mirror of https://github.com/hpcaitech/ColossalAI
78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
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import torch
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from torch import nn
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from torch import autograd
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from torch.optim import SGD, Adam, RMSprop, Optimizer
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from colossalai.pipeline.rpc.PipelineBase import FillDrainPipelineEngine, OneFOneBPipelineEngine
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from colossalai.testing import assert_close
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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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actual_stage_num = stage_num * chunk
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use_checkpoint = args.use_checkpoint
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num_microbatches = args.num_microbatches
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optimizer_class = globals()[args.optimizer]
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lr = 1e-3
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sample_num = 1024
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feat_num = 100
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h = 100
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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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checkpoint=use_checkpoint)
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engine.initialize_optimizer(optimizer_class, lr=lr)
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_ = engine.forward_backward(input_sample)
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engine.step()
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cuda_rpc_result = []
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single_result = []
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actual_stage_num = engine._get_actual_stage_num()
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# compute parameters after updating in cuda rpc
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parameters = engine.remote_parameters()
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for stage_id in range(actual_stage_num):
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for p in parameters[stage_id]:
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cuda_rpc_result.append(p)
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# compute forward result and backward grad of parameters just in rank_0
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test_model = nn.Sequential(*module_partitions).to(device)
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optimizer: Optimizer = optimizer_class(test_model.parameters(), lr=lr)
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input_sample = input_sample.requires_grad_()
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out_val = test_model(input_sample).sum()
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autograd.backward(out_val)
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optimizer.step()
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optimizer.zero_grad()
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for p in test_model.parameters():
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single_result.append(p)
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assert len(cuda_rpc_result) == len(single_result)
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for r_c, r_s in zip(cuda_rpc_result, single_result):
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assert_close(r_c, r_s, 0.001, 0.001)
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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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