wehub-resource-sync
766 lines
28 KiB
Python
766 lines
28 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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import random
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import tempfile
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import unittest
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from typing import List
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import numpy as np
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import paddle
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from parameterized import parameterized, parameterized_class
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from paddlenlp import __version__ as current_version
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from paddlenlp.transformers import (
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AutoModel,
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AutoModelForQuestionAnswering,
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AutoModelForTokenClassification,
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BertForMaskedLM,
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BertForMultipleChoice,
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BertForPretraining,
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BertForQuestionAnswering,
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BertForSequenceClassification,
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BertForTokenClassification,
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BertModel,
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)
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from paddlenlp.transformers.bert.configuration import BertConfig
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from paddlenlp.transformers.model_utils import PretrainedModel
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from paddlenlp.utils import install_package, uninstall_package
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from ...testing_utils import require_package, slow
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_common import (
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ModelTesterMixin,
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ModelTesterPretrainedMixin,
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ids_tensor,
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random_attention_mask,
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)
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class BertModelTester:
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def __init__(
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self,
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parent: BertModelTest,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=16,
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initializer_range=0.02,
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pad_token_id=0,
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pool_act="tanh",
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fuse=False,
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type_sequence_label_size=2,
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num_labels=3,
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num_choices=4,
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scope=None,
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dropout=0.56,
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return_dict=False,
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):
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self.parent: BertModelTest = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.pad_token_id = pad_token_id
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self.pool_act = pool_act
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self.fuse = fuse
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self.type_sequence_label_size = type_sequence_label_size
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.dropout = dropout
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self.return_dict = return_dict
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self) -> BertConfig:
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return BertConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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pool_act=self.pool_act,
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fuse=self.fuse,
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num_labels=self.num_labels,
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num_choices=self.num_choices,
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)
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def create_and_check_model(
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self, config: BertConfig, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = BertModel(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, token_type_ids=token_type_ids)
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result = model(input_ids)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.hidden_size])
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def create_and_check_for_masked_lm(
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self,
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config: BertConfig,
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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 = BertForMaskedLM(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size])
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def create_and_check_model_past_large_inputs(
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self,
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config: BertConfig,
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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 = BertModel(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.return_dict)
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past_key_values = outputs.past_key_values if self.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, attention_mask=next_attention_mask, output_hidden_states=True, return_dict=self.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.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 create_and_check_for_pretraining(
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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 = BertForPretraining(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=token_labels,
