training.py 36.1 KB
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# coding=utf-8
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# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
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#
# 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.

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"""Pretrain utilities."""
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from datetime import datetime
import math
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import sys
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import time
# The earliest we can measure the start time.
_TRAIN_START_TIME = time.time()

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import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP

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from megatron import get_args
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from megatron import get_timers
from megatron import get_tensorboard_writer
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from megatron import get_current_global_batch_size
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from megatron import get_num_microbatches
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from megatron import is_last_rank
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from megatron import update_num_microbatches
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from megatron import mpu
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from megatron import print_rank_0
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from megatron import print_rank_last
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from megatron.checkpointing import load_checkpoint
from megatron.checkpointing import save_checkpoint
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from megatron.model import Float16Module
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from megatron.optimizer import get_megatron_optimizer
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from megatron.initialize import initialize_megatron
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from megatron.initialize import write_args_to_tensorboard
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from megatron.learning_rates import AnnealingLR
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.utils import check_adlr_autoresume_termination
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from megatron.utils import unwrap_model
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from megatron.data.data_samplers import build_pretraining_data_loader
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from megatron.utils import calc_params_l2_norm
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from megatron.schedules import forward_backward_no_pipelining
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from megatron.schedules import forward_backward_pipelining_without_interleaving
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from megatron.schedules import forward_backward_pipelining_with_interleaving
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from megatron.utils import report_memory
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def print_datetime(string):
    """Note that this call will sync across all ranks."""
    torch.distributed.barrier()
    time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print_rank_0('[' + string + '] datetime: {} '.format(time_str))


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

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

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    # Initalize and get arguments, timers, and Tensorboard writer.
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    initialize_megatron(extra_args_provider=extra_args_provider,
                        args_defaults=args_defaults)
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    # Adjust the startup time so it reflects the largest value.
    # This will be closer to what scheduler will see (outside of
    # image ... launches.
    global _TRAIN_START_TIME
    start_time_tensor = torch.cuda.FloatTensor([_TRAIN_START_TIME])
    torch.distributed.all_reduce(start_time_tensor,
                                 op=torch.distributed.ReduceOp.MIN)
    _TRAIN_START_TIME = start_time_tensor.item()
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    print_rank_0('time to initialize megatron (seconds): {:.3f}'.format(
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        time.time() - _TRAIN_START_TIME))
    print_datetime('after megatron is initialized')

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    args = get_args()
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    timers = get_timers()
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    # Model, optimizer, and learning rate.
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    timers('model-and-optimizer-setup').start()
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    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider)
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    timers('model-and-optimizer-setup').stop()
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    print_datetime('after model, optimizer, and learning rate '
                   'scheduler are built')
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    # Data stuff.
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    timers('train/valid/test-data-iterators-setup').start()
    if args.virtual_pipeline_model_parallel_size is not None:
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        all_data_iterators = [
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            build_train_valid_test_data_iterators(train_valid_test_dataset_provider)
            for _ in range(len(model))
        ]
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        train_data_iterator = [data_iterators[0] for data_iterators in all_data_iterators]
        valid_data_iterator = [data_iterators[1] for data_iterators in all_data_iterators]
        test_data_iterator = [data_iterators[2] for data_iterators in all_data_iterators]
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    else:
        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-setup').stop()
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    print_datetime('after dataloaders are built')
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    # Print setup timing.
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    print_rank_0('done with setup ...')
    timers.log(['model-and-optimizer-setup', 'train/valid/test-data-iterators-setup'])
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    print_rank_0('training ...')
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    iteration = 0
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    if args.do_train and args.train_iters > 0:
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        iteration = train(forward_step_func,
                          model, optimizer, lr_scheduler,
                          train_data_iterator, valid_data_iterator)
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    print_datetime('after training is done')
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    if args.do_valid:
        prefix = 'the end of training for val data'
        evaluate_and_print_results(prefix, forward_step_func,
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                                   valid_data_iterator, model,
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                                   iteration, False)
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    if args.save and iteration != 0:
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        save_checkpoint(iteration, model, optimizer, lr_scheduler)
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    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,
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                                   0, True)
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def update_train_iters(args):

