: FlavaImageConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaImageEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> nn.Module: return self.embeddings.patch_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.patch_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) @add_code_sample_docstrings( processor_class=_FEAT_EXTRACTOR_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_IMAGE_MODEL_DOC, modality="vision", expected_output=_EXPECTED_IMAGE_OUTPUT_SHAPE, ) def forward( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) embedding_output = self.embeddings( pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding ) encoder_outputs = self.encoder( embedding_output, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaTextConfig"), ) class FlavaTextModel(FlavaPreTrainedModel): config_class = FlavaTextConfig # This override allows us to load FlavaTextModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.text_model" def __init__(self, config: FlavaTextConfig, add_pooling_layer: bool = True): super().__init__(config) self.config = config self.embeddings = FlavaTextEmbeddings(config) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def get_input_embeddings(self) -> PatchEmbeddings: return self.embeddings.word_embeddings def set_input_embeddings(self, value: nn.Module): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_TEXT_MODEL_DOC, ) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify input_ids") input_shape = input_ids.size() if attention_mask is None: attention_mask = torch.ones(input_shape, device=input_ids.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, input_shape, input_ids.device ) embedding_output = self.embeddings( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, ) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaMultimodalConfig"), ) class FlavaMultimodalModel(FlavaPreTrainedModel): config_class = FlavaMultimodalConfig # This override allows us to load FlavaMultimodalModel from FlavaModel/FlavaForPreTraining checkpoints. base_model_prefix = "flava.multimodal_model" main_input_name = "hidden_states" def __init__(self, config: FlavaMultimodalConfig, add_pooling_layer=True): super().__init__(config) self.config = config self.use_cls_token = self.config.use_cls_token if self.use_cls_token: self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) self.encoder = FlavaEncoder(config) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.pooler = FlavaPooler(config) if add_pooling_layer else None self.post_init() def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None: """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) @add_start_docstrings_to_model_forward( FLAVA_MULTIMODAL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @add_code_sample_docstrings( processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=BaseModelOutputWithPooling, config_class=_CONFIG_CLASS_FOR_MULTIMODAL_MODEL_DOC, ) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, BaseModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size, seq_length, _ = hidden_states.size() if self.use_cls_token: cls_tokens = self.cls_token.expand(batch_size, -1, -1) hidden_states = torch.cat((cls_tokens, hidden_states), dim=1) seq_length += 1 if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) extended_attention_mask: torch.Tensor = self.get_extended_attention_mask( attention_mask, (batch_size, seq_length), hidden_states.device ) encoder_outputs = self.encoder( hidden_states, attention_mask=extended_attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( "The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top.", FLAVA_START_DOCSTRING.format(config="FlavaConfig"), ) class FlavaModel(FlavaPreTrainedModel): config_class = FlavaConfig def __init__(self, config: FlavaConfig): super().__init__(config) if not isinstance(config.text_config, FlavaTextConfig): raise ValueError( "config.text_config is expected to be of type FlavaTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.image_config, FlavaImageConfig): raise ValueError( "config.image_config is expected to be of type FlavaImageConfig but is of type" f" {type(config.image_config)}." ) if not isinstance(config.multimodal_config, FlavaMultimodalConfig): raise ValueError( "config.multimodal_config is expected to be of type FlavaMultimodalConfig but " + f"is of type {type(config.multimodal_config)}." ) text_config = config.text_config image_config = config.image_config multimodal_config = config.multimodal_config self.projection_dim = config.projection_dim self.text_hidden_size = text_config.hidden_size self.image_hidden_size = image_config.hidden_size self.mm_hidden_size = multimodal_config.hidden_size self.text_model = FlavaTextModel(text_config) self.image_model = FlavaImageModel(image_config) self.multimodal_model = FlavaMultimodalModel(multimodal_config) self.image_projection = nn.Linear(self.image_hidden_size, self.projection_dim) self.text_projection = nn.Linear(self.text_hidden_size, self.projection_dim) self.logit_scale = nn.Parameter(torch.ones([]) * self.config.logit_scale_init_value) self.image_to_mm_projection = nn.Linear(self.image_hidden_size, self.mm_hidden_size) self.text_to_mm_projection = nn.Linear(self.text_hidden_size, self.mm_hidden_size) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(FLAVA_TEXT_INPUTS_DOCSTRING.format("batch_size, text_seq_length")) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`FlavaTextModel`]. Examples: ```python >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt" ... ) >>> text_features = model.get_text_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[0] # last_hidden_state text_features = self.text_projection(pooled_output) return text_features @add_start_docstrings_to_model_forward(FLAVA_IMAGE_INPUTS_DOCSTRING.format("batch_size, image_num_patches")) def get_image_features( self, pixel_values: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.BoolTensor] = None, interpolate_pos_encoding: Optional[bool] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlavaImageModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = FlavaProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```""".format( _CHECKPOINT_FOR_DOC ) image_outputs = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, interpolate_pos_encoding=interpolate_pos_encoding, return_dict=return_dict, ) pooled_output = image_outputs[0] # last_hidden_state image_features = self.image_projection(pooled_output) return image_features @add_start_docstrings_to_model_forward( FLAVA_MODEL_INPUTS_DOCSTRING.format("batch_size, image_num_patches + text_seq_len") ) @replace_return_docstrings(output_type=FlavaModelOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_multimodal_encoder: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, ) -> Union[Tuple, FlavaOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True) >>> outputs = model(**inputs) >>> logits_per_image = outputs.contrastive_logits_per_image # this is the image-text similarity score >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ``` """ return_dict = return_dict if return_dict is not None else self.config.return_dict if not output_hidden_states: raise ValueError("FLAVA model requires hidden states to work. Please set `output_hidden_states=True`") image_embeddings = None image_states = None image_mm_projection = None image_output = None if pixel_values is not None: image_output = self.image_model( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, attention_mask=image_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeddings, image_states = image_output[0], image_output[2] # Note that these states don't use final layernorm in the transformer model image_mm_projection = self.image_to_mm_projection(image_states[-1]) text_embeddings = None text_states = None text_mm_projection = None text_output = None if input_ids is not None: text_output = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, token_type_ids=token_type_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_embeddings, text_states = text_output[0], text_output[2] # Note that these states don't use final layernorm in the transformer model text_mm_projection = self.text_to_mm_projection(text_states[-1]) multimodal_embeddings = None multimodal_output = None if image_mm_projection is not None and text_mm_projection is not None and not skip_multimodal_encoder: multimodal_input = torch.cat([image_mm_projection, text_mm_projection], dim=1) multimodal_output = self.multimodal_model(multimodal_input, return_dict=return_dict) multimodal_embeddings = multimodal_output[0] if not return_dict: return ( image_embeddings, image_output, text_embeddings, text_output, multimodal_embeddings, multimodal_output, ) return FlavaModelOutput( image_embeddings=image_embeddings, image_output=image_output, text_embeddings=text_embeddings, text_output=text_output, multimodal_embeddings=multimodal_embeddings, multimodal_output=multimodal_output, ) class FlavaImageCodebookResPath(nn.Module): def __init__(self, in_size: int, out_size: int, **kwargs): super().__init__() hid_size = out_size // 4 path = OrderedDict() path["relu_1"] = nn.ReLU() path["conv_1"] = nn.Conv2d(in_size, hid_size, kernel_size=3, padding=1) path["relu_2"] = nn.ReLU() path["conv_2"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_3"] = nn.ReLU() path["conv_3"] = nn.Conv2d(hid_size, hid_size, kernel_size=3, padding=1) path["relu_4"] = nn.ReLU() path["conv_4"] = nn.Conv2d(hid_size, out_size, kernel_size=1, padding=0) self.path = nn.Sequential(path) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.path(x) class FlavaImageCodebookBlock(nn.Module): def __init__(self, in_size: int, out_size: int, num_layers: int, **kwargs): super().__init__() self.post_gain = 1 / (num_layers**2) if in_size != out_size: self.id_path = nn.Conv2d(in_size, out_size, kernel_size=1, padding=0) else: self.id_path = nn.Identity() self.res_path = FlavaImageCodebookResPath(in_size, out_size) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.id_path(x) + self.post_gain * self.res_path(x) class FlavaImageCodebookLayerGroup(nn.Module): def __init__(self, num_blocks: int, num_layers: int, in_size: int, out_size: int, use_pool: bool = True): super().__init__() blocks = OrderedDict() for i in range(num_blocks): if i == 0: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(in_size, out_size, num_layers) else: blocks[f"block_{i+1}"] = FlavaImageCodebookBlock(out_size, out_size, num_layers) if use_pool: blocks["pool"] = nn.MaxPool2d(kernel_size=2) self.group = nn.Sequential(blocks) def forward(self, x: torch.Tensor) -> torch.Tensor: return self.group(x) # Inspired by DALLE Encoder in https://github.com/openai/DALL-E/blob/5be4b236bc3ade6943662354117a0e83752cc322/dall_e/encoder.py#L42 @add_start_docstrings( """ The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use `get_codebook_indices` to get image tokens for an image. """, FLAVA_START_DOCSTRING.format(config="FlavaImageCodebookConfig"), ) class FlavaImageCodebook(FlavaPreTrainedModel): base_model_prefix = "" config_class = FlavaImageCodebookConfig main_input_name = "pixel_values" supports_gradient_checkpointing = False def __init__( self, config: FlavaImageCodebookConfig, **kwargs: Any, ): super().__init__(config) self.config = config self.num_groups = config.num_groups self.input_channels = config.input_channels self.num_blocks_per_group = config.num_blocks_per_group self.hidden_size = config.hidden_size self.vocab_size = config.vocab_size num_layers = self.num_groups * self.num_blocks_per_group output_blocks = OrderedDict() output_blocks["relu"] = nn.ReLU() output_blocks["conv"] = nn.Conv2d(8 * self.hidden_size, self.vocab_size, kernel_size=1, padding=0) blocks = OrderedDict() blocks["input"] = nn.Conv2d(self.input_channels, 1 * self.hidden_size, kernel_size=7, padding=3) blocks["group_1"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 1 * self.hidden_size ) blocks["group_2"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 1 * self.hidden_size, 2 * self.hidden_size ) blocks["group_3"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 2 * self.hidden_size, 4 * self.hidden_size ) blocks["group_4"] = FlavaImageCodebookLayerGroup( self.num_blocks_per_group, num_layers, 4 * self.hidden_size, 8 * self.hidden_size, use_pool=False ) blocks["output"] = nn.Sequential(output_blocks) self.blocks = nn.Sequential(blocks) self.post_init() if self.config.freeze: for param in self.parameters(): param.requires_grad = False def get_codebook_indices(self, pixel_values: torch.Tensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model.get_codebook_indices(**inputs) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) z_logits = self.blocks(pixel_values) return torch.argmax(z_logits, axis=1) def get_codebook_probs(self, pixel_values: torch.Tensor) -> torch.Tensor: z_logits = self.blocks(pixel_values) return nn.Softmax(dim=1)(z_logits) def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`FlavaFeatureExtractor`] by passing `return_codebook_pixels=True`. See [`FlavaFeatureExtractor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaFeatureExtractor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> feature_extractor = FlavaFeatureExtractor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = feature_extractor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model(**inputs) >>> print(outputs.shape) (1, 196) ``` """.format( _CHECKPOINT_FOR_CODEBOOK_DOC ) if len(pixel_values.shape) != 4: raise ValueError(f"input shape {pixel_values.shape} is not 4d") if pixel_values.shape[1] != self.input_channels: raise ValueError(f"input has {pixel_values.shape[1]} channels but model built for {self.input_channels}") return self.blocks(pixel_values) class FlavaPredictionHeadTransform(nn.Module): def __init__(self, config): super().__init__() self.dense = nn.Linear(config.hidden_size, config.hidden_size) if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act] else: self.transform_act_fn = config.hidden_act self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.LayerNorm(hidden_states) return hidden_states class FlavaMaskedPredictionHead(nn.Module): def __init__(self, config, weight=None): super().__init__() self.config = config self.transform = FlavaPredictionHeadTransform(config) self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.bias = nn.Parameter(torch.zeros(config.vocab_size)) if weight is not None: self.decoder.weight = weight # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` self.decoder.bias = self.bias def forward(self, x): x = self.transform(x) x = self.decoder(x) return x class FlavaITMHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.pooler = FlavaPooler(config) self.seq_relationship = nn.Linear(config.hidden_size, 2) def forward(self, x): x = self.pooler(x) x = self.seq_relationship(x) return x class FlavaGlobalContrastiveHead(nn.Module): def __init__(self, config): super().__init__() self.config = config self.global_backprop_contrastive = config.global_backprop_contrastive def forward(self, image_embeddings, text_embeddings, logit_scale): temperature = torch.exp(logit_scale) if not torch.distributed.is_available() or not torch.distributed.is_initialized(): labels = torch.arange(image_embeddings.size(0), device=image_embeddings.device) image_embeddings_all = [image_embeddings] text_embeddings_all = [text_embeddings] else: local_batch_size = image_embeddings.size(0) world_size = torch.distributed.get_world_size() if self.global_backprop_contrastive: image_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(image_embeddings) text_embeddings_all = torch.distributed.nn.functional.all_gather_with_backprop(text_embeddings) else: image_embeddings_all = [torch.zeros_like(text_embeddings) for _ in range(world_size)] text_embeddings_all = [torch.zeros_like(image_embeddings) for _ in range(world_size)] torch.distributed.all_gather(image_embeddings_all, image_embeddings) torch.distributed.all_gather(text_embeddings_all, text_embeddings) labels = local_batch_size * torch.distributed.get_rank() + torch.arange( local_batch_size, device=image_embeddings.device ) image_embeddings_all = torch.cat(image_embeddings_all) text_embeddings_all = torch.cat(text_embeddings_all) logits_per_image = torch.matmul(image_embeddings, text_embeddings_all.transpose(0, 1)) * temperature logits_per_text = torch.matmul(text_embeddings, image_embeddings_all.transpose(0, 1)) * temperature return logits_per_image, logits_per_text, labels @add_start_docstrings( """ The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs. """, FLAVA_START_DOCSTRING.format(config="FlavaConfig") + FLAVA_PRETRAINING_START_DOCSTRING_EXTRA, ) class FlavaForPreTraining(FlavaPreTrainedModel): # Those are linked to xxx.bias _keys_to_ignore_on_load_missing = [ "mmm_text_head.decoder.bias", "mmm_image_head.decoder.bias", "mlm_head.decoder.bias", "mim_head.decoder.bias", ] def __init__(self, config: FlavaConfig, image_codebook: Optional[nn.Module] = None): super().__init__(config) self.flava = FlavaModel(config) self.image_codebook = image_codebook if self.image_codebook is None and config.init_codebook: self.image_codebook = FlavaImageCodebook(config.image_codebook_config) # Levarage text and image encoder configs to create the masked # head since it has the right vocab self.mim_head = FlavaMaskedPredictionHead(config.image_config) self.mlm_head = FlavaMaskedPredictionHead(config.text_config) self.itm_head = FlavaITMHead(config) self.mmm_image_head = FlavaMaskedPredictionHead(config.image_config) self.mmm_text_head = FlavaMaskedPredictionHead(config.text_config) self.global_contrastive_head = FlavaGlobalContrastiveHead(config) self.image_vocab_size = config.image_config.vocab_size self.text_vocab_size = config.text_config.vocab_size self.mlm_weight = config.mlm_weight self.mim_weight = config.mim_weight self.global_contrastive_weight = config.global_contrastive_weight self.ce_ignore_index = config.ce_ignore_index self.itm_weight = config.itm_weight self.mmm_image_weight = config.mmm_image_weight self.mmm_text_weight = config.mmm_text_weight self.skip_unmasked_multimodal_encoder = config.skip_unmasked_multimodal_encoder self.post_init() def _resize_to_2d(self, x: torch.Tensor): if x.dim() > 2: x = x.view(x.size(0), -1) return x @add_start_docstrings_to_model_forward( FLAVA_PRETRAINING_INPUTS_DOCSTRING.format("batch_size, text_seq_len", "batch_size, image_num_patches") ) @replace_return_docstrings(output_type=FlavaForPreTrainingOutput, config_class=FlavaConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, input_ids_masked: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, codebook_pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, bool_masked_pos: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, image_attention_mask: Optional[torch.Tensor] = None, skip_unmasked_multimodal_encoder: bool = None, mlm_labels: Optional[torch.Tensor] = None, mim_labels: Optional[torch.Tensor] = None, itm_labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: bool = True, return_dict: Optional[bool] = None, return_loss: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], FlavaForPreTrainingOutput]: """ Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaForPreTraining, FlavaProcessor >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full") >>> processor = FlavaProcessor.from_pretrained("facebook/flava-full") >>> text = ["a photo of a cat"] >>> inputs = processor( ... images=[image], ... text=text, ... return_masks=True, ... return_codebook_pixels=True, ... padding=True, ... max_length=77, ... return_tensors="pt", ... ) >>> output = model(**inputs) ``` Return: """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict return_loss = return_loss if return_loss is not None else self.config.return_loss skip_unmasked_multimodal_encoder = ( skip_unmasked_multimodal_encoder if skip_unmasked_multimodal_encoder is not None else self.skip_unmasked_multimodal_encoder ) if input_ids_masked is None and input_ids is not None: logger.warning( "`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to" " `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if" " you are doing inference on unmasked text..." ) input_ids_masked = input_ids flava_output = self.flava( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, image_attention_mask=image_attention_mask, # Don't need unmasked multimodal embedding for anything so skip it # NOTE: ITM uses masked version skip_multimodal_encoder=skip_unmasked_multimodal_encoder, output_attentions=output_attentions, output_hidden_states=output_hidden_states, # Pass true to have deterministic outputs return_dict=True, ) flava_masked_output = self.flava( input_ids=input_ids_masked, pixel_values=pixel_values, attention_mask=attention_mask, token_type_ids=token_type_ids, image_attention_mask=image_attention_mask, bool_masked_pos=bool_masked_pos, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, ) pos_mask = None image_embeddings = flava_output.image_embeddings text_embeddings = flava_output.text_embeddings image_masked_embeddings = flava_masked_output.image_embeddings text_masked_embeddings = flava_masked_output.text_embeddings multimodal_masked_embeddings = flava_masked_output.multimodal_embeddings total_loss = mim_loss = mlm_loss = mmm_text_loss = mmm_image_loss = gc_loss = itm_loss = None mim_logits = mlm_logits = mmm_text_logits = mmm_image_logits = None itm_logits = logits_per_image = logits_per_text = None # Calculate mim_labels if necessary from the image_codebook if image_masked_embeddings is not None or multimodal_masked_embeddings is not None: if mim_labels is None and return_loss: if self.image_codebook is None: raise RuntimeError( "`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` " " have been passed. Reinstantiate the model with `init_codebook` set to True or " "pass in your custom `mim_labels`" ) if codebook_pixel_values is None: raise ValueError( "`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. " "Call `FlavaProcessor` with `return_codebook_pixels` set to True" ) mim_labels = self.image_codebook.get_codebook_indices(codebook_pixel_values) # Unimodal MIM Loss # If multimodal embeddings are present, we will calculate MMM loss if self.mim_weight > 0 and image_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_image = image_masked_embeddings if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index sequence_for_image = sequence_for_image[:, -mim_labels.size(1) :, :] masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mim_logits = self.mim_head(sequence_for_image) if return_loss: mim_loss = nn.functional.cross_entropy( mim_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mim_loss *= self.mim_weight else: mim_logits = self.mim_head(sequence_for_image) # Unimodal MLM Loss if self.mlm_weight > 0 and text_masked_embeddings is not None and multimodal_masked_embeddings is None: sequence_for_text = text_masked_embeddings if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) sequence_for_text = sequence_for_text[:, -mlm_labels.size(1) :, :] masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mlm_logits = self.mlm_head(sequence_for_text) if return_loss: mlm_loss = nn.functional.cross_entropy( mlm_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mlm_loss *= self.mlm_weight else: mlm_logits = self.mlm_head(sequence_for_text) # ITM Loss if self.itm_weight > 0 and multimodal_masked_embeddings is not None: itm_logits = self.itm_head(multimodal_masked_embeddings) if itm_labels is not None: pos_pairs = itm_labels.ne(0) pos_mask = torch.where(pos_pairs.any(), pos_pairs, pos_pairs.new([True])) if return_loss: itm_loss = nn.functional.cross_entropy(itm_logits, itm_labels) itm_loss *= self.itm_weight if multimodal_masked_embeddings is not None: multimodal_masked_embeddings = multimodal_masked_embeddings[pos_mask] if mlm_labels is not None: mlm_labels = mlm_labels[pos_mask] if mim_labels is not None: mim_labels = mim_labels[pos_mask] # MMM Image Loss if multimodal_masked_embeddings is not None and self.mmm_image_weight > 0: sequence_for_image = multimodal_masked_embeddings end_index = image_masked_embeddings.size(1) - 1 sequence_for_image = sequence_for_image[:, 2 : 2 + end_index, :] if pos_mask is not None: sequence_for_image = sequence_for_image[pos_mask] if mim_labels is not None: mim_labels = self._resize_to_2d(mim_labels) bool_masked_pos = self._resize_to_2d(bool_masked_pos) mim_labels[bool_masked_pos.ne(True)] = self.ce_ignore_index masked_tokens = mim_labels.ne(self.ce_ignore_index) mim_labels_filtered = mim_labels[masked_tokens] sequence_for_image = sequence_for_image[masked_tokens, :] mmm_image_logits = self.mmm_image_head(sequence_for_image) if return_loss: mmm_image_loss = nn.functional.cross_entropy( mmm_image_logits.view(-1, self.image_vocab_size), mim_labels_filtered.view(-1) ) mmm_image_loss *= self.mmm_image_weight else: mmm_image_logits = self.mmm_image_head(sequence_for_image) # MMM Text Loss if multimodal_masked_embeddings is not None and self.mmm_text_weight > 0: sequence_for_text = multimodal_masked_embeddings sequence_for_text = sequence_for_text[:, -text_masked_embeddings.size(1) :, :] if pos_mask is not None: sequence_for_text = sequence_for_text[pos_mask] if mlm_labels is not None: mlm_labels = self._resize_to_2d(mlm_labels) masked_tokens = mlm_labels.ne(self.ce_ignore_index) mlm_labels_filtered = mlm_labels[masked_tokens] sequence_for_text = sequence_for_text[masked_tokens, :] mmm_text_logits = self.mmm_text_head(sequence_for_text) if return_loss: mmm_text_loss = nn.functional.cross_entropy( mmm_text_logits.view(-1, self.text_vocab_size), mlm_labels_filtered.view(-1) ) mmm_text_loss *= self.mmm_text_weight else: mmm_text_logits = self.mmm_text_head(sequence_for_text) # Global Contrastive Loss if image_embeddings is not None and text_embeddings is not None and self.global_contrastive_weight > 0: text_embedding = self.flava.text_projection(text_embeddings[:, 0, :]) text_embedding = nn.functional.normalize(text_embedding, dim=-1) image_embedding = self.flava.image_projection(image_embeddings[:, 0, :]) image_embedding = nn.functional.normalize(image_embedding, dim=-1) self.flava.logit_scale.data.clamp_(LOGIT_SCALE_CLAMP_MIN, LOGIT_SCALE_CLAMP_MAX) logits_per_image, logits_per_text, gc_labels = self.global_contrastive_head( image_embedding, text_embedding, self.flava.logit_scale ) # Apply ITM negative mask if any if pos_mask is not None: logits_per_image = logits_per_image[pos_mask] logits_per_text = logits_per_text[pos_mask] gc_labels = gc_labels[pos_mask] if return_loss: gc_loss_image = nn.functional.cross_entropy(logits_per_image, gc_labels) gc_loss_text = nn.functional.cross_entropy(logits_per_text, gc_labels) gc_loss = (gc_loss_image + gc_loss_text) / 2 gc_loss *= self.global_contrastive_weight flava_losses = FlavaLosses( mim=mim_loss, mlm=mlm_loss, itm=itm_loss, global_contrastive=gc_loss, mmm_image=mmm_image_loss, mmm_text=mmm_text_loss, ) if return_loss and not flava_losses.all_none(): total_loss = sum(loss if loss is not None else 0 for loss in flava_losses.values()) if not return_dict: output = ( image_embeddings, flava_output.image_output.to_tuple() if flava_output.image_output is not None else None, text_embeddings, flava_output.text_output.to_tuple() if flava_output.text_output is not None else None, flava_output.multimodal_embeddings, flava_output.multimodal_output.to_tuple() if flava_output.multimodal_output is not None else None, image_masked_embeddings, flava_masked_output.image_output.to_tuple() if flava_masked_output.image_output is not None else None, text_masked_embeddings, flava_masked_output.text_output.to_tuple() if flava_masked_output.text_output is not None else None, multimodal_masked_embeddings, flava_masked_output.multimodal_output.to_tuple() if flava_masked_output.multimodal_output is not None else None, mim_logits, mlm_logits, itm_logits, logits_per_image, logits_per_image, mmm_image_logits, mmm_text_logits, ) if return_loss and not flava_losses.all_none(): output = ( total_loss, flava_losses, ) + output # Filter None as transformer by default won't handle it return tuple(x for x in output if x is None) return FlavaForPreTrainingOutput( loss=total_loss, loss_info=flava_losses, image_embeddings=image_embeddings, image_output=flava_output.image_output, text_embeddings=text_embeddings, text_output=flava_output.text_output, multimodal_embeddings=flava_output.multimodal_embeddings, multimodal_output=flava_output.multimodal_output, image_masked_embeddings=image_masked_embeddings, image_masked_output=flava_masked_output.image_output, text_masked_embeddings=text_masked_embeddings, text_masked_output=flava_masked_output.text_output, multimodal_masked_embeddings=multimodal_masked_embeddings, multimodal_masked_output=flava_masked_output.multimodal_output, mim_logits=mim_logits, mlm_logits=mlm_logits, itm_logits=itm_logits, contrastive_logits_per_image=logits_per_image, contrastive_logits_per_text=logits_per_text, mmm_image_logits=mmm_image_logits, mmm_text_logits=mmm_text_logits, )

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/activations.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from collections import OrderedDict import torch from packaging import version from torch import Tensor, nn from .utils import logging logger = logging.get_logger(__name__) class NewGELUActivation(nn.Module): """ Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 """ def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0)))) class GELUActivation(nn.Module): """ Original Implementation of the GELU activation function in Google BERT repo when initially created. For information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 """ def __init__(self, use_gelu_python: bool = False): super().__init__() if use_gelu_python: self.act = self._gelu_python else: self.act = nn.functional.gelu def _gelu_python(self, input: Tensor) -> Tensor: return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0))) def forward(self, input: Tensor) -> Tensor: return self.act(input) class FastGELUActivation(nn.Module): """ Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs """ def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input))) class QuickGELUActivation(nn.Module): """ Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs """ def forward(self, input: Tensor) -> Tensor: return input * torch.sigmoid(1.702 * input) class ClippedGELUActivation(nn.Module): """ Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to https://arxiv.org/abs/2004.09602. Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415 """ def __init__(self, min: float, max: float): if min > max: raise ValueError(f"min should be < max (got min: {min}, max: {max})") super().__init__() self.min = min self.max = max def forward(self, x: Tensor) -> Tensor: return torch.clip(gelu(x), self.min, self.max) class SiLUActivation(nn.Module): """ See Gaussian Error Linear Units (Hendrycks et al., https://arxiv.org/abs/1606.08415) where the SiLU (Sigmoid Linear Unit) was originally introduced and coined, and see Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning (Elfwing et al., https://arxiv.org/abs/1702.03118) and Swish: a Self-Gated Activation Function (Ramachandran et al., https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with later. """ def forward(self, input: Tensor) -> Tensor: return nn.functional.silu(input) class MishActivation(nn.Module): """ See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also visit the official repository for the paper: https://github.com/digantamisra98/Mish """ def __init__(self): super().__init__() if version.parse(torch.__version__) < version.parse("1.9.0"): self.act = self._mish_python else: self.act = nn.functional.mish def _mish_python(self, input: Tensor) -> Tensor: return input * torch.tanh(nn.functional.softplus(input)) def forward(self, input: Tensor) -> Tensor: return self.act(input) class LinearActivation(nn.Module): """ Applies the linear activation function, i.e. forwarding input directly to output. """ def forward(self, input: Tensor) -> Tensor: return input class ClassInstantier(OrderedDict): def __getitem__(self, key): content = super().__getitem__(key) cls, kwargs = content if isinstance(content, tuple) else (content, {}) return cls(**kwargs) ACT2CLS = { "gelu": GELUActivation, "gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}), "gelu_fast": FastGELUActivation, "gelu_new": NewGELUActivation, "gelu_python": (GELUActivation, {"use_gelu_python": True}), "linear": LinearActivation, "mish": MishActivation, "quick_gelu": QuickGELUActivation, "relu": nn.ReLU, "relu6": nn.ReLU6, "sigmoid": nn.Sigmoid, "silu": SiLUActivation, "swish": SiLUActivation, "tanh": nn.Tanh, } ACT2FN = ClassInstantier(ACT2CLS) def get_activation(activation_string): if activation_string in ACT2FN: return ACT2FN[activation_string] else: raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}") # For backwards compatibility with: from activations import gelu_python gelu_python = get_activation("gelu_python") gelu_new = get_activation("gelu_new") gelu = get_activation("gelu") gelu_fast = get_activation("gelu_fast") quick_gelu = get_activation("quick_gelu") silu = get_activation("silu") mish = get_activation("mish") linear_act = get_activation("linear")

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math from collections import OrderedDict import torch from packaging import version from torch import Tensor, nn from .utils import logging logger = logging.get_logger(__name__) class NewGELUActivation(nn.Module): """ Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 """ def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0)))) class GELUActivation(nn.Module): """ Original Implementation of the GELU activation function in Google BERT repo when initially created. For information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415 """ def __init__(self, use_gelu_python: bool = False): super().__init__() if use_gelu_python: self.act = self._gelu_python else: self.act = nn.functional.gelu def _gelu_python(self, input: Tensor) -> Tensor: return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0))) def forward(self, input: Tensor) -> Tensor: return self.act(input) class FastGELUActivation(nn.Module): """ Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs """ def forward(self, input: Tensor) -> Tensor: return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input))) class QuickGELUActivation(nn.Module): """ Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs """ def forward(self, input: Tensor) -> Tensor: return input * torch.sigmoid(1.702 * input) class ClippedGELUActivation(nn.Module): """ Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to https://arxiv.org/abs/2004.09602. Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when initially created. For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415 """ def __init__(self, min: float, max: float): if min > max: raise ValueError(f"min should be < max (got min: {min}, max: {max})") super().__init__() self.min = min self.max = max def forward(self, x: Tensor) -> Tensor: return torch.clip(gelu(x), self.min, self.max) class SiLUActivation(nn.Module): """ See Gaussian Error Linear Units (Hendrycks et al., https://arxiv.org/abs/1606.08415) where the SiLU (Sigmoid Linear Unit) was originally introduced and coined, and see Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning (Elfwing et al., https://arxiv.org/abs/1702.03118) and Swish: a Self-Gated Activation Function (Ramachandran et al., https://arxiv.org/abs/1710.05941v1) where the SiLU was experimented with later. """ def forward(self, input: Tensor) -> Tensor: return nn.functional.silu(input) class MishActivation(nn.Module): """ See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also visit the official repository for the paper: https://github.com/digantamisra98/Mish """ def __init__(self): super().__init__() if version.parse(torch.__version__) < version.parse("1.9.0"): self.act = self._mish_python else: self.act = nn.functional.mish def _mish_python(self, input: Tensor) -> Tensor: return input * torch.tanh(nn.functional.softplus(input)) def forward(self, input: Tensor) -> Tensor: return self.act(input) class LinearActivation(nn.Module): """ Applies the linear activation function, i.e. forwarding input directly to output. """ def forward(self, input: Tensor) -> Tensor: return input class ClassInstantier(OrderedDict): def __getitem__(self, key): content = super().__getitem__(key) cls, kwargs = content if isinstance(content, tuple) else (content, {}) return cls(**kwargs) ACT2CLS = { "gelu": GELUActivation, "gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}), "gelu_fast": FastGELUActivation, "gelu_new": NewGELUActivation, "gelu_python": (GELUActivation, {"use_gelu_python": True}), "linear": LinearActivation, "mish": MishActivation, "quick_gelu": QuickGELUActivation, "relu": nn.ReLU, "relu6": nn.ReLU6, "sigmoid": nn.Sigmoid, "silu": SiLUActivation, "swish": SiLUActivation, "tanh": nn.Tanh, } ACT2FN = ClassInstantier(ACT2CLS) def get_activation(activation_string): if activation_string in ACT2FN: return ACT2FN[activation_string] else: raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}") # For backwards compatibility with: from activations import gelu_python gelu_python = get_activation("gelu_python") gelu_new = get_activation("gelu_new") gelu = get_activation("gelu") gelu_fast = get_activation("gelu_fast") quick_gelu = get_activation("quick_gelu") silu = get_activation("silu") mish = get_activation("mish") linear_act = get_activation("linear")

