# 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_torch_available from .test_configuration_common import ConfigTester from .test_modeling_common import ModelTesterMixin, ids_tensor from .utils import CACHE_DIR, require_torch, slow, torch_device if is_torch_available(): from transformers import ( XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering, XLMForSequenceClassification, XLMForQuestionAnsweringSimple, ) from transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP @require_torch class XLMModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( ( XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering, XLMForSequenceClassification, XLMForQuestionAnsweringSimple, ) if is_torch_available() else () ) class XLMModelTester(object): def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_lengths=True, use_token_type_ids=True, use_labels=True, gelu_activation=True, sinusoidal_embeddings=False, causal=False, asm=False, n_langs=2, vocab_size=99, n_special=0, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, 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, summary_type="last", use_proj=True, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_lengths = use_input_lengths self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.gelu_activation = gelu_activation self.sinusoidal_embeddings = sinusoidal_embeddings self.asm = asm self.n_langs = n_langs self.vocab_size = vocab_size self.n_special = n_special self.summary_type = summary_type self.causal = causal self.use_proj = use_proj self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.n_langs = n_langs self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.summary_type = summary_type 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 = ids_tensor([self.batch_size, self.seq_length], 2).float() 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).float() 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, ) return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask, ) def check_loss_output(self, result): self.parent.assertListEqual(list(result["loss"].size()), []) def create_and_check_xlm_model( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask, ): model = XLMModel(config=config) model.to(torch_device) model.eval() outputs = model(input_ids, lengths=input_lengths, langs=token_type_ids) outputs = model(input_ids, langs=token_type_ids) outputs = model(input_ids) sequence_output = outputs[0] result = { "sequence_output": sequence_output, } self.parent.assertListEqual( list(result["sequence_output"].size()), [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, input_mask, ): model = XLMWithLMHeadModel(config) model.to(torch_device) model.eval() loss, logits = model(input_ids, token_type_ids=token_type_ids, labels=token_labels) result = { "loss": loss, "logits": logits, } self.parent.assertListEqual(list(result["loss"].size()), []) self.parent.assertListEqual( list(result["logits"].size()), [self.batch_size, self.seq_length, self.vocab_size] ) def create_and_check_xlm_simple_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask, ): model = XLMForQuestionAnsweringSimple(config) model.to(torch_device) model.eval() outputs = model(input_ids) outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels) loss, start_logits, end_logits = outputs result = { "loss": loss, "start_logits": start_logits, "end_logits": end_logits, } self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length]) self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length]) self.check_loss_output(result) def create_and_check_xlm_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask, ): model = XLMForQuestionAnswering(config) model.to(torch_device) model.eval() outputs = model(input_ids) start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits = outputs outputs = model( input_ids, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, p_mask=input_mask, ) outputs = model( input_ids, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, ) (total_loss,) = outputs outputs = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels) (total_loss,) = outputs result = { "loss": total_loss, "start_top_log_probs": start_top_log_probs, "start_top_index": start_top_index, "end_top_log_probs": end_top_log_probs, "end_top_index": end_top_index, "cls_logits": cls_logits, } self.parent.assertListEqual(list(result["loss"].size()), []) self.parent.assertListEqual( list(result["start_top_log_probs"].size()), [self.batch_size, model.config.start_n_top] ) self.parent.assertListEqual( list(result["start_top_index"].size()), [self.batch_size, model.config.start_n_top] ) self.parent.assertListEqual( list(result["end_top_log_probs"].size()), [self.batch_size, model.config.start_n_top * model.config.end_n_top], ) self.parent.assertListEqual( list(result["end_top_index"].size()), [self.batch_size, model.config.start_n_top * model.config.end_n_top], ) self.parent.assertListEqual(list(result["cls_logits"].size()), [self.batch_size]) def create_and_check_xlm_sequence_classif( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask, ): model = XLMForSequenceClassification(config) model.to(torch_device) model.eval() (logits,) = model(input_ids) loss, logits = model(input_ids, labels=sequence_labels) result = { "loss": loss, "logits": logits, } self.parent.assertListEqual(list(result["loss"].size()), []) self.parent.assertListEqual( list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size] ) 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, input_mask, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths} return config, inputs_dict def setUp(self): self.model_tester = XLMModelTest.XLMModelTester(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_simple_qa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_xlm_simple_qa(*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) @slow def test_model_from_pretrained(self): for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = XLMModel.from_pretrained(model_name, cache_dir=CACHE_DIR) self.assertIsNotNone(model)