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