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# 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."""
from functools import partial
import sys
import torch
from megatron import get_args, get_num_microbatches
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.model import ModelType
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 average_losses_across_data_parallel_group
from megatron.utils import calc_params_l2_norm
from megatron.utils import check_adlr_autoresume_termination
def process_batch(batch):
"""Process batch and produce inputs for the model."""
args = get_args()
tokens = batch['text'].long().cuda().contiguous()
types = batch['types'].long().cuda().contiguous()
labels = batch['label'].long().cuda().contiguous()
attention_mask = batch['padding_mask'].float().cuda().contiguous()
if args.fp16:
attention_mask = attention_mask.half()
return tokens, types, labels, attention_mask
def cross_entropy_loss_func(labels, output_tensor):
logits = output_tensor
# Cross-entropy loss.
loss_func = torch.nn.CrossEntropyLoss()
loss = loss_func(logits.contiguous().float(), labels)
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
return loss, {'training 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
tokens, types, labels, attention_mask = process_batch(batch_)
timers('batch-generator').stop()
# Forward model.
output_tensor = model(tokens, attention_mask, tokentype_ids=types)
return output_tensor, partial(cross_entropy_loss_func, labels)
def build_data_loader(dataset, micro_batch_size, num_workers, drop_last,
task_collate_fn=None):
"""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,
collate_fn=task_collate_fn)
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,
task_collate_fn=None):
"""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,
task_collate_fn)
# 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,
task_collate_fn)
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
if hasattr(train_dataset, 'sample_multiplier'):
# If our dataset as a sample_multiplier attribute that means
# each "sample" from the dataset actually has multiple samples
# that will collapse into the batch dimension (for example in
# the RACE dataset that has several options), we need to
# account for that when setting the micro batch size.
args.micro_batch_size *= train_dataset.sample_multiplier
args.global_batch_size *= train_dataset.sample_multiplier
return train_dataloader, valid_dataloader
def _train(model, optimizer, opt_param_scheduler, forward_step,
train_dataloader, valid_dataloader, end_of_epoch_callback):
"""Train the model."""
args = get_args()
timers = get_timers()
assert get_num_microbatches() == 1, "finetuning with gradient accumulation doesn't currently work"
# 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.
out = train_step(forward_step, batch, model, optimizer, opt_param_scheduler)
losses_dict, skipped_iter, grad_norm, num_zeros_in_grad = out
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, None)
# Autoresume
if args.adlr_autoresume and \
(iteration % args.adlr_autoresume_interval == 0):
check_adlr_autoresume_termination(iteration, model,
optimizer, opt_param_scheduler)
# Checkpointing
saved_checkpoint = False
if args.save and args.save_interval and \
iteration % args.save_interval == 0:
save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
saved_checkpoint = True
# 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, None, False)
if end_of_epoch_callback is not None:
end_of_epoch_callback(model, iteration)
print_rank_0('-' * 72 + '\n')
# Exiting based on iterations
if args.exit_interval and iteration % args.exit_interval == 0:
if not saved_checkpoint:
save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
torch.distributed.barrier()
print_rank_0('exiting program at iteration {}'.format(iteration))
sys.exit()
# Checkpointing at the end of each epoch.
if args.save:
save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
prefix = 'iteration {}'.format(iteration)
evaluate_and_print_results(prefix, forward_step,
valid_dataloader, model,
iteration, None, False)
if end_of_epoch_callback is not None:
end_of_epoch_callback(model, iteration)
print_rank_0('-' * 72 + '\n')
# 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,
model_type=ModelType.encoder_or_decoder,
forward_step=_cross_entropy_forward_step,
end_of_epoch_callback_provider=None,
task_collate_fn=None):
"""Main finetune function used across all tasks."""
args = get_args()
timers = get_timers()
assert args.rampup_batch_size is None, \
'batch size scaling is not supported for finetuning'
# 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, task_collate_fn)
else:
args.train_iters = 0
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, opt_param_scheduler = setup_model_and_optimizer(model_provider, model_type)
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
original_rng = args.no_load_rng
args.no_load_rng = True
_ = load_checkpoint(model, None, None)
args.load = original_load
args.no_load_rng = original_rng
# This is critical when only model is loaded. We should make sure
# main 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, opt_param_scheduler, forward_step,
train_dataloader, valid_dataloader, end_of_epoch_callback)
# Or just evaluate.
else:
print_rank_0("Not Imp")
import pdb;pdb.set_trace()
# 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 :-)')
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