run_glue_tpu.py

# coding=utf-8
# Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2019, 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.
""" Finetuning the library models for sequence classification on GLUE (Bert, DistilBert, XLNet, RoBERTa)."""

from __future__ import absolute_import, division, print_function

import argparse
from collections import defaultdict
import glob
import logging
import math
import multiprocessing
import os
import random
import time

import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from torch.utils.tensorboard import SummaryWriter
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
import torch_xla.distributed.parallel_loader as pl
import torch_xla.distributed.xla_multiprocessing as xmp

from tqdm import tqdm, trange

from transformers import (WEIGHTS_NAME, BertConfig,
                                  BertForSequenceClassification, BertTokenizer,
                                  RobertaConfig,
                                  RobertaForSequenceClassification,
                                  RobertaTokenizer,
                                  XLMConfig, XLMForSequenceClassification,
                                  XLMTokenizer, XLNetConfig,
                                  XLNetForSequenceClassification,
                                  XLNetTokenizer,
                                  DistilBertConfig,
                                  DistilBertForSequenceClassification,
                                  DistilBertTokenizer)

from transformers import AdamW, get_linear_schedule_with_warmup

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
from transformers import glue_convert_examples_to_features as convert_examples_to_features

logger = logging.getLogger(__name__)

ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (
    BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)), ())

MODEL_CLASSES = {
    'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
    'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
    'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
    'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),
    'distilbert': (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
}


def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


def get_sampler(dataset):
    if xm.xrt_world_size() <= 1:
        return RandomSampler(dataset)
    return DistributedSampler(
            dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())


def train(args, train_dataset, model, tokenizer, disable_logging=False):
    """ Train the model """
    if xm.is_master_ordinal():
        # Only master writes to Tensorboard
        tb_writer = SummaryWriter()

    train_sampler = get_sampler(train_dataset)
    dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)

    if args.max_steps > 0:
        t_total = args.max_steps
        args.num_train_epochs = args.max_steps // (len(dataloader) // args.gradient_accumulation_steps) + 1
    else:
        t_total = len(dataloader) // args.gradient_accumulation_steps * args.num_train_epochs

    # Prepare optimizer and schedule (linear warmup and decay)
    no_decay = ['bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
        {
            'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            'weight_decay': args.weight_decay,
        },
        {
            'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
            'weight_decay': 0.0,
        },
    ]
    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
    scheduler = get_linear_schedule_with_warmup(
        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)

    # Train!
    logger.info("***** Running training *****")
    logger.info("  Num examples = %d", len(dataloader) * args.train_batch_size)
    logger.info("  Num Epochs = %d", args.num_train_epochs)
    logger.info("  Instantaneous batch size per TPU core = %d", args.train_batch_size)
    logger.info("  Total train batch size (w. parallel, distributed & accumulation) = %d",
            (args.train_batch_size * args.gradient_accumulation_steps * xm.xrt_world_size()))
    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
    logger.info("  Total optimization steps = %d", t_total)

    global_step = 0
    loss = None
    model.zero_grad()
    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=disable_logging)
    set_seed(args.seed)  # Added here for reproductibility (even between python 2 and 3)
    for epoch in train_iterator:
        # Get TPU parallel loader which sends data to TPU in background.
        train_dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)
        epoch_iterator = tqdm(train_dataloader, desc="Iteration", total=len(dataloader), disable=disable_logging)
        for step, batch in enumerate(epoch_iterator):

            # Save model checkpoint.
            if args.save_steps > 0 and global_step % args.save_steps == 0:
                output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
                logger.info("Saving model checkpoint to %s", output_dir)

                if xm.is_master_ordinal():
                    if not os.path.exists(output_dir):
                        os.makedirs(output_dir)
                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))

