# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest from transformers import RobertaConfig, is_tf_available from .test_configuration_common import ConfigTester from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor from .utils import CACHE_DIR, require_tf, slow if is_tf_available(): import tensorflow as tf import numpy from transformers.modeling_tf_roberta import ( TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, TFRobertaForTokenClassification, TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP, ) @require_tf class TFRobertaModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( (TFRobertaModel, TFRobertaForMaskedLM, TFRobertaForSequenceClassification) if is_tf_available() else () ) class TFRobertaModelTester(object): def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = RobertaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def create_and_check_roberta_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFRobertaModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} sequence_output = model(inputs)[0] inputs = [input_ids, input_mask] sequence_output = model(inputs)[0] sequence_output = model(input_ids)[0] result = { "sequence_output": sequence_output.numpy(), } self.parent.assertListEqual( list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size] ) def create_and_check_roberta_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFRobertaForMaskedLM(config=config) prediction_scores = model([input_ids, input_mask, token_type_ids])[0] result = { "prediction_scores": prediction_scores.numpy(), } self.parent.assertListEqual( list(result["prediction_scores"].shape), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_roberta_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = TFRobertaForTokenClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} (logits,) = model(inputs) result = { "logits": logits.numpy(), } self.parent.assertListEqual( list(result["logits"].shape), [self.batch_size, self.seq_length, self.num_labels] ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict def setUp(self): self.model_tester = TFRobertaModelTest.TFRobertaModelTester(self) self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_roberta_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = TFRobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR) self.assertIsNotNone(model) class TFRobertaModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = TFRobertaForMaskedLM.from_pretrained("roberta-base") input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = [1, 11, 50265] self.assertEqual(list(output.numpy().shape), expected_shape) # compare the actual values for a slice. expected_slice = tf.constant( [[[33.8843, -4.3107, 22.7779], [4.6533, -2.8099, 13.6252], [1.8222, -3.6898, 8.8600]]] ) self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)) @slow def test_inference_no_head(self): model = TFRobertaModel.from_pretrained("roberta-base") input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. expected_slice = tf.constant( [[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0539, -0.0174], [0.0548, 0.0799, 0.1687]]] ) self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-3)) @slow def test_inference_classification_head(self): model = TFRobertaForSequenceClassification.from_pretrained("roberta-large-mnli") input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = [1, 3] self.assertEqual(list(output.numpy().shape), expected_shape) expected_tensor = tf.constant([[-0.9469, 0.3913, 0.5118]]) self.assertTrue(numpy.allclose(output.numpy(), expected_tensor.numpy(), atol=1e-3))