# 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 is_tf_available from transformers.testing_utils import require_tf, slow from .test_configuration_common import ConfigTester from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor if is_tf_available(): import tensorflow as tf from transformers import ( XLMConfig, TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceClassification, TFXLMForQuestionAnsweringSimple, TFXLMForTokenClassification, TFXLMForMultipleChoice, TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, ) class TFXLMModelTester: 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 = 5 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 = ids_tensor([self.batch_size, self.seq_length], 2, 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 = XLMConfig( 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_dict=True, ) return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def create_and_check_xlm_model( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFXLMModel(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_xlm_lm_head( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFXLMWithLMHeadModel(config) inputs = {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids} outputs = model(inputs) result = outputs self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_xlm_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFXLMForQuestionAnsweringSimple(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_xlm_sequence_classif( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = TFXLMForSequenceClassification(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_xlm_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 = TFXLMForTokenClassification(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_xlm_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 = TFXLMForMultipleChoice(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 TFXLMModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = ( ( TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceClassification, TFXLMForQuestionAnsweringSimple, TFXLMForTokenClassification, TFXLMForMultipleChoice, ) if is_tf_available() else () ) all_generative_model_classes = ( (TFXLMWithLMHeadModel,) if is_tf_available() else () ) # TODO (PVP): Check other models whether language generation is also applicable def setUp(self): self.model_tester = TFXLMModelTester(self) self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37) def test_config(self): self.config_tester.run_common_tests() def test_xlm_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_model(*config_and_inputs) def test_xlm_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_lm_head(*config_and_inputs) def test_xlm_qa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_qa(*config_and_inputs) def test_xlm_sequence_classif(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_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_xlm_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_xlm_for_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFXLMModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_tf class TFXLMModelLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_xlm_mlm_en_2048(self): model = TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048") input_ids = tf.convert_to_tensor([[14, 447]], dtype=tf.int32) # the president expected_output_ids = [ 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, 14, 447, ] # the president the president the president the president the president the president the president the president the president the president # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference output_ids = model.generate(input_ids, do_sample=False) self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)