mirror of https://github.com/hpcaitech/ColossalAI
114 lines
4.6 KiB
Python
114 lines
4.6 KiB
Python
import contextlib
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import os
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import torch
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import torch.nn as nn
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from dataset.webtext import WebtextDataset
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from titans.model.gpt import GPTLMLoss
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import colossalai
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import colossalai.utils as utils
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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.logging import disable_existing_loggers, get_dist_logger
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from colossalai.nn import LinearWarmupLR
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from colossalai.trainer import Trainer, hooks
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from colossalai.utils import colo_set_process_memory_fraction, is_using_pp
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from colossalai.utils.timer import MultiTimer
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from colossalai.zero.legacy.init_ctx import ZeroInitContext
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def calc_local_model_size(model: torch.nn.Module):
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numel_per_device = 0
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for p in model.parameters():
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numel_per_device += p.numel()
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return numel_per_device
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VOCAB_SIZE = 50257
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def main():
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parser = colossalai.get_default_parser()
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parser.add_argument('--from_torch', default=False, action='store_true')
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parser.add_argument('--use_dummy_dataset', default=False, action='store_true')
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args = parser.parse_args()
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disable_existing_loggers()
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if args.from_torch:
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colossalai.launch_from_torch(config=args.config)
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else:
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colossalai.launch_from_slurm(config=args.config, host=args.host, port=29500, seed=42)
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logger = get_dist_logger()
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data_path = None if args.use_dummy_dataset else os.environ['DATA']
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logger.info(f'Build data loader from path {data_path}', ranks=[0])
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train_ds = WebtextDataset(path=data_path, seq_len=gpc.config.SEQ_LEN)
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train_dataloader = utils.get_dataloader(train_ds,
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seed=42,
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batch_size=gpc.config.BATCH_SIZE,
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pin_memory=True,
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shuffle=True,
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drop_last=True)
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logger.info('Build model', ranks=[0])
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use_pipeline = is_using_pp()
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use_interleaved = hasattr(gpc.config.model, 'num_chunks')
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use_zero3 = hasattr(gpc.config, 'zero')
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ctx = contextlib.nullcontext()
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if use_zero3:
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ctx = ZeroInitContext(target_device=torch.cuda.current_device(),
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shard_strategy=gpc.config.zero.model_config.shard_strategy,
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shard_param=True)
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with ctx:
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model = gpc.config.model.pop('type')(**gpc.config.model)
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if use_pipeline and use_interleaved and not isinstance(model, nn.ModuleList):
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model = nn.ModuleList([model])
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if use_zero3:
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numel = ctx.model_numel_tensor.item()
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else:
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numel = calc_local_model_size(model)
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tflop = numel * gpc.config.BATCH_SIZE * gpc.config.SEQ_LEN \
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* gpc.get_world_size(ParallelMode.MODEL) * gpc.get_world_size(ParallelMode.DATA) * 8 / (1024 ** 4)
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criterion = getattr(gpc.config, 'loss_fn', None)
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if criterion is not None:
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criterion = criterion.type()
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else:
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criterion = GPTLMLoss()
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logger.info('Build optimizer', ranks=[0])
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optimizer = gpc.config.optimizer.pop('type')(model.parameters(), **gpc.config.optimizer)
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lr_scheduler = LinearWarmupLR(optimizer, total_steps=gpc.config.NUM_EPOCHS, warmup_steps=5)
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engine, train_dataloader, _, lr_scheduler = colossalai.initialize(model,
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optimizer,
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criterion,
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train_dataloader=train_dataloader,
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lr_scheduler=lr_scheduler)
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global_batch_size = gpc.config.BATCH_SIZE * \
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gpc.get_world_size(ParallelMode.DATA) * getattr(gpc.config, "gradient_accumulation", 1)
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logger.info(f'Init done, global batch size = {global_batch_size}', ranks=[0])
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timier = MultiTimer()
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trainer = Trainer(engine=engine, logger=logger, timer=timier)
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hook_list = [
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hooks.LossHook(),
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hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=True),
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hooks.LogMetricByEpochHook(logger),
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hooks.ThroughputHook(ignored_steps=10, tflop_per_step=tflop),
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hooks.LogMetricByStepHook(),
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hooks.LogMemoryByEpochHook(logger),
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# hooks.LogMemoryByEpochHook(logger),
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# hooks.LogTimingByEpochHook(timer, logger),
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]
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trainer.fit(train_dataloader=train_dataloader,
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epochs=gpc.config.NUM_EPOCHS,
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test_interval=1,
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hooks=hook_list,
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display_progress=True,
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return_output_label=False)
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if __name__ == '__main__':
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main()
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