training.py

# coding=utf-8
# Copyright (c) 2020, 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
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"""Pretrain utilities."""

from datetime import datetime
import math
import sys
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from apex.optimizers import FusedAdam as Adam

from megatron import get_args
from megatron import get_timers
from megatron import get_tensorboard_writer
from megatron import mpu
from megatron import print_rank_0
from megatron.checkpointing import load_checkpoint
from megatron.checkpointing import save_checkpoint
from megatron.fp16 import FP16_Module
from megatron.fp16 import FP16_Optimizer
from megatron.initialize import initialize_megatron
from megatron.learning_rates import AnnealingLR
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import get_params_for_weight_decay_optimization
from megatron.model.realm_model import ICTBertModel
from megatron.utils import check_adlr_autoresume_termination
from megatron.utils import make_data_loader
from megatron.utils import report_memory


def pretrain(train_valid_test_dataset_provider, model_provider,
             forward_step_func, extra_args_provider=None, args_defaults={}):
    """Main training program.

    This function will run the followings in the order provided:
        1) initialize Megatron.
        2) setup model, optimizer and lr schedule using the model_provider.
        3) call train_val_test_data_provider to get train/val/test datasets.
        4) train the modle using the forward_step_func.

    Arguments:
        train_valid_test_dataset_provider: a function that takes the size of
            train/valid/test dataset and returns `train, valid, test` datasets.
        model_provider: a function that returns a vanilla version of the
            model. By vanilla we mean a simple model on cpu with no fp16 or ddp.
        forward_step_func: a function that takes a `data iterator` and `model`,
            and returns a `loss` scalar with a dictionary with key:values being
            the info we would like to monitor during training, for example
            `lm-loss: value`. We also require that this function add
            `batch generator` to the timers class.
        extra_args_provider: a function that takes a parser and adds arguments
            to it. It is used for programs to add their own arguments.
        args_defaults: a dictionary from argument-name to argument-value. It
            to set already parse arguments.
    """

    # Initalize and get arguments, timers, and Tensorboard writer.
    initialize_megatron(extra_args_provider=extra_args_provider,
                        args_defaults=args_defaults)

    args = get_args()
    timers = get_timers()

    # Model, optimizer, and learning rate.
    timers('model and optimizer').start()
    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider)
    timers('model and optimizer').stop()

    # Data stuff.
    timers('train/valid/test data iterators').start()
    train_data_iterator, valid_data_iterator, test_data_iterator \
        = build_train_valid_test_data_iterators(
            train_valid_test_dataset_provider)
    timers('train/valid/test data iterators').stop()

    # Print setup timing.
    print_rank_0('done with setups ...')
    timers.log(['model and optimizer', 'train/valid/test data iterators'])
    print_rank_0('training ...')

    iteration = 0
    if args.do_train and args.train_iters > 0:
        iteration, _ = train(forward_step_func,
                             model, optimizer, lr_scheduler,
                             train_data_iterator, valid_data_iterator)

    if args.do_valid:
        prefix = 'the end of training for val data'
        evaluate_and_print_results(prefix, forward_step_func,
                                   valid_data_iterator, model,
                                   iteration, False)

    if args.save and iteration != 0:
        save_checkpoint(iteration, model, optimizer, lr_scheduler)

    if args.do_test:
        # Run on test data.
        prefix = 'the end of training for test data'
        evaluate_and_print_results(prefix, forward_step_func,
                                   test_data_iterator, model,
                                   0, True)


def get_model(model_provider_func):
    """Build the model."""
    args = get_args()

    # Build model on cpu.
    model = model_provider_func()

    # Print number of parameters.
    if mpu.get_data_parallel_rank() == 0:
        print(' > number of parameters on model parallel rank {}: {}'.format(
            mpu.get_model_parallel_rank(),
            sum([p.nelement() for p in model.parameters()])), flush=True)

    # GPU allocation.
    model.cuda(torch.cuda.current_device())

    # Fp16 conversion.
    if args.fp16:
        model = FP16_Module(model)

    # Wrap model for distributed training."""
    if args.DDP_impl == 'torch':
        i = torch.cuda.current_device()
        model = torchDDP(model, device_ids=[i], output_device=i,
                         process_group=mpu.get_data_parallel_group())
        return model
    if args.DDP_impl == 'local':
        model = LocalDDP(model)
        return model

    raise NotImplementedError('Unknown DDP implementation specified: {}. '
                              'Exiting.'.format(args.DDP_impl))


def get_optimizer(model):
    """Set up the optimizer."""
    args = get_args()