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next_sentence_label=sequence_labels,
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)
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.vocab_size])
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self.parent.assertEqual(result[2].shape, [self.batch_size, 2])
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def create_and_check_for_multiple_choice(
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self,
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config: BertConfig,
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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 = BertForMultipleChoice(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_token_type_ids = token_type_ids.unsqueeze(1).expand([-1, self.num_choices, -1])
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multiple_choice_input_mask = input_mask.unsqueeze(1).expand([-1, self.num_choices, -1])
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result = model(
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multiple_choice_inputs_ids,
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attention_mask=multiple_choice_input_mask,
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token_type_ids=multiple_choice_token_type_ids,
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labels=choice_labels,
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)
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.num_choices])
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def create_and_check_for_question_answering(
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self,
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config,
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input_ids,
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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 = BertForQuestionAnswering(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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return_dict=self.return_dict,
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)
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if sequence_labels is not None:
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result = result[1:]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length])
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length])
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def create_and_check_for_sequence_classification(
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self,
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config: BertConfig,
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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 = BertForSequenceClassification(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.num_labels])
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def create_and_check_for_token_classification(
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self,
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config,
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input_ids,
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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 = BertForTokenClassification(config)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, self.num_labels])
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def test_addition_params(self, config: BertConfig, *args, **kwargs):
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config.num_labels = 7
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config.classifier_dropout = 0.98
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model = BertForTokenClassification(config)
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model.eval()
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self.parent.assertEqual(model.classifier.weight.shape, [config.hidden_size, 7])
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self.parent.assertEqual(model.dropout.p, 0.98)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@parameterized_class(
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("return_dict", "use_labels"),
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[
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[False, False],
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[False, True],
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[True, False],
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[True, True],
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],
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)
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class BertModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = BertModel
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return_dict = False
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use_labels = False
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test_tie_weights = True
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all_model_classes = (
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BertModel,
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BertForMaskedLM,
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BertForMultipleChoice,
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BertForPretraining,
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BertForQuestionAnswering,
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BertForSequenceClassification,
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BertForTokenClassification,
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)
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def setUp(self):
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super().setUp()
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self.model_tester = BertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BertConfig, vocab_size=256, hidden_size=24)
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def test_config(self):
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# self.config_tester.create_and_test_config_from_and_save_pretrained()
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_decoder_model_past_with_large_inputs(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_past_large_inputs(*config_and_inputs)
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def test_for_multiple_choice(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
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def test_for_pretraining(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_pretraining(*config_and_inputs)