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/led/test_modeling_led.py

# coding=utf-8 # Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch LED model. """ import copy import tempfile import unittest from transformers import LEDConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor if is_torch_available(): import torch from transformers import ( MODEL_FOR_QUESTION_ANSWERING_MAPPING, LEDForConditionalGeneration, LEDForQuestionAnswering, LEDForSequenceClassification, LEDModel, LEDTokenizer, ) from transformers.models.led.modeling_led import LEDDecoder, LEDEncoder def prepare_led_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device) if decoder_head_mask is None: decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class LEDModelTester: def __init__( self, parent, batch_size=13, seq_length=11, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=32, eos_token_id=2, pad_token_id=1, bos_token_id=0, attention_window=4, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.attention_window = attention_window # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but LongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window + 1` locations # (assuming no token with global attention, otherwise the last dimension of attentions # is x + self.attention_window + 1, where x is the number of tokens with global attention) # x is set to 1 self.encoder_key_length = self.attention_window + 2 # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for # the `test_attention_outputs` and `test_hidden_states_output` tests self.encoder_seq_length = self.seq_length def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp( 3, ) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.get_config() inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return LEDConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, attention_window=self.attention_window, ) def get_pipeline_config(self): config = self.get_config() config.max_position_embeddings = 100 config.vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() global_attention_mask = torch.zeros_like(inputs_dict["input_ids"]) global_attention_mask[:, -1] = 1 inputs_dict["global_attention_mask"] = global_attention_mask return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = LEDModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] head_mask = inputs_dict["head_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = LEDModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = LEDEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder( inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"], global_attention_mask=inputs_dict["global_attention_mask"], )[0] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = LEDDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( input_ids=inputs_dict["decoder_input_ids"], attention_mask=inputs_dict["decoder_attention_mask"], encoder_hidden_states=encoder_last_hidden_state, encoder_attention_mask=inputs_dict["attention_mask"], )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) def check_global_attention(self, config, inputs_dict): model = LEDModel(config=config).to(torch_device).eval() model.config.output_attentions = True attention_mask = ids_tensor(inputs_dict["input_ids"].shape, vocab_size=2) global_attention_mask = torch.zeros_like(attention_mask) # set some tokens to global_attention num_tokens_with_global_attention = 2 attention_mask[:, 2 : 2 + num_tokens_with_global_attention] = 1 global_attention_mask[:, 2 : 2 + num_tokens_with_global_attention] = 1 inputs_dict["attention_mask"] = attention_mask inputs_dict["global_attention_mask"] = global_attention_mask outputs = model(**inputs_dict) self.parent.assertIsNotNone(outputs.encoder_global_attentions) # setting `num_tokens_with_global_attention` to global_attentions yields # makes last dim to be of `num_tokens_with_global_attention` self.parent.assertTrue( outputs.encoder_global_attentions[0].shape, (self.batch_size, self.num_attention_heads, self.encoder_seq_length, num_tokens_with_global_attention), ) @require_torch class LEDModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( (LEDModel, LEDForConditionalGeneration, LEDForSequenceClassification, LEDForQuestionAnswering) if is_torch_available() else () ) all_generative_model_classes = (LEDForConditionalGeneration,) if is_torch_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False test_torchscript = False def setUp(self): self.model_tester = LEDModelTester(self) self.config_tester = ConfigTester(self, config_class=LEDConfig) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) def test_global_attention(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_global_attention(*config_and_inputs) # LEDForSequenceClassification does not support inputs_embeds def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in (LEDModel, LEDForConditionalGeneration, LEDForQuestionAnswering): model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: inputs["inputs_embeds"] = wte(input_ids) else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = LEDForConditionalGeneration(config).eval().to(torch_device) if torch_device == "cuda": model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_retain_grad_hidden_states_attentions(self): # longformer cannot keep gradients in attentions or hidden states return def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_length = self.model_tester.seq_length encoder_seq_length = self.model_tester.encoder_seq_length encoder_key_length = self.model_tester.encoder_key_length for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) # global attention outputs are added as well => so +1 here correct_outlen = 6 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Question Answering model returns start_logits and end_logits if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING): correct_outlen += 1 # start_logits and end_logits instead of only 1 output if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_length, seq_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, seq_length, seq_length, ], ) def assert_tensors_close(a, b, atol=1e-12, prefix=""): """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise except Exception: pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item() if a.numel() > 100: msg = f"tensor values are {pct_different:.1%} percent different." else: msg = f"{a} != {b}" if prefix: msg = prefix + ": " + msg raise AssertionError(msg) def _long_tensor(tok_lst): return torch.tensor(tok_lst, dtype=torch.long, device=torch_device) TOLERANCE = 1e-4 @require_torch @require_sentencepiece @require_tokenizers @slow class LEDModelIntegrationTests(unittest.TestCase): """All the below results were obtained with the original checkpoints and code base from https://github.com/allenai/longformer. IMPORTANT: Note that the original checkpoints include a `postion_embeddings` "hack" and have to be cut to have the correct shape. See: https://github.com/huggingface/transformers/pull/9278#issue-544709661. """ @cached_property def default_tokenizer(self): return LEDTokenizer.from_pretrained("allenai/led-base-16384") def test_inference_no_head(self): model = LEDModel.from_pretrained("allenai/led-base-16384").to(torch_device) # change to intended input input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids) with torch.no_grad(): output = model(**inputs_dict).last_hidden_state expected_shape = torch.Size((1, 1024, 768)) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = torch.tensor( [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]], device=torch_device ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) def test_inference_head(self): model = LEDForConditionalGeneration.from_pretrained("allenai/led-base-16384").to(torch_device) # change to intended input input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids) with torch.no_grad(): output = model(**inputs_dict, use_cache=False).logits expected_shape = torch.Size((1, 1024, model.config.vocab_size)) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = torch.tensor( [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]], device=torch_device ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) def test_seq_to_seq_generation(self): # this test requires 16GB of RAM hf = LEDForConditionalGeneration.from_pretrained("allenai/led-large-16384-arxiv").to(torch_device) tok = LEDTokenizer.from_pretrained("allenai/led-large-16384-arxiv") ARTICLE_LEP = r"""the lep experiments at the resonance of @xmath1-boson have tested the standard model ( sm ) at quantum level , measuring the @xmath1-decay into fermion pairs with an accuracy of one part in ten thousands . the good agreement of the lep data with the sm predictions have severely constrained the behavior of new physics at the @xmath1-pole . taking these achievements into account one can imagine that the physics of @xmath1-boson will again play the central role in the frontier of particle physics if the next generation @xmath1 factory comes true with the generated @xmath1 events several orders of magnitude higher than that of the lep . this factory can be realized in the gigaz option of the international linear collider ( ilc)@xcite . the ilc is a proposed electron - positron collider with tunable energy ranging from @xmath12 to @xmath13 and polarized beams in its first phase , and the gigaz option corresponds to its operation on top of the resonance of @xmath1 boson by adding a bypass to its main beam line . given the high luminosity , @xmath14 , and the cross section at the resonance of @xmath1 boson , @xmath15 , about @xmath16 @xmath1 events can be generated in an operational year of @xmath17 of gigaz , which implies that the expected sensitivity to the branching ratio of @xmath1-decay can be improved from @xmath18 at the lep to @xmath19 at the gigaz@xcite . in light of this , the @xmath1-boson properties , especially its exotic or rare decays which are widely believed to be sensitive to new physics , should be investigated comprehensively to evaluate their potential in probing new physics . among the rare @xmath1-decays , the flavor changing ( fc ) processes were most extensively studied to explore the flavor texture in new physics @xcite , and it was found that , although these processes are severely suppressed in the sm , their branching ratios in new physics models can be greatly enhanced to @xmath19 for lepton flavor violation decays @xcite and @xmath20 for quark flavor violation decays @xcite . besides the fc processes , the @xmath1-decay into light higgs boson(s ) is another type of rare process that was widely studied , e.g. the decay @xmath21 ( @xmath22 ) with the particle @xmath0 denoting a light higgs boson was studied in @xcite , the decay @xmath23 was studied in the two higgs doublet model ( 2hdm)@xcite and the minimal supersymmetric standard model ( mssm)@xcite , and the decay @xmath4 was studied in a model independent way @xcite , in 2hdm@xcite and also in mssm@xcite . these studies indicate that , in contrast with the kinematic forbidden of these decays in the sm , the rates of these decays can be as large as @xmath18 in new physics models , which lie within the expected sensitivity of the gigaz . in this work , we extend the previous studies of these decays to some new models and investigate these decays altogether . we are motivated by some recent studies on the singlet extension of the mssm , such as the next - to - minimal supersymmetric standard model ( nmssm ) @xcite and the nearly minimal supersymmetric standard model ( nmssm ) @xcite , where a light cp - odd higgs boson @xmath0 with singlet - dominant component may naturally arise from the spontaneous breaking of some approximate global symmetry like @xmath24 or peccei - quuin symmetry @xcite . these non - minimal supersymmetric models can not only avoid the @xmath25-problem , but also alleviate the little hierarchy by having such a light higgs boson @xmath0 @xcite . we are also motivated by that , with the latest experiments , the properties of the light higgs boson are more stringently constrained than before . so it is worth updating the previous studies . so far there is no model - independent lower bound on the lightest higgs boson mass . in the sm , it must be heavier than @xmath26 gev , obtained from the null observation of the higgs boson at lep experiments . however , due to the more complex structure of the higgs sector in the extensions of the sm , this lower bound can be significantly relaxed according to recent studies , e.g. , for the cp - odd higgs boson @xmath0 we have @xmath27 gev in the nmssm @xcite , @xmath28 gev in the nmssm @xcite , and @xmath29 gev in the lepton - specific 2hdm ( l2hdm ) @xcite . with such a light cp - odd higgs boson , the z - decay into one or more @xmath0 is open up . noting that the decay @xmath30 is forbidden due to bose symmetry , we in this work study the rare @xmath1-decays @xmath6 ( @xmath22 ) , @xmath31 and @xmath4 in a comparative way for four models , namely the type - ii 2hdm@xcite , the l2hdm @xcite , the nmssm and the nmssm . in our study , we examine carefully the constraints on the light @xmath0 from many latest experimental results . this work is organized as follows . in sec . ii we briefly describe the four new physics models . in sec . iii we present the calculations of the rare @xmath1-decays . in sec . iv we list the constraints on the four new physics models . in sec . v we show the numerical results for the branching ratios of the rare @xmath1-decays in various models . finally , the conclusion is given in sec . as the most economical way , the sm utilizes one higgs doublet to break the electroweak symmetry . as a result , the sm predicts only one physical higgs boson with its properties totally determined by two free parameters . in new physics models , the higgs sector is usually extended by adding higgs doublets and/or singlets , and consequently , more physical higgs bosons are predicted along with more free parameters involved in . the general 2hdm contains two @xmath32 doublet higgs fields @xmath33 and @xmath34 , and with the assumption of cp - conserving , its scalar potential can be parameterized as@xcite : @xmath35,\end{aligned}\ ] ] where @xmath36 ( @xmath37 ) are free dimensionless parameters , and @xmath38 ( @xmath39 ) are the parameters with mass dimension . after the electroweak symmetry breaking , the spectrum of this higgs sector includes three massless goldstone modes , which become the longitudinal modes of @xmath40 and @xmath1 bosons , and five massive physical states : two cp - even higgs bosons @xmath41 and @xmath42 , one neutral cp - odd higgs particle @xmath0 and a pair of charged higgs bosons @xmath43 . noting the constraint @xmath44 with @xmath45 and @xmath46 denoting the vacuum expectation values ( vev ) of @xmath33 and @xmath34 respectively , we choose @xmath47 as the input parameters with @xmath48 , and @xmath49 being the mixing angle that diagonalizes the mass matrix of the cp - even higgs fields . the difference between the type - ii 2hdm and the l2hdm comes from the yukawa coupling of the higgs bosons to quark / lepton . in the type - ii 2hdm , one higgs doublet @xmath34 generates the masses of up - type quarks and the other doublet @xmath33 generates the masses of down - type quarks and charged leptons ; while in the l2hdm one higgs doublet @xmath33 couples only to leptons and the other doublet @xmath34 couples only to quarks . so the yukawa interactions of @xmath0 to fermions in these two models are given by @xcite @xmath50 with @xmath51 denoting generation index . obviously , in the type - ii 2hdm the @xmath52 coupling and the @xmath53 coupling can be simultaneously enhanced by @xmath54 , while in the l2hdm only the @xmath53 coupling is enhanced by @xmath55 . the structures of the nmssm and the nmssm are described by their superpotentials and corresponding soft - breaking terms , which are given by @xcite @xmath56 where @xmath57 is the superpotential of the mssm without the @xmath25 term , @xmath58 and @xmath59 are higgs doublet and singlet superfields with @xmath60 and @xmath61 being their scalar component respectively , @xmath62 , @xmath63 , @xmath64 , @xmath65 , @xmath66 and @xmath67 are soft breaking parameters , and @xmath68 and @xmath69 are coefficients of the higgs self interactions . with the superpotentials and the soft - breaking terms , one can get the higgs potentials of the nmssm and the nmssm respectively . like the 2hdm , the higgs bosons with same cp property will mix and the mass eigenstates are obtained by diagonalizing the corresponding mass matrices : @xmath70 where the fields on the right hands of the equations are component fields of @xmath71 , @xmath72 and @xmath61 defined by @xmath73 @xmath74 and @xmath75 are respectively the cp - even and cp - odd neutral higgs bosons , @xmath76 and @xmath77 are goldstone bosons eaten by @xmath1 and @xmath78 , and @xmath79 is the charged higgs boson . so both the nmssm and nmssm predict three cp - even higgs bosons , two cp - odd higgs bosons and one pair of charged higgs bosons . in general , the lighter cp - odd higgs @xmath0 in these model is the mixture of the singlet field @xmath80 and the doublet field combination , @xmath81 , i.e. @xmath82 and its couplings to down - type quarks are then proportional to @xmath83 . so for singlet dominated @xmath0 , @xmath84 is small and the couplings are suppressed . as a comparison , the interactions of @xmath0 with the squarks are given by@xcite @xmath85 i.e. the interaction does not vanish when @xmath86 approaches zero . just like the 2hdm where we use the vevs of the higgs fields as fundamental parameters , we choose @xmath68 , @xmath69 , @xmath87 , @xmath88 , @xmath66 and @xmath89 as input parameters for the nmssm@xcite and @xmath68 , @xmath54 , @xmath88 , @xmath65 , @xmath90 and @xmath91 as input parameters for the nmssm@xcite . about the nmssm and the nmssm , three points should be noted . the first is for the two models , there is no explicit @xmath92term , and the effective @xmath25 parameter ( @xmath93 ) is generated when the scalar component of @xmath59 develops a vev . the second is , the nmssm is actually same as the nmssm with @xmath94@xcite , because the tadpole terms @xmath95 and its soft breaking term @xmath96 in the nmssm do not induce any interactions , except for the tree - level higgs boson masses and the minimization conditions . and the last is despite of the similarities , the nmssm has its own peculiarity , which comes from its neutralino sector . in the basis @xmath97 , its neutralino mass matrix is given by @xcite @xmath98 where @xmath99 and @xmath100 are @xmath101 and @xmath102 gaugino masses respectively , @xmath103 , @xmath104 , @xmath105 and @xmath106 . after diagonalizing this matrix one can get the mass eigenstate of the lightest neutralino @xmath107 with mass taking the following form @xcite @xmath108 this expression implies that @xmath107 must be lighter than about @xmath109 gev for @xmath110 ( from lower bound on chargnio mass ) and @xmath111 ( perturbativity bound ) . like the other supersymmetric models , @xmath107 as the lightest sparticle acts as the dark matter in the universe , but due to its singlino - dominated nature , it is difficult to annihilate sufficiently to get the correct density in the current universe . so the relic density of @xmath107 plays a crucial way in selecting the model parameters . for example , as shown in @xcite , for @xmath112 , there is no way to get the correct relic density , and for the other cases , @xmath107 mainly annihilates by exchanging @xmath1 boson for @xmath113 , or by exchanging a light cp - odd higgs boson @xmath0 with mass satisfying the relation @xmath114 for @xmath115 . for the annihilation , @xmath54 and @xmath25 are required to be less than 10 and @xmath116 respectively because through eq.([mass - exp ] ) a large @xmath87 or @xmath25 will suppress @xmath117 to make the annihilation more difficult . the properties of the lightest cp - odd higgs boson @xmath0 , such as its mass and couplings , are also limited tightly since @xmath0 plays an important role in @xmath107 annihilation . the phenomenology of the nmssm is also rather special , and this was discussed in detail in @xcite . in the type - ii 2hdm , l2hdm , nmssm and nmssm , the rare @xmath1-decays @xmath118 ( @xmath22 ) , @xmath3 and @xmath4 may proceed by the feynman diagrams shown in fig.[fig1 ] , fig.[fig2 ] and fig.[fig3 ] respectively . for these diagrams , the intermediate state @xmath119 represents all possible cp - even higgs bosons in the corresponding model , i.e. @xmath41 and @xmath42 in type - ii 2hdm and l2hdm and @xmath41 , @xmath42 and @xmath120 in nmssm and nmssm . in order to take into account the possible resonance effects of @xmath119 in fig.[fig1](c ) for @xmath2 and fig.[fig3 ] ( a ) for @xmath11 , we have calculated all the decay modes of @xmath119 and properly included the width effect in its propagator . as to the decay @xmath121 , two points should be noted . one is , unlike the decays @xmath6 and @xmath11 , this process proceeds only through loops mediated by quarks / leptons in the type - ii 2hdm and l2hdm , and additionally by sparticles in the nmssm and nmssm . so in most cases its rate should be much smaller than the other two . the other is due to cp - invariance , loops mediated by squarks / sleptons give no contribution to the decay@xcite . in actual calculation , this is reflected by the fact that the coupling coefficient of @xmath122 differs from that of @xmath123 by a minus sign ( see eq.([asqsq ] ) ) , and as a result , the squark - mediated contributions to @xmath121 are completely canceled out . with regard to the rare decay @xmath11 , we have more explanations . in the lowest order , this decay proceeds by the diagram shown in fig.[fig3 ] ( a ) , and hence one may think that , as a rough estimate , it is enough to only consider the contributions from fig.[fig3](a ) . however , we note that in some cases of the type - ii 2hdm and l2hdm , due to the cancelation of the contributions from different @xmath119 in fig.[fig3 ] ( a ) and also due to the potentially largeness of @xmath124 couplings ( i.e. larger than the electroweak scale @xmath125 ) , the radiative correction from the higgs - mediated loops may dominate over the tree level contribution even when the tree level prediction of the rate , @xmath126 , exceeds @xmath20 . on the other hand , we find the contribution from quark / lepton - mediated loops can be safely neglected if @xmath127 in the type - ii 2hdm and the l2hdm . in the nmssm and the nmssm , besides the corrections from the higgs- and quark / lepton - mediated loops , loops involving sparticles such as squarks , charginos and neutralinos can also contribute to the decay . we numerically checked that the contributions from squarks and charginos can be safely neglected if @xmath127 . we also calculated part of potentially large neutralino correction ( note that there are totally about @xmath128 diagrams for such correction ! ) and found they can be neglected too . since considering all the radiative corrections will make our numerical calculation rather slow , we only include the most important correction , namely that from higgs - mediated loops , in presenting our results for the four models . one can intuitively understand the relative smallness of the sparticle contribution to @xmath11 as follows . first consider the squark contribution which is induced by the @xmath129 interaction ( @xmath130 denotes the squark in chirality state ) and the @xmath131 interaction through box diagrams . because the @xmath132 interaction conserves the chirality of the squarks while the @xmath133 interaction violates the chirality , to get non - zero contribution to @xmath11 from the squark loops , at least four chiral flippings are needed , with three of them provided by @xmath131 interaction and the rest provided by the left - right squark mixing . this means that , if one calculates the amplitude in the chirality basis with the mass insertion method , the amplitude is suppressed by the mixing factor @xmath134 with @xmath135 being the off diagonal element in squark mass matrix . next consider the chargino / neutralino contributions . since for a light @xmath0 , its doublet component , parameterized by @xmath84 in eq.([mixing ] ) , is usually small , the couplings of @xmath0 with the sparticles will never be tremendously large@xcite . so the chargino / neutralino contributions are not important too . in our calculation of the decays , we work in the mass eigenstates of sparticles instead of in the chirality basis . for the type - ii 2hdm and the l2hdm , we consider the following constraints @xcite : * theoretical constraints on @xmath136 from perturbativity , unitarity and requirements that the scalar potential is finit at large field values and contains no flat directions @xcite , which imply that @xmath137 * the constraints from the lep search for neutral higgs bosons . we compute the signals from the higgs - strahlung production @xmath138 ( @xmath139 ) with @xmath140 @xcite and from the associated production @xmath141 with @xmath142 @xcite , and compare them with the corresponding lep data which have been inputted into our code . we also consider the constraints from @xmath138 by looking for a peak of @xmath143 recoil mass distribution of @xmath1-boson @xcite and the constraint of @xmath144 mev when @xmath145 @xcite . + these constraints limit the quantities such as @xmath146 \times br ( h_i \to \bar{b } b ) $ ] on the @xmath147 plane with the the subscript @xmath148 denoting the coupling coefficient of the @xmath149 interaction . they also impose a model - dependent lower bound on @xmath150 , e.g. , @xmath151 for the type - ii 2hdm ( from our scan results ) , @xmath152 for the l2hdm@xcite , and @xmath153 for the nmssm @xcite . these bounds are significantly lower than that of the sm , i.e. @xmath154 , partially because in new physics models , unconventional decay modes of @xmath155 such as @xmath156 are open up . as to the nmssm , another specific reason for allowing a significantly lighter cp - even higgs boson is that the boson may be singlet - dominated in this model . + with regard to the lightest cp - odd higgs boson @xmath0 , we checked that there is no lower bound on its mass so long as the @xmath157 interaction is weak or @xmath155 is sufficiently heavy . * the constraints from the lep search for a light higgs boson via the yukawa process @xmath158 with @xmath22 and @xmath61 denoting a scalar @xcite . these constraints can limit the @xmath159 coupling versus @xmath160 in new physics models . * the constraints from the cleo - iii limit on @xmath161 and the latest babar limits on @xmath162 . these constraints will put very tight constraints on the @xmath163 coupling for @xmath164 . in our analysis , we use the results of fig.8 in the second paper of @xcite to excluded the unfavored points . * the constraints from @xmath165 couplings . since the higgs sector can give sizable higher order corrections to @xmath165 couplings , we calculate them to one loop level and require the corrected @xmath165 couplings to lie within the @xmath166 range of their fitted value . the sm predictions for the couplings at @xmath1-pole are given by @xmath167 and @xmath168 @xcite , and the fitted values are given by @xmath169 and @xmath170 , respectively@xcite . we adopt the formula in @xcite to the 2hdm in our calculation . * the constraints from @xmath171 leptonic decay . we require the new physics correction to the branching ratio @xmath172 to be in the range of @xmath173 @xcite . we use the formula in @xcite in our calculation . + about the constraints ( 5 ) and ( 6 ) , two points should be noted . one is all higgs bosons are involved in the constraints by entering the self energy of @xmath171 lepton , the @xmath174 vertex correction or the @xmath175 vertex correction , and also the box diagrams for @xmath176@xcite . since the yukawa couplings of the higgs bosons to @xmath171 lepton get enhanced by @xmath54 and so do the corrections , @xmath54 must be upper bounded for given spectrum of the higgs sector . generally speaking , the lighter @xmath0 is , the more tightly @xmath54 is limited@xcite . the other point is in the type - ii 2hdm , @xmath177 , b - physics observables as well as @xmath178 decays discussed above can constraint the model in a tighter way than the constraints ( 5 ) and ( 6 ) since the yukawa couplings of @xmath171 lepton and @xmath179 quark are simultaneously enhanced by @xmath54 . but for the l2hdm , because only the yukawa couplings of @xmath171 lepton get enhanced ( see eq.[yukawa ] ) , the constraints ( 5 ) and ( 6 ) are more important in limiting @xmath54 . * indirect constraints from the precision electroweak observables such as @xmath180 , @xmath181 and @xmath182 , or their combinations @xmath183 @xcite . we require @xmath184 to be compatible with the lep / sld data at @xmath185 confidence level@xcite . we also require new physics prediction of @xmath186 is within the @xmath187 range of its experimental value . the latest results for @xmath188 are @xmath189 ( measured value ) and @xmath190 ( sm prediction ) for @xmath191 gev @xcite . in our code , we adopt the formula for these observables presented in @xcite to the type - ii 2hdm and the l2hdm respectively . + in calculating @xmath180 , @xmath181 and @xmath182 , we note that these observables get dominant contributions from the self energies of the gauge bosons @xmath1 , @xmath192 and @xmath193 . since there is no @xmath194 coupling or @xmath195 coupling , @xmath0 must be associated with the other higgs bosons to contribute to the self energies . so by the uv convergence of these quantities , one can infer that , for the case of a light @xmath0 and @xmath196 , these quantities depend on the spectrum of the higgs sector in a way like @xmath197 at leading order , which implies that a light @xmath0 can still survive the constraints from the precision electroweak observables given the splitting between @xmath150 and @xmath198 is moderate@xcite . * the constraints from b physics observables such as the branching ratios for @xmath199 , @xmath200 and @xmath201 , and the mass differences @xmath202 and @xmath203 . we require their theoretical predications to agree with the corresponding experimental values at @xmath187 level . + in the type - ii 2hdm and the l2hdm , only the charged higgs boson contributes to these observables by loops , so one can expect that @xmath198 versus @xmath54 is to be limited . combined analysis of the limits in the type - ii 2hdm has been done by the ckmfitter group , and the lower bound of @xmath204 as a function of @xmath87 was given in fig.11 of @xcite . this analysis indicates that @xmath198 must be heavier than @xmath205 at @xmath185 c.l . regardless the value of @xmath54 . in this work , we use the results of fig.11 in @xcite to exclude the unfavored points . as for the l2hdm , b physics actually can not put any constraints@xcite because in this model the couplings of the charged higgs boson to quarks are proportional to @xmath206 and in the case of large @xmath54 which we are interested in , they are suppressed . in our analysis of the l2hdm , we impose the lep bound on @xmath198 , i.e. @xmath207@xcite . * the constraints from the muon anomalous magnetic moment @xmath208 . now both the theoretical prediction and the experimental measured value of @xmath208 have reached a remarkable precision , but a significant deviation still exists : @xmath209 @xcite . in the 2hdm , @xmath208 gets additional contributions from the one - loop diagrams induced by the higgs bosons and also from the two - loop barr - zee diagrams mediated by @xmath0 and @xmath155@xcite . if the higgs bosons are much heavier than @xmath25 lepton mass , the contributions from the barr - zee diagrams are more important , and to efficiently alleviate the discrepancy of @xmath208 , one needs a light @xmath0 along with its enhanced couplings to @xmath25 lepton and also to heavy fermions such as bottom quark and @xmath171 lepton to push up the effects of the barr - zee diagram@xcite . the cp - even higgs bosons are usually preferred to be heavy since their contributions to @xmath208 are negative . + in the type - ii 2hdm , because @xmath54 is tightly constrained by the process @xmath210 at the lep@xcite and the @xmath178 decay@xcite , the barr - zee diagram contribution is insufficient to enhance @xmath208 to @xmath187 range around its measured value@xcite . so in our analysis , we require the type - ii 2hdm to explain @xmath208 at @xmath211 level . while for the l2hdm , @xmath54 is less constrained compared with the type - ii 2hdm , and the barr - zee diagram involving the @xmath171-loop is capable to push up greatly the theoretical prediction of @xmath208@xcite . therefore , we require the l2hdm to explain the discrepancy at @xmath187 level . + unlike the other constraints discussed above , the @xmath208 constraint will put a two - sided bound on @xmath54 since on the one hand , it needs a large @xmath54 to enhance the barr - zee contribution , but on the other hand , too large @xmath54 will result in an unacceptable large @xmath208 . * since this paper concentrates on a light @xmath0 , the decay @xmath212 is open up with a possible large decay width . we require the width of any higgs boson to be smaller than its mass to avoid a too fat higgs boson@xcite . we checked that for the scenario characterized by @xmath213 , the coefficient of @xmath214 interaction is usually larger than the electroweak scale @xmath125 , and consequently a large decay width is resulted . for the nmssm and nmssm , the above constraints become more complicated because in these models , not only more higgs bosons are involved in , but also sparticles enter the constraints . so it is not easy to understand some of the constraints intuitively . take the process @xmath199 as an example . in the supersymmetric models , besides the charged higgs contribution , chargino loops , gluino loops as well as neutralino loops also contribute to the process@xcite , and depending on the susy parameters , any of these contributions may become dominated over or be canceled by other contributions . as a result , although the charged higgs affects the process in the same way as that in the type - ii 2hdm , charged higgs as light as @xmath215 is still allowed even for @xmath216@xcite . since among the constraints , @xmath208 is rather peculiar in that it needs new physics to explain the discrepancy between @xmath217 and @xmath218 , we discuss more about its dependence on susy parameters . in the nmssm and the nmssm , @xmath208 receives contributions from higgs loops and neutralino / chargino loops . for the higgs contribution , it is quite similar to that of the type - ii 2hdm except that more higgs bosons are involved in@xcite . for the neutralino / chargino contribution , in the light bino limit ( i.e. @xmath219 ) , it can be approximated by@xcite @xmath220 for @xmath221 with @xmath222 being smuon mass . so combining the two contributions together , one can learn that a light @xmath0 along with large @xmath54 and/or light smuon with moderate @xmath87 are favored to dilute the discrepancy . because more parameters are involved in the constraints on the supersymmetric models , we consider following additional constraints to further limit their parameters : * direct bounds on sparticle masses from the lep1 , the lep2 and the tevatron experiments @xcite . * the lep1 bound on invisible z decay @xmath223 ; the lep2 bound on neutralino production @xmath224 and @xmath225@xcite . * dark matter constraints from the wmap relic density 0.0975 @xmath226 0.1213 @xcite . note that among the above constraints , the constraint ( 2 ) on higgs sector and the constraint ( c ) on neutralino sector are very important . this is because in the supersymmetric models , the sm - like higgs is upper bounded by about @xmath227 at tree level and by about @xmath228 at loop level , and that the relic density restricts the lsp annihilation cross section in a certain narrow range . in our analysis of the nmssm , we calculate the constraints ( 3 ) and ( 5 - 7 ) by ourselves and utilize the code nmssmtools @xcite to implement the rest constraints . we also extend nmssmtools to the nmssm to implement the constraints . for the extension , the most difficult thing we faced is how to adapt the code micromegas@xcite to the nmssm case . we solve this problem by noting the following facts : * as we mentioned before , the nmssm is actually same as the nmssm with the trilinear singlet term setting to zero . so we can utilize the model file of the nmssm as the input of the micromegas and set @xmath229 . * since in the nmssm , the lsp is too light to annihilate into higgs pairs , there is no need to reconstruct the effective higgs potential to calculate precisely the annihilation channel @xmath230 with @xmath61 denoting any of higgs bosons@xcite . we thank the authors of the nmssmtools for helpful discussion on this issue when we finish such extension@xcite . with the above constraints , we perform four independent random scans over the parameter space of the type - ii 2hdm , the l2hdm , the nmssm and the nmssm respectively . we vary the parameters in following ranges : @xmath231 for the type - ii 2hdm , @xmath232 for the l2hdm , @xmath233 for the nmssm , and @xmath234 for the nmssm . in performing the scans , we note that for the nmssm and the nmssm , some constraints also rely on the gaugino masses and the soft breaking parameters in the squark sector and the slepton sector . since these parameters affect little on the properties of @xmath0 , we fix them to reduce the number of free parameters in our scan . for the squark sector , we adopt the @xmath235 scenario which assumes that the soft mass parameters for the third generation squarks are degenerate : @xmath236 800 gev , and that the trilinear couplings of the third generation squarks are also degenerate , @xmath237 with @xmath238 . for the slepton sector , we assume all the soft - breaking masses and trilinear parameters to be 100 gev . this setting is necessary for the nmssm since this model is difficult to explain the muon anomalous moment at @xmath239 level for heavy sleptons@xcite . finally , we assume the grand unification relation @xmath240 for the gaugino masses with @xmath241 being fine structure constants of the different gauge group . with large number of random points in the scans , we finally get about @xmath242 , @xmath243 , @xmath244 and @xmath242 samples for the type - ii 2hdm , the l2hdm , the nmssm and the nmssm respectively which survive the constraints and satisfy @xmath245 . analyzing the properties of the @xmath0 indicates that for most of the surviving points in the nmssm and the nmssm , its dominant component is the singlet field ( numerically speaking , @xmath246 ) so that its couplings to the sm fermions are suppressed@xcite . our analysis also indicates that the main decay products of @xmath0 are @xmath247 for the l2hdm@xcite , @xmath248 ( dominant ) and @xmath247 ( subdominant ) for the type - ii 2hdm , the nmssm and the nmssm , and in some rare cases , neutralino pairs in the nmssm@xcite . in fig.[fig4 ] , we project the surviving samples on the @xmath249 plane . this figure shows that the allowed range of @xmath54 is from @xmath250 to @xmath251 in the type - ii 2hdm , and from @xmath252 to @xmath253 in the l2hdm . just as we introduced before , the lower bounds of @xmath254 come from the fact that we require the models to explain the muon anomalous moment , while the upper bound is due to we have imposed the constraint from the lep process @xmath255 , which have limited the upper reach of the @xmath256 coupling for light @xmath61 @xcite(for the dependence of @xmath256 coupling on @xmath54 , see sec . this figure also indicates that for the nmssm and the nmssm , @xmath54 is upper bounded by @xmath257 . for the nmssm , this is because large @xmath87 can suppress the dark matter mass to make its annihilation difficult ( see @xcite and also sec . ii ) , but for the nmssm , this is because we choose a light slepton mass so that large @xmath54 can enhance @xmath208 too significantly to be experimentally unacceptable . we checked that for the slepton mass as heavy as @xmath258 , @xmath259 is still allowed for the nmssm . in fig.[fig5 ] and fig.[fig6 ] , we show the branching ratios of @xmath260 and @xmath261 respectively . fig.[fig5 ] indicates , among the four models , the type - ii 2hdm predicts the largest ratio for @xmath260 with its value varying from @xmath262 to @xmath263 . the underlying reason is in the type - ii 2hdm , the @xmath264 coupling is enhanced by @xmath54 ( see fig.[fig4 ] ) , while in the other three model , the coupling is suppressed either by @xmath265 or by the singlet component of the @xmath0 . fig.[fig6 ] shows that the l2hdm predicts the largest rate for @xmath266 with its value reaching @xmath5 in optimum case , and for the other three models , the ratio of @xmath261 is at least about one order smaller than that of @xmath267 . this feature can be easily understood from the @xmath268 coupling introduced in sect . we emphasize that , if the nature prefers a light @xmath0 , @xmath260 and/or @xmath269 in the type - ii 2hdm and the l2hdm will be observable at the gigaz . then by the rates of the two decays , one can determine whether the type - ii 2hdm or the l2hdm is the right theory . on the other hand , if both decays are observed with small rates or fail to be observed , the singlet extensions of the mssm are favored . in fig.[fig7 ] , we show the rate of @xmath3 as the function of @xmath270 . this figure indicates that the branching ratio of @xmath121 can reach @xmath271 , @xmath272 , @xmath273 and @xmath274 for the optimal cases of the type - ii 2hdm , the l2hdm , the nmssm and the nmssm respectively , which implies that the decay @xmath121 will never be observable at the gigaz if the studied model is chosen by nature . the reason for the smallness is , as we pointed out before , that the decay @xmath121 proceeds only at loop level . comparing the optimum cases of the type - ii 2hdm , the nmssm and the nmssm shown in fig.5 - 7 , one may find that the relation @xmath275 holds for any of the decays . this is because the decays are all induced by the yukawa couplings with similar structure for the models . in the supersymmetric models , the large singlet component of the light @xmath0 is to suppress the yukawa couplings , and the @xmath0 in the nmssm has more singlet component than that in the nmssm . next we consider the decay @xmath11 , which , unlike the above decays , depends on the higgs self interactions . in fig.[fig8 ] we plot its rate as a function of @xmath270 and this figure indicates that the @xmath276 may be the largest among the ratios of the exotic @xmath1 decays , reaching @xmath277 in the optimum cases of the type - ii 2hdm , the l2hdm and the nmssm . the underlying reason is , in some cases , the intermediate state @xmath119 in fig.[fig3 ] ( a ) may be on - shell . in fact , we find this is one of the main differences between the nmssm and the nmssm , that is , in the nmssm , @xmath119 in fig.[fig3 ] ( a ) may be on - shell ( corresponds to the points with large @xmath278 ) while in the nmssm , this seems impossible . so we conclude that the decay @xmath11 may serve as an alternative channel to test new physics models , especially it may be used to distinguish the nmssm from the nmssm if the supersymmetry is found at the lhc and the @xmath11 is observed at the gigaz with large rate . before we end our discussion , we note that in the nmssm , the higgs boson @xmath0 may be lighter than @xmath279 without conflicting with low energy data from @xmath178 decays and the other observables ( see fig.[fig4]-[fig8 ] ) . in this case , @xmath0 is axion - like as pointed out in @xcite . we checked that , among the rare @xmath1 decays discussed in this paper , the largest branching ratio comes from @xmath280 which can reach @xmath281 . since in this case , the decay product of @xmath0 is highly collinear muon pair , detecting the decay @xmath280 may need some knowledge about detectors , which is beyond our discussion . in this paper , we studied the rare @xmath1-decays @xmath2 ( @xmath7 ) , @xmath282 and @xmath4 in the type - ii 2hdm , lepton - specific 2hdm , nmssm and nmssm , which predict a light cp - odd higgs boson @xmath0 . in the parameter space allowed by current experiments , the branching ratio can be as large as @xmath5 for @xmath118 , @xmath8 for @xmath3 and @xmath9 for @xmath4 , which implies that the decays @xmath2 and @xmath283 may be accessible at the gigaz option . since different models predict different size of branching ratios , these decays can be used to distinguish different model through the measurement of these rare decays . this work was supported in part by hastit under grant no . 2009hastit004 , by the national natural science foundation of china ( nnsfc ) under grant nos . 10821504 , 10725526 , 10635030 , 10775039 , 11075045 and by the project of knowledge innovation program ( pkip ) of chinese academy of sciences under grant no . . for some reviews , see , e.g. , m. a. perez , g. tavares - velasco and j. j. toscano , int . j. mod . a * 19 * , 159 ( 2004 ) ; j. m. yang , arxiv:1006.2594 . j. i. illana , m. masip , 67 , 035004 ( 2003 ) ; j. cao , z. xiong , j. m. yang , 32 , 245 ( 2004 ) . d. atwood _ et al_. , 66 , 093005 ( 2002 ) . j. kalinowski , and s. pokorski , 219 , 116 ( 1989 ) ; a. djouadi , p. m. zerwas and j. zunft , 259 , 175 ( 1991 ) ; a. djouadi , j. kalinowski , and p. m. zerwas , z. phys . c * 54 * , 255 ( 1992 ) . m. krawczyk , _ et al . _ , 19 , 463 ( 2001 ) ; 8 , 495 ( 1999 ) . j. f. gunion , g. gamberini and s. f. novaes , 38 , 3481 ( 1988 ) ; thomas j. weiler and tzu - 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ARTICLE_MAGNET = r"""it is well known that the classical magnetoresistance ( mr ) in metals or semiconductors with a closed free electron fermi surface increases quadratically with increasing magnetic field @xmath2 for @xmath3 and saturates when @xmath4 . here @xmath5 is the zero - magnetic - field mobility . hence , the extraordinarily high and linear mr ( lmr ) , which breaks this familiar rule , has been gaining much attention as soon as its discovery . in the past decade , this unexpected lmr has been reported in silver chalcogenide,@xcite indium antimonide,@xcite silicon,@xcite mnas - gaas composite material,@xcite and graphene.@xcite kapitza s linear law@xcite indicates that the metal shows a magnetoresistance linear in perpendicular magnetic field when it has an open fermi surface and a mean free path longer than the electronic larmor radius . recently , another two models , irrespective of the open fermi surface , have been constructed to provide possible mechanisms for the lmr phenomenon . abrikosov suggested a quantum - limit origin of lmr for the homogenous system with a gapless linear energy spectrum.@xcite his model requires that landau levels are well formed and the carrier concentration is small that all electrons occupy only the lowest landau band . alternatively , parish and littlewood developed a classical model without involving linear spectrum.@xcite ignoring the concrete microscopic mechanism , they attributed this unusual mr to the mobility fluctuations in a strongly inhomogenous system . topological insulators@xcite ( tis ) are novel materials with a full energy gap in bulk , while there are gapless surface states . due to its unique band structure with only one helical dirac cone and linear energy dispersion,@xcite the surface states of the ti bi@xmath0se@xmath1 become an excellent platform for the study of quantum - limit lmr . the recent experiment in this flat surface system , however , reported that a large positive mr , which becomes very linear above a characteristic field of @xmath6@xmath7@xmath8 t , was observed even in an opposite situation where the carrier sheet density is high that electrons occupy more than one landau levels.@xcite moreover , they found that raising temperature to room temperature almost has no influence on the observed lmr . it is striking that this observation is in conflict with abrikosov s model and also with the classical parish - littlewood model . so far a reliable theoretical scheme capable of explaining this novel experiment has still been lacking . in this paper , we generalize the balance - equation approach@xcite to a system modeling the surface states of a three - dimensional ti to investigate the two - dimensional magnetotransport in it . we find that a positive , nonsaturating and dominantly linear magnetoresistance can appear within quite wide magnetic - field range in the ti surface state having a positive and finite effective g - factor . this linear magnetoresistance shows up in the system of high carrier concentration and low mobility when electrons are in extended states and spread over many smeared landau levels , and persists up to room temperature , providing a possible mechanism for the recently observed linear magnetoresistance in topological insulator bi@xmath0se@xmath1 nanoribbons.@xcite we consider the surface state of a bi@xmath0se@xmath1-type large bulk gap ti in the @xmath9-@xmath10 plane under the influence of a uniform magnetic field @xmath11 applied along the @xmath12 direction.@xcite following the experimental observation,@xcite we assume that the fermi energy locates in the gap of the bulk band and above the dirac point , i.e. the surface carriers are electrons . further , the separations of the fermi energy from the bottom of bulk band and dirac point are much larger than the highest temperature ( @xmath13 ) considered in this work . hence , the contribution from the bulk band to the magnetotransport is negligible . these electrons , scattered by randomly distributed impurities and by phonons , are driven by a uniform in - plane electric field @xmath14 in the topological surface . the hamiltonian of this many - electron and phonon system consists of an electron part @xmath15 , a phonon part @xmath16 , and electron - impurity and electron - phonon interactions @xmath17 and @xmath18 : @xmath19 here , the electron hamiltonian is taken in the form @xmath20 , \ ] ] in which @xmath21 , @xmath22 , @xmath23 and @xmath24 , stand , respectively , for the canonical momentum , coordinate , momentum and spin operators of the @xmath25th electron having charge @xmath26 , @xmath27 is the vector potential of the perpendicular magnetic field @xmath28 in the landau gauge , @xmath29 is the fermi velocity , @xmath30 is the effective g - factor of the surface electron , and @xmath31 is the bohr magneton with @xmath32 the free electron mass . the sum index @xmath25 in eq.([helectron ] ) goes over all electrons of total number @xmath33 in the surface state of unit area . in the frame work of balance equation approach,@xcite the two - dimensional center - of - mass ( c.m . ) momentum and coordinate @xmath34 and @xmath35 , and the relative - electron momenta and coordinates @xmath36 and @xmath37 are introduced to write the hamiltonian @xmath15 into the sum of a single - particle c.m . part @xmath38 and a many - particle relative - electron part @xmath39 : @xmath40 , with @xmath41.\end{aligned}\ ] ] in this , @xmath42 is the canonical momentum of the center - of - mass and @xmath43 is the canonical momentum for the @xmath25th relative electron . here we have also introduced c.m . spin operators @xmath44 and @xmath45 . the commutation relations between the c.m . spin operators @xmath46 and @xmath47 and the spin operators @xmath48 , @xmath49 and @xmath50 of the @xmath25th electron are of order of @xmath51 : @xmath52= n^{-1}2\,{\rm i}\,\varepsi lon_{\beta_1\beta_2\beta_3}\sigma_j^{\beta_3}$ ] with @xmath53 . therefore , for a macroscopic large @xmath33 system , the c.m . part @xmath38 actually commutes with the relative - electron part @xmath54 in the hamiltonian , i.e. the c.m . motion and the relative motion of electrons are truly separated from each other . the couplings between the two emerge only through the electron impurity and electron phonon interactions . furthermore , the electric field @xmath55 shows up only in @xmath38 . and , in view of @xmath56={\rm i}\delta_{\alpha \beta}(\delta_{ij}-1/n)\simeq { \rm i}\delta_{\alpha\beta}\delta_{ij}$ ] , i.e. the relative - electron momenta and coordinates can be treated as canonical conjugate variables , the relative - motion part @xmath54 is just the hamiltonian of @xmath33 electrons in the surface state of ti in the magnetic field without the presence of the electric field . in terms of the c.m . coordinate @xmath57 and the relative electron density operator @xmath58 , the electron impurity and electron phonon interactions can be written as@xcite @xmath59 here @xmath60 and @xmath61 are respectively the impurity potential ( an impurity at randomly distributed position @xmath62 ) and electron phonon coupling matrix element in the plane - wave representation , and @xmath63 with @xmath64 and @xmath65 being the creation and annihilation operators for a phonon of wavevector @xmath66 in branch @xmath67 having frequency @xmath68 . velocity ( operator ) @xmath69 is the time variation of its coordinate : @xmath70= v_{\rm f}(\sigma_{\rm c}^y\ , \hat{i}-\sigma_{\rm c}^x\ , \hat{j})$ ] . to derive a force - balance equation for steady state transport we consider the heisenberg equation for the rate of change of the c.m . canonical momentum @xmath71 : @xmath72= - n e({\bm v}\times { \bm b})- n e{\bm e}+{\bm { f}}_{\rm i}+{\bm { f}}_{\rm p},\ ] ] in which the frictional forces @xmath73 and @xmath74 share the same expressions as given in ref .. the statistical average of the operator equation can be determined to linear order in the electron impurity and electron phonon interactions @xmath17 and @xmath18 with the initial density matrix @xmath75 at temperature @xmath76 when the in - plane electric field @xmath77 is not strong . for steady - transport states we have @xmath78 , leading to a force - balance equation of the form @xmath79 here @xmath80 , the statistically averaged velocity of the moving center - of - mass , is identified as the average rate of change of its position , i.e. the drift velocity of the electron system driven by the electric field @xmath77 , and @xmath81 and @xmath82 are frictional forces experienced by the center - of - mass due to impurity and phonon scatterings : @xmath83,\label{fp}\end{aligned}\ ] ] in which @xmath84 is the bose distribution function , @xmath85 , and @xmath86 stands for the imaginary part of the fourier spectrum of the relative - electron density correlation function defined by @xmath87\big\rangle_{0},\ ] ] where @xmath88 and @xmath89 denotes the statistical averaging over the initial density matrix @xmath90.@xcite the force - balance equation describes the steady - state two - dimensional magnetotransport in the surface state of a ti . note that the frictional forces @xmath81 and @xmath82 are in the opposite direction of the drift velocity @xmath91 and their magnitudes are functions of @xmath92 only . with the drift velocity @xmath93 in the @xmath9 direction , the force - balance equation eq . yields a transverse resistivity @xmath94 , and a longitudinal resistivity @xmath95 . the linear one is in the form @xmath96 for calculating the electron density correlation function @xmath97 we proceed in the landau representation.@xcite the landau levels of the single - particle hamiltonian @xmath98 of the relative - electron system in the absence of electric field are composed of a positive `` @xmath99 '' and a negative `` @xmath100 '' branch@xcite @xmath101 with @xmath102 and @xmath103 , and a zero ( @xmath104 ) level @xmath105 the corresponding landau wave functions are @xmath106 and @xmath107 for @xmath108 ; and @xmath109 for @xmath104 . here @xmath110 is the wavevector of the system along @xmath9 direction ; @xmath111 with @xmath112 ; and @xmath113 is the harmonic oscillator eigenfunction with @xmath114 being the hermite polynomial , @xmath115 , and @xmath116 . each landau level contains @xmath117 electron states for system of unit surface area . the positive branch @xmath118 and the @xmath104 level @xmath119 of the above energy spectra are indeed quite close to those of the surface states in the bulk gap of bi@xmath0se@xmath1-family materials derived from microscopic band calculation.@xcite the landau levels are broadened due to impurity , phonon and electron - electron scatterings . we model the imaginary part of the retarded green s function , or the density - of - states , of the broadened landau level @xmath120 ( written for `` + ' ' -branch and @xmath104 levels ) , using a gaussian - type form:@xcite @xmath121,\ ] ] with a half - width @xmath122 of the form:@xcite @xmath123^{1/2}$ ] . here @xmath124 is the single - particle lifetime and @xmath125 is the cyclotron frequency of linear - energy - dispersion system with @xmath126 being the zero - temperature fermi level . using a semi - empirical parameter @xmath127 to relate @xmath124 with the transport scattering time @xmath128 , and expressing @xmath129 with the zero - field mobility @xmath5 at finite temperature,@xcite we can write the landau - level broadening as @xmath130^{1/2}.\ ] ] in the present study we consider the case of @xmath120-doping , i.e. the fermi level is high enough above the energy zero of the dirac cone in the range of `` + ' ' -branch levels and the states of `` @xmath100''-branch levels are completely filled , that they are irrelevant to electron transport . special attention has to be paid to the @xmath104 level , since , depending on the direction of exchange potential the effective g - factor of a ti surface state , @xmath30 , can be positive , zero or negative.@xcite the sign and magnitude of the effective g - factor determines how many states of the zero level should be included in or excluded from the available states for electron occupation in the case of @xmath120-doping at a magnetic field . ( i ) if @xmath131 , the @xmath104 level center is exactly at @xmath132 and the system is electron - hole symmetric . the total number of negative energy states ( including the states of the lower half of the @xmath104 level and states of the @xmath100"-branch levels ) and that of positive energy states ( including the states of the upper half of the @xmath104 level and states of the @xmath99"-branch levels ) do not change when changing magnetic field . therefore , the lower - half negative energy states of this level are always filled and the upper - half positive - energy states of it are available for the occupation of particles which are counted as electrons participating in transport in the case of @xmath120-doping . ( ii ) for a finite positive @xmath133 , the @xmath104 level @xmath134 moves downward to negative energy and its distance to the nearest @xmath100"-branch level is @xmath135 closer than to the nearest + " -branch level at finite magnetic field strength @xmath2 . this is equivalent to the opening of an increasingly enlarged ( with increasing @xmath2 ) energy gap between the + " -branch states and the states of the zero - level and the @xmath100"-branch levels . the opening of a sufficient energy gap implies that with increasing magnetic field the states in the + " -branch levels would no longer shrink into the zero - level , and thus the @xmath104 level should be completely excluded from the conduction band , i.e. only particles occupying the + " -branch states are counted as electrons participating in transport in the case of @xmath120-doping , when the magnetic field @xmath2 gets larger than a certain value ( depending on the magnitude of @xmath30 ) . ( iii ) for a finite negative @xmath136 , the @xmath104 level @xmath134 moves upward to positive energy and an increasingly enlarged energy gap will be opened between the states of the zero - level and the + " -branch and the states of @xmath100"-branch levels , and particles occupying the @xmath104 level and + " -branch states are electrons participating in transport when the magnetic field @xmath2 gets larger than a certain value . as a result , the experimentally accessible sheet density @xmath33 of electrons participating in transport is related to the fermi energy @xmath137 by the following equation valid at finite @xmath30 for the magnetic field @xmath2 larger than a certain value : @xmath138 in which @xmath139 + 1\}^{-1}$ ] is the fermi distribution function at temperature @xmath76 and the summation index @xmath120 goes over @xmath140 for @xmath133 , or @xmath141 for @xmath136 . in the case of @xmath131 , @xmath142\ ] ] valid for arbitrary magnetic field , in which @xmath143 . the imaginary part of relative - electron density correlation function in the presence of a magnetic field , @xmath86 , can be expressed in the landau representation as@xcite @xmath144 in which the transform factor @xmath145 ^ 2,\end{aligned}\ ] ] with @xmath146 , @xmath147 , @xmath148 , and @xmath149 being associated laguerre polynomials . the landau - representation correlation function @xmath150 in eq.([piqw ] ) can be constructed with the imaginary part of the retarded green s function @xmath151 , or the density - of - states , of the @xmath120th landau level as@xcite @xmath152\nonumber\\ & \hspace{1.2cm}\times{\rm im}g_n(\epsilon+\omega){\rm im}g_{n'}(\epsilon).\end{aligned}\ ] ] the summation indices @xmath120 and @xmath153 in eq.([piqw ] ) are taken over @xmath140 for @xmath133 , or @xmath154 for @xmath136 . in the case of @xmath131 , eq.([piqw ] ) still works and the summation indices @xmath120 and @xmath153 go over @xmath154 but with @xmath155 replaced by @xmath156 in eq.([p2nn ] ) . numerical calculations are performed for the magnetoresistivity @xmath157 of surface state in a uniform ti bi@xmath0se@xmath1 . at zero temperature the elastic scattering contributing to the resistivity is modeled by a coulomb potential due to charged impurities:@xcite @xmath158 with @xmath159 being the impurity density , which is determined by the zero - magnetic - field mobility @xmath5 . at temperatures higher than @xmath160,@xcite phonon scatterings play increasingly important role and the dominant inelastic contribution comes from optical phonons . for this polar material , the scattering by optical phonons via the deformation potential can be neglected . hence , we take account of inelastic scattering from optical phonons via frhlich coupling : @xmath161 . in the numerical calculation we use the following parameters:@xcite fermi velocity @xmath162 , static dielectric constant @xmath163 , optical dielectric constant @xmath164 , and phonon energy @xmath165 . the broadening parameter is taken to be @xmath166 . as a function of the magnetic field @xmath2 having different effective g - factors : @xmath167 and @xmath168 for a ti surface system with electron sheet density @xmath169 in the cases of zero - magnetic - field mobility @xmath170 ( a ) and @xmath171 ( b ) . several integer - number positions of filling factor @xmath172 are marked in ( b).,scaledwidth=40.0% ] fig.[diffg ] shows the calculated magnetoresistivity @xmath157 versus the magnetic field strength @xmath2 for a ti surface system with electron sheet density @xmath169 but having different effective g - factors : @xmath167 and @xmath168 for two values of zero - magnetic - field mobility @xmath170 and @xmath171 , representing different degree of landau - level broadening . in the case without zeeman splitting ( @xmath131 ) the resistivity @xmath157 exhibits almost no change with changing magnetic field up to 10 t , except the shubnikov - de haas ( sdh ) oscillation showing up in the case of @xmath171 . this kind of magnetoresistance behavior was indeed seen experimentally in the electron - hole symmetrical massless system of single - layer graphene.@xcite in the case of a positive g - factor , @xmath173 , the magnetoresistivity increases linearly with increasing magnetic field ; while for a negative g - factor , @xmath174 , the magnetoresistivity decreases linearly with increasing magnetic field . is shown as a function of the magnetic field @xmath2 for different values of zero - magnetic - field mobility : ( a ) @xmath175 , ( b ) @xmath176 , ( c ) @xmath177 , ( d ) @xmath178 , ( e ) @xmath179 , and ( f ) @xmath180 . the inset of ( a ) illustrates the same for a larger magnetic - field range @xmath181 . the filling factor @xmath182 is plotted versus the magnetic field in ( f ) ; and several integer - number positions of @xmath182 are also marked in ( d ) and ( e ) . here the surface electron density @xmath169 and the lattice temperature @xmath183.,scaledwidth=47.0% ] in the following we will give more detailed examination on the linearly increasing magnetoresistance in the positive @xmath30 case . fig.[rhob ] shows the calculated resistivity @xmath157 versus the magnetic field strength @xmath2 at lattice temperature @xmath183 for system of carrier sheet density @xmath169 and @xmath173 , having different zero - field mobility @xmath184 and @xmath180 . all resistivity curves for mobility @xmath185 exhibit clear linearity in the magnetic - field range and appear no tendency of saturation at the highest field shown in the figure . especially , for the case @xmath170 , the linear behavior extends even up to the magnetic field of @xmath186 , as illustrated in the inset of fig.[rhob](a ) . this feature contradicts the classical mr which saturates at sufficiently large magnetic field @xmath187 . note that here we only present the calculated @xmath157 for magnetic field @xmath2 larger than @xmath188 t , for which a sufficient energy gap @xmath135 is assumed to open that with further increase of the magnetic field the states in the `` + ' ' -branch levels no longer shrink into the zero level and thus it should be excluded from the conduction band . this is of course not true for very weak magnetic field . when @xmath189 the energy gap @xmath190 , the situation becomes similar to the case of @xmath131 : the whole upper half of the zero - level states are available to electron occupation and we should have a flat resistivity @xmath157 when changing magnetic field . with increasing @xmath2 the portion of the zero - level states available to conduction electrons decreases until the magnetic field reaches @xmath191 . as a result the resistivity @xmath157 should exhibit a crossover from a flat changing at small @xmath2 to positively linear increasing at @xmath192 . this is just the behavior observed in the ti bi@xmath0se@xmath1.@xcite note that in the case of @xmath170 , the broadened landau - level widths are always larger than the neighboring level interval : @xmath193 , which requires @xmath194 ^ 2 $ ] , even for the lowest landau level @xmath195 , i.e. the whole landau - level spectrum is smeared . with increasing the zero - field mobility the magnitude of resistivity @xmath157 decreases , and when the broadened landau - level width becomes smaller than the neighboring level interval , @xmath196 , a weak sdh oscillation begin to occur around the linearly - dependent average value of @xmath157 at higher portion of the magnetic field range , as seen in fig.[rhob](c ) , ( d ) and ( e ) for @xmath197 and @xmath198 . on the other hand , in the case of large mobility , e.g. @xmath199 , where the broadened landau - level widths @xmath200 are much smaller than the neighboring level interval even for level index @xmath120 as large as @xmath201 , the magnetoresistivity shows pronounced sdh oscillation and the linear - dependent behavior disappears , before the appearance of quantum hall effect,@xcite as shown in fig.[rhob](f ) . abrikosov s model for the lmr requires the applied magnetic field large enough to reach the quantum limit at which all the carriers are within the lowest landau level,@xcite while it is obvious that more than one landau levels are occupied in the experimental samples in the field range in which the linear and non - saturating magnetoresistivity was observed.@xcite for the given electron surface density @xmath202 , the number of occupied landau levels , or the filling factor @xmath172 , at different magnetic fields is shown in fig.[rhob](f ) , as well as in the fig.[rhob](d ) and ( e ) , where the integer - number positions of @xmath203 , i.e. filling up to entire @xmath182 landau levels , coincide with the minima of the density - of - states or the dips of sdh oscillation . this is in contrast with @xmath131 case , where the integer number of @xmath203 , which implies a filling up to the center position of the @xmath182th landau levels , locates at a peak of sdh oscillation , as shown in fig.[diffg]b . the observed sdh oscillations in the bi@xmath0se@xmath1 nanoribbon exhibiting nonsaturating surface lmr in the experiment@xcite favor the former case : a finite positive effective @xmath133 . is plotted as a function of the surface electron density @xmath33 at magnetic field @xmath204 : ( a ) at different values of zero - field mobility @xmath5 , and ( b ) at different values of zero - field conductivity @xmath205.,scaledwidth=40.0% ] at various lattice temperatures . here the zero - magnetic - field mobility at zero temperature is @xmath206.,scaledwidth=35.0% ] next , we examine the density - dependence of the linear magnetoresistivity . to compare with abrikosov s quantum magnetoresistance which suggests a @xmath207 behavior,@xcite we show the calculated @xmath208 for above lmr versus the carrier sheet density @xmath33 in fig.[rhon ] at fixed magnetic field @xmath209 t . the mobility is taken respectively to be @xmath210 and @xmath211m@xmath212/vs to make the resistivity in the lmr regime . a clearly linear dependence of @xmath213 on the surface density @xmath33 is seen in all cases , indicating that this non - saturating linear resistivity is almost inversely proportional to the carrier density . in the figure we also show @xmath208 versus @xmath33 under the condition of different given conductivity @xmath214 and @xmath215 . in this case the half - width @xmath216 is independent of surface density . the linear dependence still holds , indicating that this linear behavior is not sensitive to the modest @xmath33-dependence of landau level broadening @xmath216 as long as the system is in the overlapped landau level regime . from the above discussion , it is obvious that lmr shows up in the system having overlapped landau levels and the separation of landau levels makes the mr departure from the linear increase . at high temperature , the thermal energy would smear the level separation and phonon scatterings further broaden landau levels . hence , it is believed that this lmr will be robust against raising temperature . this is indeed the case as seen in fig.[rhot ] , where we plot the calculated magnetoresistivity @xmath157 for the above system with zero - temperature linear mobility @xmath217m@xmath212/vs versus the magnetic field at different lattice temperatures . we can see that raising temperature to room temperature has little effect on the linearity of mr . due to the decreased mobility at higher temperature from phonon scattering , the weak sdh oscillation on the linear background tends to vanish . these features are in good agreement with the experimental report.@xcite in summary , we have studied the two - dimensional magnetotransport in the flat surface of a three - dimensional ti , which arises from the surface states with a wavevector - linear energy dispersion and a finite , positive zeeman splitting within the bulk energy gap . when the level broadening is comparable to or larger than the landau - level separation and the conduction electrons spread over many landau levels , a positive , dominantly linear and non - saturating magnetoresistance appears within a quite wide range of magnetic field and persists up to room temperature . this remarkable lmr provides a possible mechanism for the recently observed linear magnetoresistance in topological insulator bi@xmath0se@xmath1 nanoribbons.@xcite in contrast to quantum hall effect which appears in the case of well formed landau levels and to abrikosov s quantum magnetotransport,@xcite which is limited to the extreme quantum limit that all electrons coalesce into the lowest landau level , the discussed lmr is a phenomena of pure classical two - dimensional magnetotransport in a system having linear - energy - dispersion , appearing in the regime of overlapped landau levels , irrespective of its showing up in relatively high magnetic field range . furthermore , the present scheme deals with spatially uniform case without invoking the mobility fluctuation in a strongly inhomogeneous system , which is required in the classical parish and littlewood model to produce a lmr.@xcite the appearance of this significant positive - increasing linear magnetoresistance depends on the existence of a positive and sizable effective g - factor . if the zeeman energy splitting is quite small the resistivity @xmath157 would exhibit little change with changing magnetic field . in the case of a negative and sizable effective g - factor the magnetoresistivity would decrease linearly with increasing magnetic field . therefore , the behavior of the longitudinal resistivity versus magnetic field may provide a useful way for judging the direction and the size of the effective zeeman energy splitting in ti surface states . this work was supported by the national science foundation of china ( grant no . 11104002 ) , the national basic research program of china ( grant no . 2012cb927403 ) and by the program for science&technology innovation talents in universities of henan province ( grant no . 2012hastit029 ) .""" dct = tok.batch_encode_plus( [ARTICLE_LEP, ARTICLE_MAGNET], max_length=6144, padding="max_length", truncation=True, return_tensors="pt", ) hypotheses_batch = hf.generate( input_ids=dct["input_ids"].to(torch_device), attention_mask=dct["attention_mask"].to(torch_device), num_beams=4, max_length=512, early_stopping=True, no_repeat_ngram_size=3, ) EXPECTED_LEP = ( " the physics of @xmath0-boson will again play the central role in the frontier of particle physics if the" " gigaz option of the international linear collider ( ilc ) can be realized in its first phase. \n the" " expected sensitivity to the branching ratio of rare decays, especially its exotic or rare processes," " should be investigated comprehensively to evaluate their potential in probing new physics. in this work" " \n, we study the rare decay into light higgs boson(s ) in the framework of the minimal supersymmetric" " standard model ( mssm ), where a light cp - odd higgs - boson with singlet - dominant component may" " naturally arise from the spontaneous breaking of some approximate global symmetry. " ) EXPECTED_MAGNET = ( " the recent experiment in the surface states of the topological insulator bi@xmath0se @xmath1, however," " reported that a large positive magnetoresistance becomes very linear in perpendicular magnetic field" " even in an opposite situation where the carrier sheet density is high that all electrons occupy more" " than one landau levels. \n it is striking that this observation is in conflict with abrikosov s model" " and also with the classical parish - littlewood model. " ) generated = tok.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated == [EXPECTED_LEP, EXPECTED_MAGNET]

