train_distributed_dataset.py

#!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a new model on one or across multiple GPUs.
"""

import argparse
import logging
import math
import os
import random
import sys
from typing import Dict, Optional, Any, List, Tuple, Callable

import numpy as np
import torch
import torch.distributed as dist
import functools
from fairseq import (
    checkpoint_utils,
    options,
    quantization_utils,
    tasks,
    utils,
)
from fairseq.data import iterators, data_utils
from fairseq.data.plasma_utils import PlasmaStore
from fairseq.dataclass.configs import FairseqConfig
from fairseq.dataclass.utils import convert_namespace_to_omegaconf
from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils
from fairseq.file_io import PathManager
from fairseq.logging import meters, metrics, progress_bar
from fairseq.model_parallel.megatron_trainer import MegatronTrainer
from fairseq.trainer import Trainer
from omegaconf import DictConfig, OmegaConf


logging.basicConfig(
    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S",
    level=os.environ.get("LOGLEVEL", "INFO").upper(),
    stream=sys.stdout,
)
logger = logging.getLogger("fairseq_cli.train")


def main(cfg: FairseqConfig) -> None:
    if isinstance(cfg, argparse.Namespace):
        cfg = convert_namespace_to_omegaconf(cfg)

    utils.import_user_module(cfg.common)

    if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg:
        # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
        logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg))

    assert (
        cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
    ), "Must specify batch size either with --max-tokens or --batch-size"
    metrics.reset()

    if cfg.common.log_file is not None:
        handler = logging.FileHandler(filename=cfg.common.log_file)
        logger.addHandler(handler)
    
    np.random.seed(cfg.common.seed)
    utils.set_torch_seed(cfg.common.seed)

    checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir)

    # Print nvidia smi stats
    logger.info(metrics.get_nvidia_smi_gpu_memory_stats_str())

    # Print args
    logger.info(cfg)

    if cfg.checkpoint.write_checkpoints_asynchronously:
        try:
            import iopath  # noqa: F401
        except ImportError:
            logging.exception(
                "Asynchronous checkpoint writing is specified but iopath is "
                "not installed: `pip install iopath`"
            )
            return

    # Setup task, e.g., translation, language modeling, etc.
    task = tasks.setup_task(cfg.task)

    assert cfg.criterion, "Please specify criterion to train a model"
    if utils.is_moe(cfg.model) and getattr(cfg.model, "moe_expert_count", 0) < distributed_utils.get_global_world_size():
        assert cfg.distributed_training.ddp_backend == 'fully_sharded', 'num_experts < num_gpus only supported by FSDP'

    # Build model and criterion
    if cfg.distributed_training.ddp_backend == "fully_sharded":
        #if cfg.distributed_training.use_sharded_state: assert cfg.checkpoint.no_save_optimizer_state, f'--use-sharded-state requires --no-save-optimizer-state'
        extra = {
            "is_moe": utils.is_moe(cfg.model),
            "use_sharded_state": cfg.distributed_training.use_sharded_state,
        }

        with fsdp_enable_wrap(cfg.distributed_training, **extra):
            model = fsdp_wrap(task.build_model(cfg.model))
    else:
        model = task.build_model(cfg.model)
    criterion = task.build_criterion(cfg.criterion)

    def is_expert_param(p):
        return getattr(p, "expert", False) or getattr(p, "base_expert", False)

    logger.info(model)
    logger.info("task: {}".format(task.__class__.__name__))
    logger.info("model: {}".format(model.__class__.__name__))
    logger.info("criterion: {}".format(criterion.__class__.__name__))
    logger.info(
        "num. non-expert model params: {:,} (num. trained: {:,})".format(
            sum(getattr(p, "_orig_size", p).numel() for p in model.parameters() if not is_expert_param(p)),
            sum(getattr(p, "_orig_size", p).numel() for p in model.parameters() if not is_expert_param(p) and p.requires_grad),
        )
    )
    logger.info(
        "num. expert model params: {:,} (num. trained: {:,})".format(
            sum(getattr(p, "_orig_size", p).numel() for p in model.parameters() if is_expert_param(p)),
            sum(getattr(p, "_orig_size", p).numel() for p in model.parameters() if is_expert_param(p) and p.requires_grad),
        )
    )
    logger.info(metrics.get_nvidia_smi_gpu_memory_stats_str())

    # Load valid dataset (we load training data below, based on the latest checkpoint)
    # We load the valid dataset AFTER building the model
    data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg)
    
    task.data_manager.args.dataset_impl='mmap'
    if cfg.dataset.combine_valid_subsets:
        task.load_dataset("valid", combine=True, epoch=1)
    else:
        for valid_sub_split in cfg.dataset.valid_subset.split(","):
            task.load_dataset(valid_sub_split, combine=False, epoch=1)
    
