repo_id
stringclasses
55 values
file_path
stringlengths
42
186
content
stringlengths
1
333k
__index_level_0__
int64
0
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/siglip/test_modeling_siglip.py
# coding=utf-8 # Copyright 2024 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 SigLIP model. """ import inspect import os import tempfile import unittest import numpy as np import requests from transformers import SiglipConfig, SiglipTextConfig, SiglipVisionConfig from transformers.testing_utils import ( require_torch, require_vision, slow, torch_device, ) from transformers.utils import 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, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import SiglipForImageClassification, SiglipModel, SiglipTextModel, SiglipVisionModel if is_vision_available(): from PIL import Image from transformers import SiglipProcessor class SiglipVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent 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.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return SiglipVisionConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = SiglipVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs_for_common def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class SiglipVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (SiglipVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = SiglipVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=SiglipVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="SIGLIP does not use inputs_embeds") def test_inputs_embeds(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) @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipVisionModel does not support standalone training") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="SiglipVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="SiglipVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation") def test_initialization(self): pass @slow def test_model_from_pretrained(self): model_name = "google/siglip-base-patch16-224" model = SiglipVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class SiglipTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, 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_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.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs 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]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return SiglipTextConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = SiglipTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) 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)) # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs_for_common def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class SiglipTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (SiglipTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False model_split_percents = [0.5, 0.8, 0.9] # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.setUp with CLIP->Siglip def setUp(self): self.model_tester = SiglipTextModelTester(self) self.config_tester = ConfigTester(self, config_class=SiglipTextConfig, hidden_size=37) # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_config def test_config(self): self.config_tester.run_common_tests() # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_model def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training def test_training(self): pass # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing_use_reentrant def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_training_gradient_checkpointing_use_reentrant_false def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Siglip does not use inputs_embeds") # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_inputs_embeds def test_inputs_embeds(self): pass @unittest.skip(reason="SiglipTextModel has no base class and is not available in MODEL_MAPPING") # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_save_load_fast_init_from_base def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="SiglipTextModel has no base class and is not available in MODEL_MAPPING") # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_save_load_fast_init_to_base def test_save_load_fast_init_to_base(self): pass @unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation") def test_initialization(self): pass @slow def test_model_from_pretrained(self): model_name = "google/siglip-base-patch16-224" model = SiglipTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class SiglipModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = SiglipTextModelTester(parent, **text_kwargs) self.vision_model_tester = SiglipVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.is_training = is_training # Copied from tests.models.clip.test_modeling_clip.CLIPModelTester.prepare_config_and_inputs def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return SiglipConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = SiglipModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": False, } return config, inputs_dict @require_torch class SiglipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (SiglipModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": SiglipModel} if is_torch_available() else {} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.setUp with CLIP->Siglip def setUp(self): self.model_tester = SiglipModelTester(self) # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model 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(reason="Hidden_states is tested in individual model tests") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_hidden_states_output def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_inputs_embeds def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_retain_grad_hidden_states_attentions def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="SiglipModel does not have input/output embeddings") # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model_common_attributes def test_model_common_attributes(self): pass @unittest.skip(reason="SiglipModel does not support training") def test_training(self): pass @unittest.skip(reason="SiglipModel does not support training") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipModel does not support training") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipModel does not support training") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation") def test_initialization(self): pass # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest._create_and_check_torchscript with CLIP->Siglip def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # Siglip needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_load_vision_text_config with CLIP->Siglip def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save SiglipConfig and check if we can load SiglipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = SiglipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save SiglipConfig and check if we can load SiglipTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = SiglipTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): model_name = "google/siglip-base-patch16-224" model = SiglipModel.from_pretrained(model_name) self.assertIsNotNone(model) class SiglipForImageClassificationModelTester(SiglipModelTester): def __init__(self, parent): super().__init__(parent) self.batch_size = self.vision_model_tester.batch_size self.num_hidden_layers = self.vision_model_tester.num_hidden_layers self.hidden_size = self.vision_model_tester.hidden_size self.seq_length = self.vision_model_tester.seq_length def prepare_config_and_inputs(self): _, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, pixel_values def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class SiglipForImageClassificationModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (SiglipForImageClassification,) if is_torch_available() else () pipeline_model_mapping = {"image-classification": SiglipForImageClassification} if is_torch_available() else {} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = SiglipForImageClassificationModelTester(self) @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds") def test_model_common_attributes(self): pass @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation") def test_initialization(self): pass # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image @require_vision @require_torch class SiglipModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "google/siglip-base-patch16-224" model = SiglipModel.from_pretrained(model_name).to(torch_device) processor = SiglipProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=["a photo of 2 cats", "a photo of 2 dogs"], images=image, padding="max_length", return_tensors="pt" ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits_per_image = outputs.logits_per_image logits_per_text = outputs.logits_per_text # verify the logits self.assertEqual( logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[-0.7567, -10.3354]], device=torch_device) self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3)) # verify the probs probs = torch.sigmoid(logits_per_image) # these are the probabilities expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device) self.assertTrue(torch.allclose(probs, expected_probs, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/siglip/test_tokenization_siglip.py
# coding=utf-8 # Copyright 2024 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 json import os import tempfile import unittest from transformers import SPIECE_UNDERLINE, AddedToken, BatchEncoding, SiglipTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property, is_tf_available, is_torch_available from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): FRAMEWORK = "pt" elif is_tf_available(): FRAMEWORK = "tf" else: FRAMEWORK = "jax" @require_sentencepiece @require_tokenizers class SiglipTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "google/siglip-base-patch16-224" tokenizer_class = SiglipTokenizer test_rust_tokenizer = False test_sentencepiece = True test_sentencepiece_ignore_case = True # Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.setUp with T5->Siglip def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = SiglipTokenizer(SAMPLE_VOCAB) tokenizer.save_pretrained(self.tmpdirname) # Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_convert_token_and_id with T5->Siglip 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>") def test_full_tokenizer(self): tokenizer = SiglipTokenizer(SAMPLE_VOCAB) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁this", "▁is", "▁a", "▁t", "est"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [66, 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, [7, 23, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 12, 66, 46, 72, 80, 6, 0]) 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 siglip_tokenizer(self): return SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224") # Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.get_tokenizer with T5->Siglip def get_tokenizer(self, **kwargs) -> SiglipTokenizer: return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) # Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_rust_and_python_full_tokenizers with T5->Siglip 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() sequence = "I was born in 92000, and this is falsé." tokens = tokenizer.tokenize(sequence) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) ids = tokenizer.encode(sequence, add_special_tokens=False) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) rust_tokenizer = self.get_rust_tokenizer() ids = tokenizer.encode(sequence) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) def test_eos_treatment(self): tokenizer = self.siglip_tokenizer batch_with_eos_added = tokenizer(["hi</s>", "I went to the gym</s>", "</s>"]) batch_without_eos_added = tokenizer(["hi", "I went to the gym", ""]) self.assertListEqual(batch_with_eos_added["input_ids"], batch_without_eos_added["input_ids"]) def test_prepare_batch(self): tokenizer = self.siglip_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] expected_src_tokens = [262, 266, 476, 8532, 270, 4460, 3949, 1682, tokenizer.eos_token_id] batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) self.assertIsInstance(batch, BatchEncoding) if FRAMEWORK != "jax": result = list(batch.input_ids.numpy()[0]) else: result = list(batch.input_ids.tolist()[0]) self.assertListEqual(expected_src_tokens, result) self.assertEqual((2, 9), batch.input_ids.shape) def test_empty_target_text(self): tokenizer = self.siglip_tokenizer src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK) # check if input_ids are returned and no decoder_input_ids self.assertIn("input_ids", batch) self.assertNotIn("decoder_input_ids", batch) self.assertNotIn("decoder_attention_mask", batch) def test_max_length(self): tokenizer = self.siglip_tokenizer tgt_text = ["Summary of the text.", "Another summary."] targets = tokenizer( text_target=tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK ) self.assertEqual(32, targets["input_ids"].shape[1]) def test_eos_in_input(self): tokenizer = self.siglip_tokenizer src_text = ["A long paragraph for summarization. </s>"] tgt_text = ["Summary of the text. </s>"] expected_src_tokens = [262, 266, 476, 8532, 270, 4460, 3949, 1682, 1] expected_tgt_tokens = [6254, 267, 260, 1443, 1] batch = tokenizer(src_text, text_target=tgt_text) self.assertEqual(expected_src_tokens, batch["input_ids"][0]) self.assertEqual(expected_tgt_tokens, batch["labels"][0]) @unittest.skip(reason="SiglipTokenizer strips the punctuation") def test_subword_regularization_tokenizer(self): pass @unittest.skip(reason="SiglipTokenizer strips the punctuation") def test_pickle_subword_regularization_tokenizer(self): pass # Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_special_tokens_initialization with T5->Siglip def test_special_tokens_initialization(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): added_tokens = [f"<extra_id_{i}>" for i in range(100)] + [AddedToken("<special>", lstrip=True)] tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) tokenizer_cr = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs, from_slow=True ) tokenizer_p = self.tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) p_output = tokenizer_p.encode("Hey this is a <special> token") r_output = tokenizer_r.encode("Hey this is a <special> token") cr_output = tokenizer_cr.encode("Hey this is a <special> token") special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0] self.assertEqual(p_output, r_output) self.assertEqual(cr_output, r_output) self.assertTrue(special_token_id in p_output) self.assertTrue(special_token_id in r_output) self.assertTrue(special_token_id in cr_output) # Copied from tests.models.t5.test_tokenization_t5.T5TokenizationTest.test_special_tokens_initialization_with_non_empty_additional_special_tokens with T5->Siglip def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self): tokenizer_list = [] if self.test_slow_tokenizer: tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())) if self.test_rust_tokenizer: tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())) for tokenizer_class, tokenizer_utils in tokenizer_list: with tempfile.TemporaryDirectory() as tmp_dir: tokenizer_utils.save_pretrained(tmp_dir) with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file: special_tokens_map = json.load(json_file) with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file: tokenizer_config = json.load(json_file) added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(100)] special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [ "an_additional_special_token" ] tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [ "an_additional_special_token" ] with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile: json.dump(special_tokens_map, outfile) with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile: json.dump(tokenizer_config, outfile) # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and # "special_tokens_map.json" files tokenizer_without_change_in_init = tokenizer_class.from_pretrained( tmp_dir, ) self.assertIn( "an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens ) # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # BySiglipTokenization no vocab self.assertEqual( ["an_additional_special_token"], tokenizer_without_change_in_init.convert_ids_to_tokens( tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]) ), ) # Now we test that we can change the value of additional_special_tokens in the from_pretrained new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)] tokenizer = tokenizer_class.from_pretrained( tmp_dir, additional_special_tokens=new_added_tokens, ) self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens) self.assertEqual( ["a_new_additional_special_token"], tokenizer.convert_ids_to_tokens( tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]) ), ) def test_sentencepiece_tokenize_and_convert_tokens_to_string(self): """Test ``_tokenize`` and ``convert_tokens_to_string``.""" if not self.test_sentencepiece: return tokenizer = self.get_tokenizer() text = "This is text to test the tokenizer." if self.test_sentencepiece_ignore_case: text = text.lower() tokens = tokenizer.tokenize(text) self.assertTrue(len(tokens) > 0) # check if converting back to original text works reverse_text = tokenizer.convert_tokens_to_string(tokens) if self.test_sentencepiece_ignore_case: reverse_text = reverse_text.lower() expected_text = "this is text to test the tokenizer" self.assertEqual(reverse_text, expected_text) special_tokens = tokenizer.all_special_tokens special_tokens_string = tokenizer.convert_tokens_to_string(special_tokens) for special_token in special_tokens: self.assertIn(special_token, special_tokens_string) if self.test_rust_tokenizer: rust_tokenizer = self.get_rust_tokenizer() special_tokens_string_rust = rust_tokenizer.convert_tokens_to_string(special_tokens) self.assertEqual(special_tokens_string, special_tokens_string_rust) @slow def test_tokenizer_integration(self): tokenizer = SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224") # fmt: off texts = [ 'the real mountain view', 'Zürich', 'San Francisco', 'a picture of a laptop with the lockscreen on, a cup of cappucino, salt and pepper grinders. The view through the window reveals lake Zürich and the Alps in the background of the city.', ] expected_input_ids = [ [260, 638, 3293, 870, 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], [262, 761, 5879, 5345, 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], [262, 264, 452, 20563, 15949, 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], [262, 266, 1357, 267, 262, 266, 4429, 275, 260, 3940, 6360, 277, 262, 266, 3064, 267, 3549, 388, 16538, 296, 298, 2617, 263, 4869, 14998, 264, 260, 870, 393, 260, 1710, 7958, 4324, 262, 761, 5879, 5345, 263, 260, 1518, 388, 264, 268, 260, 1970, 267, 260, 741, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], ] # fmt: on for text, expected in zip(texts, expected_input_ids): input_ids = tokenizer(text, padding="max_length").input_ids self.assertListEqual(input_ids, expected) def test_some_edge_cases(self): tokenizer = SiglipTokenizer.from_pretrained("google/siglip-base-patch16-224", legacy=False) sp_tokens = tokenizer.sp_model.encode("</s>>", out_type=str) self.assertEqual(sp_tokens, ["</", "s", ">", ">"]) tokens = tokenizer.tokenize("</s>>") self.assertNotEqual(sp_tokens, tokens) self.assertEqual(tokens, ["</s>"]) tokens = tokenizer.tokenize("") self.assertEqual(tokens, []) self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str)) tokens = tokenizer.tokenize(" ") self.assertEqual(tokens, []) self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str)) tokens = tokenizer.tokenize("▁") self.assertEqual(tokens, []) self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str)) tokens = tokenizer.tokenize(" ▁") self.assertEqual(tokens, []) self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str)) @require_sentencepiece @require_tokenizers class CommonSpmIntegrationTests(unittest.TestCase): """ A class that regroups important test to make sure that we properly handle the special tokens. """ @classmethod def setUpClass(cls): tokenizer = SiglipTokenizer(SAMPLE_VOCAB, extra_ids=0, legacy=False) tokenizer.add_special_tokens( {"additional_special_tokens": [AddedToken("<extra_id_0>", rstrip=False, lstrip=False)]} ) cls.tokenizer = tokenizer def test_add_dummy_prefix(self): # make sure `'▁'` is prepended, and outputs match sp_model's # `sentencepiece.NormalizerSpec.add_dummy_prefix` attribute input_ids = self.tokenizer.encode(". Hello", add_special_tokens=False) self.assertEqual(input_ids, [37, 86, 20]) self.assertEqual(input_ids, [37, 86, 20]) tokens = self.tokenizer.tokenize(". Hello") self.assertEqual(tokens, ["▁he", "ll", "o"]) tokens = self.tokenizer.tokenize("") self.assertEqual(tokens, []) self.assertEqual(tokens, self.tokenizer.sp_model.encode("", out_type=str)) tokens = self.tokenizer.tokenize(" ") self.assertEqual(tokens, []) self.assertEqual(tokens, self.tokenizer.sp_model.encode(" ", out_type=str)) tokens = self.tokenizer.tokenize("▁") self.assertEqual(tokens, []) self.assertEqual(tokens, self.tokenizer.sp_model.encode("▁", out_type=str)) def test_remove_extra_whitespaces(self): # make sure the extra spaces are eaten # sentencepiece.NormalizerSpec.remove_extra_whitespaces attribute input_ids = self.tokenizer.encode(" . Hello", add_special_tokens=False) self.assertEqual(input_ids, [37, 86, 20]) self.assertEqual(input_ids, [37, 86, 20]) tokens = self.tokenizer.tokenize(" . Hello") self.assertEqual(tokens, ["▁he", "ll", "o"]) # `'▁'` is also a whitespace input_ids = self.tokenizer.encode("▁He is not") self.assertEqual(input_ids, [37, 46, 44, 2]) tokens = self.tokenizer.tokenize("▁He is not") self.assertEqual(tokens, ["▁he", "▁is", "▁not"]) # no extra space added input_ids = self.tokenizer.encode("▁He is not ▁He") self.assertEqual(input_ids, [37, 46, 44, 37, 2]) tokens = self.tokenizer.tokenize("▁He is not ▁He") self.assertEqual(tokens, ["▁he", "▁is", "▁not", "▁he"]) # spaces are eaten by spm even if not start
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/siglip/test_image_processor_siglip.py
# coding=utf-8 # Copyright 2024 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 unittest from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from transformers import SiglipImageProcessor class SiglipImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_rescale=True, rescale_factor=1 / 255, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } def expected_output_image_shape(self, images): return self.num_channels, self.size["height"], self.size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Siglip class SiglipImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = SiglipImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = SiglipImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() # Ignore copy def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "resample")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) # Ignore copy def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 18, "width": 18}) image_processor = self.image_processing_class.from_dict( self.image_processor_dict, size={"height": 84, "width": 84} ) self.assertEqual(image_processor.size, {"height": 84, "width": 84}) @unittest.skip("not supported") # Ignore copy def test_call_numpy_4_channels(self): pass
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/deformable_detr/test_modeling_deformable_detr.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 Deformable DETR model. """ import inspect import math import unittest from typing import Dict, List, Tuple from transformers import DeformableDetrConfig, ResNetConfig, is_torch_available, is_vision_available from transformers.file_utils import cached_property from transformers.testing_utils import ( require_timm, require_torch, require_torch_accelerator, require_torch_bf16, require_vision, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import DeformableDetrForObjectDetection, DeformableDetrModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class DeformableDetrModelTester: def __init__( self, parent, batch_size=8, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=8, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, num_queries=12, num_channels=3, image_size=196, n_targets=8, num_labels=91, num_feature_levels=4, encoder_n_points=2, decoder_n_points=6, ): self.parent = parent self.batch_size = batch_size 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.num_queries = num_queries self.num_channels = num_channels self.image_size = image_size self.n_targets = n_targets self.num_labels = num_labels self.num_feature_levels = num_feature_levels self.encoder_n_points = encoder_n_points self.decoder_n_points = decoder_n_points # we also set the expected seq length for both encoder and decoder self.encoder_seq_length = ( math.ceil(self.image_size / 8) ** 2 + math.ceil(self.image_size / 16) ** 2 + math.ceil(self.image_size / 32) ** 2 + math.ceil(self.image_size / 64) ** 2 ) self.decoder_seq_length = self.num_queries def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device) labels = None if self.use_labels: # labels is a list of Dict (each Dict being the labels for a given example in the batch) labels = [] for i in range(self.batch_size): target = {} target["class_labels"] = torch.randint( high=self.num_labels, size=(self.n_targets,), device=torch_device ) target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device) target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device) labels.append(target) config = self.get_config() return config, pixel_values, pixel_mask, labels def get_config(self): resnet_config = ResNetConfig( num_channels=3, embeddings_size=10, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 2, 1], hidden_act="relu", num_labels=3, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], ) return DeformableDetrConfig( 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, num_queries=self.num_queries, num_labels=self.num_labels, num_feature_levels=self.num_feature_levels, encoder_n_points=self.encoder_n_points, decoder_n_points=self.decoder_n_points, use_timm_backbone=False, backbone=None, backbone_config=resnet_config, use_pretrained_backbone=False, ) def prepare_config_and_inputs_for_common(self): config, pixel_values, pixel_mask, labels = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask} return config, inputs_dict def create_and_check_deformable_detr_model(self, config, pixel_values, pixel_mask, labels): model = DeformableDetrModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size)) def create_and_check_deformable_detr_object_detection_head_model(self, config, pixel_values, pixel_mask, labels): model = DeformableDetrForObjectDetection(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, labels=labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, self.num_labels)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) @require_torch class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection} if is_torch_available() else {} ) is_encoder_decoder = True test_torchscript = False test_pruning = False test_head_masking = False test_missing_keys = False # special case for head models 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.__name__ == "DeformableDetrForObjectDetection": labels = [] for i in range(self.model_tester.batch_size): target = {} target["class_labels"] = torch.ones( size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long ) target["boxes"] = torch.ones( self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float ) target["masks"] = torch.ones( self.model_tester.n_targets, self.model_tester.image_size, self.model_tester.image_size, device=torch_device, dtype=torch.float, ) labels.append(target) inputs_dict["labels"] = labels return inputs_dict def setUp(self): self.model_tester = DeformableDetrModelTester(self) self.config_tester = ConfigTester(self, config_class=DeformableDetrConfig, has_text_modality=False) def test_config(self): # we don't test common_properties and arguments_init as these don't apply for Deformable DETR 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() def test_deformable_detr_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deformable_detr_model(*config_and_inputs) def test_deformable_detr_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deformable_detr_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="Deformable DETR does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Deformable DETR does not use inputs_embeds") def test_inputs_embeds_matches_input_ids(self): pass @unittest.skip(reason="Deformable DETR does not have a get_input_embeddings method") def test_model_common_attributes(self): pass @unittest.skip(reason="Deformable DETR is not a generative model") def test_generate_without_input_ids(self): pass @unittest.skip(reason="Deformable DETR does not use token embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass 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) 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, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) out_len = len(outputs) correct_outlen = 8 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Object Detection model returns pred_logits and pred_boxes if model_class.__name__ == "DeformableDetrForObjectDetection": correct_outlen += 2 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, self.model_tester.num_queries, self.model_tester.num_queries], ) # 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, self.model_tester.num_feature_levels, self.model_tester.decoder_n_points, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = 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)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.encoder_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, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): 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 isinstance(tuple_object, Dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: print("Model class:", model_class) model = model_class(config) model.to(torch_device) model.eval() 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, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) 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}) 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}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence( model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True} ) def test_retain_grad_hidden_states_attentions(self): # removed retain_grad and grad on decoder_hidden_states, as queries don't require grad config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) # we take the second output since last_hidden_state is the second item output = outputs[1] encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_attentions = outputs.encoder_attentions[0] encoder_hidden_states.retain_grad() encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) def test_forward_auxiliary_loss(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.auxiliary_loss = True # only test for object detection and segmentation model for model_class in self.all_model_classes[1:]: model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) outputs = model(**inputs) self.assertIsNotNone(outputs.auxiliary_outputs) self.assertEqual(len(outputs.auxiliary_outputs), self.model_tester.num_hidden_layers - 1) 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()] if model.config.is_encoder_decoder: expected_arg_names = ["pixel_values", "pixel_mask"] expected_arg_names.extend( ["head_mask", "decoder_head_mask", "encoder_outputs"] if "head_mask" and "decoder_head_mask" in arg_names else [] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) else: expected_arg_names = ["pixel_values", "pixel_mask"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_different_timm_backbone(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # let's pick a random timm backbone config.backbone = "tf_mobilenetv3_small_075" config.backbone_config = None config.use_timm_backbone = True config.backbone_kwargs = {"out_indices": [1, 2, 3, 4]} for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if model_class.__name__ == "DeformableDetrForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, self.model_tester.num_labels, ) self.assertEqual(outputs.logits.shape, expected_shape) # Confirm out_indices was propogated to backbone self.assertEqual(len(model.model.backbone.conv_encoder.intermediate_channel_sizes), 4) elif model_class.__name__ == "ConditionalDetrForSegmentation": # Confirm out_indices was propogated to backbone self.assertEqual(len(model.deformable_detr.model.backbone.conv_encoder.intermediate_channel_sizes), 4) else: # Confirm out_indices was propogated to backbone self.assertEqual(len(model.backbone.conv_encoder.intermediate_channel_sizes), 4) self.assertTrue(outputs) 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: print("Model class:", model_class) model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: if param.requires_grad: if ( "level_embed" in name or "sampling_offsets.bias" in name or "value_proj" in name or "output_proj" in name or "reference_points" in name ): continue 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 test_two_stage_training(self): model_class = DeformableDetrForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True config.two_stage = True config.auxiliary_loss = True config.with_box_refine = True 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 create_and_check_model_fp16_forward(self): model_class = DeformableDetrForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) model.to(torch_device) model.half() model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) output = model(**inputs)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) @require_torch_bf16 def create_and_check_model_bf16_forward(self): model_class = DeformableDetrForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config, torch_dtype=torch.bfloat16) model.to(torch_device) model.eval() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) output = model(**inputs)["last_hidden_state"] self.parent.assertFalse(torch.isnan(output).any().item()) TOLERANCE = 1e-4 # 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_timm @require_vision @slow class DeformableDetrModelIntegrationTests(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") if is_vision_available() else None def test_inference_object_detection_head(self): model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr").to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) pixel_values = encoding["pixel_values"].to(torch_device) pixel_mask = encoding["pixel_mask"].to(torch_device) with torch.no_grad(): outputs = model(pixel_values, pixel_mask) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]] ).to(torch_device) expected_boxes = torch.tensor( [[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4)) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4)) # verify postprocessing results = image_processor.post_process_object_detection( outputs, threshold=0.3, target_sizes=[image.size[::-1]] )[0] expected_scores = torch.tensor([0.7999, 0.7894, 0.6331, 0.4720, 0.4382]).to(torch_device) expected_labels = [17, 17, 75, 75, 63] expected_slice_boxes = torch.tensor([16.5028, 52.8390, 318.2544, 470.7841]).to(torch_device) self.assertEqual(len(results["scores"]), 5) self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-4)) self.assertSequenceEqual(results["labels"].tolist(), expected_labels) self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes)) def test_inference_object_detection_head_with_box_refine_two_stage(self): model = DeformableDetrForObjectDetection.from_pretrained( "SenseTime/deformable-detr-with-box-refine-two-stage" ).to(torch_device) image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt").to(torch_device) pixel_values = encoding["pixel_values"].to(torch_device) pixel_mask = encoding["pixel_mask"].to(torch_device) with torch.no_grad(): outputs = model(pixel_values, pixel_mask) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_logits = torch.tensor( [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]] ).to(torch_device) expected_boxes = torch.tensor( [[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-4)) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4)) @require_torch_accelerator def test_inference_object_detection_head_equivalence_cpu_gpu(self): image_processor = self.default_image_processor image = prepare_img() encoding = image_processor(images=image, return_tensors="pt") pixel_values = encoding["pixel_values"] pixel_mask = encoding["pixel_mask"] # 1. run model on CPU model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-single-scale") with torch.no_grad(): cpu_outputs = model(pixel_values, pixel_mask) # 2. run model on GPU model.to(torch_device) with torch.no_grad(): gpu_outputs = model(pixel_values.to(torch_device), pixel_mask.to(torch_device)) # 3. assert equivalence for key in cpu_outputs.keys(): assert torch.allclose(cpu_outputs[key], gpu_outputs[key].cpu(), atol=1e-4) expected_logits = torch.tensor( [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]] ) assert torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=1e-4)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/deformable_detr/test_image_processing_deformable_detr.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 json import pathlib import unittest from transformers.testing_utils import require_torch, require_vision, slow from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import DeformableDetrImageProcessor class DeformableDetrImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], do_rescale=True, rescale_factor=1 / 255, do_pad=True, ): # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_pad = do_pad def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_pad": self.do_pad, } def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to DeformableDetrImageProcessor, assuming do_resize is set to True with a scalar size. """ if not batched: image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size else: h, w = image.shape[1], image.shape[2] if w < h: expected_height = int(self.size["shortest_edge"] * h / w) expected_width = self.size["shortest_edge"] elif w > h: expected_height = self.size["shortest_edge"] expected_width = int(self.size["shortest_edge"] * w / h) else: expected_height = self.size["shortest_edge"] expected_width = self.size["shortest_edge"] else: expected_values = [] for image in image_inputs: expected_height, expected_width = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) expected_height = max(expected_values, key=lambda item: item[0])[0] expected_width = max(expected_values, key=lambda item: item[1])[1] return expected_height, expected_width def expected_output_image_shape(self, images): height, width = self.get_expected_values(images, batched=True) return self.num_channels, height, width def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class DeformableDetrImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase): image_processing_class = DeformableDetrImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = DeformableDetrImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "do_pad")) self.assertTrue(hasattr(image_processing, "size")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333}) self.assertEqual(image_processor.do_pad, True) image_processor = self.image_processing_class.from_dict( self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False ) self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) self.assertEqual(image_processor.do_pad, False) @slow def test_call_pytorch_with_coco_detection_annotations(self): # prepare image and target image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f: target = json.loads(f.read()) target = {"image_id": 39769, "annotations": target} # encode them image_processing = DeformableDetrImageProcessor() encoding = image_processing(images=image, annotations=target, return_tensors="pt") # verify pixel values expected_shape = torch.Size([1, 3, 800, 1066]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) # verify area expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]) self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area)) # verify boxes expected_boxes_shape = torch.Size([6, 4]) self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) # verify image_id expected_image_id = torch.tensor([39769]) self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) # verify is_crowd expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) # verify class_labels expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) # verify orig_size expected_orig_size = torch.tensor([480, 640]) self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) # verify size expected_size = torch.tensor([800, 1066]) self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) @slow def test_call_pytorch_with_coco_panoptic_annotations(self): # prepare image, target and masks_path image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f: target = json.loads(f.read()) target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") # encode them image_processing = DeformableDetrImageProcessor(format="coco_panoptic") encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt") # verify pixel values expected_shape = torch.Size([1, 3, 800, 1066]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) # verify area expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]) self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area)) # verify boxes expected_boxes_shape = torch.Size([6, 4]) self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) # verify image_id expected_image_id = torch.tensor([39769]) self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) # verify is_crowd expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) # verify class_labels expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) # verify masks expected_masks_sum = 822873 self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum) # verify orig_size expected_orig_size = torch.tensor([480, 640]) self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) # verify size expected_size = torch.tensor([800, 1066]) self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) @slow # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_detection_annotations with Detr->DeformableDetr def test_batched_coco_detection_annotations(self): image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800)) with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f: target = json.loads(f.read()) annotations_0 = {"image_id": 39769, "annotations": target} annotations_1 = {"image_id": 39769, "annotations": target} # Adjust the bounding boxes for the resized image w_0, h_0 = image_0.size w_1, h_1 = image_1.size for i in range(len(annotations_1["annotations"])): coords = annotations_1["annotations"][i]["bbox"] new_bbox = [ coords[0] * w_1 / w_0, coords[1] * h_1 / h_0, coords[2] * w_1 / w_0, coords[3] * h_1 / h_0, ] annotations_1["annotations"][i]["bbox"] = new_bbox images = [image_0, image_1] annotations = [annotations_0, annotations_1] image_processing = DeformableDetrImageProcessor() encoding = image_processing( images=images, annotations=annotations, return_segmentation_masks=True, return_tensors="pt", # do_convert_annotations=True ) # Check the pixel values have been padded postprocessed_height, postprocessed_width = 800, 1066 expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) # Check the bounding boxes have been adjusted for padded images self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) expected_boxes_0 = torch.tensor( [ [0.6879, 0.4609, 0.0755, 0.3691], [0.2118, 0.3359, 0.2601, 0.1566], [0.5011, 0.5000, 0.9979, 1.0000], [0.5010, 0.5020, 0.9979, 0.9959], [0.3284, 0.5944, 0.5884, 0.8112], [0.8394, 0.5445, 0.3213, 0.9110], ] ) expected_boxes_1 = torch.tensor( [ [0.4130, 0.2765, 0.0453, 0.2215], [0.1272, 0.2016, 0.1561, 0.0940], [0.3757, 0.4933, 0.7488, 0.9865], [0.3759, 0.5002, 0.7492, 0.9955], [0.1971, 0.5456, 0.3532, 0.8646], [0.5790, 0.4115, 0.3430, 0.7161], ] ) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) # Check the masks have also been padded self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height # format and not in the range [0, 1] encoding = image_processing( images=images, annotations=annotations, return_segmentation_masks=True, do_convert_annotations=False, return_tensors="pt", ) self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) # Convert to absolute coordinates unnormalized_boxes_0 = torch.vstack( [ expected_boxes_0[:, 0] * postprocessed_width, expected_boxes_0[:, 1] * postprocessed_height, expected_boxes_0[:, 2] * postprocessed_width, expected_boxes_0[:, 3] * postprocessed_height, ] ).T unnormalized_boxes_1 = torch.vstack( [ expected_boxes_1[:, 0] * postprocessed_width, expected_boxes_1[:, 1] * postprocessed_height, expected_boxes_1[:, 2] * postprocessed_width, expected_boxes_1[:, 3] * postprocessed_height, ] ).T # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max expected_boxes_0 = torch.vstack( [ unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, ] ).T expected_boxes_1 = torch.vstack( [ unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, ] ).T self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->DeformableDetr def test_batched_coco_panoptic_annotations(self): # prepare image, target and masks_path image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800)) with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f: target = json.loads(f.read()) annotation_0 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} annotation_1 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} w_0, h_0 = image_0.size w_1, h_1 = image_1.size for i in range(len(annotation_1["segments_info"])): coords = annotation_1["segments_info"][i]["bbox"] new_bbox = [ coords[0] * w_1 / w_0, coords[1] * h_1 / h_0, coords[2] * w_1 / w_0, coords[3] * h_1 / h_0, ] annotation_1["segments_info"][i]["bbox"] = new_bbox masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") images = [image_0, image_1] annotations = [annotation_0, annotation_1] # encode them image_processing = DeformableDetrImageProcessor(format="coco_panoptic") encoding = image_processing( images=images, annotations=annotations, masks_path=masks_path, return_tensors="pt", return_segmentation_masks=True, ) # Check the pixel values have been padded postprocessed_height, postprocessed_width = 800, 1066 expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) # Check the bounding boxes have been adjusted for padded images self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) expected_boxes_0 = torch.tensor( [ [0.2625, 0.5437, 0.4688, 0.8625], [0.7719, 0.4104, 0.4531, 0.7125], [0.5000, 0.4927, 0.9969, 0.9854], [0.1688, 0.2000, 0.2063, 0.0917], [0.5492, 0.2760, 0.0578, 0.2187], [0.4992, 0.4990, 0.9984, 0.9979], ] ) expected_boxes_1 = torch.tensor( [ [0.1576, 0.3262, 0.2814, 0.5175], [0.4634, 0.2463, 0.2720, 0.4275], [0.3002, 0.2956, 0.5985, 0.5913], [0.1013, 0.1200, 0.1238, 0.0550], [0.3297, 0.1656, 0.0347, 0.1312], [0.2997, 0.2994, 0.5994, 0.5987], ] ) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) # Check the masks have also been padded self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height # format and not in the range [0, 1] encoding = image_processing( images=images, annotations=annotations, masks_path=masks_path, return_segmentation_masks=True, do_convert_annotations=False, return_tensors="pt", ) self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) # Convert to absolute coordinates unnormalized_boxes_0 = torch.vstack( [ expected_boxes_0[:, 0] * postprocessed_width, expected_boxes_0[:, 1] * postprocessed_height, expected_boxes_0[:, 2] * postprocessed_width, expected_boxes_0[:, 3] * postprocessed_height, ] ).T unnormalized_boxes_1 = torch.vstack( [ expected_boxes_1[:, 0] * postprocessed_width, expected_boxes_1[:, 1] * postprocessed_height, expected_boxes_1[:, 2] * postprocessed_width, expected_boxes_1[:, 3] * postprocessed_height, ] ).T # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max expected_boxes_0 = torch.vstack( [ unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, ] ).T expected_boxes_1 = torch.vstack( [ unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, ] ).T self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/unispeech/test_modeling_unispeech.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 PyTorch UniSpeech model. """ import math import unittest import numpy as np import pytest from datasets import load_dataset from transformers import UniSpeechConfig, is_torch_available from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( UniSpeechForCTC, UniSpeechForPreTraining, UniSpeechForSequenceClassification, UniSpeechModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) class UniSpeechModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=2, num_attention_heads=2, hidden_dropout_prob=0.1, # this is most likely not correctly set yet intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, vocab_size=32, do_stable_layer_norm=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.scope = scope output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() return config, input_values, attention_mask def get_config(self): return UniSpeechConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, ) def create_and_check_model(self, config, input_values, attention_mask): model = UniSpeechModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = UniSpeechModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = UniSpeechForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = UniSpeechForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = UniSpeechForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lengths are at least # one shorter than logit lengths to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = UniSpeechForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = UniSpeechForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with pytest.raises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class UniSpeechRobustModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (UniSpeechForCTC, UniSpeechModel, UniSpeechForSequenceClassification, UniSpeechForPreTraining) if is_torch_available() else () ) pipeline_model_mapping = ( { "audio-classification": UniSpeechForSequenceClassification, "automatic-speech-recognition": UniSpeechForCTC, "feature-extraction": UniSpeechModel, } if is_torch_available() else {} ) test_pruning = False test_headmasking = False def setUp(self): self.model_tester = UniSpeechModelTester( self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True ) self.config_tester = ConfigTester(self, config_class=UniSpeechConfig, 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_batched_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_batch_inference(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # UniSpeech has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # UniSpeech cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # UniSpeech has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) 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(): uniform_init_parms = [ "conv.weight", "conv.parametrizations.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "feature_projection.projection.weight", "feature_projection.projection.bias", ] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: 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", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = UniSpeechForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = UniSpeechForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_feature_prob_ctc_single_batch(self): model = UniSpeechForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech", mask_time_prob=0.2, mask_feature_prob=0.2, mask_time_length=2, mask_feature_length=2, ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech", return_attention_mask=True ) batch_duration_in_seconds = [6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (1, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = UniSpeechModel.from_pretrained("microsoft/unispeech-large-1500h-cv") self.assertIsNotNone(model) @require_torch @require_soundfile @slow class UniSpeechModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter( lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)] )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def _load_superb(self, task, num_samples): ds = load_dataset("anton-l/superb_dummy", task, split="test") return ds[:num_samples] def test_inference_pretraining(self): model = UniSpeechForPreTraining.from_pretrained("microsoft/unispeech-large-1500h-cv") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-xlsr-53") input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) with torch.no_grad(): torch.manual_seed(0) outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), ) # compute cosine similarity cosine_sim = torch.cosine_similarity(outputs.projected_states, outputs.projected_quantized_states, dim=-1) # pretrained model should have learned a high cosine similarity self.assertTrue(cosine_sim.mean() > 0.5) # fmt: off expected_cosine_sim_slice = torch.tensor( [[0.8290, 0.8335, 0.8815, 0.8580, 0.8249], [0.8892, 0.9221, 0.8711, 0.8601, 0.8482]], device=torch_device, ) # fmt: on self.assertTrue(torch.allclose(cosine_sim[:, :5], expected_cosine_sim_slice, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/bark/test_modeling_bark.py
# coding=utf-8 # Copyright 2023 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 Bark model. """ import copy import inspect import tempfile import unittest import pytest from transformers import ( BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig, is_torch_available, ) from transformers.models.bark.generation_configuration_bark import ( BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkSemanticGenerationConfig, ) from transformers.testing_utils import ( require_flash_attn, require_torch, require_torch_fp16, require_torch_gpu, 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, random_attention_mask from ..encodec.test_modeling_encodec import EncodecModelTester if is_torch_available(): import torch from transformers import ( BarkCausalModel, BarkCoarseModel, BarkFineModel, BarkModel, BarkProcessor, BarkSemanticModel, ) class BarkSemanticModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden_layers seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): 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_labels = use_labels self.vocab_size = vocab_size self.output_vocab_size = output_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.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False 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]) config = self.get_config() head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) inputs_dict = { "input_ids": input_ids, "head_mask": head_mask, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkSemanticConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkSemanticModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_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)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # 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)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] 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() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkCoarseModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden_layers seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): 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_labels = use_labels self.vocab_size = vocab_size self.output_vocab_size = output_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.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False 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]) config = self.get_config() head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) inputs_dict = { "input_ids": input_ids, "head_mask": head_mask, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkCoarseConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkCoarseModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_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)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # 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)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] 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() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkFineModelTester: def __init__( self, parent, batch_size=3, # need batch_size != num_hidden_layers seq_length=4, is_training=False, # for now training is not supported use_input_mask=True, use_labels=True, vocab_size=33, output_vocab_size=33, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=15, dropout=0.1, window_size=256, initializer_range=0.02, n_codes_total=8, # for BarkFineModel n_codes_given=1, # for BarkFineModel ): 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_labels = use_labels self.vocab_size = vocab_size self.output_vocab_size = output_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.dropout = dropout self.window_size = window_size self.initializer_range = initializer_range self.bos_token_id = output_vocab_size - 1 self.eos_token_id = output_vocab_size - 1 self.pad_token_id = output_vocab_size - 1 self.n_codes_total = n_codes_total self.n_codes_given = n_codes_given self.is_encoder_decoder = False def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length, self.n_codes_total], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) # randint between self.n_codes_given - 1 and self.n_codes_total - 1 codebook_idx = ids_tensor((1,), self.n_codes_total - self.n_codes_given).item() + self.n_codes_given inputs_dict = { "codebook_idx": codebook_idx, "input_ids": input_ids, "head_mask": head_mask, "attention_mask": input_mask, } return config, inputs_dict def get_config(self): return BarkFineConfig( vocab_size=self.vocab_size, output_vocab_size=self.output_vocab_size, hidden_size=self.hidden_size, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, window_size=self.window_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 config.output_vocab_size = 300 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BarkFineModel(config=config).to(torch_device).eval() input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] # first forward pass outputs = model(input_ids, attention_mask=attention_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)["logits"] output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[ "logits" ] # 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)) # test no attention_mask works outputs = model(input_ids, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["logits"] output_from_past = model(next_tokens, past_key_values=past_key_values)["logits"] 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() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) class BarkModelTester: def __init__( self, parent, semantic_kwargs=None, coarse_acoustics_kwargs=None, fine_acoustics_kwargs=None, codec_kwargs=None, is_training=False, # for now training is not supported ): if semantic_kwargs is None: semantic_kwargs = {} if coarse_acoustics_kwargs is None: coarse_acoustics_kwargs = {} if fine_acoustics_kwargs is None: fine_acoustics_kwargs = {} if codec_kwargs is None: codec_kwargs = {} self.parent = parent self.semantic_model_tester = BarkSemanticModelTester(parent, **semantic_kwargs) self.coarse_acoustics_model_tester = BarkCoarseModelTester(parent, **coarse_acoustics_kwargs) self.fine_acoustics_model_tester = BarkFineModelTester(parent, **fine_acoustics_kwargs) self.codec_model_tester = EncodecModelTester(parent, **codec_kwargs) self.is_training = is_training def get_config(self): return BarkConfig.from_sub_model_configs( self.semantic_model_tester.get_config(), self.coarse_acoustics_model_tester.get_config(), self.fine_acoustics_model_tester.get_config(), self.codec_model_tester.get_config(), ) def get_pipeline_config(self): config = self.get_config() # follow the `get_pipeline_config` of the sub component models config.semantic_config.vocab_size = 300 config.coarse_acoustics_config.vocab_size = 300 config.fine_acoustics_config.vocab_size = 300 config.semantic_config.output_vocab_size = 300 config.coarse_acoustics_config.output_vocab_size = 300 config.fine_acoustics_config.output_vocab_size = 300 return config @require_torch class BarkSemanticModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BarkSemanticModel,) if is_torch_available() else () all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else () is_encoder_decoder = False fx_compatible = False test_missing_keys = False test_pruning = False test_model_parallel = False # no model_parallel for now test_resize_embeddings = True def setUp(self): self.model_tester = BarkSemanticModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkSemanticConfig, n_embd=37) 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_inputs_embeds(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) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings() inputs["input_embeds"] = wte(input_ids) with torch.no_grad(): model(**inputs)[0] # override as the input arg is called "input_embeds", not "inputs_embeds" def test_inputs_embeds_matches_input_ids(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) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) with torch.no_grad(): out_ids = model(**inputs)[0] input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings() inputs["input_embeds"] = wte(input_ids) with torch.no_grad(): out_embeds = model(**inputs)[0] self.assertTrue(torch.allclose(out_embeds, out_ids)) @require_torch_fp16 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 = self.all_generative_model_classes[0](config).eval().to(torch_device) 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) @require_torch class BarkCoarseModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): # Same tester as BarkSemanticModelTest, except for model_class and config_class all_model_classes = (BarkCoarseModel,) if is_torch_available() else () all_generative_model_classes = (BarkCausalModel,) if is_torch_available() else () is_encoder_decoder = False fx_compatible = False test_missing_keys = False test_pruning = False test_model_parallel = False # no model_parallel for now test_resize_embeddings = True def setUp(self): self.model_tester = BarkCoarseModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkCoarseConfig, n_embd=37) 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_inputs_embeds(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) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings() inputs["input_embeds"] = wte(input_ids) with torch.no_grad(): model(**inputs)[0] # override as the input arg is called "input_embeds", not "inputs_embeds" def test_inputs_embeds_matches_input_ids(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) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) with torch.no_grad(): out_ids = model(**inputs)[0] input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings() inputs["input_embeds"] = wte(input_ids) with torch.no_grad(): out_embeds = model(**inputs)[0] self.assertTrue(torch.allclose(out_embeds, out_ids)) @require_torch_fp16 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 = self.all_generative_model_classes[0](config).eval().to(torch_device) 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) @require_torch class BarkFineModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (BarkFineModel,) if is_torch_available() else () is_encoder_decoder = False fx_compatible = False test_missing_keys = False test_pruning = False # no model_parallel for now test_model_parallel = False # torchscript disabled for now because forward with an int test_torchscript = False test_resize_embeddings = True def setUp(self): self.model_tester = BarkFineModelTester(self) self.config_tester = ConfigTester(self, config_class=BarkFineConfig, n_embd=37) 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_inputs_embeds(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) model.to(torch_device) model.eval() inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) input_ids = inputs["input_ids"] del inputs["input_ids"] wte = model.get_input_embeddings()[inputs_dict["codebook_idx"]] inputs["input_embeds"] = wte(input_ids[:, :, inputs_dict["codebook_idx"]]) with torch.no_grad(): model(**inputs)[0] @unittest.skip("FineModel relies on codebook idx and does not return same logits") def test_inputs_embeds_matches_input_ids(self): pass @require_torch_fp16 def test_generate_fp16(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_ids = input_dict["input_ids"] # take first codebook channel model = self.all_model_classes[0](config).eval().to(torch_device) model.half() # toy generation_configs semantic_generation_config = BarkSemanticGenerationConfig(semantic_vocab_size=0) coarse_generation_config = BarkCoarseGenerationConfig(n_coarse_codebooks=config.n_codes_given) fine_generation_config = BarkFineGenerationConfig( max_fine_history_length=config.block_size // 2, max_fine_input_length=config.block_size, n_fine_codebooks=config.n_codes_total, ) codebook_size = config.vocab_size - 1 model.generate( input_ids, history_prompt=None, temperature=None, semantic_generation_config=semantic_generation_config, coarse_generation_config=coarse_generation_config, fine_generation_config=fine_generation_config, codebook_size=codebook_size, ) model.generate( input_ids, history_prompt=None, temperature=0.7, semantic_generation_config=semantic_generation_config, coarse_generation_config=coarse_generation_config, fine_generation_config=fine_generation_config, codebook_size=codebook_size, ) 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 = ["codebook_idx", "input_ids"] self.assertListEqual(arg_names[:2], expected_arg_names) def test_model_common_attributes(self): # one embedding layer per codebook config, inputs_dict = 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()[0], (torch.nn.Embedding)) model.set_input_embeddings( torch.nn.ModuleList([torch.nn.Embedding(10, 10) for _ in range(config.n_codes_total)]) ) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x[0], torch.nn.Linear)) def test_resize_tokens_embeddings(self): # resizing tokens_embeddings of a ModuleList original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config) model.to(torch_device) if self.model_tester.is_training is False: model.eval() model_vocab_size = config.vocab_size # Retrieve the embeddings and clone theme model_embed_list = model.resize_token_embeddings(model_vocab_size) cloned_embeddings_list = [model_embed.weight.clone() for model_embed in model_embed_list] # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_embed_list = model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) # Check that it actually resizes the embeddings matrix for each codebook for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list): self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model_embed_list = model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) for model_embed, cloned_embeddings in zip(model_embed_list, cloned_embeddings_list): self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that adding and removing tokens has not modified the first part of the embedding matrix. # only check for the first embedding matrix models_equal = True for p1, p2 in zip(cloned_embeddings_list[0], model_embed_list[0].weight): if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_resize_embeddings_untied(self): # resizing tokens_embeddings of a ModuleList original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.test_resize_embeddings: return original_config.tie_word_embeddings = False for model_class in self.all_model_classes: config = copy.deepcopy(original_config) model = model_class(config).to(torch_device) # if no output embeddings -> leave test if model.get_output_embeddings() is None: continue # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size model_vocab_size = config.vocab_size model.resize_token_embeddings(model_vocab_size + 10) self.assertEqual(model.config.vocab_size, model_vocab_size + 10) output_embeds_list = model.get_output_embeddings() for output_embeds in output_embeds_list: self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size model.resize_token_embeddings(model_vocab_size - 15) self.assertEqual(model.config.vocab_size, model_vocab_size - 15) # Check that it actually resizes the embeddings matrix output_embeds_list = model.get_output_embeddings() for output_embeds in output_embeds_list: self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15) # Check bias if present if output_embeds.bias is not None: self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15) # Check that the model can still do a forward pass successfully (every parameter should be resized) # Input ids should be clamped to the maximum size of the vocabulary inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 1) # Check that the model can still do a forward pass successfully (every parameter should be resized) model(**self._prepare_for_class(inputs_dict, model_class)) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.bfloat16) model.to(torch_device) dummy_input = inputs_dict["input_ids"][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] dummy_attention_mask[:, 1:] = 1 dummy_attention_mask[:, :1] = 0 outputs = model(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2) other_inputs = {"output_hidden_states": True} if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(inputs_dict["codebook_idx"], dummy_input, **other_inputs) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, **other_inputs) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa[1:], logits[1:], atol=4e-2, rtol=4e-2) # check with inference + dropout model.train() _ = model_fa(inputs_dict["codebook_idx"], dummy_input, **other_inputs) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.bfloat16, ) model.to(torch_device) dummy_input = inputs_dict["input_ids"][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] dummy_attention_mask[:, :-1] = 1 dummy_attention_mask[:, -1:] = 0 outputs = model(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa, logits, atol=4e-2, rtol=4e-2) other_inputs = { "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(inputs_dict["codebook_idx"], dummy_input, **other_inputs) outputs_fa = model_fa(inputs_dict["codebook_idx"], dummy_input, **other_inputs) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] assert torch.allclose(logits_fa[:-1], logits[:-1], atol=4e-2, rtol=4e-2) @require_torch class BarkModelIntegrationTests(unittest.TestCase): @cached_property def model(self): return BarkModel.from_pretrained("suno/bark").to(torch_device) @cached_property def processor(self): return BarkProcessor.from_pretrained("suno/bark") @cached_property def inputs(self): input_ids = self.processor("In the light of the moon, a little egg lay on a leaf", voice_preset="en_speaker_6") input_ids = input_ids.to(torch_device) return input_ids @cached_property def semantic_generation_config(self): semantic_generation_config = BarkSemanticGenerationConfig(**self.model.generation_config.semantic_config) return semantic_generation_config @cached_property def coarse_generation_config(self): coarse_generation_config = BarkCoarseGenerationConfig(**self.model.generation_config.coarse_acoustics_config) return coarse_generation_config @cached_property def fine_generation_config(self): fine_generation_config = BarkFineGenerationConfig(**self.model.generation_config.fine_acoustics_config) return fine_generation_config @slow def test_generate_semantic(self): input_ids = self.inputs # check first ids expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # greedy decoding with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) @slow def test_generate_semantic_early_stop(self): input_ids = self.inputs min_eos_p = 0.01 # check first ids expected_output_ids = [7363, 321, 41, 1461, 6915, 952, 326, 41, 41, 927,] # fmt: skip # Should be able to read min_eos_p from kwargs with torch.no_grad(): torch.manual_seed(0) output_ids_without_min_eos_p = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, ) torch.manual_seed(0) output_ids_kwargs = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, min_eos_p=min_eos_p, ) self.assertListEqual(output_ids_without_min_eos_p[0, : len(expected_output_ids)].tolist(), expected_output_ids) self.assertLess(len(output_ids_kwargs[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())) # Should be able to read min_eos_p from the semantic generation config self.semantic_generation_config.min_eos_p = min_eos_p with torch.no_grad(): torch.manual_seed(0) output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=0.9, semantic_generation_config=self.semantic_generation_config, ) self.assertEqual(output_ids.shape, output_ids_kwargs.shape) self.assertLess(len(output_ids[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist())) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) @slow def test_generate_coarse(self): input_ids = self.inputs history_prompt = input_ids["history_prompt"] # check first ids expected_output_ids = [11018, 11391, 10651, 11418, 10857, 11620, 10642, 11366, 10312, 11528, 10531, 11516, 10474, 11051, 10524, 11051, ] # fmt: skip with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) output_ids = self.model.coarse_acoustics.generate( output_ids, history_prompt=history_prompt, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, codebook_size=self.model.generation_config.codebook_size, ) self.assertListEqual(output_ids[0, : len(expected_output_ids)].tolist(), expected_output_ids) @slow def test_generate_fine(self): input_ids = self.inputs history_prompt = input_ids["history_prompt"] # fmt: off expected_output_ids = [ [1018, 651, 857, 642, 312, 531, 474, 524, 524, 776,], [367, 394, 596, 342, 504, 492, 27, 27, 822, 822,], [961, 955, 221, 955, 955, 686, 939, 939, 479, 176,], [638, 365, 218, 944, 853, 363, 639, 22, 884, 456,], [302, 912, 524, 38, 174, 209, 879, 23, 910, 227,], [440, 673, 861, 666, 372, 558, 49, 172, 232, 342,], [244, 358, 123, 356, 586, 520, 499, 877, 542, 637,], [806, 685, 905, 848, 803, 810, 921, 208, 625, 203,], ] # fmt: on with torch.no_grad(): output_ids = self.model.semantic.generate( **input_ids, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, ) output_ids = self.model.coarse_acoustics.generate( output_ids, history_prompt=history_prompt, do_sample=False, temperature=1.0, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, codebook_size=self.model.generation_config.codebook_size, ) # greedy decoding output_ids = self.model.fine_acoustics.generate( output_ids, history_prompt=history_prompt, temperature=None, semantic_generation_config=self.semantic_generation_config, coarse_generation_config=self.coarse_generation_config, fine_generation_config=self.fine_generation_config, codebook_size=self.model.generation_config.codebook_size, ) self.assertListEqual(output_ids[0, :, : len(expected_output_ids[0])].tolist(), expected_output_ids) @slow def test_generate_end_to_end(self): input_ids = self.inputs with torch.no_grad(): self.model.generate(**input_ids) self.model.generate(**{key: val for (key, val) in input_ids.items() if key != "history_prompt"}) @slow def test_generate_end_to_end_with_args(self): input_ids = self.inputs with torch.no_grad(): self.model.generate(**input_ids, do_sample=True, temperature=0.6, penalty_alpha=0.6) self.model.generate(**input_ids, do_sample=True, temperature=0.6, num_beams=4) @slow def test_generate_batching(self): args = {"do_sample": False, "temperature": None} s1 = "I love HuggingFace" s2 = "In the light of the moon, a little egg lay on a leaf" voice_preset = "en_speaker_6" input_ids = self.processor([s1, s2], voice_preset=voice_preset).to(torch_device) # generate in batch outputs, audio_lengths = self.model.generate(**input_ids, **args, return_output_lengths=True) # generate one-by-one s1 = self.processor(s1, voice_preset=voice_preset).to(torch_device) s2 = self.processor(s2, voice_preset=voice_preset).to(torch_device) output1 = self.model.generate(**s1, **args) output2 = self.model.generate(**s2, **args) # up until the coarse acoustic model (included), results are the same # the fine acoustic model introduces small differences # first verify if same length (should be the same because it's decided in the coarse model) self.assertEqual(tuple(audio_lengths), (output1.shape[1], output2.shape[1])) # then assert almost equal self.assertTrue(torch.allclose(outputs[0, : audio_lengths[0]], output1.squeeze(), atol=2e-3)) self.assertTrue(torch.allclose(outputs[1, : audio_lengths[1]], output2.squeeze(), atol=2e-3)) # now test single input with return_output_lengths = True outputs, _ = self.model.generate(**s1, **args, return_output_lengths=True) self.assertTrue((outputs == output1).all().item()) @slow def test_generate_end_to_end_with_sub_models_args(self): input_ids = self.inputs with torch.no_grad(): torch.manual_seed(0) self.model.generate( **input_ids, do_sample=False, temperature=1.0, coarse_do_sample=True, coarse_temperature=0.7 ) output_ids_without_min_eos_p = self.model.generate( **input_ids, do_sample=True, temperature=0.9, coarse_do_sample=True, coarse_temperature=0.7, fine_temperature=0.3, ) output_ids_with_min_eos_p = self.model.generate( **input_ids, do_sample=True, temperature=0.9, coarse_temperature=0.7, fine_temperature=0.3, min_eos_p=0.1, ) self.assertLess( len(output_ids_with_min_eos_p[0, :].tolist()), len(output_ids_without_min_eos_p[0, :].tolist()) ) @require_torch_gpu @slow def test_generate_end_to_end_with_offload(self): input_ids = self.inputs with torch.no_grad(): # standard generation output_with_no_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0) torch.cuda.empty_cache() memory_before_offload = torch.cuda.memory_allocated() model_memory_footprint = self.model.get_memory_footprint() # activate cpu offload self.model.enable_cpu_offload() memory_after_offload = torch.cuda.memory_allocated() # checks if the model have been offloaded # CUDA memory usage after offload should be near 0, leaving room to small differences room_for_difference = 1.1 self.assertGreater( (memory_before_offload - model_memory_footprint) * room_for_difference, memory_after_offload ) # checks if device is the correct one self.assertEqual(self.model.device.type, torch_device) # checks if hooks exist self.assertTrue(hasattr(self.model.semantic, "_hf_hook")) # output with cpu offload output_with_offload = self.model.generate(**input_ids, do_sample=False, temperature=1.0) # checks if same output self.assertListEqual(output_with_no_offload.tolist(), output_with_offload.tolist())
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/bark/test_processor_bark.py
# Copyright 2023 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 shutil import tempfile import unittest import numpy as np from transformers import AutoTokenizer, BarkProcessor from transformers.testing_utils import require_torch, slow @require_torch class BarkProcessorTest(unittest.TestCase): def setUp(self): self.checkpoint = "suno/bark-small" self.tmpdirname = tempfile.mkdtemp() self.voice_preset = "en_speaker_1" self.input_string = "This is a test string" self.speaker_embeddings_dict_path = "speaker_embeddings_path.json" self.speaker_embeddings_directory = "speaker_embeddings" def get_tokenizer(self, **kwargs): return AutoTokenizer.from_pretrained(self.checkpoint, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def test_save_load_pretrained_default(self): tokenizer = self.get_tokenizer() processor = BarkProcessor(tokenizer=tokenizer) processor.save_pretrained(self.tmpdirname) processor = BarkProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) @slow def test_save_load_pretrained_additional_features(self): processor = BarkProcessor.from_pretrained( pretrained_processor_name_or_path=self.checkpoint, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path, ) processor.save_pretrained( self.tmpdirname, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path, speaker_embeddings_directory=self.speaker_embeddings_directory, ) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") processor = BarkProcessor.from_pretrained( self.tmpdirname, self.speaker_embeddings_dict_path, bos_token="(BOS)", eos_token="(EOS)", ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) def test_speaker_embeddings(self): processor = BarkProcessor.from_pretrained( pretrained_processor_name_or_path=self.checkpoint, speaker_embeddings_dict_path=self.speaker_embeddings_dict_path, ) seq_len = 35 nb_codebooks_coarse = 2 nb_codebooks_total = 8 voice_preset = { "semantic_prompt": np.ones(seq_len), "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)), "fine_prompt": np.ones((nb_codebooks_total, seq_len)), } # test providing already loaded voice_preset inputs = processor(text=self.input_string, voice_preset=voice_preset) processed_voice_preset = inputs["history_prompt"] for key in voice_preset: self.assertListEqual(voice_preset[key].tolist(), processed_voice_preset.get(key, np.array([])).tolist()) # test loading voice preset from npz file tmpfilename = os.path.join(self.tmpdirname, "file.npz") np.savez(tmpfilename, **voice_preset) inputs = processor(text=self.input_string, voice_preset=tmpfilename) processed_voice_preset = inputs["history_prompt"] for key in voice_preset: self.assertListEqual(voice_preset[key].tolist(), processed_voice_preset.get(key, np.array([])).tolist()) # test loading voice preset from the hub inputs = processor(text=self.input_string, voice_preset=self.voice_preset) def test_tokenizer(self): tokenizer = self.get_tokenizer() processor = BarkProcessor(tokenizer=tokenizer) encoded_processor = processor(text=self.input_string) encoded_tok = tokenizer( self.input_string, padding="max_length", max_length=256, add_special_tokens=False, return_attention_mask=True, return_token_type_ids=False, ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key], encoded_processor[key].squeeze().tolist())
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/stablelm/test_modeling_stablelm.py
# coding=utf-8 # Copyright 2024 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 StableLm model. """ import unittest from parameterized import parameterized from transformers import StableLmConfig, is_torch_available, set_seed from transformers.testing_utils import ( is_flaky, require_bitsandbytes, require_flash_attn, require_torch, require_torch_sdpa, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( AutoTokenizer, StableLmForCausalLM, StableLmForSequenceClassification, StableLmModel, ) from transformers.models.stablelm.modeling_stablelm import ( StableLmDynamicNTKScalingRotaryEmbedding, StableLmLinearScalingRotaryEmbedding, StableLmRotaryEmbedding, ) # Copied from transformers.tests.models.persimmon.test_modeling_persimmon.PersimmonModelTester with Persimmon -> StableLm class StableLmModelTester: # Ignore copy def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=2, num_attention_heads=4, num_key_value_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, pad_token_id=0, 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.num_key_value_heads = num_key_value_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.pad_token_id = pad_token_id 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 = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device) 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): return StableLmConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, pad_token_id=self.pad_token_id, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = StableLmModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 = StableLmModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 = StableLmForCausalLM(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_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 = StableLmForCausalLM(config=config) model.to(torch_device) model.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 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, "attention_mask": input_mask} return config, inputs_dict @require_torch # Copied from transformers.tests.persimmon.test_modeling_persimmon.PersimmonModelTest with Persimmon -> StableLm class StableLmModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (StableLmModel, StableLmForCausalLM, StableLmForSequenceClassification) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": StableLmModel, "text-classification": StableLmForSequenceClassification, # TODO (ydshieh): check why these two fail. Fix them or skip them in a better way. # "text-generation": StableLmForCausalLM, # "zero-shot": StableLmForSequenceClassification, } if is_torch_available() else {} ) all_generative_model_classes = (StableLmForCausalLM,) if is_torch_available() else () test_headmasking = False test_pruning = False def setUp(self): self.model_tester = StableLmModelTester(self) self.config_tester = ConfigTester(self, config_class=StableLmConfig, 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_stablelm_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = StableLmForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_stablelm_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = StableLmForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_stablelm_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = StableLmForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @parameterized.expand([("linear",), ("dynamic",)]) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->StableLm def test_model_rope_scaling_from_config(self, scaling_type): config, _ = self.model_tester.prepare_config_and_inputs_for_common() short_input = ids_tensor([1, 10], config.vocab_size) long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size) set_seed(42) # Fixed seed at init time so the two models get the same random weights original_model = StableLmModel(config) original_model.to(torch_device) original_model.eval() original_short_output = original_model(short_input).last_hidden_state original_long_output = original_model(long_input).last_hidden_state set_seed(42) # Fixed seed at init time so the two models get the same random weights config.rope_scaling = {"type": scaling_type, "factor": 10.0} scaled_model = StableLmModel(config) scaled_model.to(torch_device) scaled_model.eval() scaled_short_output = scaled_model(short_input).last_hidden_state scaled_long_output = scaled_model(long_input).last_hidden_state # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original # maximum sequence length, so the outputs for the short input should match. if scaling_type == "dynamic": self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) else: self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) # The output should be different for long inputs self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5)) # Copied from tests.models.falcon.test_modeling_falcon.FalconModelTest.test_model_rope_scaling with Falcon->StableLm def test_model_rope_scaling(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() hidden_size = config.hidden_size num_heads = config.num_attention_heads head_dim = hidden_size // num_heads scaling_factor = 10 short_input_length = 10 long_input_length = int(config.max_position_embeddings * 1.5) # Inputs x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device # Sanity check original RoPE original_rope = StableLmRotaryEmbedding( head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, ).to(torch_device) original_cos_short, original_sin_short = original_rope(x, short_input_length) original_cos_long, original_sin_long = original_rope(x, long_input_length) torch.testing.assert_close(original_cos_short, original_cos_long[:short_input_length, :]) torch.testing.assert_close(original_sin_short, original_sin_long[:short_input_length, :]) # Sanity check linear RoPE scaling # New position "x" should match original position with index "x/scaling_factor" linear_scaling_rope = StableLmLinearScalingRotaryEmbedding( head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, scaling_factor=scaling_factor, ).to(torch_device) linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length) linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length) torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :]) torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :]) for new_position in range(0, long_input_length, scaling_factor): original_position = int(new_position // scaling_factor) torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :]) torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :]) # Sanity check Dynamic NTK RoPE scaling # Scaling should only be observed after a long input is fed. We can observe that the frequencies increase # with scaling_factor (or that `inv_freq` decreases) ntk_scaling_rope = StableLmDynamicNTKScalingRotaryEmbedding( head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, scaling_factor=scaling_factor, ).to(torch_device) ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length) ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length) torch.testing.assert_close(ntk_cos_short, original_cos_short) torch.testing.assert_close(ntk_sin_short, original_sin_short) with self.assertRaises(AssertionError): torch.testing.assert_close(ntk_cos_long, original_cos_long) with self.assertRaises(AssertionError): torch.testing.assert_close(ntk_sin_long, original_sin_long) self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all()) @require_torch class StableLmModelIntegrationTest(unittest.TestCase): @slow def test_model_stablelm_3b_4e1t_logits(self): input_ids = {"input_ids": torch.tensor([[510, 8588, 310, 1900, 9386]], dtype=torch.long, device=torch_device)} model = StableLmForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t").to(torch_device) model.eval() output = model(**input_ids).logits # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[2.7146, 2.4245, 1.5616, 1.4424, 2.6790]]).to(torch_device) self.assertTrue(torch.allclose(output.mean(dim=-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4)) # Expected logits sliced from [0, 0, 0:30] EXPECTED_SLICE = torch.tensor([7.1030, -1.4195, 9.9206, 7.7008, 4.9891, 4.2169, 5.5426, 3.7878, 6.7593, 5.7360, 8.4691, 5.5448, 5.0544, 10.4129, 8.5573, 13.0405, 7.3265, 3.5868, 6.1106, 5.9406, 5.6376, 5.7490, 5.4850, 4.8124, 5.1991, 4.6419, 4.5719, 9.9588, 6.7222, 4.5070]).to(torch_device) # fmt: skip self.assertTrue(torch.allclose(output[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)) @slow def test_model_stablelm_3b_4e1t_generation(self): tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t") model = StableLmForCausalLM.from_pretrained("stabilityai/stablelm-3b-4e1t") input_ids = tokenizer.encode( "My favorite food has always been pizza, but lately", return_tensors="pt", ) outputs = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(outputs[0], skip_special_tokens=True) EXPECTED_TEXT_COMPLETION = """My favorite food has always been pizza, but lately I’ve been craving something different. I’ve been trying to eat healthier and I’ve""" self.assertEqual(text, EXPECTED_TEXT_COMPLETION) @slow def test_model_tiny_random_stablelm_2_logits(self): # Check parallel residual and qk layernorm forward pass input_ids = {"input_ids": torch.tensor([[510, 8588, 310, 1900, 9386]], dtype=torch.long, device=torch_device)} model = StableLmForCausalLM.from_pretrained("stabilityai/tiny-random-stablelm-2").to(torch_device) model.eval() output = model(**input_ids).logits # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[-2.7196, -3.6099, -2.6877, -3.1973, -3.9344]]).to(torch_device) self.assertTrue(torch.allclose(output.mean(dim=-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4)) # Expected logits sliced from [0, 0, 0:30] EXPECTED_SLICE = torch.tensor([2.8364, 5.3811, 5.1659, 7.5485, 4.3219, 6.3315, 1.3967, 6.9147, 3.9679, 6.4786, 5.9176, 3.3067, 5.2917, 0.1485, 3.9630, 7.9947,10.6727, 9.6757, 8.8772, 8.3527, 7.8445, 6.6025, 5.5786, 7.0985,6.1369, 3.4259, 1.9397, 4.6157, 4.8105, 3.1768]).to(torch_device) # fmt: skip self.assertTrue(torch.allclose(output[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)) @slow def test_model_tiny_random_stablelm_2_generation(self): # Check parallel residual and qk layernorm generation tokenizer = AutoTokenizer.from_pretrained("stabilityai/tiny-random-stablelm-2") model = StableLmForCausalLM.from_pretrained("stabilityai/tiny-random-stablelm-2") input_ids = tokenizer.encode( "My favorite ride at the amusement park", return_tensors="pt", ) outputs = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(outputs[0], skip_special_tokens=True) EXPECTED_TEXT_COMPLETION = """My favorite ride at the amusement park is the 2000-mile roller coaster. It's a thrilling ride filled with roller coast""" self.assertEqual(text, EXPECTED_TEXT_COMPLETION) @require_bitsandbytes @slow @require_flash_attn def test_model_3b_long_prompt(self): EXPECTED_OUTPUT_TOKEN_IDS = [3, 3, 3] input_ids = [306, 338] * 2047 model = StableLmForCausalLM.from_pretrained( "stabilityai/stablelm-3b-4e1t", device_map="auto", torch_dtype="auto", load_in_4bit=True, attn_implementation="flash_attention_2", ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-3:].tolist()) # Copied from transformers.tests.models.llama.test_modeling_llama.LlamaModelTest.test_eager_matches_sdpa_generate with Llama->StableLm,saibo/llama-1B->stabilityai/stablelm-3b-4e1t # TODO: @Fxmarty @is_flaky(max_attempts=3, description="flaky on some models.") @require_torch_sdpa @slow def test_eager_matches_sdpa_generate(self): """ Overwritting the common test as the test is flaky on tiny models """ max_new_tokens = 30 tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-3b-4e1t") model_sdpa = StableLmForCausalLM.from_pretrained( "stabilityai/stablelm-3b-4e1t", torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(torch_device) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") model_eager = StableLmForCausalLM.from_pretrained( "stabilityai/stablelm-3b-4e1t", torch_dtype=torch.float16, low_cpu_mem_usage=True, attn_implementation="eager", ).to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: raise ValueError("The eager model should not have SDPA attention layers") has_sdpa = False for name, submodule in model_sdpa.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: has_sdpa = True break if not has_sdpa: raise ValueError("The SDPA model should have SDPA attention layers") texts = [ "hi here's a longer context, getting longer and", "Hello this is a very long sentence my friend, very long for real", "Today I am in Paris and", ] for padding_side in ["left", "right"]: tokenizer.padding_side = padding_side tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device) res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) with self.subTest(f"{padding_side}"): torch.testing.assert_close( res_eager, res_sdpa, msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}", )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/mpnet/test_tokenization_mpnet.py
# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team, Microsoft Corporation. # # 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 unittest from transformers import MPNetTokenizerFast from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class MPNetTokenizerTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "microsoft/mpnet-base" tokenizer_class = MPNetTokenizer rust_tokenizer_class = MPNetTokenizerFast test_rust_tokenizer = True space_between_special_tokens = True def setUp(self): super().setUp() vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.tokenizer_class(self.vocab_file) tokens = tokenizer.tokenize("UNwant\u00E9d,running") self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11]) @slow def test_sequence_builders(self): tokenizer = self.tokenizer_class.from_pretrained("microsoft/mpnet-base") 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 == [0] + text + [2] assert encoded_pair == [0] + text + [2] + [2] + text_2 + [2]
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/mpnet/test_modeling_mpnet.py
# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team, Microsoft Corporation. # # 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 MPNetConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MPNetForMaskedLM, MPNetForMultipleChoice, MPNetForQuestionAnswering, MPNetForSequenceClassification, MPNetForTokenClassification, MPNetModel, ) class MPNetModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=2, num_attention_heads=4, intermediate_size=64, 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 get_large_model_config(self): return MPNetConfig.from_pretrained("microsoft/mpnet-base") 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 MPNetConfig( 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, initializer_range=self.initializer_range, ) def create_and_check_mpnet_model( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MPNetModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, input_mask) 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_mpnet_for_question_answering( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MPNetForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, 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_mpnet_for_sequence_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = MPNetForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_mpnet_for_multiple_choice( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = MPNetForMultipleChoice(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_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, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_mpnet_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = MPNetForTokenClassification(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 MPNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MPNetForMaskedLM, MPNetForMultipleChoice, MPNetForQuestionAnswering, MPNetForSequenceClassification, MPNetForTokenClassification, MPNetModel, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": MPNetModel, "fill-mask": MPNetForMaskedLM, "question-answering": MPNetForQuestionAnswering, "text-classification": MPNetForSequenceClassification, "token-classification": MPNetForTokenClassification, "zero-shot": MPNetForSequenceClassification, } if is_torch_available() else {} ) test_pruning = False test_resize_embeddings = True def setUp(self): self.model_tester = MPNetModelTester(self) self.config_tester = ConfigTester(self, config_class=MPNetConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_mpnet_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpnet_model(*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_mpnet_for_sequence_classification(*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_mpnet_for_multiple_choice(*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_mpnet_for_token_classification(*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_mpnet_for_question_answering(*config_and_inputs) @unittest.skip("TFMPNet adds poolers to all models, unlike the PT model class.") def test_tf_from_pt_safetensors(self): return @require_torch class MPNetModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = MPNetModel.from_pretrained("microsoft/mpnet-base") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] ) # compare the actual values for a slice. self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/mpnet/test_modeling_tf_mpnet.py
