mirror of https://github.com/hpcaitech/ColossalAI
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
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from colossalai.utils import free_port, ColoInitContext, get_current_device
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from colossalai.testing import rerun_if_address_is_in_use
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from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction, init_colo_module
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from functools import partial
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from colossalai.core import global_context as gpc
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from colossalai.context import ParallelMode
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from colossalai.nn.parallel import ColoDDP
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import colossalai
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import torch
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import torch.multiprocessing as mp
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import pytest
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class Net(torch.nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.embed = torch.nn.Embedding(20, 4)
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self.proj = torch.nn.Linear(4, 8)
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def forward(self, x):
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# move input to cpu and restore output
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current_dev = x.device
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x = x.to('cpu')
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x = self.embed(x)
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x = x.to(current_dev)
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x = self.proj(x)
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return x
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def run_hybrid_device(use_ddp):
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with ColoInitContext(device=get_current_device()):
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model = Net()
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real_model = model
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if use_ddp:
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model = ColoDDP(model)
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real_model = model.module
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print(f'embedding weight size: {real_model.embed.weight.size()} | device: {real_model.embed.weight.device}')
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#print(f'linear weight size: {real_model.proj.weight.size()} | device: {real_model.proj.weight.device}')
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parallel_action = ParallelAction(ComputePattern.TP1D)
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init_colo_module(model, parallel_action, recursive=True, mode='col')
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# use cpu gloo to handle embedding
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real_model.embed.to('cpu')
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gloo_group_tp = gpc.get_cpu_group(ParallelMode.PARALLEL_1D)
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real_model.embed.weight.spec.dist_spec.process_group = gloo_group_tp
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print(f'embedding weight size: {real_model.embed.weight.size()} | new device: {real_model.embed.weight.device}')
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#print(f'linear weight size: {real_model.proj.weight.size()} | new device: {real_model.proj.weight.device}')
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data = torch.randint(low=0, high=20, size=(16,), device=get_current_device())
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out = model(data)
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out.sum().backward()
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def run_dist(rank, world_size, port, use_ddp):
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if use_ddp and world_size == 1:
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return
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tp_world_size = world_size // 2 if use_ddp else world_size
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config = dict(parallel=dict(tensor=dict(mode="1d", size=tp_world_size),))
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colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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run_hybrid_device(use_ddp)
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@pytest.mark.dist
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@pytest.mark.parametrize('world_size', [1, 4])
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@pytest.mark.parametrize('use_ddp', [False, True])
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@rerun_if_address_is_in_use()
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# Working for simulate the embedding(CPU DP+TP) -> nn(GPU DP+TP)
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def _test_hybrid_device(world_size, use_ddp):
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run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp)
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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_test_hybrid_device(1, False)
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