import argparse
import torch
from data.bert_helper import SequenceParallelDataIterator, get_batch_for_sequence_parallel
from data.dummy_dataloader import DummyDataloader
from loss_func.bert_loss import BertLoss
from lr_scheduler import AnnealingLR
from model.bert import BertForPretrain, build_pipeline_bert
import colossalai
from colossalai.amp import AMP_TYPE
from colossalai.context.parallel_mode import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.engine.schedule import PipelineSchedule
from colossalai.kernel import LayerNorm
from colossalai.logging import get_dist_logger
from colossalai.nn.optimizer import FusedAdam
from colossalai.utils import MultiTimer, is_using_pp
def process_batch_data(batch_data):
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask = batch_data
if gpc.is_first_rank(ParallelMode.PIPELINE):
data = dict(input_ids=tokens, attention_masks=padding_mask, tokentype_ids=types, lm_labels=lm_labels)
else:
data = dict(attention_masks=padding_mask, tokentype_ids=types, lm_labels=lm_labels)
label = dict(loss_mask=loss_mask, sentence_order=sentence_order)
return data, label
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--synthetic', action="store_true", help="whether use synthetic data")
return parser.parse_args()
def pipeline_data_process_func(stage_output, micro_batch_data):
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask = micro_batch_data
if gpc.is_first_rank(ParallelMode.PIPELINE):
data = (tokens, padding_mask, types, lm_labels)
label = (loss_mask, sentence_order)
else:
data = (stage_output, padding_mask, types, lm_labels)
label = (loss_mask, sentence_order)
return data, label
def main():
# initialize
args = parse_args()
colossalai.launch_from_torch(config='./config.py', seed=1234, backend='nccl')
logger = get_dist_logger()
# build synthetic dataloader
BATCH_SIZE_PER_GPUS = gpc.config.GLOBAL_BATCH_SIZE // gpc.get_world_size(ParallelMode.DATA)
VOCAB_SIZE = 30528
trainloader = DummyDataloader(batch_size=BATCH_SIZE_PER_GPUS,
vocab_size=VOCAB_SIZE,
seq_length=gpc.config.SEQ_LENGTH)
validloader = DummyDataloader(batch_size=BATCH_SIZE_PER_GPUS,
vocab_size=VOCAB_SIZE,
seq_length=gpc.config.SEQ_LENGTH)
logger.info("Dataloaders are built", ranks=[0])
# build model
if hasattr(gpc.config, 'fp16') and gpc.config.fp16.get('mode') == AMP_TYPE.NAIVE:
is_naive_fp16 = True
else:
is_naive_fp16 = False
use_pipeline = is_using_pp()
kwargs = dict(vocab_size=VOCAB_SIZE,
hidden_size=gpc.config.HIDDEN_SIZE,
max_sequence_length=gpc.config.SEQ_LENGTH,
num_attention_heads=gpc.config.NUM_ATTENTION_HEADS,
convert_fp16_to_fp32_in_softmax=True,
is_naive_fp16=is_naive_fp16,
add_binary_head=gpc.config.ADD_BINARY_HEAD)
if use_pipeline:
model = build_pipeline_bert(num_layers=gpc.config.DEPTH, num_chunks=1, **kwargs)
else:
model = BertForPretrain(num_layers=gpc.config.DEPTH, **kwargs)
model = model.half()
model.reset_parameters()
logger.info(f"Model is built with softmax in fp32 = {is_naive_fp16}", ranks=[0])
total_numel = 0
for p in model.parameters():
total_numel += p.numel()
logger.info(f"This model has {total_numel} parameters")
# build criterion
criterion = BertLoss()
logger.info("Criterion is built", ranks=[0])
# layernorm and bias has no weight decay
weight_decay_params = {'params': []}
no_weight_decay_params = {'params': [], 'weight_decay': 0.0}
for module_ in model.modules():
if isinstance(module_, LayerNorm):
no_weight_decay_params['params'].extend([p for p in list(module_._parameters.values()) if p is not None])
else:
weight_decay_params['params'].extend(
[p for n, p in list(module_._parameters.items()) if p is not None and n != 'bias'])
no_weight_decay_params['params'].extend(
