run_glue.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace 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.
"""BERT finetuning runner."""

from __future__ import absolute_import, division, print_function

import argparse
import logging
import os
import sys
import random
from tqdm import tqdm, trange

import numpy as np

import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from torch.nn import CrossEntropyLoss, MSELoss

from tensorboardX import SummaryWriter

from pytorch_transformers import WEIGHTS_NAME, CONFIG_NAME
from pytorch_transformers.modeling_bert import BertForSequenceClassification
from pytorch_transformers.tokenization_bert import BertTokenizer
from pytorch_transformers.optimization import BertAdam, WarmupLinearSchedule

from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics


logger = logging.getLogger(__name__)


def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--data_dir", default=None, type=str, required=True,
                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
    parser.add_argument("--bert_model", default=None, type=str, required=True,
                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
                        "bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
                        "bert-base-multilingual-cased, bert-base-chinese.")
    parser.add_argument("--task_name", default=None, type=str, required=True,
                        help="The name of the task to train.")
    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model predictions and checkpoints will be written.")

    ## Other parameters
    parser.add_argument("--cache_dir", default="", type=str,
                        help="Where do you want to store the pre-trained models downloaded from s3")
    parser.add_argument("--max_seq_length", default=128, type=int,
                        help="The maximum total input sequence length after WordPiece tokenization. \n"
                             "Sequences longer than this will be truncated, and sequences shorter \n"
                             "than this will be padded.")
    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("--do_lower_case", action='store_true',
                        help="Set this flag if you are using an uncased model.")
    parser.add_argument("--train_batch_size", default=32, type=int,
                        help="Total batch size for training.")
    parser.add_argument("--eval_batch_size", default=8, type=int,
                        help="Total batch size for eval.")
    parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
                        help="Number of updates steps to accumulate before performing a backward/update pass.")
    parser.add_argument("--learning_rate", default=5e-5, type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--num_train_epochs", default=3.0, type=float,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--warmup_proportion", default=0.1, type=float,
                        help="Proportion of training to perform linear learning rate warmup for. "
                             "E.g., 0.1 = 10%% of training.")
    parser.add_argument("--no_cuda", action='store_true',
                        help="Avoid using CUDA when available")
    parser.add_argument('--overwrite_output_dir', action='store_true',
                        help="Overwrite the content of the output directory")
    parser.add_argument('--seed', type=int, default=42,
                        help="random seed for initialization")

    parser.add_argument('--fp16', action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--loss_scale', type=float, default=0,
                        help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
                             "0 (default value): dynamic loss scaling.\n"
                             "Positive power of 2: static loss scaling value.\n")

    parser.add_argument("--local_rank", type=int, default=-1,
                        help="local_rank for distributed training on gpus")

    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
    args = parser.parse_args()

    # Setup distant debugging if needed
    if args.server_ip and args.server_port:
        # 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()

    # Setup CUDA, GPU & distributed training
    if args.local_rank == -1 or args.no_cuda:
        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
        n_gpu = torch.cuda.device_count()
    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        torch.distributed.init_process_group(backend='nccl')
        n_gpu = 1
    args.device = device

    # Setup logging
    logging.basicConfig(level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
    logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
                device, n_gpu, bool(args.local_rank != -1), args.fp16))

    # Setup seeds
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed)

    # Safety checks and create output directory
    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 os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
        raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
    if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
        os.makedirs(args.output_dir)

    # Prepare GLUE task
    task_name = args.task_name.lower()
    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))
    processor = processors[task_name]()
    output_mode = output_modes[task_name]
    label_list = processor.get_labels()
    num_labels = len(label_list)

    # Load pretrained model and tokenizer
    if args.local_rank not in [-1, 0]:
        # Make sure only the first process in distributed training will download model & vocab
        torch.distributed.barrier()

    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
    model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)

    if args.local_rank == 0:
        torch.distributed.barrier()

    # Distributed, parrallel and fp16 model
    if args.fp16:
        model.half()
    model.to(device)
    if args.local_rank != -1:
        model = torch.nn.parallel.DistributedDataParallel(model,
                                                          device_ids=[args.local_rank],
                                                          output_device=args.local_rank,
                                                          find_unused_parameters=True)
    elif n_gpu > 1:
        model = torch.nn.DataParallel(model)

    global_step = 0
    tr_loss = 0
    if args.do_train:
        if args.local_rank in [-1, 0]:
            tb_writer = SummaryWriter()

        # Load and cache data
        train_examples = processor.get_train_examples(args.data_dir)
        cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
            list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(task_name)))
        if os.path.exists(cached_train_features_file):
            train_features = torch.load(cached_train_features_file)
        else:
            train_features = convert_examples_to_features(
                train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
            if args.local_rank == -1 or torch.distributed.get_rank() == 0:
                logger.info("  Saving train features into cached file %s", cached_train_features_file)
                torch.save(train_features, cached_train_features_file)

        # Convert in tensors and build dataloader
        all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
        all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
        all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
        if output_mode == "classification":
            all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
        elif output_mode == "regression":
            all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)

        args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps

        train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
        train_sampler = RandomSampler(train_data) if args.local_rank == -1 else DistributedSampler(train_data)
        train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)

        num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs

        # 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}
            ]
        if args.fp16:
            try:
                from apex.optimizers import FP16_Optimizer, FusedAdam
            except ImportError:
                raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
            optimizer = FusedAdam(optimizer_grouped_parameters,
                                  lr=args.learning_rate,
                                  bias_correction=False,
                                  max_grad_norm=1.0)
            if args.loss_scale == 0:
                optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
            else:
                optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
            warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
                                                 t_total=num_train_optimization_steps)

        else:
            optimizer = BertAdam(optimizer_grouped_parameters,
                                 lr=args.learning_rate,
                                 warmup=args.warmup_proportion,
                                 t_total=num_train_optimization_steps)