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def test_for_question_answering(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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def test_for_custom_params(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.test_addition_params(*config_and_inputs)
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def test_model_name_list(self):
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config = self.model_tester.get_config()
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model = self.base_model_class(config)
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self.assertTrue(len(model.model_name_list) != 0)
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@slow
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def test_params_compatibility_of_init_method(self):
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"""test initing model with different params"""
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model: BertForTokenClassification = BertForTokenClassification.from_pretrained(
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"bert-base-uncased", num_classes=4, dropout=0.3
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)
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assert model.num_labels == 4
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assert model.dropout.p == 0.3
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class BertCompatibilityTest(unittest.TestCase):
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test_model_id = "hf-internal-testing/tiny-random-BertModel"
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@classmethod
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@require_package("transformers", "torch")
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def setUpClass(cls) -> None:
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from transformers import BertModel
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# when python application is done, `TemporaryDirectory` will be free
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cls.torch_model_path = tempfile.TemporaryDirectory().name
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model = BertModel.from_pretrained(cls.test_model_id)
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model.save_pretrained(cls.torch_model_path)
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def test_model_config_mapping(self):
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config = BertConfig(num_labels=22, hidden_dropout_prob=0.99)
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self.assertEqual(config.hidden_dropout_prob, 0.99)
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self.assertEqual(config.num_labels, 22)
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def setUp(self) -> None:
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self.tempdirs: List[tempfile.TemporaryDirectory] = []
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def tearDown(self) -> None:
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for tempdir in self.tempdirs:
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tempdir.cleanup()
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def get_tempdir(self) -> str:
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tempdir = tempfile.TemporaryDirectory()
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self.tempdirs.append(tempdir)
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return tempdir.name
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def run_token_for_classification(self, version: str):
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install_package("paddlenlp", version=version)
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from paddlenlp import __version__
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self.assertEqual(__version__, version)
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from paddlenlp.transformers import BertForTokenClassification, BertModel
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tempdir = self.get_tempdir()
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|
|
|
# prepare the old version of model
|
|
old_model = BertModel.from_pretrained("bert-base-uncased")
|
|
old_model_path = os.path.join(tempdir, "old-model")
|
|
old_model.save_pretrained(old_model_path)
|
|
|
|
old_model_for_token = BertForTokenClassification.from_pretrained(
|
|
"bert-base-uncased", num_classes=4, dropout=0.3
|
|
)
|
|
old_model_for_token_path = os.path.join(tempdir, "old-model-for-token")
|
|
old_model_for_token.save_pretrained(old_model_for_token_path)
|
|
|
|
uninstall_package("paddlenlp")
|
|
from paddlenlp import __version__
|
|
|
|
self.assertEqual(__version__, current_version)
|
|
|
|
from paddlenlp.transformers import BertForTokenClassification, BertModel
|
|
|
|
# bert: from old bert
|
|
model = BertModel.from_pretrained(old_model_path)
|
|
self.compare_two_model(old_model, model)
|
|
|
|
# bert: from old bert-for-token
|
|
model = BertModel.from_pretrained(old_model_for_token_path)
|
|
self.compare_two_model(old_model, model)
|
|
|
|
# bert-for-token: from old bert
|
|
model = BertForTokenClassification.from_pretrained(old_model_path)
|
|
self.compare_two_model(old_model_for_token, model)
|
|
self.assertNotEqual(model.num_labels, 4)
|
|
self.assertNotEqual(model.dropout.p, 0.3)
|
|
|
|
# bert-for-token: from old bert-for-token
|
|
model = BertForTokenClassification.from_pretrained(old_model_for_token_path)
|
|
self.compare_two_model(old_model_for_token, model)
|
|
self.assertEqual(model.num_labels, 4)
|
|
self.assertEqual(model.dropout.p, 0.3)
|
|
|
|
def compare_two_model(self, first_model: PretrainedModel, second_model: PretrainedModel):
|
|
|
|
first_weight_name = "encoder.layers.8.linear2.weight"
|
|
if first_model.__class__.__name__ != "BertModel":
|
|
first_weight_name = "bert." + first_weight_name
|
|
|
|
second_weight_name = "encoder.layers.8.linear2.weight"
|
|
if second_model.__class__.__name__ != "BertModel":
|
|
second_weight_name = "bert." + second_weight_name
|
|
|
|
first_tensor = first_model.state_dict()[first_weight_name]
|
|
second_tensor = second_model.state_dict()[second_weight_name]
|
|
self.compare_two_weight(first_tensor, second_tensor)
|
|
|
|
def compare_two_weight(self, first_tensor, second_tensor):
|
|
diff = paddle.sum(first_tensor - second_tensor).item()
|
|
self.assertEqual(diff, 0.0)
|
|
|
|
@slow
|
|
def test_paddlenlp_token_classification(self):
|
|
versions = ["3.0.0b4"]
|
|
for version in versions:
|
|
install_package("paddlenlp", version=version)
|
|
self.run_token_for_classification(version)
