[Test refactor 1/5] Per-folder tests reorganization (#15725)
* Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>
This commit is contained in:
Lysandre Debut
418
tests/bert/test_modeling_tf_bert.py
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418
tests/bert/test_modeling_tf_bert.py
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# 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 unittest
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from transformers import BertConfig, is_tf_available
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from transformers.models.auto import get_values
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from transformers.testing_utils import require_tf, slow
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
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from ..utils.test_modeling_tf_core import TFCoreModelTesterMixin
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if is_tf_available():
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import tensorflow as tf
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from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
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from transformers.models.bert.modeling_tf_bert import (
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TFBertForMaskedLM,
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TFBertForMultipleChoice,
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TFBertForNextSentencePrediction,
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TFBertForPreTraining,
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TFBertForQuestionAnswering,
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TFBertForSequenceClassification,
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TFBertForTokenClassification,
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TFBertLMHeadModel,
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TFBertModel,
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)
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class TFBertModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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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 = 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.use_labels = True
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self.vocab_size = 99
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self.hidden_size = 32
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self.num_hidden_layers = 5
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self.num_attention_heads = 4
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self.intermediate_size = 37
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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 = 16
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self.type_sequence_label_size = 2
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self.initializer_range = 0.02
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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 = 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 = 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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)
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def prepare_config_and_inputs_for_decoder(self):
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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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) = self.prepare_config_and_inputs()
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config.is_decoder = True
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encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
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encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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return (
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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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encoder_hidden_states,
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encoder_attention_mask,
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)
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def create_and_check_bert_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertModel(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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sequence_output, pooled_output = model(inputs)
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inputs = [input_ids, input_mask]
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result = model(inputs)
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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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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_bert_lm_head(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.is_decoder = True
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model = TFBertLMHeadModel(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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prediction_scores = model(inputs)["logits"]
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self.parent.assertListEqual(
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list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
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)
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def create_and_check_bert_for_masked_lm(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertForMaskedLM(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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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_bert_for_next_sequence_prediction(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertForNextSentencePrediction(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
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def create_and_check_bert_for_pretraining(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertForPreTraining(config=config)
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inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
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result = model(inputs)
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self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
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def create_and_check_bert_for_sequence_classification(
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self, config, input_ids, token_type_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 = TFBertForSequenceClassification(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_bert_for_multiple_choice(
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self, config, input_ids, token_type_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 = TFBertForMultipleChoice(config=config)
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multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
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multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
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multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
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inputs = {
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"input_ids": 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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}
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result = model(inputs)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_bert_for_token_classification(
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self, config, input_ids, token_type_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 = TFBertForTokenClassification(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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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_bert_for_question_answering(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = TFBertForQuestionAnswering(config=config)
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inputs = {
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"input_ids": input_ids,
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"attention_mask": input_mask,
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"token_type_ids": token_type_ids,
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}
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result = model(inputs)
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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 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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@require_tf
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class TFBertModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFBertModel,
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TFBertForMaskedLM,
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TFBertLMHeadModel,
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TFBertForNextSentencePrediction,
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TFBertForPreTraining,
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TFBertForQuestionAnswering,
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TFBertForSequenceClassification,
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TFBertForTokenClassification,
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TFBertForMultipleChoice,
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)
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if is_tf_available()
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else ()
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)
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test_head_masking = False
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test_onnx = True
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onnx_min_opset = 10
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# special case for ForPreTraining model
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class in get_values(TF_MODEL_FOR_PRETRAINING_MAPPING):
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inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
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return inputs_dict
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def setUp(self):
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self.model_tester = TFBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BertConfig, 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_bert_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_bert_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_bert_for_masked_lm(*config_and_inputs)
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def test_for_causal_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_bert_lm_head(*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_bert_for_multiple_choice(*config_and_inputs)
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def test_for_next_sequence_prediction(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_bert_for_next_sequence_prediction(*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_bert_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_bert_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_bert_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_bert_for_token_classification(*config_and_inputs)
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def test_model_from_pretrained(self):
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model = TFBertModel.from_pretrained("jplu/tiny-tf-bert-random")
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self.assertIsNotNone(model)
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def test_model_common_attributes(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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list_lm_models = [TFBertForMaskedLM, TFBertForPreTraining, TFBertLMHeadModel]
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for model_class in self.all_model_classes:
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model = model_class(config)
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assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
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if model_class in list_lm_models:
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x = model.get_output_embeddings()
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assert isinstance(x, tf.keras.layers.Layer)
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name = model.get_bias()
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assert isinstance(name, dict)
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for k, v in name.items():
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assert isinstance(v, tf.Variable)
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else:
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x = model.get_output_embeddings()
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assert x is None
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name = model.get_bias()
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assert name is None
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def test_custom_load_tf_weights(self):
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model, output_loading_info = TFBertForTokenClassification.from_pretrained(
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"jplu/tiny-tf-bert-random", output_loading_info=True
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)
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self.assertEqual(sorted(output_loading_info["unexpected_keys"]), [])
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for layer in output_loading_info["missing_keys"]:
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self.assertTrue(layer.split("_")[0] in ["dropout", "classifier"])
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@require_tf
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class TFBertModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_masked_lm(self):
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model = TFBertForPreTraining.from_pretrained("lysandre/tiny-bert-random")
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input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
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output = model(input_ids)[0]
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expected_shape = [1, 6, 32000]
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self.assertEqual(output.shape, expected_shape)
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print(output[:, :3, :3])
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expected_slice = tf.constant(
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[
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[
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[-0.05243197, -0.04498899, 0.05512108],
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[-0.07444685, -0.01064632, 0.04352357],
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[-0.05020351, 0.05530146, 0.00700043],
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]
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]
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)
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tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
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