# 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_pretrained_bert import (GPT2Config, GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel) from pytorch_pretrained_bert.modeling_gpt2 import PRETRAINED_MODEL_ARCHIVE_MAP class GPT2ModelTest(unittest.TestCase): class GPT2ModelTester(object): def __init__(self, parent, batch_size=13, seq_length=7, is_training=True, use_position_ids=True, use_token_type_ids=True, use_labels=True, vocab_size=99, n_special=1, n_positions=33, n_embd=32, n_layer=5, n_head=4, n_choices=3, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, scope=None): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_position_ids = use_position_ids self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.n_special = n_special self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.n_choices = n_choices self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope def prepare_config_and_inputs(self): total_num_tokens = self.vocab_size + self.n_special input_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens) position_ids = None if self.use_position_ids: position_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions) token_type_ids = None if self.use_token_type_ids: total_voc = self.vocab_size token_type_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc) mc_labels = None lm_labels = None mc_token_ids = None if self.use_labels: mc_labels = GPT2ModelTest.ids_tensor([self.batch_size], self.type_sequence_label_size) lm_labels = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels) mc_token_ids = GPT2ModelTest.ids_tensor([self.batch_size, self.n_choices], self.seq_length) config = GPT2Config( vocab_size_or_config_json_file=self.vocab_size, n_special=self.n_special, n_positions=self.n_positions, n_embd=self.n_embd, n_layer=self.n_layer, n_head=self.n_head, initializer_range=self.initializer_range) return (config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids) def create_gpt2_model(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = GPT2Model(config) model.eval() hidden_states, presents = model(input_ids, position_ids, token_type_ids) outputs = { "hidden_states": hidden_states, "presents": presents, } return outputs def check_gpt2_model_output(self, result): self.parent.assertEqual(len(result["hidden_states"]), self.n_layer + 1) self.parent.assertListEqual( list(result["hidden_states"][0].size()), [self.batch_size, self.n_choices, self.seq_length, self.n_embd]) def create_gpt2_lm_head(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = GPT2LMHeadModel(config) model.eval() loss = model(input_ids, position_ids, token_type_ids, lm_labels) lm_logits, presents = model(input_ids, position_ids, token_type_ids) outputs = { "loss": loss, "lm_logits": lm_logits, "presents": presents, } return outputs def create_gpt2_lm_head_with_output_attention(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = GPT2LMHeadModel(config, output_attentions=True) model.eval() loss = model(input_ids, position_ids, token_type_ids, lm_labels) attentions, lm_logits, presents = model(input_ids, position_ids, token_type_ids) outputs = { "loss": loss, "lm_logits": lm_logits, "presents": presents, "attentions": attentions, } return outputs def check_gpt2_lm_head_output(self, result): total_voc = self.n_special + self.vocab_size self.parent.assertListEqual( list(result["lm_logits"].size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]) self.parent.assertEqual(self.n_layer, len(result["presents"])) self.parent.assertListEqual( list(result["presents"][0].size()), [2, self.batch_size * self.n_choices, self.n_head, self.seq_length, self.n_embd // self.n_head]) def check_gpt2_lm_head_loss_output(self, result): self.parent.assertListEqual( list(result["loss"].size()), []) def create_gpt2_double_heads(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = GPT2DoubleHeadsModel(config) model.eval() loss = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels, token_type_ids=token_type_ids, position_ids=position_ids) lm_logits, mc_logits, presents = model(input_ids, mc_token_ids, position_ids=position_ids, token_type_ids=token_type_ids) outputs = { "loss": loss, "lm_logits": lm_logits, "mc_logits": mc_logits, "presents": presents, } return outputs def create_gpt2_double_heads_with_output_attention(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): model = GPT2DoubleHeadsModel(config, output_attentions=True) model.eval() loss = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels, token_type_ids=token_type_ids, position_ids=position_ids) attentions, lm_logits, mc_logits, presents = model(input_ids, mc_token_ids, position_ids=position_ids, token_type_ids=token_type_ids) outputs = { "loss": loss, "lm_logits": lm_logits, "mc_logits": mc_logits, "presents": presents, "attentions": attentions, } return outputs def check_gpt2_double_heads_output(self, result): total_voc = self.n_special + self.vocab_size self.parent.assertListEqual( list(result["lm_logits"].size()), [self.batch_size, self.n_choices, self.seq_length, total_voc]) self.parent.assertListEqual( list(result["mc_logits"].size()), [self.batch_size, self.n_choices]) def check_gpt2_double_heads_loss_output(self, result): self.parent.assertListEqual( [list(l.size()) for