# 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import unittest import json import random import shutil import pytest import torch from pytorch_transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering) from pytorch_transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP from .modeling_common_test import ConfigTester, CommonTestCases, ids_tensor class XLNetModelTest(CommonTestCases.CommonModelTester): all_model_classes=(XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering) 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, max_position_embeddings=10, 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.max_position_embeddings = max_position_embeddings 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) 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) target_mapping[:, 0, -1] = 1.0 # predict last token sequence_labels = None lm_labels = None is_impossible_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() config = XLNetConfig( vocab_size_or_config_json_file=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, max_position_embeddings=self.max_position_embeddings, 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) 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): model = XLNetModel(config) 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, } 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_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): model = XLNetLMHeadModel(config) 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): model = XLNetForQuestionAnswering(config) 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_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): model = XLNetForSequenceClassification(config) 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) = 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_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_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) @pytest.mark.slow def test_model_from_pretrained(self): cache_dir = "/tmp/pytorch_transformers_test/" for model_name in list(XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = XLNetModel.from_pretrained(model_name, cache_dir=cache_dir) shutil.rmtree(cache_dir) self.assertIsNotNone(model) if __name__ == "__main__": unittest.main()