    # Build parameter groups (weight decay and non-decay).
    while isinstance(model, (torchDDP, LocalDDP, FP16_Module)):
        model = model.module
    param_groups = get_params_for_weight_decay_optimization(model)

    # Add model parallel attribute if it is not set.
    for param_group in param_groups:
        for param in param_group['params']:
            if not hasattr(param, 'model_parallel'):
                param.model_parallel = False

    # Use Adam.
    optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay)

    # Wrap into fp16 optimizer.
    if args.fp16:
        optimizer = FP16_Optimizer(optimizer,
                                   static_loss_scale=args.loss_scale,
                                   dynamic_loss_scale=args.dynamic_loss_scale,
                                   dynamic_loss_args={
                                       'scale_window': args.loss_scale_window,
                                       'min_scale': args.min_scale,
                                       'delayed_shift': args.hysteresis})

    return optimizer


def get_learning_rate_scheduler(optimizer):
    """Build the learning rate scheduler."""
    args = get_args()

    # Add linear learning rate scheduler.
    if args.lr_decay_iters is not None:
        num_iters = args.lr_decay_iters
    else:
        num_iters = args.train_iters
    num_iters = max(1, num_iters)
    init_step = 0
    warmup_iter = args.warmup * num_iters
    lr_scheduler = AnnealingLR(
        optimizer,
        start_lr=args.lr,
        warmup_iter=warmup_iter,
        total_iters=num_iters,
        decay_style=args.lr_decay_style,
        last_iter=init_step,
        min_lr=args.min_lr,
        use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler,
        override_lr_scheduler=args.override_lr_scheduler)

    return lr_scheduler


def setup_model_and_optimizer(model_provider_func):
    """Setup model and optimizer."""
    args = get_args()

    model = get_model(model_provider_func)
    optimizer = get_optimizer(model)
    lr_scheduler = get_learning_rate_scheduler(optimizer)

    if args.load is not None:
        args.iteration = load_checkpoint(model, optimizer, lr_scheduler)
    else:
        args.iteration = 0

    # get model without FP16 and/or TorchDDP wrappers
    unwrapped_model = model
    while hasattr(unwrapped_model, 'module'):
        unwrapped_model = unwrapped_model.module

    if args.iteration == 0 and hasattr(unwrapped_model, 'init_state_dict_from_bert'):
        print("Initializing ICT from pretrained BERT model", flush=True)
        unwrapped_model.init_state_dict_from_bert()

    return model, optimizer, lr_scheduler


def backward_step(optimizer, model, loss):
    """Backward step."""
    args = get_args()
    timers = get_timers()

    # Backward pass.
    timers('backward-backward').start()
    optimizer.zero_grad(set_grads_to_None=True)
    if args.fp16:
        optimizer.backward(loss, update_master_grads=False)
    else:
        loss.backward()
    timers('backward-backward').stop()

    # All-reduce if needed.
    if args.DDP_impl == 'local':
        timers('backward-allreduce').start()
        model.allreduce_params(reduce_after=False,
                               fp32_allreduce=args.fp32_allreduce)
        timers('backward-allreduce').stop()

    # Update master gradients.
    timers('backward-master-grad').start()
    if args.fp16:
        optimizer.update_master_grads()
    timers('backward-master-grad').stop()

    # Clipping gradients helps prevent the exploding gradient.
    timers('backward-clip-grad').start()
    if args.clip_grad > 0:
        if not args.fp16:
            mpu.clip_grad_norm(model.parameters(), args.clip_grad)
        else:
            optimizer.clip_master_grads(args.clip_grad)
    timers('backward-clip-grad').stop()


def train_step(forward_step_func, data_iterator,
               model, optimizer, lr_scheduler):
    """Single training step."""
    args = get_args()
    timers = get_timers()

    # Forward model for one step.
    timers('forward').start()
    loss, loss_reduced = forward_step_func(data_iterator, model)
    timers('forward').stop()

    # Calculate gradients, reduce across processes, and clip.
    timers('backward').start()
    backward_step(optimizer, model, loss)
    timers('backward').stop()

    # Update parameters.
    timers('optimizer').start()
    optimizer.step()
    timers('optimizer').stop()

    # Update learning rate.
    skipped_iter = 0
    if not (args.fp16 and optimizer.overflow):
        lr_scheduler.step()
    else:
        skipped_iter = 1

    return loss_reduced, skipped_iter


def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
                 loss_scale, report_memory_flag):
    """Log training information such as losses, timing, ...."""
    args = get_args()
    timers = get_timers()
    writer = get_tensorboard_writer()