|
|
uninstall_package("paddlenlp")
|
|
|
|
@slow
|
|
def test_bert_save_token_load(self):
|
|
"""bert -> token"""
|
|
from paddlenlp.transformers import BertForTokenClassification, BertModel
|
|
|
|
saved_dir = os.path.join(self.get_tempdir(), "bert-saved")
|
|
bert: BertModel = BertModel.from_pretrained("bert-base-uncased")
|
|
bert.save_pretrained(saved_dir)
|
|
|
|
bert_for_token = BertForTokenClassification.from_pretrained(saved_dir)
|
|
self.compare_two_model(bert, bert_for_token)
|
|
|
|
@slow
|
|
def test_bert_save_bert_load(self):
|
|
"""bert -> bert"""
|
|
saved_dir = os.path.join(self.get_tempdir(), "bert-saved")
|
|
bert: BertModel = BertModel.from_pretrained("bert-base-uncased")
|
|
bert.save_pretrained(saved_dir)
|
|
|
|
bert_loaded = BertModel.from_pretrained(saved_dir)
|
|
self.compare_two_model(bert, bert_loaded)
|
|
|
|
@slow
|
|
def test_token_saved_bert_load(self):
|
|
"""token -> bert"""
|
|
from paddlenlp.transformers import BertForTokenClassification, BertModel
|
|
|
|
saved_dir = os.path.join(self.get_tempdir(), "bert-token-saved")
|
|
bert_for_token = BertForTokenClassification.from_pretrained("bert-base-uncased")
|
|
bert_for_token.save_pretrained(saved_dir)
|
|
|
|
bert = BertModel.from_pretrained(saved_dir)
|
|
self.compare_two_model(bert, bert_for_token)
|
|
|
|
@slow
|
|
def test_token_saved_token_load(self):
|
|
"""token -> token"""
|
|
saved_dir = os.path.join(self.get_tempdir(), "bert-token-saved")
|
|
bert_for_token = BertForTokenClassification.from_pretrained("bert-base-uncased")
|
|
bert_for_token.save_pretrained(saved_dir)
|
|
|
|
bert_for_token_loaded = BertForTokenClassification.from_pretrained(saved_dir)
|
|
self.compare_two_model(bert_for_token, bert_for_token_loaded)
|
|
|
|
@slow
|
|
def test_auto_model(self):
|
|
AutoModel.from_pretrained("bert-base-uncased")
|
|
model = AutoModelForTokenClassification.from_pretrained("bert-base-uncased", num_classes=4, dropout=0.3)
|
|
self.assertEqual(model.num_labels, 4)
|
|
self.assertEqual(model.dropout.p, 0.3)
|
|
|
|
model = AutoModelForQuestionAnswering.from_pretrained("bert-base-uncased", dropout=0.3)
|
|
self.assertEqual(model.dropout.p, 0.3)
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_bert_converter(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
|
|
# 1. create common input
|
|
input_ids = np.random.randint(100, 200, [1, 20])
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import BertModel
|
|
|
|
paddle_model = BertModel.from_pretrained(
|
|
"hf-internal-testing/tiny-random-BertModel", from_hf_hub=True, cache_dir=tempdir
|
|
)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
|
|
|
|
# 3. forward the torch model
|
|
import torch
|
|
from transformers import BertModel
|
|
|
|
torch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel", cache_dir=tempdir)
|
|
torch_model.eval()
|
|
torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_bert_converter_from_local_dir(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
|
|
# 1. create common input
|
|
input_ids = np.random.randint(100, 200, [1, 20])
|
|
|
|
# 2. forward the torch model
|
|
import torch
|
|
from transformers import BertModel
|
|
|
|
torch_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-BertModel")
|
|
torch_model.eval()
|
|
torch_model.save_pretrained(tempdir)
|
|
torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import BertModel
|
|
|
|
paddle_model = BertModel.from_pretrained(tempdir, convert_from_torch=True)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
("BertModel",),
|
|
# ("BertForMaskedLM",), TODO: need to tie weights
|
|
# ("BertForPretraining", "BertForPreTraining"), TODO: need to tie weights
|
|
("BertForMultipleChoice",),
|
|
("BertForQuestionAnswering",),
|
|
("BertForSequenceClassification",),
|
|
("BertForTokenClassification",),
|
|
]
|
|
)
|
|
@require_package("transformers", "torch")
|
|
def test_bert_classes_from_local_dir(self, class_name, pytorch_class_name: str | None = None):
|
|
pytorch_class_name = pytorch_class_name or class_name
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
|
|
# 1. create common input
|
|
input_ids = np.random.randint(100, 200, [1, 20])
|
|
|
|
# 2. forward the torch model
|
|
import torch
|
|
import transformers
|
|
|
|
torch_model_class = getattr(transformers, pytorch_class_name)
|
|
torch_model = torch_model_class.from_pretrained(self.torch_model_path)
|
|
torch_model.eval()
|
|
|
|
if "MultipleChoice" in class_name:
|
|
# construct input for MultipleChoice Model
|
|
torch_model.config.num_choices = random.randint(2, 10)
|
|
input_ids = (
|
|
paddle.to_tensor(input_ids)
|
|
.unsqueeze(1)
|
|
.expand([-1, torch_model.config.num_choices, -1])
|
|
.cpu()
|
|
.numpy()
|
|
)
|
|
|
|
torch_model.save_pretrained(tempdir)
|
|
torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
|
|
|
|
# 3. forward the paddle model
|
|
from paddlenlp import transformers
|
|
|
|
paddle_model_class = getattr(transformers, class_name)
|
|
paddle_model = paddle_model_class.from_pretrained(tempdir, convert_from_torch=True)
|
|
paddle_model.eval()
|
|
|
|
paddle_logit = paddle_model(paddle.to_tensor(input_ids), return_dict=False)[0]
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
|
|
atol=1e-3,
|
|
)
|
|
)
|
|
|
|
|
|
class BertModelIntegrationTest(ModelTesterPretrainedMixin, unittest.TestCase):
|
|
base_model_class = BertModel
|
|
hf_remote_test_model_path = "PaddleCI/tiny-random-bert"
|
|
paddlehub_remote_test_model_path = "__internal_testing__/tiny-random-bert"
|
|
|
|
@slow
|
|
def test_inference_no_attention(self):
|
|
model = BertModel.from_pretrained("bert-base-uncased")
|
|
model.eval()
|
|
input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
|
|
attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
|
|
with paddle.no_grad():
|
|
output = model(input_ids, attention_mask=attention_mask)[0]
|
|
expected_shape = [1, 11, 768]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = paddle.to_tensor(
|
|
[[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_with_attention(self):
|
|
model = BertModel.from_pretrained("bert-base-uncased")
|
|
model.eval()
|
|
input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
|
|
attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
|
|
with paddle.no_grad():
|
|
output = model(input_ids, attention_mask=attention_mask)[0]
|
|
expected_shape = [1, 11, 768]
|
|
self.assertEqual(output.shape, expected_shape)
|
|
|
|
expected_slice = paddle.to_tensor(
|
|
[[[0.4249, 0.1008, 0.7531], [0.3771, 0.1188, 0.7467], [0.4152, 0.1098, 0.7108]]]
|
|
)
|
|
self.assertTrue(paddle.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|