# 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 LayoutLMv2 model.""" import unittest from transformers.testing_utils import ( require_detectron2, require_non_xpu, require_torch, require_torch_multi_gpu, slow, torch_device, ) from transformers.utils import is_detectron2_available, is_torch_available 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 import torch.nn.functional as F from transformers import ( LayoutLMv2Config, LayoutLMv2ForQuestionAnswering, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2Model, ) if is_detectron2_available(): from detectron2.structures.image_list import ImageList class LayoutLMv2ModelTester: def __init__( self, parent, batch_size=2, num_channels=3, image_size=4, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=36, 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, image_feature_pool_shape=[7, 7, 32], coordinate_size=6, shape_size=6, num_labels=3, num_choices=4, scope=None, range_bbox=1000, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_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.image_feature_pool_shape = image_feature_pool_shape self.coordinate_size = coordinate_size self.shape_size = shape_size self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.range_bbox = range_bbox detectron2_config = LayoutLMv2Config.get_default_detectron2_config() # We need to make the model smaller detectron2_config["MODEL.RESNETS.DEPTH"] = 50 detectron2_config["MODEL.RESNETS.RES2_OUT_CHANNELS"] = 4 detectron2_config["MODEL.RESNETS.STEM_OUT_CHANNELS"] = 4 detectron2_config["MODEL.FPN.OUT_CHANNELS"] = 32 detectron2_config["MODEL.RESNETS.NUM_GROUPS"] = 1 self.detectron2_config = detectron2_config def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox) # Ensure that bbox is legal for i in range(bbox.shape[0]): for j in range(bbox.shape[1]): if bbox[i, j, 3] < bbox[i, j, 1]: t = bbox[i, j, 3] bbox[i, j, 3] = bbox[i, j, 1] bbox[i, j, 1] = t if bbox[i, j, 2] < bbox[i, j, 0]: t = bbox[i, j, 2] bbox[i, j, 2] = bbox[i, j, 0] bbox[i, j, 0] = t image = ImageList( torch.zeros(self.batch_size, self.num_channels, self.image_size, self.image_size, device=torch_device), self.image_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 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 = LayoutLMv2Config( 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, image_feature_pool_shape=self.image_feature_pool_shape, coordinate_size=self.coordinate_size, shape_size=self.shape_size, detectron2_config_args=self.detectron2_config, ) return config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels def create_and_check_model( self, config, input_ids, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): model = LayoutLMv2Model(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox=bbox, image=image, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, image=image, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, image=image) # LayoutLMv2 has a different expected sequence length, namely also visual tokens are added expected_seq_len = self.seq_length + self.image_feature_pool_shape[0] * self.image_feature_pool_shape[1] self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, 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, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): config.num_labels = self.num_labels model = LayoutLMv2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, 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, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): config.num_labels = self.num_labels model = LayoutLMv2ForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, 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, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels ): model = LayoutLMv2ForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, image=image, 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, bbox, image, token_type_ids, input_mask, sequence_labels, token_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "bbox": bbox, "image": image, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_non_xpu @require_torch @require_detectron2 class LayoutLMv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): test_mismatched_shapes = False all_model_classes = ( ( LayoutLMv2Model, LayoutLMv2ForSequenceClassification, LayoutLMv2ForTokenClassification, LayoutLMv2ForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"document-question-answering": LayoutLMv2ForQuestionAnswering, "feature-extraction": LayoutLMv2Model} if is_torch_available() else {} ) def setUp(self): self.model_tester = LayoutLMv2ModelTester(self) self.config_tester = ConfigTester(self, config_class=LayoutLMv2Config, 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) @require_torch_multi_gpu @unittest.skip( reason=( "LayoutLMV2 and its dependency `detectron2` have some layers using `add_module` which doesn't work well" " with `nn.DataParallel`" ) ) def test_multi_gpu_data_parallel_forward(self): pass 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 test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # LayoutLMv2 has a different expected sequence length expected_seq_len = ( self.model_tester.seq_length + self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1] ) 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._from_config(config, attn_implementation="eager") config = model.