# coding=utf-8 # Copyright 2020 The HuggingFace Inc. team, Microsoft Corporation. # # 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 __future__ import annotations import unittest from transformers import MPNetConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers.models.mpnet.modeling_tf_mpnet import ( TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification, TFMPNetForTokenClassification, TFMPNetModel, ) class TFMPNetModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=2, num_attention_heads=4, intermediate_size=64, 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 = MPNetConfig( 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, initializer_range=self.initializer_range, ) return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_mpnet_model( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFMPNetModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_mpnet_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFMPNetForMaskedLM(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_mpnet_for_question_answering( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFMPNetForQuestionAnswering(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, } result = model(inputs) 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_mpnet_for_sequence_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFMPNetForSequenceClassification(config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_mpnet_for_multiple_choice( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = TFMPNetForMultipleChoice(config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_mpnet_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFMPNetForTokenClassification(config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) 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_tf class TFMPNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFMPNetForMaskedLM, TFMPNetForMultipleChoice, TFMPNetForQuestionAnswering, TFMPNetForSequenceClassification, TFMPNetForTokenClassification, TFMPNetModel, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFMPNetModel, "fill-mask": TFMPNetForMaskedLM, "question-answering": TFMPNetForQuestionAnswering, "text-classification": TFMPNetForSequenceClassification, "token-classification": TFMPNetForTokenClassification, "zero-shot": TFMPNetForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFMPNetModelTester(self) self.config_tester = ConfigTester(self, config_class=MPNetConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_mpnet_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_mpnet_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_mpnet_for_masked_lm(*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_mpnet_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_mpnet_for_sequence_classification(*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_mpnet_for_multiple_choice(*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_mpnet_for_token_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in ["microsoft/mpnet-base"]: model = TFMPNetModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFMPNetModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TFMPNetModel.from_pretrained("microsoft/mpnet-base") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6, 768] self.assertEqual(output.shape, expected_shape) expected_slice = tf.constant( [ [ [-0.1067172, 0.08216473, 0.0024543], [-0.03465879, 0.8354118, -0.03252288], [-0.06569476, -0.12424111, -0.0494436], ] ] ) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/grounding_dino/test_modeling_grounding_dino.py
# coding=utf-8 # Copyright 2024 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 Grounding DINO model. """ import collections import inspect import math import re import unittest from transformers import ( GroundingDinoConfig, SwinConfig, is_torch_available, is_vision_available, ) from transformers.file_utils import cached_property from transformers.testing_utils import ( require_timm, require_torch, require_torch_gpu, require_vision, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import GroundingDinoForObjectDetection, GroundingDinoModel from transformers.pytorch_utils import id_tensor_storage if is_vision_available(): from PIL import Image from transformers import AutoProcessor class GroundingDinoModelTester: def __init__( self, parent, batch_size=4, is_training=True, use_labels=True, hidden_size=32, 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, num_queries=2, num_channels=3, image_size=98, n_targets=8, num_labels=3, num_feature_levels=4, encoder_n_points=2, decoder_n_points=6, max_text_len=7, ): self.parent = parent self.batch_size = batch_size 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.num_queries = num_queries self.num_channels = num_channels self.image_size = image_size self.n_targets = n_targets self.num_labels = num_labels self.num_feature_levels = num_feature_levels self.encoder_n_points = encoder_n_points self.decoder_n_points = decoder_n_points self.max_text_len = max_text_len # we also set the expected seq length for both encoder and decoder self.encoder_seq_length_vision = ( math.ceil(self.image_size / 8) ** 2 + math.ceil(self.image_size / 16) ** 2 + math.ceil(self.image_size / 32) ** 2 + math.ceil(self.image_size / 64) ** 2 ) self.encoder_seq_length_text = self.max_text_len self.decoder_seq_length = self.num_queries def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) pixel_mask = torch.ones([self.batch_size, self.image_size, self.image_size], device=torch_device) input_ids = ids_tensor([self.batch_size, self.max_text_len], self.num_labels) labels = None if self.use_labels: # labels is a list of Dict (each Dict being the labels for a given example in the batch) labels = [] for i in range(self.batch_size): target = {} target["class_labels"] = torch.randint( high=self.num_labels, size=(self.n_targets,), device=torch_device ) target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device) target["masks"] = torch.rand(self.n_targets, self.image_size, self.image_size, device=torch_device) labels.append(target) config = self.get_config() return config, pixel_values, pixel_mask, input_ids, labels def get_config(self): swin_config = SwinConfig( window_size=7, embed_dim=8, depths=[1, 1, 1, 1], num_heads=[1, 1, 1, 1], image_size=self.image_size, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], ) text_backbone = { "hidden_size": 8, "num_hidden_layers": 2, "num_attention_heads": 2, "intermediate_size": 8, "max_position_embeddings": 8, "model_type": "bert", } return GroundingDinoConfig( 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, num_queries=self.num_queries, num_labels=self.num_labels, num_feature_levels=self.num_feature_levels, encoder_n_points=self.encoder_n_points, decoder_n_points=self.decoder_n_points, use_timm_backbone=False, backbone_config=swin_config, max_text_len=self.max_text_len, text_config=text_backbone, ) def prepare_config_and_inputs_for_common(self): config, pixel_values, pixel_mask, input_ids, labels = self.prepare_config_and_inputs() inputs_dict = {"pixel_values": pixel_values, "pixel_mask": pixel_mask, "input_ids": input_ids} return config, inputs_dict def create_and_check_model(self, config, pixel_values, pixel_mask, input_ids, labels): model = GroundingDinoModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.num_queries, self.hidden_size)) def create_and_check_object_detection_head_model(self, config, pixel_values, pixel_mask, input_ids, labels): model = GroundingDinoForObjectDetection(config=config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) result = model(pixel_values=pixel_values, pixel_mask=pixel_mask, input_ids=input_ids, labels=labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_queries, config.max_text_len)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_queries, 4)) @require_torch class GroundingDinoModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (GroundingDinoModel, GroundingDinoForObjectDetection) if is_torch_available() else () is_encoder_decoder = True test_torchscript = False test_pruning = False test_head_masking = False test_missing_keys = False pipeline_model_mapping = ( {"image-feature-extraction": GroundingDinoModel, "zero-shot-object-detection": GroundingDinoForObjectDetection} if is_torch_available() else {} ) # special case for head models 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.__name__ == "GroundingDinoForObjectDetection": labels = [] for i in range(self.model_tester.batch_size): target = {} target["class_labels"] = torch.ones( size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long ) target["boxes"] = torch.ones( self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float ) target["masks"] = torch.ones( self.model_tester.n_targets, self.model_tester.image_size, self.model_tester.image_size, device=torch_device, dtype=torch.float, ) labels.append(target) inputs_dict["labels"] = labels return inputs_dict def setUp(self): self.model_tester = GroundingDinoModelTester(self) self.config_tester = ConfigTester(self, config_class=GroundingDinoConfig, has_text_modality=False) def test_config(self): # we don't test common_properties and arguments_init as these don't apply for Grounding DINO 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() 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_object_detection_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_object_detection_head_model(*config_and_inputs) @unittest.skip(reason="Grounding DINO does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Grounding DINO does not have a get_input_embeddings method") def test_model_common_attributes(self): pass @unittest.skip(reason="Grounding DINO does not use token embeddings") def test_resize_tokens_embeddings(self): pass @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions[-1] 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[-1] self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [ self.model_tester.num_attention_heads, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) out_len = len(outputs) correct_outlen = 10 # loss is at first position if "labels" in inputs_dict: correct_outlen += 1 # loss is added to beginning # Object Detection model returns pred_logits and pred_boxes if model_class.__name__ == "GroundingDinoForObjectDetection": correct_outlen += 2 self.assertEqual(out_len, correct_outlen) # decoder attentions decoder_attentions = outputs.decoder_attentions[0] 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, self.model_tester.num_queries, self.model_tester.num_queries], ) # cross attentions cross_attentions = outputs.decoder_attentions[-1] 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, self.model_tester.num_feature_levels, self.model_tester.decoder_n_points, ], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = 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)) self.assertEqual(out_len + 3, len(outputs)) self_attentions = outputs.encoder_attentions[-1] 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, self.model_tester.num_feature_levels, self.model_tester.encoder_n_points, ], ) # overwrite since hidden_states are called encoder_text_hidden_states 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_vision_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) seq_len = self.model_tester.encoder_seq_length_vision self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_len, self.model_tester.hidden_size], ) hidden_states = outputs.encoder_text_hidden_states self.assertEqual(len(hidden_states), expected_num_layers) seq_len = self.model_tester.encoder_seq_length_text self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_len, self.model_tester.hidden_size], ) 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) # removed retain_grad and grad on decoder_hidden_states, as queries don't require grad def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] encoder_hidden_states = outputs.encoder_vision_hidden_states[0] encoder_attentions = outputs.encoder_attentions[0][0] encoder_hidden_states.retain_grad() encoder_attentions.retain_grad() cross_attentions = outputs.decoder_attentions[-1][0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) 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", "input_ids"] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_different_timm_backbone(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # let's pick a random timm backbone config.backbone = "tf_mobilenetv3_small_075" config.use_timm_backbone = True config.backbone_config = None config.backbone_kwargs = {"in_chans": 3, "out_indices": (2, 3, 4)} for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if model_class.__name__ == "GroundingDinoForObjectDetection": expected_shape = ( self.model_tester.batch_size, self.model_tester.num_queries, config.max_text_len, ) self.assertEqual(outputs.logits.shape, expected_shape) self.assertTrue(outputs) 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(): if param.requires_grad: if ( "level_embed" in name or "sampling_offsets.bias" in name or "text_param" in name or "vision_param" in name or "value_proj" in name or "output_proj" in name or "reference_points" in name ): continue 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", ) # Copied from tests.models.deformable_detr.test_modeling_deformable_detr.DeformableDetrModelTest.test_two_stage_training with DeformableDetr->GroundingDino def test_two_stage_training(self): model_class = GroundingDinoForObjectDetection config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True config.two_stage = True config.auxiliary_loss = True config.with_box_refine = True 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_tied_weights_keys(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() config.tie_word_embeddings = True for model_class in self.all_model_classes: model_tied = model_class(config) ptrs = collections.defaultdict(list) for name, tensor in model_tied.state_dict().items(): ptrs[id_tensor_storage(tensor)].append(name) # These are all the pointers of shared tensors. tied_params = [names for _, names in ptrs.items() if len(names) > 1] tied_weight_keys = model_tied._tied_weights_keys if model_tied._tied_weights_keys is not None else [] # Detect we get a hit for each key for key in tied_weight_keys: if not any(re.search(key, p) for group in tied_params for p in group): raise ValueError(f"{key} is not a tied weight key for {model_class}.") # Removed tied weights found from tied params -> there should only be one left after for key in tied_weight_keys: for i in range(len(tied_params)): tied_params[i] = [p for p in tied_params[i] if re.search(key, p) is None] # GroundingDino when sharing weights also uses the shared ones in GroundingDinoDecoder # Therefore, differently from DeformableDetr, we expect the group lens to be 2 # one for self.bbox_embed in GroundingDinoForObejectDetection and another one # in the decoder tied_params = [group for group in tied_params if len(group) > 2] self.assertListEqual( tied_params, [], f"Missing `_tied_weights_keys` for {model_class}: add all of {tied_params} except one.", ) # 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 def prepare_text(): text = "a cat." return text @require_timm @require_vision @slow class GroundingDinoModelIntegrationTests(unittest.TestCase): @cached_property def default_processor(self): return AutoProcessor.from_pretrained("IDEA-Research/grounding-dino-tiny") if is_vision_available() else None def test_inference_object_detection_head(self): model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(torch_device) processor = self.default_processor image = prepare_img() text = prepare_text() encoding = processor(images=image, text=text, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**encoding) expected_shape_logits = torch.Size((1, model.config.num_queries, model.config.d_model)) self.assertEqual(outputs.logits.shape, expected_shape_logits) expected_boxes = torch.tensor( [[0.7674, 0.4136, 0.4572], [0.2566, 0.5463, 0.4760], [0.2585, 0.5442, 0.4641]] ).to(torch_device) expected_logits = torch.tensor( [[-4.8913, -0.1900, -0.2161], [-4.9653, -0.3719, -0.3950], [-5.9599, -3.3765, -3.3104]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_logits, atol=1e-3)) expected_shape_boxes = torch.Size((1, model.config.num_queries, 4)) self.assertEqual(outputs.pred_boxes.shape, expected_shape_boxes) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_boxes, atol=1e-4)) # verify postprocessing results = processor.image_processor.post_process_object_detection( outputs, threshold=0.35, target_sizes=[image.size[::-1]] )[0] expected_scores = torch.tensor([0.4526, 0.4082]).to(torch_device) expected_slice_boxes = torch.tensor([344.8143, 23.1796, 637.4004, 373.8295]).to(torch_device) self.assertEqual(len(results["scores"]), 2) self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-3)) self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes, atol=1e-2)) # verify grounded postprocessing expected_labels = ["a cat", "a cat"] results = processor.post_process_grounded_object_detection( outputs=outputs, input_ids=encoding.input_ids, box_threshold=0.35, text_threshold=0.3, target_sizes=[image.size[::-1]], )[0] self.assertTrue(torch.allclose(results["scores"], expected_scores, atol=1e-3)) self.assertTrue(torch.allclose(results["boxes"][0, :], expected_slice_boxes, atol=1e-2)) self.assertListEqual(results["labels"], expected_labels) @require_torch_gpu def test_inference_object_detection_head_equivalence_cpu_gpu(self): processor = self.default_processor image = prepare_img() text = prepare_text() encoding = processor(images=image, text=text, return_tensors="pt") # 1. run model on CPU model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny") with torch.no_grad(): cpu_outputs = model(**encoding) # 2. run model on GPU model.to("cuda") encoding = encoding.to("cuda") with torch.no_grad(): gpu_outputs = model(**encoding) # 3. assert equivalence for key in cpu_outputs.keys(): self.assertTrue(torch.allclose(cpu_outputs[key], gpu_outputs[key].cpu(), atol=1e-3)) expected_logits = torch.tensor( [[-4.8915, -0.1900, -0.2161], [-4.9658, -0.3716, -0.3948], [-5.9596, -3.3763, -3.3103]] ) self.assertTrue(torch.allclose(cpu_outputs.logits[0, :3, :3], expected_logits, atol=1e-3)) # assert postprocessing results_cpu = processor.image_processor.post_process_object_detection( cpu_outputs, threshold=0.35, target_sizes=[image.size[::-1]] )[0] result_gpu = processor.image_processor.post_process_object_detection( gpu_outputs, threshold=0.35, target_sizes=[image.size[::-1]] )[0] self.assertTrue(torch.allclose(results_cpu["scores"], result_gpu["scores"].cpu(), atol=1e-3)) self.assertTrue(torch.allclose(results_cpu["boxes"], result_gpu["boxes"].cpu(), atol=1e-3)) def test_cross_attention_mask(self): model = GroundingDinoForObjectDetection.from_pretrained("IDEA-Research/grounding-dino-tiny").to(torch_device) processor = self.default_processor image = prepare_img() text1 = "a cat." text2 = "a remote control." text_batched = [text1, text2] encoding1 = processor(images=image, text=text1, return_tensors="pt").to(torch_device) encoding2 = processor(images=image, text=text2, return_tensors="pt").to(torch_device) # If we batch the text and cross attention masking is working the batched result should be equal to # The singe text result encoding_batched = processor( images=[image] * len(text_batched), text=text_batched, padding="longest", return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs1 = model(**encoding1) outputs2 = model(**encoding2) outputs_batched = model(**encoding_batched) self.assertTrue(torch.allclose(outputs1.logits, outputs_batched.logits[:1], atol=1e-3)) # For some reason 12 elements are > 1e-3, but the rest are fine self.assertTrue(torch.allclose(outputs2.logits, outputs_batched.logits[1:], atol=1.8e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/grounding_dino/test_image_processing_grounding_dino.py
# coding=utf-8 # Copyright 2024 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 json import pathlib import unittest from transformers.testing_utils import require_torch, require_vision, slow from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch from transformers.models.grounding_dino.modeling_grounding_dino import GroundingDinoObjectDetectionOutput if is_vision_available(): from PIL import Image from transformers import GroundingDinoImageProcessor class GroundingDinoImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], do_rescale=True, rescale_factor=1 / 255, do_pad=True, ): # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p size = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_rescale = do_rescale self.rescale_factor = rescale_factor self.do_pad = do_pad self.num_queries = 5 self.embed_dim = 5 # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.prepare_image_processor_dict with DeformableDetr->GroundingDino def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_rescale": self.do_rescale, "rescale_factor": self.rescale_factor, "do_pad": self.do_pad, } # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.get_expected_values with DeformableDetr->GroundingDino def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to GroundingDinoImageProcessor, assuming do_resize is set to True with a scalar size. """ if not batched: image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size else: h, w = image.shape[1], image.shape[2] if w < h: expected_height = int(self.size["shortest_edge"] * h / w) expected_width = self.size["shortest_edge"] elif w > h: expected_height = self.size["shortest_edge"] expected_width = int(self.size["shortest_edge"] * w / h) else: expected_height = self.size["shortest_edge"] expected_width = self.size["shortest_edge"] else: expected_values = [] for image in image_inputs: expected_height, expected_width = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) expected_height = max(expected_values, key=lambda item: item[0])[0] expected_width = max(expected_values, key=lambda item: item[1])[1] return expected_height, expected_width # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.expected_output_image_shape with DeformableDetr->GroundingDino def expected_output_image_shape(self, images): height, width = self.get_expected_values(images, batched=True) return self.num_channels, height, width def get_fake_grounding_dino_output(self): torch.manual_seed(42) return GroundingDinoObjectDetectionOutput( pred_boxes=torch.rand(self.batch_size, self.num_queries, 4), logits=torch.rand(self.batch_size, self.num_queries, self.embed_dim), ) # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTester.prepare_image_inputs with DeformableDetr->GroundingDino def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class GroundingDinoImageProcessingTest(AnnotationFormatTestMixin, ImageProcessingTestMixin, unittest.TestCase): image_processing_class = GroundingDinoImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = GroundingDinoImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_image_processor_properties with DeformableDetr->GroundingDino def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "do_pad")) self.assertTrue(hasattr(image_processing, "size")) # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_image_processor_from_dict_with_kwargs with DeformableDetr->GroundingDino def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333}) self.assertEqual(image_processor.do_pad, True) image_processor = self.image_processing_class.from_dict( self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=False ) self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84}) self.assertEqual(image_processor.do_pad, False) def test_post_process_object_detection(self): image_processor = self.image_processing_class(**self.image_processor_dict) outputs = self.image_processor_tester.get_fake_grounding_dino_output() results = image_processor.post_process_object_detection(outputs, threshold=0.0) self.assertEqual(len(results), self.image_processor_tester.batch_size) self.assertEqual(list(results[0].keys()), ["scores", "labels", "boxes"]) self.assertEqual(results[0]["boxes"].shape, (self.image_processor_tester.num_queries, 4)) self.assertEqual(results[0]["scores"].shape, (self.image_processor_tester.num_queries,)) expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220]) self.assertTrue(torch.allclose(results[0]["scores"], expected_scores, atol=1e-4)) expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947]) self.assertTrue(torch.allclose(results[0]["boxes"][0], expected_box_slice, atol=1e-4)) @slow # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_detection_annotations with DeformableDetr->GroundingDino def test_call_pytorch_with_coco_detection_annotations(self): # prepare image and target image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f: target = json.loads(f.read()) target = {"image_id": 39769, "annotations": target} # encode them image_processing = GroundingDinoImageProcessor() encoding = image_processing(images=image, annotations=target, return_tensors="pt") # verify pixel values expected_shape = torch.Size([1, 3, 800, 1066]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) # verify area expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]) self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area)) # verify boxes expected_boxes_shape = torch.Size([6, 4]) self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) # verify image_id expected_image_id = torch.tensor([39769]) self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) # verify is_crowd expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) # verify class_labels expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) # verify orig_size expected_orig_size = torch.tensor([480, 640]) self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) # verify size expected_size = torch.tensor([800, 1066]) self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) @slow # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_detection_annotations with Detr->GroundingDino def test_batched_coco_detection_annotations(self): image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800)) with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f: target = json.loads(f.read()) annotations_0 = {"image_id": 39769, "annotations": target} annotations_1 = {"image_id": 39769, "annotations": target} # Adjust the bounding boxes for the resized image w_0, h_0 = image_0.size w_1, h_1 = image_1.size for i in range(len(annotations_1["annotations"])): coords = annotations_1["annotations"][i]["bbox"] new_bbox = [ coords[0] * w_1 / w_0, coords[1] * h_1 / h_0, coords[2] * w_1 / w_0, coords[3] * h_1 / h_0, ] annotations_1["annotations"][i]["bbox"] = new_bbox images = [image_0, image_1] annotations = [annotations_0, annotations_1] image_processing = GroundingDinoImageProcessor() encoding = image_processing( images=images, annotations=annotations, return_segmentation_masks=True, return_tensors="pt", # do_convert_annotations=True ) # Check the pixel values have been padded postprocessed_height, postprocessed_width = 800, 1066 expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) # Check the bounding boxes have been adjusted for padded images self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) expected_boxes_0 = torch.tensor( [ [0.6879, 0.4609, 0.0755, 0.3691], [0.2118, 0.3359, 0.2601, 0.1566], [0.5011, 0.5000, 0.9979, 1.0000], [0.5010, 0.5020, 0.9979, 0.9959], [0.3284, 0.5944, 0.5884, 0.8112], [0.8394, 0.5445, 0.3213, 0.9110], ] ) expected_boxes_1 = torch.tensor( [ [0.4130, 0.2765, 0.0453, 0.2215], [0.1272, 0.2016, 0.1561, 0.0940], [0.3757, 0.4933, 0.7488, 0.9865], [0.3759, 0.5002, 0.7492, 0.9955], [0.1971, 0.5456, 0.3532, 0.8646], [0.5790, 0.4115, 0.3430, 0.7161], ] ) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) # Check the masks have also been padded self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height # format and not in the range [0, 1] encoding = image_processing( images=images, annotations=annotations, return_segmentation_masks=True, do_convert_annotations=False, return_tensors="pt", ) self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) # Convert to absolute coordinates unnormalized_boxes_0 = torch.vstack( [ expected_boxes_0[:, 0] * postprocessed_width, expected_boxes_0[:, 1] * postprocessed_height, expected_boxes_0[:, 2] * postprocessed_width, expected_boxes_0[:, 3] * postprocessed_height, ] ).T unnormalized_boxes_1 = torch.vstack( [ expected_boxes_1[:, 0] * postprocessed_width, expected_boxes_1[:, 1] * postprocessed_height, expected_boxes_1[:, 2] * postprocessed_width, expected_boxes_1[:, 3] * postprocessed_height, ] ).T # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max expected_boxes_0 = torch.vstack( [ unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, ] ).T expected_boxes_1 = torch.vstack( [ unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, ] ).T self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1)) @slow # Copied from tests.models.deformable_detr.test_image_processing_deformable_detr.DeformableDetrImageProcessingTest.test_call_pytorch_with_coco_panoptic_annotations with DeformableDetr->GroundingDino def test_call_pytorch_with_coco_panoptic_annotations(self): # prepare image, target and masks_path image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f: target = json.loads(f.read()) target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") # encode them image_processing = GroundingDinoImageProcessor(format="coco_panoptic") encoding = image_processing(images=image, annotations=target, masks_path=masks_path, return_tensors="pt") # verify pixel values expected_shape = torch.Size([1, 3, 800, 1066]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4)) # verify area expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]) self.assertTrue(torch.allclose(encoding["labels"][0]["area"], expected_area)) # verify boxes expected_boxes_shape = torch.Size([6, 4]) self.assertEqual(encoding["labels"][0]["boxes"].shape, expected_boxes_shape) expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], expected_boxes_slice, atol=1e-3)) # verify image_id expected_image_id = torch.tensor([39769]) self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], expected_image_id)) # verify is_crowd expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], expected_is_crowd)) # verify class_labels expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], expected_class_labels)) # verify masks expected_masks_sum = 822873 self.assertEqual(encoding["labels"][0]["masks"].sum().item(), expected_masks_sum) # verify orig_size expected_orig_size = torch.tensor([480, 640]) self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], expected_orig_size)) # verify size expected_size = torch.tensor([800, 1066]) self.assertTrue(torch.allclose(encoding["labels"][0]["size"], expected_size)) @slow # Copied from tests.models.detr.test_image_processing_detr.DetrImageProcessingTest.test_batched_coco_panoptic_annotations with Detr->GroundingDino def test_batched_coco_panoptic_annotations(self): # prepare image, target and masks_path image_0 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") image_1 = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png").resize((800, 800)) with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f: target = json.loads(f.read()) annotation_0 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} annotation_1 = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} w_0, h_0 = image_0.size w_1, h_1 = image_1.size for i in range(len(annotation_1["segments_info"])): coords = annotation_1["segments_info"][i]["bbox"] new_bbox = [ coords[0] * w_1 / w_0, coords[1] * h_1 / h_0, coords[2] * w_1 / w_0, coords[3] * h_1 / h_0, ] annotation_1["segments_info"][i]["bbox"] = new_bbox masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") images = [image_0, image_1] annotations = [annotation_0, annotation_1] # encode them image_processing = GroundingDinoImageProcessor(format="coco_panoptic") encoding = image_processing( images=images, annotations=annotations, masks_path=masks_path, return_tensors="pt", return_segmentation_masks=True, ) # Check the pixel values have been padded postprocessed_height, postprocessed_width = 800, 1066 expected_shape = torch.Size([2, 3, postprocessed_height, postprocessed_width]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) # Check the bounding boxes have been adjusted for padded images self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) expected_boxes_0 = torch.tensor( [ [0.2625, 0.5437, 0.4688, 0.8625], [0.7719, 0.4104, 0.4531, 0.7125], [0.5000, 0.4927, 0.9969, 0.9854], [0.1688, 0.2000, 0.2063, 0.0917], [0.5492, 0.2760, 0.0578, 0.2187], [0.4992, 0.4990, 0.9984, 0.9979], ] ) expected_boxes_1 = torch.tensor( [ [0.1576, 0.3262, 0.2814, 0.5175], [0.4634, 0.2463, 0.2720, 0.4275], [0.3002, 0.2956, 0.5985, 0.5913], [0.1013, 0.1200, 0.1238, 0.0550], [0.3297, 0.1656, 0.0347, 0.1312], [0.2997, 0.2994, 0.5994, 0.5987], ] ) self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1e-3)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1e-3)) # Check the masks have also been padded self.assertEqual(encoding["labels"][0]["masks"].shape, torch.Size([6, 800, 1066])) self.assertEqual(encoding["labels"][1]["masks"].shape, torch.Size([6, 800, 1066])) # Check if do_convert_annotations=False, then the annotations are not converted to centre_x, centre_y, width, height # format and not in the range [0, 1] encoding = image_processing( images=images, annotations=annotations, masks_path=masks_path, return_segmentation_masks=True, do_convert_annotations=False, return_tensors="pt", ) self.assertEqual(encoding["labels"][0]["boxes"].shape, torch.Size([6, 4])) self.assertEqual(encoding["labels"][1]["boxes"].shape, torch.Size([6, 4])) # Convert to absolute coordinates unnormalized_boxes_0 = torch.vstack( [ expected_boxes_0[:, 0] * postprocessed_width, expected_boxes_0[:, 1] * postprocessed_height, expected_boxes_0[:, 2] * postprocessed_width, expected_boxes_0[:, 3] * postprocessed_height, ] ).T unnormalized_boxes_1 = torch.vstack( [ expected_boxes_1[:, 0] * postprocessed_width, expected_boxes_1[:, 1] * postprocessed_height, expected_boxes_1[:, 2] * postprocessed_width, expected_boxes_1[:, 3] * postprocessed_height, ] ).T # Convert from centre_x, centre_y, width, height to x_min, y_min, x_max, y_max expected_boxes_0 = torch.vstack( [ unnormalized_boxes_0[:, 0] - unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] - unnormalized_boxes_0[:, 3] / 2, unnormalized_boxes_0[:, 0] + unnormalized_boxes_0[:, 2] / 2, unnormalized_boxes_0[:, 1] + unnormalized_boxes_0[:, 3] / 2, ] ).T expected_boxes_1 = torch.vstack( [ unnormalized_boxes_1[:, 0] - unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] - unnormalized_boxes_1[:, 3] / 2, unnormalized_boxes_1[:, 0] + unnormalized_boxes_1[:, 2] / 2, unnormalized_boxes_1[:, 1] + unnormalized_boxes_1[:, 3] / 2, ] ).T self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"], expected_boxes_0, rtol=1)) self.assertTrue(torch.allclose(encoding["labels"][1]["boxes"], expected_boxes_1, rtol=1))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/grounding_dino/test_processor_grounding_dino.py
# Copyright 2024 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 import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast, GroundingDinoProcessor from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_torch, require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available if is_torch_available(): import torch from transformers.models.grounding_dino.modeling_grounding_dino import GroundingDinoObjectDetectionOutput if is_vision_available(): from PIL import Image from transformers import GroundingDinoImageProcessor @require_torch @require_vision class GroundingDinoProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() vocab_tokens = ["[UNK]","[CLS]","[SEP]","[PAD]","[MASK]","want","##want","##ed","wa","un","runn","##ing",",","low","lowest"] # fmt: skip self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) image_processor_map = { "do_resize": True, "size": None, "do_normalize": True, "image_mean": [0.5, 0.5, 0.5], "image_std": [0.5, 0.5, 0.5], "do_rescale": True, "rescale_factor": 1 / 255, "do_pad": True, } self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME) with open(self.image_processor_file, "w", encoding="utf-8") as fp: json.dump(image_processor_map, fp) self.batch_size = 7 self.num_queries = 5 self.embed_dim = 5 self.seq_length = 5 # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.get_tokenizer with CLIP->Bert def get_tokenizer(self, **kwargs): return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.get_rust_tokenizer with CLIP->Bert def get_rust_tokenizer(self, **kwargs): return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.get_image_processor with CLIP->GroundingDino def get_image_processor(self, **kwargs): return GroundingDinoImageProcessor.from_pretrained(self.tmpdirname, **kwargs) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.tearDown def tearDown(self): shutil.rmtree(self.tmpdirname) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.prepare_image_inputs 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 get_fake_grounding_dino_output(self): torch.manual_seed(42) return GroundingDinoObjectDetectionOutput( pred_boxes=torch.rand(self.batch_size, self.num_queries, 4), logits=torch.rand(self.batch_size, self.num_queries, self.embed_dim), ) def get_fake_grounding_dino_input_ids(self): input_ids = torch.tensor([101, 1037, 4937, 1012, 102]) return torch.stack([input_ids] * self.batch_size, dim=0) def test_post_process_grounded_object_detection(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor) grounding_dino_output = self.get_fake_grounding_dino_output() grounding_dino_input_ids = self.get_fake_grounding_dino_input_ids() post_processed = processor.post_process_grounded_object_detection( grounding_dino_output, grounding_dino_input_ids ) self.assertEqual(len(post_processed), self.batch_size) self.assertEqual(list(post_processed[0].keys()), ["scores", "labels", "boxes"]) self.assertEqual(post_processed[0]["boxes"].shape, (self.num_queries, 4)) self.assertEqual(post_processed[0]["scores"].shape, (self.num_queries,)) expected_scores = torch.tensor([0.7050, 0.7222, 0.7222, 0.6829, 0.7220]) self.assertTrue(torch.allclose(post_processed[0]["scores"], expected_scores, atol=1e-4)) expected_box_slice = torch.tensor([0.6908, 0.4354, 1.0737, 1.3947]) self.assertTrue(torch.allclose(post_processed[0]["boxes"][0], expected_box_slice, atol=1e-4)) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.test_save_load_pretrained_default with CLIP->GroundingDino,GroundingDinoTokenizer->BertTokenizer def test_save_load_pretrained_default(self): tokenizer_slow = self.get_tokenizer() tokenizer_fast = self.get_rust_tokenizer() image_processor = self.get_image_processor() processor_slow = GroundingDinoProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = GroundingDinoProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = GroundingDinoProcessor(tokenizer=tokenizer_fast, image_processor=image_processor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = GroundingDinoProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, BertTokenizer) self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast) self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string()) self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string()) self.assertIsInstance(processor_slow.image_processor, GroundingDinoImageProcessor) self.assertIsInstance(processor_fast.image_processor, GroundingDinoImageProcessor) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.test_save_load_pretrained_additional_features with CLIP->GroundingDino,GroundingDinoTokenizer->BertTokenizer def test_save_load_pretrained_additional_features(self): processor = GroundingDinoProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0) processor = GroundingDinoProcessor.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, BertTokenizerFast) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, GroundingDinoImageProcessor) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.test_image_processor with CLIP->GroundingDino def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() input_image_proc = image_processor(image_input, return_tensors="np") input_processor = processor(images=image_input, return_tensors="np") for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.test_tokenizer with CLIP->GroundingDino def test_tokenizer(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" 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_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual( list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values", "pixel_mask"] ) # test if it raises when no input is passed with pytest.raises(ValueError): processor() # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.test_tokenizer_decode with CLIP->GroundingDino def test_tokenizer_decode(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor) 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) # Copied from tests.models.clip.test_processor_clip.CLIPProcessorTest.test_model_input_names with CLIP->GroundingDino def test_model_input_names(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = GroundingDinoProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), processor.model_input_names)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/mra/test_modeling_mra.py
# coding=utf-8 # Copyright 2023 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 MRA model. """ import unittest from transformers import MraConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, MraModel, ) class MraModelTester: def __init__( self, parent, batch_size=2, seq_length=8, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=2, intermediate_size=36, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, 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): return MraConfig( 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 get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config 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 = MraModel(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)) 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 = MraModel(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)) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MraForMaskedLM(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_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MraForQuestionAnswering(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 = MraForSequenceClassification(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 = MraForTokenClassification(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 = MraForMultipleChoice(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 MraModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MraModel, MraForMaskedLM, MraForMultipleChoice, MraForQuestionAnswering, MraForSequenceClassification, MraForTokenClassification, ) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False has_attentions = False all_generative_model_classes = () pipeline_model_mapping = ( { "feature-extraction": MraModel, "fill-mask": MraForMaskedLM, "question-answering": MraForQuestionAnswering, "text-classification": MraForSequenceClassification, "token-classification": MraForTokenClassification, "zero-shot": MraForSequenceClassification, } if is_torch_available() else {} ) def setUp(self): self.model_tester = MraModelTester(self) self.config_tester = ConfigTester(self, config_class=MraConfig, 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_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_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): model_name = "uw-madison/mra-base-512-4" model = MraModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="MRA does not output attentions") def test_attention_outputs(self): return @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip("Model has `nan` in hidden_states, see https://github.com/huggingface/transformers/issues/29373.") def test_batching_equivalence(self): pass @require_torch class MraModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = MraModel.from_pretrained("uw-madison/mra-base-512-4") input_ids = torch.arange(256).unsqueeze(0) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 256, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_inference_masked_lm(self): model = MraForMaskedLM.from_pretrained("uw-madison/mra-base-512-4") input_ids = torch.arange(256).unsqueeze(0) with torch.no_grad(): output = model(input_ids)[0] vocab_size = 50265 expected_shape = torch.Size((1, 256, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_inference_masked_lm_long_input(self): model = MraForMaskedLM.from_pretrained("uw-madison/mra-base-4096-8-d3") input_ids = torch.arange(4096).unsqueeze(0) with torch.no_grad(): output = model(input_ids)[0] vocab_size = 50265 expected_shape = torch.Size((1, 4096, vocab_size)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/nllb_moe/test_modeling_nllb_moe.py
# coding=utf-8 # Copyright 2023 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 NLLB-MoE model. """ import copy import tempfile import unittest from transformers import NllbMoeConfig, is_torch_available, set_seed from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import NllbMoeForConditionalGeneration, NllbMoeModel, NllbTokenizer from transformers.models.nllb_moe.modeling_nllb_moe import NllbMoeDecoder, NllbMoeEncoder, NllbMoeTop2Router class NllbMoeModelTester: 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, hidden_act="relu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, encoder_layerdrop=0.0, decoder_layerdrop=0.0, max_position_embeddings=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, num_experts=4, encoder_sparse_step=2, decoder_sparse_step=1, expert_capacity=100, router_jitter_noise=0.0, ): 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_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop 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.encoder_sparse_step = encoder_sparse_step self.decoder_sparse_step = decoder_sparse_step self.expert_capacity = expert_capacity self.router_jitter_noise = router_jitter_noise self.num_experts = num_experts def prepare_nllb_moe_inputs_dict( self, 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": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids[:, -1] = self.eos_token_id # Eos Token decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) # we need to clamp the input ids here to avoid having pad token in between # this is because for NllbMoe the position_ids are prepared such that # all pad tokens have pos id = 2 and rest are between 2..seq_length # and the seq_length here is seq_length - num_pad_tokens # but when using past, there is no way of knowing if the past input ids had # pad tokens in them, which results in incorrect seq_lenth and which in turn results in # position_ids being off by num_pad_tokens in past input input_ids = input_ids.clamp(self.pad_token_id + 1) decoder_input_ids = decoder_input_ids.clamp(self.pad_token_id + 1) config = self.get_config() inputs_dict = self.prepare_nllb_moe_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return NllbMoeConfig( 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, encoder_layerdrop=self.encoder_layerdrop, decoder_layerdrop=self.decoder_layerdrop, 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, expert_capacity=self.expert_capacity, router_jitter_noise=self.router_jitter_noise, decoder_sparse_step=self.decoder_sparse_step, encoder_sparse_step=self.encoder_sparse_step, num_experts=self.num_experts, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict @require_torch def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = NllbMoeModel(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-3)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = NllbMoeModel(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 = NllbMoeEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["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 = NllbMoeDecoder.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) @require_torch class NllbMoeModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (NllbMoeModel, NllbMoeForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (NllbMoeForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "conversational": NllbMoeForConditionalGeneration, "feature-extraction": NllbMoeModel, "summarization": NllbMoeForConditionalGeneration, "text2text-generation": NllbMoeForConditionalGeneration, "translation": NllbMoeForConditionalGeneration, } if is_torch_available() else {} ) is_encoder_decoder = True fx_compatible = False test_pruning = False test_missing_keys = True test_torchscript = False # TODO: Fix the failed tests when this model gets more usage def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): # Saving the slow tokenizer after saving the fast tokenizer causes the loading of the later hanging forever. return True def setUp(self): self.model_tester = NllbMoeModelTester(self) self.config_tester = ConfigTester(self, config_class=NllbMoeConfig) 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, inputs_dict = self.model_tester.prepare_config_and_inputs() config.decoder_sparse_step = 0 self.model_tester.create_and_check_decoder_model_past_large_inputs(config, inputs_dict) 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_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in (NllbMoeModel, NllbMoeForConditionalGeneration): 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] @require_torch_fp16 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 = NllbMoeForConditionalGeneration(config).eval().to(torch_device) 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_get_loss(self): config, input_dict = self.model_tester.prepare_config_and_inputs() input_dict["output_router_logits"] = True input_dict["labels"] = input_dict["input_ids"] model = NllbMoeForConditionalGeneration(config).eval().to(torch_device) out = model(**input_dict) self.assertIsNotNone(out.loss) self.assertIsNotNone(model(**input_dict)["encoder_router_logits"][1]) self.assertIsNotNone(model(**input_dict)["decoder_router_logits"][0]) @require_torch @require_sentencepiece @require_tokenizers @slow class NllbMoeModelIntegrationTests(unittest.TestCase): @require_torch @cached_property def model_inputs(self): return { "input_ids": torch.LongTensor( [ [28768, 248, 6399, 9, 65972, 452, 1925, 629, 123543, 248075, 2, 256047], [117, 7027, 7195, 202, 44778, 248075, 2, 256047, 1, 1, 1, 1], ] ), "attention_mask": torch.Tensor( [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0]] ), "decoder_input_ids": torch.LongTensor([[2, 256057], [2, 256057]]), } @cached_property def tokenizer(self): return NllbTokenizer.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts") @cached_property def big_model(self): return NllbMoeForConditionalGeneration.from_pretrained("facebook/nllb-moe-54b") def inference_no_head(self): model = NllbMoeModel.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts").eval() with torch.no_grad(): output = model(**self.model_inputs) # fmt: off EXPECTED_ENCODER_STATE = torch.Tensor([ 0.3920, -0.1974, -0.0279, 0.3463, -0.8306, -1.0629, -0.4643, 2.0563, 1.1123, 0.3566, -0.9291, -0.3840, -0.2527, -0.9858, 1.5185, -1.1346, 0.0323, -0.9103, -0.3647, -0.4462, -0.9720, -0.3541, 0.1777, -0.4647, 1.6970, -0.9062, 0.2727, -1.0737, 0.8785, 0.4324]) EXPECTED_DECODER_STATE = torch.Tensor([-6.0425e-02, -2.0015e-01, 6.0575e-02, -8.6366e-01, -1.1310e+00, 6.8369e-01, 7.5615e-01, 7.3555e-01, 2.3071e-01, 1.5954e+00, -7.0728e-01, -2.2647e-01, -1.3292e+00, 4.8246e-01, -6.9153e-01, -1.8199e-02, -7.3664e-01, 1.5902e-03, 1.0760e-01, 1.0298e-01, -9.3933e-01, -4.6567e-01, 8.0417e-01, 1.5243e+00, 5.5844e-01, -9.9239e-02, 1.4885e+00, 7.1527e-02, -5.2612e-01, 9.4435e-02]) # fmt: on torch.testing.assert_close( output.encoder_last_hidden_state[1, 0, :30], EXPECTED_ENCODER_STATE, rtol=6e-3, atol=9e-3 ) torch.testing.assert_close(output.last_hidden_state[1, 0, :30], EXPECTED_DECODER_STATE, rtol=6e-3, atol=9e-3) def test_inference_logits(self): r""" Logits testing to check implementation consistency between `fairseq` implementation and `transformers` implementation of NLLB-MoE transformers. We only check the logits of the second sample of the batch, as it is padded. """ model = NllbMoeForConditionalGeneration.from_pretrained("hf-internal-testing/random-nllb-moe-2-experts").eval() with torch.no_grad(): output = model(**self.model_inputs) EXPECTED_LOGTIS = torch.Tensor([-0.3059, 0.0000, 9.3029, 0.6456, -0.9148, 1.7836, 0.6478, 0.9438, -0.5272, -0.6617, -1.2717, 0.4564, 0.1345, -0.2301, -1.0140, 1.1427, -1.5535, 0.1337, 0.2082, -0.8112, -0.3842, -0.3377, 0.1256, 0.6450, -0.0452, 0.0219, 1.4274, -0.4991, -0.2063, -0.4409,]) # fmt: skip torch.testing.assert_close(output.logits[1, 0, :30], EXPECTED_LOGTIS, rtol=6e-3, atol=9e-3) @unittest.skip("This requires 300GB of RAM") def test_large_logits(self): model = self.big_model with torch.no_grad(): output = model(**self.model_inputs) # fmt: off EXPECTED_ENCODER_STATE = torch.Tensor([ 0.1696, -0.0059, 0.0489, 0.0479, -0.4222, -0.2178, -0.1372, -0.0860, -0.4249, -0.0081, -0.1186, 0.6678, 0.0160, 0.4140, 0.1799, 0.0672, -0.4941, 0.0173, -0.0740, 0.0845, -0.2197, 0.4465, 0.2268, -0.1752, -0.0562, 0.1033, -0.0869, -0.5490, 0.0582, 0.2165]) EXPECTED_DECODER_STATE = torch.Tensor([ 0.0374, -0.1055, -0.1060, -0.1711, -0.0540, -0.1183, -0.0779, 0.0610, -0.0279, -0.0848, 0.0222, 0.0372, -0.0298, -0.0861, -0.0354, -0.0103, 0.0538, -0.0148, -0.0105, 0.0224, 0.0629, -0.0291, -0.0671, 0.0173, -0.0066, -0.0245, -0.0499, 0.0760, -0.0067, 0.0086]) EXPECTED_LOGTIS = torch.Tensor([ 0.3834, 0.2057, 4.5399, 0.8301, 0.4810, 0.9325, 0.9928, 0.9574, 0.5517, 0.9156, 0.2698, 0.6728, 0.7121, 0.3080, 0.4693, 0.5756, 1.0407, 0.2219, 0.3714, 0.5699, 0.5547, 0.8472, 0.3178, 0.1286, 0.1791, 0.9391, 0.5153, -0.2146, 0.1689, 0.6816]) # fmt: on torch.testing.assert_close( output.encoder_last_hidden_state[1, 0, :30], EXPECTED_ENCODER_STATE, rtol=6e-3, atol=9e-3 ) torch.testing.assert_close(output.last_hidden_state[1, 0, :30], EXPECTED_DECODER_STATE, rtol=6e-3, atol=9e-3) torch.testing.assert_close(output.logits[1, 0, :30], EXPECTED_LOGTIS, rtol=6e-3, atol=9e-3) @unittest.skip("This requires 300GB of RAM") def test_seq_to_seq_generation(self): model = self.big_model tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-moe-54b") # first 6 samples of load_dataset("facebook/flores", "eng_Latn-fra_Latn"), devtest. Truth are very similar to the fairseq translation files FIRST_6_FLORES_200 = [ 'We now have 4-month-old mice that are non-diabetic that used to be diabetic," he added.', "Dr. Ehud Ur, professor of medicine at Dalhousie University in Halifax, Nova Scotia and chair of the clinical and scientific division of the Canadian Diabetes Association cautioned that the research is still in its early days.", "Like some other experts, he is skeptical about whether diabetes can be cured, noting that these findings have no relevance to people who already have Type 1 diabetes.", "On Monday, Sara Danius, permanent secretary of the Nobel Committee for Literature at the Swedish Academy, publicly announced during a radio program on Sveriges Radio in Sweden the committee, unable to reach Bob Dylan directly about winning the 2016 Nobel Prize in Literature, had abandoned its efforts to reach him.", 'Danius said, "Right now we are doing nothing. I have called and sent emails to his closest collaborator and received very friendly replies. For now, that is certainly enough."', "Previously, Ring's CEO, Jamie Siminoff, remarked the company started when his doorbell wasn't audible from his shop in his garage.", ] inputs = tokenizer(FIRST_6_FLORES_200, padding=True, return_tensors="pt").to(torch_device) batch_translation = model.generate(**inputs, forced_bos_token_id=tokenizer.lang_code_to_id["fra_Latn"]) EXPECTED_FAIRSEQ_TRANSLATION = [ '"Nous avons maintenant des souris de 4 mois non diabétiques qui étaient diabétiques", a-t-il ajouté.', "Le docteur Ehud Ur, professeur de médecine à l'université Dalhousie, à Halifax, en Nouvelle-Écosse, et président de la division clinique et scientifique de l'Association canadienne du diabète, prévient que la recherche n'en est qu'à ses débuts.", "Comme d'autres spécialistes, il est sceptique quant à la guérison du diabète.", "Lundi, Sara Danius, secrétaire permanente du Comité Nobel de littérature à l'Académie suédoise, a annoncé publiquement lors d'une émission de radio sur Sveriges Radio en Suède que le comité, incapable de joindre Bob Dylan directement pour lui annoncer le prix Nobel de littérature 2016, avait abandonné ses efforts pour le joindre.", "Danius a déclaré: \"Pour l'instant, nous ne faisons rien. J'ai appelé et envoyé des courriels à son plus proche collaborateur et j'ai reçu des réponses très amicales. Pour l'instant, c'est certainement suffisant\".", "Auparavant, le PDG de Ring, Jamie Siminoff, a fait remarquer que la société avait commencé lorsque sa sonnette n'était pas audible depuis son magasin dans son garage.", ] translation = tokenizer.batch_decode( batch_translation.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True ) assert translation == EXPECTED_FAIRSEQ_TRANSLATION @require_torch class NllbMoeRouterTest(unittest.TestCase): r""" Switch Transformers has different blocks from classic transformer based models. The Swift MLP contains a Router class, that has to be tested to check if it is correctly implemented Original implementation of the routers here: """ config = NllbMoeConfig( num_experts=4, hidden_size=32, d_ff=16, expert_capacity=4, ) batch_size = 2 sequence_length = 20 def test_top_2_routing(self): # test routing with minimal reproduction mask = torch.ones((self.batch_size, self.sequence_length), dtype=torch.bool) mask[0][0] = False mask[1][0] = False mask = mask.reshape(-1) set_seed(0) hidden_states = torch.rand((self.batch_size, self.sequence_length, self.config.hidden_size)) classfier = torch.nn.Linear(self.config.hidden_size, self.config.num_experts) hf_router = NllbMoeTop2Router(self.config) _, _, hidden_dim = hidden_states.shape logits = classfier(hidden_states.reshape((self.batch_size * self.sequence_length), hidden_dim)) top_1_mask, router_probs = hf_router.route_tokens(logits, padding_mask=mask) torch.argmax(top_1_mask, dim=-1) router_mask = router_probs.bool() set_seed(0) experts = [ torch.nn.Linear(hidden_dim, hidden_dim), torch.nn.Linear(hidden_dim, hidden_dim), torch.nn.Linear(hidden_dim, hidden_dim), torch.nn.Linear(hidden_dim, hidden_dim), ] hidden_states = hidden_states.reshape((self.batch_size * self.sequence_length), hidden_dim) masked_hidden_states = torch.einsum("bm,be->ebm", hidden_states, router_mask) for idx, expert in enumerate(experts): token_indices = router_mask[:, idx] combining_weights = router_probs[token_indices, idx] expert_output = expert(masked_hidden_states[idx, token_indices]) expert_output *= 1 - self.config.moe_token_dropout masked_hidden_states[idx, token_indices] = torch.einsum("b,be->be", combining_weights, expert_output) hidden_states = masked_hidden_states.sum(dim=0).reshape(self.batch_size, self.sequence_length, hidden_dim) EXPECTED_MEAN_FAIRSEQ_HIDDEN_STATES = torch.Tensor([[ 7.0340e-04, 2.7997e-03, -1.3351e-02, -7.6705e-03, -3.5089e-03,3.9773e-03, 7.4593e-03, 1.2566e-02, 3.5860e-03, -2.7448e-02,-1.3731e-02, -1.0534e-02, -1.3606e-02, -1.5048e-02, -2.8914e-03,-5.0371e-03, -1.3963e-03, 6.0076e-03, -1.1380e-02, -1.4620e-02, 5.2401e-03, 8.4660e-04, -1.5319e-03, -1.6735e-02, 1.1302e-02, 3.6119e-03, 4.6084e-03, -1.3458e-02, 7.7792e-05, 1.4312e-02, 4.9107e-03, -5.0936e-03], [-4.4538e-03, 3.1026e-03, 1.4121e-04, -4.8121e-03, -5.6279e-03, 7.2493e-03, 3.9769e-03, 1.1114e-02, -1.5666e-03, -2.3477e-02, 8.7268e-03, 1.3446e-02, -2.8845e-05, -1.7287e-02, 8.7619e-03, -4.5316e-03, -1.2164e-02, 5.7461e-03, -4.5861e-03, -9.3907e-03, 2.9808e-02, 8.9206e-04, -7.6232e-04, -1.4173e-02, 3.0208e-03, 1.5310e-02, 9.7717e-03, 3.1014e-03, 7.8042e-03, 8.0197e-03, 3.4784e-03, -7.1728e-03]]) # fmt: skip self.assertTrue(torch.allclose(hidden_states.mean(1), EXPECTED_MEAN_FAIRSEQ_HIDDEN_STATES, 1e-4)) def test_batch_prioritized_routing(self): set_seed(0) config = NllbMoeConfig( num_experts=4, hidden_size=32, d_ff=16, expert_capacity=4, second_expert_policy="random" ) mask = torch.zeros((self.batch_size * self.sequence_length), dtype=torch.bool) logits = torch.rand((self.batch_size * self.sequence_length, 4)) config.batch_prioritized_routing = True router = NllbMoeTop2Router(config) top_1_mask, _ = router.route_tokens(logits, padding_mask=mask) # check that the routing is batch first. One of the last token is routed while expert capacity is very small # this means that it had a greater probability of being routed assert top_1_mask[-1, 0] == 1 def test_second_expert_policy(self): config = NllbMoeConfig( num_experts=4, hidden_size=32, d_ff=16, expert_capacity=40, ) set_seed(0) mask = torch.zeros((self.batch_size * self.sequence_length), dtype=torch.bool) logits = torch.rand((self.batch_size * self.sequence_length, 4)) set_seed(0) config.second_expert_policy = "random" router = NllbMoeTop2Router(config) top_1_mask, router_probs = router.route_tokens(logits, padding_mask=mask) set_seed(0) config.second_expert_policy = "sampling" router = NllbMoeTop2Router(config) top_1_mask_sp, router_probs_sp = router.route_tokens(logits, padding_mask=mask) set_seed(0) config.second_expert_policy = "all" router = NllbMoeTop2Router(config) top_1_mask_all, router_probs_all = router.route_tokens(logits, padding_mask=mask) # fmt: off EXPECTED_ROUTER_ALL = torch.tensor([[0.3902, 0.0000, 0.0000, 0.6098], [0.0000, 0.0000, 0.7770, 0.2230], [0.0000, 0.0000, 0.2726, 0.7274], [0.4221, 0.0000, 0.5779, 0.0000], [0.0000, 0.0000, 0.7810, 0.2190], [0.5518, 0.4482, 0.0000, 0.0000], [0.0000, 0.4060, 0.5940, 0.0000], [0.7340, 0.0000, 0.0000, 0.2660], [0.4778, 0.5222, 0.0000, 0.0000], [0.0000, 0.3984, 0.0000, 0.6016], [0.0000, 0.0548, 0.9452, 0.0000], [0.6796, 0.0000, 0.0000, 0.3204], [0.0700, 0.0000, 0.9300, 0.0000], [0.1854, 0.0000, 0.8146, 0.0000], [0.6775, 0.3225, 0.0000, 0.0000], [0.0000, 0.0000, 0.5027, 0.4973], [0.0000, 0.6577, 0.0000, 0.3423], [0.0000, 0.7767, 0.0000, 0.2233], [0.1944, 0.8056, 0.0000, 0.0000], [0.0000, 0.3073, 0.0000, 0.6927], [0.0000, 0.5655, 0.4345, 0.0000], [0.5791, 0.0000, 0.0000, 0.4209], [0.0440, 0.0000, 0.9560, 0.0000], [0.0083, 0.9917, 0.0000, 0.0000], [0.0000, 0.8395, 0.0000, 0.1605], [0.0000, 0.1458, 0.0000, 0.8542], [0.0000, 0.8534, 0.1466, 0.0000], [0.4938, 0.0000, 0.0000, 0.5062], [0.1329, 0.8671, 0.0000, 0.0000], [0.3058, 0.0000, 0.6942, 0.0000], [0.4458, 0.0000, 0.0000, 0.5542], [0.9053, 0.0947, 0.0000, 0.0000], [0.0000, 0.7563, 0.2437, 0.0000], [0.0000, 0.0000, 0.4096, 0.5904], [0.4551, 0.0000, 0.0000, 0.5449], [0.8502, 0.1498, 0.0000, 0.0000], [0.0000, 0.6312, 0.3688, 0.0000], [0.8920, 0.0000, 0.0000, 0.1080], [0.1913, 0.0000, 0.0000, 0.8087], [0.2491, 0.7509, 0.0000, 0.0000]]) EXPECTED_ROUTER_SP = torch.tensor([[0.0000, 0.6539, 0.0000, 0.3461], [0.0000, 0.0000, 0.3998, 0.6002], [0.0000, 0.5574, 0.0000, 0.4426], [0.0000, 0.0000, 0.4441, 0.5559], [0.0000, 0.6545, 0.3455, 0.0000], [0.4419, 0.5581, 0.0000, 0.0000], [0.0000, 0.4014, 0.5986, 0.0000], [0.3215, 0.0000, 0.0000, 0.6785], [0.4765, 0.5235, 0.0000, 0.0000], [0.0000, 0.5467, 0.0000, 0.4533], [0.0000, 0.4156, 0.5844, 0.0000], [0.3370, 0.0000, 0.6630, 0.0000], [0.0000, 0.0000, 0.4558, 0.5442], [0.4659, 0.0000, 0.5341, 0.0000], [0.6179, 0.3821, 0.0000, 0.0000], [0.6277, 0.0000, 0.3723, 0.0000], [0.5836, 0.4164, 0.0000, 0.0000], [0.0000, 0.6600, 0.0000, 0.3400], [0.0000, 0.4933, 0.0000, 0.5067], [0.6016, 0.0000, 0.0000, 0.3984], [0.0000, 0.5160, 0.4840, 0.0000], [0.5799, 0.0000, 0.0000, 0.4201], [0.0000, 0.0000, 0.4826, 0.5174], [0.5426, 0.4574, 0.0000, 0.0000], [0.5362, 0.4638, 0.0000, 0.0000], [0.6448, 0.0000, 0.0000, 0.3552], [0.0000, 0.5909, 0.4091, 0.0000], [0.4196, 0.0000, 0.0000, 0.5804], [0.3191, 0.6809, 0.0000, 0.0000], [0.0000, 0.0000, 0.4886, 0.5114], [0.4899, 0.0000, 0.0000, 0.5101], [0.4123, 0.0000, 0.5877, 0.0000], [0.0000, 0.3736, 0.0000, 0.6264], [0.0000, 0.0000, 0.6009, 0.3991], [0.4246, 0.0000, 0.0000, 0.5754], [0.4997, 0.0000, 0.5003, 0.0000], [0.0000, 0.3595, 0.6405, 0.0000], [0.5433, 0.0000, 0.0000, 0.4567], [0.0000, 0.6806, 0.0000, 0.3194], [0.6689, 0.3311, 0.0000, 0.0000]]) EXPECTED_ROUTER = torch.tensor([[0.4324, 0.5676, 0.0000, 0.0000], [0.0000, 0.4348, 0.0000, 0.5652], [0.4559, 0.5441, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 1.0000], [0.4744, 0.5256, 0.0000, 0.0000], [0.0000, 0.5103, 0.0000, 0.4897], [0.0000, 0.0000, 1.0000, 0.0000], [0.0000, 0.0000, 0.0000, 1.0000], [0.0000, 1.0000, 0.0000, 0.0000], [0.0000, 0.5467, 0.0000, 0.4533], [0.0000, 0.0000, 1.0000, 0.0000], [0.0000, 0.0000, 1.0000, 0.0000], [0.0000, 0.0000, 0.0000, 1.0000], [0.0000, 0.0000, 1.0000, 0.0000], [1.0000, 0.0000, 0.0000, 0.0000], [0.5063, 0.4937, 0.0000, 0.0000], [0.5396, 0.0000, 0.0000, 0.4604], [0.4576, 0.5424, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000, 1.0000], [0.5134, 0.0000, 0.4866, 0.0000], [0.0000, 0.5160, 0.4840, 0.0000], [0.5439, 0.0000, 0.4561, 0.0000], [0.4849, 0.0000, 0.0000, 0.5151], [0.5426, 0.4574, 0.0000, 0.0000], [0.5362, 0.4638, 0.0000, 0.0000], [1.0000, 0.0000, 0.0000, 0.0000], [0.0000, 1.0000, 0.0000, 0.0000], [0.0000, 0.4448, 0.0000, 0.5552], [0.0000, 1.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.4886, 0.5114], [0.4899, 0.0000, 0.0000, 0.5101], [0.0000, 0.0000, 0.5296, 0.4704], [0.0000, 0.0000, 0.4469, 0.5531], [0.0000, 0.4053, 0.5947, 0.0000], [0.0000, 0.0000, 0.4460, 0.5540], [0.4997, 0.0000, 0.5003, 0.0000], [0.0000, 0.0000, 0.5851, 0.4149], [1.0000, 0.0000, 0.0000, 0.0000], [0.0000, 0.5010, 0.4990, 0.0000], [1.0000, 0.0000, 0.0000, 0.0000]]) EXPECTED_TOP_1_ALL = torch.LongTensor([[0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0]]) EXPECTED_TOP_1_SP = torch.LongTensor([[0, 1, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 1, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [1, 0, 0, 0]]) # `sampling` and `random` do not affect the mask of the top_1 router # fmt: on torch.testing.assert_close(router_probs_all, EXPECTED_ROUTER_ALL, rtol=1e-4, atol=1e-4) torch.testing.assert_close(router_probs_sp, EXPECTED_ROUTER_SP, rtol=1e-4, atol=1e-4) torch.testing.assert_close(router_probs, EXPECTED_ROUTER, rtol=1e-4, atol=1e-4) torch.testing.assert_close(top_1_mask_all, EXPECTED_TOP_1_ALL, rtol=1e-4, atol=1e-4) torch.testing.assert_close(top_1_mask_sp, EXPECTED_TOP_1_SP, rtol=1e-4, atol=1e-4) torch.testing.assert_close(top_1_mask, EXPECTED_TOP_1_SP, rtol=1e-4, atol=1e-4)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/swin/test_modeling_tf_swin.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 TF 2.0 Swin model. """ from __future__ import annotations import inspect import unittest import numpy as np from transformers import SwinConfig from transformers.testing_utils import require_tf, require_vision, slow, to_2tuple from transformers.utils import cached_property, is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers.modeling_tf_utils import keras from transformers.models.swin.modeling_tf_swin import ( TFSwinForImageClassification, TFSwinForMaskedImageModeling, TFSwinModel, ) if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class TFSwinModelTester: def __init__( self, parent, batch_size=13, image_size=32, patch_size=2, num_channels=3, embed_dim=16, depths=[1, 2, 1], num_heads=[2, 2, 4], window_size=2, mlp_ratio=2.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", use_absolute_embeddings=False, patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, is_training=True, scope=None, use_labels=True, type_sequence_label_size=10, encoder_stride=8, ) -> None: self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.embed_dim = embed_dim self.depths = depths self.num_heads = num_heads self.window_size = window_size self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.use_absolute_embeddings = use_absolute_embeddings self.patch_norm = patch_norm self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.is_training = is_training self.scope = scope self.use_labels = use_labels self.type_sequence_label_size = type_sequence_label_size self.encoder_stride = encoder_stride 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 SwinConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, ) def create_and_check_model(self, config, pixel_values, labels): model = TFSwinModel(config=config) result = model(pixel_values) expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1)) expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1)) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim)) def create_and_check_for_masked_image_modeling(self, config, pixel_values, labels): model = TFSwinForMaskedImageModeling(config=config) result = model(pixel_values) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) ) # test greyscale images config.num_channels = 1 model = TFSwinForMaskedImageModeling(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = TFSwinForImageClassification(config) result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) # test greyscale images config.num_channels = 1 model = TFSwinForImageClassification(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) 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_tf class TFSwinModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFSwinModel, TFSwinForImageClassification, TFSwinForMaskedImageModeling, ) if is_tf_available() else () ) pipeline_model_mapping = ( {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification} if is_tf_available() else {} ) test_pruning = False test_resize_embeddings = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFSwinModelTester(self) self.config_tester = ConfigTester(self, config_class=SwinConfig, embed_dim=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 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_masked_image_modeling(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs) 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) @unittest.skip(reason="Swin does not use inputs_embeds") def test_inputs_embeds(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(), keras.layers.Layer) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, keras.layers.Dense)) 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 = len(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 window_size_squared = config.window_size**2 model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) 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)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types else: # also another +1 for reshaped_hidden_states added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) def check_hidden_states_output(self, inputs_dict, config, model_class, image_size): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # Swin has a different seq_length patch_size = to_2tuple(config.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.assertListEqual( list(hidden_states[0].shape[-2:]), [num_patches, self.model_tester.embed_dim], ) reshaped_hidden_states = outputs.reshaped_hidden_states self.assertEqual(len(reshaped_hidden_states), expected_num_layers) batch_size, num_channels, height, width = reshaped_hidden_states[0].shape reshaped_hidden_states = tf.reshape(reshaped_hidden_states[0], (batch_size, num_channels, height * width)) reshaped_hidden_states = tf.transpose(reshaped_hidden_states, (0, 2, 1)) self.assertListEqual( list(reshaped_hidden_states.shape[-2:]), [num_patches, self.model_tester.embed_dim], ) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() image_size = to_2tuple(self.model_tester.image_size) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) def test_inputs_requiring_padding(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.patch_size = 3 image_size = to_2tuple(self.model_tester.image_size) patch_size = to_2tuple(config.patch_size) padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0]) padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1]) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width)) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width)) @slow def test_model_from_pretrained(self): model_name = "microsoft/swin-tiny-patch4-window7-224" model = TFSwinModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_vision @require_tf class TFSwinModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224") image_processor = self.default_image_processor image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = image_processor(images=image, return_tensors="tf") # forward pass outputs = model(inputs) # verify the logits expected_shape = tf.TensorShape((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant([-0.0948, -0.6454, -0.0921]) self.assertTrue(np.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/swin/test_modeling_swin.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 Swin model. """ import collections import unittest from transformers import SwinConfig 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_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class SwinModelTester: def __init__( self, parent, batch_size=13, image_size=32, patch_size=2, num_channels=3, embed_dim=16, depths=[1, 2, 1], num_heads=[2, 2, 4], window_size=2, mlp_ratio=2.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", use_absolute_embeddings=False, patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, is_training=True, scope=None, use_labels=True, type_sequence_label_size=10, encoder_stride=8, out_features=["stage1", "stage2"], out_indices=[1, 2], ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.embed_dim = embed_dim self.depths = depths self.num_heads = num_heads self.window_size = window_size self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.use_absolute_embeddings = use_absolute_embeddings self.patch_norm = patch_norm self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.is_training = is_training self.scope = scope self.use_labels = use_labels self.type_sequence_label_size = type_sequence_label_size self.encoder_stride = encoder_stride self.out_features = out_features self.out_indices = out_indices 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 SwinConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, out_features=self.out_features, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels): model = SwinModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1)) expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1)) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim)) def create_and_check_backbone(self, config, pixel_values, labels): model = SwinBackbone(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), [self.batch_size, model.channels[0], 16, 16]) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = SwinBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4]) # verify channels self.parent.assertEqual(len(model.channels), 1) def create_and_check_for_masked_image_modeling(self, config, pixel_values, labels): model = SwinForMaskedImageModeling(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) ) # test greyscale images config.num_channels = 1 model = SwinForMaskedImageModeling(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = SwinForImageClassification(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)) # test greyscale images config.num_channels = 1 model = SwinForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) 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 SwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( SwinModel, SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": SwinModel, "image-classification": SwinForImageClassification} if is_torch_available() else {} ) fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = SwinModelTester(self) self.config_tester = ConfigTester(self, config_class=SwinConfig, embed_dim=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 def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) # TODO: check if this works again for PyTorch 2.x.y @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.") def test_multi_gpu_data_parallel_forward(self): pass def test_training_gradient_checkpointing(self): super().test_training_gradient_checkpointing() 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_for_masked_image_modeling(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs) 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) @unittest.skip(reason="Swin does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Swin Transformer does not use feedforward chunking") def test_feed_forward_chunking(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_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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions expected_num_attentions = len(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 window_size_squared = config.window_size**2 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.attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # also another +1 for reshaped_hidden_states added_hidden_states = 1 if model_class.__name__ == "SwinBackbone" else 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) def check_hidden_states_output(self, inputs_dict, config, model_class, image_size): 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.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # Swin has a different seq_length patch_size = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.assertListEqual( list(hidden_states[0].shape[-2:]), [num_patches, self.model_tester.embed_dim], ) if not model_class.__name__ == "SwinBackbone": reshaped_hidden_states = outputs.reshaped_hidden_states self.assertEqual(len(reshaped_hidden_states), expected_num_layers) batch_size, num_channels, height, width = reshaped_hidden_states[0].shape reshaped_hidden_states = ( reshaped_hidden_states[0].view(batch_size, num_channels, height * width).permute(0, 2, 1) ) self.assertListEqual( list(reshaped_hidden_states.shape[-2:]), [num_patches, self.model_tester.embed_dim], ) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) def test_hidden_states_output_with_padding(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.patch_size = 3 image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) patch_size = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0]) padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1]) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width)) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width)) @slow def test_model_from_pretrained(self): model_name = "microsoft/swin-tiny-patch4-window7-224" model = SwinModel.from_pretrained(model_name) self.assertIsNotNone(model) 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(): if "embeddings" not in name and 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", ) @require_vision @require_torch class SwinModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224").to(torch_device) image_processor = self.default_image_processor image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = image_processor(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([-0.0948, -0.6454, -0.0921]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)) @require_torch class SwinBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (SwinBackbone,) if is_torch_available() else () config_class = SwinConfig def setUp(self): self.model_tester = SwinModelTester(self)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/chinese_clip/test_processor_chinese_clip.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. import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor @require_vision class ChineseCLIPProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "的", "价", "格", "是", "15", "便", "alex", "##andra", ",", "。", "-", "t", "shirt", ] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) image_processor_map = { "do_resize": True, "size": {"height": 224, "width": 224}, "do_center_crop": True, "crop_size": {"height": 18, "width": 18}, "do_normalize": True, "image_mean": [0.48145466, 0.4578275, 0.40821073], "image_std": [0.26862954, 0.26130258, 0.27577711], "do_convert_rgb": True, } self.image_processor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME) with open(self.image_processor_file, "w", encoding="utf-8") as fp: json.dump(image_processor_map, fp) def get_tokenizer(self, **kwargs): return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_image_processor(self, **kwargs): return ChineseCLIPImageProcessor.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): tokenizer_slow = self.get_tokenizer() tokenizer_fast = self.get_rust_tokenizer() image_processor = self.get_image_processor() processor_slow = ChineseCLIPProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = ChineseCLIPProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = ChineseCLIPProcessor(tokenizer=tokenizer_fast, image_processor=image_processor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = ChineseCLIPProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, BertTokenizer) self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast) self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string()) self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string()) self.assertIsInstance(processor_slow.image_processor, ChineseCLIPImageProcessor) self.assertIsInstance(processor_fast.image_processor, ChineseCLIPImageProcessor) def test_save_load_pretrained_additional_features(self): processor = ChineseCLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(cls_token="(CLS)", sep_token="(SEP)") image_processor_add_kwargs = self.get_image_processor(do_normalize=False) processor = ChineseCLIPProcessor.from_pretrained( self.tmpdirname, cls_token="(CLS)", sep_token="(SEP)", do_normalize=False ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, BertTokenizerFast) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, ChineseCLIPImageProcessor) def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = ChineseCLIPProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() input_feat_extract = image_processor(image_input, return_tensors="np") input_processor = processor(images=image_input, 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): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = ChineseCLIPProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "Alexandra,T-shirt的价格是15便士。" 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_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = ChineseCLIPProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "Alexandra,T-shirt的价格是15便士。" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_tokenizer_decode(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = ChineseCLIPProcessor(tokenizer=tokenizer, image_processor=image_processor) 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): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = ChineseCLIPProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "Alexandra,T-shirt的价格是15便士。" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), processor.model_input_names)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/chinese_clip/test_modeling_chinese_clip.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 Chinese-CLIP model. """ import inspect import os import tempfile import unittest import numpy as np import requests from transformers import ChineseCLIPConfig, ChineseCLIPTextConfig, ChineseCLIPVisionConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import 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, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, ChineseCLIPModel, ChineseCLIPTextModel, ChineseCLIPVisionModel, ) if is_vision_available(): from PIL import Image from transformers import ChineseCLIPProcessor class ChineseCLIPTextModelTester: 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=2, 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 ChineseCLIPTextConfig( 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 = ChineseCLIPTextModel(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 = ChineseCLIPTextModel(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 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 class ChineseCLIPVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent 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.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, 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]) config = self.get_config() return config, pixel_values def get_config(self): return ChineseCLIPVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = ChineseCLIPVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class ChineseCLIPTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (ChineseCLIPTextModel,) if is_torch_available() else () fx_compatible = False # 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 = ChineseCLIPTextModelTester(self) self.config_tester = ConfigTester(self, config_class=ChineseCLIPTextConfig, 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, ) @slow def test_model_from_pretrained(self): model_name = "OFA-Sys/chinese-clip-vit-base-patch16" model = ChineseCLIPTextModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="ChineseCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="ChineseCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @require_torch class ChineseCLIPVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as CHINESE_CLIP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ChineseCLIPVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = ChineseCLIPVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=ChineseCLIPVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="CHINESE_CLIP does not use inputs_embeds") def test_inputs_embeds(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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="ChineseCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="ChineseCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "OFA-Sys/chinese-clip-vit-base-patch16" model = ChineseCLIPVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class ChineseCLIPModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = ChineseCLIPTextModelTester(parent, **text_kwargs) self.vision_model_tester = ChineseCLIPVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.is_training = is_training def prepare_config_and_inputs(self): ( config, input_ids, token_type_ids, attention_mask, _, __, ___, ) = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, token_type_ids, attention_mask, pixel_values def get_config(self): return ChineseCLIPConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, token_type_ids, attention_mask, pixel_values): model = ChineseCLIPModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask, token_type_ids) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, token_type_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict @require_torch class ChineseCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (ChineseCLIPModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": ChineseCLIPModel} if is_torch_available() else {} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): text_kwargs = {"use_labels": False, "batch_size": 12} vision_kwargs = {"batch_size": 12} self.model_tester = ChineseCLIPModelTester(self, text_kwargs, vision_kwargs) 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(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="ChineseCLIPModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for CHINESE_CLIP def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for sub_config_key in ("vision_config", "text_config"): sub_config = getattr(configs_no_init, sub_config_key, {}) setattr(configs_no_init, sub_config_key, _config_zero_init(sub_config)) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: 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 _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # CHINESE_CLIP needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) @slow def test_model_from_pretrained(self): model_name = "OFA-Sys/chinese-clip-vit-base-patch16" model = ChineseCLIPModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of Pikachu def prepare_img(): url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg" im = Image.open(requests.get(url, stream=True).raw) return im @require_vision @require_torch class ChineseCLIPModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "OFA-Sys/chinese-clip-vit-base-patch16" model = ChineseCLIPModel.from_pretrained(model_name).to(torch_device) processor = ChineseCLIPProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"], images=image, padding=True, return_tensors="pt" ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) probs = outputs.logits_per_image.softmax(dim=1) expected_probs = torch.tensor([[1.2686e-03, 5.4499e-02, 6.7968e-04, 9.4355e-01]], device=torch_device) self.assertTrue(torch.allclose(probs, expected_probs, atol=5e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/chinese_clip/test_image_processing_chinese_clip.py
# coding=utf-8 # Copyright 2021 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 unittest from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from transformers import ChineseCLIPImageProcessor if is_torch_available(): pass class ChineseCLIPImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, do_normalize=True, image_mean=[0.48145466, 0.4578275, 0.40821073], image_std=[0.26862954, 0.26130258, 0.27577711], do_convert_rgb=True, ): size = size if size is not None else {"height": 224, "width": 224} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_convert_rgb": self.do_convert_rgb, } def expected_output_image_shape(self, images): return 3, self.crop_size["height"], self.crop_size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class ChineseCLIPImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = ChineseCLIPImageProcessingTester(self, do_center_crop=True) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_center_crop")) self.assertTrue(hasattr(image_processing, "center_crop")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_convert_rgb")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 224, "width": 224}) self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84) self.assertEqual(image_processor.size, {"shortest_edge": 42}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) @unittest.skip("ChineseCLIPImageProcessor doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy def test_call_numpy_4_channels(self): pass @require_torch @require_vision class ChineseCLIPImageProcessingTestFourChannels(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = ChineseCLIPImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = ChineseCLIPImageProcessingTester(self, num_channels=4, do_center_crop=True) self.expected_encoded_image_num_channels = 3 @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_center_crop")) self.assertTrue(hasattr(image_processing, "center_crop")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_convert_rgb")) @unittest.skip("ChineseCLIPImageProcessor does not support 4 channels yet") # FIXME Amy def test_call_numpy(self): return super().test_call_numpy() @unittest.skip("ChineseCLIPImageProcessor does not support 4 channels yet") # FIXME Amy def test_call_pytorch(self): return super().test_call_torch() @unittest.skip("ChineseCLIPImageProcessor doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy def test_call_numpy_4_channels(self): pass
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/clipseg/test_modeling_clipseg.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 CLIPSeg model. """ import inspect import os import tempfile import unittest import numpy as np import requests import transformers from transformers import CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig from transformers.testing_utils import ( is_flax_available, is_pt_flax_cross_test, require_torch, require_vision, slow, torch_device, ) from transformers.utils import 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, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegTextModel, CLIPSegVisionModel from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES if is_vision_available(): from PIL import Image if is_flax_available(): import jax.numpy as jnp from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) class CLIPSegVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent 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.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, 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]) config = self.get_config() return config, pixel_values def get_config(self): return CLIPSegVisionConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = CLIPSegVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class CLIPSegVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as CLIPSeg does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (CLIPSegVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = CLIPSegVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=CLIPSegVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="CLIPSeg does not use inputs_embeds") def test_inputs_embeds(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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="CLIPSegVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="CLIPSegVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "CIDAS/clipseg-rd64-refined" model = CLIPSegVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class CLIPSegTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, 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_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.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range 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]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return CLIPSegTextConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = CLIPSegTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) 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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class CLIPSegTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (CLIPSegTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False model_split_percents = [0.5, 0.8, 0.9] def setUp(self): self.model_tester = CLIPSegTextModelTester(self) self.config_tester = ConfigTester(self, config_class=CLIPSegTextConfig, 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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="CLIPSeg does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="CLIPSegTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="CLIPSegTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "CIDAS/clipseg-rd64-refined" model = CLIPSegTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class CLIPSegModelTester: def __init__( self, parent, text_kwargs=None, vision_kwargs=None, is_training=True, # This should respect the `num_hidden_layers` in `CLIPSegVisionModelTester` extract_layers=(1,), ): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = CLIPSegTextModelTester(parent, **text_kwargs) self.vision_model_tester = CLIPSegVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.is_training = is_training self.extract_layers = extract_layers def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return CLIPSegConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64, reduce_dim=32, extract_layers=self.extract_layers, ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = CLIPSegModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def create_and_check_model_for_image_segmentation(self, config, input_ids, attention_maks, pixel_values): model = CLIPSegForImageSegmentation(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values) self.parent.assertEqual( result.logits.shape, ( self.vision_model_tester.batch_size, self.vision_model_tester.image_size, self.vision_model_tester.image_size, ), ) self.parent.assertEqual( result.conditional_embeddings.shape, (self.text_model_tester.batch_size, config.projection_dim) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_torch class CLIPSegModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (CLIPSegModel, CLIPSegForImageSegmentation) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": CLIPSegModel} if is_torch_available() else {} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): # CLIPSegForImageSegmentation requires special treatment if return_labels: if model_class.__name__ == "CLIPSegForImageSegmentation": batch_size, _, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [batch_size, height, width], device=torch_device, dtype=torch.float ) return inputs_dict def setUp(self): self.model_tester = CLIPSegModelTester(self) 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_for_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_for_image_segmentation(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="CLIPSegModel does not have input/output embeddings") def test_model_common_attributes(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass # override as the some parameters require custom initialization 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(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if "logit_scale" in name: self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) elif "film" in name or "transposed_conv" in name or "reduce" in name: # those parameters use PyTorch' default nn.Linear initialization scheme pass else: 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 _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # CLIPSeg needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save CLIPSegConfig and check if we can load CLIPSegVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = CLIPSegVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save CLIPSegConfig and check if we can load CLIPSegTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = CLIPSegTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) # overwrite from common since FlaxCLIPSegModel returns nested output # which is not supported in the common test @is_pt_flax_cross_test def test_equivalence_pt_to_flax(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__): # load PyTorch class pt_model = model_class(config).eval() # Flax models don't use the `use_cache` option and cache is not returned as a default. # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): return fx_model_class = getattr(transformers, fx_model_class_name) # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() # convert inputs to Flax fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)} fx_outputs = fx_model(**fx_inputs).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]): self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = fx_model_class.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**fx_inputs).to_tuple() self.assertEqual( len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch" ) for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]): self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2) # overwrite from common since FlaxCLIPSegModel returns nested output # which is not supported in the common test @is_pt_flax_cross_test def test_equivalence_flax_to_pt(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__): # load corresponding PyTorch class pt_model = model_class(config).eval() # So we disable `use_cache` here for PyTorch model. pt_model.config.use_cache = False fx_model_class_name = "Flax" + model_class.__name__ if not hasattr(transformers, fx_model_class_name): # no flax model exists for this class return fx_model_class = getattr(transformers, fx_model_class_name) # load Flax class fx_model = fx_model_class(config, dtype=jnp.float32) # make sure only flax inputs are forward that actually exist in function args fx_input_keys = inspect.signature(fx_model.__call__).parameters.keys() pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) # make sure weights are tied in PyTorch pt_model.tie_weights() # prepare inputs pt_inputs = self._prepare_for_class(inputs_dict, model_class) # remove function args that don't exist in Flax pt_inputs = {k: v for k, v in pt_inputs.items() if k in fx_input_keys} with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() fx_inputs = {k: np.array(v.to("cpu")) for k, v in pt_inputs.items() if torch.is_tensor(v)} fx_outputs = fx_model(**fx_inputs).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]): self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2) with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = model_class.from_pretrained(tmpdirname, from_flax=True) with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple() self.assertEqual( len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch" ) for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs_loaded[:4]): self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2) def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True if model_class.__name__ in MODEL_MAPPING_NAMES.values(): continue print("Model class:", model_class) model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) for k, v in inputs.items(): print(k, v.shape) loss = model(**inputs).loss loss.backward() @slow def test_model_from_pretrained(self): model_name = "CIDAS/clipseg-rd64-refined" model = CLIPSegModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image @require_vision @require_torch class CLIPSegModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_segmentation(self): model_name = "CIDAS/clipseg-rd64-refined" processor = CLIPSegProcessor.from_pretrained(model_name) model = CLIPSegForImageSegmentation.from_pretrained(model_name).to(torch_device) image = prepare_img() texts = ["a cat", "a remote", "a blanket"] inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the predicted masks self.assertEqual( outputs.logits.shape, torch.Size((3, 352, 352)), ) expected_masks_slice = torch.tensor( [[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-3)) # verify conditional and pooled output expected_conditional = torch.tensor([0.5601, -0.0314, 0.1980]).to(torch_device) expected_pooled_output = torch.tensor([0.5036, -0.2681, -0.2644]).to(torch_device) self.assertTrue(torch.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-3)) self.assertTrue(torch.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/clipseg/test_processor_clipseg.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 import numpy as np import pytest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import CLIPSegProcessor, ViTImageProcessor @require_vision class CLIPSegProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""] 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)) image_processor_map = { "do_resize": True, "size": 20, "do_center_crop": True, "crop_size": 18, "do_normalize": True, "image_mean": [0.48145466, 0.4578275, 0.40821073], "image_std": [0.26862954, 0.26130258, 0.27577711], } self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME) with open(self.image_processor_file, "w", encoding="utf-8") as fp: json.dump(image_processor_map, fp) def get_tokenizer(self, **kwargs): return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_image_processor(self, **kwargs): return ViTImageProcessor.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): tokenizer_slow = self.get_tokenizer() tokenizer_fast = self.get_rust_tokenizer() image_processor = self.get_image_processor() processor_slow = CLIPSegProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = CLIPSegProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = CLIPSegProcessor(tokenizer=tokenizer_fast, image_processor=image_processor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = CLIPSegProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer) self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast) self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string()) self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string()) self.assertIsInstance(processor_slow.image_processor, ViTImageProcessor) self.assertIsInstance(processor_fast.image_processor, ViTImageProcessor) def test_save_load_pretrained_additional_features(self): processor = CLIPSegProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0) processor = CLIPSegProcessor.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, CLIPTokenizerFast) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, ViTImageProcessor) def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() input_feat_extract = image_processor(image_input, return_tensors="np") input_processor = processor(images=image_input, 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): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" 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_processor_text(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_visual_prompt(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() visual_prompt_input = self.prepare_image_inputs() inputs = processor(images=image_input, visual_prompt=visual_prompt_input) self.assertListEqual(list(inputs.keys()), ["pixel_values", "conditional_pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_tokenizer_decode(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = CLIPSegProcessor(tokenizer=tokenizer, image_processor=image_processor) 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)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/gpt2/test_tokenization_gpt2_tf.py
import unittest from pathlib import Path from tempfile import TemporaryDirectory from transformers import AutoConfig, TFGPT2LMHeadModel, is_keras_nlp_available, is_tf_available from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer from transformers.testing_utils import require_keras_nlp, require_tf, slow if is_tf_available(): import tensorflow as tf if is_keras_nlp_available(): from transformers.models.gpt2 import TFGPT2Tokenizer TOKENIZER_CHECKPOINTS = ["openai-community/gpt2"] TINY_MODEL_CHECKPOINT = "openai-community/gpt2" if is_tf_available(): class ModelToSave(tf.Module): def __init__(self, tokenizer): super().__init__() self.tokenizer = tokenizer config = AutoConfig.from_pretrained(TINY_MODEL_CHECKPOINT) self.model = TFGPT2LMHeadModel.from_config(config) @tf.function(input_signature=(tf.TensorSpec((None,), tf.string, name="text"),)) def serving(self, text): tokenized = self.tokenizer(text) input_ids_dense = tokenized["input_ids"].to_tensor() input_mask = tf.cast(input_ids_dense > 0, tf.int32) # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN]) outputs = self.model(input_ids=input_ids_dense, attention_mask=input_mask)["logits"] return outputs @require_tf @require_keras_nlp class GPTTokenizationTest(unittest.TestCase): # The TF tokenizers are usually going to be used as pretrained tokenizers from existing model checkpoints, # so that's what we focus on here. def setUp(self): super().setUp() self.tokenizers = [GPT2Tokenizer.from_pretrained(checkpoint) for checkpoint in (TOKENIZER_CHECKPOINTS)] self.tf_tokenizers = [TFGPT2Tokenizer.from_pretrained(checkpoint) for checkpoint in TOKENIZER_CHECKPOINTS] assert len(self.tokenizers) == len(self.tf_tokenizers) self.test_sentences = [ "This is a straightforward English test sentence.", "This one has some weird characters\rto\nsee\r\nif those\u00E9break things.", "Now we're going to add some Chinese: 一 二 三 一二三", "And some much more rare Chinese: 齉 堃 齉堃", "Je vais aussi écrire en français pour tester les accents", "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ", ] self.paired_sentences = list(zip(self.test_sentences, self.test_sentences[::-1])) def test_output_equivalence(self): for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers): for test_inputs in self.test_sentences: python_outputs = tokenizer([test_inputs], return_tensors="tf") tf_outputs = tf_tokenizer([test_inputs]) for key in python_outputs.keys(): # convert them to numpy to avoid messing with ragged tensors python_outputs_values = python_outputs[key].numpy() tf_outputs_values = tf_outputs[key].numpy() self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape)) self.assertTrue(tf.reduce_all(tf.cast(python_outputs_values, tf.int64) == tf_outputs_values)) @slow def test_graph_mode(self): for tf_tokenizer in self.tf_tokenizers: compiled_tokenizer = tf.function(tf_tokenizer) for test_inputs in self.test_sentences: test_inputs = tf.constant(test_inputs) compiled_outputs = compiled_tokenizer(test_inputs) eager_outputs = tf_tokenizer(test_inputs) for key in eager_outputs.keys(): self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key])) @slow def test_saved_model(self): for tf_tokenizer in self.tf_tokenizers: model = ModelToSave(tokenizer=tf_tokenizer) test_inputs = tf.convert_to_tensor([self.test_sentences[0]]) out = model.serving(test_inputs) # Build model with some sample inputs with TemporaryDirectory() as tempdir: save_path = Path(tempdir) / "saved.model" tf.saved_model.save(model, save_path, signatures={"serving_default": model.serving}) loaded_model = tf.saved_model.load(save_path) loaded_output = loaded_model.signatures["serving_default"](test_inputs)["output_0"] # We may see small differences because the loaded model is compiled, so we need an epsilon for the test self.assertTrue(tf.reduce_all(out == loaded_output)) @slow def test_from_config(self): for tf_tokenizer in self.tf_tokenizers: test_inputs = tf.convert_to_tensor([self.test_sentences[0]]) out = tf_tokenizer(test_inputs) # Build model with some sample inputs config = tf_tokenizer.get_config() model_from_config = TFGPT2Tokenizer.from_config(config) from_config_output = model_from_config(test_inputs) for key in from_config_output.keys(): self.assertTrue(tf.reduce_all(from_config_output[key] == out[key])) @slow def test_padding(self): for tf_tokenizer in self.tf_tokenizers: # for the test to run tf_tokenizer.pad_token_id = 123123 for max_length in [3, 5, 1024]: test_inputs = tf.convert_to_tensor([self.test_sentences[0]]) out = tf_tokenizer(test_inputs, max_length=max_length) out_length = out["input_ids"].numpy().shape[1] assert out_length == max_length