    # For iteration-based training, we don't need to do anything
    if args.train_iters:
        return

    # Constant batch size with sample-based training.
    if args.rampup_batch_size is None:
        args.train_iters = args.train_samples // args.global_batch_size

    else:
        # Sample based training with rampup batch size.
        iterations = 0
        consumed_samples = 0
        # Rampup phase.
        while consumed_samples <= int(args.rampup_batch_size[2]):
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            update_num_microbatches(consumed_samples, consistency_check=False)
            consumed_samples += get_current_global_batch_size()
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            iterations += 1
        # Reset
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        update_num_microbatches(0, consistency_check=False)
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        # Constant phase
        # Note that we throw away any partial last batch.
        iterations += (args.train_samples - consumed_samples) // \
                      args.global_batch_size
        args.train_iters = iterations

    print_rank_0('setting training iterations to {}'.format(args.train_iters))

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def get_model(model_provider_func):
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    """Build the model."""
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    args = get_args()
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    # Build model.
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    if mpu.get_pipeline_model_parallel_world_size() > 1 and \
       args.virtual_pipeline_model_parallel_size is not None:
        model = []
        for i in range(args.virtual_pipeline_model_parallel_size):
            mpu.set_virtual_pipeline_model_parallel_rank(i)
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            # Set pre_process and post_process only after virtual rank is set.
            pre_process = mpu.is_pipeline_first_stage()
            post_process = mpu.is_pipeline_last_stage()
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            this_model = model_provider_func(
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                pre_process=pre_process,
                post_process=post_process
            )
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            model.append(this_model)
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    else:
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        pre_process = mpu.is_pipeline_first_stage()
        post_process = mpu.is_pipeline_last_stage()
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        model = model_provider_func(
            pre_process=pre_process,
            post_process=post_process
        )

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    if not isinstance(model, list):
        model = [model]
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    # Set tensor model parallel attributes if not set.
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    # Only parameters that are already tensor model parallel have these
    # attributes set for them. We should make sure the default attributes
    # are set for all params so the optimizer can use them.
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    for model_module in model:
        for param in model_module.parameters():
            mpu.set_defaults_if_not_set_tensor_model_parallel_attributes(param)
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    # Print number of parameters.
    if mpu.get_data_parallel_rank() == 0:
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        print(' > number of parameters on (tensor, pipeline) '
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              'model parallel rank ({}, {}): {}'.format(
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            mpu.get_tensor_model_parallel_rank(),
            mpu.get_pipeline_model_parallel_rank(),
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            sum([sum([p.nelement() for p in model_module.parameters()])
                 for model_module in model])), flush=True)
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    # GPU allocation.
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    for model_module in model:
        model_module.cuda(torch.cuda.current_device())
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    # Fp16 conversion.
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    if args.fp16 or args.bf16:
        model = [Float16Module(model_module, args) for model_module in model]
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    if args.DDP_impl == 'torch':
        i = torch.cuda.current_device()
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        model = [torchDDP(model_module, device_ids=[i], output_device=i,
                          process_group=mpu.get_data_parallel_group())
                 for model_module in model]
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        return model
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    if args.DDP_impl == 'local':
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        model = [LocalDDP(model_module,
                          args.accumulate_allreduce_grads_in_fp32,
                          args.use_contiguous_buffers_in_ddp)
                 for model_module in model]
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        return model

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    raise NotImplementedError('Unknown DDP implementation specified: {}. '
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                              'Exiting.'.format(args.DDP_impl))
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def get_learning_rate_scheduler(optimizer):
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    """Build the learning rate scheduler."""
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    args = get_args()
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    # Iteration-based training.
    if args.train_iters:
        if args.lr_decay_iters is None:
            args.lr_decay_iters = args.train_iters
        decay_steps = args.lr_decay_iters * args.global_batch_size
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        if args.lr_warmup_fraction is not None:
            warmup_steps = args.lr_warmup_fraction * decay_steps
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        else:
            warmup_steps = args.lr_warmup_iters * args.global_batch_size
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    # Sample-based training.
    elif args.train_samples:
        # We need to set training iters for later use. Technically
        # we need to adjust the training samples too (due to last
        # batch being incomplete) but we leave it as is for now.
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        update_train_iters(args)
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        if args.lr_decay_samples is None:
            args.lr_decay_samples = args.train_samples
        decay_steps = args.lr_decay_samples
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        if args.lr_warmup_fraction is not None:
            warmup_steps = args.lr_warmup_fraction * decay_steps
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        else:
            warmup_steps = args.lr_warmup_samples
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    else:
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        raise Exception(
            'either train-iters or train-samples should be provided.')

