[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>
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Lysandre Debut
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tests/roberta/test_modeling_tf_roberta.py
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tests/roberta/test_modeling_tf_roberta.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 RobertaConfig, is_tf_available
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from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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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if is_tf_available():
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import numpy
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import tensorflow as tf
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from transformers.models.roberta.modeling_tf_roberta import (
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TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFRobertaForCausalLM,
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TFRobertaForMaskedLM,
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TFRobertaForMultipleChoice,
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TFRobertaForQuestionAnswering,
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TFRobertaForSequenceClassification,
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TFRobertaForTokenClassification,
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TFRobertaModel,
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)
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class TFRobertaModelTester:
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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.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 = RobertaConfig(
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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_roberta_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 = TFRobertaModel(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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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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def create_and_check_roberta_for_causal_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 = TFRobertaForCausalLM(config=config)
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result = model([input_ids, input_mask, token_type_ids])
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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_roberta_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 = TFRobertaForMaskedLM(config=config)
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result = model([input_ids, input_mask, token_type_ids])
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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_roberta_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 = TFRobertaForTokenClassification(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, self.seq_length, self.num_labels))
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def create_and_check_roberta_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 = TFRobertaForQuestionAnswering(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.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_roberta_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 = TFRobertaForMultipleChoice(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 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 TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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TFRobertaModel,
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TFRobertaForCausalLM,
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TFRobertaForMaskedLM,
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TFRobertaForSequenceClassification,
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TFRobertaForTokenClassification,
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TFRobertaForQuestionAnswering,
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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 = False
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def setUp(self):
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self.model_tester = TFRobertaModelTester(self)
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self.config_tester = ConfigTester(self, config_class=RobertaConfig, 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_roberta_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_roberta_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_roberta_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_roberta_for_causal_lm(*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_roberta_for_token_classification(*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_roberta_for_question_answering(*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_roberta_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 TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFRobertaModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@require_tf
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@require_sentencepiece
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@require_tokenizers
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class TFRobertaModelIntegrationTest(unittest.TestCase):
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@slow
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def test_inference_masked_lm(self):
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model = TFRobertaForMaskedLM.from_pretrained("roberta-base")
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input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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expected_shape = [1, 11, 50265]
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self.assertEqual(list(output.numpy().shape), expected_shape)
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# compare the actual values for a slice.
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expected_slice = tf.constant(
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[[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
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)
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self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
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@slow
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def test_inference_no_head(self):
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model = TFRobertaModel.from_pretrained("roberta-base")
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input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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# compare the actual values for a slice.
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expected_slice = tf.constant(
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[[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0540, -0.0175], [0.0548, 0.0799, 0.1687]]]
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)
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self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))
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@slow
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def test_inference_classification_head(self):
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model = TFRobertaForSequenceClassification.from_pretrained("roberta-large-mnli")
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input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
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output = model(input_ids)[0]
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expected_shape = [1, 3]
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self.assertEqual(list(output.numpy().shape), expected_shape)
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expected_tensor = tf.constant([[-0.9469, 0.3913, 0.5118]])
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self.assertTrue(numpy.allclose(output.numpy(), expected_tensor.numpy(), atol=1e-4))
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