wehub-resource-sync
512 lines
18 KiB
Python
512 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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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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UIEM,
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ErnieMConfig,
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ErnieMForMultipleChoice,
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ErnieMForQuestionAnswering,
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ErnieMForSequenceClassification,
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ErnieMForTokenClassification,
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ErnieMModel,
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ErnieMPretrainedModel,
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)
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from ...testing_utils import slow
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from ..test_modeling_common import (
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ModelTesterMixin,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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class ErnieMModelTester:
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"""Base ErnieM Model tester which can test:"""
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def __init__(
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self,
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parent,
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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_position_ids=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=2,
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initializer_range=0.02,
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pad_token_id=0,
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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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num_classes=3,
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scope=None,
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):
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self.parent = 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_position_ids = use_position_ids
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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.type_sequence_label_size = type_sequence_label_size
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self.num_classes = num_classes
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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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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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attention_mask = None
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if self.use_input_mask:
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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position_ids = None
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if self.use_position_ids:
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ones = paddle.ones_like(input_ids, dtype="int64")
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seq_length = paddle.cumsum(ones, axis=1)
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position_ids = seq_length - ones
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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.parent.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_classes)
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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, position_ids, attention_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return ErnieMConfig(
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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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num_class=self.num_classes,
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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 prepare_config_and_inputs_for_common(self):
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config, input_ids, position_ids, attention_mask, _, _, _ = self.prepare_config_and_inputs()
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inputs_dict = {
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"input_ids": input_ids,
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"position_ids": position_ids,
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"attention_mask": attention_mask,
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}
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return config, inputs_dict
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def create_and_check_model(
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self,
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config: ErnieMConfig,
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input_ids: Tensor,
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position_ids: Tensor,
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attention_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 = ErnieMModel(config)
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model.eval()
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result = model(
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input_ids, attention_mask=attention_mask, position_ids=position_ids, return_dict=self.parent.return_dict
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)
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result = model(input_ids, position_ids=position_ids, return_dict=self.parent.return_dict)
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result = model(input_ids, attention_mask=attention_mask, return_dict=self.parent.return_dict)
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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_sequence_classification(
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self,
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config: ErnieMConfig,
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input_ids: Tensor,
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position_ids: Tensor,
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attention_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 = ErnieMForSequenceClassification(config)
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model.eval()
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result = model(
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input_ids,
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position_ids=position_ids,
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attention_mask=attention_mask,
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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_classes])
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def create_and_check_for_question_answering(
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self,
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config: ErnieMConfig,
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input_ids: Tensor,
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position_ids: Tensor,
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attention_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 = ErnieMForQuestionAnswering(config)
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model.eval()
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result = model(
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input_ids,
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position_ids=position_ids,
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attention_mask=attention_mask,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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return_dict=self.parent.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, self.num_classes])
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def create_and_check_for_uie(
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self,
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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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):
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model = UIEM(config)
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model.eval()
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start_prob, end_prob = 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(start_prob.shape, [self.batch_size, self.seq_length])
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self.parent.assertEqual(end_prob.shape, [self.batch_size, self.seq_length])
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def create_and_check_for_token_classification(
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self,
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config: ErnieMConfig,
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input_ids: Tensor,
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position_ids: Tensor,
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attention_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 = ErnieMForTokenClassification(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=attention_mask,
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position_ids=position_ids,
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labels=token_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.seq_length, self.num_classes])
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def create_and_check_for_multiple_choice(
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self,
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config: ErnieMConfig,
