# 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 @require_torch 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) @slow 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)