# coding=utf-8 # Copyright 2021 Iz Beltagy, Matthew E. Peters, Arman Cohan and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch LED model. """ import copy import tempfile import unittest from transformers import LEDConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from transformers.utils import cached_property from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor if is_torch_available(): import torch from transformers import ( MODEL_FOR_QUESTION_ANSWERING_MAPPING, LEDForConditionalGeneration, LEDForQuestionAnswering, LEDForSequenceClassification, LEDModel, LEDTokenizer, ) from transformers.models.led.modeling_led import LEDDecoder, LEDEncoder def prepare_led_inputs_dict( config, input_ids, decoder_input_ids, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) if decoder_attention_mask is None: decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id) if head_mask is None: head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device) if decoder_head_mask is None: decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) if cross_attn_head_mask is None: cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class LEDModelTester: def __init__( self, parent, batch_size=13, seq_length=11, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=32, eos_token_id=2, pad_token_id=1, bos_token_id=0, attention_window=4, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.attention_window = attention_window # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but LongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window + 1` locations # (assuming no token with global attention, otherwise the last dimension of attentions # is x + self.attention_window + 1, where x is the number of tokens with global attention) # x is set to 1 self.encoder_key_length = self.attention_window + 2 # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for # the `test_attention_outputs` and `test_hidden_states_output` tests self.encoder_seq_length = self.seq_length def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp( 3, ) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = self.get_config() inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return LEDConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, encoder_layers=self.num_hidden_layers, decoder_layers=self.num_hidden_layers, encoder_attention_heads=self.num_attention_heads, decoder_attention_heads=self.num_attention_heads, encoder_ffn_dim=self.intermediate_size, decoder_ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, attention_window=self.attention_window, ) def get_pipeline_config(self): config = self.get_config() config.max_position_embeddings = 100 config.vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() global_attention_mask = torch.zeros_like(inputs_dict["input_ids"]) global_attention_mask[:, -1] = 1 inputs_dict["global_attention_mask"] = global_attention_mask return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = LEDModel(config=config).get_decoder().to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] head_mask = inputs_dict["head_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) output, past_key_values = outputs.to_tuple() # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_attn_mask = ids_tensor((self.batch_size, 3), 2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1) output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = LEDModel(config=config).to(torch_device).eval() outputs = model(**inputs_dict) encoder_last_hidden_state = outputs.encoder_last_hidden_state last_hidden_state = outputs.last_hidden_state with tempfile.TemporaryDirectory() as tmpdirname: encoder = model.get_encoder() encoder.save_pretrained(tmpdirname) encoder = LEDEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder( inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"], global_attention_mask=inputs_dict["global_attention_mask"], )[0] self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3) with tempfile.TemporaryDirectory() as tmpdirname: decoder = model.get_decoder() decoder.save_pretrained(tmpdirname) decoder = LEDDecoder.from_pretrained(tmpdirname).to(torch_device) last_hidden_state_2 = decoder( input_ids=inputs_dict["decoder_input_ids"], attention_mask=inputs_dict["decoder_attention_mask"], encoder_hidden_states=encoder_last_hidden_state, encoder_attention_mask=inputs_dict["attention_mask"], )[0] self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3) def check_global_attention(self, config, inputs_dict): model = LEDModel(config=config).to(torch_device).eval() model.config.output_attentions = True attention_mask = ids_tensor(inputs_dict["input_ids"].shape, vocab_size=2) global_attention_mask = torch.zeros_like(attention_mask) # set some tokens to global_attention num_tokens_with_global_attention = 2 attention_mask[:, 2 : 2 + num_tokens_with_global_attention] = 1 global_attention_mask[:, 2 : 2 + num_tokens_with_global_attention] = 1 inputs_dict["attention_mask"] = attention_mask inputs_dict["global_attention_mask"] = global_attention_mask outputs = model(**inputs_dict) self.parent.assertIsNotNone(outputs.encoder_global_attentions) # setting `num_tokens_with_global_attention` to global_attentions yields # makes last dim to be of `num_tokens_with_global_attention` self.parent.assertTrue( outputs.encoder_global_attentions[0].shape, (self.batch_size, self.num_attention_heads, self.encoder_seq_length, num_tokens_with_global_attention), ) @require_torch class LEDModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( (LEDModel, LEDForConditionalGeneration, LEDForSequenceClassification, LEDForQuestionAnswering) if is_torch_available() else () ) all_generative_model_classes = (LEDForConditionalGeneration,) if is_torch_available() else () is_encoder_decoder = True test_pruning = False test_missing_keys = False test_torchscript = False def setUp(self): self.model_tester = LEDModelTester(self) self.config_tester = ConfigTester(self, config_class=LEDConfig) def test_config(self): self.config_tester.run_common_tests() def test_save_load_strict(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True) self.assertEqual(info["missing_keys"], []) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_encoder_decoder_model_standalone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs) def test_global_attention(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_global_attention(*config_and_inputs) # LEDForSequenceClassification does not support inputs_embeds def test_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in (LEDModel, LEDForConditionalGeneration, LEDForQuestionAnswering): model = model_class(config) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) if not self.is_encoder_decoder: input_ids = inputs["input_ids"] del inputs["input_ids"] else: encoder_input_ids = inputs["input_ids"] decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids) del inputs["input_ids"] inputs.pop("decoder_input_ids", None) wte = model.get_input_embeddings() if not self.is_encoder_decoder: inputs["inputs_embeds"] = wte(input_ids) else: inputs["inputs_embeds"] = wte(encoder_input_ids) inputs["decoder_inputs_embeds"] = wte(decoder_input_ids) with torch.no_grad(): model(**inputs)[0] def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) model = LEDForConditionalGeneration(config).eval().to(torch_device) if torch_device == "cuda": model.half() model.generate(input_ids, attention_mask=attention_mask) model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3) def test_retain_grad_hidden_states_attentions(self): # longformer cannot keep gradients in attentions or hidden states return def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_length = self.model_tester.seq_length encoder_seq_length = self.model_tester.encoder_seq_length encoder_key_length = self.model_tester.encoder_key_length for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) # global attention outputs are added as well => so +1 here correct_outlen = 6 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Question Answering model returns start_logits and end_logits if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING): correct_outlen += 1 # start_logits and end_logits instead of only 1 output if "past_key_values" in outputs: correct_outlen += 1 # past_key_values have been returned self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions self.assertIsInstance(decoder_attentions, (list, tuple)) self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(decoder_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_length, seq_length], ) # cross attentions cross_attentions = outputs.cross_attentions self.assertIsInstance(cross_attentions, (list, tuple)) self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(cross_attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, seq_length, seq_length, ], ) def assert_tensors_close(a, b, atol=1e-12, prefix=""): """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error.""" if a is None and b is None: return True try: if torch.allclose(a, b, atol=atol): return True raise except Exception: pct_different = (torch.gt((a - b).abs(), atol)).float().mean().item() if a.numel() > 100: msg = f"tensor values are {pct_different:.1%} percent different." else: msg = f"{a} != {b}" if prefix: msg = prefix + ": " + msg raise AssertionError(msg) def _long_tensor(tok_lst): return torch.tensor(tok_lst, dtype=torch.long, device=torch_device) TOLERANCE = 1e-4 @require_torch @require_sentencepiece @require_tokenizers @slow class LEDModelIntegrationTests(unittest.TestCase): """All the below results were obtained with the original checkpoints and code base from https://github.com/allenai/longformer. IMPORTANT: Note that the original checkpoints include a `postion_embeddings` "hack" and have to be cut to have the correct shape. See: https://github.com/huggingface/transformers/pull/9278#issue-544709661. """ @cached_property def default_tokenizer(self): return LEDTokenizer.from_pretrained("allenai/led-base-16384") def test_inference_no_head(self): model = LEDModel.from_pretrained("allenai/led-base-16384").to(torch_device) # change to intended input input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids) with torch.no_grad(): output = model(**inputs_dict).last_hidden_state expected_shape = torch.Size((1, 1024, 768)) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = torch.tensor( [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]], device=torch_device ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) def test_inference_head(self): model = LEDForConditionalGeneration.from_pretrained("allenai/led-base-16384").to(torch_device) # change to intended input input_ids = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) decoder_input_ids = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]) inputs_dict = prepare_led_inputs_dict(model.config, input_ids, decoder_input_ids) with torch.no_grad(): output = model(**inputs_dict, use_cache=False).logits expected_shape = torch.Size((1, 1024, model.config.vocab_size)) self.assertEqual(output.shape, expected_shape) # change to expected output here expected_slice = torch.tensor( [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]], device=torch_device ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE)) def test_seq_to_seq_generation(self): # this test requires 16GB of RAM hf = LEDForConditionalGeneration.from_pretrained("allenai/led-large-16384-arxiv").to(torch_device) tok = LEDTokenizer.from_pretrained("allenai/led-large-16384-arxiv") ARTICLE_LEP = r"""the lep experiments at the resonance of @xmath1-boson have tested the standard model ( sm ) at quantum level , measuring the @xmath1-decay into fermion pairs with an accuracy of one part in ten thousands . the good agreement of the lep data with the sm predictions have severely constrained the behavior of new physics at the @xmath1-pole . taking these achievements into account one can imagine that the physics of @xmath1-boson will again play the central role in the frontier of particle physics if the next generation @xmath1 factory comes true with the generated @xmath1 events several orders of magnitude higher than that of the lep . this factory can be realized in the gigaz option of the international linear collider ( ilc)@xcite . the ilc is a proposed electron - positron collider with tunable energy ranging from @xmath12 to @xmath13 and polarized beams in its first phase , and the gigaz option corresponds to its operation on top of the resonance of @xmath1 boson by adding a bypass to its main beam line . given the high luminosity , @xmath14 , and the cross section at the resonance of @xmath1 boson , @xmath15 , about @xmath16 @xmath1 events can be generated in an operational year of @xmath17 of gigaz , which implies that the expected sensitivity to the branching ratio of @xmath1-decay can be improved from @xmath18 at the lep to @xmath19 at the gigaz@xcite . in light of this , the @xmath1-boson properties , especially its exotic or rare decays which are widely believed to be sensitive to new physics , should be investigated comprehensively to evaluate their potential in probing new physics . among the rare @xmath1-decays , the flavor changing ( fc ) processes were most extensively studied to explore the flavor texture in new physics @xcite , and it was found that , although these processes are severely suppressed in the sm , their branching ratios in new physics models can be greatly enhanced to @xmath19 for lepton flavor violation decays @xcite and @xmath20 for quark flavor violation decays @xcite . besides the fc processes , the @xmath1-decay into light higgs boson(s ) is another type of rare process that was widely studied , e.g. the decay @xmath21 ( @xmath22 ) with the particle @xmath0 denoting a light higgs boson was studied in @xcite , the decay @xmath23 was studied in the two higgs doublet model ( 2hdm)@xcite and the minimal supersymmetric standard model ( mssm)@xcite , and the decay @xmath4 was studied in a model independent way @xcite , in 2hdm@xcite and also in mssm@xcite . these studies indicate that , in contrast with the kinematic forbidden of these decays in the sm , the rates of these decays can be as large as @xmath18 in new physics models , which lie within the expected sensitivity of the gigaz . in this work , we extend the previous studies of these decays to some new models and investigate these decays altogether . we are motivated by some recent studies on the singlet extension of the mssm , such as the next - to - minimal supersymmetric standard model ( nmssm ) @xcite and the nearly minimal supersymmetric standard model ( nmssm ) @xcite , where a light cp - odd higgs boson @xmath0 with singlet - dominant component may naturally arise from the spontaneous breaking of some approximate global symmetry like @xmath24 or peccei - quuin symmetry @xcite . these non - minimal supersymmetric models can not only avoid the @xmath25-problem , but also alleviate the little hierarchy by having such a light higgs boson @xmath0 @xcite . we are also motivated by that , with the latest experiments , the properties of the light higgs boson are more stringently constrained than before . so it is worth updating the previous studies . so far there is no model - independent lower bound on the lightest higgs boson mass . in the sm , it must be heavier than @xmath26 gev , obtained from the null observation of the higgs boson at lep experiments . however , due to the more complex structure of the higgs sector in the extensions of the sm , this lower bound can be significantly relaxed according to recent studies , e.g. , for the cp - odd higgs boson @xmath0 we have @xmath27 gev in the nmssm @xcite , @xmath28 gev in the nmssm @xcite , and @xmath29 gev in the lepton - specific 2hdm ( l2hdm ) @xcite . with such a light cp - odd higgs boson , the z - decay into one or more @xmath0 is open up . noting that the decay @xmath30 is forbidden due to bose symmetry , we in this work study the rare @xmath1-decays @xmath6 ( @xmath22 ) , @xmath31 and @xmath4 in a comparative way for four models , namely the type - ii 2hdm@xcite , the l2hdm @xcite , the nmssm and the nmssm . in our study , we examine carefully the constraints on the light @xmath0 from many latest experimental results . this work is organized as follows . in sec . ii we briefly describe the four new physics models . in sec . iii we present the calculations of the rare @xmath1-decays . in sec . iv we list the constraints on the four new physics models . in sec . v we show the numerical results for the branching ratios of the rare @xmath1-decays in various models . finally , the conclusion is given in sec . as the most economical way , the sm utilizes one higgs doublet to break the electroweak symmetry . as a result , the sm predicts only one physical higgs boson with its properties totally determined by two free parameters . in new physics models , the higgs sector is usually extended by adding higgs doublets and/or singlets , and consequently , more physical higgs bosons are predicted along with more free parameters involved in . the general 2hdm contains two @xmath32 doublet higgs fields @xmath33 and @xmath34 , and with the assumption of cp - conserving , its scalar potential can be parameterized as@xcite : @xmath35,\end{aligned}\ ] ] where @xmath36 ( @xmath37 ) are free dimensionless parameters , and @xmath38 ( @xmath39 ) are the parameters with mass dimension . after the electroweak symmetry breaking , the spectrum of this higgs sector includes three massless goldstone modes , which become the longitudinal modes of @xmath40 and @xmath1 bosons , and five massive physical states : two cp - even higgs bosons @xmath41 and @xmath42 , one neutral cp - odd higgs particle @xmath0 and a pair of charged higgs bosons @xmath43 . noting the constraint @xmath44 with @xmath45 and @xmath46 denoting the vacuum expectation values ( vev ) of @xmath33 and @xmath34 respectively , we choose @xmath47 as the input parameters with @xmath48 , and @xmath49 being the mixing angle that diagonalizes the mass matrix of the cp - even higgs fields . the difference between the type - ii 2hdm and the l2hdm comes from the yukawa coupling of the higgs bosons to quark / lepton . in the type - ii 2hdm , one higgs doublet @xmath34 generates the masses of up - type quarks and the other doublet @xmath33 generates the masses of down - type quarks and charged leptons ; while in the l2hdm one higgs doublet @xmath33 couples only to leptons and the other doublet @xmath34 couples only to quarks . so the yukawa interactions of @xmath0 to fermions in these two models are given by @xcite @xmath50 with @xmath51 denoting generation index . obviously , in the type - ii 2hdm the @xmath52 coupling and the @xmath53 coupling can be simultaneously enhanced by @xmath54 , while in the l2hdm only the @xmath53 coupling is enhanced by @xmath55 . the structures of the nmssm and the nmssm are described by their superpotentials and corresponding soft - breaking terms , which are given by @xcite @xmath56 where @xmath57 is the superpotential of the mssm without the @xmath25 term , @xmath58 and @xmath59 are higgs doublet and singlet superfields with @xmath60 and @xmath61 being their scalar component respectively , @xmath62 , @xmath63 , @xmath64 , @xmath65 , @xmath66 and @xmath67 are soft breaking parameters , and @xmath68 and @xmath69 are coefficients of the higgs self interactions . with the superpotentials and the soft - breaking terms , one can get the higgs potentials of the nmssm and the nmssm respectively . like the 2hdm , the higgs bosons with same cp property will mix and the mass eigenstates are obtained by diagonalizing the corresponding mass matrices : @xmath70 where the fields on the right hands of the equations are component fields of @xmath71 , @xmath72 and @xmath61 defined by @xmath73 @xmath74 and @xmath75 are respectively the cp - even and cp - odd neutral higgs bosons , @xmath76 and @xmath77 are goldstone bosons eaten by @xmath1 and @xmath78 , and @xmath79 is the charged higgs boson . so both the nmssm and nmssm predict three cp - even higgs bosons , two cp - odd higgs bosons and one pair of charged higgs bosons . in general , the lighter cp - odd higgs @xmath0 in these model is the mixture of the singlet field @xmath80 and the doublet field combination , @xmath81 , i.e. @xmath82 and its couplings to down - type quarks are then proportional to @xmath83 . so for singlet dominated @xmath0 , @xmath84 is small and the couplings are suppressed . as a comparison , the interactions of @xmath0 with the squarks are given by@xcite @xmath85 i.e. the interaction does not vanish when @xmath86 approaches zero . just like the 2hdm where we use the vevs of the higgs fields as fundamental parameters , we choose @xmath68 , @xmath69 , @xmath87 , @xmath88 , @xmath66 and @xmath89 as input parameters for the nmssm@xcite and @xmath68 , @xmath54 , @xmath88 , @xmath65 , @xmath90 and @xmath91 as input parameters for the nmssm@xcite . about the nmssm and the nmssm , three points should be noted . the first is for the two models , there is no explicit @xmath92term , and the effective @xmath25 parameter ( @xmath93 ) is generated when the scalar component of @xmath59 develops a vev . the second is , the nmssm is actually same as the nmssm with @xmath94@xcite , because the tadpole terms @xmath95 and its soft breaking term @xmath96 in the nmssm do not induce any interactions , except for the tree - level higgs boson masses and the minimization conditions . and the last is despite of the similarities , the nmssm has its own peculiarity , which comes from its neutralino sector . in the basis @xmath97 , its neutralino mass matrix is given by @xcite @xmath98 where @xmath99 and @xmath100 are @xmath101 and @xmath102 gaugino masses respectively , @xmath103 , @xmath104 , @xmath105 and @xmath106 . after diagonalizing this matrix one can get the mass eigenstate of the lightest neutralino @xmath107 with mass taking the following form @xcite @xmath108 this expression implies that @xmath107 must be lighter than about @xmath109 gev for @xmath110 ( from lower bound on chargnio mass ) and @xmath111 ( perturbativity bound ) . like the other supersymmetric models , @xmath107 as the lightest sparticle acts as the dark matter in the universe , but due to its singlino - dominated nature , it is difficult to annihilate sufficiently to get the correct density in the current universe . so the relic density of @xmath107 plays a crucial way in selecting the model parameters . for example , as shown in @xcite , for @xmath112 , there is no way to get the correct relic density , and for the other cases , @xmath107 mainly annihilates by exchanging @xmath1 boson for @xmath113 , or by exchanging a light cp - odd higgs boson @xmath0 with mass satisfying the relation @xmath114 for @xmath115 . for the annihilation , @xmath54 and @xmath25 are required to be less than 10 and @xmath116 respectively because through eq.([mass - exp ] ) a large @xmath87 or @xmath25 will suppress @xmath117 to make the annihilation more difficult . the properties of the lightest cp - odd higgs boson @xmath0 , such as its mass and couplings , are also limited tightly since @xmath0 plays an important role in @xmath107 annihilation . the phenomenology of the nmssm is also rather special , and this was discussed in detail in @xcite . in the type - ii 2hdm , l2hdm , nmssm and nmssm , the rare @xmath1-decays @xmath118 ( @xmath22 ) , @xmath3 and @xmath4 may proceed by the feynman diagrams shown in fig.[fig1 ] , fig.[fig2 ] and fig.[fig3 ] respectively . for these diagrams , the intermediate state @xmath119 represents all possible cp - even higgs bosons in the corresponding model , i.e. @xmath41 and @xmath42 in type - ii 2hdm and l2hdm and @xmath41 , @xmath42 and @xmath120 in nmssm and nmssm . in order to take into account the possible resonance effects of @xmath119 in fig.[fig1](c ) for @xmath2 and fig.[fig3 ] ( a ) for @xmath11 , we have calculated all the decay modes of @xmath119 and properly included the width effect in its propagator . as to the decay @xmath121 , two points should be noted . one is , unlike the decays @xmath6 and @xmath11 , this process proceeds only through loops mediated by quarks / leptons in the type - ii 2hdm and l2hdm , and additionally by sparticles in the nmssm and nmssm . so in most cases its rate should be much smaller than the other two . the other is due to cp - invariance , loops mediated by squarks / sleptons give no contribution to the decay@xcite . in actual calculation , this is reflected by the fact that the coupling coefficient of @xmath122 differs from that of @xmath123 by a minus sign ( see eq.([asqsq ] ) ) , and as a result , the squark - mediated contributions to @xmath121 are completely canceled out . with regard to the rare decay @xmath11 , we have more explanations . in the lowest order , this decay proceeds by the diagram shown in fig.[fig3 ] ( a ) , and hence one may think that , as a rough estimate , it is enough to only consider the contributions from fig.[fig3](a ) . however , we note that in some cases of the type - ii 2hdm and l2hdm , due to the cancelation of the contributions from different @xmath119 in fig.[fig3 ] ( a ) and also due to the potentially largeness of @xmath124 couplings ( i.e. larger than the electroweak scale @xmath125 ) , the radiative correction from the higgs - mediated loops may dominate over the tree level contribution even when the tree level prediction of the rate , @xmath126 , exceeds @xmath20 . on the other hand , we find the contribution from quark / lepton - mediated loops can be safely neglected if @xmath127 in the type - ii 2hdm and the l2hdm . in the nmssm and the nmssm , besides the corrections from the higgs- and quark / lepton - mediated loops , loops involving sparticles such as squarks , charginos and neutralinos can also contribute to the decay . we numerically checked that the contributions from squarks and charginos can be safely neglected if @xmath127 . we also calculated part of potentially large neutralino correction ( note that there are totally about @xmath128 diagrams for such correction ! ) and found they can be neglected too . since considering all the radiative corrections will make our numerical calculation rather slow , we only include the most important correction , namely that from higgs - mediated loops , in presenting our results for the four models . one can intuitively understand the relative smallness of the sparticle contribution to @xmath11 as follows . first consider the squark contribution which is induced by the @xmath129 interaction ( @xmath130 denotes the squark in chirality state ) and the @xmath131 interaction through box diagrams . because the @xmath132 interaction conserves the chirality of the squarks while the @xmath133 interaction violates the chirality , to get non - zero contribution to @xmath11 from the squark loops , at least four chiral flippings are needed , with three of them provided by @xmath131 interaction and the rest provided by the left - right squark mixing . this means that , if one calculates the amplitude in the chirality basis with the mass insertion method , the amplitude is suppressed by the mixing factor @xmath134 with @xmath135 being the off diagonal element in squark mass matrix . next consider the chargino / neutralino contributions . since for a light @xmath0 , its doublet component , parameterized by @xmath84 in eq.([mixing ] ) , is usually small , the couplings of @xmath0 with the sparticles will never be tremendously large@xcite . so the chargino / neutralino contributions are not important too . in our calculation of the decays , we work in the mass eigenstates of sparticles instead of in the chirality basis . for the type - ii 2hdm and the l2hdm , we consider the following constraints @xcite : * theoretical constraints on @xmath136 from perturbativity , unitarity and requirements that the scalar potential is finit at large field values and contains no flat directions @xcite , which imply that @xmath137 * the constraints from the lep search for neutral higgs bosons . we compute the signals from the higgs - strahlung production @xmath138 ( @xmath139 ) with @xmath140 @xcite and from the associated production @xmath141 with @xmath142 @xcite , and compare them with the corresponding lep data which have been inputted into our code . we also consider the constraints from @xmath138 by looking for a peak of @xmath143 recoil mass distribution of @xmath1-boson @xcite and the constraint of @xmath144 mev when @xmath145 @xcite . + these constraints limit the quantities such as @xmath146 \times br ( h_i \to \bar{b } b ) $ ] on the @xmath147 plane with the the subscript @xmath148 denoting the coupling coefficient of the @xmath149 interaction . they also impose a model - dependent lower bound on @xmath150 , e.g. , @xmath151 for the type - ii 2hdm ( from our scan results ) , @xmath152 for the l2hdm@xcite , and @xmath153 for the nmssm @xcite . these bounds are significantly lower than that of the sm , i.e. @xmath154 , partially because in new physics models , unconventional decay modes of @xmath155 such as @xmath156 are open up . as to the nmssm , another specific reason for allowing a significantly lighter cp - even higgs boson is that the boson may be singlet - dominated in this model . + with regard to the lightest cp - odd higgs boson @xmath0 , we checked that there is no lower bound on its mass so long as the @xmath157 interaction is weak or @xmath155 is sufficiently heavy . * the constraints from the lep search for a light higgs boson via the yukawa process @xmath158 with @xmath22 and @xmath61 denoting a scalar @xcite . these constraints can limit the @xmath159 coupling versus @xmath160 in new physics models . * the constraints from the cleo - iii limit on @xmath161 and the latest babar limits on @xmath162 . these constraints will put very tight constraints on the @xmath163 coupling for @xmath164 . in our analysis , we use the results of fig.8 in the second paper of @xcite to excluded the unfavored points . * the constraints from @xmath165 couplings . since the higgs sector can give sizable higher order corrections to @xmath165 couplings , we calculate them to one loop level and require the corrected @xmath165 couplings to lie within the @xmath166 range of their fitted value . the sm predictions for the couplings at @xmath1-pole are given by @xmath167 and @xmath168 @xcite , and the fitted values are given by @xmath169 and @xmath170 , respectively@xcite . we adopt the formula in @xcite to the 2hdm in our calculation . * the constraints from @xmath171 leptonic decay . we require the new physics correction to the branching ratio @xmath172 to be in the range of @xmath173 @xcite . we use the formula in @xcite in our calculation . + about the constraints ( 5 ) and ( 6 ) , two points should be noted . one is all higgs bosons are involved in the constraints by entering the self energy of @xmath171 lepton , the @xmath174 vertex correction or the @xmath175 vertex correction , and also the box diagrams for @xmath176@xcite . since the yukawa couplings of the higgs bosons to @xmath171 lepton get enhanced by @xmath54 and so do the corrections , @xmath54 must be upper bounded for given spectrum of the higgs sector . generally speaking , the lighter @xmath0 is , the more tightly @xmath54 is limited@xcite . the other point is in the type - ii 2hdm , @xmath177 , b - physics observables as well as @xmath178 decays discussed above can constraint the model in a tighter way than the constraints ( 5 ) and ( 6 ) since the yukawa couplings of @xmath171 lepton and @xmath179 quark are simultaneously enhanced by @xmath54 . but for the l2hdm , because only the yukawa couplings of @xmath171 lepton get enhanced ( see eq.[yukawa ] ) , the constraints ( 5 ) and ( 6 ) are more important in limiting @xmath54 . * indirect constraints from the precision electroweak observables such as @xmath180 , @xmath181 and @xmath182 , or their combinations @xmath183 @xcite . we require @xmath184 to be compatible with the lep / sld data at @xmath185 confidence level@xcite . we also require new physics prediction of @xmath186 is within the @xmath187 range of its experimental value . the latest results for @xmath188 are @xmath189 ( measured value ) and @xmath190 ( sm prediction ) for @xmath191 gev @xcite . in our code , we adopt the formula for these observables presented in @xcite to the type - ii 2hdm and the l2hdm respectively . + in calculating @xmath180 , @xmath181 and @xmath182 , we note that these observables get dominant contributions from the self energies of the gauge bosons @xmath1 , @xmath192 and @xmath193 . since there is no @xmath194 coupling or @xmath195 coupling , @xmath0 must be associated with the other higgs bosons to contribute to the self energies . so by the uv convergence of these quantities , one can infer that , for the case of a light @xmath0 and @xmath196 , these quantities depend on the spectrum of the higgs sector in a way like @xmath197 at leading order , which implies that a light @xmath0 can still survive the constraints from the precision electroweak observables given the splitting between @xmath150 and @xmath198 is moderate@xcite . * the constraints from b physics observables such as the branching ratios for @xmath199 , @xmath200 and @xmath201 , and the mass differences @xmath202 and @xmath203 . we require their theoretical predications to agree with the corresponding experimental values at @xmath187 level . + in the type - ii 2hdm and the l2hdm , only the charged higgs boson contributes to these observables by loops , so one can expect that @xmath198 versus @xmath54 is to be limited . combined analysis of the limits in the type - ii 2hdm has been done by the ckmfitter group , and the lower bound of @xmath204 as a function of @xmath87 was given in fig.11 of @xcite . this analysis indicates that @xmath198 must be heavier than @xmath205 at @xmath185 c.l . regardless the value of @xmath54 . in this work , we use the results of fig.11 in @xcite to exclude the unfavored points . as for the l2hdm , b physics actually can not put any constraints@xcite because in this model the couplings of the charged higgs boson to quarks are proportional to @xmath206 and in the case of large @xmath54 which we are interested in , they are suppressed . in our analysis of the l2hdm , we impose the lep bound on @xmath198 , i.e. @xmath207@xcite . * the constraints from the muon anomalous magnetic moment @xmath208 . now both the theoretical prediction and the experimental measured value of @xmath208 have reached a remarkable precision , but a significant deviation still exists : @xmath209 @xcite . in the 2hdm , @xmath208 gets additional contributions from the one - loop diagrams induced by the higgs bosons and also from the two - loop barr - zee diagrams mediated by @xmath0 and @xmath155@xcite . if the higgs bosons are much heavier than @xmath25 lepton mass , the contributions from the barr - zee diagrams are more important , and to efficiently alleviate the discrepancy of @xmath208 , one needs a light @xmath0 along with its enhanced couplings to @xmath25 lepton and also to heavy fermions such as bottom quark and @xmath171 lepton to push up the effects of the barr - zee diagram@xcite . the cp - even higgs bosons are usually preferred to be heavy since their contributions to @xmath208 are negative . + in the type - ii 2hdm , because @xmath54 is tightly constrained by the process @xmath210 at the lep@xcite and the @xmath178 decay@xcite , the barr - zee diagram contribution is insufficient to enhance @xmath208 to @xmath187 range around its measured value@xcite . so in our analysis , we require the type - ii 2hdm to explain @xmath208 at @xmath211 level . while for the l2hdm , @xmath54 is less constrained compared with the type - ii 2hdm , and the barr - zee diagram involving the @xmath171-loop is capable to push up greatly the theoretical prediction of @xmath208@xcite . therefore , we require the l2hdm to explain the discrepancy at @xmath187 level . + unlike the other constraints discussed above , the @xmath208 constraint will put a two - sided bound on @xmath54 since on the one hand , it needs a large @xmath54 to enhance the barr - zee contribution , but on the other hand , too large @xmath54 will result in an unacceptable large @xmath208 . * since this paper concentrates on a light @xmath0 , the decay @xmath212 is open up with a possible large decay width . we require the width of any higgs boson to be smaller than its mass to avoid a too fat higgs boson@xcite . we checked that for the scenario characterized by @xmath213 , the coefficient of @xmath214 interaction is usually larger than the electroweak scale @xmath125 , and consequently a large decay width is resulted . for the nmssm and nmssm , the above constraints become more complicated because in these models , not only more higgs bosons are involved in , but also sparticles enter the constraints . so it is not easy to understand some of the constraints intuitively . take the process @xmath199 as an example . in the supersymmetric models , besides the charged higgs contribution , chargino loops , gluino loops as well as neutralino loops also contribute to the process@xcite , and depending on the susy parameters , any of these contributions may become dominated over or be canceled by other contributions . as a result , although the charged higgs affects the process in the same way as that in the type - ii 2hdm , charged higgs as light as @xmath215 is still allowed even for @xmath216@xcite . since among the constraints , @xmath208 is rather peculiar in that it needs new physics to explain the discrepancy between @xmath217 and @xmath218 , we discuss more about its dependence on susy parameters . in the nmssm and the nmssm , @xmath208 receives contributions from higgs loops and neutralino / chargino loops . for the higgs contribution , it is quite similar to that of the type - ii 2hdm except that more higgs bosons are involved in@xcite . for the neutralino / chargino contribution , in the light bino limit ( i.e. @xmath219 ) , it can be approximated by@xcite @xmath220 for @xmath221 with @xmath222 being smuon mass . so combining the two contributions together , one can learn that a light @xmath0 along with large @xmath54 and/or light smuon with moderate @xmath87 are favored to dilute the discrepancy . because more parameters are involved in the constraints on the supersymmetric models , we consider following additional constraints to further limit their parameters : * direct bounds on sparticle masses from the lep1 , the lep2 and the tevatron experiments @xcite . * the lep1 bound on invisible z decay @xmath223 ; the lep2 bound on neutralino production @xmath224 and @xmath225@xcite . * dark matter constraints from the wmap relic density 0.0975 @xmath226 0.1213 @xcite . note that among the above constraints , the constraint ( 2 ) on higgs sector and the constraint ( c ) on neutralino sector are very important . this is because in the supersymmetric models , the sm - like higgs is upper bounded by about @xmath227 at tree level and by about @xmath228 at loop level , and that the relic density restricts the lsp annihilation cross section in a certain narrow range . in our analysis of the nmssm , we calculate the constraints ( 3 ) and ( 5 - 7 ) by ourselves and utilize the code nmssmtools @xcite to implement the rest constraints . we also extend nmssmtools to the nmssm to implement the constraints . for the extension , the most difficult thing we faced is how to adapt the code micromegas@xcite to the nmssm case . we solve this problem by noting the following facts : * as we mentioned before , the nmssm is actually same as the nmssm with the trilinear singlet term setting to zero . so we can utilize the model file of the nmssm as the input of the micromegas and set @xmath229 . * since in the nmssm , the lsp is too light to annihilate into higgs pairs , there is no need to reconstruct the effective higgs potential to calculate precisely the annihilation channel @xmath230 with @xmath61 denoting any of higgs bosons@xcite . we thank the authors of the nmssmtools for helpful discussion on this issue when we finish such extension@xcite . with the above constraints , we perform four independent random scans over the parameter space of the type - ii 2hdm , the l2hdm , the nmssm and the nmssm respectively . we vary the parameters in following ranges : @xmath231 for the type - ii 2hdm , @xmath232 for the l2hdm , @xmath233 for the nmssm , and @xmath234 for the nmssm . in performing the scans , we note that for the nmssm and the nmssm , some constraints also rely on the gaugino masses and the soft breaking parameters in the squark sector and the slepton sector . since these parameters affect little on the properties of @xmath0 , we fix them to reduce the number of free parameters in our scan . for the squark sector , we adopt the @xmath235 scenario which assumes that the soft mass parameters for the third generation squarks are degenerate : @xmath236 800 gev , and that the trilinear couplings of the third generation squarks are also degenerate , @xmath237 with @xmath238 . for the slepton sector , we assume all the soft - breaking masses and trilinear parameters to be 100 gev . this setting is necessary for the nmssm since this model is difficult to explain the muon anomalous moment at @xmath239 level for heavy sleptons@xcite . finally , we assume the grand unification relation @xmath240 for the gaugino masses with @xmath241 being fine structure constants of the different gauge group . with large number of random points in the scans , we finally get about @xmath242 , @xmath243 , @xmath244 and @xmath242 samples for the type - ii 2hdm , the l2hdm , the nmssm and the nmssm respectively which survive the constraints and satisfy @xmath245 . analyzing the properties of the @xmath0 indicates that for most of the surviving points in the nmssm and the nmssm , its dominant component is the singlet field ( numerically speaking , @xmath246 ) so that its couplings to the sm fermions are suppressed@xcite . our analysis also indicates that the main decay products of @xmath0 are @xmath247 for the l2hdm@xcite , @xmath248 ( dominant ) and @xmath247 ( subdominant ) for the type - ii 2hdm , the nmssm and the nmssm , and in some rare cases , neutralino pairs in the nmssm@xcite . in fig.