        # Train!
        logger.info("***** Running training *****")
        logger.info("  Num examples = %d", len(train_examples))
        logger.info("  Batch size = %d", args.train_batch_size)
        logger.info("  Num steps = %d", num_train_optimization_steps)
        model.train()
        for _ in trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]):
            for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
                batch = tuple(t.to(device) for t in batch)
                input_ids, input_mask, segment_ids, label_ids = batch

                ouputs = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
                loss = ouputs[0]

                if n_gpu > 1:
                    loss = loss.mean() # mean() to average on multi-gpu parallel training
                if args.gradient_accumulation_steps > 1:
                    loss = loss / args.gradient_accumulation_steps

                if args.fp16:
                    optimizer.backward(loss)
                else:
                    loss.backward()

                tr_loss += loss.item()
                if (step + 1) % args.gradient_accumulation_steps == 0:
                    if args.fp16:
                        # modify learning rate with special warm up BERT uses
                        # if args.fp16 is False, BertAdam is used that handles this automatically
                        lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion)
                        for param_group in optimizer.param_groups:
                            param_group['lr'] = lr_this_step
                    optimizer.step()
                    optimizer.zero_grad()
                    global_step += 1
                    if args.local_rank in [-1, 0]:
                        if not args.fp16:
                            tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
                        tb_writer.add_scalar('loss', loss.item(), global_step)

    ### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
    ### Example:
    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        # Save a trained model, configuration and tokenizer
        model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self

        # If we save using the predefined names, we can load using `from_pretrained`
        output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
        output_config_file = os.path.join(args.output_dir, CONFIG_NAME)

        torch.save(model_to_save.state_dict(), output_model_file)
        model_to_save.config.to_json_file(output_config_file)
        tokenizer.save_vocabulary(args.output_dir)

        # Load a trained model and vocabulary that you have fine-tuned
        model = BertForSequenceClassification.from_pretrained(args.output_dir)
        tokenizer = BertTokenizer.from_pretrained(args.output_dir)

        # Good practice: save your training arguments together with the trained model
        output_args_file = os.path.join(args.output_dir, 'training_args.bin')
        torch.save(args, output_args_file)
    else:
        model = BertForSequenceClassification.from_pretrained(args.bert_model)

    model.to(device)

    ### Evaluation
    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        eval_task_names = ("mnli", "mnli-mm") if task_name == "mnli" else (task_name,)
        eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if task_name == "mnli" else (args.output_dir,)
        for eval_task, output_dir in zip(eval_task_names, eval_outputs_dirs):
            if os.path.exists(output_dir) and os.listdir(output_dir) and args.do_train:
                raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
            if not os.path.exists(output_dir):
                os.makedirs(output_dir)

            # Load and cache data
            processor = processors[eval_task]()
            eval_examples = processor.get_dev_examples(args.data_dir)
            cached_eval_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
                list(filter(None, args.bert_model.split('/'))).pop(), str(args.max_seq_length), str(eval_task)))
            if os.path.exists(cached_eval_features_file):
                eval_features = torch.load(cached_eval_features_file)
            else:
                eval_features = convert_examples_to_features(
                    eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
                if args.local_rank == -1 or torch.distributed.get_rank() == 0:
                    logger.info("  Saving eval features into cached file %s", cached_eval_features_file)
                torch.save(eval_features, cached_eval_features_file)

            # Convert in tensors and build dataloader
            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)
            if output_mode == "classification":
                all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
            elif output_mode == "regression":
                all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)

            eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
            # Note that DistributedSampler samples randomly
            eval_sampler = SequentialSampler(eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
            eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)

            # Eval!
            logger.info("***** Running evaluation *****")
            logger.info("  Num examples = %d", len(eval_examples))
            logger.info("  Batch size = %d", args.eval_batch_size)
            model.eval()
            eval_loss = 0
            nb_eval_steps = 0
            preds = None
            out_label_ids = None
            for batch in tqdm(eval_dataloader, desc="Evaluating"):
                batch = tuple(t.to(device) for t in batch)
                input_ids, input_mask, segment_ids, label_ids = batch

                with torch.no_grad():
                    outputs = model(input_ids,
                                    token_type_ids=segment_ids,
                                    attention_mask=input_mask,
                                    labels=label_ids)
                    tmp_eval_loss, logits = outputs[:2]

                eval_loss += tmp_eval_loss.mean().item()
                nb_eval_steps += 1
                if preds is None:
                    preds = logits.detach().cpu().numpy()
                    out_label_ids = label_ids.detach().cpu().numpy()
                else:
                    preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
                    out_label_ids = np.append(out_label_ids, label_ids.detach().cpu().numpy(), axis=0)

            eval_loss = eval_loss / nb_eval_steps
            if output_mode == "classification":
                preds = np.argmax(preds, axis=1)
            elif output_mode == "regression":
                preds = np.squeeze(preds)
            result = compute_metrics(eval_task, preds, out_label_ids)

            loss = tr_loss/global_step if args.do_train else None

            result['eval_loss'] = eval_loss
            result['global_step'] = global_step
            result['loss'] = loss

            output_eval_file = os.path.join(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()