# 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 ALIGN model. """ import inspect import os import tempfile import unittest import requests from transformers import AlignConfig, AlignProcessor, AlignTextConfig, AlignVisionConfig from transformers.testing_utils import ( is_flax_available, 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 transformers import ( AlignModel, AlignTextModel, AlignVisionModel, ) from transformers.models.align.modeling_align import ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image if is_flax_available(): pass class AlignVisionModelTester: def __init__( self, parent, batch_size=12, image_size=32, num_channels=3, kernel_sizes=[3, 3, 5], in_channels=[32, 16, 24], out_channels=[16, 24, 30], hidden_dim=64, strides=[1, 1, 2], num_block_repeats=[1, 1, 2], expand_ratios=[1, 6, 6], is_training=True, hidden_act="gelu", ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.kernel_sizes = kernel_sizes self.in_channels = in_channels self.out_channels = out_channels self.hidden_dim = hidden_dim self.strides = strides self.num_block_repeats = num_block_repeats self.expand_ratios = expand_ratios self.is_training = is_training self.hidden_act = hidden_act 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 AlignVisionConfig( num_channels=self.num_channels, kernel_sizes=self.kernel_sizes, in_channels=self.in_channels, out_channels=self.out_channels, hidden_dim=self.hidden_dim, strides=self.strides, num_block_repeats=self.num_block_repeats, expand_ratios=self.expand_ratios, hidden_act=self.hidden_act, ) def create_and_check_model(self, config, pixel_values): model = AlignVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) patch_size = self.image_size // 4 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, config.hidden_dim, patch_size, patch_size) ) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, config.hidden_dim)) 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 AlignVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ALIGN does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (AlignVisionModel,) 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 = AlignVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=AlignVisionConfig, 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="AlignVisionModel does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="AlignVisionModel does not support input and output embeddings") def test_model_common_attributes(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states num_blocks = sum(config.num_block_repeats) * 4 self.assertEqual(len(hidden_states), num_blocks) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 2, self.model_tester.image_size // 2], ) 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): pass def test_training_gradient_checkpointing(self): pass @slow def test_model_from_pretrained(self): for model_name in ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = AlignVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class AlignTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, 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.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.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) config = self.get_config() return config, input_ids, token_type_ids, input_mask def get_config(self): return AlignTextConfig( 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 create_and_check_model(self, config, input_ids, token_type_ids, input_mask): model = AlignTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): 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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, ) = 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 AlignTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (AlignTextModel,) if is_torch_available() else () fx_compatible = False test_pruning = False test_head_masking = False def setUp(self): self.model_tester = AlignTextModelTester(self) self.config_tester = ConfigTester(self, config_class=AlignTextConfig, 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="ALIGN does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="AlignTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="AlignTextModel 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): for model_name in ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = AlignTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class AlignModelTester: 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 = AlignTextModelTester(parent, **text_kwargs) self.vision_model_tester = AlignVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): test_config, input_ids, token_type_ids, input_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, input_mask, pixel_values def get_config(self): return AlignConfig.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 = AlignModel(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, input_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask, "pixel_values": pixel_values, "return_loss": True, } return config, inputs_dict @require_torch class AlignModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (AlignModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": AlignModel} 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 = AlignModelTester(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="Start to fail after using torch `cu118`.") def test_multi_gpu_data_parallel_forward(self): super().test_multi_gpu_data_parallel_forward() @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="AlignModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the `temperature` parameter initilization is different for ALIGN 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 `temperature` is initilized as per the original implementation if name == "temperature": self.assertAlmostEqual( param.data.item(), 1.0, delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) elif name == "text_projection.weight": 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", ) 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"] # ALIGN 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 AlignConfig and check if we can load AlignVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = AlignVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save AlignConfig and check if we can load AlignTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = AlignTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = AlignModel.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 AlignModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "kakaobrain/align-base" model = AlignModel.from_pretrained(model_name).to(torch_device) processor = AlignProcessor.from_pretrained(model_name) image = prepare_img() texts = ["a photo of a cat", "a photo of a dog"] inputs = processor(text=texts, images=image, 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([[9.7093, 3.4679]], device=torch_device) self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))