mirror of
https://github.com/huggingface/transformers.git
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0fe17f375a
* Change the way tracing happens, enabling dynamic axes out of the box * Update the tests and modeling xlnet * Add the non recoding of leaf modules to avoid recording more values for the methods to record than what will be seen at tracing time (which would otherwise desynchronize the recorded values and the values that need to be given to the proxies during tracing, causing errors). * Comments and making tracing work for gpt-j and xlnet * Refactore things related to num_choices (and batch_size, sequence_length) * Update fx to work on PyTorch 1.10 * Postpone autowrap_function feature usage for later * Add copyrights * Remove unnecessary file * Fix issue with add_new_model_like * Apply suggestions
295 lines
12 KiB
Python
295 lines
12 KiB
Python
# coding=utf-8
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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 os
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import tempfile
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import unittest
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from transformers import DistilBertConfig, is_torch_available
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from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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if is_torch_available():
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import torch
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from transformers import (
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DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
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DistilBertForMaskedLM,
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DistilBertForMultipleChoice,
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DistilBertForQuestionAnswering,
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DistilBertForSequenceClassification,
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DistilBertForTokenClassification,
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DistilBertModel,
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)
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class DistilBertModelTester(object):
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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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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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.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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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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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, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return DistilBertConfig(
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vocab_size=self.vocab_size,
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dim=self.hidden_size,
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n_layers=self.num_hidden_layers,
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n_heads=self.num_attention_heads,
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hidden_dim=self.intermediate_size,
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hidden_act=self.hidden_act,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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)
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def create_and_check_distilbert_model(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DistilBertModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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def create_and_check_distilbert_for_masked_lm(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DistilBertForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_distilbert_for_question_answering(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = DistilBertForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids, attention_mask=input_mask, start_positions=sequence_labels, end_positions=sequence_labels
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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def create_and_check_distilbert_for_sequence_classification(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = DistilBertForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_distilbert_for_token_classification(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = DistilBertForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def create_and_check_distilbert_for_multiple_choice(
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self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_choices = self.num_choices
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model = DistilBertForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
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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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labels=choice_labels,
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)
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self.parent.assertEqual(result.logits.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, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_torch
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class DistilBertModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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DistilBertModel,
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DistilBertForMaskedLM,
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DistilBertForMultipleChoice,
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DistilBertForQuestionAnswering,
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DistilBertForSequenceClassification,
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DistilBertForTokenClassification,
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)
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if is_torch_available()
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else None
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)
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fx_compatible = True
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test_pruning = True
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test_torchscript = True
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test_resize_embeddings = True
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test_resize_position_embeddings = True
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def setUp(self):
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self.model_tester = DistilBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=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_distilbert_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_distilbert_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_distilbert_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_distilbert_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_distilbert_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_distilbert_for_token_classification(*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_distilbert_for_multiple_choice(*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 DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = DistilBertModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@slow
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@require_torch_gpu
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def test_torchscript_device_change(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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# BertForMultipleChoice behaves incorrectly in JIT environments.
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if model_class == DistilBertForMultipleChoice:
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return
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config.torchscript = True
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model = model_class(config=config)
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inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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traced_model = torch.jit.trace(
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model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu"))
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)
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with tempfile.TemporaryDirectory() as tmp:
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torch.jit.save(traced_model, os.path.join(tmp, "traced_model.pt"))
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loaded = torch.jit.load(os.path.join(tmp, "traced_model.pt"), map_location=torch_device)
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loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device))
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@require_torch
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class DistilBertModelIntergrationTest(unittest.TestCase):
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@slow
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def test_inference_no_head_absolute_embedding(self):
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model = DistilBertModel.from_pretrained("distilbert-base-uncased")
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input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
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attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
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with torch.no_grad():
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output = model(input_ids, attention_mask=attention_mask)[0]
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expected_shape = torch.Size((1, 11, 768))
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self.assertEqual(output.shape, expected_shape)
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expected_slice = torch.tensor(
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[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]
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)
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self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
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