wehub-resource-sync
507 lines
18 KiB
Python
507 lines
18 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from dataclasses import Field, dataclass, fields
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from typing import Tuple
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import paddle
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from paddle import Tensor
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from parameterized import parameterized_class
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from paddlenlp.transformers import (
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TinyBertForMultipleChoice,
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TinyBertForPretraining,
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TinyBertForQuestionAnswering,
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TinyBertForSequenceClassification,
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TinyBertModel,
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TinyBertPretrainedModel,
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)
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from paddlenlp.transformers.tinybert.configuration import TinyBertConfig
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from ...testing_utils import slow
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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@dataclass
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class TinyBertTestModelConfig:
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"""tinybert model config which keep consist with pretrained_init_configuration sub fields"""
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vocab_size: int = 100
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hidden_size: int = 100
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num_hidden_layers: int = 4
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num_attention_heads: int = 5
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intermediate_size: int = 120
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hidden_act: str = "gelu"
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hidden_dropout_prob: float = 0.1
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attention_probs_dropout_prob: float = 0.1
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max_position_embeddings: int = 62
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type_vocab_size: int = 2
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initializer_range: float = 0.02
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pad_token_id: int = 0
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@property
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def model_kwargs(self) -> dict:
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"""get the model kwargs configuration to init the model"""
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model_config_fields: Tuple[Field, ...] = fields(TinyBertTestModelConfig)
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return {field.name: getattr(self, field.name) for field in model_config_fields}
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@dataclass
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class TinyBertTestConfig(TinyBertTestModelConfig):
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"""train config under unittest code"""
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batch_size: int = 2
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seq_length: int = 7
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is_training: bool = False
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use_input_mask: bool = True
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use_token_type_ids: bool = True
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# used for sequence classification
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num_classes: int = 3
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num_choices: int = 3
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type_sequence_label_size: int = 3
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class TinyBertModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = TinyBertModel
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use_labels = False
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return_dict = False
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all_model_classes = (
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TinyBertModel,
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TinyBertForMultipleChoice,
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TinyBertForPretraining,
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TinyBertForQuestionAnswering,
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TinyBertForSequenceClassification,
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)
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def setUp(self):
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super().setUp()
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self.model_tester = TinyBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=TinyBertConfig, vocab_size=256, hidden_size=24)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
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def test_for_model_cache(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_cache(*config_and_inputs)
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@slow
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@unittest.skip("Skip for missing model weight.")
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def test_model_from_pretrained(self):
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for model_name in list(TinyBertPretrainedModel.pretrained_init_configuration)[:1]:
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model = TinyBertModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_hidden_states_output(self):
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self.skipTest("skip: test_hidden_states_output -> there is no supporting argument return_dict")
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class TinyBertModelTester:
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def __init__(
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self,
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parent: TinyBertModelTest,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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initializer_range=0.02,
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pad_token_id=0,
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pool_act="tanh",
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layer_norm_eps=1e-12,
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type_sequence_label_size=2,
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num_labels=3,
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num_choices=4,
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scope=None,
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dropout=0.56,
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return_dict=False,
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fit_size=768,
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):
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self.parent: TinyBertModelTest = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.pad_token_id = pad_token_id
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self.pool_act = pool_act
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self.type_sequence_label_size = type_sequence_label_size
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.dropout = dropout
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self.layer_norm_eps = layer_norm_eps
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self.return_dict = return_dict
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self.fit_size = fit_size
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self) -> TinyBertConfig:
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return TinyBertConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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fit_size=self.fit_size,
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pool_act=self.pool_act,
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num_labels=self.num_labels,
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num_choices=self.num_choices,
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)
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def create_and_check_model(
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self,
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config,
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input_ids: Tensor,
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token_type_ids: Tensor,
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input_mask: Tensor,
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sequence_labels: Tensor,
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token_labels: Tensor,
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choice_labels: Tensor,
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):
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model = TinyBertModel(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.hidden_size])
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def create_and_check_for_multiple_choice(
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self,
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config,
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input_ids: Tensor,
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token_type_ids: Tensor,
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input_mask: Tensor,
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sequence_labels: Tensor,
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token_labels: Tensor,
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choice_labels: Tensor,
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):
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model = TinyBertForMultipleChoice(config)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
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if token_type_ids is not None:
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token_type_ids = token_type_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
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if input_mask is not None:
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input_mask = input_mask.unsqueeze(1).expand([-1, self.num_choices, -1])
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result = model(
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multiple_choice_inputs_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=choice_labels,
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return_dict=self.return_dict,
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)
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_choices])
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def create_and_check_for_question_answering(
