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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 random | |
| 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(): | |
| import torch | |
| from transformers import ( | |
| XLNetConfig, | |
| XLNetModel, | |
| XLNetLMHeadModel, | |
| XLNetForSequenceClassification, | |
| XLNetForTokenClassification, | |
| XLNetForQuestionAnswering, | |
| ) | |
| from transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP | |
| class XLNetModelTest(ModelTesterMixin, unittest.TestCase): | |
| all_model_classes = ( | |
| ( | |
| XLNetModel, | |
| XLNetLMHeadModel, | |
| XLNetForTokenClassification, | |
| XLNetForSequenceClassification, | |
| XLNetForQuestionAnswering, | |
| ) | |
| if is_torch_available() | |
| else () | |
| ) | |
| test_pruning = False | |
| class XLNetModelTester(object): | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=13, | |
| seq_length=7, | |
| mem_len=10, | |
| clamp_len=-1, | |
| reuse_len=15, | |
| is_training=True, | |
| use_labels=True, | |
| vocab_size=99, | |
| cutoffs=[10, 50, 80], | |
| hidden_size=32, | |
| num_attention_heads=4, | |
| d_inner=128, | |
| num_hidden_layers=5, | |
| type_sequence_label_size=2, | |
| untie_r=True, | |
| bi_data=False, | |
| same_length=False, | |
| initializer_range=0.05, | |
| seed=1, | |
| type_vocab_size=2, | |
| ): | |
| self.parent = parent | |
| self.batch_size = batch_size | |
| self.seq_length = seq_length | |
| self.mem_len = mem_len | |
| # self.key_len = seq_length + mem_len | |
| self.clamp_len = clamp_len | |
| self.reuse_len = reuse_len | |
| self.is_training = is_training | |
| self.use_labels = use_labels | |
| self.vocab_size = vocab_size | |
| self.cutoffs = cutoffs | |
| self.hidden_size = hidden_size | |
| self.num_attention_heads = num_attention_heads | |
| self.d_inner = d_inner | |
| self.num_hidden_layers = num_hidden_layers | |
| self.bi_data = bi_data | |
| self.untie_r = untie_r | |
| self.same_length = same_length | |
| self.initializer_range = initializer_range | |
| self.seed = seed | |
| self.type_vocab_size = type_vocab_size | |
| self.type_sequence_label_size = type_sequence_label_size | |
| def prepare_config_and_inputs(self): | |
| input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) | |
| input_ids_2 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) | |
| segment_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) | |
| input_mask = ids_tensor([self.batch_size, self.seq_length], 2).float() | |
| input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size) | |
| perm_mask = torch.zeros( | |
| self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device | |
| ) | |
| perm_mask[:, :, -1] = 1.0 # Previous tokens don't see last token | |
| target_mapping = torch.zeros( | |
| self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device | |
| ) | |
| target_mapping[:, 0, -1] = 1.0 # predict last token | |
| sequence_labels = None | |
| lm_labels = None | |
| is_impossible_labels = None | |
| token_labels = None | |
| if self.use_labels: | |
| lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) | |
| sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) | |
| is_impossible_labels = ids_tensor([self.batch_size], 2).float() | |
| token_labels = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) | |
| config = XLNetConfig( | |
| vocab_size=self.vocab_size, | |
| d_model=self.hidden_size, | |
| n_head=self.num_attention_heads, | |
| d_inner=self.d_inner, | |
| n_layer=self.num_hidden_layers, | |
| untie_r=self.untie_r, | |
| mem_len=self.mem_len, | |
| clamp_len=self.clamp_len, | |
| same_length=self.same_length, | |
| reuse_len=self.reuse_len, | |
| bi_data=self.bi_data, | |
| initializer_range=self.initializer_range, | |
| num_labels=self.type_sequence_label_size, | |
| ) | |
| return ( | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ) | |
| def set_seed(self): | |
| random.seed(self.seed) | |
| torch.manual_seed(self.seed) | |
| def create_and_check_xlnet_base_model( | |
| self, | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ): | |
| model = XLNetModel(config) | |
| model.to(torch_device) | |
| model.eval() | |
| _, _ = model(input_ids_1, input_mask=input_mask) | |
| _, _ = model(input_ids_1, attention_mask=input_mask) | |
| _, _ = model(input_ids_1, token_type_ids=segment_ids) | |
| outputs, mems_1 = model(input_ids_1) | |
| result = { | |
| "mems_1": mems_1, | |
| "outputs": outputs, | |
| } | |
| config.mem_len = 0 | |
| model = XLNetModel(config) | |
| model.to(torch_device) | |
| model.eval() | |
| no_mems_outputs = model(input_ids_1) | |
| self.parent.assertEqual(len(no_mems_outputs), 1) | |
| self.parent.assertListEqual( | |
| list(result["outputs"].size()), [self.batch_size, self.seq_length, self.hidden_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(list(mem.size()) for mem in result["mems_1"]), | |
| [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| def create_and_check_xlnet_base_model_with_att_output( | |
| self, | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ): | |
| model = XLNetModel(config) | |
| model.to(torch_device) | |
| model.eval() | |
| _, _, attentions = model(input_ids_1, target_mapping=target_mapping) | |
| self.parent.assertEqual(len(attentions), config.n_layer) | |
| self.parent.assertIsInstance(attentions[0], tuple) | |
| self.parent.assertEqual(len(attentions[0]), 2) | |
| self.parent.assertTrue(attentions[0][0].shape, attentions[0][0].shape) | |
| def create_and_check_xlnet_lm_head( | |
