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60 lines
1.6 KiB
60 lines
1.6 KiB
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import pytest
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import torch
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from torch.fx import symbolic_trace
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from colossalai.core import global_context as gpc
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from colossalai.fx.passes import column_shard_linear_pass
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from colossalai.initialize import launch
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from colossalai.logging import disable_existing_loggers
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from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn
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class MLP(torch.nn.Module):
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def __init__(self, dim: int):
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super().__init__()
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self.linear1 = torch.nn.Linear(dim, dim)
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self.linear2 = torch.nn.Linear(dim, dim)
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self.linear3 = torch.nn.Linear(dim, dim)
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self.linear4 = torch.nn.Linear(dim, dim)
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def forward(self, x):
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x = self.linear1(x)
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x = self.linear2(x)
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x = self.linear3(x)
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x = self.linear4(x)
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return x
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CONFIG = dict(parallel=dict(tensor=dict(mode='1d', size=2)))
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def check_layer(rank, world_size, port):
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disable_existing_loggers()
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launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
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input_tensor = torch.rand(2, 16).cuda()
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model = MLP(16).cuda()
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symbolic_traced = symbolic_trace(model)
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output = model(input_tensor)
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splitted_gm = column_shard_linear_pass(symbolic_traced)
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new_output = splitted_gm(input_tensor)
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assert output.equal(new_output)
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gpc.destroy()
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torch.cuda.empty_cache()
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@pytest.mark.dist
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@clear_cache_before_run()
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@rerun_if_address_is_in_use()
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def test_1d():
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spawn(check_layer, 2)
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if __name__ == '__main__':
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test_1d()
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