[fig4 ] , we project the surviving samples on the @xmath249 plane . this figure shows that the allowed range of @xmath54 is from @xmath250 to @xmath251 in the type - ii 2hdm , and from @xmath252 to @xmath253 in the l2hdm . just as we introduced before , the lower bounds of @xmath254 come from the fact that we require the models to explain the muon anomalous moment , while the upper bound is due to we have imposed the constraint from the lep process @xmath255 , which have limited the upper reach of the @xmath256 coupling for light @xmath61 @xcite(for the dependence of @xmath256 coupling on @xmath54 , see sec . this figure also indicates that for the nmssm and the nmssm , @xmath54 is upper bounded by @xmath257 . for the nmssm , this is because large @xmath87 can suppress the dark matter mass to make its annihilation difficult ( see @xcite and also sec . ii ) , but for the nmssm , this is because we choose a light slepton mass so that large @xmath54 can enhance @xmath208 too significantly to be experimentally unacceptable . we checked that for the slepton mass as heavy as @xmath258 , @xmath259 is still allowed for the nmssm . in fig.[fig5 ] and fig.[fig6 ] , we show the branching ratios of @xmath260 and @xmath261 respectively . fig.[fig5 ] indicates , among the four models , the type - ii 2hdm predicts the largest ratio for @xmath260 with its value varying from @xmath262 to @xmath263 . the underlying reason is in the type - ii 2hdm , the @xmath264 coupling is enhanced by @xmath54 ( see fig.[fig4 ] ) , while in the other three model , the coupling is suppressed either by @xmath265 or by the singlet component of the @xmath0 . fig.[fig6 ] shows that the l2hdm predicts the largest rate for @xmath266 with its value reaching @xmath5 in optimum case , and for the other three models , the ratio of @xmath261 is at least about one order smaller than that of @xmath267 . this feature can be easily understood from the @xmath268 coupling introduced in sect . we emphasize that , if the nature prefers a light @xmath0 , @xmath260 and/or @xmath269 in the type - ii 2hdm and the l2hdm will be observable at the gigaz . then by the rates of the two decays , one can determine whether the type - ii 2hdm or the l2hdm is the right theory . on the other hand , if both decays are observed with small rates or fail to be observed , the singlet extensions of the mssm are favored . in fig.[fig7 ] , we show the rate of @xmath3 as the function of @xmath270 . this figure indicates that the branching ratio of @xmath121 can reach @xmath271 , @xmath272 , @xmath273 and @xmath274 for the optimal cases of the type - ii 2hdm , the l2hdm , the nmssm and the nmssm respectively , which implies that the decay @xmath121 will never be observable at the gigaz if the studied model is chosen by nature . the reason for the smallness is , as we pointed out before , that the decay @xmath121 proceeds only at loop level . comparing the optimum cases of the type - ii 2hdm , the nmssm and the nmssm shown in fig.5 - 7 , one may find that the relation @xmath275 holds for any of the decays . this is because the decays are all induced by the yukawa couplings with similar structure for the models . in the supersymmetric models , the large singlet component of the light @xmath0 is to suppress the yukawa couplings , and the @xmath0 in the nmssm has more singlet component than that in the nmssm . next we consider the decay @xmath11 , which , unlike the above decays , depends on the higgs self interactions . in fig.[fig8 ] we plot its rate as a function of @xmath270 and this figure indicates that the @xmath276 may be the largest among the ratios of the exotic @xmath1 decays , reaching @xmath277 in the optimum cases of the type - ii 2hdm , the l2hdm and the nmssm . the underlying reason is , in some cases , the intermediate state @xmath119 in fig.[fig3 ] ( a ) may be on - shell . in fact , we find this is one of the main differences between the nmssm and the nmssm , that is , in the nmssm , @xmath119 in fig.[fig3 ] ( a ) may be on - shell ( corresponds to the points with large @xmath278 ) while in the nmssm , this seems impossible . so we conclude that the decay @xmath11 may serve as an alternative channel to test new physics models , especially it may be used to distinguish the nmssm from the nmssm if the supersymmetry is found at the lhc and the @xmath11 is observed at the gigaz with large rate . before we end our discussion , we note that in the nmssm , the higgs boson @xmath0 may be lighter than @xmath279 without conflicting with low energy data from @xmath178 decays and the other observables ( see fig.[fig4]-[fig8 ] ) . in this case , @xmath0 is axion - like as pointed out in @xcite . we checked that , among the rare @xmath1 decays discussed in this paper , the largest branching ratio comes from @xmath280 which can reach @xmath281 . since in this case , the decay product of @xmath0 is highly collinear muon pair , detecting the decay @xmath280 may need some knowledge about detectors , which is beyond our discussion . in this paper , we studied the rare @xmath1-decays @xmath2 ( @xmath7 ) , @xmath282 and @xmath4 in the type - 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ARTICLE_MAGNET = r"""it is well known that the classical magnetoresistance ( mr ) in metals or semiconductors with a closed free electron fermi surface increases quadratically with increasing magnetic field @xmath2 for @xmath3 and saturates when @xmath4 . here @xmath5 is the zero - magnetic - field mobility . hence , the extraordinarily high and linear mr ( lmr ) , which breaks this familiar rule , has been gaining much attention as soon as its discovery . in the past decade , this unexpected lmr has been reported in silver chalcogenide,@xcite indium antimonide,@xcite silicon,@xcite mnas - gaas composite material,@xcite and graphene.@xcite kapitza s linear law@xcite indicates that the metal shows a magnetoresistance linear in perpendicular magnetic field when it has an open fermi surface and a mean free path longer than the electronic larmor radius . recently , another two models , irrespective of the open fermi surface , have been constructed to provide possible mechanisms for the lmr phenomenon . abrikosov suggested a quantum - limit origin of lmr for the homogenous system with a gapless linear energy spectrum.@xcite his model requires that landau levels are well formed and the carrier concentration is small that all electrons occupy only the lowest landau band . alternatively , parish and littlewood developed a classical model without involving linear spectrum.@xcite ignoring the concrete microscopic mechanism , they attributed this unusual mr to the mobility fluctuations in a strongly inhomogenous system . topological insulators@xcite ( tis ) are novel materials with a full energy gap in bulk , while there are gapless surface states . due to its unique band structure with only one helical dirac cone and linear energy dispersion,@xcite the surface states of the ti bi@xmath0se@xmath1 become an excellent platform for the study of quantum - limit lmr . the recent experiment in this flat surface system , however , reported that a large positive mr , which becomes very linear above a characteristic field of @xmath6@xmath7@xmath8 t , was observed even in an opposite situation where the carrier sheet density is high that electrons occupy more than one landau levels.@xcite moreover , they found that raising temperature to room temperature almost has no influence on the observed lmr . it is striking that this observation is in conflict with abrikosov s model and also with the classical parish - littlewood model . so far a reliable theoretical scheme capable of explaining this novel experiment has still been lacking . in this paper , we generalize the balance - equation approach@xcite to a system modeling the surface states of a three - dimensional ti to investigate the two - dimensional magnetotransport in it . we find that a positive , nonsaturating and dominantly linear magnetoresistance can appear within quite wide magnetic - field range in the ti surface state having a positive and finite effective g - factor . this linear magnetoresistance shows up in the system of high carrier concentration and low mobility when electrons are in extended states and spread over many smeared landau levels , and persists up to room temperature , providing a possible mechanism for the recently observed linear magnetoresistance in topological insulator bi@xmath0se@xmath1 nanoribbons.@xcite we consider the surface state of a bi@xmath0se@xmath1-type large bulk gap ti in the @xmath9-@xmath10 plane under the influence of a uniform magnetic field @xmath11 applied along the @xmath12 direction.@xcite following the experimental observation,@xcite we assume that the fermi energy locates in the gap of the bulk band and above the dirac point , i.e. the surface carriers are electrons . further , the separations of the fermi energy from the bottom of bulk band and dirac point are much larger than the highest temperature ( @xmath13 ) considered in this work . hence , the contribution from the bulk band to the magnetotransport is negligible . these electrons , scattered by randomly distributed impurities and by phonons , are driven by a uniform in - plane electric field @xmath14 in the topological surface . the hamiltonian of this many - electron and phonon system consists of an electron part @xmath15 , a phonon part @xmath16 , and electron - impurity and electron - phonon interactions @xmath17 and @xmath18 : @xmath19 here , the electron hamiltonian is taken in the form @xmath20 , \ ] ] in which @xmath21 , @xmath22 , @xmath23 and @xmath24 , stand , respectively , for the canonical momentum , coordinate , momentum and spin operators of the @xmath25th electron having charge @xmath26 , @xmath27 is the vector potential of the perpendicular magnetic field @xmath28 in the landau gauge , @xmath29 is the fermi velocity , @xmath30 is the effective g - factor of the surface electron , and @xmath31 is the bohr magneton with @xmath32 the free electron mass . the sum index @xmath25 in eq.([helectron ] ) goes over all electrons of total number @xmath33 in the surface state of unit area . in the frame work of balance equation approach,@xcite the two - dimensional center - of - mass ( c.m . ) momentum and coordinate @xmath34 and @xmath35 , and the relative - electron momenta and coordinates @xmath36 and @xmath37 are introduced to write the hamiltonian @xmath15 into the sum of a single - particle c.m . part @xmath38 and a many - particle relative - electron part @xmath39 : @xmath40 , with @xmath41.\end{aligned}\ ] ] in this , @xmath42 is the canonical momentum of the center - of - mass and @xmath43 is the canonical momentum for the @xmath25th relative electron . here we have also introduced c.m . spin operators @xmath44 and @xmath45 . the commutation relations between the c.m . spin operators @xmath46 and @xmath47 and the spin operators @xmath48 , @xmath49 and @xmath50 of the @xmath25th electron are of order of @xmath51 : @xmath52= n^{-1}2\,{\rm i}\,\varepsi lon_{\beta_1\beta_2\beta_3}\sigma_j^{\beta_3}$ ] with @xmath53 . therefore , for a macroscopic large @xmath33 system , the c.m . part @xmath38 actually commutes with the relative - electron part @xmath54 in the hamiltonian , i.e. the c.m . motion and the relative motion of electrons are truly separated from each other . the couplings between the two emerge only through the electron impurity and electron phonon interactions . furthermore , the electric field @xmath55 shows up only in @xmath38 . and , in view of @xmath56={\rm i}\delta_{\alpha \beta}(\delta_{ij}-1/n)\simeq { \rm i}\delta_{\alpha\beta}\delta_{ij}$ ] , i.e. the relative - electron momenta and coordinates can be treated as canonical conjugate variables , the relative - motion part @xmath54 is just the hamiltonian of @xmath33 electrons in the surface state of ti in the magnetic field without the presence of the electric field . in terms of the c.m . coordinate @xmath57 and the relative electron density operator @xmath58 , the electron impurity and electron phonon interactions can be written as@xcite @xmath59 here @xmath60 and @xmath61 are respectively the impurity potential ( an impurity at randomly distributed position @xmath62 ) and electron phonon coupling matrix element in the plane - wave representation , and @xmath63 with @xmath64 and @xmath65 being the creation and annihilation operators for a phonon of wavevector @xmath66 in branch @xmath67 having frequency @xmath68 . velocity ( operator ) @xmath69 is the time variation of its coordinate : @xmath70= v_{\rm f}(\sigma_{\rm c}^y\ , \hat{i}-\sigma_{\rm c}^x\ , \hat{j})$ ] . to derive a force - balance equation for steady state transport we consider the heisenberg equation for the rate of change of the c.m . canonical momentum @xmath71 : @xmath72= - n e({\bm v}\times { \bm b})- n e{\bm e}+{\bm { f}}_{\rm i}+{\bm { f}}_{\rm p},\ ] ] in which the frictional forces @xmath73 and @xmath74 share the same expressions as given in ref .. the statistical average of the operator equation can be determined to linear order in the electron impurity and electron phonon interactions @xmath17 and @xmath18 with the initial density matrix @xmath75 at temperature @xmath76 when the in - plane electric field @xmath77 is not strong . for steady - transport states we have @xmath78 , leading to a force - balance equation of the form @xmath79 here @xmath80 , the statistically averaged velocity of the moving center - of - mass , is identified as the average rate of change of its position , i.e. the drift velocity of the electron system driven by the electric field @xmath77 , and @xmath81 and @xmath82 are frictional forces experienced by the center - of - mass due to impurity and phonon scatterings : @xmath83,\label{fp}\end{aligned}\ ] ] in which @xmath84 is the bose distribution function , @xmath85 , and @xmath86 stands for the imaginary part of the fourier spectrum of the relative - electron density correlation function defined by @xmath87\big\rangle_{0},\ ] ] where @xmath88 and @xmath89 denotes the statistical averaging over the initial density matrix @xmath90.@xcite the force - balance equation describes the steady - state two - dimensional magnetotransport in the surface state of a ti . note that the frictional forces @xmath81 and @xmath82 are in the opposite direction of the drift velocity @xmath91 and their magnitudes are functions of @xmath92 only . with the drift velocity @xmath93 in the @xmath9 direction , the force - balance equation eq . yields a transverse resistivity @xmath94 , and a longitudinal resistivity @xmath95 . the linear one is in the form @xmath96 for calculating the electron density correlation function @xmath97 we proceed in the landau representation.@xcite the landau levels of the single - particle hamiltonian @xmath98 of the relative - electron system in the absence of electric field are composed of a positive `` @xmath99 '' and a negative `` @xmath100 '' branch@xcite @xmath101 with @xmath102 and @xmath103 , and a zero ( @xmath104 ) level @xmath105 the corresponding landau wave functions are @xmath106 and @xmath107 for @xmath108 ; and @xmath109 for @xmath104 . here @xmath110 is the wavevector of the system along @xmath9 direction ; @xmath111 with @xmath112 ; and @xmath113 is the harmonic oscillator eigenfunction with @xmath114 being the hermite polynomial , @xmath115 , and @xmath116 . each landau level contains @xmath117 electron states for system of unit surface area . the positive branch @xmath118 and the @xmath104 level @xmath119 of the above energy spectra are indeed quite close to those of the surface states in the bulk gap of bi@xmath0se@xmath1-family materials derived from microscopic band calculation.@xcite the landau levels are broadened due to impurity , phonon and electron - electron scatterings . we model the imaginary part of the retarded green s function , or the density - of - states , of the broadened landau level @xmath120 ( written for `` + ' ' -branch and @xmath104 levels ) , using a gaussian - type form:@xcite @xmath121,\ ] ] with a half - width @xmath122 of the form:@xcite @xmath123^{1/2}$ ] . here @xmath124 is the single - particle lifetime and @xmath125 is the cyclotron frequency of linear - energy - dispersion system with @xmath126 being the zero - temperature fermi level . using a semi - empirical parameter @xmath127 to relate @xmath124 with the transport scattering time @xmath128 , and expressing @xmath129 with the zero - field mobility @xmath5 at finite temperature,@xcite we can write the landau - level broadening as @xmath130^{1/2}.\ ] ] in the present study we consider the case of @xmath120-doping , i.e. the fermi level is high enough above the energy zero of the dirac cone in the range of `` + ' ' -branch levels and the states of `` @xmath100''-branch levels are completely filled , that they are irrelevant to electron transport . special attention has to be paid to the @xmath104 level , since , depending on the direction of exchange potential the effective g - factor of a ti surface state , @xmath30 , can be positive , zero or negative.@xcite the sign and magnitude of the effective g - factor determines how many states of the zero level should be included in or excluded from the available states for electron occupation in the case of @xmath120-doping at a magnetic field . ( i ) if @xmath131 , the @xmath104 level center is exactly at @xmath132 and the system is electron - hole symmetric . the total number of negative energy states ( including the states of the lower half of the @xmath104 level and states of the @xmath100"-branch levels ) and that of positive energy states ( including the states of the upper half of the @xmath104 level and states of the @xmath99"-branch levels ) do not change when changing magnetic field . therefore , the lower - half negative energy states of this level are always filled and the upper - half positive - energy states of it are available for the occupation of particles which are counted as electrons participating in transport in the case of @xmath120-doping . ( ii ) for a finite positive @xmath133 , the @xmath104 level @xmath134 moves downward to negative energy and its distance to the nearest @xmath100"-branch level is @xmath135 closer than to the nearest + " -branch level at finite magnetic field strength @xmath2 . this is equivalent to the opening of an increasingly enlarged ( with increasing @xmath2 ) energy gap between the + " -branch states and the states of the zero - level and the @xmath100"-branch levels . the opening of a sufficient energy gap implies that with increasing magnetic field the states in the + " -branch levels would no longer shrink into the zero - level , and thus the @xmath104 level should be completely excluded from the conduction band , i.e. only particles occupying the + " -branch states are counted as electrons participating in transport in the case of @xmath120-doping , when the magnetic field @xmath2 gets larger than a certain value ( depending on the magnitude of @xmath30 ) . ( iii ) for a finite negative @xmath136 , the @xmath104 level @xmath134 moves upward to positive energy and an increasingly enlarged energy gap will be opened between the states of the zero - level and the + " -branch and the states of @xmath100"-branch levels , and particles occupying the @xmath104 level and + " -branch states are electrons participating in transport when the magnetic field @xmath2 gets larger than a certain value . as a result , the experimentally accessible sheet density @xmath33 of electrons participating in transport is related to the fermi energy @xmath137 by the following equation valid at finite @xmath30 for the magnetic field @xmath2 larger than a certain value : @xmath138 in which @xmath139 + 1\}^{-1}$ ] is the fermi distribution function at temperature @xmath76 and the summation index @xmath120 goes over @xmath140 for @xmath133 , or @xmath141 for @xmath136 . in the case of @xmath131 , @xmath142\ ] ] valid for arbitrary magnetic field , in which @xmath143 . the imaginary part of relative - electron density correlation function in the presence of a magnetic field , @xmath86 , can be expressed in the landau representation as@xcite @xmath144 in which the transform factor @xmath145 ^ 2,\end{aligned}\ ] ] with @xmath146 , @xmath147 , @xmath148 , and @xmath149 being associated laguerre polynomials . the landau - representation correlation function @xmath150 in eq.([piqw ] ) can be constructed with the imaginary part of the retarded green s function @xmath151 , or the density - of - states , of the @xmath120th landau level as@xcite @xmath152\nonumber\\ & \hspace{1.2cm}\times{\rm im}g_n(\epsilon+\omega){\rm im}g_{n'}(\epsilon).\end{aligned}\ ] ] the summation indices @xmath120 and @xmath153 in eq.([piqw ] ) are taken over @xmath140 for @xmath133 , or @xmath154 for @xmath136 . in the case of @xmath131 , eq.([piqw ] ) still works and the summation indices @xmath120 and @xmath153 go over @xmath154 but with @xmath155 replaced by @xmath156 in eq.([p2nn ] ) . numerical calculations are performed for the magnetoresistivity @xmath157 of surface state in a uniform ti bi@xmath0se@xmath1 . at zero temperature the elastic scattering contributing to the resistivity is modeled by a coulomb potential due to charged impurities:@xcite @xmath158 with @xmath159 being the impurity density , which is determined by the zero - magnetic - field mobility @xmath5 . at temperatures higher than @xmath160,@xcite phonon scatterings play increasingly important role and the dominant inelastic contribution comes from optical phonons . for this polar material , the scattering by optical phonons via the deformation potential can be neglected . hence , we take account of inelastic scattering from optical phonons via frhlich coupling : @xmath161 . in the numerical calculation we use the following parameters:@xcite fermi velocity @xmath162 , static dielectric constant @xmath163 , optical dielectric constant @xmath164 , and phonon energy @xmath165 . the broadening parameter is taken to be @xmath166 . as a function of the magnetic field @xmath2 having different effective g - factors : @xmath167 and @xmath168 for a ti surface system with electron sheet density @xmath169 in the cases of zero - magnetic - field mobility @xmath170 ( a ) and @xmath171 ( b ) . several integer - number positions of filling factor @xmath172 are marked in ( b).,scaledwidth=40.0% ] fig.[diffg ] shows the calculated magnetoresistivity @xmath157 versus the magnetic field strength @xmath2 for a ti surface system with electron sheet density @xmath169 but having different effective g - factors : @xmath167 and @xmath168 for two values of zero - magnetic - field mobility @xmath170 and @xmath171 , representing different degree of landau - level broadening . in the case without zeeman splitting ( @xmath131 ) the resistivity @xmath157 exhibits almost no change with changing magnetic field up to 10 t , except the shubnikov - de haas ( sdh ) oscillation showing up in the case of @xmath171 . this kind of magnetoresistance behavior was indeed seen experimentally in the electron - hole symmetrical massless system of single - layer graphene.@xcite in the case of a positive g - factor , @xmath173 , the magnetoresistivity increases linearly with increasing magnetic field ; while for a negative g - factor , @xmath174 , the magnetoresistivity decreases linearly with increasing magnetic field . is shown as a function of the magnetic field @xmath2 for different values of zero - magnetic - field mobility : ( a ) @xmath175 , ( b ) @xmath176 , ( c ) @xmath177 , ( d ) @xmath178 , ( e ) @xmath179 , and ( f ) @xmath180 . the inset of ( a ) illustrates the same for a larger magnetic - field range @xmath181 . the filling factor @xmath182 is plotted versus the magnetic field in ( f ) ; and several integer - number positions of @xmath182 are also marked in ( d ) and ( e ) . here the surface electron density @xmath169 and the lattice temperature @xmath183.,scaledwidth=47.0% ] in the following we will give more detailed examination on the linearly increasing magnetoresistance in the positive @xmath30 case . fig.[rhob ] shows the calculated resistivity @xmath157 versus the magnetic field strength @xmath2 at lattice temperature @xmath183 for system of carrier sheet density @xmath169 and @xmath173 , having different zero - field mobility @xmath184 and @xmath180 . all resistivity curves for mobility @xmath185 exhibit clear linearity in the magnetic - field range and appear no tendency of saturation at the highest field shown in the figure . especially , for the case @xmath170 , the linear behavior extends even up to the magnetic field of @xmath186 , as illustrated in the inset of fig.[rhob](a ) . this feature contradicts the classical mr which saturates at sufficiently large magnetic field @xmath187 . note that here we only present the calculated @xmath157 for magnetic field @xmath2 larger than @xmath188 t , for which a sufficient energy gap @xmath135 is assumed to open that with further increase of the magnetic field the states in the `` + ' ' -branch levels no longer shrink into the zero level and thus it should be excluded from the conduction band . this is of course not true for very weak magnetic field . when @xmath189 the energy gap @xmath190 , the situation becomes similar to the case of @xmath131 : the whole upper half of the zero - level states are available to electron occupation and we should have a flat resistivity @xmath157 when changing magnetic field . with increasing @xmath2 the portion of the zero - level states available to conduction electrons decreases until the magnetic field reaches @xmath191 . as a result the resistivity @xmath157 should exhibit a crossover from a flat changing at small @xmath2 to positively linear increasing at @xmath192 . this is just the behavior observed in the ti bi@xmath0se@xmath1.@xcite note that in the case of @xmath170 , the broadened landau - level widths are always larger than the neighboring level interval : @xmath193 , which requires @xmath194 ^ 2 $ ] , even for the lowest landau level @xmath195 , i.e. the whole landau - level spectrum is smeared . with increasing the zero - field mobility the magnitude of resistivity @xmath157 decreases , and when the broadened landau - level width becomes smaller than the neighboring level interval , @xmath196 , a weak sdh oscillation begin to occur around the linearly - dependent average value of @xmath157 at higher portion of the magnetic field range , as seen in fig.[rhob](c ) , ( d ) and ( e ) for @xmath197 and @xmath198 . on the other hand , in the case of large mobility , e.g. @xmath199 , where the broadened landau - level widths @xmath200 are much smaller than the neighboring level interval even for level index @xmath120 as large as @xmath201 , the magnetoresistivity shows pronounced sdh oscillation and the linear - dependent behavior disappears , before the appearance of quantum hall effect,@xcite as shown in fig.[rhob](f ) . abrikosov s model for the lmr requires the applied magnetic field large enough to reach the quantum limit at which all the carriers are within the lowest landau level,@xcite while it is obvious that more than one landau levels are occupied in the experimental samples in the field range in which the linear and non - saturating magnetoresistivity was observed.@xcite for the given electron surface density @xmath202 , the number of occupied landau levels , or the filling factor @xmath172 , at different magnetic fields is shown in fig.[rhob](f ) , as well as in the fig.[rhob](d ) and ( e ) , where the integer - number positions of @xmath203 , i.e. filling up to entire @xmath182 landau levels , coincide with the minima of the density - of - states or the dips of sdh oscillation . this is in contrast with @xmath131 case , where the integer number of @xmath203 , which implies a filling up to the center position of the @xmath182th landau levels , locates at a peak of sdh oscillation , as shown in fig.[diffg]b . the observed sdh oscillations in the bi@xmath0se@xmath1 nanoribbon exhibiting nonsaturating surface lmr in the experiment@xcite favor the former case : a finite positive effective @xmath133 . is plotted as a function of the surface electron density @xmath33 at magnetic field @xmath204 : ( a ) at different values of zero - field mobility @xmath5 , and ( b ) at different values of zero - field conductivity @xmath205.,scaledwidth=40.0% ] at various lattice temperatures . here the zero - magnetic - field mobility at zero temperature is @xmath206.,scaledwidth=35.0% ] next , we examine the density - dependence of the linear magnetoresistivity . to compare with abrikosov s quantum magnetoresistance which suggests a @xmath207 behavior,@xcite we show the calculated @xmath208 for above lmr versus the carrier sheet density @xmath33 in fig.[rhon ] at fixed magnetic field @xmath209 t . the mobility is taken respectively to be @xmath210 and @xmath211m@xmath212/vs to make the resistivity in the lmr regime . a clearly linear dependence of @xmath213 on the surface density @xmath33 is seen in all cases , indicating that this non - saturating linear resistivity is almost inversely proportional to the carrier density . in the figure we also show @xmath208 versus @xmath33 under the condition of different given conductivity @xmath214 and @xmath215 . in this case the half - width @xmath216 is independent of surface density . the linear dependence still holds , indicating that this linear behavior is not sensitive to the modest @xmath33-dependence of landau level broadening @xmath216 as long as the system is in the overlapped landau level regime . from the above discussion , it is obvious that lmr shows up in the system having overlapped landau levels and the separation of landau levels makes the mr departure from the linear increase . at high temperature , the thermal energy would smear the level separation and phonon scatterings further broaden landau levels . hence , it is believed that this lmr will be robust against raising temperature . this is indeed the case as seen in fig.[rhot ] , where we plot the calculated magnetoresistivity @xmath157 for the above system with zero - temperature linear mobility @xmath217m@xmath212/vs versus the magnetic field at different lattice temperatures . we can see that raising temperature to room temperature has little effect on the linearity of mr . due to the decreased mobility at higher temperature from phonon scattering , the weak sdh oscillation on the linear background tends to vanish . these features are in good agreement with the experimental report.@xcite in summary , we have studied the two - dimensional magnetotransport in the flat surface of a three - dimensional ti , which arises from the surface states with a wavevector - linear energy dispersion and a finite , positive zeeman splitting within the bulk energy gap . when the level broadening is comparable to or larger than the landau - level separation and the conduction electrons spread over many landau levels , a positive , dominantly linear and non - saturating magnetoresistance appears within a quite wide range of magnetic field and persists up to room temperature . this remarkable lmr provides a possible mechanism for the recently observed linear magnetoresistance in topological insulator bi@xmath0se@xmath1 nanoribbons.@xcite in contrast to quantum hall effect which appears in the case of well formed landau levels and to abrikosov s quantum magnetotransport,@xcite which is limited to the extreme quantum limit that all electrons coalesce into the lowest landau level , the discussed lmr is a phenomena of pure classical two - dimensional magnetotransport in a system having linear - energy - dispersion , appearing in the regime of overlapped landau levels , irrespective of its showing up in relatively high magnetic field range . furthermore , the present scheme deals with spatially uniform case without invoking the mobility fluctuation in a strongly inhomogeneous system , which is required in the classical parish and littlewood model to produce a lmr.@xcite the appearance of this significant positive - increasing linear magnetoresistance depends on the existence of a positive and sizable effective g - factor . if the zeeman energy splitting is quite small the resistivity @xmath157 would exhibit little change with changing magnetic field . in the case of a negative and sizable effective g - factor the magnetoresistivity would decrease linearly with increasing magnetic field . therefore , the behavior of the longitudinal resistivity versus magnetic field may provide a useful way for judging the direction and the size of the effective zeeman energy splitting in ti surface states . this work was supported by the national science foundation of china ( grant no . 11104002 ) , the national basic research program of china ( grant no . 2012cb927403 ) and by the program for science&technology innovation talents in universities of henan province ( grant no . 2012hastit029 ) .""" dct = tok.batch_encode_plus( [ARTICLE_LEP, ARTICLE_MAGNET], max_length=6144, padding="max_length", truncation=True, return_tensors="pt", ) hypotheses_batch = hf.generate( input_ids=dct["input_ids"].to(torch_device), attention_mask=dct["attention_mask"].to(torch_device), num_beams=4, max_length=512, early_stopping=True, no_repeat_ngram_size=3, ) EXPECTED_LEP = ( " the physics of @xmath0-boson will again play the central role in the frontier of particle physics if the" " gigaz option of the international linear collider ( ilc ) can be realized in its first phase. \n the" " expected sensitivity to the branching ratio of rare decays, especially its exotic or rare processes," " should be investigated comprehensively to evaluate their potential in probing new physics. in this work" " \n, we study the rare decay into light higgs boson(s ) in the framework of the minimal supersymmetric" " standard model ( mssm ), where a light cp - odd higgs - boson with singlet - dominant component may" " naturally arise from the spontaneous breaking of some approximate global symmetry. " ) EXPECTED_MAGNET = ( " the recent experiment in the surface states of the topological insulator bi@xmath0se @xmath1, however," " reported that a large positive magnetoresistance becomes very linear in perpendicular magnetic field" " even in an opposite situation where the carrier sheet density is high that all electrons occupy more" " than one landau levels. \n it is striking that this observation is in conflict with abrikosov s model" " and also with the classical parish - littlewood model. " ) generated = tok.batch_decode( hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert generated == [EXPECTED_LEP, EXPECTED_MAGNET]