| self, | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ): | |
| model = XLNetLMHeadModel(config) | |
| model.to(torch_device) | |
| model.eval() | |
| loss_1, all_logits_1, mems_1 = model(input_ids_1, token_type_ids=segment_ids, labels=lm_labels) | |
| loss_2, all_logits_2, mems_2 = model( | |
| input_ids_2, token_type_ids=segment_ids, labels=lm_labels, mems=mems_1 | |
| ) | |
| logits, _ = model(input_ids_q, perm_mask=perm_mask, target_mapping=target_mapping) | |
| result = { | |
| "loss_1": loss_1, | |
| "mems_1": mems_1, | |
| "all_logits_1": all_logits_1, | |
| "loss_2": loss_2, | |
| "mems_2": mems_2, | |
| "all_logits_2": all_logits_2, | |
| } | |
| self.parent.assertListEqual(list(result["loss_1"].size()), []) | |
| self.parent.assertListEqual( | |
| list(result["all_logits_1"].size()), [self.batch_size, self.seq_length, self.vocab_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(list(mem.size()) for mem in result["mems_1"]), | |
| [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| self.parent.assertListEqual(list(result["loss_2"].size()), []) | |
| self.parent.assertListEqual( | |
| list(result["all_logits_2"].size()), [self.batch_size, self.seq_length, self.vocab_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(list(mem.size()) for mem in result["mems_2"]), | |
| [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| def create_and_check_xlnet_qa( | |
| self, | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ): | |
| model = XLNetForQuestionAnswering(config) | |
| model.to(torch_device) | |
| model.eval() | |
| outputs = model(input_ids_1) | |
| start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems = outputs | |
| outputs = model( | |
| input_ids_1, | |
| 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_1, | |
| start_positions=sequence_labels, | |
| end_positions=sequence_labels, | |
| cls_index=sequence_labels, | |
| is_impossible=is_impossible_labels, | |
| ) | |
| total_loss, mems = outputs | |
| outputs = model(input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels) | |
| total_loss, mems = 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, | |
| "mems": mems, | |
| } | |
| 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]) | |
| self.parent.assertListEqual( | |
| list(list(mem.size()) for mem in result["mems"]), | |
| [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| def create_and_check_xlnet_token_classif( | |
| self, | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ): | |
| model = XLNetForTokenClassification(config) | |
| model.to(torch_device) | |
| model.eval() | |
| logits, mems_1 = model(input_ids_1) | |
| loss, logits, mems_1 = model(input_ids_1, labels=token_labels) | |
| result = { | |
| "loss": loss, | |
| "mems_1": mems_1, | |
| "logits": logits, | |
| } | |
| self.parent.assertListEqual(list(result["loss"].size()), []) | |
| self.parent.assertListEqual( | |
| list(result["logits"].size()), [self.batch_size, self.seq_length, self.type_sequence_label_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(list(mem.size()) for mem in result["mems_1"]), | |
| [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| def create_and_check_xlnet_sequence_classif( | |
| self, | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ): | |
| model = XLNetForSequenceClassification(config) | |
| model.to(torch_device) | |
| model.eval() | |
| logits, mems_1 = model(input_ids_1) | |
| loss, logits, mems_1 = model(input_ids_1, labels=sequence_labels) | |
| result = { | |
| "loss": loss, | |
| "mems_1": mems_1, | |
| "logits": logits, | |
| } | |
| self.parent.assertListEqual(list(result["loss"].size()), []) | |
| self.parent.assertListEqual( | |
| list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(list(mem.size()) for mem in result["mems_1"]), | |
| [[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| def prepare_config_and_inputs_for_common(self): | |
| config_and_inputs = self.prepare_config_and_inputs() | |
| ( | |
| config, | |
| input_ids_1, | |
| input_ids_2, | |
| input_ids_q, | |
| perm_mask, | |
| input_mask, | |
| target_mapping, | |
| segment_ids, | |
| lm_labels, | |
| sequence_labels, | |
| is_impossible_labels, | |
| token_labels, | |
| ) = config_and_inputs | |
| inputs_dict = {"input_ids": input_ids_1} | |
| return config, inputs_dict | |
| def setUp(self): | |
| self.model_tester = XLNetModelTest.XLNetModelTester(self) | |
| self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37) | |
| def test_config(self): | |
| self.config_tester.run_common_tests() | |
| def test_xlnet_base_model(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs) | |
| def test_xlnet_base_model_with_att_output(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| config_and_inputs[0].output_attentions = True | |
| self.model_tester.create_and_check_xlnet_base_model_with_att_output(*config_and_inputs) | |
| def test_xlnet_lm_head(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs) | |
| def test_xlnet_sequence_classif(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs) | |
| def test_xlnet_token_classif(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_xlnet_token_classif(*config_and_inputs) | |
| def test_xlnet_qa(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| self.model_tester.create_and_check_xlnet_qa(*config_and_inputs) | |
| def test_model_from_pretrained(self): | |
| for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: | |
| model = XLNetModel.from_pretrained(model_name, cache_dir=CACHE_DIR) | |
| self.assertIsNotNone(model) | |