                # Barrier to wait for saving checkpoint.
                xm.rendezvous('mid_training_checkpoint')
                # model.save_pretrained needs to be called by all ordinals
                model.save_pretrained(output_dir, xla_device=True)

            model.train()
            inputs = {'input_ids':      batch[0],
                      'attention_mask': batch[1],
                      'labels':         batch[3]}
            if args.model_type != 'distilbert':
                # XLM, DistilBERT and RoBERTa don't use segment_ids
                inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None
            outputs = model(**inputs)
            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)

            if args.gradient_accumulation_steps > 1:
                loss = loss / args.gradient_accumulation_steps

            loss.backward()

            if (step + 1) % args.gradient_accumulation_steps == 0:
                torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)

                xm.optimizer_step(optimizer)
                scheduler.step()  # Update learning rate schedule
                model.zero_grad()
                global_step += 1

                if args.logging_steps > 0 and global_step % args.logging_steps == 0:
                    # Log metrics.
                    results = {}
                    if args.evaluate_during_training:
                        results = evaluate(args, model, tokenizer, disable_logging=disable_logging)
                    loss_scalar = loss.item()
                    logger.info('global_step: {global_step}, lr: {lr:.6f}, loss: {loss:.3f}'.format(
                        global_step=global_step, lr=scheduler.get_lr()[0], loss=loss_scalar))
                    if xm.is_master_ordinal():
                        # All values must be in CPU and not on TPU device
                        for key, value in results.items():
                            tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
                        tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
                        tb_writer.add_scalar('loss', loss_scalar, global_step)

            if args.max_steps > 0 and global_step > args.max_steps:
                epoch_iterator.close()
                break
        if args.metrics_debug:
            xm.master_print(met.metrics_report())
        if args.max_steps > 0 and global_step > args.max_steps:
            train_iterator.close()
            break

    if xm.is_master_ordinal():
        tb_writer.close()
    return global_step, loss.item()


def evaluate(args, model, tokenizer, prefix="", disable_logging=False):
    """Evaluate the model"""
    if xm.is_master_ordinal():
        # Only master writes to Tensorboard
        tb_writer = SummaryWriter()

    # Loop to handle MNLI double evaluation (matched, mis-matched)
    eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
    eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,)

    results = {}
    for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
        eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
        eval_sampler = get_sampler(eval_dataset)

        if not os.path.exists(eval_output_dir):
            os.makedirs(eval_output_dir)

        # Note that we don't shard for TPU Multiprocess as we don't reduce loss among client processes.
        dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, shuffle=False)
        eval_dataloader = pl.ParallelLoader(dataloader, [args.device]).per_device_loader(args.device)

        # Eval!
        logger.info("***** Running evaluation {} *****".format(prefix))
        logger.info("  Num examples = %d", len(dataloader) * args.eval_batch_size)
        logger.info("  Batch size = %d", args.eval_batch_size)
        eval_loss = 0.0
        nb_eval_steps = 0
        preds = None
        out_label_ids = None
        for batch in tqdm(eval_dataloader, desc="Evaluating", disable=disable_logging):
            model.eval()

            with torch.no_grad():
                inputs = {'input_ids':      batch[0],
                          'attention_mask': batch[1],
                          'labels':         batch[3]}
                if args.model_type != 'distilbert':
                    # XLM, DistilBERT and RoBERTa don't use segment_ids
                    inputs['token_type_ids'] = batch[2] if args.model_type in ['bert', 'xlnet'] else None
                outputs = model(**inputs)
                batch_eval_loss, logits = outputs[:2]

                eval_loss += batch_eval_loss
            nb_eval_steps += 1
            if preds is None:
                preds = logits.detach().cpu().numpy()
                out_label_ids = inputs['labels'].detach().cpu().numpy()
            else:
                preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
                out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)

        # Get all predictions and labels from all workers
        preds = xm.mesh_reduce('eval_preds', preds, np.concatenate)
        out_label_ids = xm.mesh_reduce(
            'eval_out_label_ids', out_label_ids, np.concatenate)

        eval_loss = eval_loss / nb_eval_steps
        if args.output_mode == "classification":
            preds = np.argmax(preds, axis=1)
        elif args.output_mode == "regression":
            preds = np.squeeze(preds)
        result = compute_metrics(eval_task, preds, out_label_ids)
        results.update(result)
        results['eval_loss'] = eval_loss.item()