    # (optionally) Configure quantization
    if cfg.common.quantization_config_path is not None:
        quantizer = quantization_utils.Quantizer(
            config_path=cfg.common.quantization_config_path,
            max_epoch=cfg.optimization.max_epoch,
            max_update=cfg.optimization.max_update,
        )
    else:
        quantizer = None

    # Build trainer
    if cfg.common.model_parallel_size == 1:
        trainer = Trainer(cfg, task, model, criterion, quantizer)
    else:
        trainer = MegatronTrainer(cfg, task, model, criterion)
    logger.info(
        "training on {} devices (GPUs/TPUs)".format(
            cfg.distributed_training.distributed_world_size
        )
    )
    logger.info(
        "max tokens per GPU = {} and batch size per GPU = {}".format(
            cfg.dataset.max_tokens,
            cfg.dataset.batch_size,
        )
    )
    logger.info(metrics.get_nvidia_smi_gpu_memory_stats_str())

    # Load the latest checkpoint if one is available and restore the
    # corresponding train iterator

    task.data_manager.args.dataset_impl='dist_mmap'
    extra_state, epoch_itr = checkpoint_utils.load_checkpoint(
        cfg.checkpoint,
        trainer,
        # don't cache epoch iterators for sharded datasets
        disable_iterator_cache=True,
        shard_batch_itr=False
    )

    max_epoch = cfg.optimization.max_epoch or math.inf
    lr = trainer.get_lr()
    train_meter = meters.StopwatchMeter()
    train_meter.start()
    
    while epoch_itr.next_epoch_idx <= max_epoch:
        if lr <= cfg.optimization.stop_min_lr:
            logger.info(
                f"stopping training because current learning rate ({lr}) is smaller "
                "than or equal to minimum learning rate "
                f"(--stop-min-lr={cfg.optimization.stop_min_lr})"
            )
            break

        # train for one epoch
        valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
        if should_stop:
            break

        # only use first validation loss to update the learning rate
        lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
        if 'train' in task.datasets:
            del task.datasets['train']
        epoch_itr = trainer.get_train_iterator(
            epoch_itr.next_epoch_idx,
            # sharded data: get train iterator for next epoch
            shard_batch_itr=False,
            load_dataset=True,
            # don't cache epoch iterators for sharded datasets
            disable_iterator_cache=True,
        )
    train_meter.stop()
    logger.info("done training in {:.1f} seconds".format(train_meter.sum))

    # ioPath implementation to wait for all asynchronous file writes to complete.
    if cfg.checkpoint.write_checkpoints_asynchronously:
        logger.info(
            "ioPath PathManager waiting for all asynchronous checkpoint "
            "writes to finish."
        )
        PathManager.async_close()
        logger.info("ioPath PathManager finished waiting.")


def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
    # skip check if no validation was done in the current epoch
    if valid_loss is None:
        return False
    if cfg.checkpoint.patience <= 0:
        return False

    def is_better(a, b):
        return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b

    prev_best = getattr(should_stop_early, "best", None)
    if prev_best is None or is_better(valid_loss, prev_best):
        should_stop_early.best = valid_loss
        should_stop_early.num_runs = 0
        return False
    else:
        should_stop_early.num_runs += 1
        if should_stop_early.num_runs >= cfg.checkpoint.patience:
            logger.info(
                "early stop since valid performance hasn't improved for last {} runs".format(
                    cfg.checkpoint.patience
                )
            )
            return True
        else:
            return False


@metrics.aggregate("train")
def train(
    cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
) -> Tuple[List[Optional[float]], bool]:
    """Train the model for one epoch and return validation losses."""
    # Initialize data iterator
    itr = epoch_itr.next_epoch_itr(
        fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
        shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
    )
    update_freq = (
        cfg.optimization.update_freq[epoch_itr.epoch - 1]
        if epoch_itr.epoch <= len(cfg.optimization.update_freq)
        else cfg.optimization.update_freq[-1]
    )
    itr = iterators.GroupedIterator(itr, update_freq)
    if cfg.common.tpu:
        itr = utils.tpu_data_loader(itr)
    progress = progress_bar.progress_bar(
        itr,
        log_format=cfg.common.log_format,
        log_file=cfg.common.log_file,
        log_interval=cfg.common.log_interval,
        epoch=epoch_itr.epoch,
        tensorboard_logdir=(
            cfg.common.tensorboard_logdir
            if distributed_utils.is_master(cfg.distributed_training)
            else None
        ),
        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
        wandb_project=(
            cfg.common.wandb_project
            if distributed_utils.is_master(cfg.distributed_training)
            else None
        ),
        wandb_run_name=os.environ.get(
            "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
        ),
        azureml_logging=(
            cfg.common.azureml_logging
            if distributed_utils.is_master(cfg.distributed_training)
            else False
        ),
    )
    progress.update_config(_flatten_config(cfg))

    trainer.begin_epoch(epoch_itr.epoch)
    if cfg.task._name in ["multilingual_language_modeling", "translation_multi_simple_epoch"]:
        valid_subsets = task.args.valid_subset.split(",")
    else:
        valid_subsets = cfg.dataset.valid_subset.split(",")
    should_stop = False
    num_updates = trainer.get_num_updates()
    logger.info("Start iterating over samples")
    
