wehub-resource-sync
431 lines
15 KiB
Python
431 lines
15 KiB
Python
# Copyright (c) 2023 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 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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SkepConfig,
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SkepCrfForTokenClassification,
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SkepForSequenceClassification,
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SkepForTokenClassification,
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SkepModel,
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SkepPretrainedModel,
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)
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from ...testing_utils import slow
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from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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class SkepModelTester:
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"""Base Skep 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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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.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) -> Tuple[SkepConfig, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
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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.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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self) -> SkepConfig:
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return SkepConfig(
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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 create_and_check_model(
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self,
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config: SkepConfig,
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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 = SkepModel(config)
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model.eval()
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result = model(
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input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, return_dict=self.parent.return_dict
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)
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result = model(input_ids, token_type_ids=token_type_ids, return_dict=self.parent.return_dict)
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result = model(input_ids, 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,
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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 = SkepForSequenceClassification(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 sequence_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if sequence_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_token_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 = SkepForTokenClassification(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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return_dict=self.parent.return_dict,
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labels=token_labels,
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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_crf_token_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 = SkepCrfForTokenClassification(config)
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model.eval()
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result = model(
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input_ids, token_type_ids=token_type_ids, return_dict=self.parent.return_dict, labels=token_labels
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)
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if paddle.is_tensor(result):
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result = [result]
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if token_labels is not None:
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self.parent.assertEqual(result[0].shape, [self.batch_size])
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else:
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length])
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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 = SkepModel(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 = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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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 SkepModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = SkepModel
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return_dict = False
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use_labels = False
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use_test_inputs_embeds = True
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all_model_classes = (
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SkepModel,
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SkepCrfForTokenClassification,
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SkepForSequenceClassification,
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SkepForTokenClassification,
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)
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def setUp(self):
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self.model_tester = SkepModelTester(self)
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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_crf_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_crf_token_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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def test_model_from_pretrained(self):
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for model_name in list(SkepPretrainedModel.pretrained_init_configuration)[:1]:
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model = SkepModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class SkepModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_attention(self):
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model = SkepModel.from_pretrained("skep_ernie_1.0_large_ch")
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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, 1024]
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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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[0.31737554, 0.58842468, 0.43969756],
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[0.20048048, 0.04142965, -0.2655520],
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[0.49883127, -0.15263288, 0.46780178],
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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 = SkepModel.from_pretrained("skep_ernie_1.0_large_ch")
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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, 1024]
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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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[0.31737554, 0.58842468, 0.43969756],
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[0.20048048, 0.04142965, -0.2655520],
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[0.49883127, -0.15263288, 0.46780178],
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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 = SkepModel.from_pretrained("skep_ernie_1.0_large_ch")
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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, 1024]
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self.assertEqual(output[0].shape, expected_shape)
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expected_slice = paddle.to_tensor(
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[
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[
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[0.31737363, 0.58842909, 0.43969074],
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[0.20047806, 0.04142847, -0.26555336],
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[0.49882850, -0.15263671, 0.46780348],
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]
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]
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)
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self.assertTrue(paddle.allclose(output[0][:, 1:4, 1:4], expected_slice, atol=1e-4))
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# insert the past key value into model
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with paddle.no_grad():
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output = model(input_ids, use_cache=True, past_key_values=output.past_key_values, return_dict=True)
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expected_slice = paddle.to_tensor(
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[
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[
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[0.29901379, 0.68195367, 0.62448436],
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[0.18537062, 0.33085057, -0.04292759],
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[0.38783669, -0.19946010, 0.24944240],
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]
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]
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)
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self.assertTrue(paddle.allclose(output[0][:, 1:4, 1:4], expected_slice, atol=1e-4))
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