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/gpt2/test_modeling_flax_gpt2.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. import tempfile import unittest import numpy as np import transformers from transformers import GPT2Config, GPT2Tokenizer, is_flax_available, is_torch_available from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_flax_available(): import jax import jax.numpy as jnp from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) from transformers.models.gpt2.modeling_flax_gpt2 import FlaxGPT2LMHeadModel, FlaxGPT2Model if is_torch_available(): import torch class FlaxGPT2ModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, 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, initializer_range=0.02, ): 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.initializer_range = initializer_range self.scope = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 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]) config = GPT2Config( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, n_positions=self.max_position_embeddings, use_cache=False, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, ) return (config, input_ids, input_mask) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask} return config, inputs_dict def prepare_config_and_inputs_for_decoder(self): config, input_ids, attention_mask = self.prepare_config_and_inputs() 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, attention_mask, encoder_hidden_states, encoder_attention_mask, ) def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length) attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4") position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1) ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], attention_mask=attention_mask, past_key_values=outputs_cache.past_key_values, position_ids=position_ids, ) outputs = model(input_ids) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) attention_mask_cache = jnp.concatenate( [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))], axis=-1, ) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length) position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1) ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask_cache, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], past_key_values=outputs_cache.past_key_values, attention_mask=attention_mask_cache, position_ids=position_ids, ) outputs = model(input_ids, attention_mask=attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_bool_attention_mask_in_generation(self, model_class_name, config, input_ids, attention_mask): model = model_class_name(config) output_int_att_mask = model.generate( input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=3, ) output_bool_att_mask = model.generate( input_ids=input_ids, attention_mask=attention_mask.astype(bool), max_new_tokens=3, ) self.parent.assertTrue( (output_bool_att_mask.sequences == output_int_att_mask.sequences).all(), "Generated response differ between boolean and integer attention mask", ) @require_flax class FlaxGPT2ModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase): all_model_classes = (FlaxGPT2Model, FlaxGPT2LMHeadModel) if is_flax_available() else () all_generative_model_classes = (FlaxGPT2LMHeadModel,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxGPT2ModelTester(self) def test_use_cache_forward(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask) def test_use_cache_forward_with_attn_mask(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward_with_attn_mask( model_class_name, config, input_ids, attention_mask ) def test_bool_attention_mask_in_generation(self): for model_class_name in self.all_generative_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_bool_attention_mask_in_generation( model_class_name, config, input_ids, attention_mask ) @slow def test_batch_generation(self): tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2", pad_token="</s>", padding_side="left") inputs = tokenizer(["Hello this is a long string", "Hey"], return_tensors="np", padding=True, truncation=True) model = FlaxGPT2LMHeadModel.from_pretrained("openai-community/gpt2") model.do_sample = False model.config.pad_token_id = model.config.eos_token_id jit_generate = jax.jit(model.generate) output_sequences = jit_generate(inputs["input_ids"], attention_mask=inputs["attention_mask"]).sequences output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True) expected_string = [ "Hello this is a long string of words. I'm going to start with the first one.\n", "Hey, I'm not sure if I'm going to be able to do", ] self.assertListEqual(output_string, expected_string) # overwrite from common since `attention_mask` in combination # with `causal_mask` behaves slighly differently @is_pt_flax_cross_test def test_equivalence_pt_to_flax(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__): # prepare inputs prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()} # load corresponding PyTorch class pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) batch_size, seq_length = pt_inputs["input_ids"].shape rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): pt_inputs["attention_mask"][batch_idx, :start_index] = 0 pt_inputs["attention_mask"][batch_idx, start_index:] = 1 prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0 prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1 pt_model = pt_model_class(config).eval() fx_model = model_class(config, dtype=jnp.float32) fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() fx_outputs = fx_model(**prepared_inputs_dict).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs, pt_outputs): self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2) with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple() self.assertEqual( len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch" ) for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs): self.assert_almost_equals(fx_output_loaded[:, -1], pt_output[:, -1].numpy(), 4e-2) # overwrite from common since `attention_mask` in combination # with `causal_mask` behaves slighly differently @is_pt_flax_cross_test def test_equivalence_flax_to_pt(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__): # prepare inputs prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class) pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()} # load corresponding PyTorch class pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning pt_model_class = getattr(transformers, pt_model_class_name) pt_model = pt_model_class(config).eval() fx_model = model_class(config, dtype=jnp.float32) pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) batch_size, seq_length = pt_inputs["input_ids"].shape rnd_start_indices = np.random.randint(0, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): pt_inputs["attention_mask"][batch_idx, :start_index] = 0 pt_inputs["attention_mask"][batch_idx, start_index:] = 1 prepared_inputs_dict["attention_mask"][batch_idx, :start_index] = 0 prepared_inputs_dict["attention_mask"][batch_idx, start_index:] = 1 # make sure weights are tied in PyTorch pt_model.tie_weights() with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() fx_outputs = fx_model(**prepared_inputs_dict).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs, pt_outputs): self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2) with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True) with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple() self.assertEqual( len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch" ) for fx_output, pt_output in zip(fx_outputs, pt_outputs_loaded): self.assert_almost_equals(fx_output[:, -1], pt_output[:, -1].numpy(), 4e-2) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("openai-community/gpt2", from_pt=True) outputs = model(np.ones((1, 1))) self.assertIsNotNone(outputs)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/gpt2/test_modeling_tf_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. from __future__ import annotations import unittest from transformers import GPT2Config, is_tf_available from transformers.testing_utils import require_tf, require_tf2onnx, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin if is_tf_available(): import tensorflow as tf from transformers import GPT2Tokenizer from transformers.models.gpt2.modeling_tf_gpt2 import ( TFGPT2DoubleHeadsModel, TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2Model, ) from transformers.tf_utils import shape_list class TFGPT2ModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_token_type_ids = True self.use_input_mask = True self.use_labels = True self.use_mc_token_ids = True self.vocab_size = 99 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None self.bos_token_id = self.vocab_size - 1 self.eos_token_id = self.vocab_size - 1 self.pad_token_id = self.vocab_size - 1 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) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], 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 = GPT2Config( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=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, n_positions=self.max_position_embeddings, # type_vocab_size=self.type_vocab_size, # initializer_range=self.initializer_range bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, return_dict=True, ) head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() 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, input_mask, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPT2Model(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) inputs = [input_ids, None, input_mask] # None is the input for 'past' result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPT2Model(config=config) # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True) outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids) outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1) output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"] output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past_key_values)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, 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-6) def create_and_check_gpt2_model_attention_mask_past( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = TFGPT2Model(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass output, past_key_values = model(input_ids, attention_mask=attn_mask).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat([attn_mask, tf.ones((shape_list(attn_mask)[0], 1), dtype=tf.int32)], axis=1) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "last_hidden_state" ] # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, 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-12) def create_and_check_gpt2_model_past_large_inputs( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = TFGPT2Model(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] token_type_ids = token_type_ids[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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 = ids_tensor((self.batch_size, 3), 2) next_token_types = ids_tensor((self.batch_size, 3), self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) next_token_type_ids = tf.concat([token_type_ids, next_token_types], axis=-1) output_from_no_past = model( next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask )["last_hidden_state"] output_from_past = model( next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past_key_values, )["last_hidden_state"] self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]) # select random slice random_slice_idx = int(ids_tensor((1,), shape_list(output_from_past)[-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 create_and_check_gpt2_lm_head(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = TFGPT2LMHeadModel(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_gpt2_double_head( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args ): model = TFGPT2DoubleHeadsModel(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "mc_token_ids": mc_token_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices)) def create_and_check_gpt2_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, "labels": sequence_labels, } model = TFGPT2ForSequenceClassification(config) result = model(inputs) 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, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, 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_tf class TFGPT2ModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (TFGPT2Model, TFGPT2LMHeadModel, TFGPT2ForSequenceClassification, TFGPT2DoubleHeadsModel) if is_tf_available() else () ) all_generative_model_classes = (TFGPT2LMHeadModel,) if is_tf_available() else () pipeline_model_mapping = ( { "feature-extraction": TFGPT2Model, "text-classification": TFGPT2ForSequenceClassification, "text-generation": TFGPT2LMHeadModel, "zero-shot": TFGPT2ForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = True onnx_min_opset = 10 def setUp(self): self.model_tester = TFGPT2ModelTester(self) self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37) def test_config(self): self.config_tester.run_common_tests() def test_gpt2_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model(*config_and_inputs) def test_gpt2_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs) def test_gpt2_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs) def test_gpt2_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs) def test_gpt2_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_lm_head(*config_and_inputs) def test_gpt2_double_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_double_head(*config_and_inputs) def test_gpt2_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "openai-community/gpt2" model = TFGPT2Model.from_pretrained(model_name) self.assertIsNotNone(model) # overwrite from common since ONNX runtime optimization doesn't work with tf.gather() when the argument # `batch_dims` > 0" @require_tf2onnx @slow def test_onnx_runtime_optimize(self): if not self.test_onnx: return import onnxruntime import tf2onnx config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # Skip these 2 classes which uses `tf.gather` with `batch_dims=1` if model_class in [TFGPT2ForSequenceClassification, TFGPT2DoubleHeadsModel]: continue model = model_class(config) model.build_in_name_scope() onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset) onnxruntime.InferenceSession(onnx_model_proto.SerializeToString()) # TODO (Joao): fix me @unittest.skip("Onnx compliancy broke with TF 2.10") def test_onnx_compliancy(self): pass @require_tf class TFGPT2ModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_greedy_distilgpt2_batch_special(self): model = TFGPT2LMHeadModel.from_pretrained("distilbert/distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilbert/distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["Today is a beautiful day and", "Yesterday was"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) generation_kwargs = { "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "do_sample": False, "repetition_penalty": 1.3, } output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = [ "Today is a beautiful day and I am so happy to be able take part in this amazing event.", "Yesterday was a very interesting time for the world to see how much of this is", ] self.assertListEqual(output_strings, expected_output_string) @slow def test_lm_generate_sample_distilgpt2_batch_special(self): model = TFGPT2LMHeadModel.from_pretrained("distilbert/distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilbert/distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["Today is a beautiful day and", "Yesterday was"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) generation_kwargs = { "do_sample": True, "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "repetition_penalty": 1.3, "temperature": 1.5, "top_k": 500, "top_p": 0.9, "seed": [42, 0], # seed set -> deterministic sampling sequence -> deterministic generation } # forces the generation to happen on CPU, to avoid GPU-related quirks with tf.device(":/CPU:0"): output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = [ "Today is a beautiful day and we will make you feel very hot/terrific in all your", "Yesterday was known by national television networks as Le Big Show or Wild Dog Jeopard", ] self.assertListEqual(output_strings, expected_output_string) @slow def test_lm_generate_greedy_distilgpt2_beam_search_special(self): model = TFGPT2LMHeadModel.from_pretrained("distilbert/distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilbert/distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["Today is a beautiful day and", "Yesterday was"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) generation_kwargs = { "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "do_sample": False, "num_beams": 2, } output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) expected_output_string = [ "Today is a beautiful day and a great day for all of us.\n\nI’m", "Yesterday was the first time that a person has been arrested in the United States for", ] self.assertListEqual(output_strings, expected_output_string) @slow def test_lm_generate_distilgpt2_left_padding(self): """Tests that the generated text is the same, regarless of left padding""" model = TFGPT2LMHeadModel.from_pretrained("distilbert/distilgpt2") tokenizer = GPT2Tokenizer.from_pretrained("distilbert/distilgpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" generation_kwargs = { "bad_words_ids": [tokenizer("is").input_ids, tokenizer("angry about").input_ids], "no_repeat_ngram_size": 2, "do_sample": False, "repetition_penalty": 1.3, } expected_output_string = ( "Today is a beautiful day and I am so happy to be able take part in this amazing event." ) sentences = ["Today is a beautiful day and"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) # using default length output_ids = model.generate(**input_ids, **generation_kwargs) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertEqual(output_strings[0], expected_output_string) sentences = ["Today is a beautiful day and", "This is a very long input that we absolutely don't care about"] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) # longer max length to capture the full length (remember: it is left padded) output_ids = model.generate(**input_ids, **generation_kwargs, max_length=27) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertEqual(output_strings[0], expected_output_string) @slow def test_lm_generate_gpt2_greedy_xla(self): model = TFGPT2LMHeadModel.from_pretrained("openai-community/gpt2") tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["The dog", "The flying machine"] expected_output_strings = [ "The dog was found in a field near the intersection of West and West Streets.\n\nThe", "The flying machine is a small, lightweight, and lightweight aircraft that can be used for any type of", ] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) output_ids = model.generate(**input_ids, do_sample=False) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) xla_generate = tf.function(model.generate, jit_compile=True) output_ids = xla_generate(**input_ids, do_sample=False) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) @slow def test_lm_generate_gpt2_sample_xla(self): # NOTE: due to the small numerical differences that are natural when we compile to XLA, sampling the same # output out of the same seed is far from guaranteed. We can, however, confirm that the results are sensible # and that we can seed both versions. # forces the generation to happen on CPU, to avoid GPU-related quirks with tf.device(":/CPU:0"): model = TFGPT2LMHeadModel.from_pretrained("openai-community/gpt2") tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentence = ["The dog", "The flying machine"] expected_output_string = [ "The dog owner asked why did our vet decide there needed to be extra ventilation inside because most" " puppies", "The flying machine was made by an artist who found it difficult to control it as it did not use", ] expected_output_string_xla = [ "The dog has been named in connection with the murder of a 20-year-old man in", "The flying machine is a new and improved system to operate and operate a new system and system " "system system", ] input_ids = tokenizer(sentence, return_tensors="tf", padding=True) output_ids = model.generate(**input_ids, do_sample=True, seed=[7, 0]) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_string) xla_generate = tf.function(model.generate, jit_compile=True) output_ids = xla_generate(**input_ids, do_sample=True, seed=[7, 0]) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_string_xla) @slow def test_lm_generate_gpt2_beam_search_xla(self): model = TFGPT2LMHeadModel.from_pretrained("openai-community/gpt2") tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" sentences = ["The dog", "The flying machine"] expected_output_strings = [ "The dog was found in the backyard of a home in the 6500 block of South Main Street", "The flying machine is a very powerful machine, but it's not a very powerful machine. It's", ] input_ids = tokenizer(sentences, return_tensors="tf", padding=True) output_ids = model.generate(**input_ids, do_sample=False, num_beams=2) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) xla_generate = tf.function(model.generate, jit_compile=True) output_ids = xla_generate(**input_ids, do_sample=False, num_beams=2) output_strings = tokenizer.batch_decode(output_ids, skip_special_tokens=True) self.assertListEqual(output_strings, expected_output_strings) @slow def test_contrastive_search_gpt2(self): article = ( "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based" ) gpt2_tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2-large") gpt2_model = TFGPT2LMHeadModel.from_pretrained("openai-community/gpt2-large") input_ids = gpt2_tokenizer(article, return_tensors="tf") outputs = gpt2_model.generate(**input_ids, penalty_alpha=0.6, top_k=4, max_length=256) generated_text = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, " "United Kingdom\n\nGoogle has a lot of data on its users and uses it to improve its products, such as " "Google Now, which helps users find the information they're looking for on the web. But the company " "is not the only one to collect data on its users. Facebook, for example, has its own facial " "recognition technology, as well as a database of millions of photos that it uses to personalize its " "News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates " "concerned about the company's ability to keep users' information private. In a blog post last " 'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our ' 'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with ' 'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at ' 'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, ' "but said in a statement to The Associated Press that" ], ) @slow def test_contrastive_search_gpt2_xla(self): article = ( "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based" ) gpt2_tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2-large") gpt2_model = TFGPT2LMHeadModel.from_pretrained("openai-community/gpt2-large") input_ids = gpt2_tokenizer(article, return_tensors="tf") xla_generate = tf.function(gpt2_model.generate, jit_compile=True) outputs = xla_generate(**input_ids, penalty_alpha=0.6, top_k=4, max_length=256) generated_text = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, " "United Kingdom\n\nGoogle has a lot of data on its users and uses it to improve its products, such as " "Google Now, which helps users find the information they're looking for on the web. But the company " "is not the only one to collect data on its users. Facebook, for example, has its own facial " "recognition technology, as well as a database of millions of photos that it uses to personalize its " "News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates " "concerned about the company's ability to keep users' information private. In a blog post last " 'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our ' 'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with ' 'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at ' 'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, ' "but said in a statement to The Associated Press that" ], )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/gpt2/test_modeling_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 datetime import gc import math import unittest import pytest from transformers import GPT2Config, is_torch_available from transformers.testing_utils import ( backend_empty_cache, require_flash_attn, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( GPT2DoubleHeadsModel, GPT2ForQuestionAnswering, GPT2ForSequenceClassification, GPT2ForTokenClassification, GPT2LMHeadModel, GPT2Model, GPT2Tokenizer, ) class GPT2ModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_token_type_ids=True, use_input_mask=True, use_labels=True, use_mc_token_ids=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, 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_token_type_ids = use_token_type_ids self.use_input_mask = use_input_mask self.use_labels = use_labels self.use_mc_token_ids = use_mc_token_ids 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 = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 def get_large_model_config(self): return GPT2Config.from_pretrained("openai-community/gpt2") def prepare_config_and_inputs( self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False ): 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) mc_token_ids = None if self.use_mc_token_ids: mc_token_ids = ids_tensor([self.batch_size, self.num_choices], 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( gradient_checkpointing=gradient_checkpointing, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn=reorder_and_upcast_attn, ) head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2) return ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) def get_config( self, gradient_checkpointing=False, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False ): return GPT2Config( vocab_size=self.vocab_size, n_embd=self.hidden_size, n_layer=self.num_hidden_layers, n_head=self.num_attention_heads, n_inner=self.intermediate_size, activation_function=self.hidden_act, resid_pdrop=self.hidden_dropout_prob, attn_pdrop=self.attention_probs_dropout_prob, n_positions=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, use_cache=True, bos_token_id=self.bos_token_id, eos_token_id=self.eos_token_id, pad_token_id=self.pad_token_id, gradient_checkpointing=gradient_checkpointing, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, reorder_and_upcast_attn=reorder_and_upcast_attn, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() 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, input_mask, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def create_and_check_gpt2_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = GPT2Model(config=config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids, head_mask=head_mask) 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(len(result.past_key_values), config.n_layer) def create_and_check_gpt2_model_past(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = GPT2Model(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, use_cache=True) outputs_use_cache_conf = model(input_ids, token_type_ids=token_type_ids) outputs_no_past = model(input_ids, token_type_ids=token_type_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) next_token_types = ids_tensor([self.batch_size, 1], self.type_vocab_size) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1) output_from_no_past = model(next_input_ids, token_type_ids=next_token_type_ids)["last_hidden_state"] output_from_past = model(next_tokens, token_type_ids=next_token_types, past_key_values=past)[ "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[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_gpt2_model_attention_mask_past( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = GPT2Model(config=config) model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = self.seq_length // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past = model(input_ids, attention_mask=attn_mask).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past, attention_mask=attn_mask)["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[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_gpt2_model_past_large_inputs( self, config, input_ids, input_mask, head_mask, token_type_ids, *args ): model = GPT2Model(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model(input_ids, token_type_ids=token_type_ids, attention_mask=input_mask, use_cache=True) output, past = 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_token_types = ids_tensor([self.batch_size, 3], self.type_vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and token_type_ids next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_token_type_ids = torch.cat([token_type_ids, next_token_types], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, token_type_ids=next_token_type_ids, attention_mask=next_attention_mask )["last_hidden_state"] output_from_past = model( next_tokens, token_type_ids=next_token_types, attention_mask=next_attention_mask, past_key_values=past )["last_hidden_state"] self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]) # 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() # 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_lm_head_model(self, config, input_ids, input_mask, head_mask, token_type_ids, *args): model = GPT2LMHeadModel(config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_forward_and_backwards( self, config, input_ids, input_mask, head_mask, token_type_ids, *args, gradient_checkpointing=False ): model = GPT2LMHeadModel(config) model.to(torch_device) if gradient_checkpointing: model.gradient_checkpointing_enable() result = model(input_ids, token_type_ids=token_type_ids, labels=input_ids) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) result.loss.backward() def create_and_check_double_lm_head_model( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, *args ): model = GPT2DoubleHeadsModel(config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_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() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() inputs = { "input_ids": multiple_choice_inputs_ids, "mc_token_ids": mc_token_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, "labels": multiple_choice_inputs_ids, } result = model(**inputs) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_choices, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.mc_logits.shape, (self.batch_size, self.num_choices)) def create_and_check_gpt2_for_question_answering( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPT2ForQuestionAnswering(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) 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_gpt2_for_sequence_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPT2ForSequenceClassification(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_gpt2_for_token_classification( self, config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, *args ): config.num_labels = self.num_labels model = GPT2ForTokenClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_gpt2_weight_initialization(self, config, *args): model = GPT2Model(config) model_std = model.config.initializer_range / math.sqrt(2 * model.config.n_layer) for key in model.state_dict().keys(): if "c_proj" in key and "weight" in key: self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std), 0.001) self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0), 0.01) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, head_mask, token_type_ids, mc_token_ids, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask, } return config, inputs_dict @require_torch class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel, GPT2ForQuestionAnswering, GPT2ForSequenceClassification, GPT2ForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": GPT2Model, "question-answering": GPT2ForQuestionAnswering, "text-classification": GPT2ForSequenceClassification, "text-generation": GPT2LMHeadModel, "token-classification": GPT2ForTokenClassification, "zero-shot": GPT2ForSequenceClassification, } if is_torch_available() else {} ) all_parallelizable_model_classes = (GPT2LMHeadModel, GPT2DoubleHeadsModel) if is_torch_available() else () fx_compatible = True test_missing_keys = False test_model_parallel = True # special case for DoubleHeads 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.__name__ == "GPT2DoubleHeadsModel": inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length), dtype=torch.long, device=torch_device, ) inputs_dict["input_ids"] = inputs_dict["labels"] inputs_dict["token_type_ids"] = inputs_dict["labels"] inputs_dict["mc_token_ids"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.num_choices), dtype=torch.long, device=torch_device, ) inputs_dict["mc_labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = GPT2ModelTester(self) self.config_tester = ConfigTester(self, config_class=GPT2Config, n_embd=37) def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() backend_empty_cache(torch_device) def test_config(self): self.config_tester.run_common_tests() def test_gpt2_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model(*config_and_inputs) def test_gpt2_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past(*config_and_inputs) def test_gpt2_model_att_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_attention_mask_past(*config_and_inputs) def test_gpt2_model_past_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_model_past_large_inputs(*config_and_inputs) def test_gpt2_lm_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lm_head_model(*config_and_inputs) def test_gpt2_double_lm_head_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_double_lm_head_model(*config_and_inputs) def test_gpt2_question_answering_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_question_answering(*config_and_inputs) def test_gpt2_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_sequence_classification(*config_and_inputs) def test_gpt2_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_for_token_classification(*config_and_inputs) def test_gpt2_gradient_checkpointing(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True) def test_gpt2_scale_attn_by_inverse_layer_idx(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(scale_attn_by_inverse_layer_idx=True) self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs) def test_gpt2_reorder_and_upcast_attn(self): config_and_inputs = self.model_tester.prepare_config_and_inputs(reorder_and_upcast_attn=True) self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs) def test_gpt2_weight_initialization(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_gpt2_weight_initialization(*config_and_inputs) @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @slow def test_batch_generation(self): model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") model.to(torch_device) tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") tokenizer.padding_side = "left" # Define PAD Token = EOS Token = 50256 tokenizer.pad_token = tokenizer.eos_token model.config.pad_token_id = model.config.eos_token_id # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] inputs = tokenizer(sentences, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) token_type_ids = torch.cat( [ input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0), input_ids.new_full((input_ids.shape[0], 1), 500), ], dim=-1, ) outputs = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), ) outputs_tt = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), token_type_ids=token_type_ids, ) inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device) output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item() inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device) output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little bit of a mess. I'm not sure if he's going", "Today, I'm going to be doing a lot of research on this. I", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence]) @slow def test_batch_generation_2heads(self): model = GPT2DoubleHeadsModel.from_pretrained("openai-community/gpt2") model.to(torch_device) tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") tokenizer.padding_side = "left" # This tokenizer has no pad token, so we have to set it in some way # Define PAD Token = EOS Token = 50256 tokenizer.pad_token = tokenizer.eos_token model.config.pad_token_id = model.config.eos_token_id # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] inputs = tokenizer(sentences, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) token_type_ids = torch.cat( [ input_ids.new_full((input_ids.shape[0], input_ids.shape[1] - 1), 0), input_ids.new_full((input_ids.shape[0], 1), 500), ], dim=-1, ) outputs = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), ) outputs_tt = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), token_type_ids=token_type_ids, ) inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device) output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item() inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device) output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) batch_out_sentence_tt = tokenizer.batch_decode(outputs_tt, skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little bit of a mess. I'm not sure if he's going", "Today, I'm going to be doing a lot of research on this. I", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertTrue(batch_out_sentence_tt != batch_out_sentence) # token_type_ids should change output self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence]) @slow def test_model_from_pretrained(self): model_name = "openai-community/gpt2" model = GPT2Model.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class GPT2ModelLanguageGenerationTest(unittest.TestCase): def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() backend_empty_cache(torch_device) def _test_lm_generate_gpt2_helper( self, gradient_checkpointing=False, reorder_and_upcast_attn=False, scale_attn_by_inverse_layer_idx=False, verify_outputs=True, ): model = GPT2LMHeadModel.from_pretrained( "openai-community/gpt2", reorder_and_upcast_attn=reorder_and_upcast_attn, scale_attn_by_inverse_layer_idx=scale_attn_by_inverse_layer_idx, ) if gradient_checkpointing: model.gradient_checkpointing_enable() else: model.gradient_checkpointing_disable() model.to(torch_device) # The dog input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device) # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog expected_output_ids = [464, 3290, 373, 1043, 287, 257, 2214, 1474, 262, 16246, 286, 2688, 290, 2688, 27262, 13, 198, 198, 464, 3290,] # fmt: skip output_ids = model.generate(input_ids, do_sample=False) if verify_outputs: self.assertListEqual(output_ids[0].tolist(), expected_output_ids) @slow def test_lm_generate_gpt2(self): self._test_lm_generate_gpt2_helper() @slow def test_lm_generate_gpt2_with_gradient_checkpointing(self): self._test_lm_generate_gpt2_helper(gradient_checkpointing=True) @slow def test_lm_generate_gpt2_with_reorder_and_upcast_attn(self): self._test_lm_generate_gpt2_helper(reorder_and_upcast_attn=True) @slow def test_lm_generate_gpt2_with_scale_attn_by_inverse_layer_idx(self): self._test_lm_generate_gpt2_helper(scale_attn_by_inverse_layer_idx=True, verify_outputs=False) @slow def test_gpt2_sample(self): tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") model.to(torch_device) torch.manual_seed(0) tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True) input_ids = tokenized.input_ids.to(torch_device) output_ids = model.generate(input_ids, do_sample=True) output_str = tokenizer.decode(output_ids[0], skip_special_tokens=True) token_type_ids = tokenized.token_type_ids.to(torch_device) output_seq = model.generate(input_ids=input_ids, do_sample=True, num_return_sequences=5) output_seq_tt = model.generate( input_ids=input_ids, token_type_ids=token_type_ids, do_sample=True, num_return_sequences=5 ) output_seq_strs = tokenizer.batch_decode(output_seq, skip_special_tokens=True) output_seq_tt_strs = tokenizer.batch_decode(output_seq_tt, skip_special_tokens=True) EXPECTED_OUTPUT_STR = ( "Today is a nice day and if you don't know anything about the state of play during your holiday" ) self.assertEqual(output_str, EXPECTED_OUTPUT_STR) self.assertTrue( all(output_seq_strs[idx] != output_seq_tt_strs[idx] for idx in range(len(output_seq_tt_strs))) ) # token_type_ids should change output @slow def test_gpt2_sample_max_time(self): tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2") model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2") model.to(torch_device) torch.manual_seed(0) tokenized = tokenizer("Today is a nice day and", return_tensors="pt", return_token_type_ids=True) input_ids = tokenized.input_ids.to(torch_device) MAX_TIME = 0.5 start = datetime.datetime.now() model.generate(input_ids, do_sample=True, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=False, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=False, num_beams=2, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=True, num_beams=2, max_time=MAX_TIME, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=MAX_TIME)) self.assertLess(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) start = datetime.datetime.now() model.generate(input_ids, do_sample=False, max_time=None, max_length=256) duration = datetime.datetime.now() - start self.assertGreater(duration, datetime.timedelta(seconds=1.5 * MAX_TIME)) @slow def test_contrastive_search_gpt2(self): article = ( "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based" ) gpt2_tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2-large") gpt2_model = GPT2LMHeadModel.from_pretrained("openai-community/gpt2-large").to(torch_device) input_ids = gpt2_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) outputs = gpt2_model.generate(input_ids, penalty_alpha=0.6, top_k=4, max_length=256) generated_text = gpt2_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "DeepMind Technologies is a British artificial intelligence subsidiary of Alphabet Inc. and research " "laboratory founded in 2010. DeepMind was acquired by Google in 2014. The company is based in London, " "United Kingdom\n\nGoogle has a lot of data on its users and uses it to improve its products, such as " "Google Now, which helps users find the information they're looking for on the web. But the company " "is not the only one to collect data on its users. Facebook, for example, has its own facial " "recognition technology, as well as a database of millions of photos that it uses to personalize its " "News Feed.\n\nFacebook's use of data is a hot topic in the tech industry, with privacy advocates " "concerned about the company's ability to keep users' information private. In a blog post last " 'year, Facebook CEO Mark Zuckerberg said his company would "do our best to be transparent about our ' 'data use and how we use it."\n\n"We have made it clear that we do not sell or share your data with ' 'third parties," Zuckerberg wrote. "If you have questions or concerns, please reach out to us at ' 'privacy@facebook.com."\n\nGoogle declined to comment on the privacy implications of its use of data, ' "but said in a statement to The Associated Press that" ], ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_left(self): """ Overwritting the common test as the test is flaky on tiny models """ model = GPT2LMHeadModel.from_pretrained("gpt2", torch_dtype=torch.float16).to(0) tokenizer = GPT2Tokenizer.from_pretrained("gpt2") texts = ["hi", "Hello this is a very long sentence"] tokenizer.padding_side = "left" tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(texts, return_tensors="pt", padding=True).to(0) output_native = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_native = tokenizer.batch_decode(output_native) model = GPT2LMHeadModel.from_pretrained( "gpt2", device_map={"": 0}, attn_implementation="flash_attention_2", torch_dtype=torch.float16 ) output_fa_2 = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_fa_2 = tokenizer.batch_decode(output_fa_2) expected_output = [ "<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>hi, who was born in the city of Kolkata, was a member of the Kolkata", "Hello this is a very long sentence. I'm sorry. I'm sorry. I'm sorry. I'm sorry. I'm sorry", ] self.assertListEqual(output_native, output_fa_2) self.assertListEqual(output_native, expected_output)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/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_jinja, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "openai-community/gpt2" 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.assertTrue(decode_s.startswith(bos_token)) self.assertTrue(all(d.startswith(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_jinja def test_tokenization_for_chat(self): tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname) test_chats = [ [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}], [ {"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Nice to meet you."}, ], [{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}], ] tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats] # fmt: off expected_tokens = [[20, 1, 20, 10, 20, 4, 3, 10, 20, 10, 20, 3, 0, 20, 20, 20, 0, 10, 20, 20, 20, 6, 20, 1, 6, 20, 20, 20, 3, 0, 0, 1, 20, 20], [20, 1, 20, 10, 20, 4, 3, 10, 20, 10, 20, 3, 0, 20, 20, 20, 0, 10, 20, 20, 20, 6, 20, 1, 6, 20, 20, 20, 3, 0, 0, 1, 20, 20, 20, 7, 20, 3, 10, 6, 1, 10, 20, 3, 3, 6, 10, 20, 1, 20, 20, 20], [20, 7, 20, 3, 10, 6, 1, 10, 20, 3, 3, 6, 10, 20, 1, 20, 20, 20, 20, 3, 0, 0, 1, 20, 20]] # fmt: on for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens): self.assertListEqual(tokenized_chat, expected_tokens) @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]) @unittest.skip("This test is failing because of a bug in the fast tokenizer") 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])
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/mega/test_modeling_mega.py
# coding=utf-8 # Copyright 2023 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 MegaConfig, is_torch_available from transformers.testing_utils import ( TestCasePlus, is_flaky, require_torch, require_torch_fp16, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MegaForCausalLM, MegaForMaskedLM, MegaForMultipleChoice, MegaForQuestionAnswering, MegaForSequenceClassification, MegaForTokenClassification, MegaModel, ) class MegaModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_positions=1024, bidirectional=False, # needed for decoding, and can't modify common generation tests; test separately by overriding ema_projection_size=16, shared_representation_size=64, use_chunking=False, chunk_size=32, attention_activation="softmax", use_normalized_ffn=True, nffn_hidden_size=24, add_token_type_embeddings=True, type_vocab_size=2, 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.add_token_type_embeddings = add_token_type_embeddings self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_positions = max_positions self.bidirectional = bidirectional self.ema_projection_size = ema_projection_size self.shared_representation_size = shared_representation_size self.use_chunking = use_chunking self.chunk_size = chunk_size self.attention_activation = attention_activation self.use_normalized_ffn = use_normalized_ffn self.nffn_hidden_size = nffn_hidden_size 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 self.num_attention_heads = 1 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.add_token_type_embeddings: 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): return MegaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, intermediate_size=self.intermediate_size, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, # added args add_token_type_embeddings=self.add_token_type_embeddings, max_positions=self.max_positions, bidirectional=self.bidirectional, ema_projection_size=self.ema_projection_size, shared_representation_size=self.shared_representation_size, use_chunking=self.use_chunking, chunk_size=self.chunk_size, attention_activation=self.attention_activation, use_normalized_ffn=self.use_normalized_ffn, nffn_hidden_size=self.nffn_hidden_size, ) def get_pipeline_config(self): config = self.get_config() config.vocab_size = 300 return config 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 config.bidirectional = False 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 = MegaModel(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 = MegaModel(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, ) 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 = MegaForCausalLM(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_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.bidirectional = False config.add_cross_attention = True model = MegaForCausalLM(config=config).to(torch_device).eval() # make sure that ids don't start with pad token mask = input_ids.ne(config.pad_token_id).long() input_ids = input_ids * mask # 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, 1), config.vocab_size) # make sure that ids don't start with pad token mask = next_tokens.ne(config.pad_token_id).long() next_tokens = next_tokens * mask next_mask = ids_tensor((self.batch_size, 1), 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_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[:, -1:, 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_decoder_model_with_chunking( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.use_chunking = True config.output_attentions = True config.attention_activation = "laplace" config.chunk_size = input_ids.size(1) * 2 model = MegaForCausalLM(config).to(torch_device).eval() input_ids = input_ids.repeat(1, 8) # multiply the sequence length by 8 since we repeat the same ids 8 times in input_ids input_mask = random_attention_mask([self.batch_size, self.seq_length * 8]) result = model(input_ids, attention_mask=input_mask) # test if the sequence length of attentions is same provided chunk_size self.parent.assertEqual(result["attentions"][0].shape[-1], config.chunk_size) def create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegaForMaskedLM(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_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = MegaForTokenClassification(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 = MegaForMultipleChoice(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 create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegaForQuestionAnswering(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)) # extra checks for Mega-specific model functionality def create_and_check_bidirectionality( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.bidirectional = True model = MegaModel(config) model.to(torch_device) model.eval() # no mask result = model(input_ids) # with mask & token types result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_chunking_shorter_sequence( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.use_chunking = True config.chunk_size = input_ids.size(1) + 25 model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_chunking_longer_sequence( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.use_chunking = True # we want the chunk size to be < sequence length, and the sequence length to be a multiple of chunk size config.chunk_size = input_ids.size(1) * 2 model = MegaModel(config) model.to(torch_device) model.eval() result = model( input_ids.repeat(1, 8), ) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length * 8, self.hidden_size)) def check_laplace_self_attention( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.attention_activation = "laplace" model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_relu2_self_attention( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.attention_activation = "relu2" model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) def check_sequence_length_beyond_max_positions( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.max_positions = self.seq_length - 2 model = MegaModel(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, (self.batch_size, self.seq_length, self.hidden_size)) 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 MegaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MegaForCausalLM, MegaForMaskedLM, MegaModel, MegaForSequenceClassification, MegaForTokenClassification, MegaForMultipleChoice, MegaForQuestionAnswering, ) if is_torch_available() else () ) all_generative_model_classes = (MegaForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": MegaModel, "fill-mask": MegaForMaskedLM, "question-answering": MegaForQuestionAnswering, "text-classification": MegaForSequenceClassification, "text-generation": MegaForCausalLM, "token-classification": MegaForTokenClassification, "zero-shot": MegaForSequenceClassification, } if is_torch_available() else {} ) fx_compatible = False test_head_masking = False test_pruning = False def setUp(self): self.model_tester = MegaModelTester(self) self.config_tester = ConfigTester(self, config_class=MegaConfig, hidden_size=37) # TODO: @ydshieh @is_flaky(description="Sometimes gives `AssertionError` on expected outputs") def test_pipeline_fill_mask(self): super().test_pipeline_fill_mask() # TODO: @ydshieh @is_flaky( description="Sometimes gives `RuntimeError: probability tensor contains either `inf`, `nan` or element < 0`" ) def test_pipeline_text_generation(self): super().test_pipeline_text_generation() 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_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_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_with_chunking(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_with_chunking(*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) 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_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_bidirectionality(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_bidirectionality(*config_and_inputs) def test_for_chunking_shorter_sequence(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_chunking_shorter_sequence(*config_and_inputs) def test_for_chunking_longer_sequence(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_chunking_longer_sequence(*config_and_inputs) def test_for_laplace_attention(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_laplace_self_attention(*config_and_inputs) def test_for_relu2_attention(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_relu2_self_attention(*config_and_inputs) def test_for_sequence_length_beyond_max_positions(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_sequence_length_beyond_max_positions(*config_and_inputs) @require_torch_fp16 def test_generate_fp16(self): config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs_for_decoder() # attention_mask = torch.LongTensor(input_ids.ne(1)).to(torch_device) model = MegaForCausalLM(config).eval().to(torch_device) 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_sequence_classification_model(self): config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs() config.num_labels = self.model_tester.num_labels sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = MegaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_sequence_classification_model_for_multi_label(self): config, input_ids, _, attention_mask, *_ = self.model_tester.prepare_config_and_inputs() config.num_labels = self.model_tester.num_labels config.problem_type = "multi_label_classification" sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = MegaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @slow def test_model_from_pretrained(self): model_name = "mnaylor/mega-base-wikitext" model = MegaModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_cpu_offload(self): super().test_cpu_offload() @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_disk_offload(self): super().test_disk_offload() @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_model_parallelism(self): super().test_model_parallelism() @unittest.skip( reason=( "Calling `self.attention_function` in `MegaMovingAverageGatedAttention.forward` changes the submodules on " "device 1 to device 0 (also changes `requires_grad`). No idea how this could happen for now." ) ) def test_multi_gpu_data_parallel_forward(self): super().test_multi_gpu_data_parallel_forward() @unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.") def test_torchscript_simple(self): super().test_torchscript_simple() @unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.") def test_torchscript_output_hidden_state(self): super().test_torchscript_output_hidden_state() @unittest.skip(reason="Tracing of the dynamically computed `MegaMultiDimensionDampedEma._kernel` doesn't work.") def test_torchscript_output_attentions(self): super().test_torchscript_output_attentions() @require_torch class MegaModelIntegrationTest(TestCasePlus): @slow def test_inference_masked_lm(self): model = MegaForMaskedLM.from_pretrained("mnaylor/mega-base-wikitext") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 50265)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.tensor( [[[67.8389, 10.1470, -32.7148], [-11.1655, 29.1152, 23.1304], [-3.8015, 66.0397, 29.6733]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) @slow def test_inference_no_head(self): model = MegaModel.from_pretrained("mnaylor/mega-base-wikitext") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 128)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. taken from output[:, :3, :3] expected_slice = torch.tensor( [[[1.1767, -0.6349, 2.8494], [-0.5109, -0.7745, 1.9495], [-0.3287, -0.2111, 3.3367]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/qwen2/test_tokenization_qwen2.py
# coding=utf-8 # Copyright 2024 The Qwen team, Alibaba Group 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. import json import os import unittest from transformers import AddedToken, Qwen2Tokenizer, Qwen2TokenizerFast from transformers.models.qwen2.tokenization_qwen2 import VOCAB_FILES_NAMES, bytes_to_unicode from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class Qwen2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "qwen/qwen-tokenizer" tokenizer_class = Qwen2Tokenizer rust_tokenizer_class = Qwen2TokenizerFast test_slow_tokenizer = True test_rust_tokenizer = True space_between_special_tokens = False from_pretrained_kwargs = None test_seq2seq = False def setUp(self): super().setUp() # this make sure the vocabuary is complete at the byte level. vocab = list(bytes_to_unicode().values()) # the vocabulary, note: # - `"\u0120n"`, `"\u0120lowest"`, `"\u0120newer"`, and `"\u0120wider"` are ineffective, because there are # not in the merges. # - `"01"` is ineffective, because the merge is ineffective due to pretokenization. vocab.extend( [ "\u0120l", "\u0120n", "\u0120lo", "\u0120low", "er", "\u0120lowest", "\u0120newer", "\u0120wider", "01", ";}", ";}\u010a", "\u00cf\u0135", "\u0120#", "##", ] ) vocab_tokens = dict(zip(vocab, range(len(vocab)))) # note: `"0 1"` is in the merges, but the pretokenization rules render it ineffective merges = [ "#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", "0 1", "; }", ";} \u010a", "\u00cf \u0135", "\u0120 #", "# #", ] self.special_tokens_map = {"eos_token": "<|endoftext|>"} 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 Qwen2Tokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_rust_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return Qwen2TokenizerFast.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): # this case should cover # - NFC normalization (code point U+03D3 has different normalization forms under NFC, NFD, NFKC, and NFKD) # - the pretokenization rules (spliting digits and merging symbols with \n\r) input_text = "lower lower newer 010;}\n<|endoftext|>\u03d2\u0301" output_text = "lower lower newer 010;}\n<|endoftext|>\u03d3" return input_text, output_text def test_python_full_tokenizer(self): tokenizer = self.get_tokenizer() sequence, _ = self.get_input_output_texts(tokenizer) bpe_tokens = [ "l", "o", "w", "er", "\u0120low", "er", "\u0120", "n", "e", "w", "er", "\u0120", "0", "1", "0", ";}\u010a", "<|endoftext|>", "\u00cf\u0135", ] tokens = tokenizer.tokenize(sequence) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens input_bpe_tokens = [75, 78, 86, 260, 259, 260, 220, 77, 68, 86, 260, 220, 15, 16, 15, 266, 270, 267] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) @unittest.skip("We disable the test of pretokenization as it is not reversible.") def test_pretokenized_inputs(self): # the test case in parent class uses str.split to "pretokenize", # which eats the whitespaces, which, in turn, is not reversible. # the results, by nature, should be different. pass @unittest.skip("We disable the test of clean up tokenization spaces as it is not applicable.") def test_clean_up_tokenization_spaces(self): # it only tests bert-base-uncased and clean_up_tokenization_spaces is not applicable to this tokenizer pass def test_nfc_normalization(self): # per https://unicode.org/faq/normalization.html, there are three characters whose normalization forms # under NFC, NFD, NFKC, and NFKD are all different # using these, we can make sure only NFC is applied input_string = "\u03d2\u0301\u03d2\u0308\u017f\u0307" # the NFD form output_string = "\u03d3\u03d4\u1e9b" # the NFC form if self.test_slow_tokenizer: tokenizer = self.get_tokenizer() tokenizer_output_string, _ = tokenizer.prepare_for_tokenization(input_string) self.assertEqual(tokenizer_output_string, output_string) if self.test_rust_tokenizer: tokenizer = self.get_rust_tokenizer() # we can check the class of the normalizer, but it would be okay if Sequence([NFD, NFC]) is used # let's check the output instead tokenizer_output_string = tokenizer.backend_tokenizer.normalizer.normalize_str(input_string) self.assertEqual(tokenizer_output_string, output_string) def test_slow_tokenizer_token_with_number_sign(self): if not self.test_slow_tokenizer: return sequence = " ###" token_ids = [268, 269] tokenizer = self.get_tokenizer() self.assertListEqual(tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sequence)), token_ids) def test_slow_tokenizer_decode_spaces_between_special_tokens_default(self): # Qwen2Tokenizer changes the default `spaces_between_special_tokens` in `decode` to False if not self.test_slow_tokenizer: return # tokenizer has a special token: `"<|endfotext|>"` as eos, but it is not `legacy_added_tokens` # special tokens in `spaces_between_special_tokens` means spaces between `legacy_added_tokens` # that would be `"<|im_start|>"` and `"<|im_end|>"` in Qwen/Qwen2 Models token_ids = [259, 260, 270, 271, 26] sequence = " lower<|endoftext|><|im_start|>;" sequence_with_space = " lower<|endoftext|> <|im_start|> ;" tokenizer = self.get_tokenizer() # let's add a legacy_added_tokens im_start = AddedToken( "<|im_start|>", single_word=False, lstrip=False, rstrip=False, special=True, normalized=False ) tokenizer.add_tokens([im_start]) # `spaces_between_special_tokens` defaults to False self.assertEqual(tokenizer.decode(token_ids), sequence) # but it can be set to True self.assertEqual(tokenizer.decode(token_ids, spaces_between_special_tokens=True), sequence_with_space) @slow def test_tokenizer_integration(self): sequences = [ "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides " "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural " "Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained " "models in 100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.", "🤗 Transformers 提供了可以轻松地下载并且训练先进的预训练模型的 API 和工具。使用预训练模型可以减少计算消耗和碳排放,并且节省从头训练所需要的时间和资源。", """```python\ntokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-tokenizer")\n""" """tokenizer("世界,你好!")```""", ] expected_encoding = {'input_ids': [[8963, 388, 320, 69514, 3881, 438, 4510, 27414, 32852, 388, 323, 4510, 27414, 21334, 35722, 1455, 529, 8, 5707, 4586, 58238, 77235, 320, 61437, 11, 479, 2828, 12, 17, 11, 11830, 61437, 64, 11, 1599, 10994, 11, 27604, 321, 33, 529, 11, 29881, 6954, 32574, 369, 18448, 11434, 45451, 320, 45, 23236, 8, 323, 18448, 11434, 23470, 320, 30042, 38, 8, 448, 916, 220, 18, 17, 10, 80669, 4119, 304, 220, 16, 15, 15, 10, 15459, 323, 5538, 94130, 2897, 1948, 619, 706, 11, 5355, 51, 21584, 323, 94986, 13], [144834, 80532, 93685, 83744, 34187, 73670, 104261, 29490, 62189, 103937, 104034, 102830, 98841, 104034, 104949, 9370, 5333, 58143, 102011, 1773, 37029, 98841, 104034, 104949, 73670, 101940, 100768, 104997, 33108, 100912, 105054, 90395, 100136, 106831, 45181, 64355, 104034, 113521, 101975, 33108, 85329, 1773, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643], [73594, 12669, 198, 85593, 284, 8979, 37434, 6387, 10442, 35722, 445, 48, 16948, 45274, 16948, 34841, 3135, 1138, 85593, 445, 99489, 3837, 108386, 6313, 899, 73594, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643, 151643]], '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, 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], [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, 0]]} # fmt: off self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="Qwen/Qwen-tokenizer", revision="5909c8222473b2c73b0b73fb054552cd4ef6a8eb", sequences=sequences, )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/qwen2/test_modeling_qwen2.py
# coding=utf-8 # Copyright 2024 The Qwen team, Alibaba Group 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 Qwen2 model. """ import gc import tempfile import unittest import pytest from transformers import AutoTokenizer, Qwen2Config, is_torch_available, set_seed from transformers.testing_utils import ( backend_empty_cache, require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, require_torch_sdpa, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( Qwen2ForCausalLM, Qwen2ForSequenceClassification, Qwen2Model, ) class Qwen2ModelTester: 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, max_window_layers=3, use_sliding_window=True, sliding_window=2, num_attention_heads=4, num_key_value_heads=2, 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, pad_token_id=0, bos_token_id=1, 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.max_window_layers = max_window_layers self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_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.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.scope = scope # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs 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 = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device) 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): return Qwen2Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, max_window_layers=self.max_window_layers, use_sliding_window=self.use_sliding_window, sliding_window=self.sliding_window, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, pad_token_id=self.pad_token_id, bos_token_id=self.bos_token_id, ) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Qwen2 def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = Qwen2Model(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Qwen2 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 = Qwen2Model(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Qwen2 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 = Qwen2ForCausalLM(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Qwen2 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 = Qwen2ForCausalLM(config=config) model.to(torch_device) model.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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common 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, "attention_mask": input_mask} return config, inputs_dict @require_torch # Copied from tests.models.mistral.test_modeling_mistral.MistralModelTest with Mistral->Qwen2 class Qwen2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Qwen2Model, Qwen2ForCausalLM, Qwen2ForSequenceClassification) if is_torch_available() else () all_generative_model_classes = (Qwen2ForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": Qwen2Model, "text-classification": Qwen2ForSequenceClassification, "text-generation": Qwen2ForCausalLM, "zero-shot": Qwen2ForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False fx_compatible = True # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146 def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True # Ignore copy # TODO: @Fxmarty @require_torch_sdpa @slow @unittest.skip(reason="Currently failing.") def test_eager_matches_sdpa_generate(self): super().test_eager_matches_sdpa_generate() def setUp(self): self.model_tester = Qwen2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Qwen2Config, 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_Qwen2_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() print(config) config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = Qwen2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Qwen2_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = Qwen2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Qwen2_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = Qwen2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @unittest.skip("Qwen2 buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass @unittest.skip("Qwen2 uses GQA on all models so the KV cache is a non standard format") def test_past_key_values_format(self): pass @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_right(self): import torch for model_class in self.all_generative_model_classes: config, _ = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to( torch_device ) dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device) dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device) model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) with self.assertRaises(ValueError): _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_use_cache(self): import torch max_new_tokens = 30 for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) # NOTE: Qwen2 apparently does not support right padding + use_cache with FA2. dummy_attention_mask[:, -1] = 1 model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) # Just test that a large cache works as expected _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False, use_cache=True, ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest("Qwen2 flash attention does not support right padding") @require_torch class Qwen2IntegrationTest(unittest.TestCase): @slow def test_model_450m_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = Qwen2ForCausalLM.from_pretrained("Qwen/Qwen2-450m-beta", device_map="auto") input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) with torch.no_grad(): out = model(input_ids).logits.cpu() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]) # fmt: skip print(out[0, 0, :30]) torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4) del model backend_empty_cache(torch_device) gc.collect() @slow def test_model_450m_generation(self): EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big""" prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-450m-beta", use_fast=False) model = Qwen2ForCausalLM.from_pretrained("Qwen/Qwen2-450m-beta", device_map="auto") input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) del model backend_empty_cache(torch_device) gc.collect() @require_bitsandbytes @slow @require_flash_attn def test_model_450m_long_prompt(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # An input with 4097 tokens that is above the size of the sliding window input_ids = [1] + [306, 338] * 2048 model = Qwen2ForCausalLM.from_pretrained( "Qwen/Qwen2-450m-beta", device_map="auto", load_in_4bit=True, attn_implementation="flash_attention_2", ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) del assistant_model del model backend_empty_cache(torch_device) gc.collect() @slow @require_torch_sdpa def test_model_450m_long_prompt_sdpa(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # An input with 4097 tokens that is above the size of the sliding window input_ids = [1] + [306, 338] * 2048 model = Qwen2ForCausalLM.from_pretrained( "Qwen/Qwen2-450m-beta", device_map="auto", attn_implementation="sdpa", ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = assistant_model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) del assistant_model backend_empty_cache(torch_device) gc.collect() EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big""" prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-450m-beta", use_fast=False) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) @slow def test_speculative_generation(self): EXPECTED_TEXT_COMPLETION = ( "My favourite condiment is 100% Sriracha. I love the heat, the tang and the fact costs" ) prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-beta", use_fast=False) model = Qwen2ForCausalLM.from_pretrained("Qwen/Qwen2-450m-beta", device_map="auto", torch_dtype=torch.float16) assistant_model = Qwen2ForCausalLM.from_pretrained( "Qwen/Qwen2-450m-beta", device_map="auto", torch_dtype=torch.float16 ) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs set_seed(0) generated_ids = model.generate( input_ids, max_new_tokens=20, do_sample=True, temperature=0.3, assistant_model=assistant_model ) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) del model backend_empty_cache(torch_device) gc.collect()
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/falcon/test_modeling_falcon.py
# coding=utf-8 # Copyright 2023 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 Falcon model. """ import tempfile import unittest from parameterized import parameterized from transformers import ( AutoModelForCausalLM, AutoTokenizer, FalconConfig, is_torch_available, set_seed, ) from transformers.testing_utils import ( is_flaky, require_bitsandbytes, require_torch, require_torch_sdpa, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( FalconForCausalLM, FalconForQuestionAnswering, FalconForSequenceClassification, FalconForTokenClassification, FalconModel, ) from transformers.models.falcon.modeling_falcon import ( FalconDynamicNTKScalingRotaryEmbedding, FalconLinearScalingRotaryEmbedding, FalconRotaryEmbedding, ) class FalconModelTester: def __init__( self, parent, batch_size=3, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, 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 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): return FalconConfig( 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, pad_token_id=1, new_decoder_architecture=True, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = FalconModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 = FalconModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 = FalconForCausalLM(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_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 = FalconForCausalLM(config=config) model.to(torch_device) model.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 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, "attention_mask": input_mask} return config, inputs_dict @require_torch class FalconModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( FalconModel, FalconForCausalLM, FalconForSequenceClassification, FalconForTokenClassification, FalconForQuestionAnswering, ) if is_torch_available() else () ) all_generative_model_classes = (FalconForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": FalconModel, "question-answering": FalconForQuestionAnswering, "text-classification": FalconForSequenceClassification, "text-generation": FalconForCausalLM, "token-classification": FalconForTokenClassification, "zero-shot": FalconForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146 def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True def setUp(self): self.model_tester = FalconModelTester(self) self.config_tester = ConfigTester(self, config_class=FalconConfig, 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_position_embedding_types(self): config, *inputs = self.model_tester.prepare_config_and_inputs() for alibi in [True, False]: config.alibi = alibi self.model_tester.create_and_check_model(config, *inputs) def test_falcon_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = FalconForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_falcon_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = FalconForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_falcon_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = FalconForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_past_key_values_format(self): # Falcon can have different numbers of KV-heads than the number of query heads, so we need # to override this test to use the right head counts. for model_class in self.all_generative_model_classes: config, inputs = self.model_tester.prepare_config_and_inputs_for_common() # If it doesn't support cache, pass the test if not hasattr(config, "use_cache"): return model = model_class(config).to(torch_device) if "use_cache" not in inputs: inputs["use_cache"] = True outputs = model(**inputs) # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format) if "past_key_values" not in outputs: return num_hidden_layers = ( getattr(config, "decoder_layers", None) or getattr(config, "num_decoder_layers", None) or config.num_hidden_layers ) num_attention_heads = getattr(config, "num_kv_heads", config.num_attention_heads) embed_dim = getattr(config, "d_model", config.hidden_size) per_head_embed_dim = embed_dim // num_attention_heads past_kv = outputs["past_key_values"] self.assertEqual(len(past_kv), num_hidden_layers) batch_size, seq_length = inputs["input_ids"].shape for i in range(num_hidden_layers): if config.new_decoder_architecture: num_attention_heads = config.num_attention_heads elif config.multi_query: num_attention_heads = 1 self.assertEqual(len(past_kv[0]), 2) # K V for the decoder = 2 self.assertEqual( past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim) ) self.assertEqual( past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim) ) @parameterized.expand([("linear",), ("dynamic",)]) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model_rope_scaling_from_config with Llama->Falcon def test_model_rope_scaling_from_config(self, scaling_type): config, _ = self.model_tester.prepare_config_and_inputs_for_common() short_input = ids_tensor([1, 10], config.vocab_size) long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size) set_seed(42) # Fixed seed at init time so the two models get the same random weights original_model = FalconModel(config) original_model.to(torch_device) original_model.eval() original_short_output = original_model(short_input).last_hidden_state original_long_output = original_model(long_input).last_hidden_state set_seed(42) # Fixed seed at init time so the two models get the same random weights config.rope_scaling = {"type": scaling_type, "factor": 10.0} scaled_model = FalconModel(config) scaled_model.to(torch_device) scaled_model.eval() scaled_short_output = scaled_model(short_input).last_hidden_state scaled_long_output = scaled_model(long_input).last_hidden_state # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original # maximum sequence length, so the outputs for the short input should match. if scaling_type == "dynamic": self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) else: self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) # The output should be different for long inputs self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5)) def test_model_rope_scaling(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() hidden_size = config.hidden_size num_heads = config.num_attention_heads head_dim = hidden_size // num_heads scaling_factor = 10 short_input_length = 10 long_input_length = int(config.max_position_embeddings * 1.5) # Inputs x = torch.randn(1, dtype=torch.float32, device=torch_device) # used exlusively to get the dtype and the device # Sanity check original RoPE original_rope = FalconRotaryEmbedding( head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, ).to(torch_device) original_cos_short, original_sin_short = original_rope(x, short_input_length) original_cos_long, original_sin_long = original_rope(x, long_input_length) torch.testing.assert_close(original_cos_short, original_cos_long[:short_input_length, :]) torch.testing.assert_close(original_sin_short, original_sin_long[:short_input_length, :]) # Sanity check linear RoPE scaling # New position "x" should match original position with index "x/scaling_factor" linear_scaling_rope = FalconLinearScalingRotaryEmbedding( head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, scaling_factor=scaling_factor, ).to(torch_device) linear_cos_short, linear_sin_short = linear_scaling_rope(x, short_input_length) linear_cos_long, linear_sin_long = linear_scaling_rope(x, long_input_length) torch.testing.assert_close(linear_cos_short, linear_cos_long[:short_input_length, :]) torch.testing.assert_close(linear_sin_short, linear_sin_long[:short_input_length, :]) for new_position in range(0, long_input_length, scaling_factor): original_position = int(new_position // scaling_factor) torch.testing.assert_close(linear_cos_long[new_position, :], original_cos_long[original_position, :]) torch.testing.assert_close(linear_sin_long[new_position, :], original_sin_long[original_position, :]) # Sanity check Dynamic NTK RoPE scaling # Scaling should only be observed after a long input is fed. We can observe that the frequencies increase # with scaling_factor (or that `inv_freq` decreases) ntk_scaling_rope = FalconDynamicNTKScalingRotaryEmbedding( head_dim, max_position_embeddings=config.max_position_embeddings, base=config.rope_theta, scaling_factor=scaling_factor, ).to(torch_device) ntk_cos_short, ntk_sin_short = ntk_scaling_rope(x, short_input_length) ntk_cos_long, ntk_sin_long = ntk_scaling_rope(x, long_input_length) torch.testing.assert_close(ntk_cos_short, original_cos_short) torch.testing.assert_close(ntk_sin_short, original_sin_short) with self.assertRaises(AssertionError): torch.testing.assert_close(ntk_cos_long, original_cos_long) with self.assertRaises(AssertionError): torch.testing.assert_close(ntk_sin_long, original_sin_long) self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all()) # TODO: @Fxmarty @is_flaky(max_attempts=3, description="flaky on some models.") @require_torch_sdpa @slow def test_eager_matches_sdpa_generate(self): max_new_tokens = 30 if len(self.all_generative_model_classes) == 0: self.skipTest(f"{self.__class__.__name__} tests a model that does support generate: skipping this test") for model_class in self.all_generative_model_classes: if not model_class._supports_sdpa: self.skipTest(f"{model_class.__name__} does not support SDPA") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) model_sdpa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(torch_device) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") model_eager = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True, attn_implementation="eager", ).to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") # NOTE: This check is disabled for Falcon as the non-SDPA/SDPA implementation is in the same class (legacy reason). # for name, submodule in model_eager.named_modules(): # if "SdpaAttention" in submodule.__class__.__name__: # raise ValueError("The eager model should not have SDPA attention layers") # has_sdpa = False # for name, submodule in model_sdpa.named_modules(): # if "SdpaAttention" in submodule.__class__.__name__: # has_sdpa = True # break # if not has_sdpa: # raise ValueError("The SDPA model should have SDPA attention layers") # Just test that a large cache works as expected res_eager = model_eager.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False ) res_sdpa = model_sdpa.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False ) self.assertTrue(torch.allclose(res_eager, res_sdpa)) @require_torch class FalconLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_falcon(self): tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b") model = FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b") model.eval() model.to(torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) EXPECTED_OUTPUT = ( "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday." ) output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=19) output_str = tokenizer.batch_decode(output_ids)[0] self.assertEqual(output_str, EXPECTED_OUTPUT) @slow def test_lm_generation_big_models(self): # The big models are way too big for the CI, so we use tiny random models that resemble their # architectures but with much smaller and fewer layers for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]: tokenizer = AutoTokenizer.from_pretrained(repo) model = FalconForCausalLM.from_pretrained(repo) model.eval() model.to(torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) # We just test that these run without errors - the models are randomly initialized # and so the actual text outputs will be garbage model.generate(**inputs, do_sample=False, max_new_tokens=4) model.generate(**inputs, do_sample=True, max_new_tokens=4) model.generate(**inputs, num_beams=2, max_new_tokens=4) @slow def test_lm_generation_use_cache(self): # The big models are way too big for the CI, so we use tiny random models that resemble their # architectures but with much smaller and fewer layers with torch.no_grad(): for repo in [ "Rocketknight1/falcon-rw-1b", "Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b", ]: tokenizer = AutoTokenizer.from_pretrained(repo) model = FalconForCausalLM.from_pretrained(repo) model.eval() model.to(device=torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) # Test results are the same with and without cache outputs_no_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=False) outputs_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=True) self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0) @require_bitsandbytes @slow def test_batched_generation(self): tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b", padding_side="left") tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained( "tiiuae/falcon-7b", device_map="auto", load_in_4bit=True, ) test_text = "A sequence: 1, 2" # should generate the rest of the sequence unpadded_inputs = tokenizer([test_text], return_tensors="pt").to("cuda:0") unpadded_gen_out = model.generate(**unpadded_inputs, max_new_tokens=20) unpadded_gen_text = tokenizer.batch_decode(unpadded_gen_out, skip_special_tokens=True) dummy_text = "This is a longer text " * 2 # forces left-padding on `test_text` padded_inputs = tokenizer([test_text, dummy_text], return_tensors="pt", padding=True).to("cuda:0") padded_gen_out = model.generate(**padded_inputs, max_new_tokens=20) padded_gen_text = tokenizer.batch_decode(padded_gen_out, skip_special_tokens=True) expected_output = "A sequence: 1, 2, 3, 4, 5, 6, 7, 8, " self.assertLess(unpadded_inputs.input_ids.shape[-1], padded_inputs.input_ids.shape[-1]) # left-padding exists self.assertEqual(unpadded_gen_text[0], expected_output) self.assertEqual(padded_gen_text[0], expected_output) @slow @require_torch_sdpa def test_falcon_alibi_sdpa_matches_eager(self): input_ids = torch.randint(0, 1000, (5, 20)) config = FalconConfig( vocab_size=1000, hidden_size=64, num_hidden_layers=3, num_attention_heads=4, new_decoder_architecture=True, alibi=True, ) falcon = FalconForCausalLM(config) falcon = falcon.eval() with torch.no_grad(): # output_attentions=True dispatches to eager path falcon_output_eager = falcon(input_ids, output_attentions=True)[0] falcon_output_sdpa = falcon(input_ids)[0] self.assertTrue(torch.allclose(falcon_output_eager, falcon_output_sdpa, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/longt5/test_modeling_flax_longt5.py
# coding=utf-8 # Copyright 2022 Google LongT5 Authors and 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 tempfile import unittest import numpy as np import transformers from transformers import is_flax_available from transformers.models.auto import get_values from transformers.testing_utils import ( is_pt_flax_cross_test, require_flax, require_sentencepiece, require_tokenizers, slow, ) from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform" import jax import jax.numpy as jnp from flax.core.frozen_dict import unfreeze from flax.traverse_util import flatten_dict from transformers import FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, FLAX_MODEL_MAPPING, AutoTokenizer, LongT5Config from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model from transformers.models.longt5.modeling_flax_longt5 import ( FlaxLongT5ForConditionalGeneration, FlaxLongT5Model, shift_tokens_right, ) class FlaxLongT5ModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, decoder_seq_length=9, local_radius=5, encoder_attention_type="local", global_block_size=3, # For common tests is_training=True, use_attention_mask=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, d_ff=37, relative_attention_num_buckets=8, dropout_rate=0.1, initializer_factor=0.002, eos_token_id=1, pad_token_id=0, decoder_start_token_id=0, scope=None, decoder_layers=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length self.local_radius = local_radius self.block_len = local_radius + 1 self.encoder_attention_type = encoder_attention_type self.global_block_size = global_block_size # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask 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.d_ff = d_ff self.relative_attention_num_buckets = relative_attention_num_buckets self.dropout_rate = dropout_rate self.initializer_factor = initializer_factor self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.scope = None self.decoder_layers = decoder_layers def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None decoder_attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) config = LongT5Config( vocab_size=self.vocab_size, d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_decoder_layers=self.decoder_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, local_radius=self.local_radius, encoder_attention_type=self.encoder_attention_type, global_block_size=self.global_block_size, ) return ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, ) def create_and_check_model( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, ): model = FlaxLongT5Model(config=config) result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) decoder_output = result.last_hidden_state encoder_output = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.shape, (self.batch_size, self.encoder_seq_length, self.hidden_size)) self.parent.assertEqual(decoder_output.shape, (self.batch_size, self.decoder_seq_length, self.hidden_size)) def check_use_cache_forward_with_attn_mask( self, model_class_name, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, ): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(input_ids) # prevent fully zero'd out attention mask decoder_attention_mask = jnp.ones_like(decoder_attention_mask) decoder_attention_mask_cache = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask_cache, past_key_values=past_key_values, ) outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=decoder_attention_mask_cache, ) outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, } return config, inputs_dict @require_flax class FlaxLongT5ModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase): all_model_classes = (FlaxLongT5Model, FlaxLongT5ForConditionalGeneration) if is_flax_available() else () all_generative_model_classes = (FlaxLongT5ForConditionalGeneration,) if is_flax_available() else () is_encoder_decoder = True def setUp(self): self.model_tester = FlaxLongT5ModelTester(self) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=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_v1_1(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() # check that gated gelu feed forward and different word embeddings work config = config_and_inputs[0] config.tie_word_embeddings = False config.feed_forward_proj = "gated-gelu" self.model_tester.create_and_check_model(config, *config_and_inputs[1:]) def test_use_cache_forward_with_attn_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, *config_and_inputs) def test_encode(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 encode_jitted(input_ids, attention_mask=None, **kwargs): return model.encode(input_ids=input_ids, attention_mask=attention_mask) with self.subTest("JIT Enabled"): jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = encode_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) def test_decode(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__): model = model_class(config) encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"]) prepared_inputs_dict = { "decoder_input_ids": inputs_dict["decoder_input_ids"], "decoder_attention_mask": inputs_dict["decoder_attention_mask"], "encoder_outputs": encoder_outputs, } @jax.jit def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs): return model.decode( decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, ) with self.subTest("JIT Enabled"): jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = decode_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) def test_shift_right(self): decoder_start_token_id = 0 pad_token_id = 1 labels = np.arange(2, 102).reshape(5, 20) labels[:2, 15:] = -100 decoder_input_ids = shift_tokens_right(labels, pad_token_id, decoder_start_token_id) np_decoder_input_ids = np.array(decoder_input_ids) padded_slice = np_decoder_input_ids[:2, (15 + 1) :] self.assertTrue((padded_slice == 1).all()) not_padded_slice = np_decoder_input_ids[2:, 1:] rolled_labels = np.roll(labels[2:], 1)[:, 1:] self.assertTrue((not_padded_slice == rolled_labels).all()) self.assertTrue((np_decoder_input_ids[:, 0] == 0).all()) # overwrite since special base model prefix is used def test_save_load_from_base(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() base_class = FLAX_MODEL_MAPPING[config.__class__] for model_class in self.all_model_classes: if model_class == base_class: continue model = base_class(config) base_params = flatten_dict(unfreeze(model.params)) # check that all base model weights are loaded correctly with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) head_model = model_class.from_pretrained(tmpdirname) base_param_from_head = flatten_dict(unfreeze(head_model.params)) for key in base_param_from_head.keys(): max_diff = (base_params[key] - base_param_from_head[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") # overwrite since special base model prefix is used def test_save_load_to_base(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() base_class = FLAX_MODEL_MAPPING[config.__class__] for model_class in self.all_model_classes: if model_class == base_class: continue model = model_class(config) base_params_from_head = flatten_dict(unfreeze(model.params)) # check that all base model weights are loaded correctly with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) base_model = base_class.from_pretrained(tmpdirname) base_params = flatten_dict(unfreeze(base_model.params)) for key in base_params_from_head.keys(): max_diff = (base_params[key] - base_params_from_head[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_length = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_length) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_length) 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) block_len = getattr(self.model_tester, "block_len", None) for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False 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) # 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, block_len, 3 * block_len], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # Question Answering model returns start_logits and end_logits if model_class in get_values(FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING): correct_outlen += 1 # start_logits and end_logits instead of only 1 output 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, 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)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 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, block_len, 3 * block_len], ) # overwrite since special base model prefix is used @is_pt_flax_cross_test def test_save_load_from_base_pt(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() base_class = FLAX_MODEL_MAPPING[config.__class__] for model_class in self.all_model_classes: if model_class == base_class: continue model = base_class(config) base_params = flatten_dict(unfreeze(model.params)) # convert Flax model to PyTorch model pt_model_class = getattr(transformers, base_class.__name__[4:]) # Skip the "Flax" at the beginning pt_model = pt_model_class(config).eval() pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params) # check that all base model weights are loaded correctly with tempfile.TemporaryDirectory() as tmpdirname: # save pt model pt_model.save_pretrained(tmpdirname) head_model = model_class.from_pretrained(tmpdirname, from_pt=True) base_param_from_head = flatten_dict(unfreeze(head_model.params)) for key in base_param_from_head.keys(): max_diff = (base_params[key] - base_param_from_head[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") # overwrite since special base model prefix is used @is_pt_flax_cross_test def test_save_load_to_base_pt(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() base_class = FLAX_MODEL_MAPPING[config.__class__] for model_class in self.all_model_classes: if model_class == base_class: continue model = model_class(config) base_params_from_head = flatten_dict(unfreeze(model.params)) # convert Flax model to PyTorch model pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning pt_model = pt_model_class(config).eval() pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params) # check that all base model weights are loaded correctly with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) base_model = base_class.from_pretrained(tmpdirname, from_pt=True) base_params = flatten_dict(unfreeze(base_model.params)) for key in base_params_from_head.keys(): max_diff = (base_params[key] - base_params_from_head[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") # overwrite since special base model prefix is used @is_pt_flax_cross_test def test_save_load_bf16_to_base_pt(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() base_class = FLAX_MODEL_MAPPING[config.__class__] for model_class in self.all_model_classes: if model_class == base_class: continue model = model_class(config) model.params = model.to_bf16(model.params) base_params_from_head = flatten_dict(unfreeze(model.params)) # convert Flax model to PyTorch model pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning pt_model = pt_model_class(config).eval() pt_model = load_flax_weights_in_pytorch_model(pt_model, model.params) # check that all base model weights are loaded correctly with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) base_model = base_class.from_pretrained(tmpdirname, from_pt=True) base_params = flatten_dict(unfreeze(base_model.params)) for key in base_params_from_head.keys(): max_diff = (base_params[key] - base_params_from_head[key]).sum().item() self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical") class FlaxLongT5TGlobalModelTest(FlaxLongT5ModelTest): def setUp(self): self.model_tester = FlaxLongT5ModelTester(self, encoder_attention_type="transient-global") self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_length = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_length) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_length) 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) block_len = getattr(self.model_tester, "block_len", None) global_block_size = getattr(self.model_tester, "global_block_size", None) global_seq_len = encoder_seq_length // global_block_size for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False 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) # 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, block_len, 3 * block_len + global_seq_len], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # Question Answering model returns start_logits and end_logits if model_class in get_values(FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING): correct_outlen += 1 # start_logits and end_logits instead of only 1 output 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, 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)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 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, block_len, 3 * block_len + global_seq_len], ) @require_sentencepiece @require_tokenizers @require_flax class FlaxLongT5ModelIntegrationTests(unittest.TestCase): model_path = "Stancld/longt5-tglobal-large-16384-pubmed-3k_steps" def expected_summary(self): return [ "background : coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in" " developing world . it provides an excellent resolution for visualization of the coronary arteries for" " catheter - based or operating interventions . although the association of this technique with major" " complications such as mortality is highly uncommon , it is frequently associated with various cardiac" " and noncardiac complications . computed tomography coronary angiography is a promising technique for the" " evaluation of cad noninvasively . it assesses disease within the coronary artery and provides" " qualitative and quantitative information about nonobstructive atherosclerotic plaque" ] @slow def test_summarization(self): model = FlaxLongT5ForConditionalGeneration.from_pretrained(self.model_path) tok = AutoTokenizer.from_pretrained(self.model_path) ARTICLE = """coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in developing world . \n it provides an excellent resolution for visualization of the coronary arteries for catheter - based or operating interventions . \n although the association of this technique with major complications such as mortality is highly uncommon , it is frequently associated with various cardiac and noncardiac complications . computed tomography ( ct ) coronary angiography is a promising technique for the evaluation of cad noninvasively . \n it assesses disease within the coronary artery and provides qualitative and quantitative information about nonobstructive atherosclerotic plaque burden within the vessel wall . \n thus , ct angiography - based disease evaluation may provide clinically more significant information than conventional angiography . the introduction of multi - slice computed tomography ( msct ) technology such as 64-slice , 12 8-slice , 256-slice , and now 320-slice msct has produced a high diagnostic accuracy of ct coronary angiography . \n it has consistently showed to have a very high negative predictive value ( well above 90% ) in ruling out patients with s ignificant cad defined as coronary luminal stenosis of > 50% . \n the american college of cardiology / american heart association recommends that coronary angiography should be performed before valve surgery in men aged > 40 years , women aged > 35 years with coronary risk factors and in postmenopausal women . \n the prevalence of cad in patients undergoing valve replacement is 2040% in developed countries . in the previous studies , \n the incidence of angiographically p roven cad in acquired valvular diseases has been shown to vary widely from 9% to 41% . in aortic stenosis , \n we aimed to report the diagnostic performance of 128-slice ct coronary angiography in 50 patients undergoing for major noncoron ary cardiac surgery referred for diagnostic invasive coronary angiography to assess the extent and severity of coronary stenosis . \n during january 2013 to december 2014 , we enrolled fifty major noncoronary cardiac surgery patients sche duled for invasive coronary angiography who fulfilled the following inclusion criteria of age 40 years , having low or intermediate probability of cad , left ventricular ejection fraction ( lvef ) > 35% , and patient giving informed conse nt for undergoing msct and conventional coronary angiography . \n those having any contraindication for contrast injection , lvef < 35% , high pretest probability of cad , and hemodynamic instability were excluded from the study . \n pati ents with heart rates of > 70 bpm received ( unless they had known overt heart failure or electrocardiogram ( ecg ) atrioventricular conduction abnormalities ) a single oral dose of 100 mg metoprolol 45 min before the scan . \n patients w ith heart rates of > 80 bpm received an additional oral dose of metoprolol if not contraindicated . \n all patients were scanned with a 128-slice ct scanner ( siemens , somatom definition as ) equipped with a new feature in msct technolog y , so - called z - axis flying - focus technology . \n the central 32 detector rows acquire 0.6-mm slices , and the flying - focus spot switches back and forth between 2 z positions between each reading . \n two slices per detector row a re acquired , which results in a higher oversampling rate in the z - axis , thereby reducing artifacts related to the spiral acquisition and improving spatial resolution down to 0.4 mm . \n a bolus of 6580 ml contrast material ( omnipaque ) was injected through an arm vein at a flow rate of 5 ml / s . \n a bolus tracking technique was used to synchronize the arrival of contrast in the coronary arteries with the initiation of the scan . to monitor the arrival of contrast m aterial , \n axial scans were obtained at the level of the ascending aorta with a delay of 10 s after the start of the contrast injection . \n the scan was automatically started when a threshold of 150 hounsfield units was reached in a re gion of interest positioned in the ascending aorta . \n images were reconstructed with ecg gating to obtain optimal , motion - free image quality . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a s ingle observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiograp hy . \n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary angiography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiograp hy . \n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of th e number , areas , and peak hounsfield units of the detected calcified lesions . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstructions to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the di agnostic performance of ct coronary angiography for the detection of significant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease p er vessel ) , and patient by patient ( no or any disease per patient ) . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a single observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiography . \n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary an giography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiography . \n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of the number , areas , and peak hounsfield units of the detected calcified lesi ons . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstruction s to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the diagnostic performance of ct coronary angiography for the detection of signif icant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease per vessel ) , and patient by patient ( no or any disease per patient ) . \n in this study , 29 ( 58% ) subjects were female , and 21 ( 42% ) were male showing an average age of 50.36 8.39 years . \n of fifty patients 24 ( 48% ) , 13 ( 26% ) , eight ( 16% ) , and five ( 10% ) underwent mitral valve replacement , double valve replacement ( dvr ) , aortic valve replacement , and other surgeries , respectively . \n high distribution of cad risk factors such as hypertension ( 24% ) , smoking ( 22% ) , and dyslipidemia ( 18% ) was observed in the stu dy group . \n the mean creatinine level was 0.766 0.17 and average dye used in conventional angiography was 48.5 26.6 whereas for ct angiography it was 72.8 6.32 . \n average radiation dose in conventional coronary angiography and msct coronary angiography was 5.2 msv and 9.2 msv , respectively . \n the majority of the patients had sinus rhythm ( 68% ) , whereas atrial fibrillation was found in 32% of the subjects . \n patients included in the study had low to intermed iate probability of cad . in this study , three patients had complications after conventional angiography . \n complications were of local site hematoma , acute kidney injury managed conservatively , and acute heart failure . \n a patient who developed hematoma was obese female patients with body mass index > 30 kg / m . \n the patient suffered from pseudoaneurysm , had hospitalized for 9 days , which leads to increased morbidity and cost of hospital stay . \n the diagnos tic accuracy of ct coronary angiography was evaluated regarding true positive , true negative values and is presented in table 1 . the overall sensitivity and \n specificity of ct angiography technique was 100% ( 95% ci : 39.76%100% ) and 91.30% ( 95% ci : 79.21%97.58% ) , respectively [ table 2 ] . \n the positive predictive value ( 50% ; 95% ci : 15.70%84.30% ) and negative predictive value ( 100% ; 95% ci : 91.59%100% ) of ct angiography were also fairly high in these patients . \n recent reports from multiple studies demonstrated that recent - generation msct scanners showed promise for noninvasive detection of coronary stenosis however , until now no studies were found regarding the clinical efficacy or prognostic value of 128-slice ct coronary angiography versus conventional invasive coronary angiography in the diagnosis of patients planned for major noncoronary surgeries such as dvr , bentall , atrial septal defect closure , etc . in our study , we reported 8% cad prevalence in patients planned for major noncoronary cardiac surgery . \n we performed conventional and msct coronary angiography in all patients and the results showed that ct coronary angiography with i nvasive coronary angiography as the reference standard had a considerably high sensitivity ( 100% ) and specificity ( 95.65% ) . \n the health economic model using invasive coronary angiography as the reference standard showed that at a p retest probability of cad of 70% or lower , ct coronary angiography resulted in lower cost per patient with a true positive diagnosis . at a pretest probability of cad of 70% or higher , invasive coronary angiography was associated with a lower cost per patient with a true positive diagnosis . in our study population , \n two patients developed local site complications in the form of hematoma and pseudoaneurysm after conventional angiography . \n hence , msct coronary ang iography will be more favorable in female obese patients with intermediate likelihood of cad . \n hence , msct coronary angiography will be cost - effective in patients of valvular heart diseases . \n however , ct angiography suffers from a drawback that average amount of dye used in msct coronary angiography were 72.8 6.32 ml which is higher than average amount of dye required for conventional angiography ( 48.6 26.6 ml ) . \n hence , the use of ct coronary angiography could not be used in patients with known renal dysfunction , where reduction of contrast dye load is highly advocated . \n our results show that 128-slice ct coronary angiography is a reliable technique to detect coronary stenosis in pat ients planned for noncoronary cardiac surgery . \n although there has been important technological progress in the development of ct coronary angiography , its clinical application remains limited . \n a study wth large numbers of patient s is required for the recommendation of only ct coronary angiography for the coronary evaluation in major non - cardiac surgeries . \n mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , guja rat , india ) . \n u.n . mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , gujarat , india ) . \n """ dct = tok( [ARTICLE], max_length=1024, padding="max_length", truncation=True, return_tensors="np", ) hypotheses_batch = model.generate( **dct, num_beams=4, length_penalty=2.0, max_length=142, min_length=56, do_sample=False, early_stopping=True, ).sequences decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False) self.assertListEqual( self.expected_summary(), decoded, )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/longt5/test_modeling_longt5.py
# coding=utf-8 # Copyright 2022 Google LongT5 Authors and 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 copy import tempfile import unittest from transformers import LongT5Config, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_QUESTION_ANSWERING_MAPPING, AutoTokenizer, LongT5EncoderModel, LongT5ForConditionalGeneration, LongT5Model, ) class LongT5ModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, decoder_seq_length=9, local_radius=5, encoder_attention_type="local", global_block_size=3, # For common tests is_training=True, use_attention_mask=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, d_ff=37, relative_attention_num_buckets=8, dropout_rate=0.1, initializer_factor=0.002, eos_token_id=1, pad_token_id=0, decoder_start_token_id=0, scope=None, decoder_layers=None, large_model_config_path="google/long-t5-local-large", ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length self.local_radius = local_radius self.block_len = local_radius + 1 self.encoder_attention_type = encoder_attention_type self.global_block_size = global_block_size # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask 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.d_ff = d_ff self.relative_attention_num_buckets = relative_attention_num_buckets self.dropout_rate = dropout_rate self.initializer_factor = initializer_factor self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.scope = None self.decoder_layers = decoder_layers self.large_model_config_path = large_model_config_path def get_large_model_config(self): return LongT5Config.from_pretrained(self.large_model_config_path) def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None decoder_attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = self.get_config() return ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) def get_pipeline_config(self): return LongT5Config( vocab_size=166, # longt5 forces 100 extra tokens d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_decoder_layers=self.decoder_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, local_radius=self.local_radius, encoder_attention_type=self.encoder_attention_type, global_block_size=self.global_block_size, ) def get_config(self): return LongT5Config( vocab_size=self.vocab_size, d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_decoder_layers=self.decoder_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, local_radius=self.local_radius, encoder_attention_type=self.encoder_attention_type, global_block_size=self.global_block_size, ) def check_prepare_lm_labels_via_shift_left( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5Model(config=config) model.to(torch_device) model.eval() # make sure that lm_labels are correctly padded from the right lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id) # add casaul pad token mask triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not() lm_labels.masked_fill_(triangular_mask, self.pad_token_id) decoder_input_ids = model._shift_right(lm_labels) for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)): # first item self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id) if i < decoder_input_ids_slice.shape[-1]: if i < decoder_input_ids.shape[-1] - 1: # items before diagonal self.parent.assertListEqual( decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist() ) # pad items after diagonal if i < decoder_input_ids.shape[-1] - 2: self.parent.assertListEqual( decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist() ) else: # all items after square self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist()) def create_and_check_model( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5Model(config=config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) decoder_output = result.last_hidden_state decoder_past = result.past_key_values encoder_output = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size)) self.parent.assertEqual(decoder_output.size(), (self.batch_size, self.decoder_seq_length, self.hidden_size)) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(decoder_past), config.num_layers) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0]), 4) def create_and_check_with_lm_head( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5ForConditionalGeneration(config=config).to(torch_device).eval() outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=lm_labels, ) self.parent.assertEqual(len(outputs), 4) self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size)) self.parent.assertEqual(outputs["loss"].size(), ()) def create_and_check_decoder_model_past( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5Model(config=config).get_decoder().to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, 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[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_decoder_model_attention_mask_past( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5Model(config=config).get_decoder() model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "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[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_decoder_model_past_large_inputs( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5Model(config=config).get_decoder().to(torch_device).eval() # first forward pass outputs = model(input_ids, attention_mask=attention_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_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([attention_mask, next_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-3)) def create_and_check_generate_with_past_key_values( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = LongT5ForConditionalGeneration(config=config).to(torch_device).eval() torch.manual_seed(0) output_without_past_cache = model.generate( input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False ) torch.manual_seed(0) output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True) self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache)) def create_and_check_encoder_decoder_shared_weights( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): for model_class in [LongT5Model, LongT5ForConditionalGeneration]: torch.manual_seed(0) model = model_class(config=config).to(torch_device).eval() # load state dict copies weights but does not tie them model.encoder.load_state_dict(model.decoder.state_dict(), strict=False) torch.manual_seed(0) tied_config = copy.deepcopy(config) tied_config.tie_encoder_decoder = True tied_model = model_class(config=tied_config).to(torch_device).eval() model_result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4 ) ) # check that outputs after saving and loading are equal with tempfile.TemporaryDirectory() as tmpdirname: tied_model.save_pretrained(tmpdirname) tied_model = model_class.from_pretrained(tmpdirname) tied_model.to(torch_device) tied_model.eval() # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4, ) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "use_cache": False, } return config, inputs_dict @require_torch class LongT5ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (LongT5Model, LongT5ForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (LongT5ForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "conversational": LongT5ForConditionalGeneration, "feature-extraction": LongT5Model, "summarization": LongT5ForConditionalGeneration, "text2text-generation": LongT5ForConditionalGeneration, "translation": LongT5ForConditionalGeneration, } if is_torch_available() else {} ) fx_compatible = False test_pruning = False test_torchscript = True test_resize_embeddings = True test_model_parallel = False is_encoder_decoder = True def setUp(self): self.model_tester = LongT5ModelTester(self) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37) def test_config(self): self.config_tester.run_common_tests() def test_shift_right(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs) 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_with_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_with_lm_head(*config_and_inputs) def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_past_with_attn_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_decoder_model_past_with_3d_attn_mask(self): ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = self.model_tester.prepare_config_and_inputs() attention_mask = ids_tensor( [self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length], vocab_size=2, ) decoder_attention_mask = ids_tensor( [self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length], vocab_size=2, ) self.model_tester.create_and_check_decoder_model_attention_mask_past( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) 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_generate_with_past_key_values(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_generate_with_past_key_values(*config_and_inputs) def test_encoder_decoder_shared_weights(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "google/long-t5-local-base" model = LongT5Model.from_pretrained(model_name) self.assertIsNotNone(model) @slow def test_export_to_onnx(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() model = LongT5Model(config_and_inputs[0]).to(torch_device) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( model, (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]), f"{tmpdirname}/longt5_test.onnx", export_params=True, opset_version=13, input_names=["input_ids", "decoder_input_ids"], ) def test_generate_with_head_masking(self): attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"] config_and_inputs = self.model_tester.prepare_config_and_inputs() config = config_and_inputs[0] max_length = config_and_inputs[1].shape[-1] + 3 model = LongT5ForConditionalGeneration(config).eval() model.to(torch_device) head_masking = { "head_mask": torch.zeros(config.num_layers, config.num_heads, device=torch_device), "decoder_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device), "cross_attn_head_mask": torch.zeros(config.num_decoder_layers, config.num_heads, device=torch_device), } for attn_name, (name, mask) in zip(attention_names, head_masking.items()): head_masks = {name: mask} # Explicitly pass decoder_head_mask as it is required from LONGT5 model when head_mask specified if name == "head_mask": head_masks["decoder_head_mask"] = torch.ones( config.num_decoder_layers, config.num_heads, device=torch_device ) out = model.generate( config_and_inputs[1], num_beams=1, max_length=max_length, output_attentions=True, return_dict_in_generate=True, **head_masks, ) # We check the state of decoder_attentions and cross_attentions just from the last step attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights]), 0.0) def test_attention_outputs(self): if not self.has_attentions: pass else: 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) chunk_length = getattr(self.model_tester, "chunk_length", None) block_len = getattr(self.model_tester, "block_len", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes 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 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) model.to(torch_device) model.eval() with torch.no_grad(): 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, block_len, 3 * block_len], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # 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, 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, 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 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, block_len, 3 * block_len], ) def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length): block_len = getattr(self.model_tester, "block_len", None) encoder_expected_shape = (batch_size, 2, config.num_attention_heads, block_len, 3 * block_len) self.assertIsInstance(attentions, tuple) self.assertListEqual( [layer_attentions.shape for layer_attentions in attentions], [encoder_expected_shape] * len(attentions), ) @require_torch class LongT5TGlobalModelTest(LongT5ModelTest): def setUp(self): self.model_tester = LongT5ModelTester( self, encoder_attention_type="transient-global", large_model_config_path="google/long-t5-tglobal-large" ) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37) def test_attention_outputs(self): if not self.has_attentions: pass else: 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) chunk_length = getattr(self.model_tester, "chunk_length", None) block_len = getattr(self.model_tester, "block_len", None) global_block_size = getattr(self.model_tester, "global_block_size", None) global_seq_len = encoder_seq_length // global_block_size if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes 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 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) model.to(torch_device) model.eval() with torch.no_grad(): 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, block_len, 3 * block_len + global_seq_len], ) out_len = len(outputs) if self.is_encoder_decoder: correct_outlen = 5 # 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, 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, 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 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, block_len, 3 * block_len + global_seq_len], ) def _check_encoder_attention_for_generate(self, attentions, batch_size, config, seq_length): block_len = getattr(self.model_tester, "block_len", None) global_block_size = getattr(self.model_tester, "global_block_size", None) global_seq_length = seq_length // global_block_size encoder_expected_shape = ( batch_size, 2, config.num_attention_heads, block_len, 3 * block_len + global_seq_length, ) self.assertIsInstance(attentions, tuple) self.assertListEqual( [layer_attentions.shape for layer_attentions in attentions], [encoder_expected_shape] * len(attentions), ) class LongT5EncoderOnlyModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, local_radius=5, encoder_attention_type="local", global_block_size=3, # For common tests use_attention_mask=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, d_ff=37, relative_attention_num_buckets=8, is_training=False, dropout_rate=0.1, initializer_factor=0.002, is_encoder_decoder=False, eos_token_id=1, pad_token_id=0, scope=None, large_model_config_path="google/long-t5-local-large", ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.local_radius = local_radius self.block_len = local_radius + 1 self.encoder_attention_type = encoder_attention_type self.global_block_size = global_block_size # For common tests self.seq_length = self.encoder_seq_length self.use_attention_mask = use_attention_mask 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.d_ff = d_ff self.relative_attention_num_buckets = relative_attention_num_buckets self.dropout_rate = dropout_rate self.initializer_factor = initializer_factor self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.is_encoder_decoder = is_encoder_decoder self.scope = None self.is_training = is_training self.large_model_config_path = large_model_config_path def get_large_model_config(self): return LongT5Config.from_pretrained(self.large_model_config_path) def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) config = LongT5Config( vocab_size=self.vocab_size, d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, is_encoder_decoder=self.is_encoder_decoder, local_radius=self.local_radius, encoder_attention_type=self.encoder_attention_type, global_block_size=self.global_block_size, ) return ( config, input_ids, attention_mask, ) def create_and_check_model( self, config, input_ids, attention_mask, ): model = LongT5EncoderModel(config=config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, attention_mask=attention_mask, ) result = model(input_ids=input_ids) encoder_output = result.last_hidden_state self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict class LongT5EncoderOnlyModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (LongT5EncoderModel,) if is_torch_available() else () test_pruning = False test_torchscript = True test_resize_embeddings = False test_model_parallel = False def setUp(self): self.model_tester = LongT5EncoderOnlyModelTester(self) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=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_attention_outputs(self): if not self.has_attentions: pass else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True block_len = getattr(self.model_tester, "block_len", 4) 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 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) model.to(torch_device) model.eval() with torch.no_grad(): 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, block_len, 3 * block_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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 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, block_len, 3 * block_len], ) class LongT5EncoderOnlyTGlobalModelTest(LongT5EncoderOnlyModelTest): def setUp(self): self.model_tester = LongT5EncoderOnlyModelTester( self, encoder_attention_type="transient-global", large_model_config_path="google/long-t5-tglobal-large" ) self.config_tester = ConfigTester(self, config_class=LongT5Config, d_model=37) def test_attention_outputs(self): if not self.has_attentions: pass else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True block_len = getattr(self.model_tester, "block_len", None) seq_len = getattr(self.model_tester, "seq_length", None) global_block_size = getattr(self.model_tester, "global_block_size", 4) global_seq_len = seq_len // global_block_size 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 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) model.to(torch_device) model.eval() with torch.no_grad(): 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, block_len, 3 * block_len + global_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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 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, block_len, 3 * block_len + global_seq_len], ) def use_task_specific_params(model, task): model.config.update(model.config.task_specific_params[task]) @require_torch @require_sentencepiece @require_tokenizers class LongT5ModelIntegrationTests(unittest.TestCase): @cached_property def model(self): return LongT5ForConditionalGeneration.