wehub-resource-sync
452 lines
16 KiB
Python
452 lines
16 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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from __future__ import annotations
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import tempfile
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import unittest
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import numpy as np
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import paddle
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from parameterized import parameterized
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from paddlenlp.transformers import (
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DistilBertForMaskedLM,
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DistilBertForQuestionAnswering,
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DistilBertForSequenceClassification,
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DistilBertForTokenClassification,
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DistilBertModel,
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)
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from paddlenlp.transformers.distilbert.configuration import DistilBertConfig
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from ...testing_utils import require_package, slow
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_common import (
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ModelTesterMixin,
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ModelTesterPretrainedMixin,
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ids_tensor,
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random_attention_mask,
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)
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class DistilBertModelTester:
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def __init__(
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self,
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parent: DistilBertModelTest,
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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_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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fuse=False,
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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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):
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self.parent: DistilBertModelTest = 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_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.fuse = fuse
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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.return_dict = return_dict
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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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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, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self) -> DistilBertConfig:
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return DistilBertConfig(
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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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pool_act=self.pool_act,
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fuse=self.fuse,
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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, config: DistilBertConfig, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DistilBertModel(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.shape, [self.batch_size, self.seq_length, self.hidden_size])
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result = model(input_ids)
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self.parent.assertEqual(result.shape, [self.batch_size, self.seq_length, self.hidden_size])
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def create_and_check_for_masked_lm(
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self,
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config: DistilBertConfig,
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input_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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):
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model = DistilBertForMaskedLM(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.shape, [self.batch_size, self.seq_length, self.vocab_size])
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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,
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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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):
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model = DistilBertForQuestionAnswering(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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)
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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: DistilBertConfig,
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input_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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):
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model = DistilBertForSequenceClassification(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.shape, [self.batch_size, self.num_labels])
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def create_and_check_for_token_classification(
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self,
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config,
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input_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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):
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model = DistilBertForTokenClassification(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.shape, [self.batch_size, self.seq_length, self.num_labels])
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def test_addition_params(self, config: DistilBertConfig, *args, **kwargs):
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config.num_labels = 7
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config.classifier_dropout = 0.98
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model = DistilBertForTokenClassification(config)
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model.eval()
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self.parent.assertEqual(model.classifier.weight.shape, [config.hidden_size, 7])
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self.parent.assertEqual(model.dropout.p, 0.98)
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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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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, "attention_mask": input_mask}
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return config, inputs_dict
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class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = DistilBertModel
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return_dict = False
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use_labels = False
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test_resize_embeddings = False
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all_model_classes = (
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DistilBertModel,
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DistilBertForMaskedLM,
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DistilBertForQuestionAnswering,
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DistilBertForSequenceClassification,
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DistilBertForTokenClassification,
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)
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def setUp(self):
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super().setUp()
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self.model_tester = DistilBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DistilBertConfig, vocab_size=256, hidden_size=24)
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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_masked_lm(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_masked_lm(*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_token_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_token_classification(*config_and_inputs)
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def test_for_custom_params(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.test_addition_params(*config_and_inputs)
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def test_model_name_list(self):
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config = self.model_tester.get_config()
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model = self.base_model_class(config)
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self.assertTrue(len(model.model_name_list) != 0)
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@slow
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def test_params_compatibility_of_init_method(self):
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"""test initing model with different params"""
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model: DistilBertForTokenClassification = DistilBertForTokenClassification.from_pretrained(
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"distilbert-base-uncased", num_classes=4, dropout=0.3
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)
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assert model.config.num_labels == 4
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assert model.config.dropout == 0.3
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class DistilBertModelCompatibilityTest(unittest.TestCase):
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model_id = "hf-internal-testing/tiny-random-DistilBertModel"
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@require_package("transformers", "torch")
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def test_distilBert_converter(self):
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the paddle model
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from paddlenlp.transformers import DistilBertModel
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paddle_model = DistilBertModel.from_pretrained(self.model_id, from_hf_hub=True, cache_dir=tempdir)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
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# 3. forward the torch model
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import torch
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from transformers import DistilBertModel
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torch_model = DistilBertModel.from_pretrained(self.model_id, cache_dir=tempdir)
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torch_model.eval()
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torch_logit = torch_model(torch.tensor(input_ids))[0]
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# 4. compare results
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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rtol=1e-4,
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)
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)
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@require_package("transformers", "torch")
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def test_distilBert_converter_from_local_dir(self):
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create common input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the torch model
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import torch
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from transformers import DistilBertModel
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torch_model = DistilBertModel.from_pretrained(self.model_id)
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torch_model.eval()
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torch_model.save_pretrained(tempdir)
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torch_logit = torch_model(torch.tensor(input_ids))[0]
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# 2. forward the paddle model
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from paddlenlp.transformers import DistilBertModel
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paddle_model = DistilBertModel.from_pretrained(tempdir, convert_from_torch=True)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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rtol=1e-4,
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)
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)
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@parameterized.expand(
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[
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("DistilBertModel",),
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("DistilBertForQuestionAnswering",),
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("DistilBertForSequenceClassification",),
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("DistilBertForTokenClassification",),
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]
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)
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@require_package("transformers", "torch")
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def test_distilBert_classes_from_local_dir(self, class_name, pytorch_class_name=None):
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pytorch_class_name = pytorch_class_name or class_name
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create common input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the torch model
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import torch
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import transformers
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torch_model_class = getattr(transformers, pytorch_class_name)
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torch_model = torch_model_class.from_pretrained(self.model_id)
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torch_model.eval()
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torch_model.save_pretrained(tempdir)
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torch_logit = torch_model(torch.tensor(input_ids))[0]
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# 3. forward the paddle model
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from paddlenlp import transformers
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paddle_model_class = getattr(transformers, class_name)
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paddle_model = paddle_model_class.from_pretrained(tempdir, convert_from_torch=True)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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atol=1e-3,
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)
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)
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class DistilBertModelIntegrationTest(ModelTesterPretrainedMixin, unittest.TestCase):
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base_model_class = DistilBertModel
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@slow
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def test_inference_no_attention(self):
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model = DistilBertModel.from_pretrained("__internal_testing__/tiny-random-distilbert")
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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)
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expected_shape = [1, 11, 8]
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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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[0.50366199, -1.33068442, -1.73558784],
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[1.72435653, 1.08600891, -0.28388503],
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[-0.19172087, -0.56781638, 0.51192915],
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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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def test_inference_with_attention(self):
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model = DistilBertModel.from_pretrained("__internal_testing__/tiny-random-distilbert")
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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)
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expected_shape = [1, 11, 8]
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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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[0.50366199, -1.33068442, -1.73558784],
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[1.72435653, 1.08600891, -0.28388503],
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[-0.19172087, -0.56781638, 0.51192915],
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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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if __name__ == "__main__":
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unittest.main()
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