# 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. from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy as np import tensorflow as tf from transformers import ( TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, FlaubertConfig, TFFlaubertForMultipleChoice, TFFlaubertForQuestionAnsweringSimple, TFFlaubertForSequenceClassification, TFFlaubertForTokenClassification, TFFlaubertModel, TFFlaubertWithLMHeadModel, ) class TFFlaubertModelTester: def __init__( self, parent, ): self.parent = parent self.batch_size = 13 self.seq_length = 7 self.is_training = True self.use_input_lengths = True self.use_token_type_ids = True self.use_labels = True self.gelu_activation = True self.sinusoidal_embeddings = False self.causal = False self.asm = False self.n_langs = 2 self.vocab_size = 99 self.n_special = 0 self.hidden_size = 32 self.num_hidden_layers = 2 self.num_attention_heads = 4 self.hidden_dropout_prob = 0.1 self.attention_probs_dropout_prob = 0.1 self.max_position_embeddings = 512 self.type_vocab_size = 16 self.type_sequence_label_size = 2 self.initializer_range = 0.02 self.num_labels = 3 self.num_choices = 4 self.summary_type = "last" self.use_proj = True self.scope = None self.bos_token_id = 0 def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = random_attention_mask([self.batch_size, self.seq_length], dtype=tf.float32) input_lengths = None if self.use_input_lengths: input_lengths = ( ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2 ) # small variation of seq_length token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs) sequence_labels = None token_labels = None is_impossible_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) is_impossible_labels = ids_tensor([self.batch_size], 2, dtype=tf.float32) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = FlaubertConfig( vocab_size=self.vocab_size, n_special=self.n_special, emb_dim=self.hidden_size, n_layers=self.num_hidden_layers, n_heads=self.num_attention_heads, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, gelu_activation=self.gelu_activation, sinusoidal_embeddings=self.sinusoidal_embeddings, asm=self.asm, causal=self.causal, n_langs=self.n_langs, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, summary_type=self.summary_type, use_proj=self.use_proj, bos_token_id=self.bos_token_id, ) return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def create_and_check_flaubert_model( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFFlaubertModel(config=config) inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_flaubert_lm_head( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFFlaubertWithLMHeadModel(config) inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_flaubert_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFFlaubertForQuestionAnsweringSimple(config) inputs = {"input_ids": input_ids, "lengths": input_lengths} result = model(inputs) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_flaubert_sequence_classif( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFFlaubertForSequenceClassification(config) inputs = {"input_ids": input_ids, "lengths": input_lengths} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_flaubert_for_token_classification( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): config.num_labels = self.num_labels model = TFFlaubertForTokenClassification(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_flaubert_for_multiple_choice( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): config.num_choices = self.num_choices model = TFFlaubertForMultipleChoice(config=config) multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1)) multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1)) multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1)) inputs = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } result = model(inputs) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "langs": token_type_ids, "lengths": input_lengths, } return config, inputs_dict @require_tf class TFFlaubertModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFFlaubertModel, TFFlaubertWithLMHeadModel, TFFlaubertForSequenceClassification, TFFlaubertForQuestionAnsweringSimple, TFFlaubertForTokenClassification, TFFlaubertForMultipleChoice, ) if is_tf_available() else () ) all_generative_model_classes = ( (TFFlaubertWithLMHeadModel,) if is_tf_available() else () ) # TODO (PVP): Check other models whether language generation is also applicable pipeline_model_mapping = ( { "feature-extraction": TFFlaubertModel, "fill-mask": TFFlaubertWithLMHeadModel, "question-answering": TFFlaubertForQuestionAnsweringSimple, "text-classification": TFFlaubertForSequenceClassification, "token-classification": TFFlaubertForTokenClassification, "zero-shot": TFFlaubertForSequenceClassification, } if is_tf_available() else {} ) test_head_masking = False test_onnx = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): if ( pipeline_test_casse_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith("Fast") ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False def setUp(self): self.model_tester = TFFlaubertModelTester(self) self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37) def test_config(self): self.config_tester.run_common_tests() def test_flaubert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_model(*config_and_inputs) def test_flaubert_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs) def test_flaubert_qa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_qa(*config_and_inputs) def test_flaubert_sequence_classif(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_sequence_classif(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_for_token_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_for_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFFlaubertModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf @require_sentencepiece @require_tokenizers class TFFlaubertModelIntegrationTest(unittest.TestCase): @slow def test_output_embeds_base_model(self): model = TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased") input_ids = tf.convert_to_tensor( [[0, 158, 735, 2592, 1424, 6727, 82, 1]], dtype=tf.int32, ) # "J'aime flaubert !" output = model(input_ids)[0] expected_shape = tf.TensorShape((1, 8, 512)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = tf.convert_to_tensor( [ [ [-1.8768773, -1.566555, 0.27072418], [-1.6920038, -0.5873505, 1.9329599], [-2.9563985, -1.6993835, 1.7972052], ] ], dtype=tf.float32, ) self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))