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps").to( torch_device ) @cached_property def tokenizer(self): return AutoTokenizer.from_pretrained("Stancld/longt5-tglobal-large-16384-pubmed-3k_steps") def expected_summary(self): return [ "background : coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in" " developing world . it provides an excellent resolution for visualization of the coronaryarteries for" " catheter - based or operating interventions . although the association of this technique with major" " complications such as mortality is highly uncommon , it is frequently associated with various cardiac" " and noncardiac complications.materials and methods : in aortic stenosis , we aimed to report the" " diagnostic performance of 128-slice computed tomography coronary angiogram in 50 patients undergoing for" " major noncoron ary cardiac surgery referred" ] @slow def test_summarization(self): model = self.model tok = self.tokenizer ARTICLE = """coronary artery disease ( cad ) is the emerging cause of morbidity and mortality in developing world . \n it provides an excellent resolution for visualization of the coronary arteries for catheter - based or operating interventions . \n although the association of this technique with major complications such as mortality is highly uncommon , it is frequently associated with various cardiac and noncardiac complications . computed tomography ( ct ) coronary angiography is a promising technique for the evaluation of cad noninvasively . \n it assesses disease within the coronary artery and provides qualitative and quantitative information about nonobstructive atherosclerotic plaque burden within the vessel wall . \n thus , ct angiography - based disease evaluation may provide clinically more significant information than conventional angiography . the introduction of multi - slice computed tomography ( msct ) technology such as 64-slice , 12 8-slice , 256-slice , and now 320-slice msct has produced a high diagnostic accuracy of ct coronary angiography . \n it has consistently showed to have a very high negative predictive value ( well above 90% ) in ruling out patients with s ignificant cad defined as coronary luminal stenosis of > 50% . \n the american college of cardiology / american heart association recommends that coronary angiography should be performed before valve surgery in men aged > 40 years , women aged > 35 years with coronary risk factors and in postmenopausal women . \n the prevalence of cad in patients undergoing valve replacement is 2040% in developed countries . in the previous studies , \n the incidence of angiographically p roven cad in acquired valvular diseases has been shown to vary widely from 9% to 41% . in aortic stenosis , \n we aimed to report the diagnostic performance of 128-slice ct coronary angiography in 50 patients undergoing for major noncoron ary cardiac surgery referred for diagnostic invasive coronary angiography to assess the extent and severity of coronary stenosis . \n during january 2013 to december 2014 , we enrolled fifty major noncoronary cardiac surgery patients sche duled for invasive coronary angiography who fulfilled the following inclusion criteria of age 40 years , having low or intermediate probability of cad , left ventricular ejection fraction ( lvef ) > 35% , and patient giving informed conse nt for undergoing msct and conventional coronary angiography . \n those having any contraindication for contrast injection , lvef < 35% , high pretest probability of cad , and hemodynamic instability were excluded from the study . \n pati ents with heart rates of > 70 bpm received ( unless they had known overt heart failure or electrocardiogram ( ecg ) atrioventricular conduction abnormalities ) a single oral dose of 100 mg metoprolol 45 min before the scan . \n patients w ith heart rates of > 80 bpm received an additional oral dose of metoprolol if not contraindicated . \n all patients were scanned with a 128-slice ct scanner ( siemens , somatom definition as ) equipped with a new feature in msct technolog y , so - called z - axis flying - focus technology . \n the central 32 detector rows acquire 0.6-mm slices , and the flying - focus spot switches back and forth between 2 z positions between each reading . \n two slices per detector row a re acquired , which results in a higher oversampling rate in the z - axis , thereby reducing artifacts related to the spiral acquisition and improving spatial resolution down to 0.4 mm . \n a bolus of 6580 ml contrast material ( omnipaque ) was injected through an arm vein at a flow rate of 5 ml / s . \n a bolus tracking technique was used to synchronize the arrival of contrast in the coronary arteries with the initiation of the scan . to monitor the arrival of contrast m aterial , \n axial scans were obtained at the level of the ascending aorta with a delay of 10 s after the start of the contrast injection . \n the scan was automatically started when a threshold of 150 hounsfield units was reached in a re gion of interest positioned in the ascending aorta . \n images were reconstructed with ecg gating to obtain optimal , motion - free image quality . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a s ingle observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiograp hy . \n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary angiography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiograp hy . \n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of th e number , areas , and peak hounsfield units of the detected calcified lesions . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstructions to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the di agnostic performance of ct coronary angiography for the detection of significant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease p er vessel ) , and patient by patient ( no or any disease per patient ) . \n all scans were performed within 2 weeks of the msct coronary diagnostic angiogram . a single observer unaware of the multi - slice ct results identified coronary lesion as a single vessel , double vessel , or triple vessel disease . \n all lesion , regardless of size , were included for comparison with ct coronary angiography . \n lesions were classified as having nonsignificant disease ( luminal irregularities or < 50% stenosis ) or as having significant stenosis . \n stenosis was evaluated in two orthogonal views and classified as significant if the mean lumen diameter reduction was 50% using a validated quantitative coronary an giography ( qca ) . \n all scans were analyzed independently by a radiologist and a cardiologist who were unaware of the results of conventional coronary angiography . \n total calcium scores of all patients were calculated with dedicated software and expressed as agatston scores . \n the agatston score is a commonly used scoring method that calculates the total amount of calcium on the basis of the number , areas , and peak hounsfield units of the detected calcified lesi ons . \n all available coronary segments were visually scored for the presence of > 50% considered as significant stenosis . \n maximum intensity projections were used to identify coronary lesions and ( curved ) multiplanar reconstruction s to classify lesions as significant or nonsignificant . \n data were analyzed using statistical system spss version 20 software ( chicago , il , usa ) . \n the diagnostic performance of ct coronary angiography for the detection of signif icant lesions in coronary arteries with qca as the standard of reference is presented as sensitivity , specificity , positive and negative predictive values , and positive and negative likelihood ratios with the corresponding exact 95% of confidence interval ( cis ) . \n comparison between ct and conventional coronary angiography was performed on the two level vessel by vessel ( no or any disease per vessel ) , and patient by patient ( no or any disease per patient ) . \n in this study , 29 ( 58% ) subjects were female , and 21 ( 42% ) were male showing an average age of 50.36 8.39 years . \n of fifty patients 24 ( 48% ) , 13 ( 26% ) , eight ( 16% ) , and five ( 10% ) underwent mitral valve replacement , double valve replacement ( dvr ) , aortic valve replacement , and other surgeries , respectively . \n high distribution of cad risk factors such as hypertension ( 24% ) , smoking ( 22% ) , and dyslipidemia ( 18% ) was observed in the stu dy group . \n the mean creatinine level was 0.766 0.17 and average dye used in conventional angiography was 48.5 26.6 whereas for ct angiography it was 72.8 6.32 . \n average radiation dose in conventional coronary angiography and msct coronary angiography was 5.2 msv and 9.2 msv , respectively . \n the majority of the patients had sinus rhythm ( 68% ) , whereas atrial fibrillation was found in 32% of the subjects . \n patients included in the study had low to intermed iate probability of cad . in this study , three patients had complications after conventional angiography . \n complications were of local site hematoma , acute kidney injury managed conservatively , and acute heart failure . \n a patient who developed hematoma was obese female patients with body mass index > 30 kg / m . \n the patient suffered from pseudoaneurysm , had hospitalized for 9 days , which leads to increased morbidity and cost of hospital stay . \n the diagnos tic accuracy of ct coronary angiography was evaluated regarding true positive , true negative values and is presented in table 1 . the overall sensitivity and \n specificity of ct angiography technique was 100% ( 95% ci : 39.76%100% ) and 91.30% ( 95% ci : 79.21%97.58% ) , respectively [ table 2 ] . \n the positive predictive value ( 50% ; 95% ci : 15.70%84.30% ) and negative predictive value ( 100% ; 95% ci : 91.59%100% ) of ct angiography were also fairly high in these patients . \n recent reports from multiple studies demonstrated that recent - generation msct scanners showed promise for noninvasive detection of coronary stenosis however , until now no studies were found regarding the clinical efficacy or prognostic value of 128-slice ct coronary angiography versus conventional invasive coronary angiography in the diagnosis of patients planned for major noncoronary surgeries such as dvr , bentall , atrial septal defect closure , etc . in our study , we reported 8% cad prevalence in patients planned for major noncoronary cardiac surgery . \n we performed conventional and msct coronary angiography in all patients and the results showed that ct coronary angiography with i nvasive coronary angiography as the reference standard had a considerably high sensitivity ( 100% ) and specificity ( 95.65% ) . \n the health economic model using invasive coronary angiography as the reference standard showed that at a p retest probability of cad of 70% or lower , ct coronary angiography resulted in lower cost per patient with a true positive diagnosis . at a pretest probability of cad of 70% or higher , invasive coronary angiography was associated with a lower cost per patient with a true positive diagnosis . in our study population , \n two patients developed local site complications in the form of hematoma and pseudoaneurysm after conventional angiography . \n hence , msct coronary ang iography will be more favorable in female obese patients with intermediate likelihood of cad . \n hence , msct coronary angiography will be cost - effective in patients of valvular heart diseases . \n however , ct angiography suffers from a drawback that average amount of dye used in msct coronary angiography were 72.8 6.32 ml which is higher than average amount of dye required for conventional angiography ( 48.6 26.6 ml ) . \n hence , the use of ct coronary angiography could not be used in patients with known renal dysfunction , where reduction of contrast dye load is highly advocated . \n our results show that 128-slice ct coronary angiography is a reliable technique to detect coronary stenosis in pat ients planned for noncoronary cardiac surgery . \n although there has been important technological progress in the development of ct coronary angiography , its clinical application remains limited . \n a study wth large numbers of patient s is required for the recommendation of only ct coronary angiography for the coronary evaluation in major non - cardiac surgeries . \n mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , guja rat , india ) . \n u.n . mehta institute of cardiology and research center ( affiliated to bj medical college , ahmedabad , gujarat , india ) . \n """ dct = tok( [ARTICLE], max_length=1024, padding="max_length", truncation=True, return_tensors="pt", ).to(torch_device) hypotheses_batch = model.generate( **dct, num_beams=4, length_penalty=2.0, max_length=142, min_length=56, no_repeat_ngram_size=3, do_sample=False, early_stopping=True, ) decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True, clean_up_tokenization_spaces=False) self.assertListEqual( self.expected_summary(), decoded, ) @slow def test_inference_hidden_states(self): model = self.model input_ids = torch.tensor( [[100, 19, 3, 9, 7142, 1200, 145, 8, 1252, 14145, 2034, 812, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.long, device=torch_device, ) decoder_input_ids = torch.tensor( [[100, 19, 3, 9, 7142, 1200, 145, 8, 1252, 14145, 2034, 812, 5, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.long, device=torch_device, ) attention_mask = torch.tensor( [[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]], dtype=torch.long, device=torch_device, ) output = model( input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, output_hidden_states=True ) # check if encoder_outputs match expected_output_slice = torch.tensor([0.0629, -0.1294, -0.0089, 0.0772, 0.0663], device=torch_device) self.assertTrue(torch.allclose(output.encoder_hidden_states[-1][0, 0, :5], expected_output_slice, atol=1e-4)) # check if logits match expected_output_slice = torch.tensor([5.5231, 6.1058, 3.1766, 8.2391, -5.9453], device=torch_device) self.assertTrue(torch.allclose(output.logits[0, 0, :5], expected_output_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/megatron_bert/test_modeling_megatron_bert.py
# Copyright 2021 The HuggingFace Inc. team. All rights reserved. # Copyright 2021 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. """ Testing suite for the PyTorch MegatronBERT model. """ import math import os import unittest from transformers import MegatronBertConfig, 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 ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, MegatronBertForCausalLM, MegatronBertForMaskedLM, MegatronBertForMultipleChoice, MegatronBertForNextSentencePrediction, MegatronBertForPreTraining, MegatronBertForQuestionAnswering, MegatronBertForSequenceClassification, MegatronBertForTokenClassification, MegatronBertModel, ) class MegatronBertModelTester: 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=64, embedding_size=32, num_hidden_layers=2, 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.embedding_size = embedding_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): return MegatronBertConfig( 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, embedding_size=self.embedding_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 create_and_check_megatron_bert_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegatronBertModel(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_megatron_bert_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegatronBertForMaskedLM(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_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegatronBertForCausalLM(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_megatron_bert_for_next_sequence_prediction( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegatronBertForNextSentencePrediction(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_megatron_bert_for_pretraining( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegatronBertForPreTraining(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_megatron_bert_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MegatronBertForQuestionAnswering(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_megatron_bert_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 = MegatronBertForSequenceClassification(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_megatron_bert_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 = MegatronBertForTokenClassification(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_megatron_bert_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 = MegatronBertForMultipleChoice(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 MegatronBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MegatronBertModel, MegatronBertForMaskedLM, MegatronBertForCausalLM, MegatronBertForMultipleChoice, MegatronBertForNextSentencePrediction, MegatronBertForPreTraining, MegatronBertForQuestionAnswering, MegatronBertForSequenceClassification, MegatronBertForTokenClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": MegatronBertModel, "fill-mask": MegatronBertForMaskedLM, "question-answering": MegatronBertForQuestionAnswering, "text-classification": MegatronBertForSequenceClassification, "text-generation": MegatronBertForCausalLM, "token-classification": MegatronBertForTokenClassification, "zero-shot": MegatronBertForSequenceClassification, } if is_torch_available() else {} ) fx_compatible = True # test_resize_embeddings = False test_head_masking = False # 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 = MegatronBertModelTester(self) self.config_tester = ConfigTester(self, config_class=MegatronBertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_megatron_bert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_megatron_bert_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_megatron_bert_for_masked_lm(*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_megatron_bert_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_megatron_bert_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_megatron_bert_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_megatron_bert_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_megatron_bert_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_megatron_bert_for_token_classification(*config_and_inputs) 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 class MegatronBertModelIntegrationTests(unittest.TestCase): @slow @unittest.skip("Model is not available.") def test_inference_no_head(self): directory = "nvidia/megatron-bert-uncased-345m" if "MYDIR" in os.environ: directory = os.path.join(os.environ["MYDIR"], directory) model = MegatronBertModel.from_pretrained(directory) model.to(torch_device) model.half() input_ids = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 9, 1024)) self.assertEqual(output.shape, expected_shape) expected = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728] for ii in range(3): for jj in range(3): a = output[0, ii, jj] b = expected[3 * ii + jj] msg = "ii={} jj={} a={} b={}".format(ii, jj, a, b) self.assertTrue(math.isclose(a, b, rel_tol=TOLERANCE, abs_tol=TOLERANCE), msg=msg)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/beit/test_modeling_flax_beit.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. import inspect import unittest import numpy as np from transformers import BeitConfig from transformers.testing_utils import require_flax, require_vision, slow from transformers.utils import cached_property, is_flax_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor if is_flax_available(): import jax from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel if is_vision_available(): from PIL import Image from transformers import BeitImageProcessor class FlaxBeitModelTester(unittest.TestCase): 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=2, 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, ): self.parent = parent self.vocab_size = vocab_size 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 # 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 if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = BeitConfig( 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, ) return config, pixel_values, labels def create_and_check_model(self, config, pixel_values, labels): model = FlaxBeitModel(config=config) result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_masked_lm(self, config, pixel_values, labels): model = FlaxBeitForMaskedImageModeling(config=config) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length - 1, self.vocab_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = FlaxBeitForImageClassification(config=config) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) # test greyscale images config.num_channels = 1 model = FlaxBeitForImageClassification(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) 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_flax class FlaxBeitModelTest(FlaxModelTesterMixin, unittest.TestCase): all_model_classes = ( (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else () ) def setUp(self) -> None: self.model_tester = FlaxBeitModelTester(self) self.config_tester = ConfigTester(self, config_class=BeitConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() # We need to override this test because Beit's forward signature is different than text models. 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) # We need to override this test because Beit expects pixel_values instead of input_ids 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(pixel_values, **kwargs): return model(pixel_values=pixel_values, **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) 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_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_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_class_name in self.all_model_classes: model = model_class_name.from_pretrained("microsoft/beit-base-patch16-224") outputs = model(np.ones((1, 3, 224, 224))) self.assertIsNotNone(outputs) # 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_vision @require_flax class FlaxBeitModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None @slow def test_inference_masked_image_modeling_head(self): model = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k") image_processor = self.default_image_processor image = prepare_img() pixel_values = image_processor(images=image, return_tensors="np").pixel_values # prepare bool_masked_pos bool_masked_pos = np.ones((1, 196), dtype=bool) # forward pass outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos) logits = outputs.logits # verify the logits expected_shape = (1, 196, 8192) self.assertEqual(logits.shape, expected_shape) expected_slice = np.array( [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ) self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2)) @slow def test_inference_image_classification_head_imagenet_1k(self): model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224") image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="np") # forward pass outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = (1, 1000) self.assertEqual(logits.shape, expected_shape) expected_slice = np.array([-1.2385, -1.0987, -1.0108]) self.assertTrue(np.allclose(logits[0, :3], expected_slice, atol=1e-4)) expected_class_idx = 281 self.assertEqual(logits.argmax(-1).item(), expected_class_idx) @slow def test_inference_image_classification_head_imagenet_22k(self): model = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k") image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="np") # forward pass outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = (1, 21841) self.assertEqual(logits.shape, expected_shape) expected_slice = np.array([1.6881, -0.2787, 0.5901]) self.assertTrue(np.allclose(logits[0, :3], expected_slice, atol=1e-4)) expected_class_idx = 2396 self.assertEqual(logits.argmax(-1).item(), expected_class_idx)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/beit/test_image_processing_beit.py
# coding=utf-8 # Copyright 2021 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 unittest from datasets import load_dataset from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import BeitImageProcessor class BeitImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], do_reduce_labels=False, ): size = size if size is not None else {"height": 20, "width": 20} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_reduce_labels = do_reduce_labels def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_reduce_labels": self.do_reduce_labels, } def expected_output_image_shape(self, images): return self.num_channels, self.crop_size["height"], self.crop_size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) def prepare_semantic_single_inputs(): dataset = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") image = Image.open(dataset[0]["file"]) map = Image.open(dataset[1]["file"]) return image, map def prepare_semantic_batch_inputs(): ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") image1 = Image.open(ds[0]["file"]) map1 = Image.open(ds[1]["file"]) image2 = Image.open(ds[2]["file"]) map2 = Image.open(ds[3]["file"]) return [image1, image2], [map1, map2] @require_torch @require_vision class BeitImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = BeitImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = BeitImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_center_crop")) self.assertTrue(hasattr(image_processing, "center_crop")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 20, "width": 20}) self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18}) self.assertEqual(image_processor.do_reduce_labels, False) image_processor = self.image_processing_class.from_dict( self.image_processor_dict, size=42, crop_size=84, reduce_labels=True ) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) self.assertEqual(image_processor.do_reduce_labels, True) def test_call_segmentation_maps(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True) maps = [] for image in image_inputs: self.assertIsInstance(image, torch.Tensor) maps.append(torch.zeros(image.shape[-2:]).long()) # Test not batched input encoding = image_processing(image_inputs[0], maps[0], return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( 1, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255) # Test batched encoding = image_processing(image_inputs, maps, return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255) # Test not batched input (PIL images) image, segmentation_map = prepare_semantic_single_inputs() encoding = image_processing(image, segmentation_map, return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( 1, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255) # Test batched input (PIL images) images, segmentation_maps = prepare_semantic_batch_inputs() encoding = image_processing(images, segmentation_maps, return_tensors="pt") self.assertEqual( encoding["pixel_values"].shape, ( 2, self.image_processor_tester.num_channels, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual( encoding["labels"].shape, ( 2, self.image_processor_tester.crop_size["height"], self.image_processor_tester.crop_size["width"], ), ) self.assertEqual(encoding["labels"].dtype, torch.long) self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255) def test_reduce_labels(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150 image, map = prepare_semantic_single_inputs() encoding = image_processing(image, map, return_tensors="pt") self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 150) image_processing.do_reduce_labels = True encoding = image_processing(image, map, return_tensors="pt") self.assertTrue(encoding["labels"].min().item() >= 0) self.assertTrue(encoding["labels"].max().item() <= 255)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/beit/test_modeling_beit.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 PyTorch BEiT model. """ import unittest from datasets import load_dataset from packaging import version from transformers import BeitConfig 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_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( BeitBackbone, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitModel, ) from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES if is_vision_available(): import PIL from PIL import Image from transformers import BeitImageProcessor class BeitModelTester: 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=[1, 2, 3, 4], out_features=["stage1", "stage2", "stage3", "stage4"], ): self.parent = parent self.vocab_size = vocab_size 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.out_features = out_features 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 BeitConfig( 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, out_features=self.out_features, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = BeitModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_backbone(self, config, pixel_values, labels, pixel_labels): model = BeitBackbone(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)) expected_height = expected_width = self.image_size // config.patch_size self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width] ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = BeitBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, expected_height, expected_width] ) # verify channels self.parent.assertEqual(len(model.channels), 1) def create_and_check_for_masked_lm(self, config, pixel_values, labels, pixel_labels): model = BeitForMaskedImageModeling(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length - 1, self.vocab_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.type_sequence_label_size model = BeitForImageClassification(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)) # test greyscale images config.num_channels = 1 model = BeitForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) 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_semantic_segmentation(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = BeitForSemanticSegmentation(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 BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as BEiT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitBackbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": BeitModel, "image-classification": BeitForImageClassification, "image-segmentation": BeitForSemanticSegmentation, } if is_torch_available() else {} ) test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = BeitModelTester(self) self.config_tester = ConfigTester(self, config_class=BeitConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="BEiT does not use inputs_embeds") def test_inputs_embeds(self): pass @require_torch_multi_gpu @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`") def test_multi_gpu_data_parallel_forward(self): pass @unittest.skip(reason="BEiT does not support feedforward chunking yet") def test_feed_forward_chunking(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_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_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_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) def test_for_semantic_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_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: # we don't test BeitForMaskedImageModeling if model_class.__name__ in [ *MODEL_MAPPING_NAMES.values(), *MODEL_FOR_BACKBONE_MAPPING_NAMES.values(), "BeitForMaskedImageModeling", ]: 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: # we don't test BeitForMaskedImageModeling if ( model_class.__name__ in [ *MODEL_MAPPING_NAMES.values(), *MODEL_FOR_BACKBONE_MAPPING_NAMES.values(), "BeitForMaskedImageModeling", ] or not model_class.supports_gradient_checkpointing ): continue 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() @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass 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", ) @slow def test_model_from_pretrained(self): model_name = "microsoft/beit-base-patch16-224" model = BeitModel.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 BeitModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") if is_vision_available() else None @slow def test_inference_masked_image_modeling_head(self): model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k").to(torch_device) image_processor = self.default_image_processor image = prepare_img() pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device) # prepare bool_masked_pos bool_masked_pos = torch.ones((1, 196), dtype=torch.bool).to(torch_device) # forward pass with torch.no_grad(): outputs = model(pixel_values=pixel_values, bool_masked_pos=bool_masked_pos) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 196, 8192)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor( [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(torch_device) self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3], expected_slice, atol=1e-2)) @slow def test_inference_image_classification_head_imagenet_1k(self): model = BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(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([-1.2385, -1.0987, -1.0108]).to(torch_device) self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4)) expected_class_idx = 281 self.assertEqual(logits.argmax(-1).item(), expected_class_idx) @slow def test_inference_image_classification_head_imagenet_22k(self): model = BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k").to( torch_device ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(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, 21841)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor([1.6881, -0.2787, 0.5901]).to(torch_device) self.assertTrue(torch.allclose(logits[0, :3], expected_slice, atol=1e-4)) expected_class_idx = 2396 self.assertEqual(logits.argmax(-1).item(), expected_class_idx) @slow def test_inference_semantic_segmentation(self): model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640") model = model.to(torch_device) image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False) ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") image = Image.open(ds[0]["file"]) inputs = image_processor(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, 150, 160, 160)) self.assertEqual(logits.shape, expected_shape) is_pillow_less_than_9 = version.parse(PIL.__version__) < version.parse("9.0.0") if is_pillow_less_than_9: expected_slice = torch.tensor( [ [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]], [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]], [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]], ], device=torch_device, ) else: expected_slice = torch.tensor( [ [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]], [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]], [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]], ], device=torch_device, ) self.assertTrue(torch.allclose(logits[0, :3, :3, :3], expected_slice, atol=1e-4)) @slow def test_post_processing_semantic_segmentation(self): model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640") model = model.to(torch_device) image_processor = BeitImageProcessor(do_resize=True, size=640, do_center_crop=False) ds = load_dataset("hf-internal-testing/fixtures_ade20k", split="test") image = Image.open(ds[0]["file"]) inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) outputs.logits = outputs.logits.detach().cpu() segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(500, 300)]) expected_shape = torch.Size((500, 300)) self.assertEqual(segmentation[0].shape, expected_shape) segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs) expected_shape = torch.Size((160, 160)) self.assertEqual(segmentation[0].shape, expected_shape) @require_torch class BeitBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (BeitBackbone,) if is_torch_available() else () config_class = BeitConfig def setUp(self): self.model_tester = BeitModelTester(self)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/convnextv2/test_modeling_convnextv2.py
# coding=utf-8 # Copyright 2023 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 ConvNextV2 model. """ import unittest from transformers import ConvNextV2Config from transformers.models.auto import get_values from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ConvNextV2Backbone, ConvNextV2ForImageClassification, ConvNextV2Model if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class ConvNextV2ModelTester: 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", num_labels=10, initializer_range=0.02, out_features=["stage2", "stage3", "stage4"], out_indices=[2, 3, 4], 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.num_labels = num_labels self.initializer_range = initializer_range self.out_features = out_features self.out_indices = out_indices 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.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return ConvNextV2Config( 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, out_features=self.out_features, out_indices=self.out_indices, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = ConvNextV2Model(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): model = ConvNextV2ForImageClassification(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 = ConvNextV2Backbone(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), [self.batch_size, self.hidden_sizes[1], 4, 4]) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) self.parent.assertListEqual(model.channels, config.hidden_sizes[1:]) # verify backbone works with out_features=None config.out_features = None model = ConvNextV2Backbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1]) # verify channels self.parent.assertEqual(len(model.channels), 1) self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]]) 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_with_labels(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values, "labels": labels} return config, inputs_dict @require_torch class ConvNextV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ConvNextV2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( ConvNextV2Model, ConvNextV2ForImageClassification, ConvNextV2Backbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification} if is_torch_available() else {} ) fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = ConvNextV2ModelTester(self) self.config_tester = ConfigTester(self, config_class=ConvNextV2Config, 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="ConvNextV2 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="ConvNextV2 does not support input and output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="ConvNextV2 does not use feedforward chunking") def test_feed_forward_chunking(self): pass def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels() config.return_dict = True if model_class.__name__ in [ *get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES), ]: 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): if not self.model_tester.is_training: return for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_with_labels() config.use_cache = False config.return_dict = True if ( model_class.__name__ in [*get_values(MODEL_MAPPING_NAMES), *get_values(MODEL_FOR_BACKBONE_MAPPING_NAMES)] or not model_class.supports_gradient_checkpointing ): continue model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() 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) # ConvNextV2'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): model_name = "facebook/convnextv2-tiny-1k-224" model = ConvNextV2Model.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 ConvNextV2ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = ConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224").to(torch_device) preprocessor = self.default_image_processor image = prepare_img() inputs = preprocessor(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([0.9996, 0.1966, -0.4386]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/convnextv2/test_modeling_tf_convnextv2.py
# coding=utf-8 # Copyright 2023 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 ConvNext model. """ from __future__ import annotations import inspect import unittest from typing import List, Tuple import numpy as np from transformers import ConvNextV2Config from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFConvNextV2ForImageClassification, TFConvNextV2Model if is_vision_available(): from PIL import Image from transformers import ConvNextImageProcessor class TFConvNextV2ModelTester: 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 ConvNextV2Config( 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 = TFConvNextV2Model(config=config) result = model(pixel_values, training=False) # expected last hidden states: batch_size, channels, height // 32, width // 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 = TFConvNextV2ForImageClassification(config) result = model(pixel_values, labels=labels, training=False) 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_tf class TFConvNextV2ModelTest(TFModelTesterMixin, PipelineTesterMixin, 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 = (TFConvNextV2Model, TFConvNextV2ForImageClassification) if is_tf_available() else () pipeline_model_mapping = ( {"feature-extraction": TFConvNextV2Model, "image-classification": TFConvNextV2ForImageClassification} if is_tf_available() else {} ) test_pruning = False test_onnx = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = TFConvNextV2ModelTester(self) self.config_tester = ConfigTester( self, config_class=ConvNextV2Config, has_text_modality=False, hidden_size=37, ) @unittest.skip(reason="ConvNext does not use inputs_embeds") def test_inputs_embeds(self): pass @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.", ) @slow def test_keras_fit(self): super().test_keras_fit() @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.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_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @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 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_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) # Since ConvNext does not have any attention we need to rewrite this test. 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, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) 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}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) 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): model = TFConvNextV2Model.from_pretrained("facebook/convnextv2-tiny-1k-224") 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 @require_vision class TFConvNextV2ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( ConvNextImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = TFConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224") image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="tf") # forward pass outputs = model(**inputs) # verify the logits expected_shape = tf.TensorShape((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = np.array([0.9996, 0.1966, -0.4386]) self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/llava_next/test_image_processor_llava_next.py
# coding=utf-8 # Copyright 2024 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 unittest import numpy as np from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD from transformers.models.llava_next.image_processing_llava_next import select_best_resolution from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import LlavaNextImageProcessor class LlavaNextImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, do_normalize=True, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD, do_convert_rgb=True, ): size = size if size is not None else {"shortest_edge": 20} crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_convert_rgb": self.do_convert_rgb, } # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.expected_output_image_shape def expected_output_image_shape(self, images): return self.num_channels, self.crop_size["height"], self.crop_size["width"] # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTester.prepare_image_inputs def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class LlavaNextImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = LlavaNextImageProcessor if is_vision_available() else None # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.setUp with CLIP->LlavaNext def setUp(self): self.image_processor_tester = LlavaNextImageProcessingTester(self) @property # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.image_processor_dict def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_center_crop")) self.assertTrue(hasattr(image_processing, "center_crop")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_convert_rgb")) self.assertTrue(hasattr(image_processing, "image_grid_pinpoints")) # Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest.test_image_processor_from_dict_with_kwargs def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"shortest_edge": 20}) self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84) self.assertEqual(image_processor.size, {"shortest_edge": 42}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84}) def test_select_best_resolution(self): possible_resolutions = [[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]] # Test with a square aspect ratio best_resolution = select_best_resolution((336, 336), possible_resolutions) self.assertEqual(best_resolution, (672, 336)) def test_call_pil(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PIL images image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 1445, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1445, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_numpy(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random numpy tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 1445, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1445, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) def test_call_pytorch(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values expected_output_image_shape = (1, 1445, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values expected_output_image_shape = (7, 1445, 3, 18, 18) self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape) @unittest.skip("LlavaNextImageProcessor doesn't treat 4 channel PIL and numpy consistently yet") # FIXME Amy def test_call_numpy_4_channels(self): pass
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/llava_next/test_modeling_llava_next.py
# coding=utf-8 # Copyright 2024 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 Llava-NeXT model. """ import gc import unittest import requests from huggingface_hub import hf_hub_download from transformers import ( AutoProcessor, LlavaNextConfig, LlavaNextForConditionalGeneration, is_torch_available, is_vision_available, ) from transformers.testing_utils import ( require_bitsandbytes, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin 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 else: is_torch_greater_or_equal_than_2_0 = False if is_vision_available(): from PIL import Image class LlavaNextVisionText2TextModelTester: def __init__( self, parent, ignore_index=-100, image_token_index=0, projector_hidden_act="gelu", seq_length=7, vision_feature_select_strategy="default", vision_feature_layer=-1, text_config={ "model_type": "llama", "seq_length": 7, "is_training": True, "use_input_mask": True, "use_token_type_ids": False, "use_labels": True, "vocab_size": 99, "hidden_size": 32, "num_hidden_layers": 2, "num_attention_heads": 4, "intermediate_size": 37, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "attention_probs_dropout_prob": 0.1, "max_position_embeddings": 580, "type_vocab_size": 16, "type_sequence_label_size": 2, "initializer_range": 0.02, "num_labels": 3, "num_choices": 4, "pad_token_id": 0, }, is_training=True, vision_config={ "image_size": 16, "patch_size": 2, "num_channels": 3, "is_training": True, "hidden_size": 32, "projection_dim": 32, "num_hidden_layers": 2, "num_attention_heads": 4, "intermediate_size": 37, "dropout": 0.1, "attention_dropout": 0.1, "initializer_range": 0.02, }, ): self.parent = parent self.ignore_index = ignore_index self.image_token_index = image_token_index self.projector_hidden_act = projector_hidden_act self.vision_feature_select_strategy = vision_feature_select_strategy self.vision_feature_layer = vision_feature_layer self.text_config = text_config self.vision_config = vision_config self.seq_length = seq_length self.num_hidden_layers = text_config["num_hidden_layers"] self.vocab_size = text_config["vocab_size"] self.hidden_size = text_config["hidden_size"] self.num_attention_heads = text_config["num_attention_heads"] self.is_training = is_training self.batch_size = 3 self.num_channels = 3 self.image_size = 30 self.encoder_seq_length = 342 self.image_grid_pinpoints = [[32, 32]] def get_config(self): return LlavaNextConfig( text_config=self.text_config, vision_config=self.vision_config, ignore_index=self.ignore_index, image_token_index=self.image_token_index, projector_hidden_act=self.projector_hidden_act, vision_feature_select_strategy=self.vision_feature_select_strategy, vision_feature_layer=self.vision_feature_layer, image_grid_pinpoints=self.image_grid_pinpoints, ) def prepare_config_and_inputs(self): pixel_values = floats_tensor( [ self.batch_size, 5, self.vision_config["num_channels"], self.vision_config["image_size"], self.vision_config["image_size"], ] ) config = self.get_config() return config, pixel_values def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1 attention_mask = input_ids.ne(1).to(torch_device) # we are giving 3 images let's make sure we pass in 3 image tokens input_ids[:, 1] = config.image_token_index inputs_dict = { "pixel_values": pixel_values, "image_sizes": torch.tensor( [[self.vision_config["image_size"], self.vision_config["image_size"]]] * self.batch_size ), "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict def create_and_check_llava_next_model_fp16_forward( self, config, input_ids, pixel_values, attention_mask, image_sizes ): model = LlavaNextForConditionalGeneration(config=config) model.to(torch_device) model.half() model.eval() logits = model( input_ids=input_ids, attention_mask=attention_mask, image_sizes=image_sizes, pixel_values=pixel_values.to(torch.bfloat16), return_dict=True, )["logits"] self.parent.assertFalse(torch.isnan(logits).any().item()) def create_and_check_llava_next_model_fp16_autocast_forward( self, config, input_ids, pixel_values, attention_mask, image_sizes ): config.torch_dtype = torch.float16 model = LlavaNextForConditionalGeneration(config=config) model.to(torch_device) model.eval() with torch.autocast(device_type="cuda", dtype=torch.float16): logits = model( input_ids=input_ids, attention_mask=attention_mask, image_sizes=image_sizes, pixel_values=pixel_values.to(torch.bfloat16), return_dict=True, )["logits"] self.parent.assertFalse(torch.isnan(logits).any().item()) @require_torch class LlavaNextForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): """ Model tester for `LlavaNextForConditionalGeneration`. """ all_model_classes = (LlavaNextForConditionalGeneration,) if is_torch_available() else () test_pruning = False test_head_masking = False def setUp(self): self.model_tester = LlavaNextVisionText2TextModelTester(self) self.config_tester = ConfigTester(self, config_class=LlavaNextConfig, has_text_modality=False) 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(): if "image_newline" in name: continue elif 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", ) @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Feedforward chunking is not yet supported") def test_feed_forward_chunking(self): pass @unittest.skip(reason="CPU offload is not yet supported") def test_cpu_offload(self): pass @require_torch class LlavaNextForConditionalGenerationIntegrationTest(unittest.TestCase): def setUp(self): self.processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true" self.image = Image.open(requests.get(url, stream=True).raw) self.prompt = "[INST] <image>\nWhat is shown in this image? [/INST]" def tearDown(self): gc.collect() torch.cuda.empty_cache() @slow @require_bitsandbytes def test_small_model_integration_test(self): model = LlavaNextForConditionalGeneration.from_pretrained( "llava-hf/llava-v1.6-mistral-7b-hf", load_in_4bit=True, ) inputs = self.processor(self.prompt, self.image, return_tensors="pt") # verify inputs against original implementation filepath = hf_hub_download( repo_id="nielsr/test-image", filename="llava_1_6_input_ids.pt", repo_type="dataset", ) original_input_ids = torch.load(filepath, map_location="cpu") # replace -200 by image_token_index (since we use token ID = 32000 for the image token) original_input_ids[original_input_ids == -200] = model.config.image_token_index assert original_input_ids[0].tolist() == inputs.input_ids[0].tolist() filepath = hf_hub_download( repo_id="nielsr/test-image", filename="llava_1_6_pixel_values.pt", repo_type="dataset", ) original_pixel_values = torch.load(filepath, map_location="cpu") assert torch.allclose(original_pixel_values, inputs.pixel_values.half()) # verify single forward pass inputs = inputs.to(torch_device) with torch.no_grad(): output = model(**inputs) expected_slice = torch.tensor( [ [-4.7695, -4.5664, -0.2786], [-10.6250, -10.8906, -2.5254], [-6.7383, -7.2461, -0.6787], ], dtype=torch.float32, device=torch_device, ) assert torch.allclose(output.logits[0, :3, :3], expected_slice, atol=1e-3) # verify generation output = model.generate(**inputs, max_new_tokens=100) EXPECTED_DECODED_TEXT = '[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays values for multiple quantitative variables represented on axes starting from the same point. This particular radar chart is showing the performance of various models or systems across different metrics or datasets.\n\nThe chart is divided into several sections, each representing a different model or dataset. The axes represent different metrics or datasets, such as "MMM-Vet," "MMM-Bench," "L' # fmt: skip self.assertEqual( self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow @require_bitsandbytes def test_small_model_integration_test_batch(self): model = LlavaNextForConditionalGeneration.from_pretrained( "llava-hf/llava-v1.6-mistral-7b-hf", load_in_4bit=True ) url = "http://images.cocodataset.org/val2017/000000039769.jpg" cats_image = Image.open(requests.get(url, stream=True).raw) inputs = self.processor( [self.prompt, self.prompt], images=[self.image, cats_image], return_tensors="pt", padding=True, ).to(torch_device) # make sure image_sizes are the same # as otherwise batched generation doesn't work inputs.image_sizes[1] = inputs.image_sizes[0] output = model.generate(**inputs, max_new_tokens=20) EXPECTED_DECODED_TEXT = ['[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays', '[INST] \nWhat is shown in this image? [/INST] The image shows two cats lying on a pink surface, which appears to be a couch or a cush'] # fmt: skip self.assertEqual( self.processor.batch_decode(output, skip_special_tokens=True), EXPECTED_DECODED_TEXT, ) @slow @require_bitsandbytes def test_small_model_integration_test_unk_token(self): # related to (#29835) model = LlavaNextForConditionalGeneration.from_pretrained( "llava-hf/llava-v1.6-mistral-7b-hf", load_in_4bit=True, ) prompt_with_unk = "[INST] <image>\nWhat is shown in this <unk> image? [/INST]" inputs = self.processor(prompt_with_unk, self.image, return_tensors="pt") # verify single forward pass inputs = inputs.to(torch_device) with torch.no_grad(): output = model(**inputs) # verify generation output = model.generate(**inputs, max_new_tokens=40) EXPECTED_DECODED_TEXT = '[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot that displays values for multiple quantitative variables represented on axes starting from the same point. This particular radar chart' # fmt: skip self.assertEqual( self.processor.decode(output[0], skip_special_tokens=True), EXPECTED_DECODED_TEXT, )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/opt/test_modeling_opt.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 PyTorch OPT model. """ import copy import tempfile import unittest import timeout_decorator # noqa from transformers import OPTConfig, is_torch_available from transformers.testing_utils import require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( GPT2Tokenizer, OPTForCausalLM, OPTForQuestionAnswering, OPTForSequenceClassification, OPTModel, ) def prepare_opt_inputs_dict( config, input_ids, decoder_input_ids=None, attention_mask=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, ): if attention_mask is None: attention_mask = input_ids.ne(config.pad_token_id) return { "input_ids": input_ids, "attention_mask": attention_mask, "head_mask": head_mask, } class OPTModelTester: 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, hidden_act="gelu", 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, embed_dim=16, num_labels=3, word_embed_proj_dim=16, type_sequence_label_size=2, ): 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.embed_dim = embed_dim self.num_labels = num_labels self.type_sequence_label_size = type_sequence_label_size self.word_embed_proj_dim = word_embed_proj_dim self.is_encoder_decoder = False def prepare_config_and_inputs(self): 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_opt_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return OPTConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, 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, embed_dim=self.embed_dim, is_encoder_decoder=False, word_embed_proj_dim=self.word_embed_proj_dim, ) def get_pipeline_config(self): config = self.get_config() config.max_position_embeddings = 100 return config def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = OPTModel(config=config).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-3)) # test no attention_mask works outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True) _, past_key_values = outputs.to_tuple() output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values)["last_hidden_state"] 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() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) @require_torch class OPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (OPTModel, OPTForCausalLM, OPTForSequenceClassification, OPTForQuestionAnswering) if is_torch_available() else () ) all_generative_model_classes = (OPTForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": OPTModel, "question-answering": OPTForQuestionAnswering, "text-classification": OPTForSequenceClassification, "text-generation": OPTForCausalLM, "zero-shot": OPTForSequenceClassification, } if is_torch_available() else {} ) is_encoder_decoder = False fx_compatible = True test_pruning = False test_missing_keys = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if ( pipeline_test_casse_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith("Fast") ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False def setUp(self): self.model_tester = OPTModelTester(self) self.config_tester = ConfigTester(self, config_class=OPTConfig) 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_inputs_embeds(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in (OPTModel,): 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] @require_torch_fp16 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 = OPTForCausalLM(config).eval().to(torch_device) 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_opt_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = OPTForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_opt_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = OPTForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @unittest.skip("Does not work on the tiny model as we keep hitting edge cases.") def test_model_parallelism(self): super().test_model_parallelism() 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) @require_torch class OPTModelIntegrationTests(unittest.TestCase): @slow def test_inference_no_head(self): model = OPTModel.from_pretrained("facebook/opt-350m").to(torch_device) input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) with torch.no_grad(): output = model(input_ids=input_ids).last_hidden_state expected_shape = torch.Size((1, 11, 512)) self.assertEqual(output.shape, expected_shape) # expected value works for CPU, as well as GPU (with TF32 disabled) expected_slice = torch.tensor( [ [-0.28726277, -1.9241608, -0.3058734], [-1.2737825, -0.13332152, -0.18766522], [0.41159445, 0.1191957, -1.3107123], ], device=torch_device, ) assert_tensors_close(output[0, :3, :3], expected_slice, atol=5e-5) @require_torch @slow class OPTEmbeddingsTest(unittest.TestCase): def setUp(self): super().setUp() self.path_model = "facebook/opt-350m" def test_load_model(self): try: _ = OPTForCausalLM.from_pretrained(self.path_model) except BaseException: self.fail("Failed loading model") def test_logits(self): model = OPTForCausalLM.from_pretrained(self.path_model) model = model.eval() tokenizer = GPT2Tokenizer.from_pretrained(self.path_model) prompts = [ "Today is a beautiful day and I want to", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False inputs = tokenizer(prompts, return_tensors="pt", padding=True, add_special_tokens=False) logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(dim=-1) # logits_meta = torch.load(self.path_logits_meta) logits_meta = torch.Tensor( [ [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670], [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822], [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703], [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477], ] ) assert torch.allclose(logits, logits_meta, atol=1e-4) @slow class OPTGenerationTest(unittest.TestCase): @property def prompts(self): return [ "Today is a beautiful day and I want", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] def test_generation_pre_attn_layer_norm(self): model_id = "facebook/opt-125m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of New York, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = OPTForCausalLM.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) def test_batch_generation(self): model_id = "facebook/opt-350m" tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = OPTForCausalLM.from_pretrained(model_id) model.to(torch_device) tokenizer.padding_side = "left" # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] inputs = tokenizer(sentences, return_tensors="pt", padding=True) input_ids = inputs["input_ids"].to(torch_device) outputs = model.generate( input_ids=input_ids, attention_mask=inputs["attention_mask"].to(torch_device), ) inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device) output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item() inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device) output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little bit of a dork.\nI'm a little bit", "Today, I was in the middle of a conversation with a friend about the", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence]) def test_generation_post_attn_layer_norm(self): model_id = "facebook/opt-350m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of San Francisco, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = OPTForCausalLM.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) @require_torch_accelerator @require_torch_fp16 def test_batched_nan_fp16(self): # a bug manifested starting at models facebook/opt-1.3 and larger when running batched generations, # therefore not using a tiny model, but the smallest model the problem was seen with which is opt-1.3b. # please refer to this github thread: https://github.com/huggingface/transformers/pull/17437 for more details model_name = "facebook/opt-1.3b" tokenizer = GPT2Tokenizer.from_pretrained(model_name, use_fast=False, padding_side="left") model = OPTForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_cache=True).to(torch_device) model = model.eval() batch = tokenizer(["Who are you?", "Joe Biden is the president of"], padding=True, return_tensors="pt") input_ids = batch["input_ids"].to(torch_device) attention_mask = batch["attention_mask"].to(torch_device) with torch.no_grad(): outputs = model(input_ids, attention_mask=attention_mask) self.assertFalse( torch.isnan(outputs.logits[0]).any().item() ) # the first logits could contain NaNs if it fails @slow def test_contrastive_search_opt(self): article = ( "A chat between a curious human and the Statue of Liberty.\n\nHuman: What is your name?\nStatue: I am the " "Statue of Liberty.\nHuman: Where do you live?\nStatue: New York City.\nHuman: How long have you lived " "there?" ) opt_tokenizer = GPT2Tokenizer.from_pretrained("facebook/opt-1.3b") opt_model = OPTForCausalLM.from_pretrained("facebook/opt-1.3b").to(torch_device) input_ids = opt_tokenizer(article, return_tensors="pt").input_ids.to(torch_device) outputs = opt_model.generate(input_ids, penalty_alpha=0.6, top_k=5, max_length=256) generated_text = opt_tokenizer.batch_decode(outputs, skip_special_tokens=True) self.assertListEqual( generated_text, [ "A chat between a curious human and the Statue of Liberty.\n\nHuman: What is your name?\nStatue: I " "am the Statue of Liberty.\nHuman: Where do you live?\nStatue: New York City.\nHuman: How long have " "you lived there?\nStatue: A hundred years.\nHuman: And you’re from what country?\nStatue: The United " "States of America.\nHuman: Why did you come to America?\nStatue: I came to escape the tyranny of my " "country.\nHuman: What tyranny?\nStatue: They didn’t let me speak my mind.\nHuman: What was your " "country?\nStatue: It was a country of immigrants.\nHuman: Who were the immigrants?\nStatue: They " "were from all over the world.\nHuman: What language did they speak?\nStatue: French, Spanish, " "Italian, German, English—you name it.\nHuman: And where did they come from?\nStatue: They came from " "every country in the world.\nHuman: And you were born in what country?\nStatue: I was born in " "France.\nHuman: And your parents were French?\nStatue" ], )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/opt/test_modeling_tf_opt.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. from __future__ import annotations import unittest import numpy as np from transformers import OPTConfig, is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import GPT2Tokenizer, TFOPTForCausalLM, TFOPTModel def prepare_opt_inputs_dict(config, input_ids, attention_mask=None, head_mask=None): if attention_mask is None: attention_mask = tf.cast(tf.math.not_equal(input_ids, config.pad_token_id), tf.int8) return {"input_ids": input_ids, "attention_mask": attention_mask} @require_tf class TFOPTModelTester: config_cls = OPTConfig 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=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=20, eos_token_id=2, pad_token_id=1, bos_token_id=0, embed_dim=16, word_embed_proj_dim=16, ): 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.embed_dim = embed_dim self.word_embed_proj_dim = word_embed_proj_dim self.is_encoder_decoder = False 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) config = self.config_cls( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, 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, embed_dim=self.embed_dim, word_embed_proj_dim=self.word_embed_proj_dim, is_encoder_decoder=False, **self.config_updates, ) inputs_dict = prepare_opt_inputs_dict(config, input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFOPTModel(config=config) input_ids = inputs_dict["input_ids"] input_ids = input_ids[:1, :] attention_mask = inputs_dict["attention_mask"][:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=attention_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) @require_tf class TFOPTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else () all_generative_model_classes = (TFOPTForCausalLM,) if is_tf_available() else () pipeline_model_mapping = ( {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {} ) is_encoder_decoder = False test_pruning = False test_onnx = False onnx_min_opset = 10 def setUp(self): self.model_tester = TFOPTModelTester(self) self.config_tester = ConfigTester(self, config_class=OPTConfig) 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_resize_token_embeddings(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def _get_word_embedding_weight(model, embedding_layer): if hasattr(embedding_layer, "weight"): return embedding_layer.weight else: # Here we build the word embeddings weights if not exists. # And then we retry to get the attribute once built. model.build_in_name_scope() if hasattr(embedding_layer, "weight"): return embedding_layer.weight else: return None for model_class in self.all_model_classes: for size in [config.vocab_size - 10, config.vocab_size + 10]: # build the embeddings model = model_class(config=config) old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) # reshape the embeddings model.resize_token_embeddings(size) new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings()) new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings()) # check that the resized embeddings size matches the desired size. assert_size = size if size is not None else config.vocab_size self.assertEqual(new_input_embeddings.shape[0], assert_size) # check that weights remain the same after resizing models_equal = True for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) if old_output_embeddings is not None and new_output_embeddings is not None: self.assertEqual(new_output_embeddings.shape[0], assert_size) models_equal = True for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()): if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0: models_equal = False self.assertTrue(models_equal) def _long_tensor(tok_lst): return tf.constant(tok_lst, dtype=tf.int32) @require_tf class TFOPTHeadTests(unittest.TestCase): vocab_size = 99 def _get_config_and_data(self): eos_column_vector = tf.ones((4, 1), dtype=tf.int32) * 2 input_ids = tf.concat([ids_tensor((4, 6), self.vocab_size - 3) + 3, eos_column_vector], axis=1) batch_size = input_ids.shape[0] config = OPTConfig( vocab_size=self.vocab_size, hidden_size=24, num_hidden_layers=2, num_attention_heads=2, ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) return config, input_ids, batch_size @require_sentencepiece @require_tf class OPTModelIntegrationTests(unittest.TestCase): @slow def test_inference_no_head(self): model = TFOPTModel.from_pretrained("facebook/opt-350m") input_ids = _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) attention_mask = tf.not_equal(input_ids, model.config.pad_token_id) with tf.GradientTape(): output = model(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state expected_shape = (1, 11, 512) self.assertEqual(output.shape, expected_shape) expected_slice = tf.constant( [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]] ) self.assertTrue(np.allclose(output[:, :3, :3], expected_slice, atol=4e-3)) xla_generate = tf.function(model, jit_compile=True) output = xla_generate(input_ids, attention_mask)[0] self.assertTrue(np.allclose(output[:, :3, :3], expected_slice, atol=4e-2)) @require_tf @slow class TFOPTEmbeddingsTest(unittest.TestCase): def setUp(self): super().setUp() self.path_model = "facebook/opt-350m" def test_logits(self): model = TFOPTForCausalLM.from_pretrained(self.path_model) tokenizer = GPT2Tokenizer.from_pretrained(self.path_model) prompts = [ "Today is a beautiful day and I want to", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False inputs = tokenizer(prompts, return_tensors="tf", padding=True, add_special_tokens=False) logits = tf.math.reduce_mean(model(inputs.input_ids, attention_mask=inputs.attention_mask)[0], axis=-1) logits_meta = tf.constant( [ [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670], [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822], [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703], [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477], ] ) self.assertTrue(np.allclose(logits, logits_meta, atol=1e-4)) xla_generate = tf.function(model, jit_compile=True) logits = tf.math.reduce_mean(xla_generate(inputs.input_ids, attention_mask=inputs.attention_mask)[0], axis=-1) self.assertTrue(np.allclose(logits, logits_meta, atol=1e-4)) @require_tf @slow class TFOPTGenerationTest(unittest.TestCase): @property def prompts(self): return [ "Today is a beautiful day and I want", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] def test_generation_pre_attn_layer_norm(self): model_id = "facebook/opt-125m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of New York, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = TFOPTForCausalLM.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="tf").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) def test_batch_generation(self): model_id = "facebook/opt-350m" tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = TFOPTForCausalLM.from_pretrained(model_id) tokenizer.padding_side = "left" # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] inputs = tokenizer(sentences, return_tensors="tf", padding=True) input_ids = inputs["input_ids"] outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"]) inputs_non_padded = tokenizer(sentences[0], return_tensors="tf").input_ids output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - tf.math.reduce_sum( tf.cast(inputs["attention_mask"][-1], tf.int64) ) inputs_padded = tokenizer(sentences[1], return_tensors="tf").input_ids output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little bit of a dork.\nI'm a little bit", "Today, I was in the middle of a conversation with a friend about the", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence]) def test_generation_post_attn_layer_norm(self): model_id = "facebook/opt-350m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of San Francisco, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = TFOPTForCausalLM.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="tf").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/opt/test_modeling_flax_opt.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 unittest import numpy as np import timeout_decorator # noqa from transformers import OPTConfig, is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform" import jax import jax.numpy as jnp from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPT2Tokenizer def prepare_opt_inputs_dict(config, input_ids, attention_mask=None, head_mask=None): if attention_mask is None: attention_mask = np.where(input_ids != config.pad_token_id, 1, 0) return { "input_ids": input_ids, "attention_mask": attention_mask, } @require_flax class FlaxOPTModelTester: 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, hidden_act="gelu", 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, embed_dim=16, word_embed_proj_dim=16, initializer_range=0.02, ): 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.embed_dim = embed_dim self.word_embed_proj_dim = word_embed_proj_dim self.initializer_range = initializer_range self.is_encoder_decoder = False def prepare_config_and_inputs(self): input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size) input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1) config = OPTConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, 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, embed_dim=self.embed_dim, is_encoder_decoder=False, word_embed_proj_dim=self.word_embed_proj_dim, initializer_range=self.initializer_range, use_cache=False, ) inputs_dict = prepare_opt_inputs_dict(config, input_ids) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, inputs_dict): max_length = 20 model = model_class_name(config) input_ids = inputs_dict["input_ids"] attention_mask = inputs_dict["attention_mask"] past_key_values = model.init_cache(input_ids.shape[0], max_length) attention_mask = jnp.ones((input_ids.shape[0], max_length), dtype="i4") position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1), ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], attention_mask=attention_mask, past_key_values=outputs_cache.past_key_values, position_ids=position_ids, ) outputs = model(input_ids) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict): max_length = 20 model = model_class_name(config) input_ids, attention_mask = ( inputs_dict["input_ids"], inputs_dict["attention_mask"], ) attention_mask_cache = jnp.concatenate( [ attention_mask, jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(input_ids.shape[0], max_length) position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1), ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask_cache, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], past_key_values=outputs_cache.past_key_values, attention_mask=attention_mask_cache, position_ids=position_ids, ) outputs = model(input_ids, attention_mask=attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class FlaxOPTModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin): all_model_classes = (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else () all_generative_model_classes = () if is_flax_available() else () def setUp(self): self.model_tester = FlaxOPTModelTester(self) def test_use_cache_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(model_class, config, inputs_dict) def test_use_cache_forward_with_attn_mask(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("facebook/opt-125m") input_ids = np.ones((1, 1)) * model.config.eos_token_id outputs = model(input_ids) self.assertIsNotNone(outputs) @require_sentencepiece @require_flax class FlaxOPTModelIntegrationTests(unittest.TestCase): @slow def test_inference_no_head(self): model = FlaxOPTModel.from_pretrained("facebook/opt-350m") input_ids = jnp.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids=input_ids).last_hidden_state expected_shape = (1, 11, 512) self.assertEqual(output.shape, expected_shape) expected_slice = jnp.array( [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]] ) self.assertTrue(jnp.allclose(output[:, :3, :3], expected_slice, atol=4e-2)) @require_flax @slow class FlaxOPTEmbeddingsTest(unittest.TestCase): def setUp(self): super().setUp() self.path_model = "facebook/opt-350m" def test_logits(self): model = FlaxOPTForCausalLM.from_pretrained(self.path_model) tokenizer = GPT2Tokenizer.from_pretrained(self.path_model) prompts = [ "Today is a beautiful day and I want to", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False inputs = tokenizer(prompts, return_tensors="jax", padding=True, add_special_tokens=False) logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1) logits_meta = jnp.array( [ [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670], [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822], [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703], [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477], ] ) self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2)) model = jax.jit(model) logits = model(inputs.input_ids, attention_mask=inputs.attention_mask)[0].mean(axis=-1) self.assertTrue(jnp.allclose(logits, logits_meta, atol=4e-2)) @require_flax @slow class FlaxOPTGenerationTest(unittest.TestCase): @property def prompts(self): return [ "Today is a beautiful day and I want", "In the city of", "Paris is the capital of France and", "Computers and mobile phones have taken", ] def test_generation_pre_attn_layer_norm(self): model_id = "facebook/opt-125m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of New York, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer = GPT2Tokenizer.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="jax").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_ids = generated_ids[0] generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) def test_generation_post_attn_layer_norm(self): model_id = "facebook/opt-350m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to", "In the city of San Francisco, the city", "Paris is the capital of France and the capital", "Computers and mobile phones have taken over the", ] predicted_outputs = [] model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer = GPT2Tokenizer.from_pretrained(model_id) for prompt in self.prompts: input_ids = tokenizer(prompt, return_tensors="jax").input_ids generated_ids = model.generate(input_ids, max_length=10) generated_ids = generated_ids[0] generated_string = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) predicted_outputs += generated_string self.assertListEqual(predicted_outputs, EXPECTED_OUTPUTS) def test_jitted_batch_generation(self): model_id = "facebook/opt-125m" EXPECTED_OUTPUTS = [ "Today is a beautiful day and I want to thank", "In the city of Rome Canaver Canaver Canaver Canaver", ] model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer = GPT2Tokenizer.from_pretrained(model_id) inputs = tokenizer( [ "Today is a beautiful day and I want to", "In the city of", ], return_tensors="jax", padding=True, ) jit_generate = jax.jit(model.generate) output_sequences = jit_generate(inputs["input_ids"], attention_mask=inputs["attention_mask"]).sequences output_string = tokenizer.batch_decode(output_sequences, skip_special_tokens=True) self.assertIsNotNone(output_string, EXPECTED_OUTPUTS) def test_batch_generation(self): model_id = "facebook/opt-350m" tokenizer = GPT2Tokenizer.from_pretrained(model_id) model = FlaxOPTForCausalLM.from_pretrained(model_id) tokenizer.padding_side = "left" # use different length sentences to test batching sentences = [ "Hello, my dog is a little", "Today, I", ] inputs = tokenizer(sentences, return_tensors="jax", padding=True) input_ids = inputs["input_ids"] outputs = model.generate(input_ids=input_ids, attention_mask=inputs["attention_mask"], trace=False) inputs_non_padded = tokenizer(sentences[0], return_tensors="jax").input_ids output_non_padded = model.generate(input_ids=inputs_non_padded) num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum() inputs_padded = tokenizer(sentences[1], return_tensors="jax").input_ids output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings) batch_out_sentence = tokenizer.batch_decode(outputs[0], skip_special_tokens=True) non_padded_sentence = tokenizer.decode(output_non_padded[0][0], skip_special_tokens=True) padded_sentence = tokenizer.decode(output_padded[0][0], skip_special_tokens=True) expected_output_sentence = [ "Hello, my dog is a little bit of a dork.\nI'm a little bit", "Today, I was in the middle of a conversation with a friend about the", ] self.assertListEqual(expected_output_sentence, batch_out_sentence) self.assertListEqual(batch_out_sentence, [non_padded_sentence, padded_sentence])
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/sew_d/test_modeling_sew_d.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 PyTorch Hubert model. """ import math import unittest import pytest from transformers import SEWDConfig, is_torch_available from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( SEWDForCTC, SEWDForSequenceClassification, SEWDModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) from transformers.models.hubert.modeling_hubert import _compute_mask_indices class SEWDModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=32, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(64, 32, 32), conv_stride=(5, 2, 1), conv_kernel=(10, 3, 1), conv_bias=False, num_conv_pos_embeddings=31, num_conv_pos_embedding_groups=2, squeeze_factor=2, max_position_embeddings=512, position_buckets=256, share_att_key=True, relative_attention=True, position_biased_input=False, pos_att_type=("p2c", "c2p"), norm_rel_ebd="layer_norm", num_hidden_layers=2, num_attention_heads=2, hidden_dropout=0.1, intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, vocab_size=32, do_stable_layer_norm=False, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.squeeze_factor = squeeze_factor self.max_position_embeddings = max_position_embeddings self.position_buckets = position_buckets self.share_att_key = share_att_key self.relative_attention = relative_attention self.position_biased_input = position_biased_input self.pos_att_type = pos_att_type self.norm_rel_ebd = norm_rel_ebd self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout = hidden_dropout self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.scope = scope output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length // self.squeeze_factor def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() return config, input_values, attention_mask def get_config(self): return SEWDConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, squeeze_factor=self.squeeze_factor, max_position_embeddings=self.max_position_embeddings, position_buckets=self.position_buckets, share_att_key=self.share_att_key, relative_attention=self.relative_attention, position_biased_input=self.position_biased_input, pos_att_type=self.pos_att_type, norm_rel_ebd=self.norm_rel_ebd, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout=self.hidden_dropout, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, ) def create_and_check_model(self, config, input_values, attention_mask): model = SEWDModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = SEWDModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = SEWDForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = SEWDForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lengths are at least # one shorter than logit lengths to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_loss(self, config, input_values, *args): model = SEWDForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = SEWDForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = SEWDForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with pytest.raises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class SEWDModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (SEWDForCTC, SEWDModel, SEWDForSequenceClassification) if is_torch_available() else () pipeline_model_mapping = ( { "audio-classification": SEWDForSequenceClassification, "automatic-speech-recognition": SEWDForCTC, "feature-extraction": SEWDModel, } if is_torch_available() else {} ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = SEWDModelTester(self) self.config_tester = ConfigTester(self, config_class=SEWDConfig, 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_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # Hubert has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # SEW cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # SEW has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) 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(): uniform_init_parms = [ "conv.weight", "masked_spec_embed", "quantizer.weight_proj.weight", ] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: 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", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = SEWDModel.from_pretrained("asapp/sew-d-tiny-100k") self.assertIsNotNone(model) @require_torch class SEWDUtilsTest(unittest.TestCase): def test_compute_mask_indices(self): batch_size = 4 sequence_length = 60 mask_prob = 0.5 mask_length = 1 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) self.assertListEqual(mask.sum(axis=-1).tolist(), [mask_prob * sequence_length for _ in range(batch_size)]) def test_compute_mask_indices_overlap(self): batch_size = 4 sequence_length = 80 mask_prob = 0.5 mask_length = 4 mask = _compute_mask_indices((batch_size, sequence_length), mask_prob, mask_length) mask = torch.from_numpy(mask).to(torch_device) # because of overlap mask don't have to add up exactly to `mask_prob * sequence_length`, but have to be smaller or equal for batch_sum in mask.sum(axis=-1): self.assertTrue(int(batch_sum) <= mask_prob * sequence_length) @require_torch @require_soundfile @slow class SEWDModelIntegrationTest(unittest.TestCase): 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").filter( lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)] )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def test_inference_pretrained_batched(self): model = SEWDModel.from_pretrained("asapp/sew-d-tiny-100k").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("asapp/sew-d-tiny-100k") input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): outputs = model(input_values).last_hidden_state # expected outputs taken from the original SEW-D implementation expected_outputs_first = torch.tensor( [ [ [-0.1619, 0.6995, 0.4062, -0.1014], [-0.1364, 0.5960, 0.0952, -0.0873], [-0.1572, 0.5718, 0.4228, -0.0864], [-0.1325, 0.6823, 0.1387, -0.0871], ], [ [-0.1296, 0.4008, 0.4952, -0.1450], [-0.1152, 0.3693, 0.3037, -0.1290], [-0.1194, 0.6074, 0.3531, -0.1466], [-0.1113, 0.3135, 0.2224, -0.1338], ], ], device=torch_device, ) expected_outputs_last = torch.tensor( [ [ [-0.1577, 0.5108, 0.8553, 0.2550], [-0.1530, 0.3580, 0.6143, 0.2672], [-0.1535, 0.4954, 0.8503, 0.1387], [-0.1572, 0.3363, 0.6217, 0.1490], ], [ [-0.1338, 0.5459, 0.9607, -0.1133], [-0.1502, 0.3738, 0.7313, -0.0986], [-0.0953, 0.4708, 1.0821, -0.0944], [-0.1474, 0.3598, 0.7248, -0.0748], ], ], device=torch_device, ) expected_output_sum = 54201.0469 self.assertTrue(torch.allclose(outputs[:, :4, :4], expected_outputs_first, atol=1e-3)) self.assertTrue(torch.allclose(outputs[:, -4:, -4:], expected_outputs_last, atol=1e-3)) self.assertTrue(abs(outputs.sum() - expected_output_sum) < 1) def test_inference_ctc_batched(self): model = SEWDForCTC.from_pretrained("asapp/sew-d-tiny-100k-ft-ls100h").to(torch_device) processor = Wav2Vec2Processor.from_pretrained("asapp/sew-d-tiny-100k-ft-ls100h", do_lower_case=True) input_speech = self._load_datasamples(2) inputs = processor(input_speech, return_tensors="pt", padding=True) input_values = inputs.input_values.to(torch_device) with torch.no_grad(): logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_trans = processor.batch_decode(predicted_ids) EXPECTED_TRANSCRIPTIONS = [ "a man said to the universe sir i exist", "swet covered breon's body trickling into the titlowing closs that was the only garmened he war", ] self.assertListEqual(predicted_trans, EXPECTED_TRANSCRIPTIONS)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/mistral/test_modeling_mistral.py
# coding=utf-8 # Copyright 2023 Mistral AI 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 Mistral model. """ import gc import tempfile import unittest import pytest from transformers import AutoTokenizer, MistralConfig, is_torch_available, set_seed from transformers.testing_utils import ( backend_empty_cache, is_flaky, require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, require_torch_sdpa, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MistralForCausalLM, MistralForSequenceClassification, MistralModel, ) class MistralModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, 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, pad_token_id=0, 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.num_key_value_heads = num_key_value_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.pad_token_id = pad_token_id self.scope = scope # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs 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 = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device) 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): return MistralConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, pad_token_id=self.pad_token_id, ) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Mistral def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = MistralModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Mistral 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 = MistralModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Mistral 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 = MistralForCausalLM(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Mistral 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 = MistralForCausalLM(config=config) model.to(torch_device) model.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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common 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, "attention_mask": input_mask} return config, inputs_dict @require_torch class MistralModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (MistralModel, MistralForCausalLM, MistralForSequenceClassification) if is_torch_available() else () ) all_generative_model_classes = (MistralForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": MistralModel, "text-classification": MistralForSequenceClassification, "text-generation": MistralForCausalLM, "zero-shot": MistralForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False fx_compatible = True # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146 def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True # TODO: @Fxmarty @is_flaky(max_attempts=3, description="flaky on some models.") @require_torch_sdpa @slow def test_eager_matches_sdpa_generate(self): super().test_eager_matches_sdpa_generate() def setUp(self): self.model_tester = MistralModelTester(self) self.config_tester = ConfigTester(self, config_class=MistralConfig, 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_Mistral_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() print(config) config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = MistralForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Mistral_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = MistralForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Mistral_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = MistralForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @unittest.skip("Mistral buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass @unittest.skip("Mistral uses GQA on all models so the KV cache is a non standard format") def test_past_key_values_format(self): pass @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_right(self): import torch for model_class in self.all_generative_model_classes: config, _ = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to( torch_device ) dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device) dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device) model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) with self.assertRaises(ValueError): _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_use_cache(self): import torch max_new_tokens = 30 for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) # NOTE: Mistral apparently does not support right padding + use_cache with FA2. dummy_attention_mask[:, -1] = 1 model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) # Just test that a large cache works as expected _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False, use_cache=True, ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest("Mistral flash attention does not support right padding") @require_torch_gpu class MistralIntegrationTest(unittest.TestCase): # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) # Depending on the hardware we get different logits / generations cuda_compute_capability_major_version = None @classmethod def setUpClass(cls): if is_torch_available() and torch.cuda.is_available(): # 8 is for A100 / A10 and 7 for T4 cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] def tearDown(self): torch.cuda.empty_cache() gc.collect() @slow def test_model_7b_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = MistralForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16 ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) with torch.no_grad(): out = model(input_ids).logits.cpu() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2) EXPECTED_SLICE = { 7: torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787, 1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781]), 8: torch.tensor([-5.8711, -5.8555, -0.1050, -4.7148, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -1.0781, 1.7568, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711, -5.8711]), } # fmt: skip print(out[0, 0, :30]) torch.testing.assert_close( out[0, 0, :30], EXPECTED_SLICE[self.cuda_compute_capability_major_version], atol=1e-4, rtol=1e-4 ) del model backend_empty_cache(torch_device) gc.collect() @slow @require_bitsandbytes def test_model_7b_generation(self): EXPECTED_TEXT_COMPLETION = { 7: "My favourite condiment is 100% ketchup. I love it on everything. I'm not a big", 8: "My favourite condiment is 100% ketchup. I’m not a fan of mustard, mayo,", } prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False) model = MistralForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", device_map={"": torch_device}, load_in_4bit=True ) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text) del model backend_empty_cache(torch_device) gc.collect() @require_bitsandbytes @slow @require_flash_attn def test_model_7b_long_prompt(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # An input with 4097 tokens that is above the size of the sliding window input_ids = [1] + [306, 338] * 2048 model = MistralForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", device_map={"": torch_device}, load_in_4bit=True, attn_implementation="flash_attention_2", ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) del assistant_model del model backend_empty_cache(torch_device) gc.collect() @slow @require_torch_sdpa def test_model_7b_long_prompt_sdpa(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # An input with 4097 tokens that is above the size of the sliding window input_ids = [1] + [306, 338] * 2048 model = MistralForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", device_map="auto", attn_implementation="sdpa", torch_dtype=torch.float16 ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) del assistant_model backend_empty_cache(torch_device) gc.collect() EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big""" prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) @slow def test_speculative_generation(self): EXPECTED_TEXT_COMPLETION = { 7: "My favourite condiment is 100% Sriracha. I love the heat, the tang and the fact costs", 8: "My favourite condiment is 100% Sriracha. I love the heat, the sweetness, the tang", } prompt = "My favourite condiment is " tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False) model = MistralForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", device_map="auto", torch_dtype=torch.float16 ) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs set_seed(0) generated_ids = model.generate( input_ids, max_new_tokens=20, do_sample=True, temperature=0.3, assistant_model=model ) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION[self.cuda_compute_capability_major_version], text) del model backend_empty_cache(torch_device) gc.collect()
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/mistral/test_modeling_flax_mistral.py
