# 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 unittest from transformers import LayoutLMConfig, 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( LayoutLMForMaskedLM, LayoutLMForQuestionAnswering, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMModel, ) class LayoutLMModelTester: """You can also import this e.g from .test_modeling_layoutlm import LayoutLMModelTester""" 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, range_bbox=1000, ): 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.range_bbox = range_bbox 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 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 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, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return LayoutLMConfig( 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_model( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LayoutLMModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids) result = model(input_ids, bbox, token_type_ids=token_type_ids) result = model(input_ids, bbox) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_for_masked_lm( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LayoutLMForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox, 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_sequence_classification( self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = LayoutLMForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, bbox, 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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = LayoutLMForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, bbox, 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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LayoutLMForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, 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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "bbox": bbox, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_torch class LayoutLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( LayoutLMModel, LayoutLMForMaskedLM, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMForQuestionAnswering, ) if is_torch_available() else None ) pipeline_model_mapping = ( { "document-question-answering": LayoutLMForQuestionAnswering, "feature-extraction": LayoutLMModel, "fill-mask": LayoutLMForMaskedLM, "text-classification": LayoutLMForSequenceClassification, "token-classification": LayoutLMForTokenClassification, "zero-shot": LayoutLMForSequenceClassification, } if is_torch_available() else {} ) def setUp(self): self.model_tester = LayoutLMModelTester(self) self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, 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_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_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) @unittest.skip( reason="This architecture 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 architecture 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 architecture 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 prepare_layoutlm_batch_inputs(): # Here we prepare a batch of 2 sequences to test a LayoutLM 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]],device=torch_device) # 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],],device=torch_device) # 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]]],device=torch_device) # 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]],device=torch_device) # noqa: E231 # these are sequence labels (i.e. at the token level) labels = torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],device=torch_device) # noqa: E231 # fmt: on return input_ids, attention_mask, bbox, token_type_ids, labels @require_torch class LayoutLMModelIntegrationTest(unittest.TestCase): @slow def test_forward_pass_no_head(self): model = LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device) input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs() # forward pass outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids) # test the sequence output on [0, :3, :3] expected_slice = torch.tensor( [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]], device=torch_device, ) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-3, atol=1e-3) # test the pooled output on [1, :3] expected_slice = torch.tensor([-0.6580, -0.0214, 0.8552], device=torch_device) torch.testing.assert_close(outputs.pooler_output[1, :3], expected_slice, rtol=1e-3, atol=1e-3) @slow def test_forward_pass_sequence_classification(self): # initialize model with randomly initialized sequence classification head model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2).to( torch_device ) input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs() # forward pass outputs = model( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=torch.tensor([1, 1], device=torch_device), ) # test whether we get a loss as a scalar loss = outputs.loss expected_shape = torch.Size([]) self.assertEqual(loss.shape, expected_shape) # test the shape of the logits logits = outputs.logits expected_shape = torch.Size((2, 2)) self.assertEqual(logits.shape, expected_shape) @slow def test_forward_pass_token_classification(self): # initialize model with randomly initialized token classification head model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13).to( torch_device ) input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs() # forward pass outputs = model( input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels ) # test the loss calculation to be around 2.65 # expected_loss = torch.tensor(2.65, device=torch_device) # The loss is currently somewhat random and can vary between 0.1-0.3 atol. # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1)) # test the shape of the logits logits = outputs.logits expected_shape = torch.Size((2, 25, 13)) self.assertEqual(logits.shape, expected_shape) @slow def test_forward_pass_question_answering(self): # initialize model with randomly initialized token classification head model = LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(torch_device) input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs() # forward pass outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids) # test the shape of the logits expected_shape = torch.Size((2, 25)) self.assertEqual(outputs.start_logits.shape, expected_shape) self.assertEqual(outputs.end_logits.shape, expected_shape)