[p for n, p in list(module_._parameters.items()) if p is not None and n == 'bias'])
logger.info(
f"without weight decay param: {len(no_weight_decay_params['params'])}, with weight decay param: {len(weight_decay_params['params'])}"
)
# optimizer
optimizer = FusedAdam((weight_decay_params, no_weight_decay_params),
lr=gpc.config.LR,
weight_decay=gpc.config.WEIGHT_DECAY)
logger.info("Optimizer is built", ranks=[0])
# lr scheduler
# follow Megatron-LM setting
warmup_steps = int(gpc.config.DECAY_ITERS * gpc.config.WARMUP_FRACTION)
lr_scheduler = AnnealingLR(optimizer=optimizer,
max_lr=gpc.config.LR,
min_lr=gpc.config.MIN_LR,
warmup_steps=warmup_steps,
decay_steps=gpc.config.DECAY_ITERS,
decay_style='linear')
logger.info(f"LR Scheduler is built with {warmup_steps} warmup steps and {gpc.config.DECAY_ITERS} decay steps")
# # init
engine, *dummy = colossalai.initialize(model, optimizer, criterion, verbose=True)
# build timer
timer = MultiTimer()
skip_iters = 0
# build loss tracker
accumulated_train_loss = torch.zeros(1, dtype=torch.float32).cuda()
accumulated_eval_loss = torch.zeros(1, dtype=torch.float32).cuda()
# build data iters for pipeline parallel
if use_pipeline:
train_data_iter = SequenceParallelDataIterator(trainloader)
valid_data_iter = SequenceParallelDataIterator(validloader)
engine.schedule.data_process_func = pipeline_data_process_func
logger.info("start training")
for step in range(1, gpc.config.TRAIN_ITERS + 1):
timer.start('train-iterations')
engine.train()
if use_pipeline:
engine.zero_grad()
_, _, train_loss = engine.execute_schedule(train_data_iter, return_output_label=False)
engine.step()
else:
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask = get_batch_for_sequence_parallel(
trainloader)
engine.zero_grad()
lm_loss, sop_output = engine(tokens, padding_mask, types, lm_labels)
train_loss = engine.criterion(lm_loss, sop_output, loss_mask, sentence_order)
engine.backward(train_loss)
engine.step()
timer.stop('train-iterations', keep_in_history=True)
if not gpc.is_initialized(ParallelMode.PIPELINE) or gpc.is_last_rank(ParallelMode.PIPELINE):
accumulated_train_loss += train_loss
lr_scheduler.step()
if step % gpc.config.EVAL_INTERVAL == 0:
engine.eval()
for j in range(gpc.config.EVAL_ITERS):
with torch.no_grad():
if use_pipeline:
_, _, eval_loss = engine.execute_schedule(valid_data_iter,
forward_only=True,
return_output_label=False)
else:
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask = get_batch_for_sequence_parallel(
validloader)
lm_loss, sop_output = engine(tokens, padding_mask, types, lm_labels)
eval_loss = engine.criterion(lm_loss, sop_output, loss_mask, sentence_order)
if not gpc.is_initialized(ParallelMode.PIPELINE) or gpc.is_last_rank(ParallelMode.PIPELINE):
accumulated_eval_loss += eval_loss
if not gpc.is_initialized(ParallelMode.PIPELINE) or gpc.is_last_rank(ParallelMode.PIPELINE):
accumulated_eval_loss /= gpc.config.EVAL_ITERS
accumulated_train_loss /= gpc.config.EVAL_INTERVAL
timer_string = []
for n, t in timer:
timer_string.append(f"{n}: {t.get_history_mean()*1000:.5f}")
timer_string = ' | '.join(timer_string)
lr = list(engine.optimizer.param_groups)[0]['lr']
loss_scale = engine.optimizer.optim.loss_scale.item()
if gpc.is_initialized(ParallelMode.PIPELINE):
ranks = [gpc.get_ranks_in_group(ParallelMode.PIPELINE)[-1]]
else:
ranks = [0]
logger.info(f'Step {step} / {gpc.config.TRAIN_ITERS} | Train Loss: {accumulated_train_loss.item():.5g} ' +
f'| Eval Loss: {accumulated_eval_loss.item():.5g} ' + f'| Loss Scale: {loss_scale}' +
f"| Learning rate: {lr} | " + timer_string,
ranks=ranks)
for n, t in timer:
t.reset()
accumulated_eval_loss.zero_()
accumulated_train_loss.zero_()
if __name__ == '__main__':
main()