mirror of https://github.com/hpcaitech/ColossalAI
50 lines
1.5 KiB
Python
50 lines
1.5 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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from rpc_test_utils import rpc_run, parse_args, RpcTestModel
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def run_master(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 = [RpcTestModel(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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if __name__ == "__main__":
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args = parse_args()
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rpc_run(args, run_master)
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