    """
    modify stop handeling to prevent dead-local, at end of the epoch.
    """
    all_stop=False
    steps_of_current_rank=len(progress)
    max_steps=distributed_utils.all_reduce(torch.tensor(steps_of_current_rank).float().cuda(), group=distributed_utils.get_data_parallel_group(), op='max')
    i=itr.n
    print('rank:{}, step:{}, max_step:{}, start:{}'.format(distributed_utils.get_data_parallel_rank(), steps_of_current_rank, max_steps, i))
    samples_size=-1
    assert steps_of_current_rank>0
    # for i, samples in enumerate(progress):
    iter_progress=iter(progress)
    while not all_stop:
        if i>=steps_of_current_rank:
            samples=[None for _ in range(samples_size)]
        else:
            samples=next(iter_progress)
            if samples_size==-1:
                samples_size=len(samples) # length of first batch
            if len(samples)!=samples_size:
                samples+=[None for _ in range(samples_size-len(samples))]
                assert len(samples)==samples_size
        with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function(
            "train_step-%d" % i
        ):
            log_output = trainer.train_step(samples)

        if log_output is not None:  # not OOM, overflow, ...
            # log mid-epoch stats
            num_updates = trainer.get_num_updates()
            if num_updates % cfg.common.log_interval == 0:
                stats = get_training_stats(metrics.get_smoothed_values("train_inner"))
                progress.log(stats, tag="train_inner", step=num_updates)

                # reset mid-epoch stats after each log interval
                # the end-of-epoch stats will still be preserved
                metrics.reset_meters("train_inner")

        i+=1
        all_stop = (i>=max_steps)
        end_of_epoch = all_stop # save model when all ranks end epoch
        valid_losses, should_stop = validate_and_save(
            cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch
        )
        if should_stop:
            break

        # it seems that we don't need to worry about ealy stop
        # if cfg.optimization.stop_time_hours==0 and cfg.optimization.max_update==0 and cfg.checkpoint.patience<=0:
            # no early stop. every processs run over all samples
            # all_stop = (i>=max_steps)
        # else:
        #     stop_num=distributed_utils.all_reduce(torch.tensor(should_stop), group=distributed_utils.get_data_parallel_group())
        #     all_stop = (stop_num==data_parallel_world_size)

    # log end-of-epoch stats
    logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch))
    stats = get_training_stats(metrics.get_smoothed_values("train"))
    progress.print(stats, tag="train", step=num_updates)

    # reset epoch-level meters
    metrics.reset_meters("train")
    return valid_losses, should_stop


def _flatten_config(cfg: DictConfig):
    config = OmegaConf.to_container(cfg)
    # remove any legacy Namespaces and replace with a single "args"
    namespace = None
    for k, v in list(config.items()):
        if isinstance(v, argparse.Namespace):
            namespace = v
            del config[k]
    if namespace is not None:
        config["args"] = vars(namespace)
    return config


def validate_and_save(
    cfg: DictConfig,
    trainer: Trainer,
    task: tasks.FairseqTask,
    epoch_itr,
    valid_subsets: List[str],
    end_of_epoch: bool,
) -> Tuple[List[Optional[float]], bool]:
    num_updates = trainer.get_num_updates()
    max_update = cfg.optimization.max_update or math.inf

    # Stopping conditions (and an additional one based on validation loss later
    # on)
    should_stop = False
    if num_updates >= max_update:
        should_stop = True
        logger.info(
            f"Stopping training due to "
            f"num_updates: {num_updates} >= max_update: {max_update}"
        )

    training_time_hours = trainer.cumulative_training_time() / (60 * 60)
    if (
        cfg.optimization.stop_time_hours > 0
        and training_time_hours > cfg.optimization.stop_time_hours
    ):
        should_stop = True
        logger.info(
            f"Stopping training due to "
            f"cumulative_training_time: {training_time_hours} > "
            f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
        )

    do_save = (
        (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0)
        or should_stop
        or (
            cfg.checkpoint.save_interval_updates > 0
            and num_updates > 0
            and num_updates % cfg.checkpoint.save_interval_updates == 0
            and num_updates >= cfg.dataset.validate_after_updates
        )
    )
    do_validate = (
        (not end_of_epoch and do_save)  # validate during mid-epoch saves
        or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0)
        or should_stop
        or (
            cfg.dataset.validate_interval_updates > 0
            and num_updates > 0
            and num_updates % cfg.dataset.validate_interval_updates == 0
        )
    ) and not cfg.dataset.disable_validation
    valid_losses = [None]
    if do_validate:
        valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets)

    should_stop |= should_stop_early(cfg, valid_losses[0])