# Copyright 2023 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 MistralConfig, is_flax_available, is_tokenizers_available from transformers.testing_utils import require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import jax.numpy as jnp from transformers.models.mistral.modeling_flax_mistral import ( FlaxMistralForCausalLM, FlaxMistralModel, ) if is_tokenizers_available(): from transformers import LlamaTokenizerFast class FlaxMistralModelTester: def __init__( self, parent, batch_size=2, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, window_size=7, initializer_range=0.02, ): 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.num_key_value_heads = num_key_value_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.window_size = window_size self.initializer_range = initializer_range self.scope = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 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 = np.tril(np.ones((self.batch_size, self.seq_length))) config = MistralConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, use_cache=True, is_decoder=False, initializer_range=self.initializer_range, sliding_window=self.window_size, ) config.pad_token_id = config.eos_token_id return (config, input_ids, input_mask) # Copied from tests.models.gpt_neo.test_modeling_flax_gpt_neo.FlaxGPTNeoModelTester.prepare_config_and_inputs_for_common def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask} return config, inputs_dict # Copied from tests.models.gpt_neo.test_modeling_flax_gpt_neo.FlaxGPTNeoModelTester.check_use_cache_forward def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length) attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4") position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1) ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], attention_mask=attention_mask, past_key_values=outputs_cache.past_key_values, position_ids=position_ids, ) outputs = model(input_ids) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") # Copied from tests.models.gpt_neo.test_modeling_flax_gpt_neo.FlaxGPTNeoModelTester.check_use_cache_forward_with_attn_mask def check_use_cache_forward_with_attn_mask(self, model_class_name, config, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) attention_mask_cache = jnp.concatenate( [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))], axis=-1, ) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length) position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1) ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask_cache, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], past_key_values=outputs_cache.past_key_values, attention_mask=attention_mask_cache, position_ids=position_ids, ) outputs = model(input_ids, attention_mask=attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class FlaxMistralModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase): all_model_classes = (FlaxMistralModel, FlaxMistralForCausalLM) if is_flax_available() else () all_generative_model_classes = (FlaxMistralForCausalLM,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxMistralModelTester(self) def test_use_cache_forward(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask) def test_use_cache_forward_with_attn_mask(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward_with_attn_mask( model_class_name, config, input_ids, attention_mask ) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("mistralai/Mistral-7B-v0.1", from_pt=True) outputs = model(np.ones((1, 1))) self.assertIsNotNone(outputs) @slow @require_flax class FlaxMistralIntegrationTest(unittest.TestCase): def setUp(self): self.model_id = "mistralai/Mistral-7B-v0.1" self.model = FlaxMistralForCausalLM.from_pretrained(self.model_id, from_pt=True) self.test_batch = jnp.arange(32).reshape(4, 8) + 1911 def test_model_logits(self): input_ids = jnp.array([[1, 306, 4658, 278, 6593, 310, 2834, 338]]) EXPECTED_MEAN = np.array([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]]) EXPECTED_SLICE = np.array([-5.8781,-5.8616,-0.1052,-4.7200,-5.8781,-5.8774,-5.8773,-5.8777,-5.8781,-5.8780,-5.8781,-5.8779,-1.0787,1.7583,-5.8779,-5.8780,-5.8783,-5.8778,-5.8776,-5.8781,-5.8784,-5.8778,-5.8778,-5.8777,-5.8779,-5.8778,-5.8776,-5.8780,-5.8779,-5.8781]) # fmt: skip flax_logits = self.model(input_ids).logits diff_mean = jnp.abs(flax_logits.mean(-1) - EXPECTED_MEAN).max() diff_slice = jnp.abs(flax_logits[0, 0, :30] - EXPECTED_SLICE).max() self.assertAlmostEqual(diff_mean, 0, places=3) self.assertAlmostEqual(diff_slice, 0, places=3) def test_generated_text(self): tokenizer = LlamaTokenizerFast.from_pretrained(self.model_id) tokenizer.pad_token_id = 2 EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big""" prompt = "My favourite condiment is " inputs = tokenizer(prompt, return_tensors="np", truncation=True, padding=True) generated_ids = self.model.generate(**inputs, max_new_tokens=20, temperature=0).sequences generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(generated_text, EXPECTED_TEXT_COMPLETION)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/dpr/test_tokenization_dpr.py
# coding=utf-8 # Copyright 2020 Huggingface # # 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 ( DPRContextEncoderTokenizer, DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast, DPRReaderOutput, DPRReaderTokenizer, DPRReaderTokenizerFast, ) from transformers.testing_utils import require_tokenizers, slow from transformers.tokenization_utils_base import BatchEncoding from ..bert.test_tokenization_bert import BertTokenizationTest @require_tokenizers class DPRContextEncoderTokenizationTest(BertTokenizationTest): tokenizer_class = DPRContextEncoderTokenizer rust_tokenizer_class = DPRContextEncoderTokenizerFast test_rust_tokenizer = True from_pretrained_id = "facebook/dpr-ctx_encoder-single-nq-base" @require_tokenizers class DPRQuestionEncoderTokenizationTest(BertTokenizationTest): tokenizer_class = DPRQuestionEncoderTokenizer rust_tokenizer_class = DPRQuestionEncoderTokenizerFast test_rust_tokenizer = True from_pretrained_id = "facebook/dpr-ctx_encoder-single-nq-base" @require_tokenizers class DPRReaderTokenizationTest(BertTokenizationTest): tokenizer_class = DPRReaderTokenizer rust_tokenizer_class = DPRReaderTokenizerFast test_rust_tokenizer = True from_pretrained_id = "facebook/dpr-ctx_encoder-single-nq-base" @slow def test_decode_best_spans(self): tokenizer = self.tokenizer_class.from_pretrained("google-bert/bert-base-uncased") text_1 = tokenizer.encode("question sequence", add_special_tokens=False) text_2 = tokenizer.encode("title sequence", add_special_tokens=False) text_3 = tokenizer.encode("text sequence " * 4, add_special_tokens=False) input_ids = [[101] + text_1 + [102] + text_2 + [102] + text_3] reader_input = BatchEncoding({"input_ids": input_ids}) start_logits = [[0] * len(input_ids[0])] end_logits = [[0] * len(input_ids[0])] relevance_logits = [0] reader_output = DPRReaderOutput(start_logits, end_logits, relevance_logits) start_index, end_index = 8, 9 start_logits[0][start_index] = 10 end_logits[0][end_index] = 10 predicted_spans = tokenizer.decode_best_spans(reader_input, reader_output) self.assertEqual(predicted_spans[0].start_index, start_index) self.assertEqual(predicted_spans[0].end_index, end_index) self.assertEqual(predicted_spans[0].doc_id, 0) @slow def test_call(self): tokenizer = self.tokenizer_class.from_pretrained("google-bert/bert-base-uncased") text_1 = tokenizer.encode("question sequence", add_special_tokens=False) text_2 = tokenizer.encode("title sequence", add_special_tokens=False) text_3 = tokenizer.encode("text sequence", add_special_tokens=False) expected_input_ids = [101] + text_1 + [102] + text_2 + [102] + text_3 encoded_input = tokenizer(questions=["question sequence"], titles=["title sequence"], texts=["text sequence"]) self.assertIn("input_ids", encoded_input) self.assertIn("attention_mask", encoded_input) self.assertListEqual(encoded_input["input_ids"][0], expected_input_ids)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/dpr/test_modeling_dpr.py
# coding=utf-8 # Copyright 2020 Huggingface # # 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 tempfile import unittest from transformers import DPRConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer class DPRModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=False, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, 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, projection_dim=0, ): 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 self.projection_dim = projection_dim 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): return DPRConfig( projection_dim=self.projection_dim, 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, initializer_range=self.initializer_range, ) def create_and_check_context_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DPRContextEncoder(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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size)) def create_and_check_question_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DPRQuestionEncoder(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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size)) def create_and_check_reader( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DPRReader(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, ) 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)) self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,)) 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} return config, inputs_dict @require_torch class DPRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DPRContextEncoder, DPRQuestionEncoder, DPRReader, ) if is_torch_available() else () ) pipeline_model_mapping = {"feature-extraction": DPRQuestionEncoder} if is_torch_available() else {} test_resize_embeddings = False test_missing_keys = False # why? test_pruning = False test_head_masking = False def setUp(self): self.model_tester = DPRModelTester(self) self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_context_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_context_encoder(*config_and_inputs) def test_question_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_question_encoder(*config_and_inputs) def test_reader_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_reader(*config_and_inputs) def test_init_changed_config(self): config = self.model_tester.prepare_config_and_inputs()[0] model = DPRQuestionEncoder(config=config) model.to(torch_device) model.eval() with tempfile.TemporaryDirectory() as tmp_dirname: model.save_pretrained(tmp_dirname) model = DPRQuestionEncoder.from_pretrained(tmp_dirname, projection_dim=512) self.assertIsNotNone(model) @slow def test_model_from_pretrained(self): model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = DPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = DPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = DPRQuestionEncoder.from_pretrained(model_name) self.assertIsNotNone(model) model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = DPRReader.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class DPRModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base", return_dict=False) model.to(torch_device) input_ids = torch.tensor( [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]], dtype=torch.long, device=torch_device ) # [CLS] hello, is my dog cute? [SEP] output = model(input_ids)[0] # embedding shape = (1, 768) # compare the actual values for a slice. expected_slice = torch.tensor( [ [ 0.03236253, 0.12753335, 0.16818509, 0.00279786, 0.3896933, 0.24264945, 0.2178971, -0.02335227, -0.08481959, -0.14324117, ] ], dtype=torch.float, device=torch_device, ) self.assertTrue(torch.allclose(output[:, :10], expected_slice, atol=1e-4)) @slow def test_reader_inference(self): tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base") model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base") model.to(torch_device) encoded_inputs = tokenizer( questions="What is love ?", titles="Haddaway", texts="What Is Love is a song recorded by the artist Haddaway", padding=True, return_tensors="pt", ) encoded_inputs.to(torch_device) outputs = model(**encoded_inputs) # compare the actual values for a slice. expected_start_logits = torch.tensor( [[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]], dtype=torch.float, device=torch_device, ) expected_end_logits = torch.tensor( [[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]], dtype=torch.float, device=torch_device, ) self.assertTrue(torch.allclose(outputs.start_logits[:, :10], expected_start_logits, atol=1e-4)) self.assertTrue(torch.allclose(outputs.end_logits[:, :10], expected_end_logits, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/dpr/test_modeling_tf_dpr.py
# coding=utf-8 # Copyright 2020 Huggingface # # 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 __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy import tensorflow as tf from transformers import ( BertConfig, DPRConfig, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) class TFDPRModelTester: 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=2, 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, projection_dim=0, ): 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 self.projection_dim = projection_dim 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: # follow test_modeling_tf_ctrl.py 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 = 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, ) config = DPRConfig(projection_dim=self.projection_dim, **config.to_dict()) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_dpr_context_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFDPRContextEncoder(config=config) 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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size)) def create_and_check_dpr_question_encoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFDPRQuestionEncoder(config=config) 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.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size)) def create_and_check_dpr_reader( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFDPRReader(config=config) result = model(input_ids, attention_mask=input_mask) 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)) self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,)) 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} return config, inputs_dict @require_tf class TFDPRModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, ) if is_tf_available() else () ) pipeline_model_mapping = {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {} test_resize_embeddings = False test_missing_keys = False test_pruning = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFDPRModelTester(self) self.config_tester = ConfigTester(self, config_class=DPRConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_dpr_context_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_context_encoder(*config_and_inputs) def test_dpr_question_encoder_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_question_encoder(*config_and_inputs) def test_dpr_reader_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_dpr_reader(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = TFDPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = TFDPRContextEncoder.from_pretrained(model_name) self.assertIsNotNone(model) model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = TFDPRQuestionEncoder.from_pretrained(model_name) self.assertIsNotNone(model) model_name = "facebook/dpr-ctx_encoder-single-nq-base" model = TFDPRReader.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFDPRModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base") input_ids = tf.constant( [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]] ) # [CLS] hello, is my dog cute? [SEP] output = model(input_ids)[0] # embedding shape = (1, 768) # compare the actual values for a slice. expected_slice = tf.constant( [ [ 0.03236253, 0.12753335, 0.16818509, 0.00279786, 0.3896933, 0.24264945, 0.2178971, -0.02335227, -0.08481959, -0.14324117, ] ] ) self.assertTrue(numpy.allclose(output[:, :10].numpy(), expected_slice.numpy(), atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/x_clip/test_modeling_x_clip.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 XCLIP model. """ import inspect import os import tempfile import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import XCLIPConfig, XCLIPTextConfig, XCLIPVisionConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import 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, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import XCLIPModel, XCLIPTextModel, XCLIPVisionModel if is_vision_available(): from transformers import XCLIPProcessor class XCLIPVisionModelTester: def __init__( self, parent, batch_size=8, image_size=30, patch_size=2, num_channels=3, num_frames=8, # important; the batch size * time must be divisible by the number of frames is_training=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, mit_hidden_size=64, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_frames = num_frames self.is_training = is_training 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.mit_hidden_size = mit_hidden_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, 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_frames, self.num_channels, self.image_size, self.image_size] ) config = self.get_config() return config, pixel_values def get_config(self): return XCLIPVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_frames=self.num_frames, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, mit_hidden_size=self.mit_hidden_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = XCLIPVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size * self.num_frames, num_patches + 1, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_frames, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class XCLIPVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as X-CLIP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (XCLIPVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = XCLIPVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=XCLIPVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="X-CLIP does not use inputs_embeds") def test_inputs_embeds(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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="XCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="XCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "microsoft/xclip-base-patch32" model = XCLIPVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue print("Model class:", model_class) config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # we add 1 here due to the special message token in X-CLIP's vision encoder seq_len = getattr(self.model_tester, "seq_length", None) + 1 encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) 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)) self.assertEqual(len(outputs.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)) self.assertEqual(len(outputs.attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(outputs.attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length], ) 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) model.to(torch_device) model.eval() with torch.no_grad(): 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), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_seq_length], ) @require_torch_multi_gpu def test_multi_gpu_data_parallel_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # some params shouldn't be scattered by nn.DataParallel # so just remove them if they are present. blacklist_non_batched_params = ["head_mask", "decoder_head_mask", "cross_attn_head_mask"] for k in blacklist_non_batched_params: inputs_dict.pop(k, None) # move input tensors to cuda:O for k, v in inputs_dict.items(): if torch.is_tensor(v): inputs_dict[k] = v.to(0) for model_class in self.all_model_classes: model = model_class(config=config) model.to(0) model.eval() # Wrap model in nn.DataParallel model = nn.DataParallel(model) with torch.no_grad(): test = self._prepare_for_class(inputs_dict, model_class) for k, v in test.items(): if isinstance(v, torch.Tensor): print(k, v.shape) else: print(k, v) _ = model(**self._prepare_for_class(inputs_dict, model_class)) class XCLIPTextModelTester: def __init__( self, parent, batch_size=8, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, 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_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.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range 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]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return XCLIPTextConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = XCLIPTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) 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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class XCLIPTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (XCLIPTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = XCLIPTextModelTester(self) self.config_tester = ConfigTester(self, config_class=XCLIPTextConfig, 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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="X-CLIP does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="XCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="XCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "microsoft/xclip-base-patch32" model = XCLIPTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class XCLIPModelTester: def __init__( self, parent, text_kwargs=None, vision_kwargs=None, projection_dim=64, mit_hidden_size=64, is_training=True, ): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.projection_dim = projection_dim self.mit_hidden_size = mit_hidden_size self.text_model_tester = XCLIPTextModelTester(parent, **text_kwargs) self.vision_model_tester = XCLIPVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, _ = self.vision_model_tester.prepare_config_and_inputs() pixel_values = floats_tensor( [ self.vision_model_tester.batch_size, self.vision_model_tester.num_frames, self.vision_model_tester.num_channels, self.vision_model_tester.image_size, self.vision_model_tester.image_size, ] ) config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return XCLIPConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=self.projection_dim, ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = XCLIPModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_video.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size), ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size), ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict @require_torch class XCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (XCLIPModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": XCLIPModel} if is_torch_available() else {} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_torchscript = False maxdiff = None def setUp(self): self.model_tester = XCLIPModelTester(self) 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(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="XCLIPModel does not have input/output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="XCLIPModel does not support feedforward chunking") def test_feed_forward_chunking(self): pass # override as the `logit_scale`, `prompts_generator.alpha` parameters require special treatment 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(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) elif name == "prompts_generator.alpha": self.assertAlmostEqual(param.data.mean().item(), model.config.prompt_alpha) else: 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 _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # X-CLIP needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save XCLIPConfig and check if we can load XCLIPVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = XCLIPVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save XCLIPConfig and check if we can load XCLIPTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = XCLIPTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): model_name = "microsoft/xclip-base-patch32" model = XCLIPModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on a spaghetti video def prepare_video(): file = hf_hub_download( repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti_8_frames.npy", repo_type="dataset" ) video = np.load(file) return list(video) @require_vision @require_torch class XCLIPModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "microsoft/xclip-base-patch32" model = XCLIPModel.from_pretrained(model_name).to(torch_device) processor = XCLIPProcessor.from_pretrained(model_name) video = prepare_video() inputs = processor( text=["playing sports", "eating spaghetti", "go shopping"], videos=video, return_tensors="pt", padding=True ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_video.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[14.0181, 20.2771, 14.4776]], device=torch_device) self.assertTrue(torch.allclose(outputs.logits_per_video, expected_logits, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/marian/test_modeling_tf_marian.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. from __future__ import annotations import unittest import warnings from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFAutoModelForSeq2SeqLM, TFMarianModel, TFMarianMTModel @require_tf class TFMarianModelTester: config_cls = MarianConfig 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=2, 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_marian_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def check_decoder_model_past_large_inputs(self, config, inputs_dict): model = TFMarianModel(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_marian_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 TFMarianModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFMarianMTModel, TFMarianModel) if is_tf_available() else () all_generative_model_classes = (TFMarianMTModel,) if is_tf_available() else () pipeline_model_mapping = ( { "conversational": TFMarianMTModel, "feature-extraction": TFMarianModel, "summarization": TFMarianMTModel, "text2text-generation": TFMarianMTModel, "translation": TFMarianMTModel, } if is_tf_available() else {} ) is_encoder_decoder = True test_pruning = False test_onnx = False def setUp(self): self.model_tester = TFMarianModelTester(self) self.config_tester = ConfigTester(self, config_class=MarianConfig) 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) @unittest.skip("Skipping for now, to fix @ArthurZ or @ydshieh") def test_pipeline_conversational(self): pass @require_tf class AbstractMarianIntegrationTest(unittest.TestCase): maxDiff = 1000 # show more chars for failing integration tests @classmethod def setUpClass(cls) -> None: cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}" return cls @cached_property def tokenizer(self) -> MarianTokenizer: return AutoTokenizer.from_pretrained(self.model_name) @property def eos_token_id(self) -> int: return self.tokenizer.eos_token_id @cached_property def model(self): warnings.simplefilter("error") model: TFMarianMTModel = TFAutoModelForSeq2SeqLM.from_pretrained(self.model_name) assert isinstance(model, TFMarianMTModel) c = model.config self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]]) self.assertEqual(c.max_length, 512) self.assertEqual(c.decoder_start_token_id, c.pad_token_id) return model def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs): generated_words = self.translate_src_text(**tokenizer_kwargs) self.assertListEqual(self.expected_text, generated_words) def translate_src_text(self, **tokenizer_kwargs): model_inputs = self.tokenizer(self.src_text, **tokenizer_kwargs, padding=True, return_tensors="tf") generated_ids = self.model.generate( model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, max_length=128 ) generated_words = self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=True) return generated_words @require_sentencepiece @require_tokenizers @require_tf class TestMarian_MT_EN(AbstractMarianIntegrationTest): """Cover low resource/high perplexity setting. This breaks if pad_token_id logits not set to LARGE_NEGATIVE.""" src = "mt" tgt = "en" src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."] expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."] @unittest.skip("Skipping until #12647 is resolved.") @slow def test_batch_generation_mt_en(self): self._assert_generated_batch_equal_expected() @require_sentencepiece @require_tokenizers @require_tf class TestMarian_en_zh(AbstractMarianIntegrationTest): src = "en" tgt = "zh" src_text = ["My name is Wolfgang and I live in Berlin"] expected_text = ["我叫沃尔夫冈 我住在柏林"] @unittest.skip("Skipping until #12647 is resolved.") @slow def test_batch_generation_en_zh(self): self._assert_generated_batch_equal_expected() @require_sentencepiece @require_tokenizers @require_tf class TestMarian_en_ROMANCE(AbstractMarianIntegrationTest): """Multilingual on target side.""" src = "en" tgt = "ROMANCE" src_text = [ ">>fr<< Don't spend so much time watching TV.", ">>pt<< Your message has been sent.", ">>es<< He's two years older than me.", ] expected_text = [ "Ne passez pas autant de temps à regarder la télé.", "A sua mensagem foi enviada.", "Es dos años más viejo que yo.", ] @unittest.skip("Skipping until #12647 is resolved.") @slow def test_batch_generation_en_ROMANCE_multi(self): self._assert_generated_batch_equal_expected() @unittest.skip("Skipping until #12647 is resolved.") @slow def test_pipeline(self): pipeline = TranslationPipeline(self.model, self.tokenizer, framework="tf") output = pipeline(self.src_text) self.assertEqual(self.expected_text, [x["translation_text"] for x in output])
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/marian/test_modeling_flax_marian.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. import unittest import numpy as np import timeout_decorator # noqa from transformers import MarianConfig, is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform" import jax import jax.numpy as jnp from transformers import MarianTokenizer from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right def prepare_marian_inputs_dict( config, input_ids, decoder_input_ids=None, 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 = np.where(input_ids != config.pad_token_id, 1, 0) if decoder_attention_mask is None: decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0) if head_mask is None: head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = np.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": attention_mask, } class FlaxMarianModelTester: 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, 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, initializer_range=0.02, ): 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.initializer_range = initializer_range def prepare_config_and_inputs(self): input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size) input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1) decoder_input_ids = shift_tokens_right(input_ids, 1, 2) config = MarianConfig( 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, initializer_range=self.initializer_range, use_cache=False, ) inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4") decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=outputs_cache.past_key_values, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) decoder_attention_mask_cache = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask_cache, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=decoder_attention_mask_cache, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class FlaxMarianModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin): is_encoder_decoder = True all_model_classes = (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else () all_generative_model_classes = (FlaxMarianMTModel,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxMarianModelTester(self) def test_use_cache_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(model_class, config, inputs_dict) def test_use_cache_forward_with_attn_mask(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict) def test_encode(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 encode_jitted(input_ids, attention_mask=None, **kwargs): return model.encode(input_ids=input_ids, attention_mask=attention_mask) with self.subTest("JIT Enabled"): jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = encode_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) def test_decode(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__): model = model_class(config) encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"]) prepared_inputs_dict = { "decoder_input_ids": inputs_dict["decoder_input_ids"], "decoder_attention_mask": inputs_dict["decoder_attention_mask"], "encoder_outputs": encoder_outputs, } @jax.jit def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs): return model.decode( decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, ) with self.subTest("JIT Enabled"): jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = decode_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) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de") # FlaxMarianForSequenceClassification expects eos token in input_ids input_ids = np.ones((1, 1)) * model.config.eos_token_id outputs = model(input_ids) self.assertIsNotNone(outputs) @unittest.skip("Skipping for now, to fix @ArthurZ or @ydshieh") def test_pipeline_conversational(self): pass @require_flax @require_sentencepiece @require_tokenizers class MarianIntegrationTest(unittest.TestCase): src = None tgt = None @classmethod def setUpClass(cls) -> None: cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}" return cls @cached_property def tokenizer(self): return MarianTokenizer.from_pretrained(self.model_name) @property def eos_token_id(self) -> int: return self.tokenizer.eos_token_id @cached_property def model(self): model: FlaxMarianMTModel = FlaxMarianMTModel.from_pretrained(self.model_name) self.assertEqual(model.config.decoder_start_token_id, model.config.pad_token_id) return model def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs): generated_words = self.translate_src_text(**tokenizer_kwargs) self.assertListEqual(self.expected_text, generated_words) def translate_src_text(self, **tokenizer_kwargs): model_inputs = self.tokenizer(self.src_text, padding=True, return_tensors="np", **tokenizer_kwargs) generated_ids = self.model.generate( model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, max_length=128, ).sequences generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True) return generated_words @require_flax @require_sentencepiece @require_tokenizers class TestMarian_EN_FR(MarianIntegrationTest): src = "en" tgt = "fr" src_text = [ "I am a small frog.", "Now I can forget the 100 words of german that I know.", ] expected_text = [ "Je suis une petite grenouille.", "Maintenant, je peux oublier les 100 mots d'allemand que je connais.", ] @slow def test_batch_generation_en_fr(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_FR_EN(MarianIntegrationTest): src = "fr" tgt = "en" src_text = [ "Donnez moi le micro.", "Tom et Mary étaient assis à une table.", # Accents ] expected_text = [ "Give me the microphone.", "Tom and Mary were sitting at a table.", ] @slow def test_batch_generation_fr_en(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_MT_EN(MarianIntegrationTest): """Cover low resource/high perplexity setting. This breaks without adjust_logits_generation overwritten""" src = "mt" tgt = "en" src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."] expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."] @slow def test_batch_generation_mt_en(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_EN_DE(MarianIntegrationTest): src = "en" tgt = "de" src_text = [ "I am a small frog.", "Now I can forget the 100 words of german that I know.", "Tom asked his teacher for advice.", "That's how I would do it.", "Tom really admired Mary's courage.", "Turn around and close your eyes.", ] expected_text = [ "Ich bin ein kleiner Frosch.", "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.", "Tom bat seinen Lehrer um Rat.", "So würde ich das machen.", "Tom bewunderte Marias Mut wirklich.", "Drehen Sie sich um und schließen Sie die Augen.", ] @slow def test_batch_generation_en_de(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_en_zh(MarianIntegrationTest): src = "en" tgt = "zh" src_text = ["My name is Wolfgang and I live in Berlin"] expected_text = ["我叫沃尔夫冈 我住在柏林"] @slow def test_batch_generation_eng_zho(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_RU_FR(MarianIntegrationTest): src = "ru" tgt = "fr" src_text = ["Он показал мне рукопись своей новой пьесы."] expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."] @slow def test_batch_generation_ru_fr(self): self._assert_generated_batch_equal_expected() @require_flax @require_sentencepiece @require_tokenizers class TestMarian_en_ROMANCE(MarianIntegrationTest): """Multilingual on target side.""" src = "en" tgt = "ROMANCE" src_text = [ ">>fr<< Don't spend so much time watching TV.", ">>pt<< Your message has been sent.", ">>es<< He's two years older than me.", ] expected_text = [ "Ne passez pas autant de temps à regarder la télé.", "A sua mensagem foi enviada.", "Es dos años más viejo que yo.", ] @slow def test_batch_generation_en_ROMANCE_multi(self): self._assert_generated_batch_equal_expected()
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/marian/test_modeling_marian.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 PyTorch Marian model. """ import tempfile import unittest from huggingface_hub.hf_api import list_models from transformers import MarianConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( AutoConfig, AutoModelWithLMHead, AutoTokenizer, MarianModel, MarianMTModel, TranslationPipeline, ) from transformers.models.marian.convert_marian_to_pytorch import ( ORG_NAME, convert_hf_name_to_opus_name, convert_opus_name_to_hf_name, ) from transformers.models.marian.modeling_marian import ( MarianDecoder, MarianEncoder, MarianForCausalLM, shift_tokens_right, ) def prepare_marian_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": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class MarianModelTester: 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, hidden_act="gelu", 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, decoder_start_token_id=3, ): 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.decoder_start_token_id = decoder_start_token_id # forcing a certain token to be generated, sets all other tokens to -inf # if however the token to be generated is already at -inf then it can lead token # `nan` values and thus break generation self.forced_bos_token_id = None self.forced_eos_token_id = None def prepare_config_and_inputs(self): 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_marian_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return MarianConfig( 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, decoder_start_token_id=self.decoder_start_token_id, forced_bos_token_id=self.forced_bos_token_id, forced_eos_token_id=self.forced_eos_token_id, ) def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = MarianModel(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-3)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = MarianModel(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 = MarianEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["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 = MarianDecoder.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) @require_torch class MarianModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (MarianModel, MarianMTModel) if is_torch_available() else () all_generative_model_classes = (MarianMTModel,) if is_torch_available() else () pipeline_model_mapping = ( { "conversational": MarianMTModel, "feature-extraction": MarianModel, "summarization": MarianMTModel, "text-generation": MarianForCausalLM, "text2text-generation": MarianMTModel, "translation": MarianMTModel, } if is_torch_available() else {} ) is_encoder_decoder = True fx_compatible = True test_pruning = False test_missing_keys = False def setUp(self): self.model_tester = MarianModelTester(self) self.config_tester = ConfigTester(self, config_class=MarianConfig) 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) @require_torch_fp16 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 = MarianMTModel(config).eval().to(torch_device) 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_share_encoder_decoder_embeddings(self): config, input_dict = self.model_tester.prepare_config_and_inputs() # check if embeddings are shared by default for model_class in self.all_model_classes: model = model_class(config) self.assertIs(model.get_encoder().embed_tokens, model.get_decoder().embed_tokens) self.assertIs(model.get_encoder().embed_tokens.weight, model.get_decoder().embed_tokens.weight) # check if embeddings are not shared when config.share_encoder_decoder_embeddings = False config.share_encoder_decoder_embeddings = False for model_class in self.all_model_classes: model = model_class(config) self.assertIsNot(model.get_encoder().embed_tokens, model.get_decoder().embed_tokens) self.assertIsNot(model.get_encoder().embed_tokens.weight, model.get_decoder().embed_tokens.weight) # check if a model with shared embeddings can be saved and loaded with share_encoder_decoder_embeddings = False config, _ = 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) model = model_class.from_pretrained(tmpdirname, share_encoder_decoder_embeddings=False) self.assertIsNot(model.get_encoder().embed_tokens, model.get_decoder().embed_tokens) self.assertIsNot(model.get_encoder().embed_tokens.weight, model.get_decoder().embed_tokens.weight) def test_resize_decoder_token_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs() # check if resize_decoder_token_embeddings raises an error when embeddings are shared for model_class in self.all_model_classes: model = model_class(config) with self.assertRaises(ValueError): model.resize_decoder_token_embeddings(config.vocab_size + 1) # check if decoder embeddings are resized when config.share_encoder_decoder_embeddings = False config.share_encoder_decoder_embeddings = False for model_class in self.all_model_classes: model = model_class(config) model.resize_decoder_token_embeddings(config.vocab_size + 1) self.assertEqual(model.get_decoder().embed_tokens.weight.shape, (config.vocab_size + 1, config.d_model)) # check if lm_head is also resized config, _ = self.model_tester.prepare_config_and_inputs() config.share_encoder_decoder_embeddings = False model = MarianMTModel(config) model.resize_decoder_token_embeddings(config.vocab_size + 1) self.assertEqual(model.lm_head.weight.shape, (config.vocab_size + 1, config.d_model)) def test_tie_word_embeddings_decoder(self): pass @unittest.skip("Skipping for now, to fix @ArthurZ or @ydshieh") def test_pipeline_conversational(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass 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) class ModelManagementTests(unittest.TestCase): @slow @require_torch def test_model_names(self): model_list = list_models() model_ids = [x.modelId for x in model_list if x.modelId.startswith(ORG_NAME)] bad_model_ids = [mid for mid in model_ids if "+" in model_ids] self.assertListEqual([], bad_model_ids) self.assertGreater(len(model_ids), 500) @require_torch @require_sentencepiece @require_tokenizers class MarianIntegrationTest(unittest.TestCase): src = "en" tgt = "de" src_text = [ "I am a small frog.", "Now I can forget the 100 words of german that I know.", "Tom asked his teacher for advice.", "That's how I would do it.", "Tom really admired Mary's courage.", "Turn around and close your eyes.", ] expected_text = [ "Ich bin ein kleiner Frosch.", "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.", "Tom bat seinen Lehrer um Rat.", "So würde ich das machen.", "Tom bewunderte Marias Mut wirklich.", "Drehen Sie sich um und schließen Sie die Augen.", ] # ^^ actual C++ output differs slightly: (1) des Deutschen removed, (2) ""-> "O", (3) tun -> machen @classmethod def setUpClass(cls) -> None: cls.model_name = f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}" return cls @cached_property def tokenizer(self): return AutoTokenizer.from_pretrained(self.model_name) @property def eos_token_id(self) -> int: return self.tokenizer.eos_token_id @cached_property def model(self): model: MarianMTModel = AutoModelWithLMHead.from_pretrained(self.model_name).to(torch_device) c = model.config self.assertListEqual(c.bad_words_ids, [[c.pad_token_id]]) self.assertEqual(c.max_length, 512) self.assertEqual(c.decoder_start_token_id, c.pad_token_id) if torch_device == "cuda": return model.half() else: return model def _assert_generated_batch_equal_expected(self, **tokenizer_kwargs): generated_words = self.translate_src_text(**tokenizer_kwargs) self.assertListEqual(self.expected_text, generated_words) def translate_src_text(self, **tokenizer_kwargs): model_inputs = self.tokenizer(self.src_text, padding=True, return_tensors="pt", **tokenizer_kwargs).to( torch_device ) self.assertEqual(self.model.device, model_inputs.input_ids.device) generated_ids = self.model.generate( model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2, max_length=128, renormalize_logits=True, # Marian should always renormalize its logits. See #25459 ) generated_words = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True) return generated_words @require_sentencepiece @require_tokenizers class TestMarian_EN_DE_More(MarianIntegrationTest): @slow def test_forward(self): src, tgt = ["I am a small frog"], ["Ich bin ein kleiner Frosch."] expected_ids = [38, 121, 14, 697, 38848, 0] model_inputs = self.tokenizer(src, text_target=tgt, return_tensors="pt").to(torch_device) self.assertListEqual(expected_ids, model_inputs.input_ids[0].tolist()) desired_keys = { "input_ids", "attention_mask", "labels", } self.assertSetEqual(desired_keys, set(model_inputs.keys())) model_inputs["decoder_input_ids"] = shift_tokens_right( model_inputs.labels, self.tokenizer.pad_token_id, self.model.config.decoder_start_token_id ) model_inputs["return_dict"] = True model_inputs["use_cache"] = False with torch.no_grad(): outputs = self.model(**model_inputs) max_indices = outputs.logits.argmax(-1) self.tokenizer.batch_decode(max_indices) def test_unk_support(self): t = self.tokenizer ids = t(["||"], return_tensors="pt").to(torch_device).input_ids[0].tolist() expected = [t.unk_token_id, t.unk_token_id, t.eos_token_id] self.assertEqual(expected, ids) def test_pad_not_split(self): input_ids_w_pad = self.tokenizer(["I am a small frog <pad>"], return_tensors="pt").input_ids[0].tolist() expected_w_pad = [38, 121, 14, 697, 38848, self.tokenizer.pad_token_id, 0] # pad self.assertListEqual(expected_w_pad, input_ids_w_pad) @slow def test_batch_generation_en_de(self): self._assert_generated_batch_equal_expected() def test_auto_config(self): config = AutoConfig.from_pretrained(self.model_name) self.assertIsInstance(config, MarianConfig) @require_sentencepiece @require_tokenizers class TestMarian_EN_FR(MarianIntegrationTest): src = "en" tgt = "fr" src_text = [ "I am a small frog.", "Now I can forget the 100 words of german that I know.", ] expected_text = [ "Je suis une petite grenouille.", "Maintenant, je peux oublier les 100 mots d'allemand que je connais.", ] @slow def test_batch_generation_en_fr(self): self._assert_generated_batch_equal_expected() @require_sentencepiece @require_tokenizers class TestMarian_FR_EN(MarianIntegrationTest): src = "fr" tgt = "en" src_text = [ "Donnez moi le micro.", "Tom et Mary étaient assis à une table.", # Accents ] expected_text = [ "Give me the microphone.", "Tom and Mary were sitting at a table.", ] @slow def test_batch_generation_fr_en(self): self._assert_generated_batch_equal_expected() @require_sentencepiece @require_tokenizers class TestMarian_RU_FR(MarianIntegrationTest): src = "ru" tgt = "fr" src_text = ["Он показал мне рукопись своей новой пьесы."] expected_text = ["Il m'a montré le manuscrit de sa nouvelle pièce."] @slow def test_batch_generation_ru_fr(self): self._assert_generated_batch_equal_expected() @require_sentencepiece @require_tokenizers class TestMarian_MT_EN(MarianIntegrationTest): """Cover low resource/high perplexity setting. This breaks without adjust_logits_generation overwritten""" src = "mt" tgt = "en" src_text = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."] expected_text = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."] @slow def test_batch_generation_mt_en(self): self._assert_generated_batch_equal_expected() @require_sentencepiece @require_tokenizers class TestMarian_en_zh(MarianIntegrationTest): src = "en" tgt = "zh" src_text = ["My name is Wolfgang and I live in Berlin"] expected_text = ["我叫沃尔夫冈 我住在柏林"] @slow def test_batch_generation_eng_zho(self): self._assert_generated_batch_equal_expected() @require_sentencepiece @require_tokenizers class TestMarian_en_ROMANCE(MarianIntegrationTest): """Multilingual on target side.""" src = "en" tgt = "ROMANCE" src_text = [ ">>fr<< Don't spend so much time watching TV.", ">>pt<< Your message has been sent.", ">>es<< He's two years older than me.", ] expected_text = [ "Ne passez pas autant de temps à regarder la télé.", "A sua mensagem foi enviada.", "Es dos años más viejo que yo.", ] @slow def test_batch_generation_en_ROMANCE_multi(self): self._assert_generated_batch_equal_expected() @slow @require_torch def test_pipeline(self): pipeline = TranslationPipeline(self.model, self.tokenizer, framework="pt", device=torch_device) output = pipeline(self.src_text) self.assertEqual(self.expected_text, [x["translation_text"] for x in output]) @require_sentencepiece @require_tokenizers class TestMarian_FI_EN_V2(MarianIntegrationTest): src = "fi" tgt = "en" src_text = [ "minä tykkään kirjojen lukemisesta", "Pidän jalkapallon katsomisesta", ] expected_text = ["I like to read books", "I like watching football"] @classmethod def setUpClass(cls) -> None: cls.model_name = "hf-internal-testing/test-opus-tatoeba-fi-en-v2" return cls @slow def test_batch_generation_fi_en(self): self._assert_generated_batch_equal_expected() @require_torch class TestConversionUtils(unittest.TestCase): def test_renaming_multilingual(self): old_names = [ "opus-mt-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi", "opus-mt-cmn+cn-fi", # no group "opus-mt-en-de", # standard name "opus-mt-en-de", # standard name ] expected = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"] self.assertListEqual(expected, [convert_opus_name_to_hf_name(x) for x in old_names]) def test_undoing_renaming(self): hf_names = ["opus-mt-ZH-fi", "opus-mt-cmn_cn-fi", "opus-mt-en-de", "opus-mt-en-de"] converted_opus_names = [convert_hf_name_to_opus_name(x) for x in hf_names] expected_opus_names = [ "cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh-fi", "cmn+cn-fi", "en-de", # standard name "en-de", ] self.assertListEqual(expected_opus_names, converted_opus_names) class MarianStandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, d_model=16, decoder_seq_length=7, is_training=True, is_decoder=True, use_attention_mask=True, use_cache=False, use_labels=True, decoder_start_token_id=2, decoder_ffn_dim=32, decoder_layers=2, encoder_attention_heads=4, decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=False, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent = parent self.batch_size = batch_size self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.d_model = d_model self.hidden_size = d_model self.num_hidden_layers = decoder_layers self.decoder_layers = decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_attention_heads = encoder_attention_heads self.decoder_attention_heads = decoder_attention_heads self.num_attention_heads = decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.max_position_embeddings = max_position_embeddings self.is_encoder_decoder = is_encoder_decoder self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = MarianConfig( vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_attention_heads=self.encoder_attention_heads, decoder_attention_heads=self.decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, max_position_embeddings=self.max_position_embeddings, is_encoder_decoder=self.is_encoder_decoder, ) return ( config, input_ids, attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = MarianDecoder(config=config).to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, 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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def create_and_check_decoder_model_attention_mask_past( self, config, input_ids, attention_mask, lm_labels, ): model = MarianDecoder(config=config).to(torch_device).eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, attention_mask=attn_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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class MarianStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (MarianDecoder, MarianForCausalLM) if is_torch_available() else () all_generative_model_classes = (MarianForCausalLM,) if is_torch_available() else () test_pruning = False is_encoder_decoder = False def setUp( self, ): self.model_tester = MarianStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=MarianConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_attn_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients return
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/marian/test_tokenization_marian.py
# coding=utf-8 # Copyright 2020 Huggingface # # 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 tempfile import unittest from pathlib import Path from shutil import copyfile from transformers import BatchEncoding, MarianTokenizer from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available if is_sentencepiece_available(): from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_SP = get_tests_dir("fixtures/test_sentencepiece.model") mock_tokenizer_config = {"target_lang": "fi", "source_lang": "en"} zh_code = ">>zh<<" ORG_NAME = "Helsinki-NLP/" if is_torch_available(): FRAMEWORK = "pt" elif is_tf_available(): FRAMEWORK = "tf" else: FRAMEWORK = "jax" @require_sentencepiece class MarianTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "Helsinki-NLP/opus-mt-en-de" tokenizer_class = MarianTokenizer test_rust_tokenizer = False test_sentencepiece = True def setUp(self): super().setUp() vocab = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"] vocab_tokens = dict(zip(vocab, range(len(vocab)))) save_dir = Path(self.tmpdirname) save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab"]) save_json(mock_tokenizer_config, save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]) if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists(): copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["source_spm"]) copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["target_spm"]) tokenizer = MarianTokenizer.from_pretrained(self.tmpdirname) tokenizer.save_pretrained(self.tmpdirname) def get_tokenizer(self, **kwargs) -> MarianTokenizer: return MarianTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): return ( "This is a test", "This is a test", ) def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "</s>" token_id = 0 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], "</s>") self.assertEqual(vocab_keys[1], "<unk>") self.assertEqual(vocab_keys[-1], "<pad>") self.assertEqual(len(vocab_keys), 9) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 9) def test_tokenizer_equivalence_en_de(self): en_de_tokenizer = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de") batch = en_de_tokenizer(["I am a small frog"], return_tensors=None) self.assertIsInstance(batch, BatchEncoding) expected = [38, 121, 14, 697, 38848, 0] self.assertListEqual(expected, batch.input_ids[0]) save_dir = tempfile.mkdtemp() en_de_tokenizer.save_pretrained(save_dir) contents = [x.name for x in Path(save_dir).glob("*")] self.assertIn("source.spm", contents) MarianTokenizer.from_pretrained(save_dir) def test_outputs_not_longer_than_maxlen(self): tok = self.get_tokenizer() batch = tok( ["I am a small frog" * 1000, "I am a small frog"], padding=True, truncation=True, return_tensors=FRAMEWORK ) self.assertIsInstance(batch, BatchEncoding) self.assertEqual(batch.input_ids.shape, (2, 512)) def test_outputs_can_be_shorter(self): tok = self.get_tokenizer() batch_smaller = tok(["I am a tiny frog", "I am a small frog"], padding=True, return_tensors=FRAMEWORK) self.assertIsInstance(batch_smaller, BatchEncoding) self.assertEqual(batch_smaller.input_ids.shape, (2, 10)) @slow def test_tokenizer_integration(self): expected_encoding = {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], '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, 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], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="Helsinki-NLP/opus-mt-en-de", revision="1a8c2263da11e68e50938f97e10cd57820bd504c", decode_kwargs={"use_source_tokenizer": True}, ) def test_tokenizer_integration_seperate_vocabs(self): tokenizer = MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs") source_text = "Tämä on testi" target_text = "This is a test" expected_src_ids = [76, 7, 2047, 2] expected_target_ids = [69, 12, 11, 940, 2] src_ids = tokenizer(source_text).input_ids self.assertListEqual(src_ids, expected_src_ids) target_ids = tokenizer(text_target=target_text).input_ids self.assertListEqual(target_ids, expected_target_ids) decoded = tokenizer.decode(target_ids, skip_special_tokens=True) self.assertEqual(decoded, target_text) def test_tokenizer_decode(self): tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-es") source_text = "Hello World" ids = tokenizer(source_text)["input_ids"] output_text = tokenizer.decode(ids, skip_special_tokens=True) self.assertEqual(source_text, output_text)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/rembert/test_tokenization_rembert.py
# coding=utf-8 # 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. """ Testing suite for the RemBert tokenizer. """ import tempfile import unittest from tests.test_tokenization_common import AddedToken, TokenizerTesterMixin from transformers import RemBertTokenizer, RemBertTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers SENTENCEPIECE_UNDERLINE = "▁" SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece @require_tokenizers class RemBertTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "google/rembert" tokenizer_class = RemBertTokenizer rust_tokenizer_class = RemBertTokenizerFast space_between_special_tokens = True test_rust_tokenizer = True test_sentencepiece_ignore_case = True pre_trained_model_path = "google/rembert" def setUp(self): super().setUp() tokenizer = RemBertTokenizer(SAMPLE_VOCAB) tokenizer.save_pretrained(self.tmpdirname) # Copied from ReformerTokenizationTest.get_input_output_texts def get_input_output_texts(self, tokenizer): input_text = "this is a test" output_text = "this is a test" return input_text, output_text 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[5], "▁the") self.assertEqual(vocab_keys[2], "</s>") def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_000) def test_full_tokenizer(self): tokenizer = RemBertTokenizer(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",".",],) # fmt: skip 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]) def test_encode_decode_round_trip(self): tokenizer = RemBertTokenizer(SAMPLE_VOCAB, keep_accents=True) text = "清水寺は京都にある。" tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ["▁", "清水寺は京都にある。"]) encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 7, 0, 1001]) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(decode_text, text) text = "That's awesome! 🤩 #HuggingFace, 🌟 Have a great day! 🌈" tokens = tokenizer.tokenize(text) self.assertListEqual( tokens, ['▁That', "'", 's', '▁a', 'w', 'es', 'ome', '!', '▁', '🤩', '▁', '#', 'H', 'u', 'g', 'g', 'ing', 'F', 'a', 'ce', ',', '▁', '🌟', '▁H', 'a', 've', '▁a', '▁great', '▁day', '!', '▁', '🌈']) # fmt: skip decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(decode_text, "That's awesome! 🤩 #HuggingFace, 🌟 Have a great day! 🌈") text = "In the sky up above" tokens = tokenizer._tokenize(text) self.assertListEqual(tokens, ["▁In", "▁the", "▁s", "k", "y", "▁up", "▁a", "b", "o", "ve"]) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 388, 5, 47, 45, 30, 118, 10, 65, 20, 123, 1001]) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "The cat. . Sat <s>.In a room" tokens = tokenizer.tokenize(text) self.assertListEqual( tokens, ["▁The", "▁c", "at", ".", "▁", ".", "▁S", "at", "▁", "<", "s", ">", ".", "I", "n", "▁a", "▁room"] ) encoded_string = tokenizer.encode(text) self.assertListEqual( encoded_string, [1000, 68, 69, 76, 4, 7, 4, 166, 76, 7, 0, 6, 0, 4, 100, 24, 10, 136, 1001] ) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "Invoice #12345, dated 2023-12-01, is due on 2024-01-15." tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ['▁In', 'v', 'o', 'ic', 'e', '▁', '#', '1', '2', '34', '5', ',', '▁da', 'ted', '▁', '2', '0', '2', '3', '-', '1', '2', '-', '0', '1', ',', '▁is', '▁d', 'u', 'e', '▁on', '▁', '2', '0', '2', '4', '-', '0', '1', '-', '1', '5', '.']) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 388, 83, 20, 113, 15, 7, 0, 356, 602, 0, 555, 3, 417, 273, 7, 602, 347, 602, 0, 33, 356, 602, 33, 347, 356, 3, 46, 229, 51, 15, 59, 7, 602, 347, 602, 0, 33, 347, 356, 33, 356, 555, 4, 1001]) # fmt: skip decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit..." tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ['▁', 'L', 'or', 'em', '▁', 'i', 'p', 's', 'um', '▁do', 'l', 'or', '▁sit', '▁am', 'e', 't', ',', '▁con', 'se', 'c', 'te', 't', 'ur', '▁a', 'd', 'i', 'p', 'is', 'c', 'ing', '▁', 'el', 'it', '.', '.', '.']) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual( encoded_string, [1000, 7, 279, 55, 300, 7, 23, 29, 6, 155, 92, 27, 55, 615, 219, 15, 14, 3, 247, 114, 28, 181, 14, 108, 10, 16, 23, 29, 125, 28, 17, 7, 168, 137, 4, 4, 4, 1001] ) # fmt: skip decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) # for multiple language in one sentence text = "Bonjour! Hello! こんにちは!" tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ["▁B", "on", "j", "o", "ur", "!", "▁He", "ll", "o", "!", "▁", "こんにちは", "!"]) encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 295, 109, 999, 20, 108, 146, 156, 86, 20, 146, 7, 0, 146, 1001]) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "Extra spaces\tand\nline breaks\r\nshould be handled." tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ['▁E', 'x', 't', 'r', 'a', '▁sp', 'a', 'ce', 's', '▁and', '▁line', '▁b', 're', 'a', 'k', 's', '▁should', '▁be', '▁hand', 'led', '.']) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual( encoded_string, [1000, 454, 297, 14, 35, 18, 277, 18, 133, 6, 12, 485, 84, 56, 18, 45, 6, 173, 36, 363, 338, 4, 1001], ) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual("Extra spaces and line breaks should be handled.", decode_text) def test_sequence_builders(self): tokenizer = RemBertTokenizer(SAMPLE_VOCAB) text = tokenizer.encode("sequence builders") text_2 = tokenizer.encode("multi-sequence build") 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 ] def test_added_tokens_serialization(self): # Utility to test the added vocab def _test_added_vocab_and_eos(expected, tokenizer_class, expected_eos, temp_dir): tokenizer = tokenizer_class.from_pretrained(temp_dir) self.assertTrue(str(expected_eos) not in tokenizer.additional_special_tokens) self.assertIn(new_eos, tokenizer.added_tokens_decoder.values()) self.assertEqual(tokenizer.added_tokens_decoder[tokenizer.eos_token_id], new_eos) self.assertDictEqual(expected, tokenizer.added_tokens_decoder) return tokenizer new_eos = AddedToken("[NEW_EOS]", rstrip=False, lstrip=True, normalized=False, special=True) new_masked_token = AddedToken("[MASK]", lstrip=True, rstrip=False, normalized=False) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): # Load a slow tokenizer from the hub, init with the new token for fast to also include it tokenizer = self.tokenizer_class.from_pretrained( pretrained_name, eos_token=new_eos, mask_token=new_masked_token ) EXPECTED_ADDED_TOKENS_DECODER = tokenizer.added_tokens_decoder with self.subTest("Hub -> Slow: Test loading a slow tokenizer from the hub)"): self.assertEqual(tokenizer._eos_token, new_eos) self.assertIn(new_eos, list(tokenizer.added_tokens_decoder.values())) with tempfile.TemporaryDirectory() as tmp_dir_2: tokenizer.save_pretrained(tmp_dir_2) with self.subTest( "Hub -> Slow -> Slow: Test saving this slow tokenizer and reloading it in the fast class" ): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.tokenizer_class, new_eos, tmp_dir_2 ) if self.rust_tokenizer_class is not None: with self.subTest( "Hub -> Slow -> Fast: Test saving this slow tokenizer and reloading it in the fast class" ): tokenizer_fast = _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_2 ) with tempfile.TemporaryDirectory() as tmp_dir_3: tokenizer_fast.save_pretrained(tmp_dir_3) with self.subTest( "Hub -> Slow -> Fast -> Fast: Test saving this fast tokenizer and reloading it in the fast class" ): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_3 ) with self.subTest( "Hub -> Slow -> Fast -> Slow: Test saving this slow tokenizer and reloading it in the slow class" ): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_3 ) with self.subTest("Hub -> Fast: Test loading a fast tokenizer from the hub)"): if self.rust_tokenizer_class is not None: tokenizer_fast = self.rust_tokenizer_class.from_pretrained(pretrained_name, eos_token=new_eos) self.assertEqual(tokenizer_fast._eos_token, new_eos) self.assertIn(new_eos, list(tokenizer_fast.added_tokens_decoder.values())) # We can't test the following because for BC we kept the default rstrip lstrip in slow not fast. Will comment once normalization is alright with self.subTest("Hub -> Fast == Hub -> Slow: make sure slow and fast tokenizer match"): self.assertDictEqual(EXPECTED_ADDED_TOKENS_DECODER, tokenizer_fast.added_tokens_decoder) EXPECTED_ADDED_TOKENS_DECODER = tokenizer_fast.added_tokens_decoder with tempfile.TemporaryDirectory() as tmp_dir_4: tokenizer_fast.save_pretrained(tmp_dir_4) with self.subTest("Hub -> Fast -> Fast: saving Fast1 locally and loading"): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_4 ) with self.subTest("Hub -> Fast -> Slow: saving Fast1 locally and loading"): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.tokenizer_class, new_eos, tmp_dir_4 )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/rembert/test_modeling_tf_rembert.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. from __future__ import annotations import unittest from transformers import RemBertConfig, is_tf_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, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForMultipleChoice, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertModel, ) class TFRemBertModelTester: 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, input_embedding_size=18, output_embedding_size=43, num_hidden_layers=2, 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 = 13 self.seq_length = 7 self.is_training = True self.use_input_mask = True self.use_token_type_ids = True self.use_labels = True self.vocab_size = 99 self.hidden_size = 32 self.input_embedding_size = input_embedding_size self.output_embedding_size = output_embedding_size self.num_hidden_layers = 2 self.num_attention_heads = 4 self.intermediate_size = 37 self.hidden_act = "gelu" self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.scope = None 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 = RemBertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, input_embedding_size=self.input_embedding_size, output_embedding_size=self.output_embedding_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, initializer_range=self.initializer_range, return_dict=True, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels 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 = TFRemBertModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_causal_lm_base_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TFRemBertModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 = TFRemBertModel(config=config) inputs = { "input_ids": 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(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) # Also check the case where encoder outputs are not passed 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)) def create_and_check_causal_lm_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } prediction_scores = model(inputs)["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_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 = TFRemBertForCausalLM(config=config) inputs = { "input_ids": 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(inputs) inputs = [input_ids, input_mask] result = model(inputs, token_type_ids=token_type_ids, encoder_hidden_states=encoder_hidden_states) prediction_scores = result["logits"] self.parent.assertListEqual( list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_causal_lm_model_past( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs.past_key_values # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and attn_mask next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) output_from_no_past = model(next_input_ids, output_hidden_states=True).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, 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-6) def create_and_check_causal_lm_model_past_with_attn_mask( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) # create attention mask half_seq_length = self.seq_length // 2 attn_mask_begin = tf.ones((self.batch_size, half_seq_length), dtype=tf.int32) attn_mask_end = tf.zeros((self.batch_size, self.seq_length - half_seq_length), dtype=tf.int32) attn_mask = tf.concat([attn_mask_begin, attn_mask_end], axis=1) # first forward pass outputs = model(input_ids, attention_mask=attn_mask, use_cache=True) # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) past_key_values = outputs.past_key_values # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).numpy() + 1 random_other_next_tokens = ids_tensor((self.batch_size, self.seq_length), config.vocab_size) vector_condition = tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change) condition = tf.transpose( tf.broadcast_to(tf.expand_dims(vector_condition, -1), (self.seq_length, self.batch_size)) ) input_ids = tf.where(condition, random_other_next_tokens, input_ids) # append to next input_ids and next_input_ids = tf.concat([input_ids, next_tokens], axis=-1) attn_mask = tf.concat( [attn_mask, tf.ones((attn_mask.shape[0], 1), dtype=tf.int32)], axis=1, ) output_from_no_past = model( next_input_ids, attention_mask=attn_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, past_key_values=past_key_values, attention_mask=attn_mask, output_hidden_states=True ).hidden_states[0] # select random slice random_slice_idx = int(ids_tensor((1,), output_from_past.shape[-1])) output_from_no_past_slice = output_from_no_past[:, -1, random_slice_idx] output_from_past_slice = output_from_past[:, 0, 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-6) def create_and_check_causal_lm_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True model = TFRemBertForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] self.batch_size = 1 # first forward pass outputs = model(input_ids, attention_mask=input_mask, use_cache=True) past_key_values = outputs.past_key_values # create hypothetical 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 = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, output_hidden_states=True, ).hidden_states[0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, ).hidden_states[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 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.add_cross_attention = True model = TFRemBertForCausalLM(config=config) input_ids = input_ids[:1, :] input_mask = input_mask[:1, :] encoder_hidden_states = encoder_hidden_states[:1, :, :] encoder_attention_mask = encoder_attention_mask[:1, :] self.batch_size = 1 # 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 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 = tf.concat([input_ids, next_tokens], axis=-1) next_attention_mask = tf.concat([input_mask, next_attn_mask], axis=-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] 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 create_and_check_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFRemBertForMaskedLM(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) 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 = TFRemBertForSequenceClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, 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 = TFRemBertForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) 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 = TFRemBertForTokenClassification(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFRemBertForQuestionAnswering(config=config) inputs = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } result = model(inputs) 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 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_tf class TFRemBertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFRemBertModel, TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertForMultipleChoice, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFRemBertModel, "fill-mask": TFRemBertForMaskedLM, "question-answering": TFRemBertForQuestionAnswering, "text-classification": TFRemBertForSequenceClassification, "text-generation": TFRemBertForCausalLM, "token-classification": TFRemBertForTokenClassification, "zero-shot": TFRemBertForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFRemBertModelTester(self) self.config_tester = ConfigTester(self, config_class=RemBertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): """Test the base model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_causal_lm_base_model(self): """Test the base model of the causal LM model is_deocder=True, no cross_attention, no encoder outputs """ config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_base_model(*config_and_inputs) def test_model_as_decoder(self): """Test the base model as a decoder (of an encoder-decoder architecture) is_deocder=True + cross_attention + pass encoder outputs """ 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_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(self): """Test the causal LM model""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model(*config_and_inputs) def test_causal_lm_model_as_decoder(self): """Test the causal LM model as a decoder""" config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_causal_lm_model_as_decoder(*config_and_inputs) def test_causal_lm_model_past(self): """Test causal LM model with `past_key_values`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past(*config_and_inputs) def test_causal_lm_model_past_with_attn_mask(self): """Test the causal LM model with `past_key_values` and `attention_mask`""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs) def test_causal_lm_model_past_with_large_inputs(self): """Test the causal LM model with `past_key_values` and a longer decoder sequence length""" config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_causal_lm_model_past_large_inputs(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): """Similar to `test_causal_lm_model_past_with_large_inputs` but with cross-attention""" 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_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_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): model = TFRemBertModel.from_pretrained("google/rembert") self.assertIsNotNone(model) @require_tf class TFRemBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_model(self): model = TFRemBertModel.from_pretrained("google/rembert") input_ids = tf.constant([[312, 56498, 313, 2125, 313]]) segment_ids = tf.constant([[0, 0, 0, 1, 1]]) output = model(input_ids, token_type_ids=segment_ids, output_hidden_states=True) hidden_size = 1152 expected_shape = [1, 5, hidden_size] self.assertEqual(output["last_hidden_state"].shape, expected_shape) expected_implementation = tf.constant( [ [ [0.0754, -0.2022, 0.1904], [-0.3354, -0.3692, -0.4791], [-0.2314, -0.6729, -0.0749], [-0.0396, -0.3105, -0.4234], [-0.1571, -0.0525, 0.5353], ] ] ) tf.debugging.assert_near(output["last_hidden_state"][:, :, :3], expected_implementation, atol=1e-4) # Running on the original tf implementation gives slightly different results here. # Not clear why this variations is present # TODO: Find reason for discrepancy # expected_original_implementation = [[ # [0.07630594074726105, -0.20146065950393677, 0.19107051193714142], # [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135], # [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716], # [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626], # [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198] # ]]
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/rembert/test_modeling_rembert.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 PyTorch RemBERT model. """ import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( RemBertConfig, RemBertForCausalLM, RemBertForMaskedLM, RemBertForMultipleChoice, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForTokenClassification, RemBertModel, ) class RemBertModelTester: 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, input_embedding_size=18, output_embedding_size=43, num_hidden_layers=2, 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.input_embedding_size = input_embedding_size self.output_embedding_size = output_embedding_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 = RemBertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, input_embedding_size=self.input_embedding_size, output_embedding_size=self.output_embedding_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, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels 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 = RemBertModel(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)) 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 = RemBertModel(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)) 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 = RemBertForCausalLM(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 = RemBertForMaskedLM(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_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 = RemBertForCausalLM(config=config) model.to(torch_device) model.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_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RemBertForQuestionAnswering(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 = RemBertForSequenceClassification(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 = RemBertForTokenClassification(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 = RemBertForMultipleChoice(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 RemBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( RemBertModel, RemBertForMaskedLM, RemBertForCausalLM, RemBertForMultipleChoice, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (RemBertForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": RemBertModel, "fill-mask": RemBertForMaskedLM, "question-answering": RemBertForQuestionAnswering, "text-classification": RemBertForSequenceClassification, "text-generation": RemBertForCausalLM, "token-classification": RemBertForTokenClassification, "zero-shot": RemBertForSequenceClassification, } if is_torch_available() else {} ) def setUp(self): self.model_tester = RemBertModelTester(self) self.config_tester = ConfigTester(self, config_class=RemBertConfig, 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_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_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_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) 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, ) @slow def test_model_from_pretrained(self): model_name = "google/rembert" model = RemBertModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class RemBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_model(self): # Test exact values at the last hidden layer model = RemBertModel.from_pretrained("google/rembert") input_ids = torch.tensor([[312, 56498, 313, 2125, 313]]) segment_ids = torch.tensor([[0, 0, 0, 1, 1]]) with torch.no_grad(): output = model(input_ids, token_type_ids=segment_ids, output_hidden_states=True) hidden_size = 1152 expected_shape = torch.Size((1, 5, hidden_size)) self.assertEqual(output["last_hidden_state"].shape, expected_shape) expected_implementation = torch.tensor( [ [ [0.0754, -0.2022, 0.1904], [-0.3354, -0.3692, -0.4791], [-0.2314, -0.6729, -0.0749], [-0.0396, -0.3105, -0.4234], [-0.1571, -0.0525, 0.5353], ] ] ) # Running on the original tf implementation gives slightly different results here. # Not clear why this variations is present # TODO: Find reason for discrepancy # expected_original_implementation = [[ # [0.07630594074726105, -0.20146065950393677, 0.19107051193714142], # [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135], # [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716], # [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626], # [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198] # ]] self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3], expected_implementation, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/pop2piano/test_processor_pop2piano.py
# Copyright 2023 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 import numpy as np import pytest from datasets import load_dataset from transformers.testing_utils import ( require_essentia, require_librosa, require_pretty_midi, require_scipy, require_torch, ) from transformers.tokenization_utils import BatchEncoding from transformers.utils.import_utils import ( is_essentia_available, is_librosa_available, is_pretty_midi_available, is_scipy_available, is_torch_available, ) requirements_available = ( is_torch_available() and is_essentia_available() and is_scipy_available() and is_librosa_available() and is_pretty_midi_available() ) if requirements_available: import pretty_midi from transformers import ( Pop2PianoFeatureExtractor, Pop2PianoForConditionalGeneration, Pop2PianoProcessor, Pop2PianoTokenizer, ) @require_scipy @require_torch @require_librosa @require_essentia @require_pretty_midi class Pop2PianoProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() feature_extractor = Pop2PianoFeatureExtractor.from_pretrained("sweetcocoa/pop2piano") tokenizer = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano") processor = Pop2PianoProcessor(feature_extractor, tokenizer) processor.save_pretrained(self.tmpdirname) def get_tokenizer(self, **kwargs): return Pop2PianoTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_feature_extractor(self, **kwargs): return Pop2PianoFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def test_save_load_pretrained_additional_features(self): processor = Pop2PianoProcessor( tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor(), ) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer( unk_token="-1", eos_token="1", pad_token="0", bos_token="2", ) feature_extractor_add_kwargs = self.get_feature_extractor() processor = Pop2PianoProcessor.from_pretrained( self.tmpdirname, unk_token="-1", eos_token="1", pad_token="0", bos_token="2", ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, Pop2PianoTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, Pop2PianoFeatureExtractor) def get_inputs(self): """get inputs for both feature extractor and tokenizer""" ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") speech_samples = ds.sort("id").select([0])["audio"] input_speech = [x["array"] for x in speech_samples][0] sampling_rate = [x["sampling_rate"] for x in speech_samples][0] feature_extractor_outputs = self.get_feature_extractor()( audio=input_speech, sampling_rate=sampling_rate, return_tensors="pt" ) model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano") token_ids = model.generate(input_features=feature_extractor_outputs["input_features"], composer="composer1") dummy_notes = [ [ pretty_midi.Note(start=0.441179, end=2.159456, pitch=70, velocity=77), pretty_midi.Note(start=0.673379, end=0.905578, pitch=73, velocity=77), pretty_midi.Note(start=0.905578, end=2.159456, pitch=73, velocity=77), pretty_midi.Note(start=1.114558, end=2.159456, pitch=78, velocity=77), pretty_midi.Note(start=1.323537, end=1.532517, pitch=80, velocity=77), ], [ pretty_midi.Note(start=0.441179, end=2.159456, pitch=70, velocity=77), ], ] return input_speech, sampling_rate, token_ids, dummy_notes def test_feature_extractor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = Pop2PianoProcessor( tokenizer=tokenizer, feature_extractor=feature_extractor, ) input_speech, sampling_rate, _, _ = self.get_inputs() feature_extractor_outputs = feature_extractor( audio=input_speech, sampling_rate=sampling_rate, return_tensors="np" ) processor_outputs = processor(audio=input_speech, sampling_rate=sampling_rate, return_tensors="np") for key in feature_extractor_outputs.keys(): self.assertTrue(np.allclose(feature_extractor_outputs[key], processor_outputs[key], atol=1e-4)) def test_processor_batch_decode(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = Pop2PianoProcessor( tokenizer=tokenizer, feature_extractor=feature_extractor, ) audio, sampling_rate, token_ids, _ = self.get_inputs() feature_extractor_output = feature_extractor(audio=audio, sampling_rate=sampling_rate, return_tensors="pt") encoded_processor = processor.batch_decode( token_ids=token_ids, feature_extractor_output=feature_extractor_output, return_midi=True, ) encoded_tokenizer = tokenizer.batch_decode( token_ids=token_ids, feature_extractor_output=feature_extractor_output, return_midi=True, ) # check start timings encoded_processor_start_timings = [token.start for token in encoded_processor["notes"]] encoded_tokenizer_start_timings = [token.start for token in encoded_tokenizer["notes"]] self.assertListEqual(encoded_processor_start_timings, encoded_tokenizer_start_timings) # check end timings encoded_processor_end_timings = [token.end for token in encoded_processor["notes"]] encoded_tokenizer_end_timings = [token.end for token in encoded_tokenizer["notes"]] self.assertListEqual(encoded_processor_end_timings, encoded_tokenizer_end_timings) # check pitch encoded_processor_pitch = [token.pitch for token in encoded_processor["notes"]] encoded_tokenizer_pitch = [token.pitch for token in encoded_tokenizer["notes"]] self.assertListEqual(encoded_processor_pitch, encoded_tokenizer_pitch) # check velocity encoded_processor_velocity = [token.velocity for token in encoded_processor["notes"]] encoded_tokenizer_velocity = [token.velocity for token in encoded_tokenizer["notes"]] self.assertListEqual(encoded_processor_velocity, encoded_tokenizer_velocity) def test_tokenizer_call(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = Pop2PianoProcessor( tokenizer=tokenizer, feature_extractor=feature_extractor, ) _, _, _, notes = self.get_inputs() encoded_processor = processor( notes=notes, ) self.assertTrue(isinstance(encoded_processor, BatchEncoding)) def test_processor(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = Pop2PianoProcessor( tokenizer=tokenizer, feature_extractor=feature_extractor, ) audio, sampling_rate, _, notes = self.get_inputs() inputs = processor( audio=audio, sampling_rate=sampling_rate, notes=notes, ) self.assertListEqual( list(inputs.keys()), ["input_features", "beatsteps", "extrapolated_beatstep", "token_ids"], ) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = Pop2PianoProcessor( tokenizer=tokenizer, feature_extractor=feature_extractor, ) audio, sampling_rate, _, notes = self.get_inputs() feature_extractor(audio, sampling_rate, return_tensors="pt") inputs = processor( audio=audio, sampling_rate=sampling_rate, notes=notes, ) self.assertListEqual( list(inputs.keys()), ["input_features", "beatsteps", "extrapolated_beatstep", "token_ids"], )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/pop2piano/test_modeling_pop2piano.py
# coding=utf-8 # Copyright 2023 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 Pop2Piano model. """ import copy import tempfile import unittest import numpy as np from datasets import load_dataset from transformers import Pop2PianoConfig from transformers.feature_extraction_utils import BatchFeature from transformers.testing_utils import ( require_essentia, require_librosa, require_onnx, require_scipy, require_torch, slow, torch_device, ) from transformers.utils import is_essentia_available, is_librosa_available, is_scipy_available, is_torch_available from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import Pop2PianoForConditionalGeneration @require_torch class Pop2PianoModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, encoder_seq_length=7, decoder_seq_length=9, # For common tests is_training=False, use_attention_mask=True, use_labels=True, hidden_size=64, num_hidden_layers=5, num_attention_heads=4, d_ff=37, relative_attention_num_buckets=8, dropout_rate=0.1, initializer_factor=0.002, eos_token_id=1, pad_token_id=0, decoder_start_token_id=0, scope=None, decoder_layers=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask 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.d_ff = d_ff self.relative_attention_num_buckets = relative_attention_num_buckets self.dropout_rate = dropout_rate self.initializer_factor = initializer_factor self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.scope = None self.decoder_layers = decoder_layers def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None decoder_attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = ( ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) if self.use_labels else None ) return self.get_config(), input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels def get_pipeline_config(self): return Pop2PianoConfig( vocab_size=166, # Pop2Piano forces 100 extra tokens d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_decoder_layers=self.decoder_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, ) def get_config(self): return Pop2PianoConfig( vocab_size=self.vocab_size, d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_decoder_layers=self.decoder_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, ) def check_prepare_lm_labels_via_shift_left( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config) model.to(torch_device) model.eval() # make sure that lm_labels are correctly padded from the right lm_labels.masked_fill_((lm_labels == self.decoder_start_token_id), self.eos_token_id) # add causal pad token mask triangular_mask = torch.tril(lm_labels.new_ones(lm_labels.shape)).logical_not() lm_labels.masked_fill_(triangular_mask, self.pad_token_id) decoder_input_ids = model._shift_right(lm_labels) for i, (decoder_input_ids_slice, lm_labels_slice) in enumerate(zip(decoder_input_ids, lm_labels)): # first item self.parent.assertEqual(decoder_input_ids_slice[0].item(), self.decoder_start_token_id) if i < decoder_input_ids_slice.shape[-1]: if i < decoder_input_ids.shape[-1] - 1: # items before diagonal self.parent.assertListEqual( decoder_input_ids_slice[1 : i + 1].tolist(), lm_labels_slice[:i].tolist() ) # pad items after diagonal if i < decoder_input_ids.shape[-1] - 2: self.parent.assertListEqual( decoder_input_ids_slice[i + 2 :].tolist(), lm_labels_slice[i + 1 : -1].tolist() ) else: # all items after square self.parent.assertListEqual(decoder_input_ids_slice[1:].tolist(), lm_labels_slice[:-1].tolist()) def create_and_check_model( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config) model.to(torch_device) model.eval() result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) result = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) decoder_past = result.past_key_values encoder_output = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size(), (self.batch_size, self.encoder_seq_length, self.hidden_size)) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(decoder_past), config.num_layers) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0]), 4) def create_and_check_with_lm_head( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config).to(torch_device).eval() outputs = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, labels=lm_labels, ) self.parent.assertEqual(len(outputs), 4) self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.decoder_seq_length, self.vocab_size)) self.parent.assertEqual(outputs["loss"].size(), ()) def create_and_check_decoder_model_past( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config).get_decoder().to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) output, past_key_values = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, 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[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_decoder_model_attention_mask_past( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config).get_decoder() model.to(torch_device) model.eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass output, past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True).to_tuple() # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "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[:, -1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # 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_decoder_model_past_large_inputs( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config).get_decoder().to(torch_device).eval() # first forward pass outputs = model(input_ids, attention_mask=attention_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_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([attention_mask, next_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-3)) def create_and_check_generate_with_past_key_values( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config).to(torch_device).eval() torch.manual_seed(0) output_without_past_cache = model.generate( input_ids[:1], num_beams=2, max_length=5, do_sample=True, use_cache=False ) torch.manual_seed(0) output_with_past_cache = model.generate(input_ids[:1], num_beams=2, max_length=5, do_sample=True) self.parent.assertTrue(torch.all(output_with_past_cache == output_without_past_cache)) def create_and_check_model_fp16_forward( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): model = Pop2PianoForConditionalGeneration(config=config).to(torch_device).half().eval() output = model(input_ids, decoder_input_ids=input_ids, attention_mask=attention_mask)[ "encoder_last_hidden_state" ] self.parent.assertFalse(torch.isnan(output).any().item()) def create_and_check_encoder_decoder_shared_weights( self, config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ): for model_class in [Pop2PianoForConditionalGeneration]: torch.manual_seed(0) model = model_class(config=config).to(torch_device).eval() # load state dict copies weights but does not tie them model.encoder.load_state_dict(model.decoder.state_dict(), strict=False) torch.manual_seed(0) tied_config = copy.deepcopy(config) tied_config.tie_encoder_decoder = True tied_model = model_class(config=tied_config).to(torch_device).eval() model_result = model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4 ) ) # check that outputs after saving and loading are equal with tempfile.TemporaryDirectory() as tmpdirname: tied_model.save_pretrained(tmpdirname) tied_model = model_class.from_pretrained(tmpdirname) tied_model.to(torch_device) tied_model.eval() # check that models has less parameters self.parent.assertLess( sum(p.numel() for p in tied_model.parameters()), sum(p.numel() for p in model.parameters()) ) random_slice_idx = ids_tensor((1,), model_result[0].shape[-1]).item() tied_model_result = tied_model( input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask, decoder_attention_mask=decoder_attention_mask, ) # check that outputs are equal self.parent.assertTrue( torch.allclose( model_result[0][0, :, random_slice_idx], tied_model_result[0][0, :, random_slice_idx], atol=1e-4, ) ) def check_resize_embeddings_pop2piano_v1_1( self, config, ): prev_vocab_size = config.vocab_size config.tie_word_embeddings = False model = Pop2PianoForConditionalGeneration(config=config).to(torch_device).eval() model.resize_token_embeddings(prev_vocab_size - 10) self.parent.assertEqual(model.get_input_embeddings().weight.shape[0], prev_vocab_size - 10) self.parent.assertEqual(model.get_output_embeddings().weight.shape[0], prev_vocab_size - 10) self.parent.assertEqual(model.config.vocab_size, prev_vocab_size - 10) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "use_cache": False, } return config, inputs_dict @require_torch class Pop2PianoModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (Pop2PianoForConditionalGeneration,) if is_torch_available() else () all_generative_model_classes = () pipeline_model_mapping = ( {"automatic-speech-recognition": Pop2PianoForConditionalGeneration} if is_torch_available() else {} ) all_parallelizable_model_classes = () fx_compatible = False test_pruning = False test_resize_embeddings = True test_model_parallel = False is_encoder_decoder = True def setUp(self): self.model_tester = Pop2PianoModelTester(self) self.config_tester = ConfigTester(self, config_class=Pop2PianoConfig, d_model=37) def test_config(self): self.config_tester.run_common_tests() def test_shift_right(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_prepare_lm_labels_via_shift_left(*config_and_inputs) 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_v1_1(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() # check that gated gelu feed forward and different word embeddings work config = config_and_inputs[0] config.tie_word_embeddings = False config.feed_forward_proj = "gated-gelu" self.model_tester.create_and_check_model(config, *config_and_inputs[1:]) def test_config_and_model_silu_gated(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() config = config_and_inputs[0] config.feed_forward_proj = "gated-silu" self.model_tester.create_and_check_model(*config_and_inputs) def test_with_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_with_lm_head(*config_and_inputs) def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_past_with_attn_mask(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_decoder_model_past_with_3d_attn_mask(self): ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = self.model_tester.prepare_config_and_inputs() attention_mask = ids_tensor( [self.model_tester.batch_size, self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length], vocab_size=2, ) decoder_attention_mask = ids_tensor( [self.model_tester.batch_size, self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length], vocab_size=2, ) self.model_tester.create_and_check_decoder_model_attention_mask_past( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) 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_shared_weights(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_encoder_decoder_shared_weights(*config_and_inputs) @unittest.skipIf(torch_device == "cpu", "Cant do half precision") def test_model_fp16_forward(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fp16_forward(*config_and_inputs) def test_v1_1_resize_embeddings(self): config = self.model_tester.prepare_config_and_inputs()[0] self.model_tester.check_resize_embeddings_pop2piano_v1_1(config) @slow def test_model_from_pretrained(self): model_name = "sweetcocoa/pop2piano" model = Pop2PianoForConditionalGeneration.from_pretrained(model_name) self.assertIsNotNone(model) @require_onnx def test_export_to_onnx(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() model = Pop2PianoForConditionalGeneration(config_and_inputs[0]).to(torch_device) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( model, (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]), f"{tmpdirname}/Pop2Piano_test.onnx", export_params=True, opset_version=9, input_names=["input_ids", "decoder_input_ids"], ) def test_pass_with_input_features(self): input_features = BatchFeature( { "input_features": torch.rand((75, 100, 512)).type(torch.float32), "beatsteps": torch.randint(size=(1, 955), low=0, high=100).type(torch.float32), "extrapolated_beatstep": torch.randint(size=(1, 900), low=0, high=100).type(torch.float32), } ) model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano") model_opts = model.generate(input_features=input_features["input_features"], return_dict_in_generate=True) self.assertEqual(model_opts.sequences.ndim, 2) def test_pass_with_batched_input_features(self): input_features = BatchFeature( { "input_features": torch.rand((220, 70, 512)).type(torch.float32), "beatsteps": torch.randint(size=(5, 955), low=0, high=100).type(torch.float32), "extrapolated_beatstep": torch.randint(size=(5, 900), low=0, high=100).type(torch.float32), "attention_mask": torch.concatenate( [ torch.ones([120, 70], dtype=torch.int32), torch.zeros([1, 70], dtype=torch.int32), torch.ones([50, 70], dtype=torch.int32), torch.zeros([1, 70], dtype=torch.int32), torch.ones([47, 70], dtype=torch.int32), torch.zeros([1, 70], dtype=torch.int32), ], axis=0, ), "attention_mask_beatsteps": torch.ones((5, 955)).type(torch.int32), "attention_mask_extrapolated_beatstep": torch.ones((5, 900)).type(torch.int32), } ) model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano") model_opts = model.generate( input_features=input_features["input_features"], attention_mask=input_features["attention_mask"], return_dict_in_generate=True, ) self.assertEqual(model_opts.sequences.ndim, 2) @require_torch class Pop2PianoModelIntegrationTests(unittest.TestCase): @slow def test_mel_conditioner_integration(self): composer = "composer1" model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano") input_embeds = torch.ones([10, 100, 512]) composer_value = model.generation_config.composer_to_feature_token[composer] composer_value = torch.tensor(composer_value) composer_value = composer_value.repeat(input_embeds.size(0)) outputs = model.mel_conditioner( input_embeds, composer_value, min(model.generation_config.composer_to_feature_token.values()) ) # check shape self.assertEqual(outputs.size(), torch.Size([10, 101, 512])) # check values EXPECTED_OUTPUTS = torch.tensor( [[1.0475305318832397, 0.29052114486694336, -0.47778210043907166], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]] ) self.assertTrue(torch.allclose(outputs[0, :3, :3], EXPECTED_OUTPUTS, atol=1e-4)) @slow @require_essentia @require_librosa @require_scipy def test_full_model_integration(self): if is_librosa_available() and is_scipy_available() and is_essentia_available() and is_torch_available(): from transformers import Pop2PianoProcessor speech_input1 = np.zeros([1_000_000], dtype=np.float32) sampling_rate = 44_100 processor = Pop2PianoProcessor.from_pretrained("sweetcocoa/pop2piano") input_features = processor.feature_extractor( speech_input1, sampling_rate=sampling_rate, return_tensors="pt" ) model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano") outputs = model.generate( input_features=input_features["input_features"], return_dict_in_generate=True ).sequences # check for shapes self.assertEqual(outputs.size(0), 70) # check for values self.assertEqual(outputs[0, :2].detach().cpu().numpy().tolist(), [0, 1]) # This is the test for a real music from K-Pop genre. @slow @require_essentia @require_librosa @require_scipy def test_real_music(self): if is_librosa_available() and is_scipy_available() and is_essentia_available() and is_torch_available(): from transformers import Pop2PianoFeatureExtractor, Pop2PianoTokenizer model = Pop2PianoForConditionalGeneration.from_pretrained("sweetcocoa/pop2piano") model.eval() feature_extractor = Pop2PianoFeatureExtractor.from_pretrained("sweetcocoa/pop2piano") tokenizer = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano") ds = load_dataset("sweetcocoa/pop2piano_ci", split="test") output_fe = feature_extractor( ds["audio"][0]["array"], sampling_rate=ds["audio"][0]["sampling_rate"], return_tensors="pt" ) output_model = model.generate(input_features=output_fe["input_features"], composer="composer1") output_tokenizer = tokenizer.batch_decode(token_ids=output_model, feature_extractor_output=output_fe) pretty_midi_object = output_tokenizer["pretty_midi_objects"][0] # Checking if no of notes are same self.assertEqual(len(pretty_midi_object.instruments[0].notes), 59) predicted_timings = [] for i in pretty_midi_object.instruments[0].notes: predicted_timings.append(i.start) # Checking note start timings(first 6) EXPECTED_START_TIMINGS = [ 0.4876190423965454, 0.7314285635948181, 0.9752380847930908, 1.4396371841430664, 1.6718367338180542, 1.904036283493042, ] np.allclose(EXPECTED_START_TIMINGS, predicted_timings[:6]) # Checking note end timings(last 6) EXPECTED_END_TIMINGS = [ 12.341403007507324, 12.567797183990479, 12.567797183990479, 12.567797183990479, 12.794191360473633, 12.794191360473633, ] np.allclose(EXPECTED_END_TIMINGS, predicted_timings[-6:])
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/pop2piano/test_feature_extraction_pop2piano.py
# coding=utf-8 # Copyright 2023 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 os import tempfile import unittest import numpy as np from datasets import load_dataset from transformers.testing_utils import ( check_json_file_has_correct_format, require_essentia, require_librosa, require_scipy, require_tf, require_torch, ) from transformers.utils.import_utils import ( is_essentia_available, is_librosa_available, is_scipy_available, is_torch_available, ) from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin requirements_available = ( is_torch_available() and is_essentia_available() and is_scipy_available() and is_librosa_available() ) if requirements_available: import torch from transformers import Pop2PianoFeatureExtractor class Pop2PianoFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, n_bars=2, sample_rate=22050, use_mel=True, padding_value=0, vocab_size_special=4, vocab_size_note=128, vocab_size_velocity=2, vocab_size_time=100, ): self.parent = parent self.n_bars = n_bars self.sample_rate = sample_rate self.use_mel = use_mel self.padding_value = padding_value self.vocab_size_special = vocab_size_special self.vocab_size_note = vocab_size_note self.vocab_size_velocity = vocab_size_velocity self.vocab_size_time = vocab_size_time def prepare_feat_extract_dict(self): return { "n_bars": self.n_bars, "sample_rate": self.sample_rate, "use_mel": self.use_mel, "padding_value": self.padding_value, "vocab_size_special": self.vocab_size_special, "vocab_size_note": self.vocab_size_note, "vocab_size_velocity": self.vocab_size_velocity, "vocab_size_time": self.vocab_size_time, } @require_torch @require_essentia @require_librosa @require_scipy class Pop2PianoFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = Pop2PianoFeatureExtractor if requirements_available else None def setUp(self): self.feat_extract_tester = Pop2PianoFeatureExtractionTester(self) def test_feat_extract_from_and_save_pretrained(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: saved_file = feat_extract_first.save_pretrained(tmpdirname)[0] check_json_file_has_correct_format(saved_file) feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() mel_1 = feat_extract_first.use_mel mel_2 = feat_extract_second.use_mel self.assertTrue(np.allclose(mel_1, mel_2)) self.assertEqual(dict_first, dict_second) def test_feat_extract_to_json_file(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "feat_extract.json") feat_extract_first.to_json_file(json_file_path) feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() mel_1 = feat_extract_first.use_mel mel_2 = feat_extract_second.use_mel self.assertTrue(np.allclose(mel_1, mel_2)) self.assertEqual(dict_first, dict_second) def test_call(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_input = np.zeros([1000000], dtype=np.float32) input_features = feature_extractor(speech_input, sampling_rate=16_000, return_tensors="np") self.assertTrue(input_features.input_features.ndim == 3) self.assertEqual(input_features.input_features.shape[-1], 512) self.assertTrue(input_features.beatsteps.ndim == 2) self.assertTrue(input_features.extrapolated_beatstep.ndim == 2) def test_integration(self): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") speech_samples = ds.sort("id").select([0])["audio"] input_speech = [x["array"] for x in speech_samples][0] sampling_rate = [x["sampling_rate"] for x in speech_samples][0] feaure_extractor = Pop2PianoFeatureExtractor.from_pretrained("sweetcocoa/pop2piano") input_features = feaure_extractor( input_speech, sampling_rate=sampling_rate, return_tensors="pt" ).input_features EXPECTED_INPUT_FEATURES = torch.tensor( [[-7.1493, -6.8701, -4.3214], [-5.9473, -5.7548, -3.8438], [-6.1324, -5.9018, -4.3778]] ) self.assertTrue(torch.allclose(input_features[0, :3, :3], EXPECTED_INPUT_FEATURES, atol=1e-4)) def test_attention_mask(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_input1 = np.zeros([1_000_000], dtype=np.float32) speech_input2 = np.random.randint(low=0, high=10, size=500_000).astype(np.float32) input_features = feature_extractor( [speech_input1, speech_input2], sampling_rate=[44_100, 16_000], return_tensors="np", return_attention_mask=True, ) self.assertTrue(hasattr(input_features, "attention_mask")) # check shapes self.assertTrue(input_features["attention_mask"].ndim == 2) self.assertEqual(input_features["attention_mask_beatsteps"].shape[0], 2) self.assertEqual(input_features["attention_mask_extrapolated_beatstep"].shape[0], 2) # check if they are any values except 0 and 1 self.assertTrue(np.max(input_features["attention_mask"]) == 1) self.assertTrue(np.max(input_features["attention_mask_beatsteps"]) == 1) self.assertTrue(np.max(input_features["attention_mask_extrapolated_beatstep"]) == 1) self.assertTrue(np.min(input_features["attention_mask"]) == 0) self.assertTrue(np.min(input_features["attention_mask_beatsteps"]) == 0) self.assertTrue(np.min(input_features["attention_mask_extrapolated_beatstep"]) == 0) def test_batch_feature(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_input1 = np.zeros([1_000_000], dtype=np.float32) speech_input2 = np.ones([2_000_000], dtype=np.float32) speech_input3 = np.random.randint(low=0, high=10, size=500_000).astype(np.float32) input_features = feature_extractor( [speech_input1, speech_input2, speech_input3], sampling_rate=[44_100, 16_000, 48_000], return_attention_mask=True, ) self.assertEqual(len(input_features["input_features"].shape), 3) # check shape self.assertEqual(input_features["beatsteps"].shape[0], 3) self.assertEqual(input_features["extrapolated_beatstep"].shape[0], 3) def test_batch_feature_np(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_input1 = np.zeros([1_000_000], dtype=np.float32) speech_input2 = np.ones([2_000_000], dtype=np.float32) speech_input3 = np.random.randint(low=0, high=10, size=500_000).astype(np.float32) input_features = feature_extractor( [speech_input1, speech_input2, speech_input3], sampling_rate=[44_100, 16_000, 48_000], return_tensors="np", return_attention_mask=True, ) # check np array or not self.assertEqual(type(input_features["input_features"]), np.ndarray) # check shape self.assertEqual(len(input_features["input_features"].shape), 3) def test_batch_feature_pt(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_input1 = np.zeros([1_000_000], dtype=np.float32) speech_input2 = np.ones([2_000_000], dtype=np.float32) speech_input3 = np.random.randint(low=0, high=10, size=500_000).astype(np.float32) input_features = feature_extractor( [speech_input1, speech_input2, speech_input3], sampling_rate=[44_100, 16_000, 48_000], return_tensors="pt", return_attention_mask=True, ) # check pt tensor or not self.assertEqual(type(input_features["input_features"]), torch.Tensor) # check shape self.assertEqual(len(input_features["input_features"].shape), 3) @require_tf def test_batch_feature_tf(self): import tensorflow as tf feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()) speech_input1 = np.zeros([1_000_000], dtype=np.float32) speech_input2 = np.ones([2_000_000], dtype=np.float32) speech_input3 = np.random.randint(low=0, high=10, size=500_000).astype(np.float32) input_features = feature_extractor( [speech_input1, speech_input2, speech_input3], sampling_rate=[44_100, 16_000, 48_000], return_tensors="tf", return_attention_mask=True, ) # check tf tensor or not self.assertTrue(tf.is_tensor(input_features["input_features"])) # check shape self.assertEqual(len(input_features["input_features"].shape), 3) @unittest.skip( "Pop2PianoFeatureExtractor does not supports padding externally (while processing audios in batches padding is automatically applied to max_length)" ) def test_padding_accepts_tensors_pt(self): pass @unittest.skip( "Pop2PianoFeatureExtractor does not supports padding externally (while processing audios in batches padding is automatically applied to max_length)" ) def test_padding_accepts_tensors_tf(self): pass @unittest.skip( "Pop2PianoFeatureExtractor does not supports padding externally (while processing audios in batches padding is automatically applied to max_length)" ) def test_padding_from_list(self): pass @unittest.skip( "Pop2PianoFeatureExtractor does not supports padding externally (while processing audios in batches padding is automatically applied to max_length)" ) def test_padding_from_array(self): pass @unittest.skip("Pop2PianoFeatureExtractor does not support truncation") def test_attention_mask_with_truncation(self): pass @unittest.skip("Pop2PianoFeatureExtractor does not supports truncation") def test_truncation_from_array(self): pass @unittest.skip("Pop2PianoFeatureExtractor does not supports truncation") def test_truncation_from_list(self): pass
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/pop2piano/test_tokenization_pop2piano.py
