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transformers/tests/models/markuplm/test_modeling_markuplm.py

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+# 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 functools import cached_property
+
+from transformers import MarkupLMConfig, 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 (
+        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_case_name,
+        config_class,
+        model_architecture,
+        tokenizer_name,
+        image_processor_name,
+        feature_extractor_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)
+
+        torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)