wehub-resource-sync
300 lines
10 KiB
Python
300 lines
10 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 numpy as np
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import paddle
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from paddlenlp.transformers import (
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ErnieDocConfig,
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ErnieDocForQuestionAnswering,
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ErnieDocForSequenceClassification,
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ErnieDocForTokenClassification,
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ErnieDocModel,
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ErnieDocPretrainedModel,
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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
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class ErnieDocModelTester:
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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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num_hidden_layers=5,
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num_attention_heads=4,
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hidden_size=32,
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hidden_dropout_prob=0.1,
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attention_dropout_prob=0.1,
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relu_dropout=0.0,
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hidden_act="gelu",
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memory_len=7,
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vocab_size=99,
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type_vocab_size=2,
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max_position_embeddings=256,
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task_type_vocab_size=3,
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normalize_before=False,
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epsilon=1e-5,
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rel_pos_params_sharing=False,
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initializer_range=0.02,
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pad_token_id=0,
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cls_token_idx=-1,
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type_sequence_label_size=2,
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num_classes=2,
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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.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_size = hidden_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_dropout_prob = attention_dropout_prob
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self.relu_dropout = relu_dropout
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self.hidden_act = hidden_act
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self.memory_len = memory_len
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.task_type_vocab_size = task_type_vocab_size
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self.type_vocab_size = type_vocab_size
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self.normalize_before = normalize_before
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self.epsilon = epsilon
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self.rel_pos_params_sharing = rel_pos_params_sharing
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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.cls_token_idx = cls_token_idx
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self.num_classes = num_classes
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self.type_sequence_label_size = type_sequence_label_size
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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, 1], 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 = paddle.ones([self.batch_size, self.seq_length, 1])
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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, 1], self.type_vocab_size, dtype="int64")
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position_ids = None
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token_labels = None
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def get_related_pos(insts, seq_len, memory_len=128):
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beg = seq_len + seq_len + memory_len
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r_position = [list(range(beg - 1, seq_len - 1, -1)) + list(range(0, seq_len)) for i in range(len(insts))]
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return np.array(r_position).astype("int64").reshape([len(insts), beg, 1])
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position_ids = paddle.to_tensor(get_related_pos(input_ids, self.seq_length, self.memory_len))
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tensor = paddle.zeros([self.batch_size, self.seq_length, self.hidden_size], dtype="float32")
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memories = [tensor for i in range(self.num_hidden_layers)]
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if self.parent.use_labels:
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_classes)
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config = self.get_config()
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return config, input_ids, memories, token_type_ids, input_mask, position_ids, token_labels
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def get_config(self):
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return ErnieDocConfig(
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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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attention_dropout_prob=self.attention_dropout_prob,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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relu_dropout=self.relu_dropout,
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memory_len=self.memory_len,
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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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task_type_vocab_size=self.task_type_vocab_size,
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normalize_before=self.normalize_before,
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epsilon=self.epsilon,
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rel_pos_params_sharing=self.rel_pos_params_sharing,
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cls_token_idx=self.cls_token_idx,
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)
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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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(config, input_ids, memories, token_type_ids, input_mask, position_ids, token_labels) = 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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"attn_mask": input_mask,
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"memories": memories,
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"position_ids": position_ids,
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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,
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input_ids,
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memories,
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token_type_ids,
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input_mask,
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position_ids,
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token_labels,
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):
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model = ErnieDocModel(config)
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model.eval()
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result = model(
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input_ids,
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memories=memories,
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attn_mask=input_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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)
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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_question_answering(
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self,
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config,
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input_ids,
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memories,
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token_type_ids,
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input_mask,
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position_ids,
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token_labels,
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):
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model = ErnieDocForQuestionAnswering(config)
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model.eval()
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result = model(
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input_ids,
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memories=memories,
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attn_mask=input_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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)
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start_logits, end_logits = result[0], result[1]
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self.parent.assertEqual(start_logits.shape, [self.batch_size, self.seq_length])
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self.parent.assertEqual(end_logits.shape, [self.batch_size, self.seq_length])
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def create_and_check_for_sequence_classification(
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self,
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config,
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input_ids,
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memories,
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token_type_ids,
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input_mask,
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position_ids,
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token_labels,
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):
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model = ErnieDocForSequenceClassification(config)
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model.eval()
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result = model(
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input_ids,
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memories=memories,
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attn_mask=input_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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)
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if position_ids 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][1].shape, [self.batch_size, self.memory_len, self.hidden_size])
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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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memories,
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token_type_ids,
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input_mask,
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position_ids,
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token_labels,
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):
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model = ErnieDocForTokenClassification(config)
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model.eval()
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result = model(
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input_ids,
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memories=memories,
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attn_mask=input_mask,
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token_type_ids=token_type_ids,
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position_ids=position_ids,
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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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class ErnieDocModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = ErnieDocModel
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return_dict: bool = False
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use_labels: bool = False
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use_test_inputs_embeds: bool = True
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all_model_classes = (
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ErnieDocModel,
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ErnieDocForSequenceClassification,
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ErnieDocForTokenClassification,
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ErnieDocForQuestionAnswering,
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)
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def setUp(self):
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self.model_tester = ErnieDocModelTester(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_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_inputs_embeds(self):
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# Direct input embedding tokens is currently not supported
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self.skipTest("Direct input embedding tokens is currently not supported")
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@slow
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@unittest.skip("Skip for missing model weight.")
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def test_model_from_pretrained(self):
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for model_name in list(ErnieDocPretrainedModel.pretrained_init_configuration)[:1]:
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model = ErnieDocModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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