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input_ids: Tensor,
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position_ids: Tensor,
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attention_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 = ErnieMForMultipleChoice(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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multiple_choice_position_ids = position_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
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multiple_choice_attention_mask = attention_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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position_ids=multiple_choice_position_ids,
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attention_mask=multiple_choice_attention_mask,
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labels=choice_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_choices])
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def create_and_check_model_cache(
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self, config: ErnieMConfig, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = ErnieMModel(config)
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model.eval()
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input_ids = ids_tensor((self.batch_size, self.seq_length), self.vocab_size)
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# create tensors for past_key_values of shape [batch_size, num_heads, seq_length, head_size]
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embed_size_per_head = self.hidden_size // self.num_attention_heads
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key_tensor = floats_tensor((self.batch_size, self.num_attention_heads, self.seq_length, embed_size_per_head))
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values_tensor = floats_tensor(
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(self.batch_size, self.num_attention_heads, self.seq_length, embed_size_per_head)
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)
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past_key_values = (
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(
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key_tensor,
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values_tensor,
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),
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) * self.num_hidden_layers
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# create fully-visible attention mask for input_ids only and input_ids + past
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attention_mask = paddle.ones([self.batch_size, self.seq_length])
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attention_mask_with_past = paddle.ones([self.batch_size, self.seq_length * 2])
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outputs_with_cache = model(
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input_ids,
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attention_mask=attention_mask_with_past,
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past_key_values=past_key_values,
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return_dict=self.parent.return_dict,
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)
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outputs_without_cache = model(input_ids, attention_mask=attention_mask, return_dict=self.parent.return_dict)
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# last_hidden_state should have the same shape but different values when given past_key_values
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if self.parent.return_dict:
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self.parent.assertEqual(
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outputs_with_cache.last_hidden_state.shape, outputs_without_cache.last_hidden_state.shape
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)
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self.parent.assertFalse(
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paddle.allclose(outputs_with_cache.last_hidden_state, outputs_without_cache.last_hidden_state)
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)
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else:
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outputs_with_cache, _ = outputs_with_cache
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outputs_without_cache, _ = outputs_without_cache
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self.parent.assertEqual(outputs_with_cache.shape, outputs_without_cache.shape)
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self.parent.assertFalse(paddle.allclose(outputs_with_cache, outputs_without_cache))
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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 ErnieMModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = ErnieMModel
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use_labels = False
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return_dict = False
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use_inputs_embeds = True
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all_model_classes = (
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ErnieMModel,
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ErnieMForSequenceClassification,
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ErnieMForTokenClassification,
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ErnieMForQuestionAnswering,
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ErnieMForMultipleChoice,
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UIEM,
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)
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def setUp(self):
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self.model_tester = ErnieMModelTester(self)
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# set attribute in setUp to overwrite the static attribute
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self.test_resize_embeddings = False
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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_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_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_token_classification(*config_and_inputs)
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def test_for_uie(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_uie(*config_and_inputs)
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def test_for_multi_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_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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def test_model_from_pretrained(self):
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for model_name in list(ErnieMPretrainedModel.pretrained_init_configuration)[:1]:
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model = ErnieMModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class ErnieMModelIntegrationTest(unittest.TestCase):
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base_model_class = ErnieMPretrainedModel
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hf_remote_test_model_path = "PaddleCI/tiny-random-ernie-m"
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@slow
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def test_inference_no_attention(self):
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model = ErnieMModel.from_pretrained("ernie-m-base")
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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, 768]
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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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[-0.02920425, -0.00768885, -0.10219190],
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[-0.10798159, 0.02311476, -0.17285497],
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[0.05675533, 0.01330730, -0.06826267],
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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 = ErnieMModel.from_pretrained("ernie-m-base")
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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, 768]
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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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[-0.02920425, -0.00768885, -0.10219190],
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[-0.10798159, 0.02311476, -0.17285497],
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[0.05675533, 0.01330730, -0.06826267],
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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_past_key_value(self):
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model = ErnieMModel.from_pretrained("ernie-m-base")
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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)
|
|
|
|
expected_shape = [1, 11, 768]
|
|
self.assertEqual(output[0].shape, expected_shape)
|
|
|
|
expected_slice = paddle.to_tensor(
|
|
[
|
|
[
|
|
[-0.02920425, -0.00768885, -0.10219190],
|
|
[-0.10798159, 0.02311476, -0.17285497],
|
|
[0.05675533, 0.01330730, -0.06826267],
|
|
]
|
|
]
|
|
)
|
|
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(
|
|
[
|
|
[
|
|
[0.05163988, -0.07475190, 0.06332156],
|
|
[0.03051429, -0.01377687, -0.12024689],
|
|
[0.03379946, 0.00674286, 0.08079184],
|
|
]
|
|
]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[0][:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|