# coding=utf-8 # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HugginFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # 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. """ OpenAI GPT model fine-tuning script. Adapted from https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/train.py It self adapted from https://github.com/openai/finetune-transformer-lm/blob/master/train.py This script with default values fine-tunes and evaluate a pretrained OpenAI GPT on the RocStories dataset """ import argparse import os import csv import random import logging from tqdm import tqdm import numpy as np import torch from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset) from sklearn.metrics import accuracy_score from sklearn.utils import shuffle from pytorch_pretrained_bert import OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer, OpenAIAdam # from analysis import rocstories as rocstories_analysis # from datasets import rocstories # from model_pytorch import DoubleHeadModel, load_openai_pretrained_model # from opt import OpenAIAdam # from text_utils import TextEncoder # from utils import (encode_dataset, iter_data, # ResultLogger, make_path) # from loss import MultipleChoiceLossCompute logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt = '%m/%d/%Y %H:%M:%S', level = logging.INFO) logger = logging.getLogger(__name__) def iter_apply(Xs, Ms, Ys): # fns = [lambda x: np.concatenate(x, 0), lambda x: float(np.sum(x))] logits = [] cost = 0 with torch.no_grad(): dh_model.eval() for xmb, mmb, ymb in iter_data(Xs, Ms, Ys, n_batch=n_batch_train, truncate=False, verbose=True): n = len(xmb) XMB = torch.tensor(xmb, dtype=torch.long).to(device) YMB = torch.tensor(ymb, dtype=torch.long).to(device) MMB = torch.tensor(mmb).to(device) _, clf_logits = dh_model(XMB) clf_logits *= n clf_losses = compute_loss_fct(XMB, YMB, MMB, clf_logits, only_return_losses=True) clf_losses *= n logits.append(clf_logits.to("cpu").numpy()) cost += clf_losses.sum().item() logits = np.concatenate(logits, 0) return logits, cost def iter_predict(Xs, Ms): logits = [] with torch.no_grad(): dh_model.eval() for xmb, mmb in iter_data(Xs, Ms, n_batch=n_batch_train, truncate=False, verbose=True): n = len(xmb) XMB = torch.tensor(xmb, dtype=torch.long).to(device) MMB = torch.tensor(mmb).to(device) _, clf_logits = dh_model(XMB) logits.append(clf_logits.to("cpu").numpy()) logits = np.concatenate(logits, 0) return logits def log(save_dir, desc): global best_score print("Logging") tr_logits, tr_cost = iter_apply(trX[:n_valid], trM[:n_valid], trY[:n_valid]) va_logits, va_cost = iter_apply(vaX, vaM, vaY) tr_cost = tr_cost / len(trY[:n_valid]) va_cost = va_cost / n_valid tr_acc = accuracy_score(trY[:n_valid], np.argmax(tr_logits, 1)) * 100. va_acc = accuracy_score(vaY, np.argmax(va_logits, 1)) * 100. logger.log(n_epochs=n_epochs, n_updates=n_updates, tr_cost=tr_cost, va_cost=va_cost, tr_acc=tr_acc, va_acc=va_acc) print('%d %d %.3f %.3f %.2f %.2f' % (n_epochs, n_updates, tr_cost, va_cost, tr_acc, va_acc)) if submit: score = va_acc if score > best_score: best_score = score path = os.path.join(save_dir, desc, 'best_params') torch.save(dh_model.state_dict(), make_path(path)) def predict(dataset, submission_dir): filename = filenames[dataset] pred_fn = pred_fns[dataset] label_decoder = label_decoders[dataset] predictions = pred_fn(iter_predict(teX, teM)) if label_decoder is not None: predictions = [label_decoder[prediction] for prediction in predictions] path = os.path.join(submission_dir, filename) os.makedirs(os.path.dirname(path), exist_ok=True) with open(path, 'w') as f: f.write('{}\t{}\n'.format('index', 'prediction')) for i, prediction in enumerate(predictions): f.write('{}\t{}\n'.format(i, prediction)) def run_epoch(): for xmb, mmb, ymb in iter_data(*shuffle(trX, trM, trYt, random_state=np.random), n_batch=n_batch_train, truncate=True, verbose=True): global n_updates dh_model.train() XMB = torch.tensor(xmb, dtype=torch.long).to(device) YMB = torch.tensor(ymb, dtype=torch.long).to(device) MMB = torch.tensor(mmb).to(device) lm_logits, clf_logits = dh_model(XMB) compute_loss_fct(XMB, YMB, MMB, clf_logits, lm_logits) n_updates += 1 if n_updates in [1000, 2000, 4000, 8000, 16000, 32000] and n_epochs == 0: log(save_dir, desc) def accuracy(out, labels): outputs = np.argmax(out, axis=1) return np.sum(outputs == labels) def load_rocstories_dataset(dataset_path): """ Output a list of tuples(story, 1st continuation, 2nd continuation, label) """ with open(dataset_path, encoding='utf_8') as f: f = csv.reader(f) output = [] next(f) # skip the first line for line in tqdm(f): output.append((' '.join(line[1:5]), line[5], line[6], int(line[-1])-1)) return output def pre_process_dataset(encoded_dataset, max_len, start_token, delimiter_token, clf_token): n_batch = len(dataset) input_ids = np.zeros((n_batch, 2, max_len), dtype=np.int32) mc_token_mask = np.zeros((n_batch, 2, max_len), dtype=np.int32) lm_labels = np.full((n_batch, 2, max_len), -1, dtype=np.float32) mc_labels = np.zeros((n_batch,), dtype=np.float32) for i, (story, cont1, cont2, mc_label), in enumerate(encoded_dataset): with_cont1 = [start_token] + story[:max_len] + [delimiter_token] + cont1[:max_len] + [clf_token] with_cont2 = [start_token] + story[:max_len] + [delimiter_token] + cont2[:max_len] + [clf_token] xmb[i, 0, :len(with_cont1)] = with_cont1 xmb[i, 1, :len(with_cont2)] = with_cont2 mc_token_mask[i, 0, len(with_cont1) - 1] = 1 lm_labels[i, 0, :len(with_cont1)-1] = with_cont1[1:] lm_labels[i, 1, :len(with_cont2)-1] = with_cont2[1:] mc_labels[i] = mc_label all_inputs = (input_ids, mc_token_mask, lm_labels, mc_labels) all_input_tensors = list(torch.tensor(t) for t in all_inputs) return all_input_tensors if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model_name', type=str, default='openai-gpt', help='pretrained model name') parser.add_argument('--data_dir', type=str, default='data/') parser.add_argument('--seed', type=int, default=42) parser.add_argument('--num_train_epochs', type=int, default=3) parser.add_argument('--train_batch_size', type=int, default=8) parser.add_argument('--max_grad_norm', type=int, default=1) parser.add_argument('--learning_rate', type=float, default=6.25e-5) parser.add_argument('--warmup_proportion', type=float, default=0.002) parser.add_argument('--max_grad_norm', type=float, default=1) parser.add_argument('--lr_schedule', type=str, default='warmup_linear') parser.add_argument('--weight_decay', type=float, default=0.01) parser.add_argument('--lm_coef', type=float, default=0.5) parser.add_argument('--n_valid', type=int, default=374) args = parser.parse_args() print(args) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_gpu = torch.cuda.device_count() logger.info("device", device, "n_gpu", n_gpu) # Load tokenizer and model # This loading functions also add new tokens and embeddings called `special tokens` # These new embeddings will be fine-tuned on the RocStories dataset special_tokens = ['_start_', '_delimiter_', '_classify_'] tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name, special_tokens=special_tokens) special_tokens_ids = list(tokenizer.convert_tokens_to_ids(token) for token in special_tokens) model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name, num_special_tokens=len(special_tokens)) # Load the dataset and prepare the inputs logger.info("Encoding dataset...") dataset = load_rocstories_dataset(args.dataset_path) tokenized_dataset = list(list(tokenizer.tokenize(x) for x in instance) for instance in dataset) encoded_dataset = list(list(tokenizer.convert_tokens_to_ids(x) for x in instance) for instance in tokenized_dataset) max_input_length = max(len(story)+max(len(cont1), len(cont2))+3 for story, cont1, cont2, _ in encoded_dataset) max_input_length = min(max_input_length, model.config.n_positions) # Max size of input for the pre-trained model