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), 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.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, expected_seq_len, expected_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 else: added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, 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, expected_seq_len, expected_seq_len], ) 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 = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # LayoutLMv2 has a different expected sequence length expected_seq_len = ( self.model_tester.seq_length + self.model_tester.image_feature_pool_shape[0] * self.model_tester.image_feature_pool_shape[1] ) self.assertListEqual( list(hidden_states[0].shape[-2:]), [expected_seq_len, 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) @slow def test_model_from_pretrained(self): model_name = "microsoft/layoutlmv2-base-uncased" model = LayoutLMv2Model.from_pretrained(model_name) self.assertIsNotNone(model) def test_batching_equivalence(self): def equivalence(tensor1, tensor2): return 1.0 - F.cosine_similarity(tensor1.float().flatten(), tensor2.float().flatten(), dim=0, eps=0) def recursive_check(batched_object, single_row_object, model_name, key): if isinstance(batched_object, (list, tuple)): for batched_object_value, single_row_object_value in zip(batched_object, single_row_object): recursive_check(batched_object_value, single_row_object_value, model_name, key) elif batched_object is None: return else: batched_row = batched_object[:1] self.assertFalse( torch.isnan(batched_row).any(), f"Batched output has `nan` in {model_name} for key={key}" ) self.assertFalse( torch.isinf(batched_row).any(), f"Batched output has `inf` in {model_name} for key={key}" ) self.assertFalse( torch.isnan(single_row_object).any(), f"Single row output has `nan` in {model_name} for key={key}" ) self.assertFalse( torch.isinf(single_row_object).any(), f"Single row output has `inf` in {model_name} for key={key}" ) self.assertTrue( (equivalence(batched_row, single_row_object)) <= 1e-03, msg=( f"Batched and Single row outputs are not equal in {model_name} for key={key}. " f"Difference={equivalence(batched_row, single_row_object)}." ), ) config, batched_input = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: config.output_hidden_states = True model_name = model_class.__name__ batched_input_prepared = self._prepare_for_class(batched_input, model_class) model = model_class(config).to(torch_device).eval() batch_size = self.model_tester.batch_size single_row_input = {} for key, value in batched_input_prepared.items(): if isinstance(value, torch.Tensor) and value.shape[0] % batch_size == 0: single_batch_shape = value.shape[0] // batch_size single_row_input[key] = value[:single_batch_shape] elif hasattr(value, "tensor"): # layoutlmv2uses ImageList instead of pixel values single_row_input[key] = value.tensor[:single_batch_shape] with torch.no_grad(): model_batched_output = model(**batched_input_prepared) model_row_output = model(**single_row_input) for key in model_batched_output: recursive_check(model_batched_output[key], model_row_output[key], model_name, key) def prepare_layoutlmv2_batch_inputs(): # Here we prepare a batch of 2 sequences to test a LayoutLMv2 forward pass on: # fmt: off input_ids = torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231 bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231 image = ImageList(torch.randn((2,3,224,224)), image_sizes=[(224,224), (224,224)]) # noqa: E231 attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231 token_type_ids = torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231 # fmt: on return input_ids, bbox, image, attention_mask, token_type_ids @require_torch @require_detectron2 class LayoutLMv2ModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = LayoutLMv2Model.from_pretrained("microsoft/layoutlmv2-base-uncased").to(torch_device) ( input_ids, bbox, image, attention_mask, token_type_ids, ) = prepare_layoutlmv2_batch_inputs() # forward pass outputs = model( input_ids=input_ids.to(torch_device), bbox=bbox.to(torch_device), image=image.to(torch_device), attention_mask=attention_mask.to(torch_device), token_type_ids=token_type_ids.to(torch_device), ) # verify the sequence output expected_shape = torch.Size( ( 2, input_ids.shape[1] + model.config.image_feature_pool_shape[0] * model.config.image_feature_pool_shape[1], model.config.hidden_size, ) ) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.1087, 0.0727, -0.3075], [0.0799, -0.0427, -0.0751], [-0.0367, 0.0480, -0.1358]], device=torch_device ) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-3, atol=1e-3) # verify the pooled output expected_shape = torch.Size((2, model.config.hidden_size)) self.assertEqual(outputs.pooler_output.shape, expected_shape)