# 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 unittest import json import random import torch from pytorch_pretrained_bert import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel) class TransfoXLModelTest(unittest.TestCase): 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], d_model=32, d_embed=32, n_head=4, d_head=8, d_inner=128, div_val=2, n_layer=5, scope=None, seed=1): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.mem_len = 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.d_model = d_model self.d_embed = d_embed self.n_head = n_head self.d_head = d_head self.d_inner = d_inner self.div_val = div_val self.n_layer = n_layer self.scope = scope self.seed = seed def prepare_config_and_inputs(self): input_ids_1 = TransfoXLModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_ids_2 = TransfoXLModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size) lm_labels = None if self.use_labels: lm_labels = TransfoXLModelTest.ids_tensor([self.batch_size, self.seq_length], self.vocab_size) config = TransfoXLConfig( vocab_size_or_config_json_file=self.vocab_size, mem_len=self.mem_len, clamp_len=self.clamp_len, cutoffs=self.cutoffs, d_model=self.d_model, d_embed=self.d_embed, n_head=self.n_head, d_head=self.d_head, d_inner=self.d_inner, div_val=self.div_val, n_layer=self.n_layer) 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.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.d_model]) self.parent.assertListEqual( list(result["hidden_states_2"].size()), [self.batch_size, self.seq_length, self.d_model]) self.parent.assertListEqual( list(list(mem.size()) for mem in result["mems_1"]), [[self.mem_len, self.batch_size, self.d_model]] * self.n_layer) self.parent.assertListEqual( list(list(mem.size()) for mem in result["mems_2"]), [[self.mem_len, self.batch_size, self.d_model]] * self.n_layer) def create_transfo_xl_lm_head(self, config, input_ids_1, input_ids_2, lm_labels): model = TransfoXLLMHeadModel(config) model.eval() loss_1, mems_1a = model(input_ids_1, target=lm_labels) lm_logits_1, mems_1b = model(input_ids_1) loss_2, mems_2a = model(input_ids_2, target=lm_labels, mems=mems_1a) lm_logits_2, mems_2b = model(input_ids_2, mems=mems_1b) outputs = { "loss_1": loss_1, "mems_1a": mems_1a, "lm_logits_1": lm_logits_1, "mems_1b": mems_1b, "loss_2": loss_2, "mems_2a": mems_2a, "lm_logits_2": lm_logits_2, "mems_2b": mems_2b, } 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_1a"]), [[self.mem_len, self.batch_size, self.d_model]] * self.n_layer) self.parent.assertListEqual( list(list(mem.size()) for mem in result["mems_1b"]), [[self.mem_len, self.batch_size, self.d_model]] * self.n_layer) self.parent.assertListEqual( list(mem[~torch.isnan(mem)].sum() for mem in result["mems_1a"]), list(mem[~torch.isnan(mem)].sum() for mem in result["mems_1b"])) 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_2a"]), [[self.mem_len, self.batch_size, self.d_model]] * self.n_layer) self.parent.assertListEqual( list(list(mem.size()) for mem in result["mems_2b"]), [[self.mem_len, self.batch_size, self.d_model]] * self.n_layer) self.parent.assertListEqual( list(mem[~torch.isnan(mem)].sum() for mem in result["mems_2a"]), list(mem[~torch.isnan(mem)].sum() for mem in result["mems_2b"])) def test_default(self): self.run_tester(TransfoXLModelTest.TransfoXLModelTester(self)) def test_config_to_json_string(self): config = TransfoXLConfig(vocab_size_or_config_json_file=96, d_embed=37) obj = json.loads(config.to_json_string()) self.assertEqual(obj["n_token"], 96) self.assertEqual(obj["d_embed"], 37) def run_tester(self, tester): config_and_inputs = tester.prepare_config_and_inputs() tester.set_seed() output_result = tester.create_transfo_xl_model(*config_and_inputs) tester.check_transfo_xl_model_output(output_result) tester.set_seed() output_result = tester.create_transfo_xl_lm_head(*config_and_inputs) tester.check_transfo_xl_lm_head_output(output_result) @classmethod def ids_tensor(cls, shape, vocab_size, rng=None, name=None): """Creates a random int32 tensor of the shape within the vocab size.""" if rng is None: rng = random.Random() total_dims = 1 for dim in shape: total_dims *= dim values = [] for _ in range(total_dims): values.append(rng.randint(0, vocab_size - 1)) return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous() if __name__ == "__main__": unittest.main()