# coding=utf-8 # Copyright 2023 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. """ Please note that Pop2PianoTokenizer is too far from our usual tokenizers and thus cannot use the TokenizerTesterMixin class. """ import os import pickle import shutil import tempfile import unittest from transformers.feature_extraction_utils import BatchFeature from transformers.testing_utils import ( is_pretty_midi_available, is_torch_available, require_pretty_midi, require_torch, ) from transformers.tokenization_utils import BatchEncoding if is_torch_available(): import torch requirements_available = is_torch_available() and is_pretty_midi_available() if requirements_available: import pretty_midi from transformers import Pop2PianoTokenizer @require_torch @require_pretty_midi class Pop2PianoTokenizerTest(unittest.TestCase): def setUp(self): super().setUp() self.tokenizer = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano") def get_input_notes(self): notes = [ [ pretty_midi.Note(start=0.441179, end=2.159456, pitch=70, velocity=77), pretty_midi.Note(start=0.673379, end=0.905578, pitch=73, velocity=77), pretty_midi.Note(start=0.905578, end=2.159456, pitch=73, velocity=77), pretty_midi.Note(start=1.114558, end=2.159456, pitch=78, velocity=77), pretty_midi.Note(start=1.323537, end=1.532517, pitch=80, velocity=77), ], [ pretty_midi.Note(start=0.441179, end=2.159456, pitch=70, velocity=77), ], ] return notes def test_call(self): notes = self.get_input_notes() output = self.tokenizer( notes, return_tensors="pt", padding="max_length", truncation=True, max_length=10, return_attention_mask=True, ) # check the output type self.assertTrue(isinstance(output, BatchEncoding)) # check the values expected_output_token_ids = torch.tensor( [[134, 133, 74, 135, 77, 132, 77, 133, 77, 82], [134, 133, 74, 136, 132, 74, 134, 134, 134, 134]] ) expected_output_attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 0, 0, 0, 0]]) self.assertTrue(torch.allclose(output["token_ids"], expected_output_token_ids, atol=1e-4)) self.assertTrue(torch.allclose(output["attention_mask"], expected_output_attention_mask, atol=1e-4)) def test_batch_decode(self): # test batch decode with model, feature-extractor outputs(beatsteps, extrapolated_beatstep) # Please note that this test does not test the accuracy of the outputs, instead it is designed to make sure that # the tokenizer's batch_decode can deal with attention_mask in feature-extractor outputs. For the accuracy check # please see the `test_batch_decode_outputs` test. model_output = torch.concatenate( [ torch.randint(size=[120, 96], low=0, high=70, dtype=torch.long), torch.zeros(size=[1, 96], dtype=torch.long), torch.randint(size=[50, 96], low=0, high=40, dtype=torch.long), torch.zeros(size=[1, 96], dtype=torch.long), ], axis=0, ) input_features = BatchFeature( { "beatsteps": torch.ones([2, 955]), "extrapolated_beatstep": torch.ones([2, 1000]), "attention_mask": torch.concatenate( [ torch.ones([120, 96], dtype=torch.long), torch.zeros([1, 96], dtype=torch.long), torch.ones([50, 96], dtype=torch.long), torch.zeros([1, 96], dtype=torch.long), ], axis=0, ), "attention_mask_beatsteps": torch.ones([2, 955]), "attention_mask_extrapolated_beatstep": torch.ones([2, 1000]), } ) output = self.tokenizer.batch_decode(token_ids=model_output, feature_extractor_output=input_features)[ "pretty_midi_objects" ] # check length self.assertTrue(len(output) == 2) # check object type self.assertTrue(isinstance(output[0], pretty_midi.pretty_midi.PrettyMIDI)) self.assertTrue(isinstance(output[1], pretty_midi.pretty_midi.PrettyMIDI)) def test_batch_decode_outputs(self): # test batch decode with model, feature-extractor outputs(beatsteps, extrapolated_beatstep) # Please note that this test tests the accuracy of the outputs of the tokenizer's `batch_decode` method. model_output = torch.tensor( [ [134, 133, 74, 135, 77, 82, 84, 136, 132, 74, 77, 82, 84], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ] ) input_features = BatchEncoding( { "beatsteps": torch.tensor([[0.0697, 0.1103, 0.1509, 0.1916]]), "extrapolated_beatstep": torch.tensor([[0.0000, 0.0406, 0.0813, 0.1219]]), } ) output = self.tokenizer.batch_decode(token_ids=model_output, feature_extractor_output=input_features) # check outputs self.assertEqual(len(output["notes"]), 4) predicted_start_timings, predicted_end_timings = [], [] for i in output["notes"]: predicted_start_timings.append(i.start) predicted_end_timings.append(i.end) # Checking note start timings expected_start_timings = torch.tensor( [ 0.069700, 0.110300, 0.110300, 0.110300, ] ) predicted_start_timings = torch.tensor(predicted_start_timings) self.assertTrue(torch.allclose(expected_start_timings, predicted_start_timings, atol=1e-4)) # Checking note end timings expected_end_timings = torch.tensor( [ 0.191600, 0.191600, 0.191600, 0.191600, ] ) predicted_end_timings = torch.tensor(predicted_end_timings) self.assertTrue(torch.allclose(expected_end_timings, predicted_end_timings, atol=1e-4)) def test_get_vocab(self): vocab_dict = self.tokenizer.get_vocab() self.assertIsInstance(vocab_dict, dict) self.assertGreaterEqual(len(self.tokenizer), len(vocab_dict)) vocab = [self.tokenizer.convert_ids_to_tokens(i) for i in range(len(self.tokenizer))] self.assertEqual(len(vocab), len(self.tokenizer)) self.tokenizer.add_tokens(["asdfasdfasdfasdf"]) vocab = [self.tokenizer.convert_ids_to_tokens(i) for i in range(len(self.tokenizer))] self.assertEqual(len(vocab), len(self.tokenizer)) def test_save_and_load_tokenizer(self): tmpdirname = tempfile.mkdtemp() sample_notes = self.get_input_notes() self.tokenizer.add_tokens(["bim", "bambam"]) additional_special_tokens = self.tokenizer.additional_special_tokens additional_special_tokens.append("new_additional_special_token") self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}) before_token_ids = self.tokenizer(sample_notes)["token_ids"] before_vocab = self.tokenizer.get_vocab() self.tokenizer.save_pretrained(tmpdirname) after_tokenizer = self.tokenizer.__class__.from_pretrained(tmpdirname) after_token_ids = after_tokenizer(sample_notes)["token_ids"] after_vocab = after_tokenizer.get_vocab() self.assertDictEqual(before_vocab, after_vocab) self.assertListEqual(before_token_ids, after_token_ids) self.assertIn("bim", after_vocab) self.assertIn("bambam", after_vocab) self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens) shutil.rmtree(tmpdirname) def test_pickle_tokenizer(self): tmpdirname = tempfile.mkdtemp() notes = self.get_input_notes() subwords = self.tokenizer(notes)["token_ids"] filename = os.path.join(tmpdirname, "tokenizer.bin") with open(filename, "wb") as handle: pickle.dump(self.tokenizer, handle) with open(filename, "rb") as handle: tokenizer_new = pickle.load(handle) subwords_loaded = tokenizer_new(notes)["token_ids"] self.assertListEqual(subwords, subwords_loaded) def test_padding_side_in_kwargs(self): tokenizer_p = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano", padding_side="left") self.assertEqual(tokenizer_p.padding_side, "left") tokenizer_p = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano", padding_side="right") self.assertEqual(tokenizer_p.padding_side, "right") self.assertRaises( ValueError, Pop2PianoTokenizer.from_pretrained, "sweetcocoa/pop2piano", padding_side="unauthorized", ) def test_truncation_side_in_kwargs(self): tokenizer_p = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano", truncation_side="left") self.assertEqual(tokenizer_p.truncation_side, "left") tokenizer_p = Pop2PianoTokenizer.from_pretrained("sweetcocoa/pop2piano", truncation_side="right") self.assertEqual(tokenizer_p.truncation_side, "right") self.assertRaises( ValueError, Pop2PianoTokenizer.from_pretrained, "sweetcocoa/pop2piano", truncation_side="unauthorized", ) def test_right_and_left_padding(self): tokenizer = self.tokenizer notes = self.get_input_notes() notes = notes[0] max_length = 20 padding_idx = tokenizer.pad_token_id # RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "right" padded_notes = tokenizer(notes, padding="max_length", max_length=max_length)["token_ids"] padded_notes_length = len(padded_notes) notes_without_padding = tokenizer(notes, padding="do_not_pad")["token_ids"] padding_size = max_length - len(notes_without_padding) self.assertEqual(padded_notes_length, max_length) self.assertEqual(notes_without_padding + [padding_idx] * padding_size, padded_notes) # LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "left" padded_notes = tokenizer(notes, padding="max_length", max_length=max_length)["token_ids"] padded_notes_length = len(padded_notes) notes_without_padding = tokenizer(notes, padding="do_not_pad")["token_ids"] padding_size = max_length - len(notes_without_padding) self.assertEqual(padded_notes_length, max_length) self.assertEqual([padding_idx] * padding_size + notes_without_padding, padded_notes) # RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding' notes_without_padding = tokenizer(notes)["token_ids"] tokenizer.padding_side = "right" padded_notes_right = tokenizer(notes, padding=False)["token_ids"] self.assertEqual(len(padded_notes_right), len(notes_without_padding)) self.assertEqual(padded_notes_right, notes_without_padding) tokenizer.padding_side = "left" padded_notes_left = tokenizer(notes, padding="longest")["token_ids"] self.assertEqual(len(padded_notes_left), len(notes_without_padding)) self.assertEqual(padded_notes_left, notes_without_padding) tokenizer.padding_side = "right" padded_notes_right = tokenizer(notes, padding="longest")["token_ids"] self.assertEqual(len(padded_notes_right), len(notes_without_padding)) self.assertEqual(padded_notes_right, notes_without_padding) tokenizer.padding_side = "left" padded_notes_left = tokenizer(notes, padding=False)["token_ids"] self.assertEqual(len(padded_notes_left), len(notes_without_padding)) self.assertEqual(padded_notes_left, notes_without_padding) def test_right_and_left_truncation(self): tokenizer = self.tokenizer notes = self.get_input_notes() notes = notes[0] truncation_size = 3 # RIGHT TRUNCATION - Check that it correctly truncates when a maximum length is specified along with the truncation flag set to True tokenizer.truncation_side = "right" full_encoded_notes = tokenizer(notes)["token_ids"] full_encoded_notes_length = len(full_encoded_notes) truncated_notes = tokenizer(notes, max_length=full_encoded_notes_length - truncation_size, truncation=True)[ "token_ids" ] self.assertEqual(full_encoded_notes_length, len(truncated_notes) + truncation_size) self.assertEqual(full_encoded_notes[:-truncation_size], truncated_notes) # LEFT TRUNCATION - Check that it correctly truncates when a maximum length is specified along with the truncation flag set to True tokenizer.truncation_side = "left" full_encoded_notes = tokenizer(notes)["token_ids"] full_encoded_notes_length = len(full_encoded_notes) truncated_notes = tokenizer(notes, max_length=full_encoded_notes_length - truncation_size, truncation=True)[ "token_ids" ] self.assertEqual(full_encoded_notes_length, len(truncated_notes) + truncation_size) self.assertEqual(full_encoded_notes[truncation_size:], truncated_notes) # RIGHT & LEFT TRUNCATION - Check that nothing is done for 'longest' and 'no_truncation' tokenizer.truncation_side = "right" truncated_notes_right = tokenizer(notes, truncation=True)["token_ids"] self.assertEqual(full_encoded_notes_length, len(truncated_notes_right)) self.assertEqual(full_encoded_notes, truncated_notes_right) tokenizer.truncation_side = "left" truncated_notes_left = tokenizer(notes, truncation="longest_first")["token_ids"] self.assertEqual(len(truncated_notes_left), full_encoded_notes_length) self.assertEqual(truncated_notes_left, full_encoded_notes) tokenizer.truncation_side = "right" truncated_notes_right = tokenizer(notes, truncation="longest_first")["token_ids"] self.assertEqual(len(truncated_notes_right), full_encoded_notes_length) self.assertEqual(truncated_notes_right, full_encoded_notes) tokenizer.truncation_side = "left" truncated_notes_left = tokenizer(notes, truncation=True)["token_ids"] self.assertEqual(len(truncated_notes_left), full_encoded_notes_length) self.assertEqual(truncated_notes_left, full_encoded_notes) def test_padding_to_multiple_of(self): notes = self.get_input_notes() if self.tokenizer.pad_token is None: self.skipTest("No padding token.") else: normal_tokens = self.tokenizer(notes[0], padding=True, pad_to_multiple_of=8) for key, value in normal_tokens.items(): self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") normal_tokens = self.tokenizer(notes[0], pad_to_multiple_of=8) for key, value in normal_tokens.items(): self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") # Should also work with truncation normal_tokens = self.tokenizer(notes[0], padding=True, truncation=True, pad_to_multiple_of=8) for key, value in normal_tokens.items(): self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") # truncation to something which is not a multiple of pad_to_multiple_of raises an error self.assertRaises( ValueError, self.tokenizer.__call__, notes[0], padding=True, truncation=True, max_length=12, pad_to_multiple_of=8, ) def test_padding_with_attention_mask(self): if self.tokenizer.pad_token is None: self.skipTest("No padding token.") if "attention_mask" not in self.tokenizer.model_input_names: self.skipTest("This model does not use attention mask.") features = [ {"token_ids": [1, 2, 3, 4, 5, 6], "attention_mask": [1, 1, 1, 1, 1, 0]}, {"token_ids": [1, 2, 3], "attention_mask": [1, 1, 0]}, ] padded_features = self.tokenizer.pad(features) if self.tokenizer.padding_side == "right": self.assertListEqual(padded_features["attention_mask"], [[1, 1, 1, 1, 1, 0], [1, 1, 0, 0, 0, 0]]) else: self.assertListEqual(padded_features["attention_mask"], [[1, 1, 1, 1, 1, 0], [0, 0, 0, 1, 1, 0]])
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/altclip/test_modeling_altclip.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 AltCLIP model. """ import inspect import os import tempfile import unittest import numpy as np import requests from transformers import AltCLIPConfig, AltCLIPProcessor, AltCLIPTextConfig, AltCLIPVisionConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import 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, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch import torch.nn as nn from transformers import AltCLIPModel, AltCLIPTextModel, AltCLIPVisionModel if is_vision_available(): from PIL import Image class AltCLIPVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent 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.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, 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]) config = self.get_config() return config, pixel_values def get_config(self): return AltCLIPVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = AltCLIPVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class AltCLIPVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as CLIP does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (AltCLIPVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = AltCLIPVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=AltCLIPVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="CLIP does not use inputs_embeds") def test_inputs_embeds(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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="AltCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="AltCLIPVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @unittest.skip(reason="AltCLIPVisionModel use the same cv backbone with CLIP model.") def test_model_from_pretrained(self): pass class AltCLIPTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, project_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, 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_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.projection_dim = projection_dim self.project_dim = project_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range 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]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return AltCLIPTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, project_dim=self.project_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, pad_token_id=1, ) def create_and_check_model(self, config, input_ids, input_mask): model = AltCLIPTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) 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.projection_dim)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class AltCLIPTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (AltCLIPTextModel,) if is_torch_available() else () fx_compatible = True test_pruning = False test_head_masking = False # TODO (@SunMarc): Fix me @unittest.skip("It's broken.") def test_resize_tokens_embeddings(self): super().test_resize_tokens_embeddings() def setUp(self): self.model_tester = AltCLIPTextModelTester(self) self.config_tester = ConfigTester(self, config_class=AltCLIPTextConfig, 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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass def test_model_outputs_equivalence(self): pass @unittest.skip(reason="Result of the model is a dict") def test_hidden_states_output(self): pass @unittest.skip(reason="AltCLIP does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="AltCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="AltCLIPTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "BAAI/AltCLIP" model = AltCLIPTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class AltCLIPModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = AltCLIPTextModelTester(parent, **text_kwargs) self.vision_model_tester = AltCLIPVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return AltCLIPConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = AltCLIPModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): model(input_ids, pixel_values, attention_mask) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @require_torch class AltCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (AltCLIPModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": AltCLIPModel} if is_torch_available() else {} fx_compatible = True test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False # TODO: Fix the failed tests when this model gets more usage def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if pipeline_test_casse_name == "FeatureExtractionPipelineTests": return True return False def setUp(self): self.model_tester = AltCLIPModelTester(self) 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(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="CLIPModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for AltCLIP 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(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: 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 _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # CLIP needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) @slow def test_model_from_pretrained(self): model_name = "BAAI/AltCLIP" model = AltCLIPModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_vision @require_torch class AltCLIPModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "BAAI/AltCLIP" model = AltCLIPModel.from_pretrained(model_name).to(torch_device) processor = AltCLIPProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor(text=["一张猫的照片", "一张狗的照片"], images=image, padding=True, return_tensors="pt").to(torch_device) # fmt: skip # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) probs = outputs.logits_per_image.softmax(dim=1) expected_probs = torch.tensor([[9.9942e-01, 5.7805e-04]], device=torch_device) self.assertTrue(torch.allclose(probs, expected_probs, atol=5e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/dinat/test_modeling_dinat.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 Dinat model. """ import collections import unittest from transformers import DinatConfig from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import DinatBackbone, DinatForImageClassification, DinatModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class DinatModelTester: def __init__( self, parent, batch_size=13, image_size=64, patch_size=4, num_channels=3, embed_dim=16, depths=[1, 2, 1], num_heads=[2, 4, 8], kernel_size=3, dilations=[[3], [1, 2], [1]], mlp_ratio=2.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, is_training=True, scope=None, use_labels=True, num_labels=10, out_features=["stage1", "stage2"], out_indices=[1, 2], ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.embed_dim = embed_dim self.depths = depths self.num_heads = num_heads self.kernel_size = kernel_size self.dilations = dilations self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.patch_norm = patch_norm self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.is_training = is_training self.scope = scope self.use_labels = use_labels self.num_labels = num_labels self.out_features = out_features self.out_indices = out_indices 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 DinatConfig( num_labels=self.num_labels, image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, kernel_size=self.kernel_size, dilations=self.dilations, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, patch_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, out_features=self.out_features, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels): model = DinatModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) expected_height = expected_width = (config.image_size // config.patch_size) // (2 ** (len(config.depths) - 1)) expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1)) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, expected_height, expected_width, expected_dim) ) def create_and_check_for_image_classification(self, config, pixel_values, labels): model = DinatForImageClassification(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)) # test greyscale images config.num_channels = 1 model = DinatForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_backbone(self, config, pixel_values, labels): model = DinatBackbone(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), [self.batch_size, model.channels[0], 16, 16]) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = DinatBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, model.channels[-1], 4, 4]) # verify channels self.parent.assertEqual(len(model.channels), 1) 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_natten @require_torch class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DinatModel, DinatForImageClassification, DinatBackbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": DinatModel, "image-classification": DinatForImageClassification} if is_torch_available() else {} ) fx_compatible = False test_torchscript = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = DinatModelTester(self) self.config_tester = ConfigTester(self, config_class=DinatConfig, embed_dim=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 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_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*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) @unittest.skip(reason="Dinat does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Dinat does not use feedforward chunking") def test_feed_forward_chunking(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_attention_outputs(self): self.skipTest("Dinat's attention operation is handled entirely by NATTEN.") def check_hidden_states_output(self, inputs_dict, config, model_class, image_size): 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.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # Dinat has a different seq_length patch_size = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) height = image_size[0] // patch_size[0] width = image_size[1] // patch_size[1] self.assertListEqual( list(hidden_states[0].shape[-3:]), [height, width, self.model_tester.embed_dim], ) if model_class.__name__ != "DinatBackbone": reshaped_hidden_states = outputs.reshaped_hidden_states self.assertEqual(len(reshaped_hidden_states), expected_num_layers) batch_size, num_channels, height, width = reshaped_hidden_states[0].shape reshaped_hidden_states = ( reshaped_hidden_states[0].view(batch_size, num_channels, height, width).permute(0, 2, 3, 1) ) self.assertListEqual( list(reshaped_hidden_states.shape[-3:]), [height, width, self.model_tester.embed_dim], ) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) @slow def test_model_from_pretrained(self): model_name = "shi-labs/dinat-mini-in1k-224" model = DinatModel.from_pretrained(model_name) self.assertIsNotNone(model) 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(): if "embeddings" not in name and 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", ) @require_natten @require_vision @require_torch class DinatModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("shi-labs/dinat-mini-in1k-224") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = DinatForImageClassification.from_pretrained("shi-labs/dinat-mini-in1k-224").to(torch_device) image_processor = self.default_image_processor image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = image_processor(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([-0.1545, -0.7667, 0.4642]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)) @require_torch @require_natten class DinatBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (DinatBackbone,) if is_torch_available() else () config_class = DinatConfig def setUp(self): self.model_tester = DinatModelTester(self)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/jukebox/test_tokenization_jukebox.py
# coding=utf-8 # 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 unittest from transformers import JukeboxTokenizer from transformers.testing_utils import require_torch class JukeboxTokenizationTest(unittest.TestCase): tokenizer_class = JukeboxTokenizer metas = { "artist": "Zac Brown Band", "genres": "Country", "lyrics": """I met a traveller from an antique land, Who said "Two vast and trunkless legs of stone Stand in the desert. . . . Near them, on the sand, Half sunk a shattered visage lies, whose frown, And wrinkled lip, and sneer of cold command, Tell that its sculptor well those passions read Which yet survive, stamped on these lifeless things, The hand that mocked them, and the heart that fed; And on the pedestal, these words appear: My name is Ozymandias, King of Kings; Look on my Works, ye Mighty, and despair! Nothing beside remains. Round the decay Of that colossal Wreck, boundless and bare The lone and level sands stretch far away """, } @require_torch def test_1b_lyrics_tokenizer(self): """ how to run the same test with openAI ... """ import torch tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics") tokens = tokenizer(**self.metas)["input_ids"] # fmt: off EXPECTED_OUTPUT = [ torch.tensor([[ 0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27, 76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32, 44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43, 47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35, 30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76, 19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31, 76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63, 76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39, 64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8, 27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45, 34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45, 27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34, 41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49, 44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64, 76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41, 32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27, 40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46, 45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49, 31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27, 45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78, 76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29, 34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48, 31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41, 40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31, 38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31, 76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39, 41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76, 27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44, 46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78, 76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45, 46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49, 41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65, 78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76, 40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33, 76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76, 76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76, 41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64, 76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76, 27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67, 78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46, 34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76, 44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47, 40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51, 78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76, 46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27, 38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47, 40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28, 27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30, 76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45, 76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44, 76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76, 76, 76]]), torch.tensor([[0, 0, 0, 1069, 11]]), torch.tensor([[0, 0, 0, 1069, 11]]), ] # fmt: on self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0])) self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1])) self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[2])) @require_torch def test_5b_lyrics_tokenizer(self): """ The outputs are similar that open AI but do not have the same format as this one is adapted to the HF integration. """ import torch tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics") tokens = tokenizer(**self.metas)["input_ids"] # fmt: off EXPECTED_OUTPUT = [ torch.tensor([[ 0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39, 31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38, 31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27, 40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41, 77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48, 27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40, 37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41, 32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77, 77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40, 77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63, 77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77, 46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31, 77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37, 77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30, 77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45, 64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49, 40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1, 40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77, 38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31, 31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29, 41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27, 46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46, 41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45, 31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44, 31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47, 44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42, 31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77, 38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35, 40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34, 27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34, 31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77, 34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32, 31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42, 31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31, 45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42, 31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77, 77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77, 15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77, 11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33, 45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12, 41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41, 44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34, 46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42, 27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77, 77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45, 35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63, 77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30, 31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77, 77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38, 41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64, 77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27, 40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77, 77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31, 77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45, 27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34, 77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77, 77]]), torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]), torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]), ] # fmt: on self.assertTrue(torch.allclose(tokens[0], EXPECTED_OUTPUT[0])) self.assertTrue(torch.allclose(tokens[1], EXPECTED_OUTPUT[1])) self.assertTrue(torch.allclose(tokens[2], EXPECTED_OUTPUT[2]))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/jukebox/test_modeling_jukebox.py
# coding=utf-8 # 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 unittest from unittest import skip from transformers import is_torch_available from transformers.testing_utils import ( require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device, ) from transformers.trainer_utils import set_seed if is_torch_available(): import torch from transformers import JukeboxModel, JukeboxPrior, JukeboxTokenizer @require_torch class Jukebox1bModelTester(unittest.TestCase): all_model_classes = (JukeboxModel,) if is_torch_available() else () model_id = "openai/jukebox-1b-lyrics" metas = { "artist": "Zac Brown Band", "genres": "Country", "lyrics": """I met a traveller from an antique land, Who said "Two vast and trunkless legs of stone Stand in the desert. . . . Near them, on the sand, Half sunk a shattered visage lies, whose frown, And wrinkled lip, and sneer of cold command, Tell that its sculptor well those passions read Which yet survive, stamped on these lifeless things, The hand that mocked them, and the heart that fed; And on the pedestal, these words appear: My name is Ozymandias, King of Kings; Look on my Works, ye Mighty, and despair! Nothing beside remains. Round the decay Of that colossal Wreck, boundless and bare The lone and level sands stretch far away """, } # fmt: off EXPECTED_OUTPUT_2 = [ 1864, 1536, 1213, 1870, 1357, 1536, 519, 880, 1323, 789, 1082, 534, 1000, 1445, 1105, 1130, 967, 515, 1434, 1620, 534, 1495, 283, 1445, 333, 1307, 539, 1631, 1528, 375, 1434, 673, 627, 710, 778, 1883, 1405, 1276, 1455, 1228 ] EXPECTED_OUTPUT_2_PT_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653 ] EXPECTED_OUTPUT_1 = [ 1125, 1751, 697, 1776, 1141, 1476, 391, 697, 1125, 684, 867, 416, 844, 1372, 1274, 717, 1274, 844, 1299, 1419, 697, 1370, 317, 1125, 191, 1440, 1370, 1440, 1370, 282, 1621, 1370, 368, 349, 867, 1872, 1262, 869, 1728, 747 ] EXPECTED_OUTPUT_1_PT_2 = [ 416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_OUTPUT_0 = [ 1755, 842, 307, 1843, 1022, 1395, 234, 1554, 806, 739, 1022, 442, 616, 556, 268, 1499, 933, 457, 1440, 1837, 755, 985, 308, 902, 293, 1443, 1671, 1141, 1533, 555, 1562, 1061, 287, 417, 1022, 2008, 1186, 1015, 1777, 268 ] EXPECTED_OUTPUT_0_PT_2 = [ 854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 307, 842, 591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591, 89 ] EXPECTED_Y_COND = [1058304, 0, 786432, 7169, 507, 76, 27, 40, 30, 76] EXPECTED_PRIMED_0 = [ 390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002, 1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002, 1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907, 1907, 1788, 596, 1626 ] EXPECTED_PRIMED_1 = [ 1236, 1668, 1484, 1920, 1848, 1409, 139, 864, 1828, 1272, 1599, 824, 1672, 139, 555, 1484, 824, 1920, 555, 596, 1579, 1599, 1231, 1599, 1637, 1407, 212, 824, 1599, 116, 1433, 824, 258, 1599, 1433, 1895, 1063, 1433, 1433, 1599 ] EXPECTED_PRIMED_2 = [ 1684, 1873, 1119, 1189, 395, 611, 1901, 972, 890, 1337, 1392, 1927, 96, 972, 672, 780, 1119, 890, 158, 771, 1073, 1927, 353, 1331, 1269, 1459, 1333, 1645, 812, 1577, 1337, 606, 353, 981, 1466, 619, 197, 391, 302, 1930 ] EXPECTED_VQVAE_ENCODE = [ 390, 1160, 1002, 1907, 1788, 1788, 1788, 1907, 1002, 1002, 1854, 1002, 1002, 1002, 1002, 1002, 1002, 1160, 1160, 1606, 596, 596, 1160, 1002, 1516, 596, 1002, 1002, 1002, 1907, 1788, 1788, 1788, 1854, 1788, 1907, 1907, 1788, 596, 1626 ] EXPECTED_VQVAE_DECODE = [ -0.0492, -0.0524, -0.0565, -0.0640, -0.0686, -0.0684, -0.0677, -0.0664, -0.0605, -0.0490, -0.0330, -0.0168, -0.0083, -0.0075, -0.0051, 0.0025, 0.0136, 0.0261, 0.0386, 0.0497, 0.0580, 0.0599, 0.0583, 0.0614, 0.0740, 0.0889, 0.1023, 0.1162, 0.1211, 0.1212, 0.1251, 0.1336, 0.1502, 0.1686, 0.1883, 0.2148, 0.2363, 0.2458, 0.2507, 0.2531 ] EXPECTED_AUDIO_COND = [ 0.0256, -0.0544, 0.1600, -0.0032, 0.1066, 0.0825, -0.0013, 0.3440, 0.0210, 0.0412, -0.1777, -0.0892, -0.0164, 0.0285, -0.0613, -0.0617, -0.0137, -0.0201, -0.0175, 0.0215, -0.0627, 0.0520, -0.0730, 0.0970, -0.0100, 0.0442, -0.0586, 0.0207, -0.0015, -0.0082 ] EXPECTED_META_COND = [ 0.0415, 0.0877, 0.0022, -0.0055, 0.0751, 0.0334, 0.0324, -0.0068, 0.0011, 0.0017, -0.0676, 0.0655, -0.0143, 0.0399, 0.0303, 0.0743, -0.0168, -0.0394, -0.1113, 0.0124, 0.0442, 0.0267, -0.0003, -0.1536, -0.0116, -0.1837, -0.0180, -0.1026, -0.0777, -0.0456 ] EXPECTED_LYRIC_COND = [ 76, 27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45, 45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46, 41, 40, 31, 78, 76 ] # fmt: on def prepare_inputs(self): tokenizer = JukeboxTokenizer.from_pretrained(self.model_id) tokens = tokenizer(**self.metas)["input_ids"] return tokens @slow def test_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs() set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=40 * model.priors[0].raw_to_tokens, save_results=False) self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]) set_seed(0) zs = model._sample(zs, labels, [1], sample_length=40 * model.priors[1].raw_to_tokens, save_results=False) self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]) set_seed(0) zs = model._sample(zs, labels, [2], sample_length=40 * model.priors[2].raw_to_tokens, save_results=False) self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]) @slow def test_conditioning(self): torch.backends.cuda.matmul.allow_tf32 = False torch.backends.cudnn.allow_tf32 = False model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs() set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long) for _ in range(3)] top_prior = model.priors[0] start = 0 music_token_conds = top_prior.get_music_tokens_conds(zs, start=start, end=start + top_prior.n_ctx) metadata = top_prior.get_metadata(labels[0].clone(), start, 1058304, 0) self.assertIsNone(music_token_conds) self.assertListEqual(metadata.numpy()[0][:10].tolist(), self.EXPECTED_Y_COND) audio_conditioning, metadata_conditioning, lyric_tokens = top_prior.get_cond(music_token_conds, metadata) torch.testing.assert_close( audio_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_AUDIO_COND), atol=1e-4, rtol=1e-4 ) torch.testing.assert_close( metadata_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_META_COND), atol=1e-4, rtol=1e-4 ) torch.testing.assert_close( lyric_tokens[0, :30].detach(), torch.tensor(self.EXPECTED_LYRIC_COND), atol=1e-4, rtol=1e-4 ) @slow def test_primed_sampling(self): torch.backends.cuda.matmul.allow_tf32 = False torch.backends.cudnn.allow_tf32 = False model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() set_seed(0) waveform = torch.rand((1, 5120, 1)) tokens = list(self.prepare_inputs()) zs = [model.vqvae.encode(waveform, start_level=2, bs_chunks=waveform.shape[0])[0], None, None] zs = model._sample( zs, tokens, sample_levels=[0], save_results=False, sample_length=40 * model.priors[0].raw_to_tokens ) torch.testing.assert_close(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0)) upper_2 = torch.cat((zs[0], torch.zeros(1, 2048 - zs[0].shape[-1])), dim=-1).long() zs = [upper_2, model.vqvae.encode(waveform, start_level=1, bs_chunks=waveform.shape[0])[0], None] zs = model._sample( zs, tokens, sample_levels=[1], save_results=False, sample_length=40 * model.priors[1].raw_to_tokens ) torch.testing.assert_close(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1)) upper_1 = torch.cat((zs[1], torch.zeros(1, 2048 - zs[1].shape[-1])), dim=-1).long() zs = [upper_2, upper_1, model.vqvae.encode(waveform, start_level=0, bs_chunks=waveform.shape[0])[0]] zs = model._sample( zs, tokens, sample_levels=[2], save_results=False, sample_length=40 * model.priors[2].raw_to_tokens ) torch.testing.assert_close(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2)) @slow def test_vqvae(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() set_seed(0) x = torch.rand((1, 5120, 1)) with torch.no_grad(): zs = model.vqvae.encode(x, start_level=2, bs_chunks=x.shape[0]) torch.testing.assert_close(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE)) with torch.no_grad(): x = model.vqvae.decode(zs, start_level=2, bs_chunks=x.shape[0]) torch.testing.assert_close(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4) @require_torch class Jukebox5bModelTester(unittest.TestCase): all_model_classes = (JukeboxModel,) if is_torch_available() else () model_id = "openai/jukebox-5b-lyrics" metas = { "artist": "Zac Brown Band", "genres": "Country", "lyrics": """I met a traveller from an antique land, Who said "Two vast and trunkless legs of stone Stand in the desert. . . . Near them, on the sand, Half sunk a shattered visage lies, whose frown, And wrinkled lip, and sneer of cold command, Tell that its sculptor well those passions read Which yet survive, stamped on these lifeless things, The hand that mocked them, and the heart that fed; And on the pedestal, these words appear: My name is Ozymandias, King of Kings; Look on my Works, ye Mighty, and despair! Nothing beside remains. Round the decay Of that colossal Wreck, boundless and bare The lone and level sands stretch far away """, } # fmt: off EXPECTED_OUTPUT_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 1489, 1489, 1489, 1489, 1150, 1853, 1509, 1150, 1357, 1509, 6, 1272 ] EXPECTED_OUTPUT_2_PT_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653 ] EXPECTED_OUTPUT_1 = [ 1125, 416, 1125, 1125, 1125, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_OUTPUT_1_PT_2 = [ 416, 416, 1125, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_OUTPUT_0 = [ 1755, 1061, 234, 1755, 1061, 1755, 185, 290, 307, 307, 616, 616, 616, 616, 616, 616, 307, 290, 417, 1755, 234, 1755, 185, 290, 290, 290, 307, 616, 616, 616, 616, 616, 290, 234, 234, 1755, 234, 234, 1755, 234, 185, 185, 307, 616, 616, 616, 616, 290, 1755, 1755, 1755, 234, 234, 1755, 1572, 290, 307, 616, 34, 616 ] EXPECTED_OUTPUT_0_PT_2 = [ 854, 842, 1353, 114, 1353, 842, 185, 842, 185, 114, 591, 842, 185, 417, 185, 842, 307, 842, 591, 842, 185, 842, 185, 842, 591, 842, 1353, 842, 185, 842, 591, 842, 591, 114, 591, 842, 185, 842, 591, 89, 591, 842, 591, 842, 591, 417, 1372, 842, 1372, 842, 34, 842, 185, 89, 591, 842, 185, 842, 591, 632 ] EXPECTED_GPU_OUTPUTS_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653 ] EXPECTED_GPU_OUTPUTS_2_PT_2 = [ 1489, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 653, 1853, 1177, 1536, 1228, 710, 475, 1489, 1229, 1224, 231, 1224, 252, 1434, 653, 475, 1106, 1877, 1599, 1228, 1600, 1683, 1182, 1853, 475, 1864, 252, 1229, 1434, 2001 ] EXPECTED_GPU_OUTPUTS_1 = [ 1125, 1125, 416, 1125, 1125, 416, 1125, 1125, 416, 416, 1125, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416 ] EXPECTED_GPU_OUTPUTS_0 = [ 491, 1755, 34, 1613, 1755, 417, 992, 1613, 222, 842, 1353, 1613, 844, 632, 185, 1613, 844, 632, 185, 1613, 185, 842, 677, 1613, 185, 114, 1353, 1613, 307, 89, 844, 1613, 307, 1332, 234, 1979, 307, 89, 1353, 616, 34, 842, 185, 842, 34, 842, 185, 842, 307, 114, 185, 89, 34, 1268, 185, 89, 34, 842, 185, 89 ] # fmt: on def prepare_inputs(self, model_id): tokenizer = JukeboxTokenizer.from_pretrained(model_id) tokens = tokenizer(**self.metas)["input_ids"] return tokens @slow def test_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = self.prepare_inputs(self.model_id) set_seed(0) zs = [torch.zeros(1, 0, dtype=torch.long).cpu() for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) self.assertIn(zs[0][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_2, self.EXPECTED_OUTPUT_2_PT_2]) set_seed(0) zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False) self.assertIn(zs[1][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_1, self.EXPECTED_OUTPUT_1_PT_2]) set_seed(0) zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False) self.assertIn(zs[2][0].detach().cpu().tolist(), [self.EXPECTED_OUTPUT_0, self.EXPECTED_OUTPUT_0_PT_2]) @slow @require_torch_accelerator @skip("Not enough GPU memory on CI runners") def test_slow_sampling(self): model = JukeboxModel.from_pretrained(self.model_id, min_duration=0).eval() labels = [i.to(torch_device) for i in self.prepare_inputs(self.model_id)] set_seed(0) model.priors[0].to(torch_device) zs = [torch.zeros(1, 0, dtype=torch.long).to(torch_device) for _ in range(3)] zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False) torch.testing.assert_close(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2)) model.priors[0].cpu() set_seed(0) model.priors[1].to(torch_device) zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False) torch.testing.assert_close(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1)) model.priors[1].cpu() set_seed(0) model.priors[2].to(torch_device) zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False) torch.testing.assert_close(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0)) @slow @require_torch_accelerator @require_torch_fp16 def test_fp16_slow_sampling(self): prior_id = "ArthurZ/jukebox_prior_0" model = JukeboxPrior.from_pretrained(prior_id, min_duration=0).eval().half().to(torch_device) labels = self.prepare_inputs(prior_id)[0].to(torch_device) metadata = model.get_metadata(labels, 0, 7680, 0) set_seed(0) outputs = model.sample(1, metadata=metadata, sample_tokens=60) self.assertIn(outputs[0].cpu().tolist(), [self.EXPECTED_GPU_OUTPUTS_2, self.EXPECTED_GPU_OUTPUTS_2_PT_2])
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/owlvit/test_modeling_owlvit.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 OwlViT model. """ import inspect import os import tempfile import unittest import numpy as np import requests from transformers import OwlViTConfig, OwlViTTextConfig, OwlViTVisionConfig from transformers.testing_utils import ( require_torch, require_torch_accelerator, require_torch_fp16, require_vision, slow, torch_device, ) from transformers.utils import 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, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import OwlViTForObjectDetection, OwlViTModel, OwlViTTextModel, OwlViTVisionModel if is_vision_available(): from PIL import Image from transformers import OwlViTProcessor class OwlViTVisionModelTester: def __init__( self, parent, batch_size=12, image_size=32, patch_size=2, num_channels=3, is_training=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent 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.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, 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]) config = self.get_config() return config, pixel_values def get_config(self): return OwlViTVisionConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = OwlViTVisionModel(config=config).to(torch_device) model.eval() pixel_values = pixel_values.to(torch.float32) with torch.no_grad(): 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)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class OwlViTVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as OWLVIT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (OwlViTVisionModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = OwlViTVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=OwlViTVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="OWLVIT does not use inputs_embeds") def test_inputs_embeds(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) @unittest.skip(reason="OWL-ViT does not support training yet") def test_training(self): pass @unittest.skip(reason="OWL-ViT does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "google/owlvit-base-patch32" model = OwlViTVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class OwlViTTextModelTester: def __init__( self, parent, batch_size=12, num_queries=4, seq_length=16, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=64, num_hidden_layers=12, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=16, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.num_queries = num_queries self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask 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.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size * self.num_queries, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size * self.num_queries, self.seq_length]) if input_mask is not None: num_text, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(num_text,)) for idx, start_index in enumerate(rnd_start_indices): input_mask[idx, :start_index] = 1 input_mask[idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return OwlViTTextConfig( 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, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = OwlViTTextModel(config=config).to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids=input_ids, attention_mask=input_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size * self.num_queries, self.seq_length, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_queries, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class OwlViTTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (OwlViTTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = OwlViTTextModelTester(self) self.config_tester = ConfigTester(self, config_class=OwlViTTextConfig, 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) @unittest.skip(reason="OWL-ViT does not support training yet") def test_training(self): pass @unittest.skip(reason="OWL-ViT does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="OWLVIT does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): model_name = "google/owlvit-base-patch32" model = OwlViTTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class OwlViTModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = OwlViTTextModelTester(parent, **text_kwargs) self.vision_model_tester = OwlViTVisionModelTester(parent, **vision_kwargs) self.is_training = is_training self.text_config = self.text_model_tester.get_config().to_dict() self.vision_config = self.vision_model_tester.get_config().to_dict() self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = OwlViTModel(config).to(torch_device).eval() with torch.no_grad(): result = model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, ) image_logits_size = ( self.vision_model_tester.batch_size, self.text_model_tester.batch_size * self.text_model_tester.num_queries, ) text_logits_size = ( self.text_model_tester.batch_size * self.text_model_tester.num_queries, self.vision_model_tester.batch_size, ) self.parent.assertEqual(result.logits_per_image.shape, image_logits_size) self.parent.assertEqual(result.logits_per_text.shape, text_logits_size) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, "return_loss": False, } return config, inputs_dict @require_torch class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (OwlViTModel,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": OwlViTModel, "zero-shot-object-detection": OwlViTForObjectDetection, } if is_torch_available() else {} ) fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = OwlViTModelTester(self) 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(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="OwlViTModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `logit_scale` parameter initilization is different for OWLVIT 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(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: 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 _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init).to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # OWLVIT needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") loaded_model = loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save OwlViTConfig and check if we can load OwlViTVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = OwlViTVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save OwlViTConfig and check if we can load OwlViTTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = OwlViTTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): model_name = "google/owlvit-base-patch32" model = OwlViTModel.from_pretrained(model_name) self.assertIsNotNone(model) class OwlViTForObjectDetectionTester: def __init__(self, parent, is_training=True): self.parent = parent self.text_model_tester = OwlViTTextModelTester(parent) self.vision_model_tester = OwlViTVisionModelTester(parent) self.is_training = is_training self.text_config = self.text_model_tester.get_config().to_dict() self.vision_config = self.vision_model_tester.get_config().to_dict() self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, pixel_values, input_ids, attention_mask def get_config(self): return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64) def create_and_check_model(self, config, pixel_values, input_ids, attention_mask): model = OwlViTForObjectDetection(config).to(torch_device).eval() with torch.no_grad(): result = model( pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask, return_dict=True, ) pred_boxes_size = ( self.vision_model_tester.batch_size, (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2, 4, ) pred_logits_size = ( self.vision_model_tester.batch_size, (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2, 4, ) pred_class_embeds_size = ( self.vision_model_tester.batch_size, (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2, self.text_model_tester.hidden_size, ) self.parent.assertEqual(result.pred_boxes.shape, pred_boxes_size) self.parent.assertEqual(result.logits.shape, pred_logits_size) self.parent.assertEqual(result.class_embeds.shape, pred_class_embeds_size) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, input_ids, attention_mask = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class OwlViTForObjectDetectionTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (OwlViTForObjectDetection,) if is_torch_available() else () fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = OwlViTForObjectDetectionTester(self) 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(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="OwlViTModel does not have input/output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="Test_initialization is tested in individual model tests") def test_initialization(self): pass @unittest.skip(reason="Test_forward_signature is tested in individual model tests") def test_forward_signature(self): pass @unittest.skip(reason="Test_save_load_fast_init_from_base is tested in individual model tests") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="OWL-ViT does not support training yet") def test_training(self): pass @unittest.skip(reason="OWL-ViT does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init).to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # OWLVIT needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") loaded_model = loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) @slow def test_model_from_pretrained(self): model_name = "google/owlvit-base-patch32" model = OwlViTForObjectDetection.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" im = Image.open(requests.get(url, stream=True).raw) return im @require_vision @require_torch class OwlViTModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "google/owlvit-base-patch32" model = OwlViTModel.from_pretrained(model_name).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=[["a photo of a cat", "a photo of a dog"]], images=image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[3.4613, 0.9403]], device=torch_device) self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3)) @slow def test_inference_object_detection(self): model_name = "google/owlvit-base-patch32" model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=[["a photo of a cat", "a photo of a dog"]], images=image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = model(**inputs) num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4))) expected_slice_boxes = torch.tensor( [[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4)) @slow def test_inference_one_shot_object_detection(self): model_name = "google/owlvit-base-patch32" model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() query_image = prepare_img() inputs = processor( images=image, query_images=query_image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = model.image_guided_detection(**inputs) num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4))) expected_slice_boxes = torch.tensor( [[0.0691, 0.0445, 0.1373], [0.1592, 0.0456, 0.3192], [0.1632, 0.0423, 0.2478]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.target_pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4)) @slow @require_torch_accelerator @require_torch_fp16 def test_inference_one_shot_object_detection_fp16(self): model_name = "google/owlvit-base-patch32" model = OwlViTForObjectDetection.from_pretrained(model_name, torch_dtype=torch.float16).to(torch_device) processor = OwlViTProcessor.from_pretrained(model_name) image = prepare_img() query_image = prepare_img() inputs = processor( images=image, query_images=query_image, max_length=16, padding="max_length", return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = model.image_guided_detection(**inputs) # No need to check the logits, we just check inference runs fine. num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2) self.assertEqual(outputs.target_pred_boxes.shape, torch.Size((1, num_queries, 4)))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/owlvit/test_processor_owlvit.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 import numpy as np import pytest from transformers import CLIPTokenizer, CLIPTokenizerFast from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import OwlViTImageProcessor, OwlViTProcessor @require_vision class OwlViTProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""] 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)) image_processor_map = { "do_resize": True, "size": 20, "do_center_crop": True, "crop_size": 18, "do_normalize": True, "image_mean": [0.48145466, 0.4578275, 0.40821073], "image_std": [0.26862954, 0.26130258, 0.27577711], } self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME) with open(self.image_processor_file, "w", encoding="utf-8") as fp: json.dump(image_processor_map, fp) def get_tokenizer(self, **kwargs): return CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="!", **kwargs) def get_rust_tokenizer(self, **kwargs): return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="!", **kwargs) def get_image_processor(self, **kwargs): return OwlViTImageProcessor.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): tokenizer_slow = self.get_tokenizer() tokenizer_fast = self.get_rust_tokenizer() image_processor = self.get_image_processor() processor_slow = OwlViTProcessor(tokenizer=tokenizer_slow, image_processor=image_processor) processor_slow.save_pretrained(self.tmpdirname) processor_slow = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=False) processor_fast = OwlViTProcessor(tokenizer=tokenizer_fast, image_processor=image_processor) processor_fast.save_pretrained(self.tmpdirname) processor_fast = OwlViTProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab()) self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab()) self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab()) self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer) self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast) self.assertEqual(processor_slow.image_processor.to_json_string(), image_processor.to_json_string()) self.assertEqual(processor_fast.image_processor.to_json_string(), image_processor.to_json_string()) self.assertIsInstance(processor_slow.image_processor, OwlViTImageProcessor) self.assertIsInstance(processor_fast.image_processor, OwlViTImageProcessor) def test_save_load_pretrained_additional_features(self): processor = OwlViTProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor()) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") image_processor_add_kwargs = self.get_image_processor(do_normalize=False) processor = OwlViTProcessor.from_pretrained( self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", pad_token="!", do_normalize=False ) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab()) self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, OwlViTImageProcessor) def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() input_image_proc = image_processor(image_input, return_tensors="np") input_processor = processor(images=image_input, return_tensors="np") for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum(), input_processor[key].sum(), delta=1e-2) def test_tokenizer(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" encoded_processor = processor(text=input_str, return_tensors="np") encoded_tok = tokenizer(input_str, return_tensors="np") for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist()) def test_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_with_text_list(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_text = ["cat", "nasa badge"] inputs = processor(text=input_text) seq_length = 16 self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (2, seq_length)) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_with_nested_text_list(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_texts = [["cat", "nasa badge"], ["person"]] inputs = processor(text=input_texts) seq_length = 16 batch_size = len(input_texts) num_max_text_queries = max([len(texts) for texts in input_texts]) self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (batch_size * num_max_text_queries, seq_length)) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_processor_case(self): model_name = "google/owlvit-base-patch32" processor = OwlViTProcessor.from_pretrained(model_name) input_texts = ["cat", "nasa badge"] inputs = processor(text=input_texts) seq_length = 16 input_ids = inputs["input_ids"] predicted_ids = [ [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ] self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"]) self.assertEqual(inputs["input_ids"].shape, (2, seq_length)) self.assertListEqual(list(input_ids[0]), predicted_ids[0]) self.assertListEqual(list(input_ids[1]), predicted_ids[1]) def test_processor_case2(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() query_input = self.prepare_image_inputs() inputs = processor(images=image_input, query_images=query_input) self.assertListEqual(list(inputs.keys()), ["query_pixel_values", "pixel_values"]) # test if it raises when no input is passed with pytest.raises(ValueError): processor() def test_tokenizer_decode(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = OwlViTProcessor(tokenizer=tokenizer, image_processor=image_processor) 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)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/owlvit/test_image_processing_owlvit.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 unittest from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from transformers import OwlViTImageProcessor class OwlViTImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_center_crop=True, crop_size=None, do_normalize=True, image_mean=[0.48145466, 0.4578275, 0.40821073], image_std=[0.26862954, 0.26130258, 0.27577711], do_convert_rgb=True, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size if size is not None else {"height": 18, "width": 18} self.do_center_crop = do_center_crop self.crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18} self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std self.do_convert_rgb = do_convert_rgb def prepare_image_processor_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, "do_convert_rgb": self.do_convert_rgb, } def expected_output_image_shape(self, images): return self.num_channels, self.crop_size["height"], self.crop_size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class OwlViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = OwlViTImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = OwlViTImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_center_crop")) self.assertTrue(hasattr(image_processing, "center_crop")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_convert_rgb")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 18, "width": 18}) self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/dinov2/test_modeling_dinov2.py
# coding=utf-8 # Copyright 2023 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 Dinov2 model. """ import unittest from transformers import Dinov2Config from transformers.testing_utils import ( is_flaky, require_torch, require_vision, slow, torch_device, ) from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import Dinov2Backbone, Dinov2ForImageClassification, Dinov2Model if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class Dinov2ModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, 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, scope=None, ): self.parent = parent 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 # in Dinov2, 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 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 Dinov2Config( 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, ) def create_and_check_model(self, config, pixel_values, labels): model = Dinov2Model(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_backbone(self, config, pixel_values, labels): model = Dinov2Backbone(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)) expected_size = self.image_size // config.patch_size self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], expected_size, expected_size] ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) # verify backbone works with out_features=None config.out_features = None model = Dinov2Backbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, model.channels[0], expected_size, expected_size] ) # verify channels self.parent.assertEqual(len(model.channels), 1) # verify backbone works with apply_layernorm=False and reshape_hidden_states=False config.apply_layernorm = False config.reshape_hidden_states = False model = Dinov2Backbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.seq_length, self.hidden_size] ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = Dinov2ForImageClassification(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)) # test greyscale images config.num_channels = 1 model = Dinov2ForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) 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 Dinov2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Dinov2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( Dinov2Model, Dinov2ForImageClassification, Dinov2Backbone, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"image-feature-extraction": Dinov2Model, "image-classification": Dinov2ForImageClassification} if is_torch_available() else {} ) fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = Dinov2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Dinov2Config, has_text_modality=False, hidden_size=37) @is_flaky(max_attempts=3, description="`torch.nn.init.trunc_normal_` is flaky.") def test_initialization(self): super().test_initialization() def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Dinov2 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(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_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_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) @unittest.skip(reason="Dinov2 does not support feedforward chunking yet") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model_name = "facebook/dinov2-base" model = Dinov2Model.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 Dinov2ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("facebook/dinov2-base") if is_vision_available() else None @slow def test_inference_no_head(self): model = Dinov2Model.from_pretrained("facebook/dinov2-base").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the last hidden states expected_shape = torch.Size((1, 257, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-2.1747, -0.4729, 1.0936], [-3.2780, -0.8269, -0.9210], [-2.9129, 1.1284, -0.7306]], device=torch_device, ) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4)) @require_torch class Dinov2BackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (Dinov2Backbone,) if is_torch_available() else () config_class = Dinov2Config has_attentions = False def setUp(self): self.model_tester = Dinov2ModelTester(self)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/cpm/test_tokenization_cpm.py
# coding=utf-8 # Copyright 2018 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.models.cpm.tokenization_cpm import CpmTokenizer from transformers.testing_utils import custom_tokenizers from ..xlnet.test_modeling_xlnet import XLNetModelTest @custom_tokenizers class CpmTokenizationTest(XLNetModelTest): # There is no `CpmModel` def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True def test_pre_tokenization(self): tokenizer = CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate") text = "Hugging Face大法好,谁用谁知道。" normalized_text = "Hugging Face大法好,谁用谁知道。<unk>" bpe_tokens = "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split() tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, bpe_tokens) input_tokens = tokens + [tokenizer.unk_token] input_bpe_tokens = [13789, 13283, 1421, 8, 10, 1164, 13608, 16528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) reconstructed_text = tokenizer.decode(input_bpe_tokens) self.assertEqual(reconstructed_text, normalized_text)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/speech_to_text_2/test_tokenization_speech_to_text_2.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. import inspect import json import os import tempfile import unittest from transformers.models.speech_to_text_2 import Speech2Text2Tokenizer from transformers.models.speech_to_text_2.tokenization_speech_to_text_2 import VOCAB_FILES_NAMES from ...test_tokenization_common import TokenizerTesterMixin class SpeechToTextTokenizerTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "facebook/s2t-wav2vec2-large-en-de" tokenizer_class = Speech2Text2Tokenizer test_rust_tokenizer = False def setUp(self): super().setUp() vocab = "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" ") merges = ["he re</w> 123", "here a 1456"] vocab_tokens = dict(zip(vocab, range(len(vocab)))) self.special_tokens_map = {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"} self.tmpdirname = tempfile.mkdtemp() 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") as fp: fp.write("\n".join(merges)) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "<s>") self.assertEqual(vocab_keys[1], "<pad>") self.assertEqual(vocab_keys[-1], "vocab") self.assertEqual(len(vocab_keys), 14) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 14) def test_tokenizer_decode(self): tokenizer = Speech2Text2Tokenizer.from_pretrained(self.tmpdirname) # make sure @@ is correctly concatenated token_ids = [4, 6, 8, 7, 10] # ["here@@", "couple", "words", "of@@", "the"] output_string = tokenizer.decode(token_ids) self.assertTrue(output_string == "herecouple words ofthe") def test_load_no_merges_file(self): tokenizer = Speech2Text2Tokenizer.from_pretrained(self.tmpdirname) with tempfile.TemporaryDirectory() as tmp_dirname: tokenizer.save_pretrained(tmp_dirname) os.remove(os.path.join(tmp_dirname, "merges.txt")) # load tokenizer without merges file should not throw an error tokenizer = Speech2Text2Tokenizer.from_pretrained(tmp_dirname) with tempfile.TemporaryDirectory() as tmp_dirname: # save tokenizer and load again tokenizer.save_pretrained(tmp_dirname) tokenizer = Speech2Text2Tokenizer.from_pretrained(tmp_dirname) self.assertIsNotNone(tokenizer) # overwrite since merges_file is optional def test_tokenizer_slow_store_full_signature(self): if not self.test_slow_tokenizer: return signature = inspect.signature(self.tokenizer_class.__init__) tokenizer = self.get_tokenizer() for parameter_name, parameter in signature.parameters.items(): if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file": self.assertIn(parameter_name, tokenizer.init_kwargs)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/speech_to_text_2/test_modeling_speech_to_text_2.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 PyTorch Speech2Text model. """ import unittest from transformers import Speech2Text2Config from transformers.testing_utils import is_torch_available, require_torch, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers.models.speech_to_text_2.modeling_speech_to_text_2 import ( Speech2Text2Decoder, Speech2Text2ForCausalLM, ) @require_torch class Speech2Text2StandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, d_model=16, decoder_seq_length=7, is_training=True, is_decoder=True, use_attention_mask=True, use_cache=False, use_labels=True, decoder_start_token_id=2, decoder_ffn_dim=32, decoder_layers=2, decoder_attention_heads=4, max_position_embeddings=30, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent = parent self.batch_size = batch_size self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.d_model = d_model self.hidden_size = d_model self.num_hidden_layers = decoder_layers self.decoder_layers = decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.decoder_attention_heads = decoder_attention_heads self.num_attention_heads = decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.max_position_embeddings = max_position_embeddings self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = Speech2Text2Config( vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, decoder_attention_heads=self.decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, max_position_embeddings=self.max_position_embeddings, ) return ( config, input_ids, attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = Speech2Text2Decoder(config=config).to(torch_device).eval() input_ids = input_ids[:2] input_ids[input_ids == 0] += 1 # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((2, 1), config.vocab_size - 1) + 1 # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) print(next_input_ids) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, 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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class Speech2Text2StandaloneDecoderModelTest( ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase ): all_model_classes = (Speech2Text2Decoder, Speech2Text2ForCausalLM) if is_torch_available() else () all_generative_model_classes = (Speech2Text2ForCausalLM,) if is_torch_available() else () pipeline_model_mapping = {"text-generation": Speech2Text2ForCausalLM} if is_torch_available() else {} fx_compatible = True test_pruning = False def setUp( self, ): self.model_tester = Speech2Text2StandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=Speech2Text2Config) # not implemented currently def test_inputs_embeds(self): pass # speech2text2 has no base model def test_save_load_fast_init_from_base(self): pass # speech2text2 has no base model def test_save_load_fast_init_to_base(self): pass def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) # decoder cannot keep gradients def test_retain_grad_hidden_states_attentions(self): return
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/cohere/test_tokenization_cohere.py
# coding=utf-8 # 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 unittest from transformers import CohereTokenizerFast from transformers.testing_utils import require_jinja, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class CohereTokenizationTest(TokenizerTesterMixin, unittest.TestCase): slow_tokenizer_class = None rust_tokenizer_class = CohereTokenizerFast tokenizer_class = CohereTokenizerFast test_rust_tokenizer = True test_slow_tokenizer = False from_pretrained_vocab_key = "tokenizer_file" from_pretrained_id = "CohereForAI/c4ai-command-r-v01" special_tokens_map = { "bos_token": "<BOS_TOKEN>", "eos_token": "<|END_OF_TURN_TOKEN|>", "unk_token": "<UNK>", "pad_token": "<PAD>", } def setUp(self): super().setUp() tokenizer = CohereTokenizerFast.from_pretrained("CohereForAI/c4ai-command-r-v01") tokenizer.save_pretrained(self.tmpdirname) def get_rust_tokenizer(self, **kwargs): kwargs.update(self.special_tokens_map) return CohereTokenizerFast.from_pretrained(self.tmpdirname, **kwargs) @unittest.skip("This needs a slow tokenizer. Cohere does not have one!") def test_encode_decode_with_spaces(self): return def test_encodings_from_sample_data(self): """ Assert that the created tokens are the same than the hard-coded ones """ tokenizer = self.get_rust_tokenizer() INPUT_SENTENCES = ["The quick brown fox<|END_OF_TURN_TOKEN|>", "jumps over the lazy dog<|END_OF_TURN_TOKEN|>"] TARGET_TOKENS = [[5, 2162, 6629, 19883, 73388, 255001], [5, 81, 25092, 2515, 1690, 46189, 9507, 255001]] computed_tokens = tokenizer.batch_encode_plus(INPUT_SENTENCES)["input_ids"] self.assertListEqual(TARGET_TOKENS, computed_tokens) INPUT_SENTENCES_W_BOS = [ "<BOS_TOKEN>The quick brown fox<|END_OF_TURN_TOKEN|>", "<BOS_TOKEN>jumps over the lazy dog<|END_OF_TURN_TOKEN|>", ] decoded_tokens = tokenizer.batch_decode(computed_tokens) self.assertListEqual(decoded_tokens, INPUT_SENTENCES_W_BOS) def test_padding(self, max_length=10): 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) # tokenizer_r.pad_token = None # Hotfixing padding = None # 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 try: tokenizer_r.encode(s, max_length=max_length) tokenizer_r.encode_plus(s, max_length=max_length) tokenizer_r.batch_encode_plus(s2, max_length=max_length) tokenizer_r.encode(p, max_length=max_length) tokenizer_r.batch_encode_plus(p2, max_length=max_length) except ValueError: self.fail("Cohere Tokenizer should be able to deal with padding") tokenizer_r.pad_token = None # Hotfixing padding = None 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_pretrained_model_lists(self): # No `max_model_input_sizes` for Cohere model self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map), 1) self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0]), 1) @require_jinja def test_tokenization_for_chat(self): tokenizer = self.get_rust_tokenizer() test_chats = [ [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}], [ {"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Nice to meet you."}, ], ] tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats] expected_tokens = [ [5, 255000, 255008, 5659, 1955, 1671, 19264, 171597, 21, 255001, 255000, 255006, 28339, 8, 255001], [ 5, 255000, 255008, 5659, 1955, 1671, 19264, 171597, 21, 255001, 255000, 255006, 28339, 8, 255001, 255000, 255007, 97190, 1726, 5694, 1933, 21, 255001, ], ] for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens): self.assertListEqual(tokenized_chat, expected_tokens) @require_jinja def test_tokenization_for_tool_use(self): tokenizer = self.get_rust_tokenizer() conversation = [{"role": "user", "content": "Whats the biggest penguin in the world?"}] tools = [ { "name": "internet_search", "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet", "parameter_definitions": { "query": {"description": "Query to search the internet with", "type": "str", "required": True} }, }, { "name": "directly_answer", "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history", "parameter_definitions": {}, }, ] tool_use_prompt = tokenizer.apply_tool_use_template( conversation, tools=tools, tokenize=False, add_generation_prompt=True, ) expected_prompt = '''<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling. ## Available Tools Here is a list of tools that you have available to you: ```python def internet_search(query: str) -> List[Dict]: """Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query (str): Query to search the internet with """ pass ``` ```python def directly_answer() -> List[Dict]: """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass ```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example: ```json [ { "tool_name": title of the tool in the specification, "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters } ]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>''' self.assertEqual(tool_use_prompt, expected_prompt) @require_jinja def test_tokenization_for_grounded_generation(self): tokenizer = self.get_rust_tokenizer() conversation = [{"role": "user", "content": "Whats the biggest penguin in the world?"}] documents = [ {"title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height."}, {"title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."}, ] grounded_generation_prompt = tokenizer.apply_grounded_generation_template( conversation, documents=documents, citation_mode="accurate", # or "fast" tokenize=False, add_generation_prompt=True, ) expected_prompt = """<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results> Document: 0 title: Tall penguins text: Emperor penguins are the tallest growing up to 122 cm in height. Document: 1 title: Penguin habitats text: Emperor penguins only live in Antarctica. </results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line. Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'. Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'. Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup. Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>""" self.assertEqual(grounded_generation_prompt, expected_prompt) def test_add_prefix_space_fast(self): tokenizer_w_prefix = self.get_rust_tokenizer(add_prefix_space=True) tokenizer_wo_prefix = self.get_rust_tokenizer(add_prefix_space=False) tokens_w_prefix = tokenizer_w_prefix.tokenize("Hey") tokens_wo_prefix = tokenizer_wo_prefix.tokenize("Hey") self.assertNotEqual(tokens_w_prefix, tokens_wo_prefix)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/cohere/test_modeling_cohere.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 Cohere model. """ import unittest from parameterized import parameterized from transformers import CohereConfig, is_torch_available from transformers.testing_utils import ( require_bitsandbytes, require_torch, require_torch_multi_gpu, require_torch_sdpa, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, CohereForCausalLM, CohereModel # Copied from transformers.tests.models.llama.LlamaModelTester with Llama->Cohere class CohereModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, 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, pad_token_id=0, 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.pad_token_id = pad_token_id 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 = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device) 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 # Ignore copy def get_config(self): return CohereConfig( 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, pad_token_id=self.pad_token_id, eos_token_id=self.pad_token_id, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = CohereModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 = CohereModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, 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 = CohereForCausalLM(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_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 = CohereForCausalLM(config=config) model.to(torch_device) model.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 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, "attention_mask": input_mask} return config, inputs_dict @require_torch class CohereModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (CohereModel, CohereForCausalLM) if is_torch_available() else () all_generative_model_classes = (CohereForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": CohereModel, "text-generation": CohereForCausalLM, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False fx_compatible = True # Need to use `0.8` instead of `0.9` for `test_cpu_offload` # This is because we are hitting edge cases with the causal_mask buffer model_split_percents = [0.5, 0.7, 0.8] def setUp(self): self.model_tester = CohereModelTester(self) self.config_tester = ConfigTester(self, config_class=CohereConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip("TODO @gante fix this for Cohere") @parameterized.expand([(1, False), (1, True), (4, False)]) def test_new_cache_format(self, num_beams, do_sample): pass 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) @require_bitsandbytes @require_torch_sdpa @require_torch_multi_gpu @slow def test_eager_matches_sdpa_generate(self): """ Overwritting the common test as the test is flaky on tiny models """ max_new_tokens = 30 model_id = "CohereForAI/c4ai-command-r-v01-4bit" tokenizer = AutoTokenizer.from_pretrained(model_id) model_sdpa = CohereForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto" ) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") model_eager = CohereForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, attn_implementation="eager", device_map="auto" ) self.assertTrue(model_eager.config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: raise ValueError("The eager model should not have SDPA attention layers") has_sdpa = False for name, submodule in model_sdpa.named_modules(): if "SdpaAttention" in submodule.__class__.__name__: has_sdpa = True break if not has_sdpa: raise ValueError("The SDPA model should have SDPA attention layers") texts = [ "hi here's a longer context, getting longer and", "Hello this is a very long sentence my friend, very long for real", "Today I am in Paris and", ] for padding_side in ["left", "right"]: tokenizer.padding_side = padding_side tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device) res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) with self.subTest(f"{padding_side}"): torch.testing.assert_close( res_eager, res_sdpa, msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}", ) @require_torch @slow class CohereIntegrationTest(unittest.TestCase): @require_torch_multi_gpu @require_bitsandbytes def test_batched_4bit(self): model_id = "CohereForAI/c4ai-command-r-v01-4bit" EXPECTED_TEXT = [ 'Hello today I am going to show you how to make a simple and easy card using the new stamp set called "Hello" from the Occasions catalog. This set is so versatile and can be used for many occasions. I used the new In', "Hi there, here we are again with another great collection of free fonts. This time we have gathered 10 free fonts that you can download and use in your designs. These fonts are free for personal and commercial use. So", ] model = CohereForCausalLM.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token = tokenizer.eos_token text = ["Hello today I am going to show you how to", "Hi there, here we are"] inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=40, do_sample=False) self.assertEqual(tokenizer.batch_decode(output, skip_special_tokens=True), EXPECTED_TEXT) @require_torch_sdpa def test_batched_small_model_logits(self): # Since the model is very large, we created a random cohere model so that we can do a simple # logits check on it. model_id = "hf-internal-testing/cohere-random" EXPECTED_LOGITS = torch.Tensor( [ [[0.0000, 0.1866, -0.1997], [0.0000, -0.0736, 0.1785], [0.0000, -0.1965, -0.0569]], [[0.0000, -0.0302, 0.1488], [0.0000, -0.0402, 0.1351], [0.0000, -0.0341, 0.1116]], ] ).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) model = CohereForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) tokenizer.pad_token = tokenizer.eos_token text = ["Hello today I am going to show you how to", "Hi there, here we are"] inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device) with torch.no_grad(): output = model(**inputs) logits = output.logits self.assertTrue(torch.allclose(EXPECTED_LOGITS, logits[:, :3, :3], rtol=1e-3, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/visual_bert/test_modeling_visual_bert.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 PyTorch VisualBERT model. """ import copy import unittest from transformers import VisualBertConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( VisualBertForMultipleChoice, VisualBertForPreTraining, VisualBertForQuestionAnswering, VisualBertForRegionToPhraseAlignment, VisualBertForVisualReasoning, VisualBertModel, ) class VisualBertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, visual_seq_length=5, is_training=True, use_attention_mask=True, use_visual_attention_mask=True, use_token_type_ids=True, use_visual_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, 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, visual_embedding_dim=20, 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.visual_seq_length = visual_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_visual_attention_mask = use_visual_attention_mask self.use_token_type_ids = use_token_type_ids self.use_visual_token_type_ids = use_visual_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.visual_embedding_dim = visual_embedding_dim 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 get_config(self): return VisualBertConfig( 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, visual_embedding_dim=self.visual_embedding_dim, num_labels=self.num_labels, is_decoder=False, initializer_range=self.initializer_range, ) def prepare_config_and_inputs_for_common(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) visual_embeds = floats_tensor([self.batch_size, self.visual_seq_length, self.visual_embedding_dim]) attention_mask = None if self.use_attention_mask: attention_mask = torch.ones((self.batch_size, self.seq_length), dtype=torch.long, device=torch_device) visual_attention_mask = None if self.use_visual_attention_mask: visual_attention_mask = torch.ones( (self.batch_size, self.visual_seq_length), dtype=torch.long, device=torch_device ) 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) visual_token_type_ids = None if self.use_visual_token_type_ids: visual_token_type_ids = ids_tensor([self.batch_size, self.visual_seq_length], self.type_vocab_size) config = self.get_config() return config, { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask, "visual_embeds": visual_embeds, "visual_token_type_ids": visual_token_type_ids, "visual_attention_mask": visual_attention_mask, } def prepare_config_and_inputs_for_pretraining(self): masked_lm_labels = None sentence_image_labels = None if self.use_labels: masked_lm_labels = ids_tensor([self.batch_size, self.seq_length + self.visual_seq_length], self.vocab_size) sentence_image_labels = ids_tensor( [self.batch_size], self.type_sequence_label_size, ) config, input_dict = self.prepare_config_and_inputs_for_common() input_dict.update({"labels": masked_lm_labels, "sentence_image_labels": sentence_image_labels}) return config, input_dict def prepare_config_and_inputs_for_multiple_choice(self): input_ids = ids_tensor([self.batch_size, self.num_choices, self.seq_length], self.vocab_size) visual_embeds = floats_tensor( [self.batch_size, self.num_choices, self.visual_seq_length, self.visual_embedding_dim] ) attention_mask = None if self.use_attention_mask: attention_mask = torch.ones( (self.batch_size, self.num_choices, self.seq_length), dtype=torch.long, device=torch_device ) visual_attention_mask = None if self.use_visual_attention_mask: visual_attention_mask = torch.ones( (self.batch_size, self.num_choices, self.visual_seq_length), dtype=torch.long, device=torch_device ) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.num_choices, self.seq_length], self.type_vocab_size) visual_token_type_ids = None if self.use_visual_token_type_ids: visual_token_type_ids = ids_tensor( [self.batch_size, self.num_choices, self.visual_seq_length], self.type_vocab_size ) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return config, { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask, "visual_embeds": visual_embeds, "visual_token_type_ids": visual_token_type_ids, "visual_attention_mask": visual_attention_mask, "labels": labels, } def prepare_config_and_inputs_for_vqa(self): vqa_labels = None if self.use_labels: vqa_labels = floats_tensor([self.batch_size, self.num_labels]) config, input_dict = self.prepare_config_and_inputs_for_common() input_dict.update({"labels": vqa_labels}) return config, input_dict def prepare_config_and_inputs_for_nlvr(self): nlvr_labels = None if self.use_labels: nlvr_labels = ids_tensor([self.batch_size], self.num_labels) config, input_dict = self.prepare_config_and_inputs_for_common() input_dict.update({"labels": nlvr_labels}) return config, input_dict def prepare_config_and_inputs_for_flickr(self): region_to_phrase_position = torch.cat( ( ids_tensor([self.batch_size, self.seq_length], self.visual_seq_length), torch.ones(self.batch_size, self.visual_seq_length, dtype=torch.long, device=torch_device) * -1, ), dim=-1, ) flickr_labels = None if self.use_labels: flickr_labels = floats_tensor( [self.batch_size, self.seq_length + self.visual_seq_length, self.visual_seq_length] ) config, input_dict = self.prepare_config_and_inputs_for_common() input_dict.update({"region_to_phrase_position": region_to_phrase_position, "labels": flickr_labels}) return config, input_dict def create_and_check_model(self, config, input_dict): model = VisualBertModel(config=config) model.to(torch_device) model.eval() result = model(**input_dict) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.seq_length + self.visual_seq_length, self.hidden_size), ) def create_and_check_for_pretraining(self, config, input_dict): model = VisualBertForPreTraining(config=config) model.to(torch_device) model.eval() result = model(**input_dict) self.parent.assertEqual( result.prediction_logits.shape, (self.batch_size, self.seq_length + self.visual_seq_length, self.vocab_size), ) def create_and_check_for_vqa(self, config, input_dict): model = VisualBertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model(**input_dict) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_multiple_choice(self, config, input_dict): model = VisualBertForMultipleChoice(config=config) model.to(torch_device) model.eval() result = model(**input_dict) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def create_and_check_for_nlvr(self, config, input_dict): model = VisualBertForVisualReasoning(config=config) model.to(torch_device) model.eval() result = model(**input_dict) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_flickr(self, config, input_dict): model = VisualBertForRegionToPhraseAlignment(config=config) model.to(torch_device) model.eval() result = model(**input_dict) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.seq_length + self.visual_seq_length, self.visual_seq_length) ) @require_torch class VisualBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( VisualBertModel, VisualBertForMultipleChoice, VisualBertForVisualReasoning, VisualBertForRegionToPhraseAlignment, VisualBertForQuestionAnswering, VisualBertForPreTraining, ) if is_torch_available() else () ) pipeline_model_mapping = {"feature-extraction": VisualBertModel} if is_torch_available() else {} test_torchscript = False test_pruning = False def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if model_class == VisualBertForMultipleChoice: for key in inputs_dict.keys(): value = inputs_dict[key] if isinstance(value, torch.Tensor) and value.ndim > 1: if key != "visual_embeds": inputs_dict[key] = ( inputs_dict[key].unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() ) else: inputs_dict[key] = ( inputs_dict[key] .unsqueeze(1) .expand(-1, self.model_tester.num_choices, -1, self.model_tester.visual_embedding_dim) .contiguous() ) elif model_class == VisualBertForRegionToPhraseAlignment: total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length batch_size = self.model_tester.batch_size inputs_dict["region_to_phrase_position"] = torch.zeros( (batch_size, total_length), dtype=torch.long, device=torch_device, ) if return_labels: if model_class == VisualBertForMultipleChoice: inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class == VisualBertForPreTraining: total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length batch_size = self.model_tester.batch_size inputs_dict["labels"] = torch.zeros( (batch_size, total_length), dtype=torch.long, device=torch_device, ) inputs_dict["sentence_image_labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) # Flickr expects float labels elif model_class == VisualBertForRegionToPhraseAlignment: batch_size = self.model_tester.batch_size total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length inputs_dict["labels"] = torch.ones( ( batch_size, total_length, self.model_tester.visual_seq_length, ), dtype=torch.float, device=torch_device, ) # VQA expects float labels elif model_class == VisualBertForQuestionAnswering: inputs_dict["labels"] = torch.ones( (self.model_tester.batch_size, self.model_tester.num_labels), dtype=torch.float, device=torch_device, ) elif model_class == VisualBertForVisualReasoning: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size), dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = VisualBertModelTester(self) self.config_tester = ConfigTester(self, config_class=VisualBertConfig, hidden_size=37) 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) visual_seq_len = getattr(self.model_tester, "visual_seq_length", None) encoder_seq_length = (seq_len if seq_len is not None else 0) + ( visual_seq_len if visual_seq_len is not None else 0 ) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes 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 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) model.to(torch_device) model.eval() with torch.no_grad(): 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) if chunk_length is not None: self.assertListEqual( list(attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types elif self.is_encoder_decoder: added_hidden_states = 2 else: added_hidden_states = 1 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) if chunk_length is not None: self.assertListEqual( list(self_attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) 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_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 if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1: seq_length = seq_length * self.model_tester.chunk_length else: seq_length = self.model_tester.seq_length + self.model_tester.visual_seq_length self.assertListEqual( list(hidden_states[0].shape[-2:]), [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_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common() 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_common() 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_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_pretraining() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) def test_model_for_vqa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_vqa() self.model_tester.create_and_check_for_vqa(*config_and_inputs) def test_model_for_nlvr(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_nlvr() self.model_tester.create_and_check_for_nlvr(*config_and_inputs) def test_model_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_multiple_choice() self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs) def test_model_for_flickr(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_flickr() self.model_tester.create_and_check_for_flickr(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "uclanlp/visualbert-vqa" model = VisualBertModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @require_torch class VisualBertModelIntegrationTest(unittest.TestCase): @slow def test_inference_vqa_coco_pre(self): model = VisualBertForPreTraining.from_pretrained("uclanlp/visualbert-vqa-coco-pre") input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1) token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1) visual_embeds = torch.ones(size=(1, 10, 2048), dtype=torch.float32) * 0.5 visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long) attention_mask = torch.tensor([1] * 6).reshape(1, -1) visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1) with torch.no_grad(): output = model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, ) vocab_size = 30522 expected_shape = torch.Size((1, 16, vocab_size)) self.assertEqual(output.prediction_logits.shape, expected_shape) expected_slice = torch.tensor( [[[-5.1858, -5.1903, -4.9142], [-6.2214, -5.9238, -5.8381], [-6.3027, -5.9939, -5.9297]]] ) self.assertTrue(torch.allclose(output.prediction_logits[:, :3, :3], expected_slice, atol=1e-4)) expected_shape_2 = torch.Size((1, 2)) self.assertEqual(output.seq_relationship_logits.shape, expected_shape_2) expected_slice_2 = torch.tensor([[0.7393, 0.1754]]) self.assertTrue(torch.allclose(output.seq_relationship_logits, expected_slice_2, atol=1e-4)) @slow def test_inference_vqa(self): model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa") input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1) token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1) visual_embeds = torch.ones(size=(1, 10, 2048), dtype=torch.float32) * 0.5 visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long) attention_mask = torch.tensor([1] * 6).reshape(1, -1) visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1) with torch.no_grad(): output = model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, ) # vocab_size = 30522 expected_shape = torch.Size((1, 3129)) self.assertEqual(output.logits.shape, expected_shape) expected_slice = torch.tensor( [[-8.9898, 3.0803, -1.8016, 2.4542, -8.3420, -2.0224, -3.3124, -4.4139, -3.1491, -3.8997]] ) self.assertTrue(torch.allclose(output.logits[:, :10], expected_slice, atol=1e-4)) @slow def test_inference_nlvr(self): model = VisualBertForVisualReasoning.from_pretrained("uclanlp/visualbert-nlvr2") input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1) token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1) visual_embeds = torch.ones(size=(1, 10, 1024), dtype=torch.float32) * 0.5 visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long) attention_mask = torch.tensor([1] * 6).reshape(1, -1) visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1) with torch.no_grad(): output = model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, ) # vocab_size = 30522 expected_shape = torch.Size((1, 2)) self.assertEqual(output.logits.shape, expected_shape) expected_slice = torch.tensor([[-1.1436, 0.8900]]) self.assertTrue(torch.allclose(output.logits, expected_slice, atol=1e-4)) @slow def test_inference_vcr(self): model = VisualBertForMultipleChoice.from_pretrained("uclanlp/visualbert-vcr") input_ids = torch.tensor([[[1, 2, 3, 4, 5, 6] for i in range(4)]], dtype=torch.long) attention_mask = torch.ones_like(input_ids) token_type_ids = torch.ones_like(input_ids) visual_embeds = torch.ones(size=(1, 4, 10, 512), dtype=torch.float32) * 0.5 visual_token_type_ids = torch.ones(size=(1, 4, 10), dtype=torch.long) visual_attention_mask = torch.ones_like(visual_token_type_ids) with torch.no_grad(): output = model( input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, visual_embeds=visual_embeds, visual_attention_mask=visual_attention_mask, visual_token_type_ids=visual_token_type_ids, ) # vocab_size = 30522 expected_shape = torch.Size((1, 4)) self.assertEqual(output.logits.shape, expected_shape) expected_slice = torch.tensor([[-7.7697, -7.7697, -7.7697, -7.7697]]) self.assertTrue(torch.allclose(output.logits, expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/gemma/test_modeling_flax_gemma.py
# Copyright 2024 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 AutoTokenizer, GemmaConfig, is_flax_available from transformers.testing_utils import require_flax, require_read_token, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import jax import jax.numpy as jnp from transformers.models.gemma.modeling_flax_gemma import ( FlaxGemmaForCausalLM, FlaxGemmaModel, ) class FlaxGemmaModelTester: def __init__( self, parent, batch_size=2, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, initializer_range=0.02, ): 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.num_key_value_heads = num_key_value_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.initializer_range = initializer_range self.scope = None self.bos_token_id = vocab_size - 1 self.eos_token_id = vocab_size - 1 self.pad_token_id = vocab_size - 1 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 = np.tril(np.ones((self.batch_size, self.seq_length))) config = GemmaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, head_dim=self.hidden_size // 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, use_cache=True, is_decoder=False, initializer_range=self.initializer_range, ) return config, input_ids, input_mask def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask} return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length) attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4") position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1) ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], attention_mask=attention_mask, past_key_values=outputs_cache.past_key_values, position_ids=position_ids, ) outputs = model(input_ids) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, input_ids, attention_mask): max_decoder_length = 20 model = model_class_name(config) attention_mask_cache = jnp.concatenate( [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))], axis=-1, ) past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length) position_ids = jnp.broadcast_to( jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1) ) outputs_cache = model( input_ids[:, :-1], attention_mask=attention_mask_cache, past_key_values=past_key_values, position_ids=position_ids, ) position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model( input_ids[:, -1:], past_key_values=outputs_cache.past_key_values, attention_mask=attention_mask_cache, position_ids=position_ids, ) outputs = model(input_ids, attention_mask=attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class FlaxGemmaModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase): all_model_classes = (FlaxGemmaModel, FlaxGemmaForCausalLM) if is_flax_available() else () all_generative_model_classes = (FlaxGemmaForCausalLM,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxGemmaModelTester(self) def test_use_cache_forward(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask) def test_use_cache_forward_with_attn_mask(self): for model_class_name in self.all_model_classes: config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() self.model_tester.check_use_cache_forward_with_attn_mask( model_class_name, config, input_ids, attention_mask ) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("google/gemma-2b", from_pt=True) outputs = model(np.ones((1, 1))) self.assertIsNotNone(outputs) @slow @require_flax @require_read_token class FlaxGemmaIntegrationTest(unittest.TestCase): input_text = ["The capital of France is", "To play the perfect cover drive"] model_id = "google/gemma-2b" revision = "flax" def setUp(self): self.model, self.params = FlaxGemmaForCausalLM.from_pretrained( self.model_id, revision=self.revision, _do_init=False ) self.tokenizer = AutoTokenizer.from_pretrained(self.model_id) self.tokenizer.padding_side = "left" def test_logits(self): inputs = self.tokenizer(self.input_text, return_tensors="np", padding=True) # fmt: off EXPECTED_MEAN = [ [-16.427, -21.386, -35.491, -36.258, -31.401, -36.370, -37.598], [-21.386, -32.150, -33.155, -34.344, -34.706, -34.678, -38.495], ] EXPECTED_SLICE = [-33.462, -16.481, -30.837, -32.195, -33.113] # fmt: on logits = self.model(**inputs, params=self.params).logits diff_mean = jnp.abs(logits.mean(-1) - np.array(EXPECTED_MEAN)).max() diff_slice = jnp.abs(logits[0, -1, 475:480] - np.array(EXPECTED_SLICE)).max() self.assertAlmostEqual(diff_mean, 0, places=3) self.assertAlmostEqual(diff_slice, 0, places=3) def test_generation(self): EXPECTED_TEXTS = [ "The capital of France is a city of contrasts. It is a city of history, of art, of culture, of fashion", "To play the perfect cover drive, you need to have a good technique and a good mindset.\n\nThe cover drive is a shot", ] inputs = self.tokenizer(self.input_text, return_tensors="np", padding=True) output = self.model.generate(**inputs, params=self.params, max_new_tokens=20, do_sample=False) output_text = self.tokenizer.batch_decode(output.sequences, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) def test_jit_generation(self): EXPECTED_TEXTS = [ "The capital of France is a city of contrasts. It is a city of history, culture, and art, but it is", "To play the perfect cover drive, you need to have a good technique and a good mindset.\n\nThe cover drive is a shot", ] inputs = self.tokenizer(self.input_text, return_tensors="np", padding=True) def generate(input_ids, attention_mask): outputs = self.model.generate( input_ids, attention_mask=attention_mask, params=self.params, max_new_tokens=20, do_sample=False ) return outputs jit_generate = jax.jit(generate) output_sequences = jit_generate(**inputs).sequences output_text = self.tokenizer.batch_decode(output_sequences, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/gemma/test_modeling_gemma.py
# coding=utf-8 # Copyright 2024 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 Gemma model. """ import tempfile import unittest import pytest from parameterized import parameterized from transformers import AutoModelForCausalLM, AutoTokenizer, GemmaConfig, is_torch_available from transformers.testing_utils import ( is_flaky, require_bitsandbytes, require_flash_attn, require_read_token, require_torch, require_torch_gpu, require_torch_sdpa, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import GemmaForCausalLM, GemmaForSequenceClassification, GemmaModel class GemmaModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, 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, pad_token_id=0, 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.num_key_value_heads = num_key_value_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.pad_token_id = pad_token_id self.scope = scope self.head_dim = self.hidden_size // self.num_attention_heads # Copied from tests.models.mistral.test_modeling_mistral.MistralModelTester.prepare_config_and_inputs 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 = torch.tril(torch.ones(self.batch_size, self.seq_length)).to(torch_device) 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 # Ignore copy def get_config(self): return GemmaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_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, pad_token_id=self.pad_token_id, head_dim=self.head_dim, ) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->Gemma def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = GemmaModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model_as_decoder with Llama->Gemma 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 = GemmaModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_for_causal_lm with Llama->Gemma 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 = GemmaForCausalLM(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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_decoder_model_past_large_inputs with Llama->Gemma 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 = GemmaForCausalLM(config=config) model.to(torch_device) model.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)) # Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common with Llama->Gemma 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, "attention_mask": input_mask} return config, inputs_dict @require_torch class GemmaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (GemmaModel, GemmaForCausalLM, GemmaForSequenceClassification) if is_torch_available() else () all_generative_model_classes = (GemmaForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": GemmaModel, "text-classification": GemmaForSequenceClassification, "text-generation": GemmaForCausalLM, "zero-shot": GemmaForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False # Need to remove 0.9 in `test_cpu_offload` # This is because we are hitting edge cases with the causal_mask buffer model_split_percents = [0.5, 0.6] # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146 def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True def setUp(self): self.model_tester = GemmaModelTester(self) self.config_tester = ConfigTester(self, config_class=GemmaConfig, 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_Gemma_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() print(config) config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = GemmaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Gemma_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = GemmaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) def test_Gemma_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = GemmaForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @unittest.skip("TODO @gante fix this for Llama") @parameterized.expand([(1, False), (1, True), (4, False)]) def test_new_cache_format(self, num_beams, do_sample): pass @unittest.skip("Gemma buffers include complex numbers, which breaks this test") def test_save_load_fast_init_from_base(self): pass @unittest.skip("Gemma uses GQA on all models so the KV cache is a non standard format") def test_past_key_values_format(self): pass @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_use_cache(self): import torch max_new_tokens = 30 for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) # NOTE: Gemma apparently does not support right padding + use_cache with FA2. dummy_attention_mask[:, -1] = 1 model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) # Just test that a large cache works as expected _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False, use_cache=True, ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest("Gemma flash attention does not support right padding") @require_torch_sdpa @require_torch_gpu @slow def test_sdpa_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_sdpa: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_sdpa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="sdpa" ) model_sdpa.to(torch_device) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager") model.to(torch_device) dummy_input = inputs_dict[model_class.main_input_name] dummy_input = dummy_input.to(torch_device) outputs = model(dummy_input, output_hidden_states=True) outputs_sdpa = model_sdpa(dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_sdpa = outputs_sdpa.hidden_states[-1] # gemma sdpa needs a high tolerance assert torch.allclose(logits_sdpa, logits, atol=3e-3) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @is_flaky @slow def test_flash_attn_2_equivalence(self): for model_class in self.all_model_classes: if not model_class._supports_flash_attn_2: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="eager") model.to(torch_device) dummy_input = inputs_dict[model_class.main_input_name] dummy_input = dummy_input.to(torch_device) outputs = model(dummy_input, output_hidden_states=True) outputs_fa = model_fa(dummy_input, output_hidden_states=True) logits = outputs.hidden_states[-1] logits_fa = outputs_fa.hidden_states[-1] # gemma flash attention 2 needs a high tolerance assert torch.allclose(logits_fa, logits, atol=3e-3) @slow @require_torch_gpu class GemmaIntegrationTest(unittest.TestCase): input_text = ["Hello I am doing", "Hi today"] # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) # Depending on the hardware we get different logits / generations cuda_compute_capability_major_version = None @classmethod def setUpClass(cls): if is_torch_available() and torch.cuda.is_available(): # 8 is for A100 / A10 and 7 for T4 cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] @require_read_token def test_model_2b_fp32(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_read_token def test_model_2b_fp16(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_read_token def test_model_2b_fp16_static_cache(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) model.generation_config.cache_implementation = "static" tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_read_token def test_model_2b_bf16(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = { 7: [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi", ], 8: [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ], } model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) @require_read_token def test_model_2b_eager(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = { 7: [ "Hello I am doing a project on the 1990s and I am looking for some information on the ", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ], 8: [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ], } model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="eager" ) model.to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) @require_torch_sdpa @require_read_token def test_model_2b_sdpa(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = { 7: [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Khichdi", ], 8: [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ], } model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="sdpa" ) model.to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) @pytest.mark.flash_attn_test @require_flash_attn @require_read_token def test_model_2b_flash_attn(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project on the 1990s and I need to know what the most popular music", "Hi today I am going to share with you a very easy and simple recipe of <strong><em>Kaju Kat", ] model = AutoModelForCausalLM.from_pretrained( model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model.to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_bitsandbytes @require_read_token def test_model_2b_4bit(self): model_id = "google/gemma-2b" EXPECTED_TEXTS = [ "Hello I am doing a project and I need to make a 3d model of a house. I have been using", "Hi today I'd like to share with you my experience with the new wattpad wattpad wattpad wattpad wattpad wattpad wattpad", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @unittest.skip("The test will not fit our CI runners") @require_read_token def test_model_7b_fp32(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ "Hello my name is ***** ***** I will be assisting you today. I am sorry to hear about your issue. I will", "Hi,\n\nI have a problem with my 2005 1.6 16", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(torch_device) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_read_token def test_model_7b_fp16(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ """Hello I am doing a project on a 1999 4.0L 4x4. I""", "Hi today I am going to show you how to make a simple and easy to make a DIY 3D", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_read_token def test_model_7b_bf16(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = { 7: [ """Hello I am doing a project on a 1991 240sx and I am trying to find""", "Hi today I am going to show you how to make a very simple and easy to make a very simple and", ], 8: [ "Hello I am doing a project for my school and I am trying to make a program that will read a .txt file", "Hi today I am going to show you how to make a very simple and easy to make a very simple and", ], } model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16).to( torch_device ) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) @require_read_token def test_model_7b_fp16_static_cache(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = [ """Hello I am doing a project on a 1999 4.0L 4x4. I""", "Hi today I am going to show you how to make a simple and easy to make a DIY 3D", ] model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, torch_dtype=torch.float16).to( torch_device ) model.generation_config.cache_implementation = "static" tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS) @require_bitsandbytes @require_read_token def test_model_7b_4bit(self): model_id = "google/gemma-7b" EXPECTED_TEXTS = { 7: [ "Hello I am doing a project for my school and I am trying to make a program that will take a number and then", """Hi today I am going to talk about the new update for the game called "The new update" and I""", ], 8: [ "Hello I am doing a project for my school and I am trying to make a program that will take a number and then", "Hi today I am going to talk about the best way to get rid of acne. miniaturing is a very", ], } model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained(model_id) inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device) output = model.generate(**inputs, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output, skip_special_tokens=True) self.assertEqual(output_text, EXPECTED_TEXTS[self.cuda_compute_capability_major_version])
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/gemma/test_tokenization_gemma.py