        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
        if xm.is_master_ordinal():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results {} *****".format(prefix))
                for key in sorted(results.keys()):
                    logger.info("  %s = %s", key, str(results[key]))
                    writer.write("%s = %s\n" % (key, str(results[key])))
                    tb_writer.add_scalar(key, results[key])

    if args.metrics_debug:
        xm.master_print(met.metrics_report())

    if xm.is_master_ordinal():
        tb_writer.close()

    return results


def load_and_cache_examples(args, task, tokenizer, evaluate=False):
    if not xm.is_master_ordinal():
        xm.rendezvous('load_and_cache_examples')

    processor = processors[task]()
    output_mode = output_modes[task]
    cached_features_file = os.path.join(
        args.data_dir, 'cached_{}_{}_{}_{}'.format(
            'dev' if evaluate else 'train',
            list(filter(None, args.model_name_or_path.split('/'))).pop(),
            str(args.max_seq_length),
            str(task)))

    # Load data features from cache or dataset file
    if os.path.exists(cached_features_file) and not args.overwrite_cache:
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
    else:
        logger.info("Creating features from dataset file at %s", args.data_dir)
        label_list = processor.get_labels()
        if task in ['mnli', 'mnli-mm'] and args.model_type in ['roberta']:
            # HACK(label indices are swapped in RoBERTa pretrained model)
            label_list[1], label_list[2] = label_list[2], label_list[1]
        examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
        features = convert_examples_to_features(examples,
                                                tokenizer,
                                                label_list=label_list,
                                                max_length=args.max_seq_length,
                                                output_mode=output_mode,
                                                pad_on_left=bool(args.model_type in ['xlnet']),  # pad on the left for xlnet
                                                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
                                                pad_token_segment_id=4 if args.model_type in ['xlnet'] else 0,
        )
        logger.info("Saving features into cached file %s", cached_features_file)
        torch.save(features, cached_features_file)

    if xm.is_master_ordinal():
        xm.rendezvous('load_and_cache_examples')

    # Convert to Tensors and build dataset
    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
    if output_mode == "classification":
        all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
    elif output_mode == "regression":
        all_labels = torch.tensor([f.label for f in features], dtype=torch.float)

    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
    return dataset


def main(args):
    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))

    # Get TPU/XLA Device
    args.device = xm.xla_device()

    # Setup logging
    logging.basicConfig(
            format='[xla:{}] %(asctime)s - %(levelname)s - %(name)s -   %(message)s'.format(xm.get_ordinal()),
            datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO)
    disable_logging = False
    if not xm.is_master_ordinal() and args.only_log_master:
        # Disable all non-master loggers below CRITICAL.
        logging.disable(logging.CRITICAL)
        disable_logging = True
    logger.warning("Process rank: %s, device: %s, num_cores: %s",
            xm.get_ordinal(), args.device, args.num_cores)

    # Set seed to have same initialization
    set_seed(args.seed)

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

    if not xm.is_master_ordinal():
        xm.rendezvous('download_only_once')  # Make sure only the first process in distributed training will download model & vocab

    # Load pretrained model and tokenizer
    args.model_type = args.model_type.lower()
    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
                                          num_labels=num_labels,
                                          finetuning_task=args.task_name,
                                          cache_dir=args.cache_dir if args.cache_dir else None,
                                          xla_device=True)
    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
                                                do_lower_case=args.do_lower_case,
                                                cache_dir=args.cache_dir if args.cache_dir else None,
                                                xla_device=True)
    model = model_class.from_pretrained(args.model_name_or_path,
                                        from_tf=bool('.ckpt' in args.model_name_or_path),
                                        config=config,
                                        cache_dir=args.cache_dir if args.cache_dir else None,
                                        xla_device=True)

    if xm.is_master_ordinal():
        xm.rendezvous('download_only_once')

    # Send model to TPU/XLA device.
    model.to(args.device)

    logger.info("Training/evaluation parameters %s", args)

    if args.do_train:
        # Train the model.
        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
        global_step, tr_loss = train(args, train_dataset, model, tokenizer, disable_logging=disable_logging)
        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)


        if xm.is_master_ordinal():
            # Save trained model.
            # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()

