mirror of https://github.com/hpcaitech/ColossalAI
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
import os
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import argparse
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import torch
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from torch import nn
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import torch.multiprocessing as mp
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import torch.distributed.rpc as rpc
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from colossalai.pipeline.rpc.PipelineBase import FillDrainPipelineEngine, OneFOneBPipelineEngine
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class TestModel(nn.Module):
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def __init__(self, stage_id, actual_stage_num, feat_num, h) -> None:
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super().__init__()
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self.rank = stage_id
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self.is_last_rank = stage_id == actual_stage_num - 1
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self.linear_name = f'linear_{stage_id}'
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if stage_id == 0:
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setattr(self, self.linear_name, nn.Linear(feat_num, h))
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elif stage_id == actual_stage_num - 1:
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setattr(self, self.linear_name, nn.Linear(h, 1))
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else:
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setattr(self, self.linear_name, nn.Linear(h, h))
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def forward(self, x) -> torch.Tensor:
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linear: nn.Module = getattr(self, self.linear_name)
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out: torch.Tensor = linear(x)
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if self.is_last_rank:
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out = out.sum()
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return out
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def run_main(args):
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torch.manual_seed(100)
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device = args.device
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stage_num = args.world_size
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chunk = args.chunk
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num_microbatches = args.num_microbatches
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actual_stage_num = stage_num * chunk
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use_interleave = args.use_interleave
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use_checkpoint = args.use_checkpoint
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sample_num = 1024
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feat_num = 10
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h = 10
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batch_size = 1024
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assert sample_num % batch_size == 0
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batch_num = sample_num // batch_size
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input_sample = torch.randn((sample_num, feat_num), device=device)
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module_partitions = [TestModel(pp_rank, actual_stage_num, feat_num, h) for pp_rank in range(actual_stage_num)]
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engine = OneFOneBPipelineEngine(module_partitions=module_partitions,
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stage_num=stage_num,
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num_microbatches=num_microbatches,
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device=device,
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chunk=chunk,
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use_interleave=use_interleave,
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checkpoint=use_checkpoint)
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_ = engine.forward_backward(input_sample)
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def run_worker(rank, args):
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os.environ['MASTER_ADDR'] = args.master_addr
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os.environ['MASTER_PORT'] = args.master_port
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# config rpc
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# if cuda is used, set_device_map is a must is configured
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# for cuda is not supported in torch rpc by default
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options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=args.num_worker_threads)
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world_size = args.world_size
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for rank_idx in range(world_size):
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options.set_device_map(f'work{rank_idx}', {rank: rank_idx})
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rpc.init_rpc(name=f'work{rank}', rank=rank, world_size=world_size, rpc_backend_options=options)
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# in rpc mode, only rank 0 is needed to be coded
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if rank == 0:
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run_main(args)
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# barrier here
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rpc.shutdown()
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--world_size', type=int, default=2)
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parser.add_argument('--num_microbatches', type=int, default=2)
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parser.add_argument('--device', type=str, default='cuda')
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parser.add_argument('--chunk', type=int, default=1)
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parser.add_argument('--use_checkpoint', action='store_true')
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parser.add_argument('--use_interleave', action='store_true')
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parser.add_argument('--master_addr', type=str, default='localhost')
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parser.add_argument('--master_port', type=str, default='29020')
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parser.add_argument('--num_worker_threads', type=str, default=128)
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return parser.parse_args()
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if __name__ == "__main__":
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args = parse_args()
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world_size = args.world_size
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assert args.device in ['cpu', 'cuda'], "device must be cpu or cuda!"
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mp.spawn(run_worker, args=(args,), nprocs=world_size)
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