    # Save checkpoint
    if do_save or should_stop:
        checkpoint_utils.save_checkpoint(
            cfg.checkpoint, trainer, epoch_itr, valid_losses[0], training_finished=should_stop,
            async_callback_fn=functools.partial(post_checkpoint_callback, cfg) if cfg.checkpoint.s3_upload_path else None,
        )

    trainer.reset_dummy_batch(epoch_itr.first_batch)
    return valid_losses, should_stop

def post_checkpoint_callback(cfg, filename):
    if cfg.checkpoint.s3_upload_path is not None:
        try:
            # PathManager only supports writing to S3, but this function call
            # can be replaced with other APIs for copying checkpoints.
            PathManager.copy_from_local(
                filename,
                os.path.join(cfg.checkpoint.s3_upload_path, os.path.basename(filename)),
                overwrite=True,
            )
        except (FileNotFoundError, AssertionError) as e:
            logger.info(f'could not upload {filename}: {e}')

def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
    stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0)
    return stats


def validate(
    cfg: DictConfig,
    trainer: Trainer,
    task: tasks.FairseqTask,
    epoch_itr,
    subsets: List[str],
) -> List[Optional[float]]:
    """Evaluate the model on the validation set(s) and return the losses."""

    if cfg.dataset.fixed_validation_seed is not None:
        # set fixed seed for every validation
        utils.set_torch_seed(cfg.dataset.fixed_validation_seed)

    trainer.begin_valid_epoch(epoch_itr.epoch)
    valid_losses = []
    for subset in subsets:
        logger.info('begin validation on "{}" subset on rank {}'.format(
            subset, distributed_utils.get_global_rank()))

        # Initialize data iterator
        itr = trainer.get_valid_iterator(subset).next_epoch_itr(
            shuffle=False, set_dataset_epoch=False  # use a fixed valid set
        )
        if cfg.common.tpu:
            itr = utils.tpu_data_loader(itr)

        logger.info('got valid iterator on "{}" subset on rank {}'.format(
                subset,
                distributed_utils.get_global_rank()
            )
        )

        progress = progress_bar.progress_bar(
            itr,
            log_format=cfg.common.log_format,
            log_interval=cfg.common.log_interval,
            epoch=epoch_itr.epoch,
            prefix=f"valid on '{subset}' subset",
            tensorboard_logdir=(
                cfg.common.tensorboard_logdir
                if distributed_utils.is_master(cfg.distributed_training)
                else None
            ),
            default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
            wandb_project=(
                cfg.common.wandb_project
                if distributed_utils.is_master(cfg.distributed_training)
                else None
            ),
            wandb_run_name=os.environ.get(
                "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
            ),
        )

        logger.warning('rank:{} Begin looping over validation "{}" subset with length "{}"'.format(distributed_utils.get_global_rank(),subset, len(progress)))

        # create a new root metrics aggregator so validation metrics
        # don't pollute other aggregators (e.g., train meters)
        with metrics.aggregate(new_root=True) as agg:
            for i, sample in enumerate(progress):
                if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps:
                    break
                trainer.valid_step(sample)
        # log validation stats
        stats = get_valid_stats(cfg, trainer, agg.get_smoothed_values())
        progress.print(stats, tag=subset, step=trainer.get_num_updates())
        valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
    return valid_losses


def get_valid_stats(
    cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
) -> Dict[str, Any]:
    stats["num_updates"] = trainer.get_num_updates()
    if hasattr(checkpoint_utils.save_checkpoint, "best"):
        key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
        best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
        stats[key] = best_function(
            checkpoint_utils.save_checkpoint.best,
            stats[cfg.checkpoint.best_checkpoint_metric],
        )
    return stats


def cli_main(
    modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
) -> None:
    parser = options.get_training_parser()
    args = options.parse_args_and_arch(parser, modify_parser=modify_parser)

    cfg = convert_namespace_to_omegaconf(args)

    if cfg.common.use_plasma_view:
        server = PlasmaStore(path=cfg.common.plasma_path)
        logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}")

    if args.profile:
        with torch.cuda.profiler.profile():
            with torch.autograd.profiler.emit_nvtx():
                distributed_utils.call_main(cfg, main)
    else:
        distributed_utils.call_main(cfg, main)

    # if cfg.common.use_plasma_view:
    #     server.server.kill()


if __name__ == "__main__":
    cli_main()