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    lr_scheduler = AnnealingLR(
        optimizer,
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        max_lr=args.lr,
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        min_lr=args.min_lr,
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        warmup_steps=warmup_steps,
        decay_steps=decay_steps,
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        decay_style=args.lr_decay_style,
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        use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler,
        override_lr_scheduler=args.override_lr_scheduler)

    return lr_scheduler


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def setup_model_and_optimizer(model_provider_func):
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    """Setup model and optimizer."""
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    args = get_args()
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    model = get_model(model_provider_func)
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    unwrapped_model = unwrap_model(model,
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                                   (torchDDP, LocalDDP, Float16Module))
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    optimizer = get_megatron_optimizer(unwrapped_model)

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    lr_scheduler = get_learning_rate_scheduler(optimizer)
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    if args.load is not None:
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        timers = get_timers()
        # Extra barrier is added to make sure all ranks report the
        # max time.
        torch.distributed.barrier()
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        timers('load-checkpoint').start()
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        args.iteration = load_checkpoint(model, optimizer, lr_scheduler)
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        torch.distributed.barrier()
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        timers('load-checkpoint').stop()
        timers.log(['load-checkpoint'])
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    else:
        args.iteration = 0

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    # We only support local DDP with multiple micro-batches.
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    if len(model) > 1 or mpu.get_pipeline_model_parallel_world_size() > 1:
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        assert args.DDP_impl == 'local'

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    # get model without FP16 and/or TorchDDP wrappers
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    if args.iteration == 0 and len(unwrapped_model) == 1 \
        and hasattr(unwrapped_model[0], 'init_state_dict_from_bert'):
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        print_rank_0("Initializing ICT from pretrained BERT model")
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        unwrapped_model[0].init_state_dict_from_bert()
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        if args.fp16:
            optimizer.reload_model_params()
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    return model, optimizer, lr_scheduler


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

    # Set grad to zero.
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    if args.DDP_impl == 'local' and args.use_contiguous_buffers_in_ddp:
        for partition in model:
            partition.zero_grad_buffer()
    else:
        optimizer.zero_grad()
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    if mpu.get_pipeline_model_parallel_world_size() > 1:
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        if args.virtual_pipeline_model_parallel_size is not None:
            forward_backward_func = forward_backward_pipelining_with_interleaving
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            assert get_num_microbatches() % args.pipeline_model_parallel_size == 0, \
                'number of microbatches is not divisible by pipeline-parallel ' \
                'size when using interleaved schedule'
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        else:
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            forward_backward_func = forward_backward_pipelining_without_interleaving
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    else:
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        forward_backward_func = forward_backward_no_pipelining
    losses_reduced = forward_backward_func(
        forward_step_func, data_iterator, model,
        optimizer, timers, forward_only=False)
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    # All-reduce if needed.
    if args.DDP_impl == 'local':
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        timers('backward-params-all-reduce').start()
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        for model_module in model:
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            model_module.allreduce_gradients()
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        timers('backward-params-all-reduce').stop()
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    # All-reduce word_embeddings' grad across first and last stages to ensure
    # that word_embeddings parameters stay in sync.
    # This should only run for models that support pipelined model parallelism
    # (BERT and GPT-2).
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    timers('backward-embedding-all-reduce').start()
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    if (mpu.is_pipeline_first_stage(ignore_virtual=True) or
        mpu.is_pipeline_last_stage(ignore_virtual=True)) and \
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            mpu.get_pipeline_model_parallel_world_size() > 1:
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        if mpu.is_pipeline_first_stage(ignore_virtual=True):
            unwrapped_model = model[0]
        elif mpu.is_pipeline_last_stage(ignore_virtual=True):
            unwrapped_model = model[-1]
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        unwrapped_model = unwrap_model(
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            unwrapped_model, (torchDDP, LocalDDP, Float16Module))
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        if unwrapped_model.share_word_embeddings:
            word_embeddings_weight = unwrapped_model.word_embeddings_weight()
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            if args.DDP_impl == 'local':
                grad = word_embeddings_weight.main_grad
            else:
                grad = word_embeddings_weight.grad
            torch.distributed.all_reduce(grad, group=mpu.get_embedding_group())
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    timers('backward-embedding-all-reduce').stop()
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    # Update parameters.
    timers('optimizer').start()
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    update_successful, grad_norm, num_zeros_in_grad = optimizer.step()
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    timers('optimizer').stop()

