NVIDIA-GPU-Bootcamp-Training/ai/Megatron/English/Python/jupyter_notebook/Megatron-LM/tasks/vision/finetune_utils.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
# limitations under the License.

"""Finetune utilities."""

import torch
import torch.nn.functional as F
from functools import partial
from megatron import get_args
from megatron import print_rank_0
from megatron import get_timers
from megatron import mpu
from megatron.checkpointing import load_checkpoint
from megatron.checkpointing import save_checkpoint
from megatron.training import evaluate_and_print_results
from megatron.training import setup_model_and_optimizer
from megatron.training import train_step
from megatron.training import training_log
from megatron.utils import check_adlr_autoresume_termination
from megatron.utils import average_losses_across_data_parallel_group


def process_batch(batch):
    """Process batch and produce inputs for the model."""
    images = batch[0].cuda().contiguous()
    labels = batch[1].cuda().contiguous()
    return images, labels


def cross_entropy_loss_func(labels, output_tensor):
    logits = output_tensor

    # Cross-entropy loss.
    loss = F.cross_entropy(logits.contiguous().float(), labels)

    # Reduce loss for logging.
    averaged_loss = average_losses_across_data_parallel_group([loss])

    return loss, {'lm loss': averaged_loss[0]}


def _cross_entropy_forward_step(batch, model):
    """Simple forward step with cross-entropy loss."""
    timers = get_timers()

    # Get the batch.
    timers("batch generator").start()
    try:
        batch_ = next(batch)
    except BaseException:
        batch_ = batch
    images, labels = process_batch(batch_)
    timers("batch generator").stop()

   # Forward model.
    output_tensor = model(images)
  
    return output_tensor, partial(cross_entropy_loss_func, labels)


def build_data_loader(dataset, micro_batch_size, num_workers, drop_last):
    """Data loader. Note that batch-size is the local (per GPU) batch-size."""

    # Sampler.
    world_size = mpu.get_data_parallel_world_size()
    rank = mpu.get_data_parallel_rank()
    sampler = torch.utils.data.distributed.DistributedSampler(
        dataset, num_replicas=world_size, rank=rank
    )

    # Data loader. Note that batch size is the per GPU batch size.
    data_loader = torch.utils.data.DataLoader(
        dataset,
        batch_size=micro_batch_size,
        sampler=sampler,
        shuffle=False,
        num_workers=num_workers,
        drop_last=drop_last,
        pin_memory=True,
    )

    return data_loader


def _build_infinite_size_dataloader(dataloader):
    """Build a looped dataloader with infinite size."""

    iterator = dataloader.__iter__()
    while True:
        try:
            yield iterator.__next__()
        except StopIteration:
            iterator = dataloader.__iter__()


def _build_train_valid_dataloaders(train_dataset, valid_dataset):
    """Traing and validation dataloaders."""
    args = get_args()

    print_rank_0('building train and validation dataloaders ...')
    # Training dataset.
    train_dataloader = build_data_loader(train_dataset, args.micro_batch_size,
                                           args.num_workers, not args.keep_last)
    # Set the training iterations.
    args.train_iters_per_epoch = len(train_dataloader)
    args.train_iters = args.epochs * args.train_iters_per_epoch
    # Validation dataset. For this dataset, we do not need to set up
    # shuffling so we can just use a simple infinite loop.
    valid_dataloader_ = build_data_loader(valid_dataset, args.micro_batch_size,
                                            args.num_workers, not args.keep_last)
    valid_dataloader = _build_infinite_size_dataloader(valid_dataloader_)

    # Now that we've built the data loaders, set batch_size arguments
    # to the actual batch size the model will see for this dataset.
    # This is necessary so pipeline transfers know what size they are
    # and the LR schedule, which is based on samples seen, gets set
    # correctly.
    args.orig_micro_batch_size = args.micro_batch_size
    args.orig_global_batch_size = args.global_batch_size

    return train_dataloader, valid_dataloader

def _train(
    model,
    optimizer,
    lr_scheduler,
    forward_step,
    train_dataloader,
    valid_dataloader,
    end_of_epoch_callback,
):
    """Train the model."""
    args = get_args()
    timers = get_timers()

