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# 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 | |
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 | |
class MarkupLMModelIntegrationTest(unittest.TestCase): | |
def default_processor(self): | |
# TODO use from_pretrained here | |
feature_extractor = MarkupLMFeatureExtractor() | |
tokenizer = MarkupLMTokenizer.from_pretrained("microsoft/markuplm-base") | |
return MarkupLMProcessor(feature_extractor, tokenizer) | |
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)) | |