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| # 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 | |
| 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) | |
| 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) | |