l in result["loss"]], [[], []]) def create_and_check_gpt2_for_headmasking(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): for model_class in (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel): model = model_class(config=config, keep_multihead_output=True) model.eval() head_mask = torch.zeros(self.n_layer, self.n_head).to(input_ids.device) head_mask[0, 1:-1] = 1.0 # Mask all but the first and last heads on the first layer head_mask[-1, 1:] = 1.0 # Mask all but the first head on the last layer if isinstance(model, GPT2DoubleHeadsModel): output = model(input_ids, mc_token_ids, head_mask=head_mask) else: output = model(input_ids, head_mask=head_mask) if isinstance(model, GPT2Model): output = sum(t.sum() for t in output[0]) elif isinstance(output, (list, tuple)): output = sum(t.sum() for t in output[:-1]) output = output.sum() output.backward() multihead_outputs = (model if isinstance(model, GPT2Model) else model.transformer).get_multihead_outputs() self.parent.assertEqual(len(multihead_outputs), self.n_layer) self.parent.assertListEqual( list(multihead_outputs[0].size()), [self.batch_size * self.n_choices, self.n_head, self.seq_length, self.n_embd // self.n_head]) self.parent.assertEqual( len(multihead_outputs[0][:, 1:(self.n_head-1), :, :].nonzero()), 0) self.parent.assertEqual( len(multihead_outputs[0][:, 0, :, :].nonzero()), self.batch_size * self.n_choices * self.seq_length * self.n_embd // self.n_head) self.parent.assertEqual( len(multihead_outputs[0][:, self.n_head-1, :, :].nonzero()), self.batch_size * self.n_choices * self.seq_length * self.n_embd // self.n_head) self.parent.assertListEqual( list(multihead_outputs[1].size()), [self.batch_size * self.n_choices, self.n_head, self.seq_length, self.n_embd // self.n_head]) self.parent.assertEqual( len(multihead_outputs[1].nonzero()), multihead_outputs[1].numel()) self.parent.assertListEqual( list(multihead_outputs[-1].size()), [self.batch_size * self.n_choices, self.n_head, self.seq_length, self.n_embd // self.n_head]) self.parent.assertEqual( len(multihead_outputs[-1][:, 1:, :, :].nonzero()), 0) self.parent.assertEqual( len(multihead_outputs[-1][:, 0, :, :].nonzero()), self.batch_size * self.n_choices * self.seq_length * self.n_embd // self.n_head) def create_and_check_gpt2_for_head_pruning(self, config, input_ids, token_type_ids, position_ids, mc_labels, lm_labels, mc_token_ids): for model_class in (GPT2Model, GPT2LMHeadModel, GPT2DoubleHeadsModel): model = model_class(config=config, keep_multihead_output=True) model.eval() transformer = model if isinstance(model, GPT2Model) else model.transformer heads_to_prune = {0: list(range(1, self.n_head)), -1: [0]} transformer.prune_heads(heads_to_prune) if isinstance(model, GPT2DoubleHeadsModel): output = model(input_ids, mc_token_ids) else: output = model(input_ids) if isinstance(model, GPT2Model): output = sum(t.sum() for t in output[0]) elif isinstance(output, (list, tuple)): output = sum(t.sum() for t in output[:-1]) output = output.sum() output.backward() multihead_outputs = transformer.get_multihead_outputs() self.parent.assertEqual(len(multihead_outputs), self.n_layer) self.parent.assertListEqual( list(multihead_outputs[0].size()), [self.batch_size * self.n_choices, 1, self.seq_length, self.n_embd // self.n_head]) self.parent.assertListEqual( list(multihead_outputs[1].size()), [self.batch_size * self.n_choices, self.n_head, self.seq_length, self.n_embd // self.n_head]) self.parent.assertListEqual( list(multihead_outputs[-1].size()), [self.batch_size * self.n_choices, self.n_head-1, self.seq_length, self.n_embd // self.n_head]) def test_default(self): self.run_tester(GPT2ModelTest.GPT2ModelTester(self)) def test_config_to_json_string(self): config = GPT2Config(vocab_size_or_config_json_file=99, n_embd=37) obj = json.loads(config.to_json_string()) self.assertEqual(obj["vocab_size"], 99) self.assertEqual(obj["n_embd"], 37) def test_config_to_json_file(self): config_first = GPT2Config(vocab_size_or_config_json_file=99, n_embd=37) json_file_path = "/tmp/config.json" config_first.to_json_file(json_file_path) config_second = GPT2Config.from_json_file(json_file_path) os.remove(json_file_path) self.assertEqual(config_second.to_dict(), config_first.to_dict()) @pytest.mark.slow def test_model_from_pretrained(self): cache_dir = "/tmp/pytorch_pretrained_bert_test/" for model_name in list(PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir) shutil.rmtree(cache_dir) self.assertIsNotNone(model) def run_tester(self, tester): config_and_inputs = tester.prepare_config_and_inputs() output_result = tester.create_gpt2_model(*config_and_inputs) tester.check_gpt2_model_output(output_result) output_result = tester.create_gpt2_lm_head(*config_and_inputs) tester.check_gpt2_lm_head_output(output_result) tester.check_gpt2_lm_head_loss_output(output_result) output_result = tester.create_gpt2_double_heads(*config_and_inputs) tester.check_gpt2_double_heads_output(output_result) tester.check_gpt2_double_heads_loss_output(output_result) tester.create_and_check_gpt2_for_headmasking(*config_and_inputs) tester.create_and_check_gpt2_for_head_pruning(*config_and_inputs) @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()