    # Update losses.
    for key in loss_dict:
        total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key]

    # Logging.
    timers_to_log = []

    def add_to_logging(name):
        if name in timers.timers:
            timers_to_log.append(name)
    add_to_logging('forward')
    add_to_logging('backward')
    add_to_logging('backward-backward')
    add_to_logging('backward-allreduce')
    add_to_logging('backward-master-grad')
    add_to_logging('backward-clip-grad')
    add_to_logging('optimizer')
    add_to_logging('batch generator')

    # Tensorboard values.
    if writer and torch.distributed.get_rank() == 0:
        writer.add_scalar('learning_rate', learning_rate, iteration)
        for key in loss_dict:
            writer.add_scalar(key, loss_dict[key], iteration)
        if args.fp16:
            writer.add_scalar('loss_scale', loss_scale, iteration)
        normalizer = iteration % args.log_interval
        if normalizer == 0:
            normalizer = args.log_interval
        timers.write(timers_to_log, writer, iteration,
                     normalizer=normalizer)

    if iteration % args.log_interval == 0:
        elapsed_time = timers('interval time').elapsed()
        if writer and torch.distributed.get_rank() == 0:
            writer.add_scalar('iteration_time',
                              elapsed_time / args.log_interval, iteration)
        log_string = ' iteration {:8d}/{:8d} |'.format(iteration,
                                                       args.train_iters)
        log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
            elapsed_time * 1000.0 / args.log_interval)
        log_string += ' learning rate: {:.3E} |'.format(learning_rate)
        for key in total_loss_dict:
            avg = total_loss_dict[key].item() / args.log_interval
            log_string += ' {}: {:.6E} |'.format(key, avg)
            total_loss_dict[key] = 0.0
        if args.fp16:
            log_string += ' loss scale: {:.1f} |'.format(loss_scale)
        print_rank_0(log_string)
        if report_memory_flag:
            report_memory('after {} iterations'.format(iteration))
            report_memory_flag = False
        timers.log(timers_to_log, normalizer=args.log_interval)

    return report_memory_flag


def train(forward_step_func, model, optimizer, lr_scheduler,
          train_data_iterator, valid_data_iterator):
    """Train the model function."""
    args = get_args()
    timers = get_timers()

    # Turn on training mode which enables dropout.
    model.train()

    # Tracking loss.
    total_loss_dict = {}

    # Iterations.
    iteration = args.iteration
    skipped_iters = 0

    timers('interval time').start()
    report_memory_flag = True
    while iteration < args.train_iters:
        loss_dict, skipped_iter = train_step(forward_step_func,
                                             train_data_iterator,
                                             model,
                                             optimizer,
                                             lr_scheduler)
        skipped_iters += skipped_iter
        iteration += 1

        # Logging.
        loss_scale = None
        if args.fp16:
            loss_scale = optimizer.loss_scale
        report_memory_flag = training_log(loss_dict, total_loss_dict,
                                          optimizer.param_groups[0]['lr'],
                                          iteration, loss_scale,
                                          report_memory_flag)

        # Autoresume
        if args.adlr_autoresume and \
           (iteration % args.adlr_autoresume_interval == 0):
            check_adlr_autoresume_termination(iteration, model, optimizer,
                                              lr_scheduler)

        # Checkpointing
        if args.save and args.save_interval and \
           iteration % args.save_interval == 0:
            save_checkpoint(iteration, model, optimizer, lr_scheduler)

        # Evaluation
        if args.eval_interval and iteration % args.eval_interval == 0 and \
           args.do_valid:
            prefix = 'iteration {}'.format(iteration)
            evaluate_and_print_results(prefix, forward_step_func,
                                       valid_data_iterator, model,
                                       iteration, False)

        if args.exit_interval and iteration % args.exit_interval == 0:
            torch.distributed.barrier()
            time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
            rank = torch.distributed.get_rank()
            print_rank_0('rank: {} | time: {} | exiting the program at '
                         'iteration {}'.format(rank, time_str, iteration))
            sys.exit()

    return iteration, skipped_iters


def evaluate(forward_step_func, data_iterator, model, verbose=False):
    """Evaluation."""
    args = get_args()