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self,
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config,
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input_ids: Tensor,
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token_type_ids: Tensor,
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input_mask: Tensor,
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sequence_labels: Tensor,
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token_labels: Tensor,
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choice_labels: Tensor,
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):
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model = TinyBertForQuestionAnswering(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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return_dict=self.return_dict,
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)
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length])
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length])
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def create_and_check_for_sequence_classification(
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self,
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config,
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input_ids: Tensor,
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token_type_ids: Tensor,
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input_mask: Tensor,
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sequence_labels: Tensor,
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token_labels: Tensor,
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choice_labels: Tensor,
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):
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model = TinyBertForSequenceClassification(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=sequence_labels,
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return_dict=self.parent.return_dict,
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)
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_labels])
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def create_and_check_model_cache(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TinyBertModel(config)
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model.eval()
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# first forward pass
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outputs = model(input_ids, attention_mask=input_mask, use_cache=True, return_dict=self.parent.return_dict)
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past_key_values = outputs.past_key_values if self.parent.return_dict else outputs[2]
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# create hypothetical multiple next token and extent to next_input_ids
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next_tokens = ids_tensor((self.batch_size, 3), self.vocab_size)
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next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
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# append to next input_ids and
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next_input_ids = paddle.concat([input_ids, next_tokens], axis=-1)
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next_attention_mask = paddle.concat([input_mask, next_mask], axis=-1)
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outputs = model(
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next_input_ids,
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attention_mask=next_attention_mask,
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output_hidden_states=True,
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return_dict=self.parent.return_dict,
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)
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output_from_no_past = outputs[2][0]
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outputs = model(
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next_tokens,
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attention_mask=next_attention_mask,
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past_key_values=past_key_values,
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output_hidden_states=True,
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return_dict=self.parent.return_dict,
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)
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output_from_past = outputs[2][0]
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# select random slice
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random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
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output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
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output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
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self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
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# test that outputs are equal for slice
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self.parent.assertTrue(paddle.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@parameterized_class(
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("return_dict", "use_labels"),
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[
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[False, False],
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[False, True],
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[True, False],
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[True, True],
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],
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)
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class TinyBertModelIntegrationTest(unittest.TestCase):
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@slow
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@unittest.skip("Skip for missing model weight.")
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def test_inference_no_attention(self):
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model = TinyBertModel.from_pretrained("tinybert-4l-312d")
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model.eval()
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input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
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with paddle.no_grad():
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output = model(input_ids)[0]
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expected_shape = [1, 11, 312]
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self.assertEqual(output.shape, expected_shape)
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expected_slice = paddle.to_tensor(
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[
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[
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[-0.76857519, -0.04066351, -0.36538580],
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[-0.79803109, -0.04977923, -0.37076530],
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[-0.76121056, -0.07496471, -0.35906711],
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]
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]
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)
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self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
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@slow
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@unittest.skip("Skip for missing model weight.")
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def test_inference_with_attention(self):
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model = TinyBertModel.from_pretrained("tinybert-4l-312d")
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model.eval()
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input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
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attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
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with paddle.no_grad():
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output = model(input_ids, attention_mask=attention_mask)[0]
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expected_shape = [1, 11, 312]
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self.assertEqual(output.shape, expected_shape)
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expected_slice = paddle.to_tensor(
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[
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[
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[-0.76857519, -0.04066351, -0.36538580],
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[-0.79803109, -0.04977923, -0.37076530],
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[-0.76121056, -0.07496471, -0.35906711],
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]
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]
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)
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self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
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@slow
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@unittest.skip("Skip for missing model weight.")
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def test_inference_with_past_key_value(self):
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model = TinyBertModel.from_pretrained("tinybert-4l-312d")
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model.eval()
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input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
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attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
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with paddle.no_grad():
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output = model(input_ids, attention_mask=attention_mask, use_cache=True, return_dict=True)
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expected_shape = [1, 11, 312]
|
|
self.assertEqual(output[0].shape, expected_shape)
|
|
expected_slice = paddle.to_tensor(
|
|
[
|
|
[
|
|
[-0.76857519, -0.04066351, -0.36538580],
|
|
[-0.79803109, -0.04977923, -0.37076530],
|
|
[-0.76121056, -0.07496471, -0.35906711],
|
|
]
|
|
]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[0][:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
# insert the past key value into model
|
|
with paddle.no_grad():
|
|
output = model(input_ids, use_cache=True, past_key_values=output.past_key_values, return_dict=True)
|
|
expected_slice = paddle.to_tensor(
|
|
[
|
|
[
|
|
[-0.61422300, -0.05978593, -0.23719205],
|
|
[-0.64617568, -0.04066525, -0.26458248],
|
|
[-0.65170693, -0.04711169, -0.29544356],
|
|
]
|
|
]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[0][:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|