# 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 @require_torch 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) @slow 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)