    # Update learning rate.
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    if update_successful:
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        increment = get_num_microbatches() * \
                    args.micro_batch_size * \
                    args.data_parallel_size
        lr_scheduler.step(increment=increment)
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        skipped_iter = 0
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    else:
        skipped_iter = 1

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    if mpu.is_pipeline_last_stage(ignore_virtual=True):
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        # Average loss across microbatches.
        loss_reduced = {}
        for key in losses_reduced[0]:
            losses_reduced_for_key = [x[key] for x in losses_reduced]
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            loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key)
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        return loss_reduced, skipped_iter, grad_norm, num_zeros_in_grad
    return {}, skipped_iter, grad_norm, num_zeros_in_grad
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def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
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                 loss_scale, report_memory_flag, skipped_iter,
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                 grad_norm, params_norm, num_zeros_in_grad):
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    """Log training information such as losses, timing, ...."""
    args = get_args()
    timers = get_timers()
    writer = get_tensorboard_writer()
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    # Advanced, skipped, and Nan iterations.
    advanced_iters_key = 'advanced iterations'
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    skipped_iters_key = 'skipped iterations'
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    nan_iters_key = 'nan iterations'
    # Advanced iterations.
    if not skipped_iter:
        total_loss_dict[advanced_iters_key] = total_loss_dict.get(
            advanced_iters_key, 0) + 1
    else:
        if advanced_iters_key not in total_loss_dict:
            total_loss_dict[advanced_iters_key] = 0
    # Skipped iterations.
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    total_loss_dict[skipped_iters_key] = total_loss_dict.get(
        skipped_iters_key, 0) + skipped_iter
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    # Update losses and set nan iterations
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    got_nan = False
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    for key in loss_dict:
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        if not skipped_iter:
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            total_loss_dict[key] = total_loss_dict.get(
                key, torch.cuda.FloatTensor([0.0])) + loss_dict[key]
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        else:
            value = loss_dict[key].float().sum().item()
            is_nan = value == float('inf') or \
                     value == -float('inf') or \
                     value != value
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            got_nan = got_nan or is_nan
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    total_loss_dict[nan_iters_key] = total_loss_dict.get(
        nan_iters_key, 0) + int(got_nan)
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    # Logging.
    timers_to_log = []
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    def add_to_logging(name):
        if name in timers.timers:
            timers_to_log.append(name)
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    add_to_logging('forward-compute')
    add_to_logging('forward-recv')
    add_to_logging('forward-send')
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    add_to_logging('forward-backward-send-forward-backward-recv')
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    add_to_logging('backward-compute')
    add_to_logging('backward-recv')
    add_to_logging('backward-send')
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    add_to_logging('backward-send-forward-recv')
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    add_to_logging('backward-send-backward-recv')
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    add_to_logging('backward-params-all-reduce')
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    add_to_logging('backward-embedding-all-reduce')
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    add_to_logging('optimizer-copy-to-main-grad')
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    add_to_logging('optimizer-unscale-and-check-inf')
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    add_to_logging('optimizer-clip-main-grad')
    add_to_logging('optimizer-copy-main-to-model-params')
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    add_to_logging('optimizer')
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    add_to_logging('batch-generator')
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    # Calculate batch size.
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    batch_size = args.micro_batch_size * args.data_parallel_size * \
        get_num_microbatches()

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    total_iterations = total_loss_dict[advanced_iters_key] + \
                       total_loss_dict[skipped_iters_key]

