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https://github.com/huggingface/transformers.git
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b670c26684
Caching models across test cases and across runs of the test suite makes slow tests somewhat more bearable. Use gettempdir() instead of /tmp in tests. This makes it easier to change the location of the cache with semi-standard TMPDIR/TEMP/TMP environment variables. Fix #2222.
238 lines
10 KiB
Python
238 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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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 absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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from transformers import is_torch_available
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from .modeling_common_test import (CommonTestCases, ids_tensor)
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from .configuration_common_test import ConfigTester
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from .utils import CACHE_DIR, require_torch, slow, torch_device
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if is_torch_available():
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from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM,
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AlbertForSequenceClassification, AlbertForQuestionAnswering,
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)
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from transformers.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP
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@require_torch
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class AlbertModelTest(CommonTestCases.CommonModelTester):
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all_model_classes = (AlbertModel, AlbertForMaskedLM) if is_torch_available() else ()
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class AlbertModelTester(object):
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def __init__(self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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embedding_size=16,
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hidden_size=36,
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num_hidden_layers=6,
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num_hidden_groups=6,
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num_attention_heads=6,
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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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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.embedding_size = embedding_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.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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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.num_hidden_groups = num_hidden_groups
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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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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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 = 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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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def check_loss_output(self, result):
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self.parent.assertListEqual(
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list(result["loss"].size()),
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[])
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def create_and_check_albert_model(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = AlbertModel(config=config)
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model.to(torch_device)
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model.eval()
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sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids)
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sequence_output, pooled_output = model(input_ids)
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result = {
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"sequence_output": sequence_output,
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"pooled_output": pooled_output,
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}
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self.parent.assertListEqual(
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list(result["sequence_output"].size()),
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[self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size])
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def create_and_check_albert_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = AlbertForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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loss, prediction_scores = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels)
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result = {
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"loss": loss,
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"prediction_scores": prediction_scores,
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}
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self.parent.assertListEqual(
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list(result["prediction_scores"].size()),
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[self.batch_size, self.seq_length, self.vocab_size])
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self.check_loss_output(result)
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def create_and_check_albert_for_question_answering(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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model = AlbertForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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loss, start_logits, end_logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids,
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start_positions=sequence_labels, end_positions=sequence_labels)
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result = {
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"loss": loss,
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"start_logits": start_logits,
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"end_logits": end_logits,
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}
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self.parent.assertListEqual(
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list(result["start_logits"].size()),
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[self.batch_size, self.seq_length])
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self.parent.assertListEqual(
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list(result["end_logits"].size()),
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[self.batch_size, self.seq_length])
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self.check_loss_output(result)
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def create_and_check_albert_for_sequence_classification(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
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config.num_labels = self.num_labels
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model = AlbertForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
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result = {
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"loss": loss,
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"logits": logits,
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}
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self.parent.assertListEqual(
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list(result["logits"].size()),
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[self.batch_size, self.num_labels])
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self.check_loss_output(result)
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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,
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sequence_labels, token_labels, choice_labels) = 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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def setUp(self):
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self.model_tester = AlbertModelTest.AlbertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_albert_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_albert_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_albert_for_masked_lm(*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_albert_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_albert_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(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
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model = AlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
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self.assertIsNotNone(model)
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if __name__ == "__main__":
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unittest.main()
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