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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 TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel | |
| from transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP | |
| class TransfoXLModelTest(ModelTesterMixin, unittest.TestCase): | |
| all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel) if is_torch_available() else () | |
| test_pruning = False | |
| test_torchscript = False | |
| test_resize_embeddings = False | |
| class TransfoXLModelTester(object): | |
| def __init__( | |
| self, | |
| parent, | |
| batch_size=13, | |
| seq_length=7, | |
| mem_len=30, | |
| clamp_len=15, | |
| is_training=True, | |
| use_labels=True, | |
| vocab_size=99, | |
| cutoffs=[10, 50, 80], | |
| hidden_size=32, | |
| d_embed=32, | |
| num_attention_heads=4, | |
| d_head=8, | |
| d_inner=128, | |
| div_val=2, | |
| num_hidden_layers=5, | |
| scope=None, | |
| seed=1, | |
| ): | |
| self.parent = parent | |
| self.batch_size = batch_size | |
| self.seq_length = seq_length | |
| self.mem_len = mem_len | |
| self.key_length = seq_length + mem_len | |
| self.clamp_len = clamp_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.d_embed = d_embed | |
| self.num_attention_heads = num_attention_heads | |
| self.d_head = d_head | |
| self.d_inner = d_inner | |
| self.div_val = div_val | |
| self.num_hidden_layers = num_hidden_layers | |
| self.scope = scope | |
| self.seed = seed | |
| 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) | |
| lm_labels = None | |
| if self.use_labels: | |
| lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) | |
| config = TransfoXLConfig( | |
| vocab_size=self.vocab_size, | |
| mem_len=self.mem_len, | |
| clamp_len=self.clamp_len, | |
| cutoffs=self.cutoffs, | |
| d_model=self.hidden_size, | |
| d_embed=self.d_embed, | |
| n_head=self.num_attention_heads, | |
| d_head=self.d_head, | |
| d_inner=self.d_inner, | |
| div_val=self.div_val, | |
| n_layer=self.num_hidden_layers, | |
| ) | |
| return (config, input_ids_1, input_ids_2, lm_labels) | |
| def set_seed(self): | |
| random.seed(self.seed) | |
| torch.manual_seed(self.seed) | |
| def create_transfo_xl_model(self, config, input_ids_1, input_ids_2, lm_labels): | |
| model = TransfoXLModel(config) | |
| model.to(torch_device) | |
| model.eval() | |
| hidden_states_1, mems_1 = model(input_ids_1) | |
| hidden_states_2, mems_2 = model(input_ids_2, mems_1) | |
| outputs = { | |
| "hidden_states_1": hidden_states_1, | |
| "mems_1": mems_1, | |
| "hidden_states_2": hidden_states_2, | |
| "mems_2": mems_2, | |
| } | |
| return outputs | |
| def check_transfo_xl_model_output(self, result): | |
| self.parent.assertListEqual( | |
| list(result["hidden_states_1"].size()), [self.batch_size, self.seq_length, self.hidden_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(result["hidden_states_2"].size()), [self.batch_size, self.seq_length, self.hidden_size] | |
| ) | |
| self.parent.assertListEqual( | |
| list(list(mem.size()) for mem in result["mems_1"]), | |
| [[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| 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_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels): | |
| model = TransfoXLLMHeadModel(config) | |
| model.to(torch_device) | |
| model.eval() | |
| lm_logits_1, mems_1 = model(input_ids_1) | |
| loss_1, _, mems_1 = model(input_ids_1, labels=lm_labels) | |
| lm_logits_2, mems_2 = model(input_ids_2, mems=mems_1) | |
| loss_2, _, mems_2 = model(input_ids_2, labels=lm_labels, mems=mems_1) | |
| outputs = { | |
| "loss_1": loss_1, | |
| "mems_1": mems_1, | |
| "lm_logits_1": lm_logits_1, | |
| "loss_2": loss_2, | |
| "mems_2": mems_2, | |
| "lm_logits_2": lm_logits_2, | |
| } | |
| return outputs | |
| def check_transfo_xl_lm_head_output(self, result): | |
| self.parent.assertListEqual(list(result["loss_1"].size()), [self.batch_size, self.seq_length]) | |
| self.parent.assertListEqual( | |
| list(result["lm_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.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers, | |
| ) | |
| self.parent.assertListEqual(list(result["loss_2"].size()), [self.batch_size, self.seq_length]) | |
| self.parent.assertListEqual( | |
| list(result["lm_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 prepare_config_and_inputs_for_common(self): | |
| config_and_inputs = self.prepare_config_and_inputs() | |
| (config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs | |
| inputs_dict = {"input_ids": input_ids_1} | |
| return config, inputs_dict | |
| def setUp(self): | |
| self.model_tester = TransfoXLModelTest.TransfoXLModelTester(self) | |
| self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37) | |
| def test_config(self): | |
| self.config_tester.run_common_tests() | |
| def test_transfo_xl_model(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs) | |
| self.model_tester.check_transfo_xl_model_output(output_result) | |
| def test_transfo_xl_lm_head(self): | |
| self.model_tester.set_seed() | |
| config_and_inputs = self.model_tester.prepare_config_and_inputs() | |
| output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs) | |
| self.model_tester.check_transfo_xl_lm_head_output(output_result) | |
| def test_model_from_pretrained(self): | |
| for model_name in list(TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: | |
| model = TransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR) | |
| self.assertIsNotNone(model) | |