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    # Tensorboard values.
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    if writer and (iteration % args.tensorboard_log_interval == 0 ) and \
       is_last_rank():
        if args.log_learning_rate_to_tensorboard:
            writer.add_scalar('learning-rate', learning_rate, iteration)
            writer.add_scalar('learning-rate vs samples', learning_rate,
                              args.consumed_train_samples)
        if args.log_batch_size_to_tensorboard:
            writer.add_scalar('batch-size', batch_size, iteration)
            writer.add_scalar('batch-size vs samples', batch_size,
                              args.consumed_train_samples)
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        for key in loss_dict:
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            writer.add_scalar(key , loss_dict[key], iteration)
            writer.add_scalar(key + ' vs samples', loss_dict[key],
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                              args.consumed_train_samples)
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        if args.log_loss_scale_to_tensorboard:
            writer.add_scalar('loss-scale', loss_scale, iteration)
            writer.add_scalar('loss-scale vs samples', loss_scale,
                              args.consumed_train_samples)
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        if grad_norm is not None:
            writer.add_scalar('grad-norm', grad_norm, iteration)
            writer.add_scalar('grad-norm vs samples', grad_norm,
                              args.consumed_train_samples)
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        if num_zeros_in_grad is not None:
            writer.add_scalar('num-zeros', num_zeros_in_grad, iteration)
            writer.add_scalar('num-zeros vs samples', num_zeros_in_grad,
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                              args.consumed_train_samples)
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        if params_norm is not None:
            writer.add_scalar('params-norm', params_norm, iteration)
            writer.add_scalar('params-norm vs samples', params_norm,
                              args.consumed_train_samples)
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        if args.log_timers_to_tensorboard:
            timers.write(timers_to_log, writer, iteration,
                         normalizer=total_iterations)
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        if args.log_memory_to_tensorboard:
            mem_stats = torch.cuda.memory_stats()
            writer.add_scalar(
                "mem-reserved-bytes",
                mem_stats["reserved_bytes.all.current"],
                iteration,
            )
            writer.add_scalar(
                "mem-allocated-bytes",
                mem_stats["allocated_bytes.all.current"],
                iteration,
            )
            writer.add_scalar(
                "mem-allocated-count",
                mem_stats["allocation.all.current"],
                iteration,
            )
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    if iteration % args.log_interval == 0:
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        elapsed_time = timers('interval-time').elapsed()
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        elapsed_time_per_iteration = elapsed_time / total_iterations
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        if writer and torch.distributed.get_rank() == 0:
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            if args.log_timers_to_tensorboard:
                writer.add_scalar('iteration-time',
                                  elapsed_time_per_iteration, iteration)
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        log_string = ' iteration {:8d}/{:8d} |'.format(
            iteration, args.train_iters)
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        log_string += ' consumed samples: {:12d} |'.format(
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            args.consumed_train_samples)
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        log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
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            elapsed_time_per_iteration * 1000.0)
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        log_string += ' learning rate: {:.3E} |'.format(learning_rate)
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        log_string += ' global batch size: {:5d} |'.format(batch_size)
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        for key in total_loss_dict:
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            if key not in [advanced_iters_key, skipped_iters_key,
                           nan_iters_key]:
                avg = total_loss_dict[key].item() / \
                      float(max(1, total_loss_dict[advanced_iters_key]))
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                if avg > 0.0:
                    log_string += ' {}: {:.6E} |'.format(key, avg)
                total_loss_dict[key] = torch.cuda.FloatTensor([0.0])
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        log_string += ' loss scale: {:.1f} |'.format(loss_scale)
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        if grad_norm is not None:
            log_string += ' grad norm: {:.3f} |'.format(grad_norm)
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        if num_zeros_in_grad is not None:
            log_string += ' num zeros: {:.1f} |'.format(num_zeros_in_grad)
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        if params_norm is not None:
            log_string += ' params norm: {:.3f} |'.format(params_norm)
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        log_string += ' number of skipped iterations: {:3d} |'.format(
            total_loss_dict[skipped_iters_key])
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        log_string += ' number of nan iterations: {:3d} |'.format(
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            total_loss_dict[nan_iters_key])
        total_loss_dict[advanced_iters_key] = 0
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        total_loss_dict[skipped_iters_key] = 0
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        total_loss_dict[nan_iters_key] = 0
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        print_rank_last(log_string)
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        if report_memory_flag and learning_rate > 0.:
            # Report memory after optimizer state has been initialized.
            report_memory('(after {} iterations)'.format(iteration))
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            report_memory_flag = False
        timers.log(timers_to_log, normalizer=args.log_interval)