# coding=utf-8 # Copyright 2024 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 datasets import load_dataset from transformers import ( AddedToken, GemmaTokenizer, GemmaTokenizerFast, is_torch_available, ) from transformers.convert_slow_tokenizer import convert_slow_tokenizer from transformers.testing_utils import ( get_tests_dir, nested_simplify, require_jinja, require_read_token, require_sentencepiece, require_tokenizers, require_torch, slow, ) from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") if is_torch_available(): pass @require_sentencepiece @require_tokenizers class GemmaTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "google/gemma-7b" tokenizer_class = GemmaTokenizer rust_tokenizer_class = GemmaTokenizerFast test_rust_tokenizer = False test_sentencepiece = True from_pretrained_kwargs = {} def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True) tokenizer.pad_token = tokenizer.eos_token tokenizer.save_pretrained(self.tmpdirname) @require_torch def test_batch_tokenization(self): if not self.test_seq2seq: return tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Longer text that will definitely require truncation. text = [ " UN Chief Says There Is No Military Solution in Syria", " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for" " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons" " will only worsen the violence and misery for millions of people.", ] try: batch = tokenizer( text=text, max_length=3, max_target_length=10, return_tensors="pt", ) except NotImplementedError: return self.assertEqual(batch.input_ids.shape[1], 3) # max_target_length will default to max_length if not specified batch = tokenizer(text, max_length=3, return_tensors="pt") self.assertEqual(batch.input_ids.shape[1], 3) batch_encoder_only = tokenizer(text=text, max_length=3, max_target_length=10, return_tensors="pt") self.assertEqual(batch_encoder_only.input_ids.shape[1], 3) self.assertEqual(batch_encoder_only.attention_mask.shape[1], 3) self.assertNotIn("decoder_input_ids", batch_encoder_only) @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece.") def test_save_slow_from_fast_and_reload_fast(self): pass def test_special_tokens_initialization(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): added_tokens = [AddedToken("<special>", lstrip=True)] tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) r_output = tokenizer_r.encode("Hey this is a <special> token") special_token_id = tokenizer_r.encode("<special>", add_special_tokens=False)[0] self.assertTrue(special_token_id in r_output) if self.test_slow_tokenizer: tokenizer_cr = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs, # , from_slow=True <- unfortunately too slow to convert ) tokenizer_p = self.tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) p_output = tokenizer_p.encode("Hey this is a <special> token") cr_output = tokenizer_cr.encode("Hey this is a <special> token") self.assertEqual(p_output, r_output) self.assertEqual(cr_output, r_output) self.assertTrue(special_token_id in p_output) self.assertTrue(special_token_id in cr_output) @slow @require_read_token def test_tokenizer_integration(self): expected_encoding = {'input_ids': [[2, 158434, 591, 84193, 3836, 685, 6599, 31223, 235290, 140247, 578, 6599, 31223, 235290, 145139, 235290, 3491, 235275, 6572, 3311, 235290, 38197, 109959, 591, 25894, 235269, 162174, 235290, 235284, 235269, 1791, 6362, 12481, 235269, 1576, 18622, 235269, 2900, 1136, 86684, 235269, 29092, 4632, 16994, 604, 13146, 14944, 40371, 591, 19700, 235327, 235275, 578, 13146, 14944, 25511, 591, 235300, 12474, 235275, 675, 1163, 235248, 235304, 235284, 235340, 229903, 5377, 575, 235248, 235274, 235276, 235276, 235340, 17044, 578, 5271, 1061, 118345, 1865, 125247, 235269, 8745, 111226, 578, 176888, 235265], [2, 25894, 603, 6869, 577, 953, 235290, 8297, 5271, 209099, 41642, 774, 748, 78253, 2793, 731, 51506, 34346, 611, 2145, 2731, 578, 1833, 4807, 575, 832, 16630, 235265], [2, 651, 4320, 8426, 25341, 36271, 1163, 573, 27894, 5929, 235265]], '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, 1]]} # fmt: skip self.tokenizer_integration_test_util( expected_encoding=expected_encoding, model_name="google/gemma-2b", revision="", padding=False, ) @unittest.skip("worker 'gw4' crashed on CI, passing locally.") def test_pickle_subword_regularization_tokenizer(self): pass @unittest.skip("worker 'gw4' crashed on CI, passing locally.") def test_subword_regularization_tokenizer(self): pass @unittest.skip("Skipping") def test_torch_encode_plus_sent_to_model(self): pass @require_torch @require_sentencepiece @require_tokenizers class GemmaIntegrationTest(unittest.TestCase): @classmethod def setUpClass(cls): checkpoint_name = "hf-internal-testing/dummy-gemma" cls.tokenizer: GemmaTokenizer = GemmaTokenizer.from_pretrained( checkpoint_name, eos_token="<s>" ) # add this token cls.rust_tokenizer = GemmaTokenizerFast.from_pretrained( checkpoint_name, eos_token="<s>", from_slow=True ) # add this token return cls @require_torch def integration_tests(self): inputs = self.tokenizer( ["The following string should be properly encoded: Hello.", "But ird and ปี ird ด"], return_tensors="pt", ) self.assertEqual( nested_simplify(inputs), { "input_ids": [ [2, 450, 1494, 1347, 881, 367, 6284, 18511, 29901, 15043, 29889], [2, 1205, 29871, 1823, 322, 29871, 31010, 30691, 1678, 1823, 1678, 30718], ], "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]], }, ) def test_fast_special_tokens(self): slow_tokenizer = self.tokenizer fast_tokenizer = self.rust_tokenizer slow = slow_tokenizer.encode("A sample test", add_special_tokens=True) assert slow == [2, 235280, 6453, 2121] fast_tokenizer.add_eos_token = False fast = fast_tokenizer.encode("A sample test", add_special_tokens=True) assert fast == [2, 235280, 6453, 2121] fast_tokenizer.add_eos_token = True fast = fast_tokenizer.encode("A sample test", add_special_tokens=True) assert fast == [2, 235280, 6453, 2121, 204] slow_tokenizer.add_eos_token = True slow = slow_tokenizer.encode("A sample test", add_special_tokens=True) assert slow == [2, 235280, 6453, 2121, 204] self.tokenizer.add_eos_token = False self.rust_tokenizer.add_eos_token = False @unittest.skip("Not super important and always failing. Let's skip it") @slow def test_conversion(self): # This is excruciatingly slow since it has to recreate the entire merge # list from the original vocabulary in spm self.rust_tokenizer.save_pretrained("./out") with tempfile.TemporaryDirectory() as dirname: self.rust_tokenizer.save_pretrained(dirname) with open(os.path.join(dirname, "tokenizer.json"), "r") as f: old_serialized = f.read() new_tokenizer = convert_slow_tokenizer(self.tokenizer) with tempfile.NamedTemporaryFile() as f: new_tokenizer.save(f.name) # Re-opening since `f` is in bytes. new_serialized = open(f.name, "r").read() with open("out_tokenizer.json", "w") as g: g.write(new_serialized) self.assertEqual(old_serialized, new_serialized) def test_simple_encode_decode(self): pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer self.tokenizer.add_eos_token = False self.rust_tokenizer.add_eos_token = False self.assertEqual(pyth_tokenizer.encode("This is a test"), [2, 1596, 603, 476, 2121]) self.assertEqual(rust_tokenizer.encode("This is a test"), [2, 1596, 603, 476, 2121]) self.assertEqual(pyth_tokenizer.decode([2, 1596, 603, 476, 2121], skip_special_tokens=True), "This is a test") self.assertEqual(rust_tokenizer.decode([2, 1596, 603, 476, 2121], skip_special_tokens=True), "This is a test") # bytefallback showcase self.assertEqual(pyth_tokenizer.encode("生活的真谛是"), [2, 122182, 235710, 245467, 235427] ) # fmt: skip self.assertEqual(rust_tokenizer.encode("生活的真谛是"), [2, 122182, 235710, 245467, 235427] ) # fmt: skip self.assertEqual( pyth_tokenizer.decode([2, 122182, 235710, 245467, 235427], skip_special_tokens=True), "生活的真谛是", ) self.assertEqual( rust_tokenizer.decode([2, 122182, 235710, 245467, 235427], skip_special_tokens=True), "生活的真谛是", ) # Inner spaces showcase self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [2, 2151, 139, 4521]) self.assertEqual(rust_tokenizer.encode("Hi Hello"), [2, 2151, 139, 4521]) self.assertEqual(pyth_tokenizer.decode([2, 2151, 139, 4521], skip_special_tokens=True), "Hi Hello") self.assertEqual(rust_tokenizer.decode([2, 2151, 139, 4521], skip_special_tokens=True), "Hi Hello") self.assertEqual(pyth_tokenizer.encode("Hi Hello"), [2, 2151, 140, 4521]) self.assertEqual(rust_tokenizer.encode("Hi Hello"), [2, 2151, 140, 4521]) self.assertEqual(pyth_tokenizer.decode([2, 2151, 140, 4521], skip_special_tokens=True), "Hi Hello") self.assertEqual(rust_tokenizer.decode([2, 2151, 140, 4521], skip_special_tokens=True), "Hi Hello") self.assertEqual(pyth_tokenizer.encode(""), [2]) self.assertEqual(rust_tokenizer.encode(""), [2]) self.assertEqual(pyth_tokenizer.encode(" "), [2, 235248]) self.assertEqual(rust_tokenizer.encode(" "), [2, 235248]) self.assertEqual(pyth_tokenizer.encode(" "), [2, 139]) self.assertEqual(rust_tokenizer.encode(" "), [2, 139]) self.assertEqual(pyth_tokenizer.encode(" Hello"), [2, 25957]) self.assertEqual(rust_tokenizer.encode(" Hello"), [2, 25957]) def test_no_differences_decode(self): self.tokenizer.add_eos_token = False self.rust_tokenizer.add_eos_token = False pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer self.assertEqual(pyth_tokenizer.decode([869]), "og") self.assertEqual(rust_tokenizer.decode([869]), "og") self.assertEqual(pyth_tokenizer.decode([30112, 869]), " expenditureog") self.assertEqual(rust_tokenizer.decode([30112, 869]), " expenditureog") def test_no_differences_special_tokens(self): pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer self.assertEqual(pyth_tokenizer.encode(""), [2]) self.assertEqual(rust_tokenizer.encode(""), [2]) self.assertEqual(pyth_tokenizer.encode("<s>"), [2, 204]) self.assertEqual(rust_tokenizer.encode("<s>"), [2, 204]) @unittest.skipIf( os.getenv("RUN_TOKENIZER_INTEGRATION", "0") == "0", "RUN_TOKENIZER_INTEGRATION=1 to run tokenizer integration tests", ) def test_integration_test_xnli(self): import tqdm pyth_tokenizer = self.tokenizer rust_tokenizer = self.rust_tokenizer dataset = load_dataset("code_x_glue_ct_code_to_text", "go") for item in tqdm.tqdm(dataset["validation"]): string = item["code"] encoded1 = pyth_tokenizer.encode(string) encoded2 = rust_tokenizer.encode(string) self.assertEqual( encoded1, encoded2, msg="Hint: the following tokenization diff were obtained for slow vs fast:\n " f"elements in slow: {set(pyth_tokenizer.tokenize(string))-set(rust_tokenizer.tokenize(string))} \nvs\n " f"elements in fast: {set(rust_tokenizer.tokenize(string))-set(pyth_tokenizer.tokenize(string))} \n\n{string}", ) decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True) decoded2 = rust_tokenizer.decode(encoded1, skip_special_tokens=True) self.assertEqual(decoded1, decoded2) dataset = load_dataset("xnli", "all_languages") for item in tqdm.tqdm(dataset["train"]): for string in item["premise"].values(): encoded1 = pyth_tokenizer.encode(string) encoded2 = rust_tokenizer.encode(string) self.assertEqual(encoded1, encoded2, msg=f"failed on {string}") decoded1 = pyth_tokenizer.decode(encoded1, skip_special_tokens=True) decoded2 = rust_tokenizer.decode(encoded2, skip_special_tokens=True) self.assertEqual(decoded1, decoded2) def test_special_token_special_word(self): # the word inform should be split as ['in', 'form'] tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") tokenizer.add_tokens([AddedToken("<REPR_END>", rstrip=True, lstrip=True)], special_tokens=False) out1 = tokenizer.decode( tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=False ) self.assertEqual(out1, "<REPR_END>inform") out2 = tokenizer.decode( tokenizer.encode("<REPR_END>inform", add_special_tokens=False), spaces_between_special_tokens=True ) # decoding strips the added prefix space. self.assertEqual(out2, "<REPR_END> inform") input_ids = tokenizer.encode("<REPR_END>inform", add_special_tokens=False) self.assertEqual(input_ids, [256000, 43910]) out2 = tokenizer.decode( tokenizer.encode(" <REPR_END> inform", add_special_tokens=False), spaces_between_special_tokens=False ) # TODO @ArthurZ currently we strip left and right, so this will not keep the spaces self.assertEqual(out2, "<REPR_END>inform") ### Let's make sure decoding does not add extra spaces here and there # TODO @ArthurZ this should be affected by the lstrip/rstrip/single word /normalize refactoring # Since currently we always strip left and right of the token, results are as such input_ids = tokenizer.encode("<s> Hello<s>how", add_special_tokens=False) self.assertEqual(input_ids, [204, 25957, 204, 1139]) tokens = tokenizer.tokenize("<s> Hello<s>how", add_special_tokens=False) self.assertEqual(tokens, ["<s>", "▁Hello", "<s>", "how"]) decoded_tokens = tokenizer.decode(input_ids) self.assertEqual(decoded_tokens, "<s> Hello<s>how") # Let's make sure that if there are any spaces, we don't remove them! input_ids = tokenizer.encode(" <s> Hello<s> how", add_special_tokens=False) self.assertEqual(input_ids, [235248, 204, 25957, 204, 1368]) tokens = tokenizer.tokenize(" <s> Hello<s> how", add_special_tokens=False) self.assertEqual(tokens, ["▁", "<s>", "▁Hello", "<s>", "▁how"]) decoded_tokens = tokenizer.decode(input_ids) self.assertEqual(decoded_tokens, " <s> Hello<s> how") def test_some_edge_cases(self): tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") sp_tokens = tokenizer.sp_model.encode("<s>>", out_type=str) self.assertEqual(sp_tokens, ["<s>", ">"]) tokens = tokenizer.tokenize("<s>>") self.assertEqual(sp_tokens, tokens) self.assertEqual(tokens, ["<s>", ">"]) tokens = tokenizer.tokenize("") self.assertEqual(tokens, []) self.assertEqual(tokens, tokenizer.sp_model.encode("", out_type=str)) tokens = tokenizer.tokenize(" ") self.assertEqual(tokens, ["▁"]) # a dummy prefix space is not added by the sp_model as it was de-activated self.assertEqual(tokens, tokenizer.sp_model.encode(" ", out_type=str)) tokens = tokenizer.tokenize("▁") self.assertEqual(tokens, ["▁"]) # a dummy prefix space is not added by the sp_model as it was de-activated self.assertEqual(tokens, tokenizer.sp_model.encode("▁", out_type=str)) tokens = tokenizer.tokenize(" ▁") self.assertEqual(tokens, ["▁▁"]) # a dummy prefix space is not added by the sp_model as it was de-activated self.assertEqual(tokens, tokenizer.sp_model.encode("▁▁", out_type=str)) @require_jinja def test_tokenization_for_chat(self): tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") test_chats = [ [{"role": "user", "content": "Hello!"}], [ {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Nice to meet you."}, ], [{"role": "user", "content": "Hello!"}], ] # Matt: The third test case tests the default system message, but if this is ever changed in the # class/repo code then that test will fail, and the case will need to be updated. tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats] expected_tokens = [[235322, 235371, 571, 235298, 2997, 73786, 1645, 108, 4521, 149907, 235371, 571, 235298, 615, 73786, 108], [235322, 235371, 571, 235298, 2997, 73786, 1645, 108, 4521, 149907, 235371, 571, 235298, 615, 73786, 108, 235322, 235371, 571, 235298, 2997, 73786, 105776, 108, 7731, 577, 4664, 692, 35606, 235371, 571, 235298, 615, 73786, 108], [235322, 235371, 571, 235298, 2997, 73786, 1645, 108, 4521, 149907, 235371, 571, 235298, 615, 73786, 108]] # fmt: skip for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens): self.assertListEqual(tokenized_chat, expected_tokens) @require_sentencepiece @require_tokenizers class CommonSpmIntegrationTests(unittest.TestCase): """ A class that regroups important test to make sure that we properly handle the special tokens. """ def test_edge_case_tabulation(self): fast_tokenizer = GemmaTokenizerFast.from_pretrained("hf-internal-testing/dummy-gemma") slow_tokenizer = GemmaTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") input_text = "Hey<eos>. \t\t \n\nyou é @#😈 🤗! , 1234 15 5,61" EXPECTED_IDS = [ 2, 6750, 1, 235265, 235248, 255969, 235248, 109, 4747, 139, 235335, 139, 216311, 241316, 139, 239880, 235341, 144, 235269, 235248, 235274, 235284, 235304, 235310, 235248, 235274, 235308, 235248, 235308, 235269, 235318, 235274] # fmt: skip EXPECTED_TOKENS = [ "Hey", "<eos>", ".", "▁", "\t\t", "▁", "\n\n", "you", "▁▁", "é", "▁▁", "@#", "😈", "▁▁", "🤗", "!", "▁▁▁▁▁▁▁", ",", "▁", "1", "2", "3", "4", "▁", "1", "5", "▁", "5", ",", "6", "1"] # fmt: skip tokens = fast_tokenizer.tokenize(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(tokens, EXPECTED_TOKENS) tokens = slow_tokenizer.tokenize(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(tokens, EXPECTED_TOKENS) input_ids = fast_tokenizer.encode(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(input_ids, EXPECTED_IDS) input_ids = slow_tokenizer.encode(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(input_ids, EXPECTED_IDS) text = fast_tokenizer.decode(EXPECTED_IDS) with self.subTest("test fast edge case fast"): self.assertEqual(text, "<bos>Hey<eos>. \t\t \n\nyou é @#😈 🤗! , 1234 15 5,61") text = slow_tokenizer.decode(EXPECTED_IDS) with self.subTest("test fast edge case fast"): self.assertEqual(text, "<bos>Hey<eos>. \t\t \n\nyou é @#😈 🤗! , 1234 15 5,61") input_text = "\t\t\t\t \n\n61" EXPECTED_IDS = [2, 255971, 235248, 109, 235318, 235274] EXPECTED_TOKENS = ["\t\t\t\t", "▁", "\n\n", "6", "1"] tokens = fast_tokenizer.tokenize(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(tokens, EXPECTED_TOKENS) tokens = slow_tokenizer.tokenize(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(tokens, EXPECTED_TOKENS) input_ids = fast_tokenizer.encode(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(input_ids, EXPECTED_IDS) input_ids = slow_tokenizer.encode(input_text) with self.subTest("test fast edge case fast"): self.assertEqual(input_ids, EXPECTED_IDS) text = fast_tokenizer.decode(EXPECTED_IDS) with self.subTest("test fast edge case fast"): self.assertEqual(text, "<bos>\t\t\t\t \n\n61") text = slow_tokenizer.decode(EXPECTED_IDS) with self.subTest("test fast edge case fast"): self.assertEqual(text, "<bos>\t\t\t\t \n\n61")
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/univnet/test_feature_extraction_univnet.py
# Copyright 2023 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 itertools import os import random import tempfile import unittest import numpy as np from datasets import Audio, load_dataset from transformers import UnivNetFeatureExtractor from transformers.testing_utils import check_json_file_has_correct_format, require_torch, slow from transformers.utils.import_utils import is_torch_available from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin if is_torch_available(): import torch global_rng = random.Random() # Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list 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 class UnivNetFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, min_seq_length=400, max_seq_length=2000, feature_size=1, sampling_rate=24000, padding_value=0.0, do_normalize=True, num_mel_bins=100, hop_length=256, win_length=1024, win_function="hann_window", filter_length=1024, max_length_s=10, fmin=0.0, fmax=12000, mel_floor=1e-9, center=False, compression_factor=1.0, compression_clip_val=1e-5, normalize_min=-11.512925148010254, normalize_max=2.3143386840820312, model_in_channels=64, pad_end_length=10, ): 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.sampling_rate = sampling_rate self.padding_value = padding_value self.do_normalize = do_normalize self.num_mel_bins = num_mel_bins self.hop_length = hop_length self.win_length = win_length self.win_function = win_function self.filter_length = filter_length self.max_length_s = max_length_s self.fmin = fmin self.fmax = fmax self.mel_floor = mel_floor self.center = center self.compression_factor = compression_factor self.compression_clip_val = compression_clip_val self.normalize_min = normalize_min self.normalize_max = normalize_max self.model_in_channels = model_in_channels self.pad_end_length = pad_end_length def prepare_feat_extract_dict(self): return { "feature_size": self.feature_size, "sampling_rate": self.sampling_rate, "padding_value": self.padding_value, "do_normalize": self.do_normalize, "num_mel_bins": self.num_mel_bins, "hop_length": self.hop_length, "win_length": self.win_length, "win_function": self.win_function, "filter_length": self.filter_length, "max_length_s": self.max_length_s, "fmin": self.fmin, "fmax": self.fmax, "mel_floor": self.mel_floor, "center": self.center, "compression_factor": self.compression_factor, "compression_clip_val": self.compression_clip_val, "normalize_min": self.normalize_min, "normalize_max": self.normalize_max, "model_in_channels": self.model_in_channels, "pad_end_length": self.pad_end_length, } 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.batch_size, self.max_seq_length)) else: # make sure that inputs increase in size speech_inputs = [ _flatten(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 class UnivNetFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase): feature_extraction_class = UnivNetFeatureExtractor def setUp(self): self.feat_extract_tester = UnivNetFeatureExtractionTester(self) # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_feat_extract_from_and_save_pretrained def test_feat_extract_from_and_save_pretrained(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: saved_file = feat_extract_first.save_pretrained(tmpdirname)[0] check_json_file_has_correct_format(saved_file) feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() mel_1 = feat_extract_first.mel_filters mel_2 = feat_extract_second.mel_filters self.assertTrue(np.allclose(mel_1, mel_2)) self.assertEqual(dict_first, dict_second) # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_feat_extract_to_json_file def test_feat_extract_to_json_file(self): feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict) with tempfile.TemporaryDirectory() as tmpdirname: json_file_path = os.path.join(tmpdirname, "feat_extract.json") feat_extract_first.to_json_file(json_file_path) feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path) dict_first = feat_extract_first.to_dict() dict_second = feat_extract_second.to_dict() mel_1 = feat_extract_first.mel_filters mel_2 = feat_extract_second.mel_filters self.assertTrue(np.allclose(mel_1, mel_2)) self.assertEqual(dict_first, dict_second) 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 1200 speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] # Test feature size input_features = feature_extractor( np_speech_inputs, padding="max_length", max_length=1600, return_tensors="np" ).input_features self.assertTrue(input_features.ndim == 3) # Note: for some reason I get a weird padding error when feature_size > 1 # self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size) # Note: we use the shape convention (batch_size, seq_len, num_mel_bins) self.assertTrue(input_features.shape[-1] == feature_extractor.num_mel_bins) # 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)) # Test 2-D numpy arrays are batched. speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)] np_speech_inputs = np.asarray(speech_inputs) 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)) # Test truncation required speech_inputs = [ floats_list((1, x))[0] for x in range((feature_extractor.num_max_samples - 100), (feature_extractor.num_max_samples + 500), 200) ] np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs] speech_inputs_truncated = [x[: feature_extractor.num_max_samples] for x in speech_inputs] np_speech_inputs_truncated = [np.asarray(speech_input) for speech_input in speech_inputs_truncated] encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor(np_speech_inputs_truncated, 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_batched_unbatched_consistency(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = floats_list((1, 800))[0] np_speech_inputs = np.asarray(speech_inputs) # Test unbatched vs batched list encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor([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)) # Test np.ndarray vs List[np.ndarray] encoded_sequences_1 = feature_extractor(np_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)) # Test unbatched np.ndarray vs batched np.ndarray encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features encoded_sequences_2 = feature_extractor( np.expand_dims(np_speech_inputs, axis=0), 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_generate_noise(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] features = feature_extractor(speech_inputs, return_noise=True) input_features = features.input_features noise_features = features.noise_sequence for spectrogram, noise in zip(input_features, noise_features): self.assertEqual(spectrogram.shape[0], noise.shape[0]) def test_pad_end(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] input_features1 = feature_extractor(speech_inputs, padding=False, pad_end=False).input_features input_features2 = feature_extractor(speech_inputs, padding=False, pad_end=True).input_features for spectrogram1, spectrogram2 in zip(input_features1, input_features2): self.assertEqual(spectrogram1.shape[0] + self.feat_extract_tester.pad_end_length, spectrogram2.shape[0]) def test_generate_noise_and_pad_end(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)] features = feature_extractor(speech_inputs, padding=False, return_noise=True, pad_end=True) input_features = features.input_features noise_features = features.noise_sequence for spectrogram, noise in zip(input_features, noise_features): self.assertEqual(spectrogram.shape[0], noise.shape[0]) @require_torch def test_batch_decode(self): import torch feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) input_lengths = list(range(800, 1400, 200)) pad_samples = feature_extractor.pad_end_length * feature_extractor.hop_length output_features = { "waveforms": torch.tensor(floats_list((3, max(input_lengths) + pad_samples))), "waveform_lengths": torch.tensor(input_lengths), } waveforms = feature_extractor.batch_decode(**output_features) for input_length, waveform in zip(input_lengths, waveforms): self.assertTrue(len(waveform.shape) == 1, msg="Individual output waveforms should be 1D") self.assertEqual(waveform.shape[0], input_length) @require_torch # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad 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 _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") ds = ds.cast_column("audio", Audio(sampling_rate=self.feat_extract_tester.sampling_rate)) # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples], [x["sampling_rate"] for x in speech_samples] @slow @require_torch def test_integration(self): # fmt: off EXPECTED_INPUT_FEATURES = torch.tensor( [ -5.0229, -6.1358, -5.8346, -5.4447, -5.6707, -5.8577, -5.0464, -5.0058, -5.6015, -5.6410, -5.4325, -5.6116, -5.3700, -5.7956, -5.3196, -5.3274, -5.9655, -5.6057, -5.8382, -5.9602, -5.9005, -5.9123, -5.7669, -6.1441, -5.5168, -5.1405, -5.3927, -6.0032, -5.5784, -5.3728 ], ) # fmt: on input_speech, sr = self._load_datasamples(1) feature_extractor = UnivNetFeatureExtractor() input_features = feature_extractor(input_speech, sampling_rate=sr[0], return_tensors="pt").input_features self.assertEqual(input_features.shape, (1, 548, 100)) input_features_mean = torch.mean(input_features) input_features_stddev = torch.std(input_features) EXPECTED_MEAN = torch.tensor(-6.18862009) EXPECTED_STDDEV = torch.tensor(2.80845642) torch.testing.assert_close(input_features_mean, EXPECTED_MEAN, atol=5e-5, rtol=5e-6) torch.testing.assert_close(input_features_stddev, EXPECTED_STDDEV) torch.testing.assert_close(input_features[0, :30, 0], EXPECTED_INPUT_FEATURES, atol=1e-4, rtol=1e-5)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/univnet/test_modeling_univnet.py
# Copyright 2023 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 gc import inspect import random import unittest from datasets import Audio, load_dataset from transformers import UnivNetConfig, UnivNetFeatureExtractor from transformers.testing_utils import ( is_torch_available, require_torch, require_torch_gpu, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, floats_tensor, ) if is_torch_available(): import torch from transformers import UnivNetModel class UnivNetModelTester: def __init__( self, parent, batch_size=2, seq_length=7, in_channels=8, hidden_channels=8, num_mel_bins=20, kernel_predictor_hidden_channels=8, seed=0, is_training=False, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.in_channels = in_channels self.hidden_channels = hidden_channels self.num_mel_bins = num_mel_bins self.kernel_predictor_hidden_channels = kernel_predictor_hidden_channels self.seed = seed self.is_training = is_training def prepare_noise_sequence(self): generator = torch.manual_seed(self.seed) noise_shape = (self.batch_size, self.seq_length, self.in_channels) # Create noise on CPU for reproducibility noise_sequence = torch.randn(noise_shape, generator=generator, dtype=torch.float) return noise_sequence def prepare_config_and_inputs(self): spectrogram = floats_tensor([self.batch_size, self.seq_length, self.num_mel_bins], scale=1.0) noise_sequence = self.prepare_noise_sequence() noise_sequence = noise_sequence.to(spectrogram.device) config = self.get_config() return config, spectrogram, noise_sequence def get_config(self): return UnivNetConfig( model_in_channels=self.in_channels, model_hidden_channels=self.hidden_channels, num_mel_bins=self.num_mel_bins, kernel_predictor_hidden_channels=self.kernel_predictor_hidden_channels, ) def create_and_check_model(self, config, spectrogram, noise_sequence): model = UnivNetModel(config=config).to(torch_device).eval() result = model(spectrogram, noise_sequence)[0] self.parent.assertEqual(result.shape, (self.batch_size, self.seq_length * 256)) def prepare_config_and_inputs_for_common(self): config, spectrogram, noise_sequence = self.prepare_config_and_inputs() inputs_dict = {"input_features": spectrogram, "noise_sequence": noise_sequence} return config, inputs_dict @require_torch class UnivNetModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (UnivNetModel,) if is_torch_available() else () # UnivNetModel currently cannot be traced with torch.jit.trace. test_torchscript = False # The UnivNetModel is not a transformer and does not use any attention mechanisms, so skip transformer/attention # related tests. test_pruning = False test_resize_embeddings = False test_resize_position_embeddings = False test_head_masking = False # UnivNetModel is not a sequence classification model. test_mismatched_shapes = False # UnivNetModel does not have a base_model_prefix attribute. test_missing_keys = False # UnivNetModel does not implement a parallelize method. test_model_parallel = False is_encoder_decoder = False has_attentions = False input_name = "input_features" def setUp(self): self.model_tester = UnivNetModelTester(self) self.config_tester = ConfigTester(self, config_class=UnivNetConfig) @unittest.skip(reason="fix this once it gets more usage") def test_multi_gpu_data_parallel_forward(self): super().test_multi_gpu_data_parallel_forward() def test_config(self): 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_from_and_save_pretrained_subfolder() 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 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_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 = [ "input_features", ] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @unittest.skip(reason="UnivNetModel does not output hidden_states.") def test_hidden_states_output(self): pass @unittest.skip(reason="UnivNetModel.forward does not accept an inputs_embeds argument.") def test_inputs_embeds(self): pass @unittest.skip(reason="UnivNetModel does not use input embeddings and thus has no get_input_embeddings method.") def test_model_common_attributes(self): pass @unittest.skip(reason="UnivNetModel does not support all arguments tested, such as output_hidden_states.") def test_model_outputs_equivalence(self): pass @unittest.skip(reason="UnivNetModel does not output hidden_states.") def test_retain_grad_hidden_states_attentions(self): pass def test_batched_inputs_outputs(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() batched_spectrogram = inputs["input_features"] batched_noise_sequence = inputs["noise_sequence"] with torch.no_grad(): batched_outputs = model( batched_spectrogram.to(torch_device), batched_noise_sequence.to(torch_device), )[0] self.assertEqual( batched_spectrogram.shape[0], batched_outputs.shape[0], msg="Got different batch dims for input and output", ) def test_unbatched_inputs_outputs(self): config, inputs = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model( inputs["input_features"][:1].to(torch_device), inputs["noise_sequence"][:1].to(torch_device) )[0] self.assertTrue(outputs.shape[0] == 1, msg="Unbatched input should create batched output with bsz = 1") @require_torch_gpu @slow class UnivNetModelIntegrationTests(unittest.TestCase): def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def _load_datasamples(self, num_samples, sampling_rate=24000): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") ds = ds.cast_column("audio", Audio(sampling_rate=sampling_rate)) # automatic decoding with librispeech speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"] return [x["array"] for x in speech_samples], [x["sampling_rate"] for x in speech_samples] def get_inputs(self, device, num_samples: int = 3, noise_length: int = 10, seed: int = 0): generator = torch.manual_seed(seed) # Note: hardcode model_in_channels -> 64 if num_samples == 1: noise_sequence_shape = (64, noise_length) else: noise_sequence_shape = (num_samples, 64, noise_length) # Explicity generate noise_sequence on CPU for consistency. noise_sequence = torch.randn(noise_sequence_shape, generator=generator, dtype=torch.float32, device="cpu") # Put noise_sequence on the desired device. noise_sequence = noise_sequence.to(device) # Note: hardcode num_mel_channels -> 100 if num_samples == 1: spectrogram_shape = [100, noise_length] else: spectrogram_shape = [num_samples, 100, noise_length] spectrogram = floats_tensor(spectrogram_shape, scale=1.0, rng=random.Random(seed)) # Note: spectrogram should already be on torch_device # Permute to match diffusers implementation if num_samples == 1: noise_sequence = noise_sequence.transpose(1, 0) spectrogram = spectrogram.transpose(1, 0) else: noise_sequence = noise_sequence.transpose(2, 1) spectrogram = spectrogram.transpose(2, 1) inputs = { "input_features": spectrogram, "noise_sequence": noise_sequence, "generator": generator, } return inputs def test_model_inference_batched(self): # Load sample checkpoint from Tortoise TTS model = UnivNetModel.from_pretrained("dg845/univnet-dev") model.eval().to(torch_device) # Get batched noise and spectrogram inputs. input_speech = self.get_inputs(torch_device, num_samples=3) with torch.no_grad(): waveform = model(**input_speech)[0] waveform = waveform.cpu() waveform_mean = torch.mean(waveform) waveform_stddev = torch.std(waveform) waveform_slice = waveform[-1, -9:].flatten() EXPECTED_MEAN = torch.tensor(-0.19989729) EXPECTED_STDDEV = torch.tensor(0.35230172) EXPECTED_SLICE = torch.tensor([-0.3408, -0.6045, -0.5052, 0.1160, -0.1556, -0.0405, -0.3024, -0.5290, -0.5019]) torch.testing.assert_close(waveform_mean, EXPECTED_MEAN, atol=1e-4, rtol=1e-5) torch.testing.assert_close(waveform_stddev, EXPECTED_STDDEV, atol=1e-4, rtol=1e-5) torch.testing.assert_close(waveform_slice, EXPECTED_SLICE, atol=5e-4, rtol=1e-5) def test_model_inference_unbatched(self): # Load sample checkpoint from Tortoise TTS model = UnivNetModel.from_pretrained("dg845/univnet-dev") model.eval().to(torch_device) # Get unbatched noise and spectrogram inputs. input_speech = self.get_inputs(torch_device, num_samples=1) with torch.no_grad(): waveform = model(**input_speech)[0] waveform = waveform.cpu() waveform_mean = torch.mean(waveform) waveform_stddev = torch.std(waveform) waveform_slice = waveform[-1, -9:].flatten() EXPECTED_MEAN = torch.tensor(-0.22895093) EXPECTED_STDDEV = torch.tensor(0.33986747) EXPECTED_SLICE = torch.tensor([-0.3276, -0.5504, -0.3484, 0.3574, -0.0373, -0.1826, -0.4880, -0.6431, -0.5162]) torch.testing.assert_close(waveform_mean, EXPECTED_MEAN, atol=1e-4, rtol=1e-5) torch.testing.assert_close(waveform_stddev, EXPECTED_STDDEV, atol=1e-4, rtol=1e-5) torch.testing.assert_close(waveform_slice, EXPECTED_SLICE, atol=1e-3, rtol=1e-5) def test_integration(self): feature_extractor = UnivNetFeatureExtractor.from_pretrained("dg845/univnet-dev") model = UnivNetModel.from_pretrained("dg845/univnet-dev") model.eval().to(torch_device) audio, sr = self._load_datasamples(1, sampling_rate=feature_extractor.sampling_rate) input_features = feature_extractor(audio, sampling_rate=sr[0], return_tensors="pt").input_features input_features = input_features.to(device=torch_device) input_speech = self.get_inputs(torch_device, num_samples=1, noise_length=input_features.shape[1]) input_speech["input_features"] = input_features with torch.no_grad(): waveform = model(**input_speech)[0] waveform = waveform.cpu() waveform_mean = torch.mean(waveform) waveform_stddev = torch.std(waveform) waveform_slice = waveform[-1, -9:].flatten() EXPECTED_MEAN = torch.tensor(0.00051374) EXPECTED_STDDEV = torch.tensor(0.058105603) # fmt: off EXPECTED_SLICE = torch.tensor([-4.3934e-04, -1.8203e-04, -3.3033e-04, -3.8716e-04, -1.6125e-04, 3.5389e-06, -3.3149e-04, -3.7613e-04, -2.3331e-04]) # fmt: on torch.testing.assert_close(waveform_mean, EXPECTED_MEAN, atol=5e-6, rtol=1e-5) torch.testing.assert_close(waveform_stddev, EXPECTED_STDDEV, atol=1e-4, rtol=1e-5) torch.testing.assert_close(waveform_slice, EXPECTED_SLICE, atol=5e-6, rtol=1e-5)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/megatron_gpt2/test_modeling_megatron_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 os import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): import torch from transformers import GPT2LMHeadModel @require_torch @require_sentencepiece @require_tokenizers class MegatronGPT2IntegrationTest(unittest.TestCase): @slow @unittest.skip("Model is not available.") def test_inference_no_head(self): directory = "nvidia/megatron-gpt2-345m/" if "MYDIR" in os.environ: directory = os.path.join(os.environ["MYDIR"], directory) model = GPT2LMHeadModel.from_pretrained(directory) model.to(torch_device) model.half() input_ids = torch.tensor( [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]], device=torch_device, dtype=torch.long, ) with torch.no_grad(): output = model(input_ids).logits expected_shape = torch.Size((1, 9, 50257)) self.assertEqual(output.shape, expected_shape) expected_diag = torch.tensor( [ 4.9414, -0.2920, -1.2148, -4.0273, -0.5161, -5.2109, -1.2412, -1.8301, -1.7734, -4.7148, -0.2317, -1.0811, -2.1777, 0.4141, -3.7969, -4.0586, -2.5332, -3.3809, 4.3867, ], device=torch_device, dtype=torch.half, ) for i in range(19): r, c = 8 * i // 17, 2792 * i # along the diagonal computed, expected = output[0, r, c], expected_diag[i] msg = f"row={r} col={c} computed={computed} expected={expected}" self.assertAlmostEqual(computed, expected, delta=1e-4, msg=msg)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/unispeech_sat/test_modeling_unispeech_sat.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 PyTorch UniSpeechSat model. """ import math import unittest import numpy as np import pytest from datasets import load_dataset from transformers import UniSpeechSatConfig, is_torch_available from transformers.testing_utils import require_soundfile, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( UniSpeechSatForAudioFrameClassification, UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, UniSpeechSatModel, Wav2Vec2FeatureExtractor, Wav2Vec2Processor, ) class UniSpeechSatModelTester: def __init__( self, parent, batch_size=13, seq_length=1024, # speech is longer is_training=False, hidden_size=16, feat_extract_norm="group", feat_extract_dropout=0.0, feat_extract_activation="gelu", conv_dim=(32, 32, 32), conv_stride=(4, 4, 4), conv_kernel=(8, 8, 8), conv_bias=False, num_conv_pos_embeddings=16, num_conv_pos_embedding_groups=2, num_hidden_layers=2, num_attention_heads=2, hidden_dropout_prob=0.1, # this is most likely not correctly set yet intermediate_size=20, layer_norm_eps=1e-5, hidden_act="gelu", initializer_range=0.02, mask_time_prob=0.5, mask_time_length=2, vocab_size=32, do_stable_layer_norm=False, tdnn_dim=(32, 32), tdnn_kernel=(3, 3), tdnn_dilation=(1, 1), xvector_output_dim=32, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.hidden_size = hidden_size self.feat_extract_norm = feat_extract_norm self.feat_extract_dropout = feat_extract_dropout self.feat_extract_activation = feat_extract_activation self.conv_dim = conv_dim self.conv_stride = conv_stride self.conv_kernel = conv_kernel self.conv_bias = conv_bias self.num_conv_pos_embeddings = num_conv_pos_embeddings self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.intermediate_size = intermediate_size self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.initializer_range = initializer_range self.vocab_size = vocab_size self.do_stable_layer_norm = do_stable_layer_norm self.mask_time_prob = mask_time_prob self.mask_time_length = mask_time_length self.tdnn_dim = tdnn_dim self.tdnn_kernel = tdnn_kernel self.tdnn_dilation = tdnn_dilation self.xvector_output_dim = xvector_output_dim self.scope = scope output_seq_length = self.seq_length for kernel, stride in zip(self.conv_kernel, self.conv_stride): output_seq_length = (output_seq_length - (kernel - 1)) / stride self.output_seq_length = int(math.ceil(output_seq_length)) self.encoder_seq_length = self.output_seq_length def prepare_config_and_inputs(self): input_values = floats_tensor([self.batch_size, self.seq_length], scale=1.0) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) config = self.get_config() return config, input_values, attention_mask def get_config(self): return UniSpeechSatConfig( hidden_size=self.hidden_size, feat_extract_norm=self.feat_extract_norm, feat_extract_dropout=self.feat_extract_dropout, feat_extract_activation=self.feat_extract_activation, conv_dim=self.conv_dim, conv_stride=self.conv_stride, conv_kernel=self.conv_kernel, conv_bias=self.conv_bias, num_conv_pos_embeddings=self.num_conv_pos_embeddings, num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups, mask_time_prob=self.mask_time_prob, mask_time_length=self.mask_time_length, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, hidden_dropout_prob=self.hidden_dropout_prob, intermediate_size=self.intermediate_size, layer_norm_eps=self.layer_norm_eps, hidden_act=self.hidden_act, initializer_range=self.initializer_range, vocab_size=self.vocab_size, tdnn_dim=self.tdnn_dim, tdnn_kernel=self.tdnn_kernel, tdnn_dilation=self.tdnn_dilation, xvector_output_dim=self.xvector_output_dim, ) def create_and_check_model(self, config, input_values, attention_mask): model = UniSpeechSatModel(config=config) model.to(torch_device) model.eval() result = model(input_values, attention_mask=attention_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size) ) def create_and_check_batch_inference(self, config, input_values, *args): # test does not pass for models making use of `group_norm` # check: https://github.com/pytorch/fairseq/issues/3227 model = UniSpeechSatModel(config=config) model.to(torch_device) model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0.0 batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state for i in range(input_values.shape[0]): input_slice = input_values[i : i + 1, : input_lengths[i]] output = model(input_slice).last_hidden_state batch_output = batch_outputs[i : i + 1, : output.shape[1]] self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3)) def check_ctc_loss(self, config, input_values, *args): model = UniSpeechSatForCTC(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 model.config.ctc_loss_reduction = "sum" sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() model.config.ctc_loss_reduction = "mean" mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() self.parent.assertTrue(isinstance(sum_loss, float)) self.parent.assertTrue(isinstance(mean_loss, float)) def check_seq_classifier_loss(self, config, input_values, *args): model = UniSpeechSatForSequenceClassification(config=config) model.to(torch_device) # make sure that dropout is disabled model.eval() input_values = input_values[:3] attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 attention_mask[i, input_lengths[i] :] = 0 masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item() unmasked_loss = model(input_values, labels=labels).loss.item() self.parent.assertTrue(isinstance(masked_loss, float)) self.parent.assertTrue(isinstance(unmasked_loss, float)) self.parent.assertTrue(masked_loss != unmasked_loss) def check_ctc_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = UniSpeechSatForCTC(config=config) model.to(torch_device) model.train() # freeze feature encoder model.freeze_feature_encoder() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 if max_length_labels[i] < labels.shape[-1]: # it's important that we make sure that target lengths are at least # one shorter than logit lengths to prevent -inf labels[i, max_length_labels[i] - 1 :] = -100 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_seq_classifier_training(self, config, input_values, *args): config.ctc_zero_infinity = True model = UniSpeechSatForSequenceClassification(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_xvector_training(self, config, *args): config.ctc_zero_infinity = True model = UniSpeechSatForXVector(config=config) model.to(torch_device) model.train() # freeze everything but the classification head model.freeze_base_model() # use a longer sequence length to account for TDNN temporal downsampling input_values = floats_tensor([self.batch_size, self.seq_length * 2], scale=1.0) input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label)) # pad input for i in range(len(input_lengths)): input_values[i, input_lengths[i] :] = 0.0 loss = model(input_values, labels=labels).loss self.parent.assertFalse(torch.isinf(loss).item()) loss.backward() def check_labels_out_of_vocab(self, config, input_values, *args): model = UniSpeechSatForCTC(config) model.to(torch_device) model.train() input_values = input_values[:3] input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]] max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths)) labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100) with pytest.raises(ValueError): model(input_values, labels=labels) def prepare_config_and_inputs_for_common(self): config, input_values, attention_mask = self.prepare_config_and_inputs() inputs_dict = {"input_values": input_values, "attention_mask": attention_mask} return config, inputs_dict @require_torch class UniSpeechSatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification, UniSpeechSatForAudioFrameClassification, UniSpeechSatForXVector, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "audio-classification": UniSpeechSatForSequenceClassification, "automatic-speech-recognition": UniSpeechSatForCTC, "feature-extraction": UniSpeechSatModel, } if is_torch_available() else {} ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = UniSpeechSatModelTester(self) self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, 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_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_xvector_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_xvector_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # UniSpeechSat has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # UniSpeechSat cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # UniSpeechSat has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) 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(): uniform_init_parms = [ "conv.weight", "conv.parametrizations.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "feature_projection.projection.weight", "feature_projection.projection.bias", "label_embeddings_concat", "objective.weight", ] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: 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", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus") self.assertIsNotNone(model) @require_torch class UniSpeechSatRobustModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( (UniSpeechSatForCTC, UniSpeechSatForPreTraining, UniSpeechSatModel, UniSpeechSatForSequenceClassification) if is_torch_available() else () ) test_pruning = False test_headmasking = False test_torchscript = False def setUp(self): self.model_tester = UniSpeechSatModelTester( self, conv_stride=(3, 3, 3), feat_extract_norm="layer", do_stable_layer_norm=True ) self.config_tester = ConfigTester(self, config_class=UniSpeechSatConfig, 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_batched_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_batch_inference(*config_and_inputs) def test_ctc_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_loss(*config_and_inputs) def test_seq_classifier_loss_inference(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_loss(*config_and_inputs) def test_ctc_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_ctc_training(*config_and_inputs) def test_seq_classifier_train(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_seq_classifier_training(*config_and_inputs) def test_labels_out_of_vocab(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.check_labels_out_of_vocab(*config_and_inputs) # UniSpeechSat has no inputs_embeds def test_inputs_embeds(self): pass # `input_ids` is renamed to `input_values` def test_forward_signature(self): pass # UniSpeechSat cannot resize token embeddings # since it has no tokens embeddings def test_resize_tokens_embeddings(self): pass # UniSpeechSat has no inputs_embeds # and thus the `get_input_embeddings` fn # is not implemented def test_model_common_attributes(self): pass def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) # set layer drop to 0 model.config.layerdrop = 0.0 input_values = inputs_dict["input_values"] input_lengths = torch.tensor( [input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device ) output_lengths = model._get_feat_extract_output_lengths(input_lengths) labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size) inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"]) inputs_dict["labels"] = labels outputs = model(**inputs_dict) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] attentions = outputs.attentions[0] hidden_states.retain_grad() attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) self.assertIsNotNone(attentions.grad) 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(): uniform_init_parms = [ "conv.weight", "conv.parametrizations.weight", "masked_spec_embed", "codevectors", "quantizer.weight_proj.weight", "project_hid.weight", "project_hid.bias", "project_q.weight", "project_q.bias", "feature_projection.projection.weight", "feature_projection.projection.bias", "label_embeddings_concat", "objective.weight", ] if param.requires_grad: if any(x in name for x in uniform_init_parms): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: 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", ) # overwrite from test_modeling_common def _mock_init_weights(self, module): if hasattr(module, "weight") and module.weight is not None: module.weight.data.fill_(3) if hasattr(module, "weight_g") and module.weight_g is not None: module.weight_g.data.fill_(3) if hasattr(module, "weight_v") and module.weight_v is not None: module.weight_v.data.fill_(3) if hasattr(module, "bias") and module.bias is not None: module.bias.data.fill_(3) if hasattr(module, "codevectors") and module.codevectors is not None: module.codevectors.data.fill_(3) if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: module.masked_spec_embed.data.fill_(3) def test_mask_feature_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_feature_prob=0.2, mask_feature_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_prob_ctc(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_time_length=2 ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [1, 3, 2, 6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (4, 1498, 32)) def test_mask_time_feature_prob_ctc_single_batch(self): model = UniSpeechSatForCTC.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", mask_time_prob=0.2, mask_feature_prob=0.2, mask_time_length=2, mask_feature_length=2, ) model.to(torch_device).train() processor = Wav2Vec2Processor.from_pretrained( "hf-internal-testing/tiny-random-unispeech-sat", return_attention_mask=True ) batch_duration_in_seconds = [6] input_features = [np.random.random(16_000 * s) for s in batch_duration_in_seconds] batch = processor( input_features, padding=True, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt" ) logits = model( input_values=batch["input_values"].to(torch_device), attention_mask=batch["attention_mask"].to(torch_device), ).logits self.assertEqual(logits.shape, (1, 1498, 32)) @unittest.skip(reason="Feed forward chunking is not implemented") def test_feed_forward_chunking(self): pass @slow def test_model_from_pretrained(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large") self.assertIsNotNone(model) @require_torch @require_soundfile @slow class UniSpeechSatModelIntegrationTest(unittest.TestCase): def _load_datasamples(self, num_samples): ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") # automatic decoding with librispeech speech_samples = ds.sort("id").filter( lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)] )[:num_samples]["audio"] return [x["array"] for x in speech_samples] def _load_superb(self, task, num_samples): ds = load_dataset("anton-l/superb_dummy", task, split="test") return ds[:num_samples] def test_inference_encoder_base(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-base-plus") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( "facebook/wav2vec2-base", return_attention_mask=True ) input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), ) # fmt: off expected_hidden_states_slice = torch.tensor( [[[-0.0743, 0.1384], [-0.0845, 0.1704]], [[-0.0954, 0.1936], [-0.1123, 0.2095]]], device=torch_device, ) # fmt: on self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3)) def test_inference_encoder_large(self): model = UniSpeechSatModel.from_pretrained("microsoft/unispeech-sat-large") model.to(torch_device) feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("facebook/wav2vec2-large-xlsr-53") input_speech = self._load_datasamples(2) inputs_dict = feature_extractor(input_speech, return_tensors="pt", padding=True) with torch.no_grad(): outputs = model( inputs_dict.input_values.to(torch_device), attention_mask=inputs_dict.attention_mask.to(torch_device), ) # fmt: off expected_hidden_states_slice = torch.tensor( [[[-0.1172, -0.0797], [-0.0012, 0.0213]], [[-0.1225, -0.1277], [-0.0668, -0.0585]]], device=torch_device, ) # fmt: on self.assertTrue(torch.allclose(outputs.last_hidden_state[:, :2, -2:], expected_hidden_states_slice, atol=1e-3)) def test_inference_diarization(self): model = UniSpeechSatForAudioFrameClassification.from_pretrained("microsoft/unispeech-sat-base-plus-sd").to( torch_device ) processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sd") input_data = self._load_superb("sd", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True, sampling_rate=16_000) input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) with torch.no_grad(): outputs = model(input_values, attention_mask=attention_mask) # labels is a one-hot array of shape (num_frames, num_speakers) labels = (outputs.logits > 0).long() # s3prl logits for the same batch expected_logits = torch.tensor( [ [[-5.6119, -5.5845], [-3.7772, -5.4824], [-3.6914, -5.1619], [-4.7560, -5.0496]], [[-6.3785, -4.8365], [-5.5863, -5.4149], [-5.5639, -4.8469], [-6.1511, -4.0052]], [[-6.0355, -3.7414], [-5.5968, -4.8061], [-5.4620, -4.7310], [-5.5864, -4.6078]], [[-5.9493, -4.8963], [-4.4050, -5.4476], [-4.1755, -5.1395], [-4.0272, -4.3705]], ], device=torch_device, ) self.assertEqual(labels[0, :, 0].sum(), 270) self.assertEqual(labels[0, :, 1].sum(), 647) self.assertTrue(torch.allclose(outputs.logits[:, :4], expected_logits, atol=1e-2)) def test_inference_speaker_verification(self): model = UniSpeechSatForXVector.from_pretrained("microsoft/unispeech-sat-base-plus-sv").to(torch_device) processor = Wav2Vec2FeatureExtractor.from_pretrained("microsoft/unispeech-sat-base-plus-sv") input_data = self._load_superb("si", 4) inputs = processor(input_data["speech"], return_tensors="pt", padding=True) labels = torch.tensor([5, 1, 1, 3], device=torch_device).T with torch.no_grad(): input_values = inputs.input_values.to(torch_device) attention_mask = inputs.attention_mask.to(torch_device) outputs = model(input_values, attention_mask=attention_mask, labels=labels) embeddings = torch.nn.functional.normalize(outputs.embeddings, dim=-1) cosine_sim = torch.nn.CosineSimilarity(dim=-1) # id10002 vs id10002 self.assertAlmostEqual(cosine_sim(embeddings[1], embeddings[2]).item(), 0.9671, 3) # id10006 vs id10002 self.assertAlmostEqual(cosine_sim(embeddings[0], embeddings[1]).item(), 0.4941, 3) # id10002 vs id10004 self.assertAlmostEqual(cosine_sim(embeddings[2], embeddings[3]).item(), 0.5616, 3) self.assertAlmostEqual(outputs.loss.item(), 18.5925, 2)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/pvt/test_modeling_pvt.py
# coding=utf-8 # Copyright 2023 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 Pvt model. """ import unittest from transformers import is_torch_available, is_vision_available from transformers.testing_utils import ( require_accelerate, require_torch, require_torch_accelerator, require_torch_fp16, slow, torch_device, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import PvtConfig, PvtForImageClassification, PvtImageProcessor, PvtModel from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES if is_vision_available(): from PIL import Image class PvtConfigTester(ConfigTester): def run_common_tests(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class PvtModelTester: 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 PvtConfig( 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, ) def create_and_check_model(self, config, pixel_values, labels): model = PvtModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertIsNotNone(result.last_hidden_state) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = PvtForImageClassification(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)) # test greyscale images config.num_channels = 1 model = PvtForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) 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 # 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 class PvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (PvtModel, PvtForImageClassification) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": PvtModel, "image-classification": PvtForImageClassification} if is_torch_available() else {} ) test_head_masking = False test_pruning = False test_resize_embeddings = False test_torchscript = False has_attentions = False def setUp(self): self.model_tester = PvtModelTester(self) self.config_tester = PvtConfigTester(self, config_class=PvtConfig) 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) @unittest.skip("Pvt does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("Pvt does not have get_input_embeddings method and get_output_embeddings methods") def test_model_common_attributes(self): pass 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, param in model.named_parameters(): self.assertTrue( -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0, 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.hidden_states expected_num_layers = sum(self.model_tester.depths) + 1 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.batch_size, (self.model_tester.image_size // 4) ** 2, 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_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.__name__ in MODEL_MAPPING_NAMES.values(): 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() @slow def test_model_from_pretrained(self): model_name = "Zetatech/pvt-tiny-224" model = PvtModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class PvtModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_classification(self): # only resize + normalize image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224") model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval() image = prepare_img() encoded_inputs = image_processor(images=image, return_tensors="pt") pixel_values = encoded_inputs.pixel_values.to(torch_device) with torch.no_grad(): outputs = model(pixel_values) expected_shape = torch.Size((1, model.config.num_labels)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-1.4192, -1.9158, -0.9702]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)) @slow def test_inference_model(self): model = PvtModel.from_pretrained("Zetatech/pvt-tiny-224").to(torch_device).eval() image_processor = PvtImageProcessor.from_pretrained("Zetatech/pvt-tiny-224") image = prepare_img() inputs = image_processor(images=image, return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device) # forward pass with torch.no_grad(): outputs = model(pixel_values) # verify the logits expected_shape = torch.Size((1, 50, 512)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.3086, 1.0402, 1.1816], [-0.2880, 0.5781, 0.6124], [0.1480, 0.6129, -0.0590]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4)) @slow @require_accelerate @require_torch_accelerator @require_torch_fp16 def test_inference_fp16(self): r""" A small test to make sure that inference work in half precision without any problem. """ model = PvtForImageClassification.from_pretrained("Zetatech/pvt-tiny-224", torch_dtype=torch.float16) model.to(torch_device) image_processor = PvtImageProcessor(size=224) image = prepare_img() inputs = image_processor(images=image, return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device, dtype=torch.float16) # forward pass to make sure inference works in fp16 with torch.no_grad(): _ = model(pixel_values)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/pvt/test_image_processing_pvt.py
# coding=utf-8 # Copyright 2023 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 unittest from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from transformers import PvtImageProcessor class PvtImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225], ): size = size if size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_image_processor_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, } def expected_output_image_shape(self, images): return self.num_channels, self.size["height"], self.size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class PvtImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = PvtImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = PvtImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 18, "width": 18}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"height": 42, "width": 42})
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/blenderbot/test_tokenization_blenderbot.py
#!/usr/bin/env python3 # 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. """Tests for Blenderbot Tokenizers, including common tests for BlenderbotSmallTokenizer.""" import unittest from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast from transformers.testing_utils import require_jinja from transformers.utils import cached_property class Blenderbot3BTokenizerTests(unittest.TestCase): @cached_property def tokenizer_3b(self): return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B") @cached_property def rust_tokenizer_3b(self): return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B") def test_encode_decode_cycle(self): tok = self.tokenizer_3b src_text = " I am a small frog." encoded = tok([src_text], padding=False, truncation=False)["input_ids"] decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] assert src_text == decoded def test_encode_decode_cycle_rust_tokenizer(self): tok = self.rust_tokenizer_3b src_text = " I am a small frog." encoded = tok([src_text], padding=False, truncation=False)["input_ids"] decoded = tok.batch_decode(encoded, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] assert src_text == decoded def test_3B_tokenization_same_as_parlai(self): assert self.tokenizer_3b.add_prefix_space assert self.tokenizer_3b([" Sam", "Sam"]).input_ids == [[5502, 2], [5502, 2]] def test_3B_tokenization_same_as_parlai_rust_tokenizer(self): assert self.rust_tokenizer_3b.add_prefix_space assert self.rust_tokenizer_3b([" Sam", "Sam"]).input_ids == [[5502, 2], [5502, 2]] @require_jinja def test_tokenization_for_chat(self): tok = self.tokenizer_3b test_chats = [ [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}], [ {"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Nice to meet you."}, ], [{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}], ] tokenized_chats = [tok.apply_chat_template(test_chat) for test_chat in test_chats] expected_tokens = [ [553, 366, 265, 4792, 3879, 73, 311, 21, 228, 228, 6950, 8, 2], [553, 366, 265, 4792, 3879, 73, 311, 21, 228, 228, 6950, 8, 228, 3490, 287, 2273, 304, 21, 2], [3490, 287, 2273, 304, 21, 228, 228, 6950, 8, 2], ] for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens): self.assertListEqual(tokenized_chat, expected_tokens)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/blenderbot/test_modeling_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. """ Testing suite for the PyTorch Blenderbot model. """ import tempfile import unittest from transformers import BlenderbotConfig, is_torch_available from transformers.testing_utils import ( backend_empty_cache, require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotTokenizer from transformers.models.blenderbot.modeling_blenderbot import ( BlenderbotDecoder, BlenderbotEncoder, BlenderbotForCausalLM, ) 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 = 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": attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } class BlenderbotModelTester: 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, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=50, 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_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 # forcing a certain token to be generated, sets all other tokens to -inf # if however the token to be generated is already at -inf then it can lead token # `nan` values and thus break generation self.forced_bos_token_id = None self.forced_eos_token_id = None def prepare_config_and_inputs(self): 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_blenderbot_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def get_config(self): return BlenderbotConfig( 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, forced_bos_token_id=self.forced_bos_token_id, forced_eos_token_id=self.forced_eos_token_id, ) 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() return config, inputs_dict def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict): model = BlenderbotModel(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-3)) def check_encoder_decoder_model_standalone(self, config, inputs_dict): model = BlenderbotModel(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 = BlenderbotEncoder.from_pretrained(tmpdirname).to(torch_device) encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["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 = BlenderbotDecoder.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) @require_torch class BlenderbotModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (BlenderbotModel, BlenderbotForConditionalGeneration) if is_torch_available() else () all_generative_model_classes = (BlenderbotForConditionalGeneration,) if is_torch_available() else () pipeline_model_mapping = ( { "conversational": BlenderbotForConditionalGeneration, "feature-extraction": BlenderbotModel, "summarization": BlenderbotForConditionalGeneration, "text-generation": BlenderbotForCausalLM, "text2text-generation": BlenderbotForConditionalGeneration, "translation": BlenderbotForConditionalGeneration, } if is_torch_available() else {} ) is_encoder_decoder = True fx_compatible = True test_pruning = False test_missing_keys = False def setUp(self): self.model_tester = BlenderbotModelTester(self) self.config_tester = ConfigTester(self, config_class=BlenderbotConfig) 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) @require_torch_fp16 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 = BlenderbotForConditionalGeneration(config).eval().to(torch_device) 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 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) @unittest.skipUnless(torch_device != "cpu", "3B test too slow on CPU.") @require_torch @require_sentencepiece @require_tokenizers class Blenderbot3BIntegrationTests(unittest.TestCase): ckpt = "facebook/blenderbot-3B" @cached_property def tokenizer(self): return BlenderbotTokenizer.from_pretrained(self.ckpt) @slow def test_generation_from_short_input_same_as_parlai_3B(self): FASTER_GEN_KWARGS = {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25} TOK_DECODE_KW = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True} backend_empty_cache(torch_device) model = BlenderbotForConditionalGeneration.from_pretrained(self.ckpt).half().to(torch_device) src_text = ["Sam"] model_inputs = self.tokenizer(src_text, return_tensors="pt").to(torch_device) generated_utterances = model.generate(**model_inputs, **FASTER_GEN_KWARGS) tgt_text = 'Sam is a great name. It means "sun" in Gaelic.' generated_txt = self.tokenizer.batch_decode(generated_utterances, **TOK_DECODE_KW) assert generated_txt[0].strip() == tgt_text src_text = ( "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel" " like i'm going to throw up.\nand why is that?" ) model_inputs = self.tokenizer([src_text], return_tensors="pt").to(torch_device) generated_ids = model.generate(**model_inputs, **FASTER_GEN_KWARGS)[0] reply = self.tokenizer.decode(generated_ids, **TOK_DECODE_KW) assert "I think it's because we are so worried about what people think of us." == reply.strip() del model class BlenderbotStandaloneDecoderModelTester: def __init__( self, parent, vocab_size=99, batch_size=13, d_model=16, decoder_seq_length=7, is_training=True, is_decoder=True, use_attention_mask=True, use_cache=False, use_labels=True, decoder_start_token_id=2, decoder_ffn_dim=32, decoder_layers=2, encoder_attention_heads=4, decoder_attention_heads=4, max_position_embeddings=30, is_encoder_decoder=False, encoder_no_repeat_ngram_size=0, pad_token_id=0, bos_token_id=1, eos_token_id=2, scope=None, ): self.parent = parent self.batch_size = batch_size self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask self.use_labels = use_labels self.vocab_size = vocab_size self.d_model = d_model self.hidden_size = d_model self.num_hidden_layers = decoder_layers self.decoder_layers = decoder_layers self.decoder_ffn_dim = decoder_ffn_dim self.encoder_attention_heads = encoder_attention_heads self.decoder_attention_heads = decoder_attention_heads self.num_attention_heads = decoder_attention_heads self.eos_token_id = eos_token_id self.bos_token_id = bos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.max_position_embeddings = max_position_embeddings self.is_encoder_decoder = is_encoder_decoder self.encoder_no_repeat_ngram_size = encoder_no_repeat_ngram_size self.scope = None self.decoder_key_length = decoder_seq_length self.base_model_out_len = 2 self.decoder_attention_idx = 1 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = BlenderbotConfig( vocab_size=self.vocab_size, d_model=self.d_model, decoder_layers=self.decoder_layers, decoder_ffn_dim=self.decoder_ffn_dim, encoder_attention_heads=self.encoder_attention_heads, decoder_attention_heads=self.decoder_attention_heads, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, use_cache=self.use_cache, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, max_position_embeddings=self.max_position_embeddings, is_encoder_decoder=self.is_encoder_decoder, encoder_no_repeat_ngram_size=self.encoder_no_repeat_ngram_size, ) return ( config, input_ids, attention_mask, lm_labels, ) def create_and_check_decoder_model_past( self, config, input_ids, attention_mask, lm_labels, ): config.use_cache = True model = BlenderbotDecoder(config=config).to(torch_device).eval() # first forward pass outputs = model(input_ids, use_cache=True) outputs_use_cache_conf = model(input_ids) outputs_no_past = model(input_ids, use_cache=False) self.parent.assertTrue(len(outputs) == len(outputs_use_cache_conf)) self.parent.assertTrue(len(outputs) == len(outputs_no_past) + 1) past_key_values = outputs["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) output_from_no_past = model(next_input_ids)["last_hidden_state"] output_from_past = model(next_tokens, 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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def create_and_check_decoder_model_attention_mask_past( self, config, input_ids, attention_mask, lm_labels, ): model = BlenderbotDecoder(config=config).to(torch_device).eval() # create attention mask attn_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device) half_seq_length = input_ids.shape[-1] // 2 attn_mask[:, half_seq_length:] = 0 # first forward pass past_key_values = model(input_ids, attention_mask=attn_mask, use_cache=True)["past_key_values"] # past_key_values = model(input_ids, use_cache=True)["past_key_values"] # create hypothetical next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size) # change a random masked slice from input_ids random_seq_idx_to_change = ids_tensor((1,), half_seq_length).item() + 1 random_other_next_tokens = ids_tensor((self.batch_size, 1), config.vocab_size).squeeze(-1) input_ids[:, -random_seq_idx_to_change] = random_other_next_tokens # append to next input_ids and attn_mask next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) attn_mask = torch.cat( [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1, ) # get two different outputs output_from_no_past = model(next_input_ids, attention_mask=attn_mask)["last_hidden_state"] output_from_past = model(next_tokens, past_key_values=past_key_values, attention_mask=attn_mask)[ "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[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach() output_from_past_slice = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice assert torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, attention_mask, lm_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class BlenderbotStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (BlenderbotDecoder, BlenderbotForCausalLM) if is_torch_available() else () all_generative_model_classes = (BlenderbotForCausalLM,) if is_torch_available() else () test_pruning = False is_encoder_decoder = False def setUp( self, ): self.model_tester = BlenderbotStandaloneDecoderModelTester(self, is_training=False) self.config_tester = ConfigTester(self, config_class=BlenderbotConfig) def test_config(self): self.config_tester.run_common_tests() def test_decoder_model_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past(*config_and_inputs) def test_decoder_model_attn_mask_past(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_attention_mask_past(*config_and_inputs) def test_retain_grad_hidden_states_attentions(self): # decoder cannot keep gradients return
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/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. from __future__ import annotations import unittest from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available from transformers.testing_utils import require_tf, require_tokenizers, slow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin 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=2, num_attention_heads=4, intermediate_size=37, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=50, 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, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else () all_generative_model_classes = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else () pipeline_model_mapping = ( { "conversational": TFBlenderbotForConditionalGeneration, "feature-extraction": TFBlenderbotModel, "summarization": TFBlenderbotForConditionalGeneration, "text2text-generation": TFBlenderbotForConditionalGeneration, "translation": 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) @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?" )
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/blenderbot/test_modeling_flax_blenderbot.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. import unittest import numpy as np import timeout_decorator # noqa from transformers import BlenderbotConfig, is_flax_available from transformers.testing_utils import jax_device, require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html os.environ["XLA_PYTHON_CLIENT_ALLOCATOR"] = "platform" import jax import jax.numpy as jnp from transformers import BlenderbotTokenizer from transformers.models.blenderbot.modeling_flax_blenderbot import ( FlaxBlenderbotForConditionalGeneration, FlaxBlenderbotModel, shift_tokens_right, ) def prepare_blenderbot_inputs_dict( config, input_ids, decoder_input_ids=None, 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 = np.where(input_ids != config.pad_token_id, 1, 0) if decoder_attention_mask is None: decoder_attention_mask = np.where(decoder_input_ids != config.pad_token_id, 1, 0) if head_mask is None: head_mask = np.ones((config.encoder_layers, config.encoder_attention_heads)) if decoder_head_mask is None: decoder_head_mask = np.ones((config.decoder_layers, config.decoder_attention_heads)) if cross_attn_head_mask is None: cross_attn_head_mask = np.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": attention_mask, } class FlaxBlenderbotModelTester: 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, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=50, eos_token_id=2, pad_token_id=1, bos_token_id=0, initializer_range=0.02, ): 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.initializer_range = initializer_range def prepare_config_and_inputs(self): input_ids = np.clip(ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size), 3, self.vocab_size) input_ids = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1), dtype=np.int64)), -1) decoder_input_ids = shift_tokens_right(input_ids, 1, 2) config = BlenderbotConfig( 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, initializer_range=self.initializer_range, use_cache=False, ) inputs_dict = prepare_blenderbot_inputs_dict(config, input_ids, decoder_input_ids) return config, inputs_dict def prepare_config_and_inputs_for_common(self): config, inputs_dict = self.prepare_config_and_inputs() return config, inputs_dict def check_use_cache_forward(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_attention_mask = jnp.ones((decoder_input_ids.shape[0], max_decoder_length), dtype="i4") decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=outputs_cache.past_key_values, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") def check_use_cache_forward_with_attn_mask(self, model_class_name, config, inputs_dict): max_decoder_length = 20 model = model_class_name(config) encoder_outputs = model.encode(inputs_dict["input_ids"]) decoder_input_ids, decoder_attention_mask = ( inputs_dict["decoder_input_ids"], inputs_dict["decoder_attention_mask"], ) decoder_attention_mask_cache = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])), ], axis=-1, ) past_key_values = model.init_cache(decoder_input_ids.shape[0], max_decoder_length, encoder_outputs) decoder_position_ids = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :], (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1), ) outputs_cache = model.decode( decoder_input_ids[:, :-1], encoder_outputs, decoder_attention_mask=decoder_attention_mask_cache, past_key_values=past_key_values, decoder_position_ids=decoder_position_ids, ) decoder_position_ids = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]], dtype="i4") outputs_cache_next = model.decode( decoder_input_ids[:, -1:], encoder_outputs, past_key_values=outputs_cache.past_key_values, decoder_attention_mask=decoder_attention_mask_cache, decoder_position_ids=decoder_position_ids, ) outputs = model.decode(decoder_input_ids, encoder_outputs, decoder_attention_mask=decoder_attention_mask) diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]))) self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}") @require_flax class BlenderbotHeadTests(unittest.TestCase): vocab_size = 99 def _get_config_and_data(self): input_ids = np.array( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ], dtype=np.int64, ) batch_size = input_ids.shape[0] config = BlenderbotConfig( vocab_size=self.vocab_size, d_model=24, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=32, decoder_ffn_dim=32, max_position_embeddings=48, eos_token_id=2, pad_token_id=1, bos_token_id=0, ) return config, input_ids, batch_size # @timeout_decorator.timeout(1) # not working with the decorator so far def test_lm_forward(self): config, input_ids, batch_size = self._get_config_and_data() lm_model = FlaxBlenderbotForConditionalGeneration(config) outputs = lm_model(input_ids=input_ids) expected_shape = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) def test_lm_uneven_forward(self): config = BlenderbotConfig( vocab_size=self.vocab_size, d_model=14, encoder_layers=2, decoder_layers=2, encoder_attention_heads=2, decoder_attention_heads=2, encoder_ffn_dim=8, decoder_ffn_dim=8, max_position_embeddings=48, ) lm_model = FlaxBlenderbotForConditionalGeneration(config) context = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], dtype=np.int64) summary = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]], dtype=np.int64) outputs = lm_model(input_ids=context, decoder_input_ids=summary) expected_shape = (*summary.shape, config.vocab_size) self.assertEqual(outputs["logits"].shape, expected_shape) def test_shift_tokens_right(self): input_ids = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=np.int64) shifted = shift_tokens_right(input_ids, 1, 2) n_pad_before = np.equal(input_ids, 1).astype(np.float32).sum() n_pad_after = np.equal(shifted, 1).astype(np.float32).sum() self.assertEqual(shifted.shape, input_ids.shape) self.assertEqual(n_pad_after, n_pad_before - 1) self.assertTrue(np.equal(shifted[:, 0], 2).all()) @require_flax class FlaxBlenderbotModelTest(FlaxModelTesterMixin, unittest.TestCase, FlaxGenerationTesterMixin): is_encoder_decoder = True all_model_classes = ( ( FlaxBlenderbotModel, FlaxBlenderbotForConditionalGeneration, ) if is_flax_available() else () ) all_generative_model_classes = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else () def setUp(self): self.model_tester = FlaxBlenderbotModelTester(self) def test_use_cache_forward(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(model_class, config, inputs_dict) def test_use_cache_forward_with_attn_mask(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(model_class, config, inputs_dict) def test_encode(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 encode_jitted(input_ids, attention_mask=None, **kwargs): return model.encode(input_ids=input_ids, attention_mask=attention_mask) with self.subTest("JIT Enabled"): jitted_outputs = encode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = encode_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) def test_decode(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__): model = model_class(config) encoder_outputs = model.encode(inputs_dict["input_ids"], inputs_dict["attention_mask"]) prepared_inputs_dict = { "decoder_input_ids": inputs_dict["decoder_input_ids"], "decoder_attention_mask": inputs_dict["decoder_attention_mask"], "encoder_outputs": encoder_outputs, } @jax.jit def decode_jitted(decoder_input_ids, decoder_attention_mask, encoder_outputs): return model.decode( decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, encoder_outputs=encoder_outputs, ) with self.subTest("JIT Enabled"): jitted_outputs = decode_jitted(**prepared_inputs_dict).to_tuple() with self.subTest("JIT Disabled"): with jax.disable_jit(): outputs = decode_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) @slow def test_model_from_pretrained(self): for model_class_name in self.all_model_classes: model = model_class_name.from_pretrained("facebook/blenderbot-400M-distill") # FlaxBlenderbotForSequenceClassification expects eos token in input_ids input_ids = np.ones((1, 1)) * model.config.eos_token_id outputs = model(input_ids) self.assertIsNotNone(outputs) @unittest.skipUnless(jax_device != "cpu", "3B test too slow on CPU.") @slow def test_generation_from_short_input_same_as_parlai_3B(self): FASTER_GEN_KWARGS = {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25} TOK_DECODE_KW = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True} model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B", from_pt=True) tokenizer = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B") src_text = ["Sam"] model_inputs = tokenizer(src_text, return_tensors="jax") generated_utterances = model.generate(**model_inputs, **FASTER_GEN_KWARGS) tgt_text = 'Sam is a great name. It means "sun" in Gaelic.' generated_txt = tokenizer.batch_decode(generated_utterances, **TOK_DECODE_KW) assert generated_txt[0].strip() == tgt_text
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/markuplm/test_feature_extraction_markuplm.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 unittest from transformers.testing_utils import require_bs4 from transformers.utils import is_bs4_available from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin if is_bs4_available(): from transformers import MarkupLMFeatureExtractor class MarkupLMFeatureExtractionTester(unittest.TestCase): def __init__(self, parent): self.parent = parent def prepare_feat_extract_dict(self): return {} def get_html_strings(): html_string_1 = """<HTML> <HEAD> <TITLE>sample document</TITLE> </HEAD> <BODY BGCOLOR="FFFFFF"> <HR> <a href="http://google.com">Goog</a> <H1>This is one header</H1> <H2>This is a another Header</H2> <P>Travel from <P> <B>SFO to JFK</B> <BR> <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B> <HR> <div style="color:#0000FF"> <h3>Traveler <b> name </b> is <p> John Doe </p> </div>""" html_string_2 = """ <!DOCTYPE html> <html> <body> <h1>My First Heading</h1> <p>My first paragraph.</p> </body> </html> """ return [html_string_1, html_string_2] @require_bs4 class MarkupLMFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = MarkupLMFeatureExtractor if is_bs4_available() else None def setUp(self): self.feature_extract_tester = MarkupLMFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_call(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class() # Test not batched input html_string = get_html_strings()[0] encoding = feature_extractor(html_string) # fmt: off expected_nodes = [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']] expected_xpaths = [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']] # fmt: on self.assertEqual(encoding.nodes, expected_nodes) self.assertEqual(encoding.xpaths, expected_xpaths) # Test batched html_strings = get_html_strings() encoding = feature_extractor(html_strings) # fmt: off expected_nodes = expected_nodes + [['My First Heading', 'My first paragraph.']] expected_xpaths = expected_xpaths + [['/html/body/h1', '/html/body/p']] self.assertEqual(len(encoding.nodes), 2) self.assertEqual(len(encoding.xpaths), 2) self.assertEqual(encoding.nodes, expected_nodes) self.assertEqual(encoding.xpaths, expected_xpaths)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/markuplm/test_processor_markuplm.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 from transformers import ( MarkupLMProcessor, MarkupLMTokenizer, PreTrainedTokenizer, PreTrainedTokenizerBase, PreTrainedTokenizerFast, ) from transformers.models.markuplm.tokenization_markuplm import VOCAB_FILES_NAMES from transformers.testing_utils import require_bs4, require_tokenizers, require_torch, slow from transformers.utils import FEATURE_EXTRACTOR_NAME, cached_property, is_bs4_available, is_tokenizers_available if is_bs4_available(): from transformers import MarkupLMFeatureExtractor if is_tokenizers_available(): from transformers import MarkupLMTokenizerFast @require_bs4 @require_tokenizers class MarkupLMProcessorTest(unittest.TestCase): tokenizer_class = MarkupLMTokenizer rust_tokenizer_class = MarkupLMTokenizerFast 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", "\u0120hello", "\u0120world", "<unk>",] # fmt: skip 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.tags_dict = {"a": 0, "abbr": 1, "acronym": 2, "address": 3} 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"]) self.tokenizer_config_file = os.path.join(self.tmpdirname, "tokenizer_config.json") 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)) with open(self.tokenizer_config_file, "w", encoding="utf-8") as fp: fp.write(json.dumps({"tags_dict": self.tags_dict})) feature_extractor_map = {"feature_extractor_type": "MarkupLMFeatureExtractor"} 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 MarkupLMFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs) def tearDown(self): shutil.rmtree(self.tmpdirname) def test_save_load_pretrained_default(self): feature_extractor = self.get_feature_extractor() tokenizers = self.get_tokenizers() for tokenizer in tokenizers: processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.save_pretrained(self.tmpdirname) processor = MarkupLMProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertIsInstance(processor.tokenizer, (MarkupLMTokenizer, MarkupLMTokenizerFast)) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string()) self.assertIsInstance(processor.feature_extractor, MarkupLMFeatureExtractor) def test_save_load_pretrained_additional_features(self): processor = MarkupLMProcessor(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 = MarkupLMProcessor.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, MarkupLMTokenizer) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, MarkupLMFeatureExtractor) # 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 = MarkupLMProcessor.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, MarkupLMTokenizerFast) self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string()) self.assertIsInstance(processor.feature_extractor, MarkupLMFeatureExtractor) def test_model_input_names(self): feature_extractor = self.get_feature_extractor() tokenizer = self.get_tokenizer() processor = MarkupLMProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor) self.assertListEqual( processor.model_input_names, tokenizer.model_input_names, msg="`processor` and `tokenizer` model input names do not match", ) # different use cases tests @require_bs4 @require_torch class MarkupLMProcessorIntegrationTests(unittest.TestCase): @cached_property def get_html_strings(self): html_string_1 = """ <!DOCTYPE html> <html> <head> <title>Hello world</title> </head> <body> <h1>Welcome</h1> <p>Here is my website.</p> </body> </html>""" html_string_2 = """ <!DOCTYPE html> <html> <body> <h2>HTML Images</h2> <p>HTML images are defined with the img tag:</p> <img src="w3schools.jpg" alt="W3Schools.com" width="104" height="142"> </body> </html> """ return [html_string_1, html_string_2] @cached_property def get_tokenizers(self): slow_tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base") fast_tokenizer = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base", from_slow=True) return [slow_tokenizer, fast_tokenizer] @slow def test_processor_case_1(self): # case 1: web page classification (training, inference) + token classification (inference) feature_extractor = MarkupLMFeatureExtractor() tokenizers = self.get_tokenizers html_strings = self.get_html_strings for tokenizer in tokenizers: processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched inputs = processor(html_strings[0], return_tensors="pt") # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected = [0, 31414, 232, 25194, 11773, 16, 127, 998, 4, 2] self.assertSequenceEqual(inputs.input_ids.squeeze().tolist(), expected) # batched inputs = processor(html_strings, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected = [0, 48085, 2209, 48085, 3156, 32, 6533, 19, 5, 48599, 6694, 35, 2] self.assertSequenceEqual(inputs.input_ids[1].tolist(), expected) @slow def test_processor_case_2(self): # case 2: web page classification (training, inference) + token classification (inference), parse_html=False feature_extractor = MarkupLMFeatureExtractor() tokenizers = self.get_tokenizers for tokenizer in tokenizers: processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.parse_html = False # not batched nodes = ["hello", "world", "how", "are"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"] inputs = processor(nodes=nodes, xpaths=xpaths, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = list(inputs.keys()) for key in expected_keys: self.assertIn(key, actual_keys) # verify input_ids expected_decoding = "<s>helloworldhoware</s>" decoding = processor.decode(inputs.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched nodes = [["hello", "world"], ["my", "name", "is"]] xpaths = [ ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"], ["html/body", "html/body/div", "html/body"], ] inputs = processor(nodes=nodes, xpaths=xpaths, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s>helloworld</s><pad>" decoding = processor.decode(inputs.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) @slow def test_processor_case_3(self): # case 3: token classification (training), parse_html=False feature_extractor = MarkupLMFeatureExtractor() tokenizers = self.get_tokenizers for tokenizer in tokenizers: processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.parse_html = False # not batched nodes = ["hello", "world", "how", "are"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"] node_labels = [1, 2, 2, 1] inputs = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt") # verify keys expected_keys = [ "attention_mask", "input_ids", "labels", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq", ] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_ids = [0, 42891, 8331, 9178, 1322, 2] self.assertSequenceEqual(inputs.input_ids[0].tolist(), expected_ids) # verify labels expected_labels = [-100, 1, 2, 2, 1, -100] self.assertListEqual(inputs.labels.squeeze().tolist(), expected_labels) # batched nodes = [["hello", "world"], ["my", "name", "is"]] xpaths = [ ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"], ["html/body", "html/body/div", "html/body"], ] node_labels = [[1, 2], [6, 3, 10]] inputs = processor( nodes=nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20, truncation=True, return_tensors="pt", ) # verify keys expected_keys = [ "attention_mask", "input_ids", "labels", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq", ] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_ids = [0, 4783, 13650, 354, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] self.assertSequenceEqual(inputs.input_ids[1].tolist(), expected_ids) # verify xpath_tags_seq expected_xpaths_tags_seq = [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]] # fmt: skip self.assertSequenceEqual(inputs.xpath_tags_seq[1].tolist(), expected_xpaths_tags_seq) # verify labels expected_labels = [-100, 6, 3, 10, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100] # fmt: skip self.assertListEqual(inputs.labels[1].tolist(), expected_labels) @slow def test_processor_case_4(self): # case 4: question answering (inference), parse_html=True feature_extractor = MarkupLMFeatureExtractor() tokenizers = self.get_tokenizers html_strings = self.get_html_strings for tokenizer in tokenizers: processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) # not batched question = "What's his name?" inputs = processor(html_strings[0], questions=question, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s>What's his name?</s>Hello worldWelcomeHere is my website.</s>" # fmt: skip decoding = processor.decode(inputs.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] inputs = processor( html_strings, questions=questions, padding="max_length", max_length=20, truncation=True, return_tensors="pt", ) # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = ( "<s>what's the time</s>HTML ImagesHTML images are defined with the img tag:</s><pad><pad>" ) decoding = processor.decode(inputs.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify xpath_subs_seq expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]] # fmt: skip self.assertListEqual(inputs.xpath_subs_seq[1].tolist(), expected_xpath_subs_seq) @slow def test_processor_case_5(self): # case 5: question answering (inference), parse_html=False feature_extractor = MarkupLMFeatureExtractor(parse_html=False) tokenizers = self.get_tokenizers for tokenizer in tokenizers: processor = MarkupLMProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer) processor.parse_html = False # not batched question = "What's his name?" nodes = ["hello", "world", "how", "are"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"] inputs = processor(nodes=nodes, xpaths=xpaths, questions=question, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s>What's his name?</s>helloworldhoware</s>" decoding = processor.decode(inputs.input_ids.squeeze().tolist()) self.assertSequenceEqual(decoding, expected_decoding) # batched questions = ["How old is he?", "what's the time"] nodes = [["hello", "world"], ["my", "name", "is"]] xpaths = [ ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"], ["html/body", "html/body/div", "html/body"], ] inputs = processor(nodes=nodes, xpaths=xpaths, questions=questions, padding=True, return_tensors="pt") # verify keys expected_keys = ["attention_mask", "input_ids", "token_type_ids", "xpath_subs_seq", "xpath_tags_seq"] actual_keys = sorted(inputs.keys()) self.assertListEqual(actual_keys, expected_keys) # verify input_ids expected_decoding = "<s>How old is he?</s>helloworld</s>" decoding = processor.decode(inputs.input_ids[0].tolist()) self.assertSequenceEqual(decoding, expected_decoding) expected_decoding = "<s>what's the time</s>mynameis</s>" decoding = processor.decode(inputs.input_ids[1].tolist()) self.assertSequenceEqual(decoding, expected_decoding) # verify xpath_subs_seq expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]] # fmt: skip self.assertListEqual(inputs.xpath_subs_seq[1].tolist()[-5:], expected_xpath_subs_seq)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/markuplm/test_modeling_markuplm.py