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/swinv2/convert_swinv2_timm_to_pytorch.py

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swinv2 checkpoints from the timm library.""" import argparse import json from pathlib import Path import torch from PIL import Image import requests import timm from huggingface_hub import hf_hub_download from transformers import AutoFeatureExtractor, Swinv2Config, Swinv2ForImageClassification def get_swinv2_config(swinv2_name): config = Swinv2Config() name_split = swinv2_name.split("_") model_size = name_split[1] if "to" in name_split[3]: img_size = int(name_split[3][-3:]) else: img_size = int(name_split[3]) if "to" in name_split[2]: window_size = int(name_split[2][-2:]) else: window_size = int(name_split[2][6:]) if model_size == "tiny": embed_dim = 96 depths = (2, 2, 6, 2) num_heads = (3, 6, 12, 24) elif model_size == "small": embed_dim = 96 depths = (2, 2, 18, 2) num_heads = (3, 6, 12, 24) elif model_size == "base": embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) else: embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) if "to" in swinv2_name: config.pretrained_window_sizes = (12, 12, 12, 6) if ("22k" in swinv2_name) and ("to" not in swinv2_name): num_classes = 21841 repo_id = "huggingface/label-files" filename = "imagenet-22k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} else: num_classes = 1000 repo_id = "huggingface/label-files" filename = "imagenet-1k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} config.image_size = img_size config.num_labels = num_classes config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads config.window_size = window_size return config def rename_key(name): if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "embeddings.norm") if "layers" in name: name = "encoder." + name if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if "q_bias" in name: name = name.replace("q_bias", "query.bias") if "k_bias" in name: name = name.replace("k_bias", "key.bias") if "v_bias" in name: name = name.replace("v_bias", "value.bias") if "cpb_mlp" in name: name = name.replace("cpb_mlp", "continuous_position_bias_mlp") if name == "norm.weight": name = "layernorm.weight" if name == "norm.bias": name = "layernorm.bias" if "head" in name: name = name.replace("head", "classifier") else: name = "swinv2." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "mask" in key: continue elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[1]) block_num = int(key_split[3]) dim = model.swinv2.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight" ] = val[dim : dim * 2, :] orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias" ] = val[:dim] orig_state_dict[f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias" ] = val[-dim:] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swinv2_checkpoint(swinv2_name, pytorch_dump_folder_path): timm_model = timm.create_model(swinv2_name, pretrained=True) timm_model.eval() config = get_swinv2_config(swinv2_name) model = Swinv2ForImageClassification(config) model.eval() new_state_dict = convert_state_dict(timm_model.state_dict(), model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/{}".format(swinv2_name.replace("_", "-"))) image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") timm_outs = timm_model(inputs["pixel_values"]) hf_outs = model(**inputs).logits assert torch.allclose(timm_outs, hf_outs, atol=1e-3) print(f"Saving model {swinv2_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) model.push_to_hub( repo_path_or_name=Path(pytorch_dump_folder_path, swinv2_name), organization="nandwalritik", commit_message="Add model", ) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--swinv2_name", default="swinv2_tiny_patch4_window8_256", type=str, help="Name of the Swinv2 timm model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_swinv2_checkpoint(args.swinv2_name, args.pytorch_dump_folder_path)

# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Convert Swinv2 checkpoints from the timm library.""" import argparse import json from pathlib import Path import torch from PIL import Image import requests import timm from huggingface_hub import hf_hub_download from transformers import AutoFeatureExtractor, Swinv2Config, Swinv2ForImageClassification def get_swinv2_config(swinv2_name): config = Swinv2Config() name_split = swinv2_name.split("_") model_size = name_split[1] if "to" in name_split[3]: img_size = int(name_split[3][-3:]) else: img_size = int(name_split[3]) if "to" in name_split[2]: window_size = int(name_split[2][-2:]) else: window_size = int(name_split[2][6:]) if model_size == "tiny": embed_dim = 96 depths = (2, 2, 6, 2) num_heads = (3, 6, 12, 24) elif model_size == "small": embed_dim = 96 depths = (2, 2, 18, 2) num_heads = (3, 6, 12, 24) elif model_size == "base": embed_dim = 128 depths = (2, 2, 18, 2) num_heads = (4, 8, 16, 32) else: embed_dim = 192 depths = (2, 2, 18, 2) num_heads = (6, 12, 24, 48) if "to" in swinv2_name: config.pretrained_window_sizes = (12, 12, 12, 6) if ("22k" in swinv2_name) and ("to" not in swinv2_name): num_classes = 21841 repo_id = "huggingface/label-files" filename = "imagenet-22k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} else: num_classes = 1000 repo_id = "huggingface/label-files" filename = "imagenet-1k-id2label.json" id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} config.id2label = id2label config.label2id = {v: k for k, v in id2label.items()} config.image_size = img_size config.num_labels = num_classes config.embed_dim = embed_dim config.depths = depths config.num_heads = num_heads config.window_size = window_size return config def rename_key(name): if "patch_embed.proj" in name: name = name.replace("patch_embed.proj", "embeddings.patch_embeddings.projection") if "patch_embed.norm" in name: name = name.replace("patch_embed.norm", "embeddings.norm") if "layers" in name: name = "encoder." + name if "attn.proj" in name: name = name.replace("attn.proj", "attention.output.dense") if "attn" in name: name = name.replace("attn", "attention.self") if "norm1" in name: name = name.replace("norm1", "layernorm_before") if "norm2" in name: name = name.replace("norm2", "layernorm_after") if "mlp.fc1" in name: name = name.replace("mlp.fc1", "intermediate.dense") if "mlp.fc2" in name: name = name.replace("mlp.fc2", "output.dense") if "q_bias" in name: name = name.replace("q_bias", "query.bias") if "k_bias" in name: name = name.replace("k_bias", "key.bias") if "v_bias" in name: name = name.replace("v_bias", "value.bias") if "cpb_mlp" in name: name = name.replace("cpb_mlp", "continuous_position_bias_mlp") if name == "norm.weight": name = "layernorm.weight" if name == "norm.bias": name = "layernorm.bias" if "head" in name: name = name.replace("head", "classifier") else: name = "swinv2." + name return name def convert_state_dict(orig_state_dict, model): for key in orig_state_dict.copy().keys(): val = orig_state_dict.pop(key) if "mask" in key: continue elif "qkv" in key: key_split = key.split(".") layer_num = int(key_split[1]) block_num = int(key_split[3]) dim = model.swinv2.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.weight" ] = val[:dim, :] orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.weight" ] = val[dim : dim * 2, :] orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.weight" ] = val[-dim:, :] else: orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.query.bias" ] = val[:dim] orig_state_dict[f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.key.bias"] = val[ dim : dim * 2 ] orig_state_dict[ f"swinv2.encoder.layers.{layer_num}.blocks.{block_num}.attention.self.value.bias" ] = val[-dim:] else: orig_state_dict[rename_key(key)] = val return orig_state_dict def convert_swinv2_checkpoint(swinv2_name, pytorch_dump_folder_path): timm_model = timm.create_model(swinv2_name, pretrained=True) timm_model.eval() config = get_swinv2_config(swinv2_name) model = Swinv2ForImageClassification(config) model.eval() new_state_dict = convert_state_dict(timm_model.state_dict(), model) model.load_state_dict(new_state_dict) url = "http://images.cocodataset.org/val2017/000000039769.jpg" feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/{}".format(swinv2_name.replace("_", "-"))) image = Image.open(requests.get(url, stream=True).raw) inputs = feature_extractor(images=image, return_tensors="pt") timm_outs = timm_model(inputs["pixel_values"]) hf_outs = model(**inputs).logits assert torch.allclose(timm_outs, hf_outs, atol=1e-3) print(f"Saving model {swinv2_name} to {pytorch_dump_folder_path}") model.save_pretrained(pytorch_dump_folder_path) print(f"Saving feature extractor to {pytorch_dump_folder_path}") feature_extractor.save_pretrained(pytorch_dump_folder_path) model.push_to_hub( repo_path_or_name=Path(pytorch_dump_folder_path, swinv2_name), organization="nandwalritik", commit_message="Add model", ) if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--swinv2_name", default="swinv2_tiny_patch4_window8_256", type=str, help="Name of the Swinv2 timm model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) args = parser.parse_args() convert_swinv2_checkpoint(args.swinv2_name, args.pytorch_dump_folder_path)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/integrations.py

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Integrations with other Python libraries. """ import functools import importlib.util import json import numbers import os import pickle import shutil import sys import tempfile from dataclasses import asdict from pathlib import Path from typing import TYPE_CHECKING, Dict, Optional import numpy as np from . import __version__ as version from .utils import flatten_dict, is_datasets_available, is_torch_available, logging logger = logging.get_logger(__name__) if is_torch_available(): import torch # comet_ml requires to be imported before any ML frameworks _has_comet = importlib.util.find_spec("comet_ml") is not None and os.getenv("COMET_MODE", "").upper() != "DISABLED" if _has_comet: try: import comet_ml # noqa: F401 if hasattr(comet_ml, "config") and comet_ml.config.get_config("comet.api_key"): _has_comet = True else: if os.getenv("COMET_MODE", "").upper() != "DISABLED": logger.warning("comet_ml is installed but `COMET_API_KEY` is not set.") _has_comet = False except (ImportError, ValueError): _has_comet = False _has_neptune = importlib.util.find_spec("neptune") is not None if TYPE_CHECKING and _has_neptune: from neptune.new.metadata_containers.run import Run from .trainer_callback import ProgressCallback, TrainerCallback # noqa: E402 from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy # noqa: E402 from .training_args import ParallelMode # noqa: E402 from .utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available # noqa: E402 # Integration functions: def is_wandb_available(): # any value of WANDB_DISABLED disables wandb if os.getenv("WANDB_DISABLED", "").upper() in ENV_VARS_TRUE_VALUES: logger.warning( "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the " "--report_to flag to control the integrations used for logging result (for instance --report_to none)." ) return False return importlib.util.find_spec("wandb") is not None def is_clearml_available(): return importlib.util.find_spec("clearml") is not None def is_comet_available(): return _has_comet def is_tensorboard_available(): return importlib.util.find_spec("tensorboard") is not None or importlib.util.find_spec("tensorboardX") is not None def is_optuna_available(): return importlib.util.find_spec("optuna") is not None def is_ray_available(): return importlib.util.find_spec("ray") is not None def is_ray_tune_available(): if not is_ray_available(): return False return importlib.util.find_spec("ray.tune") is not None def is_sigopt_available(): return importlib.util.find_spec("sigopt") is not None def is_azureml_available(): if importlib.util.find_spec("azureml") is None: return False if importlib.util.find_spec("azureml.core") is None: return False return importlib.util.find_spec("azureml.core.run") is not None def is_mlflow_available(): if os.getenv("DISABLE_MLFLOW_INTEGRATION", "FALSE").upper() == "TRUE": return False return importlib.util.find_spec("mlflow") is not None def is_fairscale_available(): return importlib.util.find_spec("fairscale") is not None def is_neptune_available(): return _has_neptune def is_codecarbon_available(): return importlib.util.find_spec("codecarbon") is not None def hp_params(trial): if is_optuna_available(): import optuna if isinstance(trial, optuna.Trial): return trial.params if is_ray_tune_available(): if isinstance(trial, dict): return trial if is_sigopt_available(): if isinstance(trial, dict): return trial if is_wandb_available(): if isinstance(trial, dict): return trial raise RuntimeError(f"Unknown type for trial {trial.__class__}") def default_hp_search_backend(): if is_optuna_available(): return "optuna" elif is_ray_tune_available(): return "ray" elif is_sigopt_available(): return "sigopt" def run_hp_search_optuna(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: import optuna if trainer.args.process_index == 0: def _objective(trial, checkpoint_dir=None): checkpoint = None if checkpoint_dir: for subdir in os.listdir(checkpoint_dir): if subdir.startswith(PREFIX_CHECKPOINT_DIR): checkpoint = os.path.join(checkpoint_dir, subdir) trainer.objective = None if trainer.args.world_size > 1: if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP optuna HPO for ParallelMode.DISTRIBUTED currently.") trainer._hp_search_setup(trial) torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0) trainer.train(resume_from_checkpoint=checkpoint) else: trainer.train(resume_from_checkpoint=checkpoint, trial=trial) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) return trainer.objective timeout = kwargs.pop("timeout", None) n_jobs = kwargs.pop("n_jobs", 1) study = optuna.create_study(direction=direction, **kwargs) study.optimize(_objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs) best_trial = study.best_trial return BestRun(str(best_trial.number), best_trial.value, best_trial.params) else: for i in range(n_trials): trainer.objective = None args_main_rank = list(pickle.dumps(trainer.args)) if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP optuna HPO for ParallelMode.DISTRIBUTED currently.") torch.distributed.broadcast_object_list(args_main_rank, src=0) args = pickle.loads(bytes(args_main_rank)) for key, value in asdict(args).items(): if key != "local_rank": setattr(trainer.args, key, value) trainer.train(resume_from_checkpoint=None) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) return None def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: import ray def _objective(trial, local_trainer, checkpoint_dir=None): try: from transformers.utils.notebook import NotebookProgressCallback if local_trainer.pop_callback(NotebookProgressCallback): local_trainer.add_callback(ProgressCallback) except ModuleNotFoundError: pass checkpoint = None if checkpoint_dir: for subdir in os.listdir(checkpoint_dir): if subdir.startswith(PREFIX_CHECKPOINT_DIR): checkpoint = os.path.join(checkpoint_dir, subdir) local_trainer.objective = None local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial) # If there hasn't been any evaluation during the training loop. if getattr(local_trainer, "objective", None) is None: metrics = local_trainer.evaluate() local_trainer.objective = local_trainer.compute_objective(metrics) local_trainer._tune_save_checkpoint() ray.tune.report(objective=local_trainer.objective, **metrics, done=True) if not trainer._memory_tracker.skip_memory_metrics: from .trainer_utils import TrainerMemoryTracker logger.warning( "Memory tracking for your Trainer is currently " "enabled. Automatically disabling the memory tracker " "since the memory tracker is not serializable." ) trainer._memory_tracker = TrainerMemoryTracker(skip_memory_metrics=True) # The model and TensorBoard writer do not pickle so we have to remove them (if they exists) # while doing the ray hp search. _tb_writer = trainer.pop_callback(TensorBoardCallback) trainer.model = None # Setup default `resources_per_trial`. if "resources_per_trial" not in kwargs: # Default to 1 CPU and 1 GPU (if applicable) per trial. kwargs["resources_per_trial"] = {"cpu": 1} if trainer.args.n_gpu > 0: kwargs["resources_per_trial"]["gpu"] = 1 resource_msg = "1 CPU" + (" and 1 GPU" if trainer.args.n_gpu > 0 else "") logger.info( "No `resources_per_trial` arg was passed into " "`hyperparameter_search`. Setting it to a default value " f"of {resource_msg} for each trial." ) # Make sure each trainer only uses GPUs that were allocated per trial. gpus_per_trial = kwargs["resources_per_trial"].get("gpu", 0) trainer.args._n_gpu = gpus_per_trial # Setup default `progress_reporter`. if "progress_reporter" not in kwargs: from ray.tune import CLIReporter kwargs["progress_reporter"] = CLIReporter(metric_columns=["objective"]) if "keep_checkpoints_num" in kwargs and kwargs["keep_checkpoints_num"] > 0: # `keep_checkpoints_num=0` would disabled checkpointing trainer.use_tune_checkpoints = True if kwargs["keep_checkpoints_num"] > 1: logger.warning( f"Currently keeping {kwargs['keep_checkpoints_num']} checkpoints for each trial. " "Checkpoints are usually huge, " "consider setting `keep_checkpoints_num=1`." ) if "scheduler" in kwargs: from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining # Check if checkpointing is enabled for PopulationBasedTraining if isinstance(kwargs["scheduler"], PopulationBasedTraining): if not trainer.use_tune_checkpoints: logger.warning( "You are using PopulationBasedTraining but you haven't enabled checkpointing. " "This means your trials will train from scratch everytime they are exploiting " "new configurations. Consider enabling checkpointing by passing " "`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`." ) # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting. if isinstance( kwargs["scheduler"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining) ) and (not trainer.args.do_eval or trainer.args.evaluation_strategy == IntervalStrategy.NO): raise RuntimeError( "You are using {cls} as a scheduler but you haven't enabled evaluation during training. " "This means your trials will not report intermediate results to Ray Tune, and " "can thus not be stopped early or used to exploit other trials parameters. " "If this is what you want, do not use {cls}. If you would like to use {cls}, " "make sure you pass `do_eval=True` and `evaluation_strategy='steps'` in the " "Trainer `args`.".format(cls=type(kwargs["scheduler"]).__name__) ) trainable = ray.tune.with_parameters(_objective, local_trainer=trainer) @functools.wraps(trainable) def dynamic_modules_import_trainable(*args, **kwargs): """ Wrapper around `tune.with_parameters` to ensure datasets_modules are loaded on each Actor. Without this, an ImportError will be thrown. See https://github.com/huggingface/transformers/issues/11565. Assumes that `_objective`, defined above, is a function. """ if is_datasets_available(): import datasets.load dynamic_modules_path = os.path.join(datasets.load.init_dynamic_modules(), "__init__.py") # load dynamic_modules from path spec = importlib.util.spec_from_file_location("datasets_modules", dynamic_modules_path) datasets_modules = importlib.util.module_from_spec(spec) sys.modules[spec.name] = datasets_modules spec.loader.exec_module(datasets_modules) return trainable(*args, **kwargs) # special attr set by tune.with_parameters if hasattr(trainable, "__mixins__"): dynamic_modules_import_trainable.__mixins__ = trainable.__mixins__ analysis = ray.tune.run( dynamic_modules_import_trainable, config=trainer.hp_space(None), num_samples=n_trials, **kwargs, ) best_trial = analysis.get_best_trial(metric="objective", mode=direction[:3], scope=trainer.args.ray_scope) best_run = BestRun(best_trial.trial_id, best_trial.last_result["objective"], best_trial.config) if _tb_writer is not None: trainer.add_callback(_tb_writer) return best_run def run_hp_search_sigopt(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: import sigopt from transformers.utils.versions import importlib_metadata if trainer.args.process_index == 0: if importlib_metadata.version("sigopt") >= "8.0.0": sigopt.set_project("huggingface") experiment = sigopt.create_experiment( name="huggingface-tune", type="offline", parameters=trainer.hp_space(None), metrics=[dict(name="objective", objective=direction, strategy="optimize")], parallel_bandwidth=1, budget=n_trials, ) logger.info(f"created experiment: https://app.sigopt.com/experiment/{experiment.id}") for run in experiment.loop(): with run: trainer.objective = None if trainer.args.world_size > 1: if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.") trainer._hp_search_setup(run.run) torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0) trainer.train(resume_from_checkpoint=None) else: trainer.train(resume_from_checkpoint=None, trial=run.run) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) run.log_metric("objective", trainer.objective) best = list(experiment.get_best_runs())[0] best_run = BestRun(best.id, best.values["objective"].value, best.assignments) else: from sigopt import Connection conn = Connection() proxies = kwargs.pop("proxies", None) if proxies is not None: conn.set_proxies(proxies) experiment = conn.experiments().create( name="huggingface-tune", parameters=trainer.hp_space(None), metrics=[dict(name="objective", objective=direction, strategy="optimize")], parallel_bandwidth=1, observation_budget=n_trials, project="huggingface", ) logger.info(f"created experiment: https://app.sigopt.com/experiment/{experiment.id}") while experiment.progress.observation_count < experiment.observation_budget: suggestion = conn.experiments(experiment.id).suggestions().create() trainer.objective = None if trainer.args.world_size > 1: if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.") trainer._hp_search_setup(suggestion) torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0) trainer.train(resume_from_checkpoint=None) else: trainer.train(resume_from_checkpoint=None, trial=suggestion) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) values = [dict(name="objective", value=trainer.objective)] obs = conn.experiments(experiment.id).observations().create(suggestion=suggestion.id, values=values) logger.info(f"[suggestion_id, observation_id]: [{suggestion.id}, {obs.id}]") experiment = conn.experiments(experiment.id).fetch() best = list(conn.experiments(experiment.id).best_assignments().fetch().iterate_pages())[0] best_run = BestRun(best.id, best.value, best.assignments) return best_run else: for i in range(n_trials): trainer.objective = None args_main_rank = list(pickle.dumps(trainer.args)) if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.") torch.distributed.broadcast_object_list(args_main_rank, src=0) args = pickle.loads(bytes(args_main_rank)) for key, value in asdict(args).items(): if key != "local_rank": setattr(trainer.args, key, value) trainer.train(resume_from_checkpoint=None) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) return None def run_hp_search_wandb(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: from .integrations import is_wandb_available if not is_wandb_available(): raise ImportError("This function needs wandb installed: `pip install wandb`") import wandb # add WandbCallback if not already added in trainer callbacks reporting_to_wandb = False for callback in trainer.callback_handler.callbacks: if isinstance(callback, WandbCallback): reporting_to_wandb = True break if not reporting_to_wandb: trainer.add_callback(WandbCallback()) trainer.args.report_to = "wandb" best_trial = {"run_id": None, "objective": None, "hyperparameters": None} sweep_id = kwargs.pop("sweep_id", None) project = kwargs.pop("project", None) name = kwargs.pop("name", None) entity = kwargs.pop("entity", None) metric = kwargs.pop("metric", "eval/loss") sweep_config = trainer.hp_space(None) sweep_config["metric"]["goal"] = direction sweep_config["metric"]["name"] = metric if name: sweep_config["name"] = name def _objective(): run = wandb.run if wandb.run else wandb.init() trainer.state.trial_name = run.name run.config.update({"assignments": {}, "metric": metric}) config = wandb.config trainer.objective = None trainer.train(resume_from_checkpoint=None, trial=vars(config)["_items"]) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) format_metrics = rewrite_logs(metrics) if metric not in format_metrics: logger.warning( f"Provided metric {metric} not found. This might result in unexpected sweeps charts. The available" f" metrics are {format_metrics.keys()}" ) best_score = False if best_trial["run_id"] is not None: if direction == "minimize": best_score = trainer.objective < best_trial["objective"] elif direction == "maximize": best_score = trainer.objective > best_trial["objective"] if best_score or best_trial["run_id"] is None: best_trial["run_id"] = run.id best_trial["objective"] = trainer.objective best_trial["hyperparameters"] = dict(config) return trainer.objective sweep_id = wandb.sweep(sweep_config, project=project, entity=entity) if not sweep_id else sweep_id logger.info(f"wandb sweep id - {sweep_id}") wandb.agent(sweep_id, function=_objective, count=n_trials) return BestRun(best_trial["run_id"], best_trial["objective"], best_trial["hyperparameters"]) def get_available_reporting_integrations(): integrations = [] if is_azureml_available(): integrations.append("azure_ml") if is_comet_available(): integrations.append("comet_ml") if is_mlflow_available(): integrations.append("mlflow") if is_neptune_available(): integrations.append("neptune") if is_tensorboard_available(): integrations.append("tensorboard") if is_wandb_available(): integrations.append("wandb") if is_codecarbon_available(): integrations.append("codecarbon") if is_clearml_available(): integrations.append("clearml") return integrations def rewrite_logs(d): new_d = {} eval_prefix = "eval_" eval_prefix_len = len(eval_prefix) test_prefix = "test_" test_prefix_len = len(test_prefix) for k, v in d.items(): if k.startswith(eval_prefix): new_d["eval/" + k[eval_prefix_len:]] = v elif k.startswith(test_prefix): new_d["test/" + k[test_prefix_len:]] = v else: new_d["train/" + k] = v return new_d class TensorBoardCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard). Args: tb_writer (`SummaryWriter`, *optional*): The writer to use. Will instantiate one if not set. """ def __init__(self, tb_writer=None): has_tensorboard = is_tensorboard_available() if not has_tensorboard: raise RuntimeError( "TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or" " install tensorboardX." ) if has_tensorboard: try: from torch.utils.tensorboard import SummaryWriter # noqa: F401 self._SummaryWriter = SummaryWriter except ImportError: try: from tensorboardX import SummaryWriter self._SummaryWriter = SummaryWriter except ImportError: self._SummaryWriter = None else: self._SummaryWriter = None self.tb_writer = tb_writer def _init_summary_writer(self, args, log_dir=None): log_dir = log_dir or args.logging_dir if self._SummaryWriter is not None: self.tb_writer = self._SummaryWriter(log_dir=log_dir) def on_train_begin(self, args, state, control, **kwargs): if not state.is_world_process_zero: return log_dir = None if state.is_hyper_param_search: trial_name = state.trial_name if trial_name is not None: log_dir = os.path.join(args.logging_dir, trial_name) if self.tb_writer is None: self._init_summary_writer(args, log_dir) if self.tb_writer is not None: self.tb_writer.add_text("args", args.to_json_string()) if "model" in kwargs: model = kwargs["model"] if hasattr(model, "config") and model.config is not None: model_config_json = model.config.to_json_string() self.tb_writer.add_text("model_config", model_config_json) # Version of TensorBoard coming from tensorboardX does not have this method. if hasattr(self.tb_writer, "add_hparams"): self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={}) def on_log(self, args, state, control, logs=None, **kwargs): if not state.is_world_process_zero: return if self.tb_writer is None: self._init_summary_writer(args) if self.tb_writer is not None: logs = rewrite_logs(logs) for k, v in logs.items(): if isinstance(v, (int, float)): self.tb_writer.add_scalar(k, v, state.global_step) else: logger.warning( "Trainer is attempting to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a scalar. ' "This invocation of Tensorboard's writer.add_scalar() " "is incorrect so we dropped this attribute." ) self.tb_writer.flush() def on_train_end(self, args, state, control, **kwargs): if self.tb_writer: self.tb_writer.close() self.tb_writer = None class WandbCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [Weight and Biases](https://www.wandb.com/). """ def __init__(self): has_wandb = is_wandb_available() if not has_wandb: raise RuntimeError("WandbCallback requires wandb to be installed. Run `pip install wandb`.") if has_wandb: import wandb self._wandb = wandb self._initialized = False # log outputs self._log_model = os.getenv("WANDB_LOG_MODEL", "FALSE").upper() in ENV_VARS_TRUE_VALUES.union({"TRUE"}) def setup(self, args, state, model, **kwargs): """ Setup the optional Weights & Biases (*wandb*) integration. One can subclass and override this method to customize the setup if needed. Find more information [here](https://docs.wandb.ai/integrations/huggingface). You can also override the following environment variables: Environment: WANDB_LOG_MODEL (`bool`, *optional*, defaults to `False`): Whether or not to log model as artifact at the end of training. Use along with *TrainingArguments.load_best_model_at_end* to upload best model. WANDB_WATCH (`str`, *optional* defaults to `"gradients"`): Can be `"gradients"`, `"all"` or `"false"`. Set to `"false"` to disable gradient logging or `"all"` to log gradients and parameters. WANDB_PROJECT (`str`, *optional*, defaults to `"huggingface"`): Set this to a custom string to store results in a different project. WANDB_DISABLED (`bool`, *optional*, defaults to `False`): Whether or not to disable wandb entirely. Set *WANDB_DISABLED=true* to disable. """ if self._wandb is None: return self._initialized = True if state.is_world_process_zero: logger.info( 'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"' ) combined_dict = {**args.to_sanitized_dict()} if hasattr(model, "config") and model.config is not None: model_config = model.config.to_dict() combined_dict = {**model_config, **combined_dict} trial_name = state.trial_name init_args = {} if trial_name is not None: run_name = trial_name init_args["group"] = args.run_name else: run_name = args.run_name if self._wandb.run is None: self._wandb.init( project=os.getenv("WANDB_PROJECT", "huggingface"), name=run_name, **init_args, ) # add config parameters (run may have been created manually) self._wandb.config.update(combined_dict, allow_val_change=True) # define default x-axis (for latest wandb versions) if getattr(self._wandb, "define_metric", None): self._wandb.define_metric("train/global_step") self._wandb.define_metric("*", step_metric="train/global_step", step_sync=True) # keep track of model topology and gradients, unsupported on TPU if not is_torch_tpu_available() and os.getenv("WANDB_WATCH") != "false": self._wandb.watch( model, log=os.getenv("WANDB_WATCH", "gradients"), log_freq=max(100, args.logging_steps) ) def on_train_begin(self, args, state, control, model=None, **kwargs): if self._wandb is None: return hp_search = state.is_hyper_param_search if hp_search: self._wandb.finish() self._initialized = False args.run_name = None if not self._initialized: self.setup(args, state, model, **kwargs) def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs): if self._wandb is None: return if self._log_model and self._initialized and state.is_world_process_zero: from .trainer import Trainer fake_trainer = Trainer(args=args, model=model, tokenizer=tokenizer) with tempfile.TemporaryDirectory() as temp_dir: fake_trainer.save_model(temp_dir) metadata = ( { k: v for k, v in dict(self._wandb.summary).items() if isinstance(v, numbers.Number) and not k.startswith("_") } if not args.load_best_model_at_end else { f"eval/{args.metric_for_best_model}": state.best_metric, "train/total_floss": state.total_flos, } ) artifact = self._wandb.Artifact(name=f"model-{self._wandb.run.id}", type="model", metadata=metadata) for f in Path(temp_dir).glob("*"): if f.is_file(): with artifact.new_file(f.name, mode="wb") as fa: fa.write(f.read_bytes()) self._wandb.run.log_artifact(artifact) def on_log(self, args, state, control, model=None, logs=None, **kwargs): if self._wandb is None: return if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: logs = rewrite_logs(logs) self._wandb.log({**logs, "train/global_step": state.global_step}) class CometCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.ml/site/). """ def __init__(self): if not _has_comet: raise RuntimeError("CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.") self._initialized = False self._log_assets = False def setup(self, args, state, model): """ Setup the optional Comet.ml integration. Environment: COMET_MODE (`str`, *optional*): Whether to create an online, offline experiment or disable Comet logging. Can be "OFFLINE", "ONLINE", or "DISABLED". Defaults to "ONLINE". COMET_PROJECT_NAME (`str`, *optional*): Comet project name for experiments COMET_OFFLINE_DIRECTORY (`str`, *optional*): Folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE" COMET_LOG_ASSETS (`str`, *optional*): Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be "TRUE", or "FALSE". Defaults to "TRUE". For a number of configurable items in the environment, see [here](https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables). """ self._initialized = True log_assets = os.getenv("COMET_LOG_ASSETS", "FALSE").upper() if log_assets in {"TRUE", "1"}: self._log_assets = True if state.is_world_process_zero: comet_mode = os.getenv("COMET_MODE", "ONLINE").upper() experiment = None experiment_kwargs = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")} if comet_mode == "ONLINE": experiment = comet_ml.Experiment(**experiment_kwargs) experiment.log_other("Created from", "transformers") logger.info("Automatic Comet.ml online logging enabled") elif comet_mode == "OFFLINE": experiment_kwargs["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./") experiment = comet_ml.OfflineExperiment(**experiment_kwargs) experiment.log_other("Created from", "transformers") logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished") if experiment is not None: experiment._set_model_graph(model, framework="transformers") experiment._log_parameters(args, prefix="args/", framework="transformers") if hasattr(model, "config"): experiment._log_parameters(model.config, prefix="config/", framework="transformers") def on_train_begin(self, args, state, control, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) def on_log(self, args, state, control, model=None, logs=None, **kwargs): if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: experiment = comet_ml.config.get_global_experiment() if experiment is not None: experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework="transformers") def on_train_end(self, args, state, control, **kwargs): if self._initialized and state.is_world_process_zero: experiment = comet_ml.config.get_global_experiment() if experiment is not None: if self._log_assets is True: logger.info("Logging checkpoints. This may take time.") experiment.log_asset_folder( args.output_dir, recursive=True, log_file_name=True, step=state.global_step ) experiment.end() class AzureMLCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/). """ def __init__(self, azureml_run=None): if not is_azureml_available(): raise RuntimeError("AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`.") self.azureml_run = azureml_run def on_init_end(self, args, state, control, **kwargs): from azureml.core.run import Run if self.azureml_run is None and state.is_world_process_zero: self.azureml_run = Run.get_context() def on_log(self, args, state, control, logs=None, **kwargs): if self.azureml_run and state.is_world_process_zero: for k, v in logs.items(): if isinstance(v, (int, float)): self.azureml_run.log(k, v, description=k) class MLflowCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [MLflow](https://www.mlflow.org/). Can be disabled by setting environment variable `DISABLE_MLFLOW_INTEGRATION = TRUE`. """ def __init__(self): if not is_mlflow_available(): raise RuntimeError("MLflowCallback requires mlflow to be installed. Run `pip install mlflow`.") import mlflow self._MAX_PARAM_VAL_LENGTH = mlflow.utils.validation.MAX_PARAM_VAL_LENGTH self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH self._initialized = False self._auto_end_run = False self._log_artifacts = False self._ml_flow = mlflow def setup(self, args, state, model): """ Setup the optional MLflow integration. Environment: HF_MLFLOW_LOG_ARTIFACTS (`str`, *optional*): Whether to use MLflow .log_artifact() facility to log artifacts. This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or *1*, will copy each saved checkpoint on each save in [`TrainingArguments`]'s `output_dir` to the local or remote artifact storage. Using it without a remote storage will just copy the files to your artifact location. MLFLOW_EXPERIMENT_NAME (`str`, *optional*): Whether to use an MLflow experiment_name under which to launch the run. Default to "None" which will point to the "Default" experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be activated. If an experiment with this name does not exist, a new experiment with this name is created. MLFLOW_TAGS (`str`, *optional*): A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example: os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}' MLFLOW_NESTED_RUN (`str`, *optional*): Whether to use MLflow nested runs. If set to `True` or *1*, will create a nested run inside the current run. MLFLOW_RUN_ID (`str`, *optional*): Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When MLFLOW_RUN_ID environment variable is set, start_run attempts to resume a run with the specified run ID and other parameters are ignored. MLFLOW_FLATTEN_PARAMS (`str`, *optional*): Whether to flatten the parameters dictionary before logging. Default to `False`. """ self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES self._nested_run = os.getenv("MLFLOW_NESTED_RUN", "FALSE").upper() in ENV_VARS_TRUE_VALUES self._experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None) self._flatten_params = os.getenv("MLFLOW_FLATTEN_PARAMS", "FALSE").upper() in ENV_VARS_TRUE_VALUES self._run_id = os.getenv("MLFLOW_RUN_ID", None) logger.debug( f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run}," f" tags={self._nested_run}" ) if state.is_world_process_zero: if self._ml_flow.active_run() is None or self._nested_run or self._run_id: if self._experiment_name: # Use of set_experiment() ensure that Experiment is created if not exists self._ml_flow.set_experiment(self._experiment_name) self._ml_flow.start_run(run_name=args.run_name, nested=self._nested_run) logger.debug(f"MLflow run started with run_id={self._ml_flow.active_run().info.run_id}") self._auto_end_run = True combined_dict = args.to_dict() if hasattr(model, "config") and model.config is not None: model_config = model.config.to_dict() combined_dict = {**model_config, **combined_dict} combined_dict = flatten_dict(combined_dict) if self._flatten_params else combined_dict # remove params that are too long for MLflow for name, value in list(combined_dict.items()): # internally, all values are converted to str in MLflow if len(str(value)) > self._MAX_PARAM_VAL_LENGTH: logger.warning( f'Trainer is attempting to log a value of "{value}" for key "{name}" as a parameter. MLflow\'s' " log_param() only accepts values no longer than 250 characters so we dropped this attribute." " You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and" " avoid this message." ) del combined_dict[name] # MLflow cannot log more than 100 values in one go, so we have to split it combined_dict_items = list(combined_dict.items()) for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH): self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH])) mlflow_tags = os.getenv("MLFLOW_TAGS", None) if mlflow_tags: mlflow_tags = json.loads(mlflow_tags) self._ml_flow.set_tags(mlflow_tags) self._initialized = True def on_train_begin(self, args, state, control, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) def on_log(self, args, state, control, logs, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: metrics = {} for k, v in logs.items(): if isinstance(v, (int, float)): metrics[k] = v else: logger.warning( f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. ' "MLflow's log_metric() only accepts float and int types so we dropped this attribute." ) self._ml_flow.log_metrics(metrics=metrics, step=state.global_step) def on_train_end(self, args, state, control, **kwargs): if self._initialized and state.is_world_process_zero: if self._auto_end_run and self._ml_flow.active_run(): self._ml_flow.end_run() def on_save(self, args, state, control, **kwargs): if self._initialized and state.is_world_process_zero and self._log_artifacts: ckpt_dir = f"checkpoint-{state.global_step}" artifact_path = os.path.join(args.output_dir, ckpt_dir) logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. This may take time.") self._ml_flow.pyfunc.log_model( ckpt_dir, artifacts={"model_path": artifact_path}, python_model=self._ml_flow.pyfunc.PythonModel(), ) def __del__(self): # if the previous run is not terminated correctly, the fluent API will # not let you start a new run before the previous one is killed if ( self._auto_end_run and callable(getattr(self._ml_flow, "active_run", None)) and self._ml_flow.active_run() is not None ): self._ml_flow.end_run() class NeptuneMissingConfiguration(Exception): def __init__(self): super().__init__( """ ------ Unsupported ---- We were not able to create new runs. You provided a custom Neptune run to `NeptuneCallback` with the `run` argument. For the integration to work fully, provide your `api_token` and `project` by saving them as environment variables or passing them to the callback. """ ) class NeptuneCallback(TrainerCallback): """TrainerCallback that sends the logs to [Neptune](https://neptune.ai). Args: api_token (`str`, optional): Neptune API token obtained upon registration. You can leave this argument out if you have saved your token to the `NEPTUNE_API_TOKEN` environment variable (strongly recommended). See full setup instructions in the [docs](https://docs.neptune.ai/getting-started/installation). project (`str`, optional): Name of an existing Neptune project, in the form: "workspace-name/project-name". You can find and copy the name from the project Settings -> Properties in Neptune. If None (default), the value of the `NEPTUNE_PROJECT` environment variable will be used. name (`str`, optional): Custom name for the run. base_namespace (`str`, optional, defaults to "finetuning"): In the Neptune run, the root namespace that will contain all of the logged metadata. log_parameters (`bool`, optional, defaults to True): If True, logs all Trainer arguments and model parameters provided by the Trainer. log_checkpoints (`str`, optional, defaults to None): If "same", uploads checkpoints whenever they are saved by the Trainer. If "last", uploads only the most recently saved checkpoint. If "best", uploads the best checkpoint (among the ones saved by the Trainer). If None, does not upload checkpoints. run (`Run`, optional): Pass a Neptune run object if you want to continue logging to an existing run. Read more about resuming runs in the [docs](https://docs.neptune.ai/how-to-guides/neptune-api/resume-run). **neptune_run_kwargs (optional): Additional keyword arguments to be passed directly to the [neptune.init_run()](https://docs.neptune.ai/api-reference/neptune#.init_run) function when a new run is created. """ integration_version_key = "source_code/integrations/transformers" model_parameters_key = "model_parameters" trial_name_key = "trial" trial_params_key = "trial_params" trainer_parameters_key = "trainer_parameters" flat_metrics = {"train/epoch"} def __init__( self, *, api_token: Optional[str] = None, project: Optional[str] = None, name: Optional[str] = None, base_namespace: str = "finetuning", run: Optional["Run"] = None, log_parameters: bool = True, log_checkpoints: Optional[str] = None, **neptune_run_kwargs ): if not is_neptune_available(): raise ValueError( "NeptuneCallback requires the Neptune client library to be installed. " "To install the library, run `pip install neptune-client`." ) from neptune.new.metadata_containers.run import Run try: from neptune.new.integrations.utils import verify_type except ImportError: from neptune.new.internal.utils import verify_type verify_type("api_token", api_token, (str, type(None))) verify_type("project", project, (str, type(None))) verify_type("name", name, (str, type(None))) verify_type("base_namespace", base_namespace, str) verify_type("run", run, (Run, type(None))) verify_type("log_parameters", log_parameters, bool) verify_type("log_checkpoints", log_checkpoints, (str, type(None))) self._base_namespace_path = base_namespace self._log_parameters = log_parameters self._log_checkpoints = log_checkpoints self._initial_run: Optional[Run] = run self._run = None self._is_monitoring_run = False self._run_id = None self._force_reset_monitoring_run = False self._init_run_kwargs = {"api_token": api_token, "project": project, "name": name, **neptune_run_kwargs} self._volatile_checkpoints_dir = None self._should_upload_checkpoint = self._log_checkpoints is not None self._recent_checkpoint_path = None if self._log_checkpoints in {"last", "best"}: self._target_checkpoints_namespace = f"checkpoints/{self._log_checkpoints}" self._should_clean_recently_uploaded_checkpoint = True else: self._target_checkpoints_namespace = "checkpoints" self._should_clean_recently_uploaded_checkpoint = False def _stop_run_if_exists(self): if self._run: self._run.stop() del self._run self._run = None def _initialize_run(self, **additional_neptune_kwargs): from neptune.new import init_run from neptune.new.exceptions import NeptuneMissingApiTokenException, NeptuneMissingProjectNameException self._stop_run_if_exists() try: self._run = init_run(**self._init_run_kwargs, **additional_neptune_kwargs) self._run_id = self._run["sys/id"].fetch() except (NeptuneMissingProjectNameException, NeptuneMissingApiTokenException) as e: raise NeptuneMissingConfiguration() from e def _use_initial_run(self): self._run = self._initial_run self._is_monitoring_run = True self._run_id = self._run["sys/id"].fetch() self._initial_run = None def _ensure_run_with_monitoring(self): if self._initial_run is not None: self._use_initial_run() else: if not self._force_reset_monitoring_run and self._is_monitoring_run: return if self._run and not self._is_monitoring_run and not self._force_reset_monitoring_run: self._initialize_run(run=self._run_id) self._is_monitoring_run = True else: self._initialize_run() self._force_reset_monitoring_run = False def _ensure_at_least_run_without_monitoring(self): if self._initial_run is not None: self._use_initial_run() else: if not self._run: self._initialize_run( run=self._run_id, capture_stdout=False, capture_stderr=False, capture_hardware_metrics=False, capture_traceback=False, ) self._is_monitoring_run = False @property def run(self): if self._run is None: self._ensure_at_least_run_without_monitoring() return self._run @property def _metadata_namespace(self): return self.run[self._base_namespace_path] def _log_integration_version(self): self.run[NeptuneCallback.integration_version_key] = version def _log_trainer_parameters(self, args): self._metadata_namespace[NeptuneCallback.trainer_parameters_key] = args.to_sanitized_dict() def _log_model_parameters(self, model): if model and hasattr(model, "config") and model.config is not None: self._metadata_namespace[NeptuneCallback.model_parameters_key] = model.config.to_dict() def _log_hyper_param_search_parameters(self, state): if state and hasattr(state, "trial_name"): self._metadata_namespace[NeptuneCallback.trial_name_key] = state.trial_name if state and hasattr(state, "trial_params") and state.trial_params is not None: self._metadata_namespace[NeptuneCallback.trial_params_key] = state.trial_params def _log_model_checkpoint(self, source_directory: str, checkpoint: str): target_path = relative_path = os.path.join(source_directory, checkpoint) if self._volatile_checkpoints_dir is not None: consistent_checkpoint_path = os.path.join(self._volatile_checkpoints_dir, checkpoint) try: shutil.copytree(relative_path, os.path.join(consistent_checkpoint_path, relative_path)) target_path = consistent_checkpoint_path except IOError as e: logger.warning( "NeptuneCallback was unable to made a copy of checkpoint due to I/O exception: '{}'." "Could fail trying to upload.".format(e) ) self._metadata_namespace[self._target_checkpoints_namespace].upload_files(target_path) if self._should_clean_recently_uploaded_checkpoint and self._recent_checkpoint_path is not None: self._metadata_namespace[self._target_checkpoints_namespace].delete_files(self._recent_checkpoint_path) self._recent_checkpoint_path = relative_path def on_init_end(self, args, state, control, **kwargs): self._volatile_checkpoints_dir = None if self._log_checkpoints and (args.overwrite_output_dir or args.save_total_limit is not None): self._volatile_checkpoints_dir = tempfile.TemporaryDirectory().name if self._log_checkpoints == "best" and not args.load_best_model_at_end: raise ValueError("To save the best model checkpoint, the load_best_model_at_end argument must be enabled.") def on_train_begin(self, args, state, control, model=None, **kwargs): if not state.is_world_process_zero: return self._ensure_run_with_monitoring() self._force_reset_monitoring_run = True self._log_integration_version() if self._log_parameters: self._log_trainer_parameters(args) self._log_model_parameters(model) if state.is_hyper_param_search: self._log_hyper_param_search_parameters(state) def on_train_end(self, args, state, control, **kwargs): self._stop_run_if_exists() def __del__(self): if self._volatile_checkpoints_dir is not None: shutil.rmtree(self._volatile_checkpoints_dir, ignore_errors=True) self._stop_run_if_exists() def on_save(self, args, state, control, **kwargs): if self._should_upload_checkpoint: self._log_model_checkpoint(args.output_dir, f"checkpoint-{state.global_step}") def on_evaluate(self, args, state, control, metrics=None, **kwargs): if self._log_checkpoints == "best": best_metric_name = args.metric_for_best_model if not best_metric_name.startswith("eval_"): best_metric_name = f"eval_{best_metric_name}" metric_value = metrics.get(best_metric_name) operator = np.greater if args.greater_is_better else np.less self._should_upload_checkpoint = state.best_metric is None or operator(metric_value, state.best_metric) @classmethod def get_run(cls, trainer): for callback in trainer.callback_handler.callbacks: if isinstance(callback, cls): return callback.run raise Exception("The trainer doesn't have a NeptuneCallback configured.") def on_log(self, args, state, control, logs: Optional[Dict[str, float]] = None, **kwargs): if not state.is_world_process_zero: return if logs is not None: for name, value in rewrite_logs(logs).items(): if isinstance(value, (int, float)): if name in NeptuneCallback.flat_metrics: self._metadata_namespace[name] = value else: self._metadata_namespace[name].log(value, step=state.global_step) class CodeCarbonCallback(TrainerCallback): """ A [`TrainerCallback`] that tracks the CO2 emission of training. """ def __init__(self): if not is_codecarbon_available(): raise RuntimeError( "CodeCarbonCallback requires `codecarbon` to be installed. Run `pip install codecarbon`." ) import codecarbon self._codecarbon = codecarbon self.tracker = None def on_init_end(self, args, state, control, **kwargs): if self.tracker is None and state.is_local_process_zero: # CodeCarbon will automatically handle environment variables for configuration self.tracker = self._codecarbon.EmissionsTracker(output_dir=args.output_dir) def on_train_begin(self, args, state, control, model=None, **kwargs): if self.tracker and state.is_local_process_zero: self.tracker.start() def on_train_end(self, args, state, control, **kwargs): if self.tracker and state.is_local_process_zero: self.tracker.stop() class ClearMLCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/). Environment: CLEARML_PROJECT (`str`, *optional*, defaults to `"HuggingFace Transformers"`): ClearML project name. CLEARML_TASK (`str`, *optional* defaults to `"Trainer"`): ClearML task name. """ def __init__(self): if is_clearml_available(): import clearml self._clearml = clearml else: raise RuntimeError("ClearMLCallback requires 'clearml' to be installed. Run `pip install clearml`.") self._initialized = False self._clearml_task = None def setup(self, args, state, model, tokenizer, **kwargs): if self._clearml is None: return if state.is_world_process_zero: logger.info("Automatic ClearML logging enabled.") if self._clearml_task is None: self._clearml_task = self._clearml.Task.init( project_name=os.getenv("CLEARML_PROJECT", "HuggingFace Transformers"), task_name=os.getenv("CLEARML_TASK", "Trainer"), auto_connect_frameworks={"tensorboard": False, "pytorch": False}, output_uri=True, ) self._initialized = True logger.info("ClearML Task has been initialized.") self._clearml_task.connect(args, "Args") if hasattr(model, "config") and model.config is not None: self._clearml_task.connect(model.config, "Model Configuration") def on_train_begin(self, args, state, control, model=None, tokenizer=None, **kwargs): if self._clearml is None: return if state.is_hyper_param_search: self._initialized = False if not self._initialized: self.setup(args, state, model, tokenizer, **kwargs) def on_train_end(self, args, state, control, model=None, tokenizer=None, metrics=None, logs=None, **kwargs): if self._clearml is None: return if self._clearml_task and state.is_world_process_zero: # Close ClearML Task at the end end of training self._clearml_task.close() def on_log(self, args, state, control, model=None, tokenizer=None, logs=None, **kwargs): if self._clearml is None: return if not self._initialized: self.setup(args, state, model, tokenizer, **kwargs) if state.is_world_process_zero: eval_prefix = "eval_" eval_prefix_len = len(eval_prefix) test_prefix = "test_" test_prefix_len = len(test_prefix) single_value_scalars = [ "train_runtime", "train_samples_per_second", "train_steps_per_second", "train_loss", "total_flos", "epoch", ] for k, v in logs.items(): if isinstance(v, (int, float)): if k in single_value_scalars: self._clearml_task.get_logger().report_single_value(name=k, value=v) elif k.startswith(eval_prefix): self._clearml_task.get_logger().report_scalar( title=k[eval_prefix_len:], series="eval", value=v, iteration=state.global_step ) elif k.startswith(test_prefix): self._clearml_task.get_logger().report_scalar( title=k[test_prefix_len:], series="test", value=v, iteration=state.global_step ) else: self._clearml_task.get_logger().report_scalar( title=k, series="train", value=v, iteration=state.global_step ) else: logger.warning( "Trainer is attempting to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a scalar. ' "This invocation of ClearML logger's report_scalar() " "is incorrect so we dropped this attribute." ) def on_save(self, args, state, control, **kwargs): if self._clearml_task and state.is_world_process_zero: ckpt_dir = f"checkpoint-{state.global_step}" artifact_path = os.path.join(args.output_dir, ckpt_dir) logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. This may take time.") self._clearml_task.update_output_model(artifact_path, iteration=state.global_step, auto_delete_file=False) INTEGRATION_TO_CALLBACK = { "azure_ml": AzureMLCallback, "comet_ml": CometCallback, "mlflow": MLflowCallback, "neptune": NeptuneCallback, "tensorboard": TensorBoardCallback, "wandb": WandbCallback, "codecarbon": CodeCarbonCallback, "clearml": ClearMLCallback, } def get_reporting_integration_callbacks(report_to): for integration in report_to: if integration not in INTEGRATION_TO_CALLBACK: raise ValueError( f"{integration} is not supported, only {', '.join(INTEGRATION_TO_CALLBACK.keys())} are supported." ) return [INTEGRATION_TO_CALLBACK[integration] for integration in report_to]