    return report_memory_flag


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def save_checkpoint_and_time(iteration, model, optimizer, lr_scheduler):
    timers = get_timers()
    # Extra barrier is added to make sure
    # all ranks report the max time.
    torch.distributed.barrier()
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    timers('save-checkpoint').start()
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    save_checkpoint(iteration, model, optimizer, lr_scheduler)
    torch.distributed.barrier()
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    timers('save-checkpoint').stop()
    timers.log(['save-checkpoint'])
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def train(forward_step_func, model, optimizer, lr_scheduler,
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          train_data_iterator, valid_data_iterator):
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    """Train the model function."""
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    args = get_args()
    timers = get_timers()
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    # Write args to tensorboard
    write_args_to_tensorboard()

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    # Turn on training mode which enables dropout.
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    for model_module in model:
        model_module.train()
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    # Tracking loss.
    total_loss_dict = {}

    # Iterations.
    iteration = args.iteration

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    timers('interval-time').start()
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    print_datetime('before the start of training step')
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    report_memory_flag = True
    while iteration < args.train_iters:
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        update_num_microbatches(args.consumed_train_samples)
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        loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = \
            train_step(forward_step_func,
                       train_data_iterator,
                       model,
                       optimizer,
                       lr_scheduler)
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        iteration += 1
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        args.consumed_train_samples += mpu.get_data_parallel_world_size() * \
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                                       args.micro_batch_size * \
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                                       get_num_microbatches()
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        # Logging.
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        loss_scale = optimizer.get_loss_scale().item()
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        params_norm = None
        if args.log_params_norm:
            params_norm = calc_params_l2_norm(model)
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        report_memory_flag = training_log(loss_dict, total_loss_dict,
                                          optimizer.param_groups[0]['lr'],
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                                          iteration, loss_scale,
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                                          report_memory_flag, skipped_iter,
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                                          grad_norm, params_norm, num_zeros_in_grad)
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        # Autoresume
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        if args.adlr_autoresume and \
           (iteration % args.adlr_autoresume_interval == 0):
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            check_adlr_autoresume_termination(iteration, model, optimizer,
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                                              lr_scheduler)
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        # 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,
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                                       valid_data_iterator, model,
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                                       iteration, False)
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        # Checkpointing
        saved_checkpoint = False
        if args.save and args.save_interval and \
           iteration % args.save_interval == 0:
            save_checkpoint_and_time(iteration, model, optimizer,
                                     lr_scheduler)
            saved_checkpoint = True

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        # Exiting based on duration
        if args.exit_duration_in_mins:
            train_time = (time.time() - _TRAIN_START_TIME) / 60.0
            done_cuda = torch.cuda.IntTensor(
                [train_time > args.exit_duration_in_mins])
            torch.distributed.all_reduce(
                done_cuda, op=torch.distributed.ReduceOp.MAX)
            done = done_cuda.item()
            if done:
                if not saved_checkpoint:
                    save_checkpoint_and_time(iteration, model, optimizer,
                                             lr_scheduler)
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                print_datetime('exiting program after {} minutes'.format(train_time))
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                sys.exit()

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        # Exiting based on iterations
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        if args.exit_interval and iteration % args.exit_interval == 0:
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            if not saved_checkpoint:
                save_checkpoint_and_time(iteration, model, optimizer,
                                         lr_scheduler)
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            torch.distributed.barrier()
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            print_datetime('exiting program at iteration {}'.format(iteration))
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            sys.exit()
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    return iteration
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def evaluate(forward_step_func, data_iterator, model, verbose=False):
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    """Evaluation."""
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    args = get_args()
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    # Turn on evaluation mode which disables dropout.
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    for model_module in model:
        model_module.eval()
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    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))
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            if mpu.get_pipeline_model_parallel_world_size() > 1:
                if args.virtual_pipeline_model_parallel_size is not None:
                    forward_backward_func = forward_backward_pipelining_with_interleaving
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                else:
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                    forward_backward_func = forward_backward_pipelining_without_interleaving
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            else:
                forward_backward_func = forward_backward_no_pipelining
            loss_dicts = forward_backward_func(
                forward_step_func, data_iterator, model, optimizer=None,
                timers=None, forward_only=True)