# coding=utf-8 # Copyright 2022 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. import unittest from transformers import MarkupLMConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MarkupLMForQuestionAnswering, MarkupLMForSequenceClassification, MarkupLMForTokenClassification, MarkupLMModel, ) # TODO check dependencies from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer class MarkupLMModelTester: """You can also import this e.g from .test_modeling_markuplm import MarkupLMModelTester""" 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=2, 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, scope=None, max_xpath_tag_unit_embeddings=20, max_xpath_subs_unit_embeddings=30, tag_pad_id=2, subs_pad_id=2, max_depth=10, ): 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.scope = scope self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings self.tag_pad_id = tag_pad_id self.subs_pad_id = subs_pad_id self.max_depth = max_depth def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) xpath_tags_seq = ids_tensor( [self.batch_size, self.seq_length, self.max_depth], self.max_xpath_tag_unit_embeddings ) xpath_subs_seq = ids_tensor( [self.batch_size, self.seq_length, self.max_depth], self.max_xpath_subs_unit_embeddings ) input_mask = None if self.use_input_mask: input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) 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 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) config = self.get_config() return ( config, input_ids, xpath_tags_seq, xpath_subs_seq, token_type_ids, input_mask, sequence_labels, token_labels, ) def get_config(self): return MarkupLMConfig( 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, initializer_range=self.initializer_range, max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings, max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings, tag_pad_id=self.tag_pad_id, subs_pad_id=self.subs_pad_id, max_depth=self.max_depth, ) def create_and_check_model( self, config, input_ids, xpath_tags_seq, xpath_subs_seq, token_type_ids, input_mask, sequence_labels, token_labels, ): model = MarkupLMModel(config=config) model.to(torch_device) model.eval() print("Configs:", model.config.tag_pad_id, model.config.subs_pad_id) 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_for_sequence_classification( self, config, input_ids, xpath_tags_seq, xpath_subs_seq, token_type_ids, input_mask, sequence_labels, token_labels, ): config.num_labels = self.num_labels model = MarkupLMForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, 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, xpath_tags_seq, xpath_subs_seq, token_type_ids, input_mask, sequence_labels, token_labels, ): config.num_labels = self.num_labels model = MarkupLMForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, 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_question_answering( self, config, input_ids, xpath_tags_seq, xpath_subs_seq, token_type_ids, input_mask, sequence_labels, token_labels, ): model = MarkupLMForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, xpath_tags_seq=xpath_tags_seq, xpath_subs_seq=xpath_subs_seq, 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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, xpath_tags_seq, xpath_subs_seq, token_type_ids, input_mask, sequence_labels, token_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "xpath_tags_seq": xpath_tags_seq, "xpath_subs_seq": xpath_subs_seq, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_torch class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MarkupLMModel, MarkupLMForSequenceClassification, MarkupLMForTokenClassification, MarkupLMForQuestionAnswering, ) if is_torch_available() else None ) pipeline_model_mapping = ( { "feature-extraction": MarkupLMModel, "question-answering": MarkupLMForQuestionAnswering, "text-classification": MarkupLMForSequenceClassification, "token-classification": MarkupLMForTokenClassification, "zero-shot": MarkupLMForSequenceClassification, } if is_torch_available() else {} ) # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): # ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]` # (batch of pretokenized examples). return True def setUp(self): self.model_tester = MarkupLMModelTester(self) self.config_tester = ConfigTester(self, config_class=MarkupLMConfig, 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_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) 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 prepare_html_string(): html_string = """ <!DOCTYPE html> <html> <head> <title>Page Title</title> </head> <body> <h1>This is a Heading</h1> <p>This is a paragraph.</p> </body> </html> """ return html_string @require_torch class MarkupLMModelIntegrationTest(unittest.TestCase): @cached_property def default_processor(self): # TODO use from_pretrained here feature_extractor = MarkupLMFeatureExtractor() tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base") return MarkupLMProcessor(feature_extractor, tokenizer) @slow def test_forward_pass_no_head(self): model = MarkupLMModel.from_pretrained("microsoft/markuplm-base").to(torch_device) processor = self.default_processor inputs = processor(prepare_html_string(), return_tensors="pt") inputs = inputs.to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the last hidden states expected_shape = torch.Size([1, 14, 768]) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]] ).to(torch_device) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/markuplm/test_tokenization_markuplm.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. import inspect import json import os import re import shutil import tempfile import unittest from typing import List from transformers import ( AddedToken, MarkupLMTokenizerFast, SpecialTokensMixin, is_tf_available, is_torch_available, logging, ) from transformers.models.markuplm.tokenization_markuplm import VOCAB_FILES_NAMES, MarkupLMTokenizer from transformers.testing_utils import is_pt_tf_cross_test, require_tokenizers, require_torch, slow from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, merge_model_tokenizer_mappings logger = logging.get_logger(__name__) @require_tokenizers class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "microsoft/markuplm-base" tokenizer_class = MarkupLMTokenizer rust_tokenizer_class = MarkupLMTokenizerFast test_rust_tokenizer = True from_pretrained_kwargs = {"cls_token": "<s>"} 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", "\u0120hello", "\u0120world", "<unk>",] # fmt: skip vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] self.tags_dict = {"a": 0, "abbr": 1, "acronym": 2, "address": 3} 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"]) self.tokenizer_config_file = os.path.join(self.tmpdirname, "tokenizer_config.json") 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)) with open(self.tokenizer_config_file, "w", encoding="utf-8") as fp: fp.write(json.dumps({"tags_dict": self.tags_dict})) def get_nodes_and_xpaths(self): nodes = ["hello", "world"] xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"] return nodes, xpaths def get_nodes_and_xpaths_batch(self): nodes = [["hello world", "running"], ["hello my name is bob"]] xpaths = [ ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"], ["/html/body/div/li[2]/div/span"], ] return nodes, xpaths def get_question_nodes_and_xpaths(self): question = "what's his name?" nodes = ["hello world"] xpaths = ["/html/body/div/li[1]/div/span"] # , "/html/body/div/li[1]/div/span"] return question, nodes, xpaths def get_question_nodes_and_xpaths_batch(self): questions = ["what's his name?", "how is he called?"] nodes = [["hello world", "running"], ["hello my name is bob"]] xpaths = [ ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"], ["/html/body/div/li[2]/div/span"], ] return questions, nodes, xpaths @unittest.skip("Chat template tests don't play well with table/layout models.") def test_chat_template_batched(self): pass def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_add_special_tokens(self): tokenizers: List[MarkupLMTokenizer] = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): special_token = "[SPECIAL_TOKEN]" special_token_xpath = "/html/body/div/li[1]/div/span" tokenizer.add_special_tokens({"cls_token": special_token}) encoded_special_token = tokenizer.encode( [special_token], xpaths=[special_token_xpath], add_special_tokens=False ) self.assertEqual(len(encoded_special_token), 1) decoded = tokenizer.decode(encoded_special_token, skip_special_tokens=True) self.assertTrue(special_token not in decoded) def test_add_tokens_tokenizer(self): tokenizers: List[MarkupLMTokenizer] = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): vocab_size = tokenizer.vocab_size all_size = len(tokenizer) self.assertNotEqual(vocab_size, 0) # We usually have added tokens from the start in tests because our vocab fixtures are # smaller than the original vocabs - let's not assert this # self.assertEqual(vocab_size, all_size) new_toks = [ AddedToken("aaaaa", rstrip=True, lstrip=True), AddedToken("bbbbbb", rstrip=True, lstrip=True), AddedToken("cccccccccdddddddd", rstrip=True, lstrip=True), ] added_toks = tokenizer.add_tokens(new_toks) vocab_size_2 = tokenizer.vocab_size all_size_2 = len(tokenizer) self.assertNotEqual(vocab_size_2, 0) self.assertEqual(vocab_size + 3, vocab_size_2 + 3) self.assertEqual(added_toks, len(new_toks)) self.assertEqual(all_size_2, all_size + len(new_toks)) nodes = "aaaaa bbbbbb low cccccccccdddddddd l".split() xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))] tokens = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) self.assertGreaterEqual(len(tokens), 4) self.assertGreater(tokens[0], tokenizer.vocab_size - 1) self.assertGreater(tokens[-2], tokenizer.vocab_size - 1) new_toks_2 = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"} added_toks_2 = tokenizer.add_special_tokens(new_toks_2) vocab_size_3 = tokenizer.vocab_size all_size_3 = len(tokenizer) self.assertNotEqual(vocab_size_3, 0) self.assertEqual(vocab_size, vocab_size_3) self.assertEqual(added_toks_2, len(new_toks_2)) self.assertEqual(all_size_3, all_size_2 + len(new_toks_2)) nodes = ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l".split() xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))] tokens = tokenizer.encode( nodes, xpaths=xpaths, add_special_tokens=False, ) self.assertGreaterEqual(len(tokens), 6) self.assertGreater(tokens[0], tokenizer.vocab_size - 1) self.assertGreater(tokens[0], tokens[1]) self.assertGreater(tokens[-2], tokenizer.vocab_size - 1) self.assertGreater(tokens[-2], tokens[-3]) self.assertEqual(tokens[0], tokenizer.eos_token_id) self.assertEqual(tokens[-2], tokenizer.pad_token_id) @require_tokenizers def test_encode_decode_with_spaces(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() new_toks = [AddedToken("[ABC]", normalized=False), AddedToken("[DEF]", normalized=False)] tokenizer.add_tokens(new_toks) input = "[ABC][DEF][ABC][DEF]" if self.space_between_special_tokens: output = "[ABC] [DEF] [ABC] [DEF]" else: output = input encoded = tokenizer.encode(input.split(), xpaths=xpaths, add_special_tokens=False) decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens) self.assertIn(decoded, [output, output.lower()]) @unittest.skip("Not implemented") def test_right_and_left_truncation(self): pass def test_encode_plus_with_padding(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, nodes) padding_size = 10 padding_idx = tokenizer.pad_token_id encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, return_special_tokens_mask=True) input_ids = encoded_sequence["input_ids"] special_tokens_mask = encoded_sequence["special_tokens_mask"] sequence_length = len(input_ids) # Test 'longest' and 'no_padding' don't do anything tokenizer.padding_side = "right" not_padded_sequence = tokenizer.encode_plus( nodes, xpaths=xpaths, padding=False, return_special_tokens_mask=True, ) not_padded_input_ids = not_padded_sequence["input_ids"] not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"] not_padded_sequence_length = len(not_padded_input_ids) self.assertTrue(sequence_length == not_padded_sequence_length) self.assertTrue(input_ids == not_padded_input_ids) self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask) not_padded_sequence = tokenizer.encode_plus( nodes, xpaths=xpaths, padding=False, return_special_tokens_mask=True, ) not_padded_input_ids = not_padded_sequence["input_ids"] not_padded_special_tokens_mask = not_padded_sequence["special_tokens_mask"] not_padded_sequence_length = len(not_padded_input_ids) self.assertTrue(sequence_length == not_padded_sequence_length) self.assertTrue(input_ids == not_padded_input_ids) self.assertTrue(special_tokens_mask == not_padded_special_tokens_mask) # Test right padding tokenizer.padding_side = "right" right_padded_sequence = tokenizer.encode_plus( nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length", return_special_tokens_mask=True, ) right_padded_input_ids = right_padded_sequence["input_ids"] right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"] right_padded_sequence_length = len(right_padded_input_ids) self.assertTrue(sequence_length + padding_size == right_padded_sequence_length) self.assertTrue(input_ids + [padding_idx] * padding_size == right_padded_input_ids) self.assertTrue(special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask) # Test left padding tokenizer.padding_side = "left" left_padded_sequence = tokenizer.encode_plus( nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length", return_special_tokens_mask=True, ) left_padded_input_ids = left_padded_sequence["input_ids"] left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"] left_padded_sequence_length = len(left_padded_input_ids) self.assertTrue(sequence_length + padding_size == left_padded_sequence_length) self.assertTrue([padding_idx] * padding_size + input_ids == left_padded_input_ids) self.assertTrue([1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask) if "token_type_ids" in tokenizer.model_input_names: token_type_ids = encoded_sequence["token_type_ids"] left_padded_token_type_ids = left_padded_sequence["token_type_ids"] right_padded_token_type_ids = right_padded_sequence["token_type_ids"] assert token_type_ids + [0] * padding_size == right_padded_token_type_ids assert [0] * padding_size + token_type_ids == left_padded_token_type_ids if "attention_mask" in tokenizer.model_input_names: attention_mask = encoded_sequence["attention_mask"] right_padded_attention_mask = right_padded_sequence["attention_mask"] left_padded_attention_mask = left_padded_sequence["attention_mask"] self.assertTrue(attention_mask + [0] * padding_size == right_padded_attention_mask) self.assertTrue([0] * padding_size + attention_mask == left_padded_attention_mask) def test_internal_consistency(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() tokens = [] for word in nodes: tokens.extend(tokenizer.tokenize(word)) ids = tokenizer.convert_tokens_to_ids(tokens) ids_2 = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) self.assertListEqual(ids, ids_2) tokens_2 = tokenizer.convert_ids_to_tokens(ids) self.assertNotEqual(len(tokens_2), 0) text_2 = tokenizer.decode(ids) self.assertIsInstance(text_2, str) def test_mask_output(self): tokenizers = self.get_tokenizers(fast=False, do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() if ( tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer" and "token_type_ids" in tokenizer.model_input_names ): information = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True) sequences, mask = information["input_ids"], information["token_type_ids"] self.assertEqual(len(sequences), len(mask)) def test_number_of_added_tokens(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # test 1: single sequence nodes, xpaths = self.get_nodes_and_xpaths() sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) attached_sequences = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True) # Method is implemented (e.g. not GPT-2) if len(attached_sequences) != 2: self.assertEqual( tokenizer.num_special_tokens_to_add(pair=False), len(attached_sequences) - len(sequences) ) # test 2: two sequences question, nodes, xpaths = self.get_question_nodes_and_xpaths() sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=False) attached_sequences = tokenizer.encode(question, nodes, xpaths=xpaths, add_special_tokens=True) # Method is implemented (e.g. not GPT-2) if len(attached_sequences) != 2: self.assertEqual( tokenizer.num_special_tokens_to_add(pair=True), len(attached_sequences) - len(sequences) ) def test_padding_to_max_length(self): """We keep this test for backward compatibility but it should be removed when `pad_to_max_length` will be deprecated""" tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() padding_size = 10 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, nodes) padding_idx = tokenizer.pad_token_id # Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "right" encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths) sequence_length = len(encoded_sequence) # FIXME: the next line should be padding(max_length) to avoid warning padded_sequence = tokenizer.encode( nodes, xpaths=xpaths, max_length=sequence_length + padding_size, pad_to_max_length=True ) padded_sequence_length = len(padded_sequence) assert sequence_length + padding_size == padded_sequence_length assert encoded_sequence + [padding_idx] * padding_size == padded_sequence # Check that nothing is done when a maximum length is not specified encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths) sequence_length = len(encoded_sequence) tokenizer.padding_side = "right" padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, pad_to_max_length=True) padded_sequence_right_length = len(padded_sequence_right) assert sequence_length == padded_sequence_right_length assert encoded_sequence == padded_sequence_right def test_padding(self, max_length=50): 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) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id) pad_token_id = tokenizer_p.pad_token_id # Encode - Simple input nodes, xpaths = self.get_nodes_and_xpaths() input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True) input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True) self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length") input_p = tokenizer_p.encode(nodes, xpaths=xpaths, max_length=max_length, padding="max_length") self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.encode(nodes, xpaths=xpaths, padding="longest") input_p = tokenizer_p.encode(nodes, xpaths=xpaths, padding=True) self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id) # Encode - Pair input question, nodes, xpaths = self.get_question_nodes_and_xpaths() input_r = tokenizer_r.encode( question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True ) input_p = tokenizer_p.encode( question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True ) self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.encode( question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length" ) input_p = tokenizer_p.encode( question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length" ) self.assert_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.encode(question, nodes, xpaths=xpaths, padding=True) input_p = tokenizer_p.encode(question, nodes, xpaths=xpaths, padding="longest") self.assert_padded_input_match(input_r, input_p, len(input_r), pad_token_id) # Encode_plus - Simple input nodes, xpaths = self.get_nodes_and_xpaths() input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True) input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True) self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, max_length=max_length, padding="max_length") input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, max_length=max_length, padding="max_length") self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, padding="longest") input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, padding=True) self.assert_padded_input_match( input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id ) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) # Encode_plus - Pair input question, nodes, xpaths = self.get_question_nodes_and_xpaths() input_r = tokenizer_r.encode_plus( question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True ) input_p = tokenizer_p.encode_plus( question, nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True ) self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) input_r = tokenizer_r.encode_plus( question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length" ) input_p = tokenizer_p.encode_plus( question, nodes, xpaths=xpaths, max_length=max_length, padding="max_length" ) self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) input_r = tokenizer_r.encode_plus(question, nodes, xpaths=xpaths, padding="longest") input_p = tokenizer_p.encode_plus(question, nodes, xpaths=xpaths, padding=True) self.assert_padded_input_match( input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id ) self.assertSequenceEqual(input_r["attention_mask"], input_p["attention_mask"]) # Batch_encode_plus - Simple input nodes, xpaths = self.get_nodes_and_xpaths_batch() input_r = tokenizer_r.batch_encode_plus( nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True, ) input_p = tokenizer_p.batch_encode_plus( nodes, xpaths=xpaths, max_length=max_length, pad_to_max_length=True, ) self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.batch_encode_plus( nodes, xpaths=xpaths, max_length=max_length, padding="max_length", ) input_p = tokenizer_p.batch_encode_plus( nodes, xpaths=xpaths, max_length=max_length, padding="max_length", ) self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.batch_encode_plus( nodes, xpaths=xpaths, max_length=max_length, padding="longest", ) input_p = tokenizer_p.batch_encode_plus( nodes, xpaths=xpaths, max_length=max_length, padding=True, ) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, padding="longest") input_p = tokenizer_p.batch_encode_plus(nodes, xpaths=xpaths, padding=True) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) # Batch_encode_plus - Pair input questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch() input_r = tokenizer_r.batch_encode_plus( list(zip(questions, nodes)), is_pair=True, xpaths=xpaths, max_length=max_length, truncation=True, padding="max_length", ) input_p = tokenizer_p.batch_encode_plus( list(zip(questions, nodes)), is_pair=True, xpaths=xpaths, max_length=max_length, truncation=True, padding="max_length", ) self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) input_r = tokenizer_r.batch_encode_plus( list(zip(questions, nodes)), is_pair=True, xpaths=xpaths, padding=True, ) input_p = tokenizer_p.batch_encode_plus( list(zip(questions, nodes)), is_pair=True, xpaths=xpaths, padding="longest", ) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) # Using pad on single examples after tokenization nodes, xpaths = self.get_nodes_and_xpaths() input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths) input_r = tokenizer_r.pad(input_r) input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths) input_p = tokenizer_r.pad(input_p) self.assert_padded_input_match( input_r["input_ids"], input_p["input_ids"], len(input_r["input_ids"]), pad_token_id ) # Using pad on single examples after tokenization input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths) input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length") input_p = tokenizer_r.encode_plus(nodes, xpaths=xpaths) input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length") self.assert_padded_input_match(input_r["input_ids"], input_p["input_ids"], max_length, pad_token_id) # Using pad after tokenization nodes, xpaths = self.get_nodes_and_xpaths_batch() input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths) input_r = tokenizer_r.pad(input_r) input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths) input_p = tokenizer_r.pad(input_p) self.assert_batch_padded_input_match(input_r, input_p, len(input_r["input_ids"][0]), pad_token_id) # Using pad after tokenization nodes, xpaths = self.get_nodes_and_xpaths_batch() input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths) input_r = tokenizer_r.pad(input_r, max_length=max_length, padding="max_length") input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths) input_p = tokenizer_r.pad(input_p, max_length=max_length, padding="max_length") self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id) def test_call(self): # Tests that all call wrap to encode_plus and batch_encode_plus tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Test not batched nodes, xpaths = self.get_nodes_and_xpaths() encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths) encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths) self.assertEqual(encoded_sequences_1, encoded_sequences_2) # Test not batched pairs question, nodes, xpaths = self.get_question_nodes_and_xpaths() encoded_sequences_1 = tokenizer.encode_plus(nodes, xpaths=xpaths) encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths) self.assertEqual(encoded_sequences_1, encoded_sequences_2) # Test batched nodes, xpaths = self.get_nodes_and_xpaths_batch() encoded_sequences_1 = tokenizer.batch_encode_plus(nodes, is_pair=False, xpaths=xpaths) encoded_sequences_2 = tokenizer(nodes, xpaths=xpaths) self.assertEqual(encoded_sequences_1, encoded_sequences_2) def test_batch_encode_plus_batch_sequence_length(self): # Tests that all encoded values have the correct size tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths_batch() encoded_sequences = [ tokenizer.encode_plus(nodes_example, xpaths=xpaths_example) for nodes_example, xpaths_example in zip(nodes, xpaths) ] encoded_sequences_batch = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, padding=False ) self.assertListEqual( encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch) ) maximum_length = len( max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len) ) # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, nodes) encoded_sequences_padded = [ tokenizer.encode_plus( nodes_example, xpaths=xpaths_example, max_length=maximum_length, padding="max_length" ) for nodes_example, xpaths_example in zip(nodes, xpaths) ] encoded_sequences_batch_padded = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, padding=True ) self.assertListEqual( encoded_sequences_padded, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded), ) # check 'longest' is unsensitive to a max length encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, padding=True ) encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding="longest" ) for key in encoded_sequences_batch_padded_1.keys(): self.assertListEqual( encoded_sequences_batch_padded_1[key], encoded_sequences_batch_padded_2[key], ) # check 'no_padding' is unsensitive to a max length encoded_sequences_batch_padded_1 = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, padding=False ) encoded_sequences_batch_padded_2 = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, max_length=maximum_length + 10, padding=False ) for key in encoded_sequences_batch_padded_1.keys(): self.assertListEqual( encoded_sequences_batch_padded_1[key], encoded_sequences_batch_padded_2[key], ) @unittest.skip("batch_encode_plus does not handle overflowing tokens.") def test_batch_encode_plus_overflowing_tokens(self): pass def test_batch_encode_plus_padding(self): # Test that padded sequences are equivalent between batch_encode_plus and encode_plus # Right padding tests tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths_batch() max_length = 100 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, nodes) encoded_sequences = [ tokenizer.encode_plus( nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length" ) for nodes_example, xpaths_example in zip(nodes, xpaths) ] encoded_sequences_batch = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, max_length=max_length, padding="max_length" ) self.assertListEqual( encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch) ) # Left padding tests tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): tokenizer.padding_side = "left" nodes, xpaths = self.get_nodes_and_xpaths_batch() max_length = 100 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, nodes) encoded_sequences = [ tokenizer.encode_plus( nodes_example, xpaths=xpaths_example, max_length=max_length, padding="max_length" ) for nodes_example, xpaths_example in zip(nodes, xpaths) ] encoded_sequences_batch = tokenizer.batch_encode_plus( nodes, is_pair=False, xpaths=xpaths, max_length=max_length, padding="max_length" ) self.assertListEqual( encoded_sequences, self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch) ) def test_padding_to_multiple_of(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): if tokenizer.pad_token is None: self.skipTest("No padding token.") else: nodes, xpaths = self.get_nodes_and_xpaths() # empty_tokens = tokenizer([""], [[]], padding=True, pad_to_multiple_of=8) normal_tokens = tokenizer(nodes, xpaths=xpaths, padding=True, pad_to_multiple_of=8) # for key, value in empty_tokens.items(): # self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") for key, value in normal_tokens.items(): self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") normal_tokens = tokenizer(nodes, xpaths=xpaths, pad_to_multiple_of=8) for key, value in normal_tokens.items(): self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") # Should also work with truncation normal_tokens = tokenizer( nodes, xpaths=xpaths, padding=True, truncation=True, pad_to_multiple_of=8 ) for key, value in normal_tokens.items(): self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8") # truncation to something which is not a multiple of pad_to_multiple_of raises an error self.assertRaises( ValueError, tokenizer.__call__, nodes, xpaths=xpaths, padding=True, truncation=True, max_length=12, pad_to_multiple_of=8, ) def test_tokenizer_slow_store_full_signature(self): signature = inspect.signature(self.tokenizer_class.__init__) tokenizer = self.get_tokenizer() for parameter_name, parameter in signature.parameters.items(): if parameter.default != inspect.Parameter.empty: self.assertIn(parameter_name, tokenizer.init_kwargs) def test_build_inputs_with_special_tokens(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return 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) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) # Input tokens id nodes, xpaths = self.get_nodes_and_xpaths() input_simple = tokenizer_p.encode(nodes, xpaths=xpaths, add_special_tokens=False) input_pair = tokenizer_p.encode(nodes, xpaths=xpaths, add_special_tokens=False) # Generate output output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple) output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple) self.assertEqual(output_p, output_r) # Generate pair output output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair) output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair) self.assertEqual(output_p, output_r) def test_special_tokens_mask_input_pairs(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( nodes, xpaths=xpaths, add_special_tokens=True, return_special_tokens_mask=True, # add_prefix_space=False, ) 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) def test_special_tokens_mask(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() # Testing single inputs encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( nodes, xpaths=xpaths, 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 for i, x in enumerate(encoded_sequence_w_special) if not special_tokens_mask[i]] self.assertEqual(encoded_sequence, filtered_sequence) def test_save_and_load_tokenizer(self): # safety check on max_len default value so we are sure the test works tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): self.assertNotEqual(tokenizer.model_max_length, 42) # Now let's start the test tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Isolate this from the other tests because we save additional tokens/etc nodes, xpaths = self.get_nodes_and_xpaths() tmpdirname = tempfile.mkdtemp() before_tokens = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) before_vocab = tokenizer.get_vocab() tokenizer.save_pretrained(tmpdirname) after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname) after_tokens = after_tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) after_vocab = after_tokenizer.get_vocab() self.assertListEqual(before_tokens, after_tokens) self.assertDictEqual(before_vocab, after_vocab) shutil.rmtree(tmpdirname) def test_right_and_left_padding(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() sequence = "Sequence" padding_size = 10 # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer, sequence) padding_idx = tokenizer.pad_token_id # RIGHT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "right" encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths) sequence_length = len(encoded_sequence) padded_sequence = tokenizer.encode( nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length" ) padded_sequence_length = len(padded_sequence) assert sequence_length + padding_size == padded_sequence_length assert encoded_sequence + [padding_idx] * padding_size == padded_sequence # LEFT PADDING - Check that it correctly pads when a maximum length is specified along with the padding flag set to True tokenizer.padding_side = "left" encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths) sequence_length = len(encoded_sequence) padded_sequence = tokenizer.encode( nodes, xpaths=xpaths, max_length=sequence_length + padding_size, padding="max_length" ) padded_sequence_length = len(padded_sequence) assert sequence_length + padding_size == padded_sequence_length assert [padding_idx] * padding_size + encoded_sequence == padded_sequence # RIGHT & LEFT PADDING - Check that nothing is done for 'longest' and 'no_padding' encoded_sequence = tokenizer.encode(nodes, xpaths=xpaths) sequence_length = len(encoded_sequence) tokenizer.padding_side = "right" padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths, padding=True) padded_sequence_right_length = len(padded_sequence_right) assert sequence_length == padded_sequence_right_length assert encoded_sequence == padded_sequence_right tokenizer.padding_side = "left" padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding="longest") padded_sequence_left_length = len(padded_sequence_left) assert sequence_length == padded_sequence_left_length assert encoded_sequence == padded_sequence_left tokenizer.padding_side = "right" padded_sequence_right = tokenizer.encode(nodes, xpaths=xpaths) padded_sequence_right_length = len(padded_sequence_right) assert sequence_length == padded_sequence_right_length assert encoded_sequence == padded_sequence_right tokenizer.padding_side = "left" padded_sequence_left = tokenizer.encode(nodes, xpaths=xpaths, padding=False) padded_sequence_left_length = len(padded_sequence_left) assert sequence_length == padded_sequence_left_length assert encoded_sequence == padded_sequence_left def test_token_type_ids(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # test 1: single sequence nodes, xpaths = self.get_nodes_and_xpaths() output = tokenizer(nodes, xpaths=xpaths, return_token_type_ids=True) # Assert that the token type IDs have the same length as the input IDs self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"])) # Assert that the token type IDs have the same length as the attention mask self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"])) self.assertIn(0, output["token_type_ids"]) self.assertNotIn(1, output["token_type_ids"]) # test 2: two sequences (question + nodes) question, nodes, xpaths = self.get_question_nodes_and_xpaths() output = tokenizer(question, nodes, xpaths, return_token_type_ids=True) # Assert that the token type IDs have the same length as the input IDs self.assertEqual(len(output["token_type_ids"]), len(output["input_ids"])) # Assert that the token type IDs have the same length as the attention mask self.assertEqual(len(output["token_type_ids"]), len(output["attention_mask"])) self.assertIn(0, output["token_type_ids"]) def test_offsets_mapping(self): 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) text = ["a", "wonderful", "test"] xpaths = ["html/body" for _ in range(len(text))] # No pair tokens_with_offsets = tokenizer_r.encode_plus( text, xpaths=xpaths, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True, ) added_tokens = tokenizer_r.num_special_tokens_to_add(False) offsets = tokens_with_offsets["offset_mapping"] # Assert there is the same number of tokens and offsets self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"])) # Assert there is online added_tokens special_tokens self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens) # Pairs text = "what's his name" pair = ["a", "wonderful", "test"] xpaths = ["html/body" for _ in range(len(pair))] tokens_with_offsets = tokenizer_r.encode_plus( text, pair, xpaths=xpaths, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True, ) added_tokens = tokenizer_r.num_special_tokens_to_add(True) offsets = tokens_with_offsets["offset_mapping"] # Assert there is the same number of tokens and offsets self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"])) # Assert there is online added_tokens special_tokens self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens) @require_torch @slow def test_torch_encode_plus_sent_to_model(self): import torch from transformers import MODEL_MAPPING, TOKENIZER_MAPPING MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(MODEL_MAPPING, TOKENIZER_MAPPING) tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING: return config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__] config = config_class() if config.is_encoder_decoder or config.pad_token_id is None: return model = model_class(config) # Make sure the model contains at least the full vocabulary size in its embedding matrix is_using_common_embeddings = hasattr(model.get_input_embeddings(), "weight") assert ( (model.get_input_embeddings().weight.shape[0] >= len(tokenizer)) if is_using_common_embeddings else True ) # Build sequence nodes, xpaths = self.get_nodes_and_xpaths() encoded_sequence = tokenizer.encode_plus(nodes, xpaths=xpaths, return_tensors="pt") batch_encoded_sequence = tokenizer.batch_encode_plus( [nodes, nodes], [xpaths, xpaths], return_tensors="pt" ) # This should not fail with torch.no_grad(): # saves some time model(**encoded_sequence) model(**batch_encoded_sequence) def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: return if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer() nodes, xpaths = self.get_nodes_and_xpaths() ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) rust_ids = rust_tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) self.assertListEqual(ids, rust_ids) ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True) rust_ids = rust_tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=True) self.assertListEqual(ids, rust_ids) def test_tokenization_python_rust_equals(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return 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) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) nodes, xpaths = self.get_nodes_and_xpaths() # Ensure basic input match input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths) input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths) for key in filter( lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"], input_p.keys(), ): self.assertSequenceEqual(input_p[key], input_r[key]) input_pairs_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths) input_pairs_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths) for key in filter( lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"], input_p.keys(), ): self.assertSequenceEqual(input_pairs_p[key], input_pairs_r[key]) nodes = ["hello" for _ in range(1000)] xpaths = ["html/body" for _ in range(1000)] # Ensure truncation match input_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, max_length=512, truncation=True) input_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, max_length=512, truncation=True) for key in filter( lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"], input_p.keys(), ): self.assertSequenceEqual(input_p[key], input_r[key]) # Ensure truncation with stride match input_p = tokenizer_p.encode_plus( nodes, xpaths=xpaths, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True ) input_r = tokenizer_r.encode_plus( nodes, xpaths=xpaths, max_length=512, truncation=True, stride=3, return_overflowing_tokens=True ) for key in filter( lambda x: x in ["input_ids", "token_type_ids", "attention_mask", "xpath_tags_seq", "xpath_subs_seq"], input_p.keys(), ): self.assertSequenceEqual(input_p[key], input_r[key][0]) def test_embeded_special_tokens(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return 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) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) nodes, xpaths = self.get_nodes_and_xpaths() tokens_r = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True) tokens_p = tokenizer_p.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True) for key in tokens_p.keys(): self.assertEqual(tokens_r[key], tokens_p[key]) if "token_type_ids" in tokens_r: self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"])) tokens_r = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]) tokens_p = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]) self.assertSequenceEqual(tokens_r, tokens_p) def test_compare_add_special_tokens(self): 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_num_special_tokens_to_add = tokenizer_r.num_special_tokens_to_add(pair=False) nodes, xpaths = self.get_nodes_and_xpaths() # tokenize() no_special_tokens = tokenizer_r.tokenize(" ".join(nodes), add_special_tokens=False) with_special_tokens = tokenizer_r.tokenize(" ".join(nodes), add_special_tokens=True) self.assertEqual(len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add) # encode() no_special_tokens = tokenizer_r.encode(nodes, xpaths=xpaths, add_special_tokens=False) with_special_tokens = tokenizer_r.encode(nodes, xpaths=xpaths, add_special_tokens=True) self.assertEqual(len(no_special_tokens), len(with_special_tokens) - simple_num_special_tokens_to_add) # encode_plus() no_special_tokens = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=False) with_special_tokens = tokenizer_r.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True) for key in no_special_tokens.keys(): self.assertEqual( len(no_special_tokens[key]), len(with_special_tokens[key]) - simple_num_special_tokens_to_add, ) # # batch_encode_plus nodes, xpaths = self.get_nodes_and_xpaths_batch() no_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, add_special_tokens=False) with_special_tokens = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths, add_special_tokens=True) for key in no_special_tokens.keys(): for i_no, i_with in zip(no_special_tokens[key], with_special_tokens[key]): self.assertEqual(len(i_no), len(i_with) - simple_num_special_tokens_to_add) @slow def test_markuplm_truncation_integration_test(self): nodes, xpaths = self.get_nodes_and_xpaths() tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", model_max_length=512) for i in range(12, 512): new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, max_length=i, truncation=True) # Ensure that the input IDs are less than the max length defined. self.assertLessEqual(len(new_encoded_inputs), i) tokenizer.model_max_length = 20 new_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True) dropped_encoded_inputs = tokenizer.encode(nodes, xpaths=xpaths, truncation=True) # Ensure that the input IDs are still truncated when no max_length is specified self.assertListEqual(new_encoded_inputs, dropped_encoded_inputs) self.assertLessEqual(len(new_encoded_inputs), 20) @is_pt_tf_cross_test def test_batch_encode_plus_tensors(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths_batch() # A Tensor cannot be build by sequences which are not the same size self.assertRaises(ValueError, tokenizer.batch_encode_plus, nodes, xpaths=xpaths, return_tensors="pt") self.assertRaises(ValueError, tokenizer.batch_encode_plus, nodes, xpaths=xpaths, return_tensors="tf") if tokenizer.pad_token_id is None: self.assertRaises( ValueError, tokenizer.batch_encode_plus, nodes, xpaths=xpaths, padding=True, return_tensors="pt", ) self.assertRaises( ValueError, tokenizer.batch_encode_plus, nodes, xpaths=xpaths, padding="longest", return_tensors="tf", ) else: pytorch_tensor = tokenizer.batch_encode_plus( nodes, xpaths=xpaths, padding=True, return_tensors="pt" ) tensorflow_tensor = tokenizer.batch_encode_plus( nodes, xpaths=xpaths, padding="longest", return_tensors="tf" ) encoded_sequences = tokenizer.batch_encode_plus(nodes, xpaths=xpaths, padding=True) for key in encoded_sequences.keys(): pytorch_value = pytorch_tensor[key].tolist() tensorflow_value = tensorflow_tensor[key].numpy().tolist() encoded_value = encoded_sequences[key] self.assertEqual(pytorch_value, tensorflow_value, encoded_value) def test_sequence_ids(self): tokenizers = self.get_tokenizers() for tokenizer in tokenizers: if not tokenizer.is_fast: continue with self.subTest(f"{tokenizer.__class__.__name__}"): seq_0 = "Test this method." seq_1 = ["With", "these", "inputs."] xpaths = ["html/body" for _ in range(len(seq_1))] # We want to have sequence 0 and sequence 1 are tagged # respectively with 0 and 1 token_ids # (regardless of whether the model use token type ids) # We use this assumption in the QA pipeline among other place output = tokenizer(seq_0.split(), xpaths=xpaths) self.assertIn(0, output.sequence_ids()) output = tokenizer(seq_0, seq_1, xpaths=xpaths) self.assertIn(0, output.sequence_ids()) self.assertIn(1, output.sequence_ids()) if tokenizer.num_special_tokens_to_add(pair=True): self.assertIn(None, output.sequence_ids()) def test_special_tokens_initialization(self): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): added_tokens = [AddedToken("<special>", lstrip=True)] tokenizer_r = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) nodes = "Hey this is a <special> token".split() xpaths = ["html/body" for _ in range(len(nodes))] r_output = tokenizer_r.encode(nodes, xpaths=xpaths) special_token_id = tokenizer_r.encode(["<special>"], xpaths=["html/body"], add_special_tokens=False)[0] self.assertTrue(special_token_id in r_output) if self.test_slow_tokenizer: tokenizer_cr = self.rust_tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) tokenizer_p = self.tokenizer_class.from_pretrained( pretrained_name, additional_special_tokens=added_tokens, **kwargs ) nodes = "Hey this is a <special> token".split() xpaths = ["html/body" for _ in range(len(nodes))] p_output = tokenizer_p.encode(nodes, xpaths=xpaths) cr_output = tokenizer_cr.encode(nodes, xpaths=xpaths) self.assertEqual(p_output, r_output) self.assertEqual(cr_output, r_output) self.assertTrue(special_token_id in p_output) self.assertTrue(special_token_id in cr_output) def test_split_special_tokens(self): # TODO this is only possible for slow currently tokenizer = self.get_tokenizer() special_token = "[SPECIAL_TOKEN]" tokenizer.add_special_tokens({"additional_special_tokens": [special_token]}) encoded_special_token = tokenizer.tokenize(special_token, add_special_tokens=False) self.assertEqual(len(encoded_special_token), 1) encoded_split_special_token = tokenizer.tokenize( special_token, add_special_tokens=False, split_special_tokens=True ) self.assertTrue(len(encoded_split_special_token) > 1) def test_training_new_tokenizer(self): # This feature only exists for fast tokenizers if not self.test_rust_tokenizer: return tokenizer = self.get_rust_tokenizer() new_tokenizer = tokenizer.train_new_from_iterator(SMALL_TRAINING_CORPUS, 100) # Test we can use the new tokenizer with something not seen during training text = [["this", "is", "the"], ["how", "are", "you"]] xpaths = [["html/body"] * 3, ["html/body"] * 3] inputs = new_tokenizer(text, xpaths=xpaths) self.assertEqual(len(inputs["input_ids"]), 2) decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True) expected_result = ( # original expected result "this is the" seems contradicts to FacebookAI/roberta-based tokenizer "thisisthe" ) if tokenizer.backend_tokenizer.normalizer is not None: expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result) self.assertEqual(expected_result, decoded_input) # We check that the parameters of the tokenizer remained the same # Check we have the same number of added_tokens for both pair and non-pair inputs. self.assertEqual(tokenizer.num_special_tokens_to_add(False), new_tokenizer.num_special_tokens_to_add(False)) self.assertEqual(tokenizer.num_special_tokens_to_add(True), new_tokenizer.num_special_tokens_to_add(True)) # Check we have the correct max_length for both pair and non-pair inputs. self.assertEqual(tokenizer.max_len_single_sentence, new_tokenizer.max_len_single_sentence) self.assertEqual(tokenizer.max_len_sentences_pair, new_tokenizer.max_len_sentences_pair) # Assert the set of special tokens match as we didn't ask to change them self.assertSequenceEqual( tokenizer.all_special_tokens_extended, new_tokenizer.all_special_tokens_extended, ) self.assertDictEqual(tokenizer.special_tokens_map, new_tokenizer.special_tokens_map) def test_training_new_tokenizer_with_special_tokens_change(self): # This feature only exists for fast tokenizers if not self.test_rust_tokenizer: return tokenizer = self.get_rust_tokenizer() # Test with a special tokens map class_signature = inspect.signature(tokenizer.__class__) if "cls_token" in class_signature.parameters: new_tokenizer = tokenizer.train_new_from_iterator( SMALL_TRAINING_CORPUS, 100, special_tokens_map={tokenizer.cls_token: "<cls>"} ) cls_id = new_tokenizer.get_vocab()["<cls>"] self.assertEqual(new_tokenizer.cls_token, "<cls>") self.assertEqual(new_tokenizer.cls_token_id, cls_id) # Create a new mapping from the special tokens defined in the original tokenizer special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy() special_tokens_list.remove("additional_special_tokens") special_tokens_map = {} for token in special_tokens_list: # Get the private one to avoid unnecessary warnings. if getattr(tokenizer, f"_{token}") is not None: special_token = getattr(tokenizer, token) special_tokens_map[special_token] = f"{special_token}a" # Train new tokenizer new_tokenizer = tokenizer.train_new_from_iterator( SMALL_TRAINING_CORPUS, 100, special_tokens_map=special_tokens_map ) # Check the changes for token in special_tokens_list: # Get the private one to avoid unnecessary warnings. if getattr(tokenizer, f"_{token}") is None: continue special_token = getattr(tokenizer, token) if special_token in special_tokens_map: new_special_token = getattr(new_tokenizer, token) self.assertEqual(special_tokens_map[special_token], new_special_token) new_id = new_tokenizer.get_vocab()[new_special_token] self.assertEqual(getattr(new_tokenizer, f"{token}_id"), new_id) # Check if the AddedToken / string format has been kept for special_token in tokenizer.all_special_tokens_extended: if isinstance(special_token, AddedToken) and special_token.content not in special_tokens_map: # The special token must appear identically in the list of the new tokenizer. self.assertTrue( special_token in new_tokenizer.all_special_tokens_extended, f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}", ) elif isinstance(special_token, AddedToken): # The special token must appear in the list of the new tokenizer as an object of type AddedToken with # the same parameters as the old AddedToken except the content that the user has requested to change. special_token_str = special_token.content new_special_token_str = special_tokens_map[special_token_str] find = False for candidate in new_tokenizer.all_special_tokens_extended: if ( isinstance(candidate, AddedToken) and candidate.content == new_special_token_str and candidate.lstrip == special_token.lstrip and candidate.rstrip == special_token.rstrip and candidate.normalized == special_token.normalized and candidate.single_word == special_token.single_word ): find = True break self.assertTrue( find, f"'{new_special_token_str}' doesn't appear in the list " f"'{new_tokenizer.all_special_tokens_extended}' as an AddedToken with the same parameters as " f"'{special_token}' in the list {tokenizer.all_special_tokens_extended}", ) elif special_token not in special_tokens_map: # The special token must appear identically in the list of the new tokenizer. self.assertTrue( special_token in new_tokenizer.all_special_tokens_extended, f"'{special_token}' should be in {new_tokenizer.all_special_tokens_extended}", ) else: # The special token must appear in the list of the new tokenizer as an object of type string. self.assertTrue(special_tokens_map[special_token] in new_tokenizer.all_special_tokens_extended) # Test we can use the new tokenizer with something not seen during training nodes = [["this", "is"], ["hello", "🤗"]] xpaths = [["html/body"] * 2, ["html/body"] * 2] inputs = new_tokenizer(nodes, xpaths=xpaths) self.assertEqual(len(inputs["input_ids"]), 2) decoded_input = new_tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True) expected_result = "thisis" # same as line 1399 if tokenizer.backend_tokenizer.normalizer is not None: expected_result = tokenizer.backend_tokenizer.normalizer.normalize_str(expected_result) self.assertEqual(expected_result, decoded_input) def test_prepare_for_model(self): tokenizers = self.get_tokenizers(do_lower_case=False) for tokenizer in tokenizers: # only test prepare_for_model for the slow tokenizer if tokenizer.__class__.__name__ == "MarkupLMTokenizerFast": continue with self.subTest(f"{tokenizer.__class__.__name__}"): nodes, xpaths = self.get_nodes_and_xpaths() prepared_input_dict = tokenizer.prepare_for_model(nodes, xpaths=xpaths, add_special_tokens=True) input_dict = tokenizer.encode_plus(nodes, xpaths=xpaths, add_special_tokens=True) self.assertEqual(input_dict, prepared_input_dict) def test_padding_different_model_input_name(self): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return 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) tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) self.assertEqual(tokenizer_p.pad_token_id, tokenizer_r.pad_token_id) pad_token_id = tokenizer_p.pad_token_id nodes, xpaths = self.get_nodes_and_xpaths_batch() input_r = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths) input_p = tokenizer_r.batch_encode_plus(nodes, xpaths=xpaths) # rename encoded batch to "inputs" input_r["inputs"] = input_r[tokenizer_r.model_input_names[0]] del input_r[tokenizer_r.model_input_names[0]] input_p["inputs"] = input_p[tokenizer_p.model_input_names[0]] del input_p[tokenizer_p.model_input_names[0]] # Renaming `input_ids` to `inputs` tokenizer_r.model_input_names = ["inputs"] + tokenizer_r.model_input_names[1:] tokenizer_p.model_input_names = ["inputs"] + tokenizer_p.model_input_names[1:] input_r = tokenizer_r.pad(input_r, padding="longest") input_p = tokenizer_r.pad(input_p, padding="longest") max_length = len(input_p["inputs"][0]) self.assert_batch_padded_input_match( input_r, input_p, max_length, pad_token_id, model_main_input_name="inputs" ) def test_batch_encode_dynamic_overflowing(self): """ When calling batch_encode with multiple sequences, it can return different number of overflowing encoding for each sequence: [ Sequence 1: [Encoding 1, Encoding 2], Sequence 2: [Encoding 1], Sequence 3: [Encoding 1, Encoding 2, ... Encoding N] ] This needs to be padded so that it can represented as a tensor """ for tokenizer, pretrained_name, kwargs in self.tokenizers_list: tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name}, {tokenizer.__class__.__name__})"): if is_torch_available(): returned_tensor = "pt" elif is_tf_available(): returned_tensor = "tf" else: returned_tensor = "jax" # Single example nodes, xpaths = self.get_nodes_and_xpaths() tokens = tokenizer.encode_plus( nodes, xpaths=xpaths, max_length=1, padding=True, truncation=True, return_tensors=returned_tensor, return_overflowing_tokens=True, ) for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()): if "xpath" not in key: self.assertEqual(len(tokens[key].shape), 2) else: self.assertEqual(len(tokens[key].shape), 3) # Batch of examples # For these 2 examples, 3 training examples will be created nodes, xpaths = self.get_nodes_and_xpaths_batch() tokens = tokenizer.batch_encode_plus( nodes, xpaths=xpaths, max_length=6, padding=True, truncation="only_first", return_tensors=returned_tensor, return_overflowing_tokens=True, ) for key in filter(lambda x: "overflow_to_sample_mapping" not in x, tokens.keys()): if "xpath" not in key: self.assertEqual(len(tokens[key].shape), 2) self.assertEqual(tokens[key].shape[-1], 6) else: self.assertEqual(len(tokens[key].shape), 3) self.assertEqual(tokens[key].shape[-2], 6) @unittest.skip("TO DO: overwrite this very extensive test.") def test_alignement_methods(self): pass def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5): toks = [(i, tokenizer.decode([i], clean_up_tokenization_spaces=False)) for i in range(len(tokenizer))] toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks)) toks = list( filter( lambda t: [t[0]] == tokenizer.encode(t[1].split(" "), xpaths=len(t[1]) * ["html/body"], add_special_tokens=False), toks, ) ) if max_length is not None and len(toks) > max_length: toks = toks[:max_length] if min_length is not None and len(toks) < min_length and len(toks) > 0: while len(toks) < min_length: toks = toks + toks # toks_str = [t[1] for t in toks] toks_ids = [t[0] for t in toks] # Ensure consistency output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False) # an extra blank will cause inconsistency: ["a","b",] & "a b" """ if " " not in output_txt and len(toks_ids) > 1: output_txt = ( tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False) + " " + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False) ) """ if with_prefix_space: output_txt = " " + output_txt nodes = output_txt.split(" ") xpaths = ["html/body" for i in range(len(nodes))] output_ids = tokenizer.encode(nodes, xpaths=xpaths, add_special_tokens=False) return nodes, xpaths, output_ids def test_maximum_encoding_length_pair_input(self): # slow part fixed, fast part not tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): # Build a sequence from our model's vocabulary stride = 2 seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20) question_0 = " ".join(map(str, seq_0)) if len(ids) <= 2 + stride: seq_0 = (seq_0 + " ") * (2 + stride) ids = None seq0_tokens = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False) self.assertGreater(len(seq0_tokens["input_ids"]), 2 + stride) question_1 = "This is another sentence to be encoded." seq_1 = ["hello", "world"] xpaths_1 = ["html/body" for i in range(len(seq_1))] seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False) if abs(len(seq0_tokens["input_ids"]) - len(seq1_tokens["input_ids"])) <= 2: seq1_tokens_input_ids = seq1_tokens["input_ids"] + seq1_tokens["input_ids"] seq_1 = tokenizer.decode(seq1_tokens_input_ids, clean_up_tokenization_spaces=False) seq_1 = seq_1.split(" ") xpaths_1 = ["html/body" for i in range(len(seq_1))] seq1_tokens = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False) self.assertGreater(len(seq1_tokens["input_ids"]), 2 + stride) smallest = ( seq1_tokens["input_ids"] if len(seq0_tokens["input_ids"]) > len(seq1_tokens["input_ids"]) else seq0_tokens["input_ids"] ) # We are not using the special tokens - a bit too hard to test all the tokenizers with this # TODO try this again later sequence = tokenizer(question_0, seq_1, xpaths=xpaths_1, add_special_tokens=False) # Test with max model input length model_max_length = tokenizer.model_max_length self.assertEqual(model_max_length, 100) seq_2 = seq_0 * model_max_length question_2 = " ".join(map(str, seq_2)) xpaths_2 = xpaths_0 * model_max_length # assertgreater -> assertgreaterequal self.assertGreaterEqual(len(seq_2), model_max_length) sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False) total_length1 = len(sequence1["input_ids"]) sequence2 = tokenizer(question_2, seq_1, xpaths=xpaths_1, add_special_tokens=False) total_length2 = len(sequence2["input_ids"]) self.assertLess(total_length1, model_max_length, "Issue with the testing sequence, please update it.") self.assertGreater( total_length2, model_max_length, "Issue with the testing sequence, please update it." ) # Simple padding_strategies = ( [False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False] ) for padding_state in padding_strategies: with self.subTest(f"{tokenizer.__class__.__name__} Padding: {padding_state}"): for truncation_state in [True, "longest_first", "only_first"]: with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"): output = tokenizer( question_2, seq_1, xpaths=xpaths_1, padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"]), model_max_length) self.assertEqual(len(output["xpath_tags_seq"]), model_max_length) self.assertEqual(len(output["xpath_subs_seq"]), model_max_length) output = tokenizer( [question_2], [seq_1], xpaths=[xpaths_1], padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"][0]), model_max_length) self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length) self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length) # Simple output = tokenizer( question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation="only_second" ) self.assertEqual(len(output["input_ids"]), model_max_length) self.assertEqual(len(output["xpath_tags_seq"]), model_max_length) self.assertEqual(len(output["xpath_subs_seq"]), model_max_length) output = tokenizer( [question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation="only_second" ) self.assertEqual(len(output["input_ids"][0]), model_max_length) self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length) self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length) # Simple with no truncation # Reset warnings tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer( question_1, seq_2, xpaths=xpaths_2, padding=padding_state, truncation=False ) self.assertNotEqual(len(output["input_ids"]), model_max_length) self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length) self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer( [question_1], [seq_2], xpaths=[xpaths_2], padding=padding_state, truncation=False ) self.assertNotEqual(len(output["input_ids"][0]), model_max_length) self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length) self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) # Check the order of Sequence of input ids, overflowing tokens and xpath_tags_seq sequence with truncation truncated_first_sequence = ( tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][:-2] + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"] ) truncated_second_sequence = ( tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"] + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][:-2] ) truncated_longest_sequence = ( truncated_first_sequence if len(seq0_tokens) > len(seq1_tokens) else truncated_second_sequence ) overflow_first_sequence = ( tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"][-(2 + stride) :] + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"] ) overflow_second_sequence = ( tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"] + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["input_ids"][-(2 + stride) :] ) overflow_longest_sequence = ( overflow_first_sequence if len(seq0_tokens) > len(seq1_tokens) else overflow_second_sequence ) xpath_tags_seq_first = [[5] * 50] * ( len(tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"]) - 2 ) xpath_tags_seq_first_sequence = ( xpath_tags_seq_first + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"] ) overflowing_token_xpath_tags_seq_first_sequence_slow = [[5] * 50] * (2 + stride) overflowing_token_xpath_tags_seq_first_sequence_fast = [[5] * 50] * (2 + stride) + tokenizer( seq_1, xpaths=xpaths_1, add_special_tokens=False )["xpath_tags_seq"] xpath_tags_seq_second = [[5] * 50] * len( tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"] ) xpath_tags_seq_second_sequence = ( xpath_tags_seq_second + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][:-2] ) overflowing_token_xpath_tags_seq_second_sequence_slow = tokenizer( seq_1, xpaths=xpaths_1, add_special_tokens=False )["xpath_tags_seq"][-(2 + stride) :] overflowing_token_xpath_tags_seq_second_sequence_fast = [[5] * 50] * len( tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False)["input_ids"] ) + tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False)["xpath_tags_seq"][-(2 + stride) :] xpath_tags_seq_longest_sequence = ( xpath_tags_seq_first_sequence if len(seq0_tokens) > len(seq1_tokens) else xpath_tags_seq_second_sequence ) overflowing_token_xpath_tags_seq_longest_sequence_fast = ( overflowing_token_xpath_tags_seq_first_sequence_fast if len(seq0_tokens) > len(seq1_tokens) else overflowing_token_xpath_tags_seq_second_sequence_fast ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, MarkupLMTokenizerFast): information = tokenizer( question_0, seq_1, xpaths=xpaths_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="longest_first", return_overflowing_tokens=True, # add_prefix_space=False, ) truncated_sequence = information["input_ids"][0] overflowing_tokens = information["input_ids"][1] xpath_tags_seq = information["xpath_tags_seq"][0] overflowing_xpath_tags_seq = information["xpath_tags_seq"][1] self.assertEqual(len(information["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_longest_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest)) self.assertEqual(overflowing_tokens, overflow_longest_sequence) self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence) self.assertEqual(len(overflowing_xpath_tags_seq), 2 + stride + len(smallest)) self.assertEqual( overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast ) else: # No overflowing tokens when using 'longest' in python tokenizers with self.assertRaises(ValueError) as context: information = tokenizer( question_0, seq_1, xpaths=xpaths_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="longest_first", return_overflowing_tokens=True, # add_prefix_space=False, ) self.assertTrue( context.exception.args[0].startswith( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, MarkupLMTokenizerFast): information = tokenizer( question_0, seq_1, xpaths=xpaths_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation=True, return_overflowing_tokens=True, ) truncated_sequence = information["input_ids"][0] overflowing_tokens = information["input_ids"][1] xpath_tags_seq = information["xpath_tags_seq"][0] overflowing_xpath_tags_seq = information["xpath_tags_seq"][1] self.assertEqual(len(information["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_longest_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(smallest)) self.assertEqual(overflowing_tokens, overflow_longest_sequence) self.assertEqual(xpath_tags_seq, xpath_tags_seq_longest_sequence) self.assertEqual( overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_longest_sequence_fast ) else: # No overflowing tokens when using 'longest' in python tokenizers with self.assertRaises(ValueError) as context: information = tokenizer( question_0, seq_1, xpaths=xpaths_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation=True, return_overflowing_tokens=True, ) self.assertTrue( context.exception.args[0].startswith( "Not possible to return overflowing tokens for pair of sequences with the " "`longest_first`. Please select another truncation strategy than `longest_first`, " "for instance `only_second` or `only_first`." ) ) information_first_truncated = tokenizer( question_0, seq_1, xpaths=xpaths_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="only_first", return_overflowing_tokens=True, ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, MarkupLMTokenizerFast): truncated_sequence = information_first_truncated["input_ids"][0] overflowing_tokens = information_first_truncated["input_ids"][1] xpath_tags_seq = information_first_truncated["xpath_tags_seq"][0] overflowing_xpath_tags_seq = information_first_truncated["xpath_tags_seq"][1] self.assertEqual(len(information_first_truncated["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_first_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq1_tokens["input_ids"])) self.assertEqual(overflowing_tokens, overflow_first_sequence) self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence) # ISSUE HAPPENS HERE ↓ self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_fast) else: truncated_sequence = information_first_truncated["input_ids"] overflowing_tokens = information_first_truncated["overflowing_tokens"] overflowing_xpath_tags_seq = information_first_truncated["overflowing_xpath_tags_seq"] xpath_tags_seq = information_first_truncated["xpath_tags_seq"] self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_first_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, seq0_tokens["input_ids"][-(2 + stride) :]) self.assertEqual(xpath_tags_seq, xpath_tags_seq_first_sequence) self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_first_sequence_slow) information_second_truncated = tokenizer( question_0, seq_1, xpaths=xpaths_1, max_length=len(sequence["input_ids"]) - 2, add_special_tokens=False, stride=stride, truncation="only_second", return_overflowing_tokens=True, # add_prefix_space=False, ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, MarkupLMTokenizerFast): truncated_sequence = information_second_truncated["input_ids"][0] overflowing_tokens = information_second_truncated["input_ids"][1] xpath_tags_seq = information_second_truncated["xpath_tags_seq"][0] overflowing_xpath_tags_seq = information_second_truncated["xpath_tags_seq"][1] self.assertEqual(len(information_second_truncated["input_ids"]), 2) self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_second_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride + len(seq0_tokens["input_ids"])) self.assertEqual(overflowing_tokens, overflow_second_sequence) self.assertEqual(xpath_tags_seq, xpath_tags_seq_second_sequence) self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_second_sequence_fast) else: truncated_sequence = information_second_truncated["input_ids"] overflowing_tokens = information_second_truncated["overflowing_tokens"] xpath_tags_seq = information_second_truncated["xpath_tags_seq"] overflowing_xpath_tags_seq = information_second_truncated["overflowing_xpath_tags_seq"] self.assertEqual(len(truncated_sequence), len(sequence["input_ids"]) - 2) self.assertEqual(truncated_sequence, truncated_second_sequence) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, seq1_tokens["input_ids"][-(2 + stride) :]) self.assertEqual(xpath_tags_seq, xpath_tags_seq_second_sequence) self.assertEqual(overflowing_xpath_tags_seq, overflowing_token_xpath_tags_seq_second_sequence_slow) def test_maximum_encoding_length_single_input(self): tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100) for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): seq_0, xpaths_0, ids = self.get_clean_sequence(tokenizer, max_length=20) sequence = tokenizer(seq_0, xpaths=xpaths_0, add_special_tokens=False) total_length = len(sequence["input_ids"]) self.assertGreater(total_length, 4, "Issue with the testing sequence, please update it it's too short") # Test with max model input length model_max_length = tokenizer.model_max_length self.assertEqual(model_max_length, 100) seq_1 = seq_0 * model_max_length xpaths_1 = xpaths_0 * model_max_length sequence1 = tokenizer(seq_1, xpaths=xpaths_1, add_special_tokens=False) total_length1 = len(sequence1["input_ids"]) self.assertGreater( total_length1, model_max_length, "Issue with the testing sequence, please update it it's too short" ) # Simple padding_strategies = ( [False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False] ) for padding_state in padding_strategies: with self.subTest(f"Padding: {padding_state}"): for truncation_state in [True, "longest_first", "only_first"]: with self.subTest(f"Truncation: {truncation_state}"): output = tokenizer( seq_1, xpaths=xpaths_1, padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"]), model_max_length) self.assertEqual(len(output["xpath_tags_seq"]), model_max_length) self.assertEqual(len(output["xpath_subs_seq"]), model_max_length) output = tokenizer( [seq_1], xpaths=[xpaths_1], padding=padding_state, truncation=truncation_state, ) self.assertEqual(len(output["input_ids"][0]), model_max_length) self.assertEqual(len(output["xpath_tags_seq"][0]), model_max_length) self.assertEqual(len(output["xpath_subs_seq"][0]), model_max_length) # Simple with no truncation # Reset warnings tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer(seq_1, xpaths=xpaths_1, padding=padding_state, truncation=False) self.assertNotEqual(len(output["input_ids"]), model_max_length) self.assertNotEqual(len(output["xpath_tags_seq"]), model_max_length) self.assertNotEqual(len(output["xpath_subs_seq"]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) tokenizer.deprecation_warnings = {} with self.assertLogs("transformers", level="WARNING") as cm: output = tokenizer([seq_1], xpaths=[xpaths_1], padding=padding_state, truncation=False) self.assertNotEqual(len(output["input_ids"][0]), model_max_length) self.assertNotEqual(len(output["xpath_tags_seq"][0]), model_max_length) self.assertNotEqual(len(output["xpath_subs_seq"][0]), model_max_length) self.assertEqual(len(cm.records), 1) self.assertTrue( cm.records[0].message.startswith( "Token indices sequence length is longer than the specified maximum sequence length" " for this model" ) ) # Check the order of Sequence of input ids, overflowing tokens, xpath_tags_seq and xpath_subs_seq sequence with truncation stride = 2 information = tokenizer( seq_0, xpaths=xpaths_0, max_length=total_length - 2, add_special_tokens=False, stride=stride, truncation=True, return_overflowing_tokens=True, ) # Overflowing tokens are handled quite differently in slow and fast tokenizers if isinstance(tokenizer, MarkupLMTokenizerFast): truncated_sequence = information["input_ids"][0] overflowing_tokens = information["input_ids"][1] xpath_tags_seq = information["xpath_tags_seq"][0] overflowing_xpath_tags_seq = information["xpath_tags_seq"][1] self.assertEqual(len(information["input_ids"]), 2) self.assertEqual(len(truncated_sequence), total_length - 2) self.assertEqual(truncated_sequence, sequence["input_ids"][:-2]) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :]) self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2]) self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :]) else: truncated_sequence = information["input_ids"] overflowing_tokens = information["overflowing_tokens"] xpath_tags_seq = information["xpath_tags_seq"] overflowing_xpath_tags_seq = information["overflowing_xpath_tags_seq"] self.assertEqual(len(truncated_sequence), total_length - 2) self.assertEqual(truncated_sequence, sequence["input_ids"][:-2]) self.assertEqual(len(overflowing_tokens), 2 + stride) self.assertEqual(overflowing_tokens, sequence["input_ids"][-(2 + stride) :]) self.assertEqual(xpath_tags_seq, sequence["xpath_tags_seq"][:-2]) self.assertEqual(overflowing_xpath_tags_seq, sequence["xpath_tags_seq"][-(2 + stride) :]) @unittest.skip("MarkupLM tokenizer requires xpaths besides sequences.") def test_pretokenized_inputs(self): pass @unittest.skip("MarkupLM tokenizer always expects pretokenized inputs.") def test_compare_pretokenized_inputs(self): pass @unittest.skip("MarkupLM fast tokenizer does not support prepare_for_model") def test_compare_prepare_for_model(self): pass @slow def test_only_label_first_subword(self): nodes = ["hello", "niels"] xpaths = ["/html/body/div/li[1]/div/span" for _ in range(len(nodes))] node_labels = [0, 1] # test slow tokenizer tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base") encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100]) tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False) encoding = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100]) # test fast tokenizer tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base") encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, -100, -100]) tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base", only_label_first_subword=False) encoding = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels) self.assertListEqual(encoding.labels, [-100, 0, 1, 1, -100]) def test_markuplm_integration_test(self): tokenizer_p = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base") tokenizer_r = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base") # There are 3 cases: # CASE 1: document image classification (training + inference), document image token classification (inference), # in which case only nodes and normalized bounding xpaths are provided to the tokenizer # CASE 2: document image token classification (training), # in which case one also provides word labels to the tokenizer # CASE 3: document image visual question answering (inference), # in which case one also provides a question to the tokenizer # We need to test all 3 cases both on batched and non-batched inputs. # CASE 1: not batched nodes, xpaths = self.get_nodes_and_xpaths() expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 1: batched nodes, xpaths = self.get_nodes_and_xpaths_batch() expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 2: not batched nodes, xpaths = self.get_nodes_and_xpaths() node_labels = [1, 2, 3] expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [-100, 1, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], 'attention_mask': [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 2: batched nodes, xpaths = self.get_nodes_and_xpaths_batch() node_labels = [[1, 2, 3], [2, 46, 17, 22, 3]] expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'labels': [[-100, 1, -100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100], [-100, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]], 'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) encoding_r = tokenizer_r(nodes, xpaths=xpaths, node_labels=node_labels, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 3: not batched question, nodes, xpaths = self.get_question_nodes_and_xpaths() expected_results = {'input_ids': [0, 12196, 18, 39, 766, 116, 2, 42891, 232, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], 'xpath_subs_seq': [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], 'token_type_ids': [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} # fmt: skip encoding_p = tokenizer_p(question, nodes, xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(question, nodes, xpaths, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) self.assertDictEqual(dict(encoding_r), expected_results) # CASE 3: batched questions, nodes, xpaths = self.get_question_nodes_and_xpaths_batch() expected_results = {'input_ids': [[0, 12196, 18, 39, 766, 116, 2, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 9178, 16, 37, 373, 116, 2, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]], [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 50, 120, 50, 178, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216]]], 'xpath_subs_seq': [[[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 1, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]], [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [0, 0, 0, 2, 0, 0, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001], [1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001]]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]]} # fmt: skip encoding_p = tokenizer_p(questions, nodes, xpaths, padding="max_length", max_length=20) encoding_r = tokenizer_r(questions, nodes, xpaths, padding="max_length", max_length=20) self.assertDictEqual(dict(encoding_p), expected_results) self.assertDictEqual(dict(encoding_r), expected_results) @unittest.skip("Doesn't support another framework than PyTorch") def test_np_encode_plus_sent_to_model(self): pass def test_padding_warning_message_fast_tokenizer(self): if not self.test_rust_tokenizer: return nodes, xpaths = self.get_nodes_and_xpaths() tokenizer_fast = self.get_rust_tokenizer() # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer_fast, nodes) encoding_fast = tokenizer_fast(nodes, xpaths=xpaths) with self.assertLogs("transformers", level="WARNING") as cm: tokenizer_fast.pad(encoding_fast) self.assertEqual(len(cm.records), 1) self.assertIn( "Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to" " encode the text followed by a call to the `pad` method to get a padded encoding.", cm.records[0].message, ) if not self.test_slow_tokenizer: return tokenizer_slow = self.get_tokenizer() # check correct behaviour if no pad_token_id exists and add it eventually self._check_no_pad_token_padding(tokenizer_slow, nodes) encoding_slow = tokenizer_slow(nodes, xpaths=xpaths) with self.assertLogs(level="WARNING") as cm: # We want to assert there are no warnings, but the 'assertLogs' method does not support that. # Therefore, we are adding a dummy warning, and then we will assert it is the only warning. logger.warning("Dummy warning") tokenizer_slow.pad(encoding_slow) self.assertEqual(len(cm.records), 1) self.assertIn( "Dummy warning", cm.records[0].message, ) @unittest.skip("Chat is not supported") def test_chat_template(self): pass @unittest.skip("The model tested fails `Hub -> Fast == Hub -> Slow`, nothing much we can do") def test_added_tokens_serialization(self): pass
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/vision_text_dual_encoder/test_modeling_flax_vision_text_dual_encoder.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 PyTorch VisionTextDualEncoder model. """ import collections import tempfile import unittest import numpy as np from transformers.testing_utils import ( is_pt_flax_cross_test, require_flax, require_torch, require_vision, slow, torch_device, ) from transformers.utils import is_flax_available, is_torch_available, is_vision_available from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask from ..bert.test_modeling_flax_bert import FlaxBertModelTester from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester from ..vit.test_modeling_flax_vit import FlaxViTModelTester if is_flax_available(): from transformers import ( FlaxBertModel, FlaxCLIPVisionModel, FlaxVisionTextDualEncoderModel, FlaxViTModel, VisionTextDualEncoderConfig, VisionTextDualEncoderProcessor, ) from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) if is_torch_available(): import torch from transformers import VisionTextDualEncoderModel if is_vision_available(): from PIL import Image # Inspired by # https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py # From PyTorch internals def to_2tuple(x): if isinstance(x, collections.abc.Iterable): return x return (x, x) @require_flax class VisionTextDualEncoderMixin: def get_vision_text_model(self, config, text_config): pass def prepare_config_and_inputs(self): pass def get_pretrained_model_and_inputs(self): pass def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).") def check_model_from_pretrained_configs( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config) model = FlaxVisionTextDualEncoderModel(config) output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim)) def check_vision_text_dual_encoder_from_pretrained( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) kwargs = {"vision_model": vision_model, "text_model": text_model} model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs) output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim)) def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) kwargs = {"vision_model": vision_model, "text_model": text_model} model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs) output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) out_1 = output[0] with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname) after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) out_2 = after_output[0] max_diff = np.amax(np.abs(out_2 - out_1)) self.assertLessEqual(max_diff, 1e-3) def check_vision_text_output_attention( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) kwargs = {"vision_model": vision_model, "text_model": text_model} model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs) output = model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True ) vision_attentions = output.vision_model_output.attentions self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers) # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token) image_size = to_2tuple(vision_model.config.image_size) patch_size = to_2tuple(vision_model.config.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 1 self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len)) text_attentions = output.text_model_output.attentions self.assertEqual(len(text_attentions), text_config.num_hidden_layers) self.assertEqual( text_attentions[0].shape[-3:], (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]), ) def check_pt_flax_equivalence(self, pt_model, fx_model, inputs_dict): pt_model.to(torch_device) pt_model.eval() # prepare inputs flax_inputs = inputs_dict pt_inputs = {k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()} with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() fx_outputs = fx_model(**inputs_dict).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]): self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2) # PT -> Flax with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**inputs_dict).to_tuple() self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]): self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2) # Flax -> PT with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = VisionTextDualEncoderModel.from_pretrained(tmpdirname, from_flax=True) pt_model_loaded.to(torch_device) pt_model_loaded.eval() with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output_loaded in zip(fx_outputs[:4], pt_outputs_loaded[:4]): self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 4e-2) def check_equivalence_pt_to_flax(self, vision_config, text_config, inputs_dict): config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config) pt_model = VisionTextDualEncoderModel(config) fx_model = FlaxVisionTextDualEncoderModel(config) fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict) def check_equivalence_flax_to_pt(self, vision_config, text_config, inputs_dict): config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config) pt_model = VisionTextDualEncoderModel(config) fx_model = FlaxVisionTextDualEncoderModel(config) pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) self.check_pt_flax_equivalence(pt_model, fx_model, inputs_dict) def test_model_from_pretrained_configs(self): inputs_dict = self.prepare_config_and_inputs() self.check_model_from_pretrained_configs(**inputs_dict) def test_vision_text_dual_encoder_from_pretrained(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict) def test_save_load(self): inputs_dict = self.prepare_config_and_inputs() self.check_save_load(**inputs_dict) def test_vision_text_output_attention(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_output_attention(**inputs_dict) @is_pt_flax_cross_test def test_pt_flax_equivalence(self): config_inputs_dict = self.prepare_config_and_inputs() vision_config = config_inputs_dict.pop("vision_config") text_config = config_inputs_dict.pop("text_config") inputs_dict = config_inputs_dict self.check_equivalence_pt_to_flax(vision_config, text_config, inputs_dict) self.check_equivalence_flax_to_pt(vision_config, text_config, inputs_dict) @slow def test_real_model_save_load_from_pretrained(self): model_2, inputs = self.get_pretrained_model_and_inputs() outputs = model_2(**inputs) out_2 = outputs[0] with tempfile.TemporaryDirectory() as tmp_dirname: model_2.save_pretrained(tmp_dirname) model_1 = FlaxVisionTextDualEncoderModel.from_pretrained(tmp_dirname) after_outputs = model_1(**inputs) out_1 = after_outputs[0] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @require_flax class FlaxViTBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( "hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-bert", vision_from_pt=True, text_from_pt=True, ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.config.vision_config.num_channels, model.config.vision_config.image_size, model.config.vision_config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.config.text_config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def get_vision_text_model(self, vision_config, text_config): vision_model = FlaxViTModel(vision_config) text_model = FlaxBertModel(text_config) return vision_model, text_model def prepare_config_and_inputs(self): vit_model_tester = FlaxViTModelTester(self) bert_model_tester = FlaxBertModelTester(self) vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values = vision_config_and_inputs text_config, input_ids, token_type_ids, attention_mask = text_config_and_inputs # make sure that cross attention layers are added return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": attention_mask, "input_ids": input_ids, "token_type_ids": token_type_ids, } @require_torch class FlaxCLIPVisionBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): model = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( "hf-internal-testing/tiny-random-clip", "hf-internal-testing/tiny-bert", vision_from_pt=True, text_from_pt=True, ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.config.vision_config.num_channels, model.config.vision_config.image_size, model.config.vision_config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.config.text_config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def get_vision_text_model(self, vision_config, text_config): vision_model = FlaxCLIPVisionModel(vision_config) text_model = FlaxBertModel(text_config) return vision_model, text_model def prepare_config_and_inputs(self): clip_model_tester = FlaxCLIPVisionModelTester(self) bert_model_tester = FlaxBertModelTester(self) vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values = vision_config_and_inputs text_config, input_ids, token_type_ids, attention_mask = text_config_and_inputs # make sure that cross attention layers are added return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": attention_mask, "input_ids": input_ids, "token_type_ids": token_type_ids, } @require_flax @require_vision class FlaxVisionTextDualEncoderIntegrationTest(unittest.TestCase): @slow def test_inference(self): model = FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1.0) processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian") image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor( text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="np" ) outputs = model(**inputs) # verify the logits self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])) self.assertEqual( outputs.logits_per_text.shape, (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]), ) expected_logits = np.array([[1.2284727, 0.3104122]]) self.assertTrue(np.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/vision_text_dual_encoder/test_modeling_vision_text_dual_encoder.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 PyTorch VisionTextDualEncoder model. """ import collections import tempfile import unittest import numpy as np from transformers.testing_utils import is_pt_flax_cross_test, require_torch, require_vision, slow, torch_device from transformers.utils import is_flax_available, is_torch_available, is_vision_available from ...test_modeling_common import floats_tensor, ids_tensor, random_attention_mask from ..bert.test_modeling_bert import BertModelTester from ..clip.test_modeling_clip import CLIPVisionModelTester from ..deit.test_modeling_deit import DeiTModelTester from ..roberta.test_modeling_roberta import RobertaModelTester from ..vit.test_modeling_vit import ViTModelTester if is_torch_available(): import torch from transformers import ( BertModel, CLIPVisionModel, DeiTModel, RobertaModel, VisionTextDualEncoderConfig, VisionTextDualEncoderModel, ViTModel, ) if is_flax_available(): from transformers import FlaxVisionTextDualEncoderModel from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) if is_vision_available(): from PIL import Image from transformers import VisionTextDualEncoderProcessor # Inspired by # https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py # From PyTorch internals def to_2tuple(x): if isinstance(x, collections.abc.Iterable): return x return (x, x) @require_torch class VisionTextDualEncoderMixin: def get_vision_text_model(self, config, text_config): pass def prepare_config_and_inputs(self): pass def get_pretrained_model_and_inputs(self): pass def check_model_from_pretrained_configs( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config) model = VisionTextDualEncoderModel(config) model.to(torch_device) model.eval() output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim)) def check_vision_text_dual_encoder_model( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) model.to(torch_device) model.eval() output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim)) def check_vision_text_dual_encoder_from_pretrained( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) kwargs = {"vision_model": vision_model, "text_model": text_model} model = VisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs) model.to(torch_device) model.eval() output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim)) def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) model.to(torch_device) model.eval() with torch.no_grad(): output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) out_1 = output[0].cpu().numpy() with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = VisionTextDualEncoderModel.from_pretrained(tmpdirname).eval() model.to(torch_device) after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) out_2 = after_output[0].cpu().numpy() max_diff = np.amax(np.abs(out_2 - out_1)) self.assertLessEqual(max_diff, 1e-5) def check_vision_text_output_attention( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) model.to(torch_device) model.eval() output = model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True ) vision_attentions = output.vision_model_output.attentions self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers) # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token) image_size = to_2tuple(vision_model.config.image_size) patch_size = to_2tuple(vision_model.config.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 1 self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len)) text_attentions = output.text_model_output.attentions self.assertEqual(len(text_attentions), text_config.num_hidden_layers) self.assertEqual( text_attentions[0].shape[-3:], (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]), ) def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).") def check_pt_flax_equivalence(self, pt_model, fx_model, input_ids, attention_mask, pixel_values, **kwargs): pt_model.to(torch_device) pt_model.eval() # prepare inputs inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values} pt_inputs = inputs_dict flax_inputs = {k: v.numpy() for k, v in pt_inputs.items()} with torch.no_grad(): pt_outputs = pt_model(**pt_inputs).to_tuple() fx_outputs = fx_model(**flax_inputs).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]): self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2) # PT -> Flax with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(tmpdirname) fx_model_loaded = FlaxVisionTextDualEncoderModel.from_pretrained(tmpdirname, from_pt=True) fx_outputs_loaded = fx_model_loaded(**flax_inputs).to_tuple() self.assertEqual(len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch") for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]): self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2) # Flax -> PT with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(tmpdirname) pt_model_loaded = VisionTextDualEncoderModel.from_pretrained(tmpdirname, from_flax=True) pt_model_loaded.to(torch_device) pt_model_loaded.eval() with torch.no_grad(): pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple() self.assertEqual(len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch") for fx_output, pt_output_loaded in zip(fx_outputs[:4], pt_outputs_loaded[:4]): self.assert_almost_equals(fx_output, pt_output_loaded.numpy(), 4e-2) def check_equivalence_pt_to_flax(self, vision_config, text_config, inputs_dict): config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config) pt_model = VisionTextDualEncoderModel(config) fx_model = FlaxVisionTextDualEncoderModel(config) fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model) fx_model.params = fx_state self.check_pt_flax_equivalence(pt_model, fx_model, **inputs_dict) def check_equivalence_flax_to_pt(self, vision_config, text_config, inputs_dict): config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config) pt_model = VisionTextDualEncoderModel(config) fx_model = FlaxVisionTextDualEncoderModel(config) pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params) self.check_pt_flax_equivalence(pt_model, fx_model, **inputs_dict) def test_vision_text_dual_encoder_model(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_dual_encoder_model(**inputs_dict) def test_model_from_pretrained_configs(self): inputs_dict = self.prepare_config_and_inputs() self.check_model_from_pretrained_configs(**inputs_dict) def test_vision_text_dual_encoder_from_pretrained(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict) def test_save_load(self): inputs_dict = self.prepare_config_and_inputs() self.check_save_load(**inputs_dict) def test_vision_text_output_attention(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_output_attention(**inputs_dict) @is_pt_flax_cross_test def test_pt_flax_equivalence(self): config_inputs_dict = self.prepare_config_and_inputs() vision_config = config_inputs_dict.pop("vision_config") text_config = config_inputs_dict.pop("text_config") inputs_dict = config_inputs_dict self.check_equivalence_pt_to_flax(vision_config, text_config, inputs_dict) self.check_equivalence_flax_to_pt(vision_config, text_config, inputs_dict) @slow def test_real_model_save_load_from_pretrained(self): model_2, inputs = self.get_pretrained_model_and_inputs() model_2.to(torch_device) with torch.no_grad(): outputs = model_2(**inputs) out_2 = outputs[0].cpu().numpy() with tempfile.TemporaryDirectory() as tmp_dirname: model_2.save_pretrained(tmp_dirname) model_1 = VisionTextDualEncoderModel.from_pretrained(tmp_dirname) model_1.to(torch_device) after_outputs = model_1(**inputs) out_1 = after_outputs[0].cpu().numpy() max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @require_torch class ViTBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): model = VisionTextDualEncoderModel.from_vision_text_pretrained( "hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-bert" ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.vision_model.config.num_channels, model.vision_model.config.image_size, model.vision_model.config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def get_vision_text_model(self, vision_config, text_config): vision_model = ViTModel(vision_config).eval() text_model = BertModel(text_config).eval() return vision_model, text_model def prepare_config_and_inputs(self): vit_model_tester = ViTModelTester(self) bert_model_tester = BertModelTester(self) vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values, _ = vision_config_and_inputs ( text_config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = text_config_and_inputs return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": input_mask, "input_ids": input_ids, "text_token_type_ids": token_type_ids, "text_sequence_labels": sequence_labels, "text_token_labels": token_labels, "text_choice_labels": choice_labels, } @require_torch class DeiTRobertaModelTest(VisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): model = VisionTextDualEncoderModel.from_vision_text_pretrained( "hf-internal-testing/tiny-random-deit", "hf-internal-testing/tiny-random-roberta" ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.vision_model.config.num_channels, model.vision_model.config.image_size, model.vision_model.config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def check_vision_text_output_attention( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = VisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) model.to(torch_device) model.eval() output = model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True ) vision_attentions = output.vision_model_output.attentions self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers) # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens) image_size = to_2tuple(vision_model.config.image_size) patch_size = to_2tuple(vision_model.config.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 2 self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len)) text_attentions = output.text_model_output.attentions self.assertEqual(len(text_attentions), text_config.num_hidden_layers) self.assertEqual( text_attentions[0].shape[-3:], (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]), ) def get_vision_text_model(self, vision_config, text_config): vision_model = DeiTModel(vision_config).eval() text_model = RobertaModel(text_config).eval() return vision_model, text_model def prepare_config_and_inputs(self): vit_model_tester = DeiTModelTester(self) bert_model_tester = RobertaModelTester(self) vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values, _ = vision_config_and_inputs ( text_config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = text_config_and_inputs return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": input_mask, "input_ids": input_ids, "text_token_type_ids": token_type_ids, "text_sequence_labels": sequence_labels, "text_token_labels": token_labels, "text_choice_labels": choice_labels, } # skip as DeiT is not available in Flax def test_pt_flax_equivalence(self): pass @require_torch class CLIPVisionBertModelTest(VisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): model = VisionTextDualEncoderModel.from_vision_text_pretrained( "hf-internal-testing/tiny-random-clip", "hf-internal-testing/tiny-bert" ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.vision_model.config.num_channels, model.vision_model.config.image_size, model.vision_model.config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def get_vision_text_model(self, vision_config, text_config): vision_model = CLIPVisionModel(vision_config).eval() text_model = BertModel(text_config).eval() return vision_model, text_model def prepare_config_and_inputs(self): clip_model_tester = CLIPVisionModelTester(self) bert_model_tester = BertModelTester(self) vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values = vision_config_and_inputs ( text_config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = text_config_and_inputs return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": input_mask, "input_ids": input_ids, "text_token_type_ids": token_type_ids, "text_sequence_labels": sequence_labels, "text_token_labels": token_labels, "text_choice_labels": choice_labels, } @require_vision @require_torch class VisionTextDualEncoderIntegrationTest(unittest.TestCase): @slow def test_inference(self): model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", logit_scale_init_value=1.0) processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian") image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor( text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="pt" ) outputs = model(**inputs) # verify the logits self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])) self.assertEqual( outputs.logits_per_text.shape, (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]), ) expected_logits = torch.tensor([[1.2284727, 0.3104122]]) self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/vision_text_dual_encoder/test_modeling_tf_vision_text_dual_encoder.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 PyTorch VisionTextDualEncoder model. """ from __future__ import annotations import collections import tempfile import unittest import numpy as np from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import is_tf_available, is_vision_available from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask from ..bert.test_modeling_tf_bert import TFBertModelTester from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester from ..deit.test_modeling_tf_deit import TFDeiTModelTester from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester from ..vit.test_modeling_tf_vit import TFViTModelTester if is_tf_available(): from transformers import ( TFBertModel, TFCLIPVisionModel, TFDeiTModel, TFRobertaModel, TFVisionTextDualEncoderModel, TFViTModel, VisionTextDualEncoderConfig, ) if is_vision_available(): from PIL import Image from transformers import VisionTextDualEncoderProcessor # Inspired by # https://github.com/rwightman/pytorch-image-models/blob/b9bd960a032c75ca6b808ddeed76bee5f3ed4972/timm/models/layers/helpers.py # From PyTorch internals def to_2tuple(x): if isinstance(x, collections.abc.Iterable): return x return (x, x) @require_tf class TFVisionTextDualEncoderMixin: def get_vision_text_model(self, config, text_config): pass def prepare_config_and_inputs(self): pass def get_pretrained_model_and_inputs(self): pass def check_model_from_pretrained_configs( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): config = VisionTextDualEncoderConfig.from_vision_text_configs(vision_config, text_config) model = TFVisionTextDualEncoderModel(config) output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], config.projection_dim)) def check_vision_text_dual_encoder_model( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim)) def check_vision_text_dual_encoder_from_pretrained( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) kwargs = {"vision_model": vision_model, "text_model": text_model} model = TFVisionTextDualEncoderModel.from_vision_text_pretrained(**kwargs) output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) self.assertEqual(output["text_embeds"].shape, (input_ids.shape[0], model.config.projection_dim)) self.assertEqual(output["image_embeds"].shape, (pixel_values.shape[0], model.config.projection_dim)) def check_save_load(self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) out_1 = output[0].numpy() with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = TFVisionTextDualEncoderModel.from_pretrained(tmpdirname) after_output = model(input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask) out_2 = after_output[0].numpy() max_diff = np.amax(np.abs(out_2 - out_1)) self.assertLessEqual(max_diff, 1e-5) def check_vision_text_output_attention( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) output = model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True ) vision_attentions = output.vision_model_output.attentions self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers) # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token) image_size = to_2tuple(vision_model.config.image_size) patch_size = to_2tuple(vision_model.config.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 1 self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len)) text_attentions = output.text_model_output.attentions self.assertEqual(len(text_attentions), text_config.num_hidden_layers) self.assertEqual( text_attentions[0].shape[-3:], (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]), ) def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float): diff = np.abs((a - b)).max() self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).") def test_vision_text_dual_encoder_model(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_dual_encoder_model(**inputs_dict) def test_model_from_pretrained_configs(self): inputs_dict = self.prepare_config_and_inputs() self.check_model_from_pretrained_configs(**inputs_dict) def test_vision_text_dual_encoder_from_pretrained(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_dual_encoder_from_pretrained(**inputs_dict) def test_save_load(self): inputs_dict = self.prepare_config_and_inputs() self.check_save_load(**inputs_dict) def test_vision_text_output_attention(self): inputs_dict = self.prepare_config_and_inputs() self.check_vision_text_output_attention(**inputs_dict) @slow def test_real_model_save_load_from_pretrained(self): model_2, inputs = self.get_pretrained_model_and_inputs() outputs = model_2(**inputs) out_2 = outputs[0].numpy() with tempfile.TemporaryDirectory() as tmp_dirname: model_2.save_pretrained(tmp_dirname) model_1 = TFVisionTextDualEncoderModel.from_pretrained(tmp_dirname) after_outputs = model_1(**inputs) out_1 = after_outputs[0].numpy() max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @require_tf class TFViTBertModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): model = TFVisionTextDualEncoderModel.from_vision_text_pretrained( "hf-internal-testing/tiny-random-vit", "hf-internal-testing/tiny-random-bert" ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.vision_model.config.num_channels, model.vision_model.config.image_size, model.vision_model.config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def get_vision_text_model(self, vision_config, text_config): vision_model = TFViTModel(vision_config, name="vision_model") text_model = TFBertModel(text_config, name="text_model") return vision_model, text_model def prepare_config_and_inputs(self): vit_model_tester = TFViTModelTester(self) bert_model_tester = TFBertModelTester(self) vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values, _ = vision_config_and_inputs ( text_config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = text_config_and_inputs return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": input_mask, "input_ids": input_ids, "text_token_type_ids": token_type_ids, "text_sequence_labels": sequence_labels, "text_token_labels": token_labels, "text_choice_labels": choice_labels, } @require_tf class TFDeiTRobertaModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): # DeiT repo doesn't have TF weights, but we don't actually use the weights at all so let's # just reinitialize it. model = TFVisionTextDualEncoderModel.from_vision_text_pretrained( "Rocketknight1/tiny-random-deit-tf", "hf-internal-testing/tiny-random-roberta" ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.vision_model.config.num_channels, model.vision_model.config.image_size, model.vision_model.config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def check_vision_text_output_attention( self, text_config, input_ids, attention_mask, vision_config, pixel_values=None, **kwargs ): vision_model, text_model = self.get_vision_text_model(vision_config, text_config) model = TFVisionTextDualEncoderModel(vision_model=vision_model, text_model=text_model) output = model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, output_attentions=True ) vision_attentions = output.vision_model_output.attentions self.assertEqual(len(vision_attentions), vision_config.num_hidden_layers) # in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens) image_size = to_2tuple(vision_model.config.image_size) patch_size = to_2tuple(vision_model.config.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 2 self.assertEqual(vision_attentions[0].shape[-3:], (vision_config.num_attention_heads, seq_len, seq_len)) text_attentions = output.text_model_output.attentions self.assertEqual(len(text_attentions), text_config.num_hidden_layers) self.assertEqual( text_attentions[0].shape[-3:], (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]), ) def get_vision_text_model(self, vision_config, text_config): vision_model = TFDeiTModel(vision_config, name="vision_model") text_model = TFRobertaModel(text_config, name="text_model") return vision_model, text_model def prepare_config_and_inputs(self): vit_model_tester = TFDeiTModelTester(self) bert_model_tester = TFRobertaModelTester(self) vision_config_and_inputs = vit_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values, _ = vision_config_and_inputs ( text_config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = text_config_and_inputs return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": input_mask, "input_ids": input_ids, "text_token_type_ids": token_type_ids, "text_sequence_labels": sequence_labels, "text_token_labels": token_labels, "text_choice_labels": choice_labels, } @require_tf class TFCLIPVisionBertModelTest(TFVisionTextDualEncoderMixin, unittest.TestCase): def get_pretrained_model_and_inputs(self): model = TFVisionTextDualEncoderModel.from_vision_text_pretrained( "Rocketknight1/tiny-random-clip-tf", "hf-internal-testing/tiny-random-bert" ) batch_size = 13 pixel_values = floats_tensor( [ batch_size, model.vision_model.config.num_channels, model.vision_model.config.image_size, model.vision_model.config.image_size, ] ) input_ids = ids_tensor([batch_size, 4], model.text_model.config.vocab_size) attention_mask = random_attention_mask([batch_size, 4]) inputs = {"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask} return model, inputs def get_vision_text_model(self, vision_config, text_config): vision_model = TFCLIPVisionModel(vision_config, name="vision_model") text_model = TFBertModel(text_config, name="text_model") return vision_model, text_model def prepare_config_and_inputs(self): clip_model_tester = TFCLIPVisionModelTester(self) bert_model_tester = TFBertModelTester(self) vision_config_and_inputs = clip_model_tester.prepare_config_and_inputs() text_config_and_inputs = bert_model_tester.prepare_config_and_inputs() vision_config, pixel_values = vision_config_and_inputs ( text_config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = text_config_and_inputs return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": input_mask, "input_ids": input_ids, "text_token_type_ids": token_type_ids, "text_sequence_labels": sequence_labels, "text_token_labels": token_labels, "text_choice_labels": choice_labels, } @require_vision @require_tf class TFVisionTextDualEncoderIntegrationTest(unittest.TestCase): @slow def test_inference(self): model = TFVisionTextDualEncoderModel.from_pretrained( "clip-italian/clip-italian", logit_scale_init_value=1.0, from_pt=True ) processor = VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian") image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor( text=["una foto di un gatto", "una foto di un cane"], images=image, padding=True, return_tensors="np" ) outputs = model(**inputs) # verify the logits self.assertEqual(outputs.logits_per_image.shape, (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])) self.assertEqual( outputs.logits_per_text.shape, (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]), ) expected_logits = np.array([[1.2284727, 0.3104122]]) self.assertTrue(np.allclose(outputs.logits_per_image.numpy(), expected_logits, atol=1e-3))
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/vision_text_dual_encoder/test_processor_vision_text_dual_encoder.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. import json import os import shutil import tempfile import unittest import numpy as np from transformers import BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer from transformers.testing_utils import require_tokenizers, require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor @require_tokenizers @require_vision class VisionTextDualEncoderProcessorTest(unittest.TestCase): def setUp(self): self.tmpdirname = tempfile.mkdtemp() vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] # fmt: skip self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) image_processor_map = { "do_resize": True, "size": {"height": 18, "width": 18}, "do_normalize": True, "image_mean": [0.5, 0.5, 0.5], "image_std": [0.5, 0.5, 0.5], } self.image_processor_file = os.path.join(self.tmpdirname, IMAGE_PROCESSOR_NAME) with open(self.image_processor_file, "w", encoding="utf-8") as fp: json.dump(image_processor_map, fp) def get_tokenizer(self, **kwargs): return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_image_processor(self, **kwargs): return ViTImageProcessor.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): tokenizer = self.get_tokenizer() image_processor = self.get_image_processor() processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor) processor.save_pretrained(self.tmpdirname) processor = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname) self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab()) self.assertIsInstance(processor.tokenizer, (BertTokenizer, BertTokenizerFast)) self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string()) self.assertIsInstance(processor.image_processor, ViTImageProcessor) def test_save_load_pretrained_additional_features(self): processor = VisionTextDualEncoderProcessor( tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname) tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)") image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0) processor = VisionTextDualEncoderProcessor.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, (BertTokenizer, BertTokenizerFast)) self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string()) self.assertIsInstance(processor.image_processor, ViTImageProcessor) def test_image_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor) image_input = self.prepare_image_inputs() input_feat_extract = image_processor(image_input, return_tensors="np") input_processor = processor(images=image_input, 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): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" 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_processor(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"]) # test if it raises when no input is passed with self.assertRaises(ValueError): processor() def test_tokenizer_decode(self): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor) 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): image_processor = self.get_image_processor() tokenizer = self.get_tokenizer() processor = VisionTextDualEncoderProcessor(tokenizer=tokenizer, image_processor=image_processor) input_str = "lower newer" image_input = self.prepare_image_inputs() inputs = processor(text=input_str, images=image_input) self.assertListEqual(list(inputs.keys()), processor.model_input_names)
0
mavonic_private_repos/transformers/tests/models
mavonic_private_repos/transformers/tests/models/layoutlm/test_tokenization_layoutlm.py
# coding=utf-8 # Copyright 2018 The Microsoft Research Asia LayoutLM Team 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. import os import unittest from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class LayoutLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "microsoft/layoutlm-base-uncased" tokenizer_class = LayoutLMTokenizer rust_tokenizer_class = LayoutLMTokenizerFast test_rust_tokenizer = True space_between_special_tokens = True def setUp(self): super().setUp() vocab_tokens = [ "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest", ] self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) def get_tokenizer(self, **kwargs): return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **kwargs) def get_input_output_texts(self, tokenizer): input_text = "UNwant\u00E9d,running" output_text = "unwanted, running" return input_text, output_text def test_full_tokenizer(self): tokenizer = self.tokenizer_class(self.vocab_file) tokens = tokenizer.tokenize("UNwant\u00E9d,running") self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"]) self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [7, 4, 5, 10, 8, 9]) def test_special_tokens_as_you_expect(self): """If you are training a seq2seq model that expects a decoder_prefix token make sure it is prepended to decoder_input_ids""" pass
0