# Copyright 2020 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Integrations with other Python libraries. """ import functools import importlib.util import json import numbers import os import pickle import shutil import sys import tempfile from dataclasses import asdict from pathlib import Path from typing import TYPE_CHECKING, Dict, Optional import numpy as np from . import __version__ as version from .utils import flatten_dict, is_datasets_available, is_torch_available, logging logger = logging.get_logger(__name__) if is_torch_available(): import torch # comet_ml requires to be imported before any ML frameworks _has_comet = importlib.util.find_spec("comet_ml") is not None and os.getenv("COMET_MODE", "").upper() != "DISABLED" if _has_comet: try: import comet_ml # noqa: F401 if hasattr(comet_ml, "config") and comet_ml.config.get_config("comet.api_key"): _has_comet = True else: if os.getenv("COMET_MODE", "").upper() != "DISABLED": logger.warning("comet_ml is installed but `COMET_API_KEY` is not set.") _has_comet = False except (ImportError, ValueError): _has_comet = False _has_neptune = importlib.util.find_spec("neptune") is not None if TYPE_CHECKING and _has_neptune: from neptune.new.metadata_containers.run import Run from .trainer_callback import ProgressCallback, TrainerCallback # noqa: E402 from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy # noqa: E402 from .training_args import ParallelMode # noqa: E402 from .utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available # noqa: E402 # Integration functions: def is_wandb_available(): # any value of WANDB_DISABLED disables wandb if os.getenv("WANDB_DISABLED", "").upper() in ENV_VARS_TRUE_VALUES: logger.warning( "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the " "--report_to flag to control the integrations used for logging result (for instance --report_to none)." ) return False return importlib.util.find_spec("wandb") is not None def is_clearml_available(): return importlib.util.find_spec("clearml") is not None def is_comet_available(): return _has_comet def is_tensorboard_available(): return importlib.util.find_spec("tensorboard") is not None or importlib.util.find_spec("tensorboardX") is not None def is_optuna_available(): return importlib.util.find_spec("optuna") is not None def is_ray_available(): return importlib.util.find_spec("ray") is not None def is_ray_tune_available(): if not is_ray_available(): return False return importlib.util.find_spec("ray.tune") is not None def is_sigopt_available(): return importlib.util.find_spec("sigopt") is not None def is_azureml_available(): if importlib.util.find_spec("azureml") is None: return False if importlib.util.find_spec("azureml.core") is None: return False return importlib.util.find_spec("azureml.core.run") is not None def is_mlflow_available(): if os.getenv("DISABLE_MLFLOW_INTEGRATION", "FALSE").upper() == "TRUE": return False return importlib.util.find_spec("mlflow") is not None def is_fairscale_available(): return importlib.util.find_spec("fairscale") is not None def is_neptune_available(): return _has_neptune def is_codecarbon_available(): return importlib.util.find_spec("codecarbon") is not None def hp_params(trial): if is_optuna_available(): import optuna if isinstance(trial, optuna.Trial): return trial.params if is_ray_tune_available(): if isinstance(trial, dict): return trial if is_sigopt_available(): if isinstance(trial, dict): return trial if is_wandb_available(): if isinstance(trial, dict): return trial raise RuntimeError(f"Unknown type for trial {trial.__class__}") def default_hp_search_backend(): if is_optuna_available(): return "optuna" elif is_ray_tune_available(): return "ray" elif is_sigopt_available(): return "sigopt" def run_hp_search_optuna(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: import optuna if trainer.args.process_index == 0: def _objective(trial, checkpoint_dir=None): checkpoint = None if checkpoint_dir: for subdir in os.listdir(checkpoint_dir): if subdir.startswith(PREFIX_CHECKPOINT_DIR): checkpoint = os.path.join(checkpoint_dir, subdir) trainer.objective = None if trainer.args.world_size > 1: if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP optuna HPO for ParallelMode.DISTRIBUTED currently.") trainer._hp_search_setup(trial) torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0) trainer.train(resume_from_checkpoint=checkpoint) else: trainer.train(resume_from_checkpoint=checkpoint, trial=trial) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) return trainer.objective timeout = kwargs.pop("timeout", None) n_jobs = kwargs.pop("n_jobs", 1) study = optuna.create_study(direction=direction, **kwargs) study.optimize(_objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs) best_trial = study.best_trial return BestRun(str(best_trial.number), best_trial.value, best_trial.params) else: for i in range(n_trials): trainer.objective = None args_main_rank = list(pickle.dumps(trainer.args)) if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP optuna HPO for ParallelMode.DISTRIBUTED currently.") torch.distributed.broadcast_object_list(args_main_rank, src=0) args = pickle.loads(bytes(args_main_rank)) for key, value in asdict(args).items(): if key != "local_rank": setattr(trainer.args, key, value) trainer.train(resume_from_checkpoint=None) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) return None def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: import ray def _objective(trial, local_trainer, checkpoint_dir=None): try: from transformers.utils.notebook import NotebookProgressCallback if local_trainer.pop_callback(NotebookProgressCallback): local_trainer.add_callback(ProgressCallback) except ModuleNotFoundError: pass checkpoint = None if checkpoint_dir: for subdir in os.listdir(checkpoint_dir): if subdir.startswith(PREFIX_CHECKPOINT_DIR): checkpoint = os.path.join(checkpoint_dir, subdir) local_trainer.objective = None local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial) # If there hasn't been any evaluation during the training loop. if getattr(local_trainer, "objective", None) is None: metrics = local_trainer.evaluate() local_trainer.objective = local_trainer.compute_objective(metrics) local_trainer._tune_save_checkpoint() ray.tune.report(objective=local_trainer.objective, **metrics, done=True) if not trainer._memory_tracker.skip_memory_metrics: from .trainer_utils import TrainerMemoryTracker logger.warning( "Memory tracking for your Trainer is currently " "enabled. Automatically disabling the memory tracker " "since the memory tracker is not serializable." ) trainer._memory_tracker = TrainerMemoryTracker(skip_memory_metrics=True) # The model and TensorBoard writer do not pickle so we have to remove them (if they exists) # while doing the ray hp search. _tb_writer = trainer.pop_callback(TensorBoardCallback) trainer.model = None # Setup default `resources_per_trial`. if "resources_per_trial" not in kwargs: # Default to 1 CPU and 1 GPU (if applicable) per trial. kwargs["resources_per_trial"] = {"cpu": 1} if trainer.args.n_gpu > 0: kwargs["resources_per_trial"]["gpu"] = 1 resource_msg = "1 CPU" + (" and 1 GPU" if trainer.args.n_gpu > 0 else "") logger.info( "No `resources_per_trial` arg was passed into " "`hyperparameter_search`. Setting it to a default value " f"of {resource_msg} for each trial." ) # Make sure each trainer only uses GPUs that were allocated per trial. gpus_per_trial = kwargs["resources_per_trial"].get("gpu", 0) trainer.args._n_gpu = gpus_per_trial # Setup default `progress_reporter`. if "progress_reporter" not in kwargs: from ray.tune import CLIReporter kwargs["progress_reporter"] = CLIReporter(metric_columns=["objective"]) if "keep_checkpoints_num" in kwargs and kwargs["keep_checkpoints_num"] > 0: # `keep_checkpoints_num=0` would disabled checkpointing trainer.use_tune_checkpoints = True if kwargs["keep_checkpoints_num"] > 1: logger.warning( f"Currently keeping {kwargs['keep_checkpoints_num']} checkpoints for each trial. " "Checkpoints are usually huge, " "consider setting `keep_checkpoints_num=1`." ) if "scheduler" in kwargs: from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining # Check if checkpointing is enabled for PopulationBasedTraining if isinstance(kwargs["scheduler"], PopulationBasedTraining): if not trainer.use_tune_checkpoints: logger.warning( "You are using PopulationBasedTraining but you haven't enabled checkpointing. " "This means your trials will train from scratch everytime they are exploiting " "new configurations. Consider enabling checkpointing by passing " "`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`." ) # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting. if isinstance( kwargs["scheduler"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining) ) and (not trainer.args.do_eval or trainer.args.evaluation_strategy == IntervalStrategy.NO): raise RuntimeError( "You are using {cls} as a scheduler but you haven't enabled evaluation during training. " "This means your trials will not report intermediate results to Ray Tune, and " "can thus not be stopped early or used to exploit other trials parameters. " "If this is what you want, do not use {cls}. If you would like to use {cls}, " "make sure you pass `do_eval=True` and `evaluation_strategy='steps'` in the " "Trainer `args`.".format(cls=type(kwargs["scheduler"]).__name__) ) trainable = ray.tune.with_parameters(_objective, local_trainer=trainer) @functools.wraps(trainable) def dynamic_modules_import_trainable(*args, **kwargs): """ Wrapper around `tune.with_parameters` to ensure datasets_modules are loaded on each Actor. Without this, an ImportError will be thrown. See https://github.com/huggingface/transformers/issues/11565. Assumes that `_objective`, defined above, is a function. """ if is_datasets_available(): import datasets.load dynamic_modules_path = os.path.join(datasets.load.init_dynamic_modules(), "__init__.py") # load dynamic_modules from path spec = importlib.util.spec_from_file_location("datasets_modules", dynamic_modules_path) datasets_modules = importlib.util.module_from_spec(spec) sys.modules[spec.name] = datasets_modules spec.loader.exec_module(datasets_modules) return trainable(*args, **kwargs) # special attr set by tune.with_parameters if hasattr(trainable, "__mixins__"): dynamic_modules_import_trainable.__mixins__ = trainable.__mixins__ analysis = ray.tune.run( dynamic_modules_import_trainable, config=trainer.hp_space(None), num_samples=n_trials, **kwargs, ) best_trial = analysis.get_best_trial(metric="objective", mode=direction[:3], scope=trainer.args.ray_scope) best_run = BestRun(best_trial.trial_id, best_trial.last_result["objective"], best_trial.config) if _tb_writer is not None: trainer.add_callback(_tb_writer) return best_run def run_hp_search_sigopt(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: import sigopt from transformers.utils.versions import importlib_metadata if trainer.args.process_index == 0: if importlib_metadata.version("sigopt") >= "8.0.0": sigopt.set_project("huggingface") experiment = sigopt.create_experiment( name="huggingface-tune", type="offline", parameters=trainer.hp_space(None), metrics=[dict(name="objective", objective=direction, strategy="optimize")], parallel_bandwidth=1, budget=n_trials, ) logger.info(f"created experiment: https://app.sigopt.com/experiment/{experiment.id}") for run in experiment.loop(): with run: trainer.objective = None if trainer.args.world_size > 1: if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.") trainer._hp_search_setup(run.run) torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0) trainer.train(resume_from_checkpoint=None) else: trainer.train(resume_from_checkpoint=None, trial=run.run) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) run.log_metric("objective", trainer.objective) best = list(experiment.get_best_runs())[0] best_run = BestRun(best.id, best.values["objective"].value, best.assignments) else: from sigopt import Connection conn = Connection() proxies = kwargs.pop("proxies", None) if proxies is not None: conn.set_proxies(proxies) experiment = conn.experiments().create( name="huggingface-tune", parameters=trainer.hp_space(None), metrics=[dict(name="objective", objective=direction, strategy="optimize")], parallel_bandwidth=1, observation_budget=n_trials, project="huggingface", ) logger.info(f"created experiment: https://app.sigopt.com/experiment/{experiment.id}") while experiment.progress.observation_count < experiment.observation_budget: suggestion = conn.experiments(experiment.id).suggestions().create() trainer.objective = None if trainer.args.world_size > 1: if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.") trainer._hp_search_setup(suggestion) torch.distributed.broadcast_object_list(pickle.dumps(trainer.args), src=0) trainer.train(resume_from_checkpoint=None) else: trainer.train(resume_from_checkpoint=None, trial=suggestion) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) values = [dict(name="objective", value=trainer.objective)] obs = conn.experiments(experiment.id).observations().create(suggestion=suggestion.id, values=values) logger.info(f"[suggestion_id, observation_id]: [{suggestion.id}, {obs.id}]") experiment = conn.experiments(experiment.id).fetch() best = list(conn.experiments(experiment.id).best_assignments().fetch().iterate_pages())[0] best_run = BestRun(best.id, best.value, best.assignments) return best_run else: for i in range(n_trials): trainer.objective = None args_main_rank = list(pickle.dumps(trainer.args)) if trainer.args.parallel_mode != ParallelMode.DISTRIBUTED: raise RuntimeError("only support DDP Sigopt HPO for ParallelMode.DISTRIBUTED currently.") torch.distributed.broadcast_object_list(args_main_rank, src=0) args = pickle.loads(bytes(args_main_rank)) for key, value in asdict(args).items(): if key != "local_rank": setattr(trainer.args, key, value) trainer.train(resume_from_checkpoint=None) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) return None def run_hp_search_wandb(trainer, n_trials: int, direction: str, **kwargs) -> BestRun: from .integrations import is_wandb_available if not is_wandb_available(): raise ImportError("This function needs wandb installed: `pip install wandb`") import wandb # add WandbCallback if not already added in trainer callbacks reporting_to_wandb = False for callback in trainer.callback_handler.callbacks: if isinstance(callback, WandbCallback): reporting_to_wandb = True break if not reporting_to_wandb: trainer.add_callback(WandbCallback()) trainer.args.report_to = "wandb" best_trial = {"run_id": None, "objective": None, "hyperparameters": None} sweep_id = kwargs.pop("sweep_id", None) project = kwargs.pop("project", None) name = kwargs.pop("name", None) entity = kwargs.pop("entity", None) metric = kwargs.pop("metric", "eval/loss") sweep_config = trainer.hp_space(None) sweep_config["metric"]["goal"] = direction sweep_config["metric"]["name"] = metric if name: sweep_config["name"] = name def _objective(): run = wandb.run if wandb.run else wandb.init() trainer.state.trial_name = run.name run.config.update({"assignments": {}, "metric": metric}) config = wandb.config trainer.objective = None trainer.train(resume_from_checkpoint=None, trial=vars(config)["_items"]) # If there hasn't been any evaluation during the training loop. if getattr(trainer, "objective", None) is None: metrics = trainer.evaluate() trainer.objective = trainer.compute_objective(metrics) format_metrics = rewrite_logs(metrics) if metric not in format_metrics: logger.warning( f"Provided metric {metric} not found. This might result in unexpected sweeps charts. The available" f" metrics are {format_metrics.keys()}" ) best_score = False if best_trial["run_id"] is not None: if direction == "minimize": best_score = trainer.objective < best_trial["objective"] elif direction == "maximize": best_score = trainer.objective > best_trial["objective"] if best_score or best_trial["run_id"] is None: best_trial["run_id"] = run.id best_trial["objective"] = trainer.objective best_trial["hyperparameters"] = dict(config) return trainer.objective sweep_id = wandb.sweep(sweep_config, project=project, entity=entity) if not sweep_id else sweep_id logger.info(f"wandb sweep id - {sweep_id}") wandb.agent(sweep_id, function=_objective, count=n_trials) return BestRun(best_trial["run_id"], best_trial["objective"], best_trial["hyperparameters"]) def get_available_reporting_integrations(): integrations = [] if is_azureml_available(): integrations.append("azure_ml") if is_comet_available(): integrations.append("comet_ml") if is_mlflow_available(): integrations.append("mlflow") if is_neptune_available(): integrations.append("neptune") if is_tensorboard_available(): integrations.append("tensorboard") if is_wandb_available(): integrations.append("wandb") if is_codecarbon_available(): integrations.append("codecarbon") if is_clearml_available(): integrations.append("clearml") return integrations def rewrite_logs(d): new_d = {} eval_prefix = "eval_" eval_prefix_len = len(eval_prefix) test_prefix = "test_" test_prefix_len = len(test_prefix) for k, v in d.items(): if k.startswith(eval_prefix): new_d["eval/" + k[eval_prefix_len:]] = v elif k.startswith(test_prefix): new_d["test/" + k[test_prefix_len:]] = v else: new_d["train/" + k] = v return new_d class TensorBoardCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [TensorBoard](https://www.tensorflow.org/tensorboard). Args: tb_writer (`SummaryWriter`, *optional*): The writer to use. Will instantiate one if not set. """ def __init__(self, tb_writer=None): has_tensorboard = is_tensorboard_available() if not has_tensorboard: raise RuntimeError( "TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or" " install tensorboardX." ) if has_tensorboard: try: from torch.utils.tensorboard import SummaryWriter # noqa: F401 self._SummaryWriter = SummaryWriter except ImportError: try: from tensorboardX import SummaryWriter self._SummaryWriter = SummaryWriter except ImportError: self._SummaryWriter = None else: self._SummaryWriter = None self.tb_writer = tb_writer def _init_summary_writer(self, args, log_dir=None): log_dir = log_dir or args.logging_dir if self._SummaryWriter is not None: self.tb_writer = self._SummaryWriter(log_dir=log_dir) def on_train_begin(self, args, state, control, **kwargs): if not state.is_world_process_zero: return log_dir = None if state.is_hyper_param_search: trial_name = state.trial_name if trial_name is not None: log_dir = os.path.join(args.logging_dir, trial_name) if self.tb_writer is None: self._init_summary_writer(args, log_dir) if self.tb_writer is not None: self.tb_writer.add_text("args", args.to_json_string()) if "model" in kwargs: model = kwargs["model"] if hasattr(model, "config") and model.config is not None: model_config_json = model.config.to_json_string() self.tb_writer.add_text("model_config", model_config_json) # Version of TensorBoard coming from tensorboardX does not have this method. if hasattr(self.tb_writer, "add_hparams"): self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={}) def on_log(self, args, state, control, logs=None, **kwargs): if not state.is_world_process_zero: return if self.tb_writer is None: self._init_summary_writer(args) if self.tb_writer is not None: logs = rewrite_logs(logs) for k, v in logs.items(): if isinstance(v, (int, float)): self.tb_writer.add_scalar(k, v, state.global_step) else: logger.warning( "Trainer is attempting to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a scalar. ' "This invocation of Tensorboard's writer.add_scalar() " "is incorrect so we dropped this attribute." ) self.tb_writer.flush() def on_train_end(self, args, state, control, **kwargs): if self.tb_writer: self.tb_writer.close() self.tb_writer = None class WandbCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [Weight and Biases](https://www.wandb.com/). """ def __init__(self): has_wandb = is_wandb_available() if not has_wandb: raise RuntimeError("WandbCallback requires wandb to be installed. Run `pip install wandb`.") if has_wandb: import wandb self._wandb = wandb self._initialized = False # log outputs self._log_model = os.getenv("WANDB_LOG_MODEL", "FALSE").upper() in ENV_VARS_TRUE_VALUES.union({"TRUE"}) def setup(self, args, state, model, **kwargs): """ Setup the optional Weights & Biases (*wandb*) integration. One can subclass and override this method to customize the setup if needed. Find more information [here](https://docs.wandb.ai/integrations/huggingface). You can also override the following environment variables: Environment: WANDB_LOG_MODEL (`bool`, *optional*, defaults to `False`): Whether or not to log model as artifact at the end of training. Use along with *TrainingArguments.load_best_model_at_end* to upload best model. WANDB_WATCH (`str`, *optional* defaults to `"gradients"`): Can be `"gradients"`, `"all"` or `"false"`. Set to `"false"` to disable gradient logging or `"all"` to log gradients and parameters. WANDB_PROJECT (`str`, *optional*, defaults to `"huggingface"`): Set this to a custom string to store results in a different project. WANDB_DISABLED (`bool`, *optional*, defaults to `False`): Whether or not to disable wandb entirely. Set *WANDB_DISABLED=true* to disable. """ if self._wandb is None: return self._initialized = True if state.is_world_process_zero: logger.info( 'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"' ) combined_dict = {**args.to_sanitized_dict()} if hasattr(model, "config") and model.config is not None: model_config = model.config.to_dict() combined_dict = {**model_config, **combined_dict} trial_name = state.trial_name init_args = {} if trial_name is not None: run_name = trial_name init_args["group"] = args.run_name else: run_name = args.run_name if self._wandb.run is None: self._wandb.init( project=os.getenv("WANDB_PROJECT", "huggingface"), name=run_name, **init_args, ) # add config parameters (run may have been created manually) self._wandb.config.update(combined_dict, allow_val_change=True) # define default x-axis (for latest wandb versions) if getattr(self._wandb, "define_metric", None): self._wandb.define_metric("train/global_step") self._wandb.define_metric("*", step_metric="train/global_step", step_sync=True) # keep track of model topology and gradients, unsupported on TPU if not is_torch_tpu_available() and os.getenv("WANDB_WATCH") != "false": self._wandb.watch( model, log=os.getenv("WANDB_WATCH", "gradients"), log_freq=max(100, args.logging_steps) ) def on_train_begin(self, args, state, control, model=None, **kwargs): if self._wandb is None: return hp_search = state.is_hyper_param_search if hp_search: self._wandb.finish() self._initialized = False args.run_name = None if not self._initialized: self.setup(args, state, model, **kwargs) def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs): if self._wandb is None: return if self._log_model and self._initialized and state.is_world_process_zero: from .trainer import Trainer fake_trainer = Trainer(args=args, model=model, tokenizer=tokenizer) with tempfile.TemporaryDirectory() as temp_dir: fake_trainer.save_model(temp_dir) metadata = ( { k: v for k, v in dict(self._wandb.summary).items() if isinstance(v, numbers.Number) and not k.startswith("_") } if not args.load_best_model_at_end else { f"eval/{args.metric_for_best_model}": state.best_metric, "train/total_floss": state.total_flos, } ) artifact = self._wandb.Artifact(name=f"model-{self._wandb.run.id}", type="model", metadata=metadata) for f in Path(temp_dir).glob("*"): if f.is_file(): with artifact.new_file(f.name, mode="wb") as fa: fa.write(f.read_bytes()) self._wandb.run.log_artifact(artifact) def on_log(self, args, state, control, model=None, logs=None, **kwargs): if self._wandb is None: return if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: logs = rewrite_logs(logs) self._wandb.log({**logs, "train/global_step": state.global_step}) class CometCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [Comet ML](https://www.comet.ml/site/). """ def __init__(self): if not _has_comet: raise RuntimeError("CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.") self._initialized = False self._log_assets = False def setup(self, args, state, model): """ Setup the optional Comet.ml integration. Environment: COMET_MODE (`str`, *optional*): Whether to create an online, offline experiment or disable Comet logging. Can be "OFFLINE", "ONLINE", or "DISABLED". Defaults to "ONLINE". COMET_PROJECT_NAME (`str`, *optional*): Comet project name for experiments COMET_OFFLINE_DIRECTORY (`str`, *optional*): Folder to use for saving offline experiments when `COMET_MODE` is "OFFLINE" COMET_LOG_ASSETS (`str`, *optional*): Whether or not to log training assets (tf event logs, checkpoints, etc), to Comet. Can be "TRUE", or "FALSE". Defaults to "TRUE". For a number of configurable items in the environment, see [here](https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables). """ self._initialized = True log_assets = os.getenv("COMET_LOG_ASSETS", "FALSE").upper() if log_assets in {"TRUE", "1"}: self._log_assets = True if state.is_world_process_zero: comet_mode = os.getenv("COMET_MODE", "ONLINE").upper() experiment = None experiment_kwargs = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")} if comet_mode == "ONLINE": experiment = comet_ml.Experiment(**experiment_kwargs) experiment.log_other("Created from", "transformers") logger.info("Automatic Comet.ml online logging enabled") elif comet_mode == "OFFLINE": experiment_kwargs["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./") experiment = comet_ml.OfflineExperiment(**experiment_kwargs) experiment.log_other("Created from", "transformers") logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished") if experiment is not None: experiment._set_model_graph(model, framework="transformers") experiment._log_parameters(args, prefix="args/", framework="transformers") if hasattr(model, "config"): experiment._log_parameters(model.config, prefix="config/", framework="transformers") def on_train_begin(self, args, state, control, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) def on_log(self, args, state, control, model=None, logs=None, **kwargs): if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: experiment = comet_ml.config.get_global_experiment() if experiment is not None: experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework="transformers") def on_train_end(self, args, state, control, **kwargs): if self._initialized and state.is_world_process_zero: experiment = comet_ml.config.get_global_experiment() if experiment is not None: if self._log_assets is True: logger.info("Logging checkpoints. This may take time.") experiment.log_asset_folder( args.output_dir, recursive=True, log_file_name=True, step=state.global_step ) experiment.end() class AzureMLCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [AzureML](https://pypi.org/project/azureml-sdk/). """ def __init__(self, azureml_run=None): if not is_azureml_available(): raise RuntimeError("AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`.") self.azureml_run = azureml_run def on_init_end(self, args, state, control, **kwargs): from azureml.core.run import Run if self.azureml_run is None and state.is_world_process_zero: self.azureml_run = Run.get_context() def on_log(self, args, state, control, logs=None, **kwargs): if self.azureml_run and state.is_world_process_zero: for k, v in logs.items(): if isinstance(v, (int, float)): self.azureml_run.log(k, v, description=k) class MLflowCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [MLflow](https://www.mlflow.org/). Can be disabled by setting environment variable `DISABLE_MLFLOW_INTEGRATION = TRUE`. """ def __init__(self): if not is_mlflow_available(): raise RuntimeError("MLflowCallback requires mlflow to be installed. Run `pip install mlflow`.") import mlflow self._MAX_PARAM_VAL_LENGTH = mlflow.utils.validation.MAX_PARAM_VAL_LENGTH self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH self._initialized = False self._auto_end_run = False self._log_artifacts = False self._ml_flow = mlflow def setup(self, args, state, model): """ Setup the optional MLflow integration. Environment: HF_MLFLOW_LOG_ARTIFACTS (`str`, *optional*): Whether to use MLflow .log_artifact() facility to log artifacts. This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or *1*, will copy each saved checkpoint on each save in [`TrainingArguments`]'s `output_dir` to the local or remote artifact storage. Using it without a remote storage will just copy the files to your artifact location. MLFLOW_EXPERIMENT_NAME (`str`, *optional*): Whether to use an MLflow experiment_name under which to launch the run. Default to "None" which will point to the "Default" experiment in MLflow. Otherwise, it is a case sensitive name of the experiment to be activated. If an experiment with this name does not exist, a new experiment with this name is created. MLFLOW_TAGS (`str`, *optional*): A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example: os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}' MLFLOW_NESTED_RUN (`str`, *optional*): Whether to use MLflow nested runs. If set to `True` or *1*, will create a nested run inside the current run. MLFLOW_RUN_ID (`str`, *optional*): Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint. When MLFLOW_RUN_ID environment variable is set, start_run attempts to resume a run with the specified run ID and other parameters are ignored. MLFLOW_FLATTEN_PARAMS (`str`, *optional*): Whether to flatten the parameters dictionary before logging. Default to `False`. """ self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES self._nested_run = os.getenv("MLFLOW_NESTED_RUN", "FALSE").upper() in ENV_VARS_TRUE_VALUES self._experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None) self._flatten_params = os.getenv("MLFLOW_FLATTEN_PARAMS", "FALSE").upper() in ENV_VARS_TRUE_VALUES self._run_id = os.getenv("MLFLOW_RUN_ID", None) logger.debug( f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run}," f" tags={self._nested_run}" ) if state.is_world_process_zero: if self._ml_flow.active_run() is None or self._nested_run or self._run_id: if self._experiment_name: # Use of set_experiment() ensure that Experiment is created if not exists self._ml_flow.set_experiment(self._experiment_name) self._ml_flow.start_run(run_name=args.run_name, nested=self._nested_run) logger.debug(f"MLflow run started with run_id={self._ml_flow.active_run().info.run_id}") self._auto_end_run = True combined_dict = args.to_dict() if hasattr(model, "config") and model.config is not None: model_config = model.config.to_dict() combined_dict = {**model_config, **combined_dict} combined_dict = flatten_dict(combined_dict) if self._flatten_params else combined_dict # remove params that are too long for MLflow for name, value in list(combined_dict.items()): # internally, all values are converted to str in MLflow if len(str(value)) > self._MAX_PARAM_VAL_LENGTH: logger.warning( f'Trainer is attempting to log a value of "{value}" for key "{name}" as a parameter. MLflow\'s' " log_param() only accepts values no longer than 250 characters so we dropped this attribute." " You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and" " avoid this message." ) del combined_dict[name] # MLflow cannot log more than 100 values in one go, so we have to split it combined_dict_items = list(combined_dict.items()) for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH): self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH])) mlflow_tags = os.getenv("MLFLOW_TAGS", None) if mlflow_tags: mlflow_tags = json.loads(mlflow_tags) self._ml_flow.set_tags(mlflow_tags) self._initialized = True def on_train_begin(self, args, state, control, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) def on_log(self, args, state, control, logs, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: metrics = {} for k, v in logs.items(): if isinstance(v, (int, float)): metrics[k] = v else: logger.warning( f'Trainer is attempting to log a value of "{v}" of type {type(v)} for key "{k}" as a metric. ' "MLflow's log_metric() only accepts float and int types so we dropped this attribute." ) self._ml_flow.log_metrics(metrics=metrics, step=state.global_step) def on_train_end(self, args, state, control, **kwargs): if self._initialized and state.is_world_process_zero: if self._auto_end_run and self._ml_flow.active_run(): self._ml_flow.end_run() def on_save(self, args, state, control, **kwargs): if self._initialized and state.is_world_process_zero and self._log_artifacts: ckpt_dir = f"checkpoint-{state.global_step}" artifact_path = os.path.join(args.output_dir, ckpt_dir) logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. This may take time.") self._ml_flow.pyfunc.log_model( ckpt_dir, artifacts={"model_path": artifact_path}, python_model=self._ml_flow.pyfunc.PythonModel(), ) def __del__(self): # if the previous run is not terminated correctly, the fluent API will # not let you start a new run before the previous one is killed if ( self._auto_end_run and callable(getattr(self._ml_flow, "active_run", None)) and self._ml_flow.active_run() is not None ): self._ml_flow.end_run() class NeptuneMissingConfiguration(Exception): def __init__(self): super().__init__( """ ------ Unsupported ---- We were not able to create new runs. You provided a custom Neptune run to `NeptuneCallback` with the `run` argument. For the integration to work fully, provide your `api_token` and `project` by saving them as environment variables or passing them to the callback. """ ) class NeptuneCallback(TrainerCallback): """TrainerCallback that sends the logs to [Neptune](https://neptune.ai). Args: api_token (`str`, optional): Neptune API token obtained upon registration. You can leave this argument out if you have saved your token to the `NEPTUNE_API_TOKEN` environment variable (strongly recommended). See full setup instructions in the [docs](https://docs.neptune.ai/getting-started/installation). project (`str`, optional): Name of an existing Neptune project, in the form: "workspace-name/project-name". You can find and copy the name from the project Settings -> Properties in Neptune. If None (default), the value of the `NEPTUNE_PROJECT` environment variable will be used. name (`str`, optional): Custom name for the run. base_namespace (`str`, optional, defaults to "finetuning"): In the Neptune run, the root namespace that will contain all of the logged metadata. log_parameters (`bool`, optional, defaults to True): If True, logs all Trainer arguments and model parameters provided by the Trainer. log_checkpoints (`str`, optional, defaults to None): If "same", uploads checkpoints whenever they are saved by the Trainer. If "last", uploads only the most recently saved checkpoint. If "best", uploads the best checkpoint (among the ones saved by the Trainer). If None, does not upload checkpoints. run (`Run`, optional): Pass a Neptune run object if you want to continue logging to an existing run. Read more about resuming runs in the [docs](https://docs.neptune.ai/how-to-guides/neptune-api/resume-run). **neptune_run_kwargs (optional): Additional keyword arguments to be passed directly to the [neptune.init_run()](https://docs.neptune.ai/api-reference/neptune#.init_run) function when a new run is created. """ integration_version_key = "source_code/integrations/transformers" model_parameters_key = "model_parameters" trial_name_key = "trial" trial_params_key = "trial_params" trainer_parameters_key = "trainer_parameters" flat_metrics = {"train/epoch"} def __init__( self, *, api_token: Optional[str] = None, project: Optional[str] = None, name: Optional[str] = None, base_namespace: str = "finetuning", run: Optional["Run"] = None, log_parameters: bool = True, log_checkpoints: Optional[str] = None, **neptune_run_kwargs ): if not is_neptune_available(): raise ValueError( "NeptuneCallback requires the Neptune client library to be installed. " "To install the library, run `pip install neptune-client`." ) from neptune.new.metadata_containers.run import Run try: from neptune.new.integrations.utils import verify_type except ImportError: from neptune.new.internal.utils import verify_type verify_type("api_token", api_token, (str, type(None))) verify_type("project", project, (str, type(None))) verify_type("name", name, (str, type(None))) verify_type("base_namespace", base_namespace, str) verify_type("run", run, (Run, type(None))) verify_type("log_parameters", log_parameters, bool) verify_type("log_checkpoints", log_checkpoints, (str, type(None))) self._base_namespace_path = base_namespace self._log_parameters = log_parameters self._log_checkpoints = log_checkpoints self._initial_run: Optional[Run] = run self._run = None self._is_monitoring_run = False self._run_id = None self._force_reset_monitoring_run = False self._init_run_kwargs = {"api_token": api_token, "project": project, "name": name, **neptune_run_kwargs} self._volatile_checkpoints_dir = None self._should_upload_checkpoint = self._log_checkpoints is not None self._recent_checkpoint_path = None if self._log_checkpoints in {"last", "best"}: self._target_checkpoints_namespace = f"checkpoints/{self._log_checkpoints}" self._should_clean_recently_uploaded_checkpoint = True else: self._target_checkpoints_namespace = "checkpoints" self._should_clean_recently_uploaded_checkpoint = False def _stop_run_if_exists(self): if self._run: self._run.stop() del self._run self._run = None def _initialize_run(self, **additional_neptune_kwargs): from neptune.new import init_run from neptune.new.exceptions import NeptuneMissingApiTokenException, NeptuneMissingProjectNameException self._stop_run_if_exists() try: self._run = init_run(**self._init_run_kwargs, **additional_neptune_kwargs) self._run_id = self._run["sys/id"].fetch() except (NeptuneMissingProjectNameException, NeptuneMissingApiTokenException) as e: raise NeptuneMissingConfiguration() from e def _use_initial_run(self): self._run = self._initial_run self._is_monitoring_run = True self._run_id = self._run["sys/id"].fetch() self._initial_run = None def _ensure_run_with_monitoring(self): if self._initial_run is not None: self._use_initial_run() else: if not self._force_reset_monitoring_run and self._is_monitoring_run: return if self._run and not self._is_monitoring_run and not self._force_reset_monitoring_run: self._initialize_run(run=self._run_id) self._is_monitoring_run = True else: self._initialize_run() self._force_reset_monitoring_run = False def _ensure_at_least_run_without_monitoring(self): if self._initial_run is not None: self._use_initial_run() else: if not self._run: self._initialize_run( run=self._run_id, capture_stdout=False, capture_stderr=False, capture_hardware_metrics=False, capture_traceback=False, ) self._is_monitoring_run = False @property def run(self): if self._run is None: self._ensure_at_least_run_without_monitoring() return self._run @property def _metadata_namespace(self): return self.run[self._base_namespace_path] def _log_integration_version(self): self.run[NeptuneCallback.integration_version_key] = version def _log_trainer_parameters(self, args): self._metadata_namespace[NeptuneCallback.trainer_parameters_key] = args.to_sanitized_dict() def _log_model_parameters(self, model): if model and hasattr(model, "config") and model.config is not None: self._metadata_namespace[NeptuneCallback.model_parameters_key] = model.config.to_dict() def _log_hyper_param_search_parameters(self, state): if state and hasattr(state, "trial_name"): self._metadata_namespace[NeptuneCallback.trial_name_key] = state.trial_name if state and hasattr(state, "trial_params") and state.trial_params is not None: self._metadata_namespace[NeptuneCallback.trial_params_key] = state.trial_params def _log_model_checkpoint(self, source_directory: str, checkpoint: str): target_path = relative_path = os.path.join(source_directory, checkpoint) if self._volatile_checkpoints_dir is not None: consistent_checkpoint_path = os.path.join(self._volatile_checkpoints_dir, checkpoint) try: shutil.copytree(relative_path, os.path.join(consistent_checkpoint_path, relative_path)) target_path = consistent_checkpoint_path except IOError as e: logger.warning( "NeptuneCallback was unable to made a copy of checkpoint due to I/O exception: '{}'." "Could fail trying to upload.".format(e) ) self._metadata_namespace[self._target_checkpoints_namespace].upload_files(target_path) if self._should_clean_recently_uploaded_checkpoint and self._recent_checkpoint_path is not None: self._metadata_namespace[self._target_checkpoints_namespace].delete_files(self._recent_checkpoint_path) self._recent_checkpoint_path = relative_path def on_init_end(self, args, state, control, **kwargs): self._volatile_checkpoints_dir = None if self._log_checkpoints and (args.overwrite_output_dir or args.save_total_limit is not None): self._volatile_checkpoints_dir = tempfile.TemporaryDirectory().name if self._log_checkpoints == "best" and not args.load_best_model_at_end: raise ValueError("To save the best model checkpoint, the load_best_model_at_end argument must be enabled.") def on_train_begin(self, args, state, control, model=None, **kwargs): if not state.is_world_process_zero: return self._ensure_run_with_monitoring() self._force_reset_monitoring_run = True self._log_integration_version() if self._log_parameters: self._log_trainer_parameters(args) self._log_model_parameters(model) if state.is_hyper_param_search: self._log_hyper_param_search_parameters(state) def on_train_end(self, args, state, control, **kwargs): self._stop_run_if_exists() def __del__(self): if self._volatile_checkpoints_dir is not None: shutil.rmtree(self._volatile_checkpoints_dir, ignore_errors=True) self._stop_run_if_exists() def on_save(self, args, state, control, **kwargs): if self._should_upload_checkpoint: self._log_model_checkpoint(args.output_dir, f"checkpoint-{state.global_step}") def on_evaluate(self, args, state, control, metrics=None, **kwargs): if self._log_checkpoints == "best": best_metric_name = args.metric_for_best_model if not best_metric_name.startswith("eval_"): best_metric_name = f"eval_{best_metric_name}" metric_value = metrics.get(best_metric_name) operator = np.greater if args.greater_is_better else np.less self._should_upload_checkpoint = state.best_metric is None or operator(metric_value, state.best_metric) @classmethod def get_run(cls, trainer): for callback in trainer.callback_handler.callbacks: if isinstance(callback, cls): return callback.run raise Exception("The trainer doesn't have a NeptuneCallback configured.") def on_log(self, args, state, control, logs: Optional[Dict[str, float]] = None, **kwargs): if not state.is_world_process_zero: return if logs is not None: for name, value in rewrite_logs(logs).items(): if isinstance(value, (int, float)): if name in NeptuneCallback.flat_metrics: self._metadata_namespace[name] = value else: self._metadata_namespace[name].log(value, step=state.global_step) class CodeCarbonCallback(TrainerCallback): """ A [`TrainerCallback`] that tracks the CO2 emission of training. """ def __init__(self): if not is_codecarbon_available(): raise RuntimeError( "CodeCarbonCallback requires `codecarbon` to be installed. Run `pip install codecarbon`." ) import codecarbon self._codecarbon = codecarbon self.tracker = None def on_init_end(self, args, state, control, **kwargs): if self.tracker is None and state.is_local_process_zero: # CodeCarbon will automatically handle environment variables for configuration self.tracker = self._codecarbon.EmissionsTracker(output_dir=args.output_dir) def on_train_begin(self, args, state, control, model=None, **kwargs): if self.tracker and state.is_local_process_zero: self.tracker.start() def on_train_end(self, args, state, control, **kwargs): if self.tracker and state.is_local_process_zero: self.tracker.stop() class ClearMLCallback(TrainerCallback): """ A [`TrainerCallback`] that sends the logs to [ClearML](https://clear.ml/). Environment: CLEARML_PROJECT (`str`, *optional*, defaults to `"HuggingFace Transformers"`): ClearML project name. CLEARML_TASK (`str`, *optional* defaults to `"Trainer"`): ClearML task name. """ def __init__(self): if is_clearml_available(): import clearml self._clearml = clearml else: raise RuntimeError("ClearMLCallback requires 'clearml' to be installed. Run `pip install clearml`.") self._initialized = False self._clearml_task = None def setup(self, args, state, model, tokenizer, **kwargs): if self._clearml is None: return if state.is_world_process_zero: logger.info("Automatic ClearML logging enabled.") if self._clearml_task is None: self._clearml_task = self._clearml.Task.init( project_name=os.getenv("CLEARML_PROJECT", "HuggingFace Transformers"), task_name=os.getenv("CLEARML_TASK", "Trainer"), auto_connect_frameworks={"tensorboard": False, "pytorch": False}, output_uri=True, ) self._initialized = True logger.info("ClearML Task has been initialized.") self._clearml_task.connect(args, "Args") if hasattr(model, "config") and model.config is not None: self._clearml_task.connect(model.config, "Model Configuration") def on_train_begin(self, args, state, control, model=None, tokenizer=None, **kwargs): if self._clearml is None: return if state.is_hyper_param_search: self._initialized = False if not self._initialized: self.setup(args, state, model, tokenizer, **kwargs) def on_train_end(self, args, state, control, model=None, tokenizer=None, metrics=None, logs=None, **kwargs): if self._clearml is None: return if self._clearml_task and state.is_world_process_zero: # Close ClearML Task at the end end of training self._clearml_task.close() def on_log(self, args, state, control, model=None, tokenizer=None, logs=None, **kwargs): if self._clearml is None: return if not self._initialized: self.setup(args, state, model, tokenizer, **kwargs) if state.is_world_process_zero: eval_prefix = "eval_" eval_prefix_len = len(eval_prefix) test_prefix = "test_" test_prefix_len = len(test_prefix) single_value_scalars = [ "train_runtime", "train_samples_per_second", "train_steps_per_second", "train_loss", "total_flos", "epoch", ] for k, v in logs.items(): if isinstance(v, (int, float)): if k in single_value_scalars: self._clearml_task.get_logger().report_single_value(name=k, value=v) elif k.startswith(eval_prefix): self._clearml_task.get_logger().report_scalar( title=k[eval_prefix_len:], series="eval", value=v, iteration=state.global_step ) elif k.startswith(test_prefix): self._clearml_task.get_logger().report_scalar( title=k[test_prefix_len:], series="test", value=v, iteration=state.global_step ) else: self._clearml_task.get_logger().report_scalar( title=k, series="train", value=v, iteration=state.global_step ) else: logger.warning( "Trainer is attempting to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a scalar. ' "This invocation of ClearML logger's report_scalar() " "is incorrect so we dropped this attribute." ) def on_save(self, args, state, control, **kwargs): if self._clearml_task and state.is_world_process_zero: ckpt_dir = f"checkpoint-{state.global_step}" artifact_path = os.path.join(args.output_dir, ckpt_dir) logger.info(f"Logging checkpoint artifacts in {ckpt_dir}. This may take time.") self._clearml_task.update_output_model(artifact_path, iteration=state.global_step, auto_delete_file=False) INTEGRATION_TO_CALLBACK = { "azure_ml": AzureMLCallback, "comet_ml": CometCallback, "mlflow": MLflowCallback, "neptune": NeptuneCallback, "tensorboard": TensorBoardCallback, "wandb": WandbCallback, "codecarbon": CodeCarbonCallback, "clearml": ClearMLCallback, } def get_reporting_integration_callbacks(report_to): for integration in report_to: if integration not in INTEGRATION_TO_CALLBACK: raise ValueError( f"{integration} is not supported, only {', '.join(INTEGRATION_TO_CALLBACK.keys())} are supported." ) return [INTEGRATION_TO_CALLBACK[integration] for integration in report_to]