            if mpu.is_pipeline_last_stage(ignore_virtual=True):
                # Reduce across processes.
                for loss_dict in loss_dicts:
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                    for key in loss_dict:
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                        total_loss_dict[key] = total_loss_dict.get(
                            key, torch.cuda.FloatTensor([0.0])) + loss_dict[key]
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            args.consumed_valid_samples += mpu.get_data_parallel_world_size() \
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                                           * args.micro_batch_size \
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                                           * get_num_microbatches()
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    # Move model back to the train mode.
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    for model_module in model:
        model_module.train()
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    for key in total_loss_dict:
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        total_loss_dict[key] /= args.eval_iters * get_num_microbatches()
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    return total_loss_dict

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

    total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose)
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    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)
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        if writer and is_last_rank():
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            writer.add_scalar('{} validation'.format(key),
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                              total_loss_dict[key].item(),
                              iteration)
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            writer.add_scalar('{} validation vs samples'.format(key),
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                              total_loss_dict[key].item(),
                              args.consumed_train_samples)
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            if args.log_validation_ppl_to_tensorboard:
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                writer.add_scalar('{} validation ppl'.format(key), ppl,
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                                  iteration)
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                writer.add_scalar('{} validation ppl vs samples'.format(key),
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                                  ppl, args.consumed_train_samples)
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    length = len(string) + 1
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    print_rank_last('-' * length)
    print_rank_last(string)
    print_rank_last('-' * length)
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def cyclic_iter(iter):
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    while True:
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        for x in iter:
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            yield x

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def build_train_valid_test_data_iterators(
        build_train_valid_test_datasets_provider):
    """XXX"""
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    args = get_args()
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    (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None)

    print_rank_0('> building train, validation, and test datasets ...')
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    # Backward compatibility, assume fixed batch size.
    if args.iteration > 0 and args.consumed_train_samples == 0:
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        assert args.train_samples is None, \
            'only backward compatiblity support for iteration-based training'
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        args.consumed_train_samples = args.iteration * args.global_batch_size
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    if args.iteration > 0 and args.consumed_valid_samples == 0:
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        assert args.train_samples is None, \
            'only backward compatiblity support for iteration-based training'
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        args.consumed_valid_samples = (args.iteration // args.eval_interval) * \
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            args.eval_iters * args.global_batch_size
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    # Data loader only on rank 0 of each model parallel group.
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    if mpu.get_tensor_model_parallel_rank() == 0:
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        # Number of train/valid/test samples.
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        if args.train_samples:
            train_samples = args.train_samples
        else:
            train_samples = args.train_iters * args.global_batch_size
        eval_iters = (args.train_iters // args.eval_interval + 1) * \
                     args.eval_iters
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        test_iters = args.eval_iters
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        train_val_test_num_samples = [train_samples,
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                                      eval_iters * args.global_batch_size,
                                      test_iters * args.global_batch_size]
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        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.
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        train_dataloader = build_pretraining_data_loader(
            train_ds, args.consumed_train_samples)
        valid_dataloader = build_pretraining_data_loader(
            valid_ds, args.consumed_valid_samples)
        test_dataloader = build_pretraining_data_loader(test_ds, 0)
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        # 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,
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                                mpu.get_tensor_model_parallel_src_rank(),
                                group=mpu.get_tensor_model_parallel_group())
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    args.do_train = flags[0].item()
    args.do_valid = flags[1].item()
    args.do_test = flags[2].item()

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    # Build iterators.
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    dl_type = args.dataloader_type
    assert dl_type in ['single', 'cyclic']

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    if train_dataloader is not None:
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        train_data_iterator = iter(train_dataloader) if dl_type == 'single' \
                              else iter(cyclic_iter(train_dataloader))
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    else:
        train_data_iterator = None

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    if valid_dataloader is not None:
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        valid_data_iterator = iter(valid_dataloader) if dl_type == 'single' \
                              else iter(cyclic_iter(valid_dataloader))
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    else:
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        valid_data_iterator = None
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    if test_dataloader is not None:
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        test_data_iterator = iter(test_dataloader) if dl_type == 'single' \
                             else iter(cyclic_iter(test_dataloader))
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    else:
        test_data_iterator = None

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    return train_data_iterator, valid_data_iterator, test_data_iterator