    # Turn on evaluation mode which disables dropout.
    model.eval()

    total_loss_dict = {}

    with torch.no_grad():
        iteration = 0
        while iteration < args.eval_iters:
            iteration += 1
            if verbose and iteration % args.log_interval == 0:
                print_rank_0('Evaluating iter {}/{}'.format(iteration,
                                                            args.eval_iters))
            # Forward evaluation.
            _, loss_dict = forward_step_func(data_iterator, model)
            # Reduce across processes.
            for key in loss_dict:
                total_loss_dict[key] = total_loss_dict.get(key, 0.) + \
                    loss_dict[key]
    # Move model back to the train mode.
    model.train()

    for key in total_loss_dict:
        total_loss_dict[key] /= args.eval_iters

    return total_loss_dict


def evaluate_and_print_results(prefix, forward_step_func,
                               data_iterator, model,
                               iteration, verbose=False):
    """Helper function to evaluate and dump results on screen."""
    writer = get_tensorboard_writer()

    total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose)
    string = ' validation loss at {} | '.format(prefix)
    for key in total_loss_dict:
        string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item())
        ppl = math.exp(min(20, total_loss_dict[key].item()))
        string += '{} PPL: {:.6E} | '.format(key, ppl)
        if writer and torch.distributed.get_rank() == 0:
            writer.add_scalar('{} value'.format(key),
                              total_loss_dict[key].item(),
                              iteration)
            writer.add_scalar('{} ppl'.format(key), ppl, iteration)

    length = len(string) + 1
    print_rank_0('-' * length)
    print_rank_0(string)
    print_rank_0('-' * length)


def build_train_valid_test_data_iterators(
        build_train_valid_test_datasets_provider):
    """XXX"""
    args = get_args()

    (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None)

    print_rank_0('> building train, validation, and test datasets ...')
    # Data loader only on rank 0 of each model parallel group.
    if mpu.get_model_parallel_rank() == 0:
        # Rank, size, and global batch size.
        data_parallel_size = mpu.get_data_parallel_world_size()
        global_batch_size = args.batch_size * data_parallel_size

        # Number of train/valid/test samples.
        train_iters = args.train_iters
        eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
        test_iters = args.eval_iters
        train_val_test_num_samples = [train_iters * global_batch_size,
                                      eval_iters * global_batch_size,
                                      test_iters * global_batch_size]
        print_rank_0(' > datasets target sizes (minimum size):')
        print_rank_0('    train:      {}'.format(train_val_test_num_samples[0]))
        print_rank_0('    validation: {}'.format(train_val_test_num_samples[1]))
        print_rank_0('    test:       {}'.format(train_val_test_num_samples[2]))

        # Build the datasets.
        train_ds, valid_ds, test_ds = build_train_valid_test_datasets_provider(
            train_val_test_num_samples)

        # Build dataloders.
        train_dataloader = make_data_loader(train_ds)
        valid_dataloader = make_data_loader(valid_ds)
        test_dataloader = make_data_loader(test_ds)

        # Flags to know if we need to do training/validation/testing.
        do_train = train_dataloader is not None and args.train_iters > 0
        do_valid = valid_dataloader is not None and args.eval_iters > 0
        do_test = test_dataloader is not None and args.eval_iters > 0
        # Need to broadcast num_tokens and num_type_tokens.
        flags = torch.cuda.LongTensor(
            [int(do_train), int(do_valid), int(do_test)])
    else:
        flags = torch.cuda.LongTensor([0, 0, 0])

    # Broadcast num tokens.
    torch.distributed.broadcast(flags,
                                mpu.get_model_parallel_src_rank(),
                                group=mpu.get_model_parallel_group())
    args.do_train = flags[0].item()
    args.do_valid = flags[1].item()
    args.do_test = flags[2].item()

    # Shift the start iterations.
    if train_dataloader is not None:
        train_dataloader.batch_sampler.start_iter = args.iteration % \
            len(train_dataloader)
        print_rank_0('setting training data start iteration to {}'.
                     format(train_dataloader.batch_sampler.start_iter))
    if valid_dataloader is not None:
        start_iter_val = (args.iteration // args.eval_interval) * \
            args.eval_iters
        valid_dataloader.batch_sampler.start_iter = start_iter_val % \
            len(valid_dataloader)
        print_rank_0('setting validation data start iteration to {}'.
                     format(valid_dataloader.batch_sampler.start_iter))

    # Build iterators.
    if train_dataloader is not None:
        train_data_iterator = iter(train_dataloader)
    else:
        train_data_iterator = None

    if valid_dataloader is not None:
        valid_data_iterator = iter(valid_dataloader)
    else:
        valid_data_iterator = None

    if test_dataloader is not None:
        test_data_iterator = iter(test_dataloader)
    else:
        test_data_iterator = None

    return train_data_iterator, valid_data_iterator, test_data_iterator