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./tests/models/vision_text_dual_encoder/__init__.py

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/flava/__init__.py

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _import_structure = { "configuration_flava": [ "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlavaConfig", "FlavaImageCodebookConfig", "FlavaImageConfig", "FlavaMultimodalConfig", "FlavaTextConfig", ], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_flava"] = ["FlavaFeatureExtractor"] _import_structure["image_processing_flava"] = ["FlavaImageProcessor"] _import_structure["processing_flava"] = ["FlavaProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flava"] = [ "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST", "FlavaForPreTraining", "FlavaImageCodebook", "FlavaImageModel", "FlavaModel", "FlavaMultimodalModel", "FlavaPreTrainedModel", "FlavaTextModel", ] if TYPE_CHECKING: from .configuration_flava import ( FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_flava import FlavaFeatureExtractor from .image_processing_flava import FlavaImageProcessor from .processing_flava import FlavaProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flava import ( FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST, FlavaForPreTraining, FlavaImageCodebook, FlavaImageModel, FlavaModel, FlavaMultimodalModel, FlavaPreTrainedModel, FlavaTextModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

# flake8: noqa # There's no way to ignore "F401 '...' imported but unused" warnings in this # module, but to preserve other warnings. So, don't check this module at all. # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available _import_structure = { "configuration_flava": [ "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlavaConfig", "FlavaImageCodebookConfig", "FlavaImageConfig", "FlavaMultimodalConfig", "FlavaTextConfig", ], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["feature_extraction_flava"] = ["FlavaFeatureExtractor"] _import_structure["image_processing_flava"] = ["FlavaImageProcessor"] _import_structure["processing_flava"] = ["FlavaProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _import_structure["modeling_flava"] = [ "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST", "FlavaForPreTraining", "FlavaImageCodebook", "FlavaImageModel", "FlavaModel", "FlavaMultimodalModel", "FlavaPreTrainedModel", "FlavaTextModel", ] if TYPE_CHECKING: from .configuration_flava import ( FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP, FlavaConfig, FlavaImageCodebookConfig, FlavaImageConfig, FlavaMultimodalConfig, FlavaTextConfig, ) try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_flava import FlavaFeatureExtractor from .image_processing_flava import FlavaImageProcessor from .processing_flava import FlavaProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flava import ( FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST, FlavaForPreTraining, FlavaImageCodebook, FlavaImageModel, FlavaModel, FlavaMultimodalModel, FlavaPreTrainedModel, FlavaTextModel, ) else: import sys sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

huggingface/transformers

Add AutoBackbone + ResNetBackbone

# What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

NielsRogge

904ac21020c1b178940abea70306988e543db60b

6b217c52e626729bd5de7142358dbaf67402bb40

Add AutoBackbone + ResNetBackbone. # What does this PR do? As #20204 is a big PR, this PR adds a part of it as a standalone PR. This PR adds the AutoBackbone class, along with one example class that it supports, namely ResNetBackbone. ## Usage Usage is as follows: ``` from transformers import AutoImageProcessor, AutoBackbone import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") model = AutoBackbone.from_pretrained("microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]) inputs = processor(image, return_tensors="pt") outputs = model(**inputs) for k,v in zip(outputs.stage_names, outputs.hidden_states): print(k, v.shape) ``` which prints: ``` stage1 torch.Size([1, 256, 56, 56]) stage2 torch.Size([1, 512, 28, 28]) stage3 torch.Size([1, 1024, 14, 14]) stage4 torch.Size([1, 2048, 7, 7]) ``` Besides this, one can also obtain information about the channel dimension and stride for each of the requested stages, like so: ``` print(model.channels) print(model.strides) ``` This is handy as frameworks (like MaskFormer) need to know this information at initialization time. ## To do's/questions - [ ] We don't want `xxxBackbone` classes to be tested by all tests defined in `test_modeling_common.py`(i.e. it should probably not be part of `all_model_classes`), hence I added the class to IGNORE_NON_TESTED, and added a separate test for it. Let me know if this is ok. - [ ] It would probably be best to not have our backbones included in the documentation from the start. For now they are just an internal part of models like DETR and MaskFormer. Could we not include them in the docs for now? Currently I'm getting: ``` Exception: The following objects are in the public init so should be documented: - AutoBackbone - ResNetBackbone ``` An alternative option could be to add backbones to the list of PRIVATE_MODELS in utils/check_repo.py for now.

./src/transformers/models/opt/configuration_opt.py

# coding=utf-8 # Copyright 2022 The Metaseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ OPT model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) OPT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json", "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json", "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json", "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json", "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json", "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json", } class OPTConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the OPT [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50272): Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OPTModel`] hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of decoder layers. ffn_dim (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer decoder. activation_function (`str` or `function`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). do_layer_norm_before (`bool`, *optional*, defaults to `True`): Whether to perform layer normalization before the attention block. word_embed_proj_dim (`int`, *optional*): `word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to `hidden_size`. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. layerdrop: (`float`, *optional*, defaults to 0.0): The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Example: ```python >>> from transformers import OPTConfig, OPTModel >>> # Initializing a OPT facebook/opt-large style configuration >>> configuration = OPTConfig() >>> # Initializing a model (with random weights) from the facebook/opt-large style configuration >>> model = OPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "opt" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=50272, hidden_size=768, num_hidden_layers=12, ffn_dim=3072, max_position_embeddings=2048, do_layer_norm_before=True, _remove_final_layer_norm=False, word_embed_proj_dim=None, dropout=0.1, attention_dropout=0.0, num_attention_heads=12, activation_function="relu", layerdrop=0.0, init_std=0.02, use_cache=True, pad_token_id=1, bos_token_id=2, eos_token_id=2, **kwargs ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.num_attention_heads = num_attention_heads self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size self.ffn_dim = ffn_dim self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.dropout = dropout self.attention_dropout = attention_dropout self.activation_function = activation_function self.init_std = init_std self.layerdrop = layerdrop self.use_cache = use_cache self.do_layer_norm_before = do_layer_norm_before # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility # with checkpoints that have been fine-tuned before transformers v4.20.1 # see https://github.com/facebookresearch/metaseq/pull/164 self._remove_final_layer_norm = _remove_final_layer_norm

# coding=utf-8 # Copyright 2022 The Metaseq Authors and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ OPT model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) OPT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json", "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json", "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json", "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json", "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json", "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json", } class OPTConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the OPT [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 50272): Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`OPTModel`] hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of decoder layers. ffn_dim (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer decoder. activation_function (`str` or `function`, *optional*, defaults to `"relu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). do_layer_norm_before (`bool`, *optional*, defaults to `True`): Whether to perform layer normalization before the attention block. word_embed_proj_dim (`int`, *optional*): `word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to `hidden_size`. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. layerdrop: (`float`, *optional*, defaults to 0.0): The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Example: ```python >>> from transformers import OPTConfig, OPTModel >>> # Initializing a OPT facebook/opt-large style configuration >>> configuration = OPTConfig() >>> # Initializing a model (with random weights) from the facebook/opt-large style configuration >>> model = OPTModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "opt" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=50272, hidden_size=768, num_hidden_layers=12, ffn_dim=3072, max_position_embeddings=2048, do_layer_norm_before=True, _remove_final_layer_norm=False, word_embed_proj_dim=None, dropout=0.1, attention_dropout=0.0, num_attention_heads=12, activation_function="relu", layerdrop=0.0, init_std=0.02, use_cache=True, pad_token_id=1, bos_token_id=2, eos_token_id=2, **kwargs ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.num_attention_heads = num_attention_heads self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size self.ffn_dim = ffn_dim self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.dropout = dropout self.attention_dropout = attention_dropout self.activation_function = activation_function self.init_std = init_std self.layerdrop = layerdrop self.use_cache = use_cache self.do_layer_norm_before = do_layer_norm_before # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility # with checkpoints that have been fine-tuned before transformers v4.20.1 # see https://github.com/facebookresearch/metaseq/pull/164 self._remove_final_layer_norm = _remove_final_layer_norm