run_openai_gpt.py

# 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, trange

import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)

from pytorch_pretrained_bert import OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer, OpenAIAdam

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 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_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
    """ Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)

        To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation:
        input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
    """
    tensor_datasets = []
    for dataset in encoded_datasets:
        n_batch = len(dataset)
        input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)
        mc_token_mask = np.zeros((n_batch, 2, input_len), dtype=np.int64)
        lm_labels = np.full((n_batch, 2, input_len), -1, dtype=np.int64)
        mc_labels = np.zeros((n_batch,), dtype=np.int64)
        for i, (story, cont1, cont2, mc_label), in enumerate(dataset):
            with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
            with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
            input_ids[i, 0, :len(with_cont1)] = with_cont1
            input_ids[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)
        tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs))
    return tensor_datasets

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_name', type=str, default='openai-gpt',
                        help='pretrained model name')
    parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
    parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.")
    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model predictions and checkpoints will be written.")
    parser.add_argument('--train_dataset', type=str, default='cloze_test_val__spring2016 - cloze_test_ALL_val.tsv')
    parser.add_argument('--eval_dataset', type=str, default='test_spring2016.tsv')
    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('--eval_batch_size', type=int, default=16)
    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('--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)

    parser.add_argument('--server_ip', type=str, default='')
    parser.add_argument('--server_port', type=str, default='')
    args = parser.parse_args()
    print(args)

    # Some distant debugging
    # See https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd
    print("Waiting for debugger attach")
    ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
    ptvsd.wait_for_attach()


    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: {}, n_gpu {}".format(device, n_gpu))

    if not args.do_train and not args.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    if not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir)

    # 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 and encode the datasets
    def tokenize_and_encode(obj):
        """ Tokenize and encode a nested object """
        if isinstance(obj, str):
            return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
        elif isinstance(obj, int):
            return obj
        return list(tokenize_and_encode(o) for o in obj)

    logger.info("Encoding dataset...")
    train_dataset = load_rocstories_dataset(args.train_dataset)
    eval_dataset = load_rocstories_dataset(args.eval_dataset)
    datasets = (train_dataset, eval_dataset)
    encoded_datasets = tokenize_and_encode(datasets)

    # Compute the mex input length for the Transformer
    input_length = max(len(story) + max(len(cont1), len(cont2)) + 3  \
                           for dataset in encoded_datasets for story, cont1, cont2, _ in dataset)
    input_length = min(input_length, model.config.n_positions)  # Max size of input for the pre-trained model
    max_sub_part_length = input_length // 2 - 2

    # Prepare inputs tensors and dataloaders
    tensor_datasets = pre_process_datasets(encoded_datasets, input_length, max_sub_part_length, *special_tokens_ids)
    train_tensor_dataset, eval_tensor_dataset = tensor_datasets[0], tensor_datasets[1]

    train_data = TensorDataset(*train_tensor_dataset)
    train_sampler = RandomSampler(train_data)
    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)

    eval_data = TensorDataset(*eval_tensor_dataset)
    eval_sampler = SequentialSampler(eval_data)
    eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_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=args.weight_decay,
                           t_total=num_train_optimization_steps)

    if args.do_train:
        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()
                optimizer.step()
                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.from_pretrained(args.model_name, state_dict=model_state_dict,
                                                      num_special_tokens=len(special_tokens))
    model.to(device)

    if args.do_eval:
        model.eval()
        eval_loss, eval_accuracy = 0, 0
        nb_eval_steps, nb_eval_examples = 0, 0
        for batch in tqdm(eval_dataloader, desc="Evaluating"):
            batch = tuple(t.to(device) for t in batch)
            input_ids, mc_token_mask, lm_labels, mc_labels = batch
            with torch.no_grad():
                _, mc_loss = model(input_ids, mc_token_mask, lm_labels, mc_labels)
                _, mc_logits = model(input_ids, mc_token_mask)

            mc_logits = mc_logits.detach().cpu().numpy()
            mc_labels = mc_labels.to('cpu').numpy()
            tmp_eval_accuracy = accuracy(mc_logits, mc_labels)

            eval_loss += mc_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
        train_loss = tr_loss/nb_tr_steps if args.do_train else None
        result = {'eval_loss': eval_loss,
                  'eval_accuracy': eval_accuracy,
                  'train_loss': train_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()