max_sub_part_length = max_input_length // 2 - 2 # Prepare dataloader dataset_tensors = pre_process_dataset(encoded_dataset, max_sub_part_length, *special_tokens_ids) train_data = TensorDataset(*dataset_tensors) train_sampler = RandomSampler(train_data) train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] num_train_optimization_steps = len(train_data) // args.train_batch_size optimizer = OpenAIAdam(optimizer_grouped_parameters, lr=args.learning_rate, warmup=args.warmup_proportion, max_grad_norm=args.max_grad_norm, weight_decay=arsg.weight_decay, t_total=num_train_optimization_steps) if args.do_train: global_step = 0 nb_tr_steps = 0 tr_loss = 0 model.train() for _ in trange(int(args.num_train_epochs), desc="Epoch"): tr_loss = 0 nb_tr_examples, nb_tr_steps = 0, 0 for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): batch = tuple(t.to(device) for t in batch) input_ids, mc_token_mask, lm_labels, mc_labels = batch losses = model(input_ids, mc_token_mask, lm_labels, mc_labels) loss = args.lm_coef * losses[0] + losses[1] loss.backward() tr_loss += loss.item() nb_tr_examples += input_ids.size(0) nb_tr_steps += 1 # Save a trained model model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self output_model_file = os.path.join(args.output_dir, "pytorch_model.bin") if args.do_train: torch.save(model_to_save.state_dict(), output_model_file) # Load a trained model that you have fine-tuned model_state_dict = torch.load(output_model_file) model = OpenAIGPTDoubleHeadsModel(args.mode, state_dict=model_state_dict, num_labels=num_labels) model.to(device) if args.do_eval: eval_examples = processor.get_dev_examples(args.data_dir) eval_features = convert_examples_to_features( eval_examples, label_list, args.max_seq_length, tokenizer) logger.info("***** Running evaluation *****") logger.info(" Num examples = %d", len(eval_examples)) logger.info(" Batch size = %d", args.eval_batch_size) all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long) all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long) eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Run prediction for full data eval_sampler = SequentialSampler(eval_data) eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size) model.eval() eval_loss, eval_accuracy = 0, 0 nb_eval_steps, nb_eval_examples = 0, 0 for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"): input_ids = input_ids.to(device) input_mask = input_mask.to(device) segment_ids = segment_ids.to(device) label_ids = label_ids.to(device) with torch.no_grad(): tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) logits = model(input_ids, segment_ids, input_mask) logits = logits.detach().cpu().numpy() label_ids = label_ids.to('cpu').numpy() tmp_eval_accuracy = accuracy(logits, label_ids) eval_loss += tmp_eval_loss.mean().item() eval_accuracy += tmp_eval_accuracy nb_eval_examples += input_ids.size(0) nb_eval_steps += 1 eval_loss = eval_loss / nb_eval_steps eval_accuracy = eval_accuracy / nb_eval_examples loss = tr_loss/nb_tr_steps if args.do_train else None result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'global_step': global_step, 'loss': loss} output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: logger.info("***** Eval results *****") for key in sorted(result.keys()): logger.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if __name__ == "__main__": main() n_updates = 0 n_epochs = 0 if dataset != 'stsb': trYt = trY if submit: path = os.path.join(save_dir, desc, 'best_params') torch.save(dh_model.state_dict(), make_path(path)) best_score = 0 for i in range(args.n_iter): print("running epoch", i) run_epoch() n_epochs += 1 log(save_dir, desc) if submit: path = os.path.join(save_dir, desc, 'best_params') dh_model.load_state_dict(torch.load(path)) predict(dataset, args.submission_dir) if args.analysis: rocstories_analysis(data_dir, os.path.join(args.submission_dir, 'ROCStories.tsv'), os.path.join(log_dir, 'rocstories.jsonl'))