wehub-resource-sync
480 lines
17 KiB
Python
480 lines
17 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 random
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import tempfile
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import unittest
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import numpy as np
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import paddle
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from paddle import Tensor
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from parameterized import parameterized, parameterized_class
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from paddlenlp.transformers import (
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AlbertConfig,
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AlbertForMaskedLM,
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AlbertForMultipleChoice,
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AlbertForQuestionAnswering,
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AlbertForSequenceClassification,
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AlbertForTokenClassification,
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AlbertModel,
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)
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from ...testing_utils import require_package, slow
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from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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class AlbertModelTester:
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def __init__(
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self,
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parent,
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):
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self.parent = parent
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self.batch_size = 13
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self.seq_length = 7
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self.is_training = True
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self.use_input_mask = True
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self.use_token_type_ids = True
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self.vocab_size = 99
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self.embedding_size = 16
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self.hidden_size = 36
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self.num_hidden_layers = 6
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self.num_hidden_groups = 6
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self.num_attention_heads = 6
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self.intermediate_size = 37
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self.inner_group_num = 1
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self.hidden_act = "gelu"
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self.hidden_dropout_prob = 0.1
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self.attention_probs_dropout_prob = 0.1
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self.max_position_embeddings = 512
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self.type_vocab_size = 2
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self.initializer_range = 0.02
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self.layer_norm_eps = 1e-12
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self.pad_token_id = (0,)
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self.bos_token_id = (2,)
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self.eos_token_id = (3,)
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self.add_pooling_layer = True
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self.type_sequence_label_size = 2
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self.num_labels = 3
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self.num_choices = 4
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self.scope = None
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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.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_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):
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return AlbertConfig(
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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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num_hidden_groups=self.num_hidden_groups,
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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,
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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 = AlbertModel(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)
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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_masked_lm(
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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 = AlbertForMaskedLM(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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return_dict=self.parent.return_dict,
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)
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size])
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def create_and_check_for_question_answering(
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self,
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config,
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input_ids: 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 = AlbertForQuestionAnswering(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.parent.return_dict,
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)
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length])
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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,
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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 = AlbertForSequenceClassification(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 token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_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: 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 = AlbertForTokenClassification(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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return_dict=self.parent.return_dict,
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)
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.num_labels])
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def create_and_check_for_multiple_choice(
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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 = AlbertForMultipleChoice(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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return_dict=self.parent.return_dict,
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)
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if not self.parent.return_dict and token_labels is None:
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self.parent.assertTrue(paddle.is_tensor(result))
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if token_labels is not None:
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result = result[1:]
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elif paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_choices])
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def 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, token_type_ids, input_mask, _, _, _) = 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 AlbertModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = AlbertModel
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use_labels = False
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return_dict = False
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test_tie_weights = True
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all_model_classes = (
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AlbertModel,
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AlbertForMaskedLM,
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AlbertForMultipleChoice,
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AlbertForSequenceClassification,
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AlbertForTokenClassification,
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AlbertForQuestionAnswering,
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)
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def setUp(self):
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self.model_tester = AlbertModelTester(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_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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def test_for_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_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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@slow
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def test_model_from_pretrained(self):
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# for model_name in list(AlbertPretrainedModel.pretrained_init_configuration.keys())[:1]:
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for model_name in ["albert/albert-base-v1"]:
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model = AlbertModel.from_pretrained(model_name, from_hf_hub=True, convert_from_torch=True)
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self.assertIsNotNone(model)
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class AlbertModelCompatibilityTest(unittest.TestCase):
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model_id = "hf-internal-testing/tiny-random-AlbertModel"
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@require_package("transformers", "torch")
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def test_albert_converter(self):
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the paddle model
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from paddlenlp.transformers import AlbertModel
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paddle_model = AlbertModel.from_pretrained(self.model_id, from_hf_hub=True, cache_dir=tempdir)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
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# 3. forward the torch model
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import torch
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from transformers import AlbertModel
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torch_model = AlbertModel.from_pretrained(self.model_id, cache_dir=tempdir)
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torch_model.eval()
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torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
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# 4. compare results
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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rtol=1e-4,
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)
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)
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@require_package("transformers", "torch")
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def test_albert_converter_from_local_dir(self):
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create common input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the torch model
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import torch
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from transformers import AlbertModel
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torch_model = AlbertModel.from_pretrained(self.model_id)
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torch_model.eval()
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torch_model.save_pretrained(tempdir)
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torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
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# 2. forward the paddle model
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from paddlenlp.transformers import AlbertModel
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paddle_model = AlbertModel.from_pretrained(tempdir, convert_from_torch=True)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids))[0]
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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rtol=1e-4,
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)
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)
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@parameterized.expand(
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[
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("AlbertModel",),
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# ("AlbertForMaskedLM",), TODO: need to tie weights
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# ("AlbertForPretraining",), TODO: need to tie weights
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("AlbertForMultipleChoice",),
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("AlbertForQuestionAnswering",),
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("AlbertForSequenceClassification",),
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("AlbertForTokenClassification",),
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]
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)
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@require_package("transformers", "torch")
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def test_albert_classes_from_local_dir(self, class_name, pytorch_class_name=None):
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pytorch_class_name = pytorch_class_name or class_name
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with tempfile.TemporaryDirectory() as tempdir:
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# 1. create common input
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input_ids = np.random.randint(100, 200, [1, 20])
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# 2. forward the torch model
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import torch
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import transformers
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torch_model_class = getattr(transformers, pytorch_class_name)
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torch_model = torch_model_class.from_pretrained(self.model_id)
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torch_model.eval()
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if "MultipleChoice" in class_name:
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# construct input for MultipleChoice Model
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torch_model.config.num_choices = random.randint(2, 10)
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input_ids = (
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paddle.to_tensor(input_ids)
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.unsqueeze(1)
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.expand([-1, torch_model.config.num_choices, -1])
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.cpu()
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.numpy()
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)
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torch_model.save_pretrained(tempdir)
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torch_logit = torch_model(torch.tensor(input_ids), return_dict=False)[0]
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# 3. forward the paddle model
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from paddlenlp import transformers
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paddle_model_class = getattr(transformers, class_name)
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paddle_model = paddle_model_class.from_pretrained(tempdir, convert_from_torch=True)
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paddle_model.eval()
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paddle_logit = paddle_model(paddle.to_tensor(input_ids), return_dict=False)[0]
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self.assertTrue(
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np.allclose(
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paddle_logit.detach().cpu().reshape([-1])[:9].numpy(),
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torch_logit.detach().cpu().reshape([-1])[:9].numpy(),
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atol=1e-3,
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)
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)
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class AlbertModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head_absolute_embedding(self):
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model = AlbertModel.from_pretrained("albert-base-v2")
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input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
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attention_mask = paddle.to_tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
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with paddle.no_grad():
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output = model(input_ids, attention_mask=attention_mask)[0]
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expected_shape = [1, 11, 768]
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self.assertEqual(output.shape, expected_shape)
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expected_slice = paddle.to_tensor(
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[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]
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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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