    # Turn on training mode which enables dropout.
    for m in model:
        m.train()

    # Tracking loss.
    losses_dict_sum = {}

    # Starting epoch and iteration
    start_epoch = args.iteration // args.train_iters_per_epoch
    start_iteration = args.iteration % args.train_iters_per_epoch
    iteration = args.iteration

    # Memory reporting flag.
    report_memory_flag = True

    # For each remaining epoch
    timers("interval-time").start()
    for epoch in range(start_epoch, args.epochs):
        print_rank_0("working on epoch {} ...".format(epoch + 1))

        # Set the data loader epoch to shuffle the index iterator.
        train_dataloader.sampler.set_epoch(args.seed + epoch)

        # For all the batches in the dataset.
        for iteration_, batch in enumerate(train_dataloader):

            # Ignore the iterations before starting value
            if iteration_ < start_iteration:
                continue
            # Set to zero so the next epoch does not skip any batches.
            start_iteration = 0

            # Train for one step.
            losses_dict, skipped_iter, grad_norm, num_zeros_in_grad = train_step(
                forward_step, batch, model, optimizer, lr_scheduler
            )
            iteration += 1

            # Logging.
            params_norm = None
            if args.log_params_norm:
                params_norm = calc_params_l2_norm(model)

            report_memory_flag = training_log(
                losses_dict,
                losses_dict_sum,
                optimizer.param_groups[0]["lr"],
                iteration,
                optimizer.get_loss_scale().item(),
                report_memory_flag,
                skipped_iter,
                grad_norm,
                params_norm,
                num_zeros_in_grad
            )

            # 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:
                prefix = "iteration {}".format(iteration)
                evaluate_and_print_results(
                    prefix,
                    forward_step,
                    valid_dataloader,
                    model,
                    iteration,
                    False,
                )

        # Checkpointing at the end of each epoch.
        if args.save:
            save_checkpoint(iteration, model, optimizer, lr_scheduler)

        # Callback at the end of each epoch.
        if end_of_epoch_callback is not None:
            end_of_epoch_callback(model, epoch)


def finetune(
    train_valid_datasets_provider,
    model_provider,
    forward_step=_cross_entropy_forward_step,
    end_of_epoch_callback_provider=None,
):
    """Main finetune function used across all tasks."""
    args = get_args()
    timers = get_timers()

    # Train and validation data loaders.
    timers("train/valid/test dataset/dataloder").start()
    if args.epochs > 0:
        train_dataset, valid_dataset = train_valid_datasets_provider()
        train_dataloader, valid_dataloader = _build_train_valid_dataloaders(
            train_dataset, valid_dataset
        )
    timers("train/valid/test dataset/dataloder").stop()

    # Build calback function.
    timers("callback function").start()
    end_of_epoch_callback = None
    if end_of_epoch_callback_provider is not None:
        end_of_epoch_callback = end_of_epoch_callback_provider()
    timers("callback function").stop()

    # Build model, optimizer and learning rate scheduler.
    timers("model and optimizer").start()
    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider)
    timers("model and optimizer").stop()

    # If pretrained checkpoint is provided and we have not trained for
    # any iteration (i.e., iteration is zero), then load the pretrained
    # checkpoint.
    timers("pretrained checkpoint").start()
    if args.iteration == 0 and args.pretrained_checkpoint is not None:
        original_load = args.load
        args.load = args.pretrained_checkpoint
        _ = load_checkpoint(model, None, None, strict=False)
        args.load = original_load
        # This is critical when only model is loaded. We should make sure
        # master parameters are also updated.
        optimizer.reload_model_params()
    timers("pretrained checkpoint").stop()

    # Print setup timing.
    print_rank_0("done with setups ...")
    timers.log(
        [
            "train/valid/test dataset/dataloder",
            "callback function",
            "model and optimizer",
            "pretrained checkpoint",
        ]
    )
    print_rank_0("training ...")

    # Finetune the model.
    if args.epochs > 0:
        _train(
            model,
            optimizer,
            lr_scheduler,
            forward_step,
            train_dataloader,
            valid_dataloader,
            end_of_epoch_callback,
        )
    # Or just evaluate.
    else:
        if end_of_epoch_callback is not None:
            print_rank_0("evaluation only mode, setting epoch to -1")
            end_of_epoch_callback(model, epoch=-1, output_predictions=True)

    print_rank_0("done :-)")