            # Create output directory if needed
            if not os.path.exists(args.output_dir):
                os.makedirs(args.output_dir)

            logger.info("Saving model checkpoint to %s", args.output_dir)
            # Save a trained model, configuration and tokenizer using `save_pretrained()`.
            # They can then be reloaded using `from_pretrained()`
            tokenizer.save_pretrained(args.output_dir)
            # Good practice: save your training arguments together with the trained.
            torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))

        xm.rendezvous('post_training_checkpoint')
        # model.save_pretrained needs to be called by all ordinals
        model.save_pretrained(args.output_dir, xla_device=True)

        # Load a trained model and vocabulary that you have fine-tuned
        model = model_class.from_pretrained(args.output_dir, xla_device=True)
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, xla_device=True)
        model.to(args.device)


    # Evaluation
    results = {}
    if args.do_eval:
        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case, xla_device=True)
        checkpoints = [args.output_dir]
        if args.eval_all_checkpoints:
            checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
        logger.info("Evaluate the following checkpoints: %s", checkpoints)
        for checkpoint in checkpoints:
            global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
            prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""

            model = model_class.from_pretrained(checkpoint, xla_device=True)
            model.to(args.device)
            result = evaluate(args, model, tokenizer, prefix=prefix, disable_logging=disable_logging)
            result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
            results.update(result)

    return results


def get_args():
    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("--model_type", default=None, type=str, required=True,
                        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
    parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
                        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
    parser.add_argument("--task_name", default=None, type=str, required=True,
                        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()))
    parser.add_argument("--output_dir", default=None, type=str, required=True,
                        help="The output directory where the model predictions and checkpoints will be written.")

    # TPU Parameters
    parser.add_argument('--num_cores', default=8, type=int, help='Number of TPU cores to use.')
    parser.add_argument('--metrics_debug', action='store_true', help='Whether to print debug metrics.')

    ## Other parameters
    parser.add_argument("--config_name", default="", type=str,
                        help="Pretrained config name or path if not the same as model_name")
    parser.add_argument("--tokenizer_name", default="", type=str,
                        help="Pretrained tokenizer name or path if not the same as model_name")
    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 tokenization. Sequences longer "
                             "than this will be truncated, sequences shorter 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("--evaluate_during_training", action='store_true',
                        help="Rul evaluation during training at each logging step.")
    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=8, type=int,
                        help="Per core batch size for training.")
    parser.add_argument("--eval_batch_size", default=8, type=int,
                        help="Per core batch size for evaluation.")
    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("--weight_decay", default=0.0, type=float,
                        help="Weight deay if we apply some.")
    parser.add_argument("--adam_epsilon", default=1e-8, type=float,
                        help="Epsilon for Adam optimizer.")
    parser.add_argument("--max_grad_norm", default=1.0, type=float,
                        help="Max gradient norm.")
    parser.add_argument("--num_train_epochs", default=3.0, type=float,
                        help="Total number of training epochs to perform.")
    parser.add_argument("--max_steps", default=-1, type=int,
                        help="If > 0: set total number of training steps to perform. Override num_train_epochs.")
    parser.add_argument("--warmup_steps", default=0, type=int,
                        help="Linear warmup over warmup_steps.")

    parser.add_argument('--logging_steps', type=int, default=50,
                        help="Log every X update steps.")
    parser.add_argument('--only_log_master', action='store_true',
                        help='Whether to log only from each hosts master.')
    parser.add_argument('--save_steps', type=int, default=50,
                        help="Save checkpoint every X update steps.")
    parser.add_argument("--eval_all_checkpoints", action='store_true',
                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
    parser.add_argument('--overwrite_output_dir', action='store_true',
                        help="Overwrite the content of the output directory")
    parser.add_argument('--overwrite_cache', action='store_true',
                        help="Overwrite the cached training and evaluation sets")
    parser.add_argument('--seed', type=int, default=42,
                        help="random seed for initialization")

    return parser.parse_args()


def _mp_fn(rank, args):
    main(args)

def main_cli():
    args = get_args()
    xmp.spawn(_mp_fn, args=(args,), nprocs=args.num_cores)

if __name__ == "__main__":
    main_cli()