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https://github.com/hpcaitech/ColossalAI.git
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fff98f06ed
* [hotfix] meta tensor default device. * [siu] add experimental submodules to main branch. * [siu] * [siu] * [analyzer] init. * [analyzer] readme. * [analyzer] readme. * [analyzer] readme. * [analyzer] readme. * [test] add test. * Update symbolic_trace.py * mark skip tests. * try except. * try except. * try except. * s * init * init * fix * skip * skip --------- Co-authored-by: Daniel Shao <superdainiu@MININT-PVARVID.fareast.corp.microsoft.com> Co-authored-by: Daniel Shao <superdainiu@Daniels-Mac.local>
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
import pytest
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import torch
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try:
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from colossalai._analyzer.fx import symbolic_trace
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except:
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pass
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class LinearModel(torch.nn.Module):
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def __init__(self, in_features, out_features, bias):
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super().__init__()
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self.linear = torch.nn.Linear(in_features, out_features, bias=bias)
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def forward(self, x):
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x = self.linear(x)
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return x
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class ConvModel(torch.nn.Module):
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def __init__(self, in_channel, out_channels, kernel_size, bias) -> None:
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super().__init__()
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self.conv = torch.nn.Conv2d(in_channel,
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out_channels,
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kernel_size,
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bias=bias,
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padding=1,
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stride=2,
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dilation=2,
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groups=3)
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self.conv_transpose = torch.nn.ConvTranspose2d(out_channels,
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out_channels,
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kernel_size,
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bias=bias,
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padding=1,
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stride=2,
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dilation=2,
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groups=3)
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def forward(self, x):
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x = self.conv(x)
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x = self.conv_transpose(x)
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return x
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class AModel(torch.nn.Module):
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def __init__(self, bias) -> None:
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super().__init__()
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self.linear_1 = LinearModel(3, 3, bias)
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self.linear_2 = LinearModel(3, 3, bias)
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self.conv = ConvModel(3, 6, 3, bias)
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def forward(self, x):
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for i in range(x.shape[0]):
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x = self.linear_1(x)
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x = self.linear_2(x)
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x = self.conv(x)
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return x
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@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
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@pytest.mark.parametrize("bias", [True, False])
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@pytest.mark.parametrize("bias_addition_split", [True, False])
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@pytest.mark.parametrize("shape", [(3, 3, 3), (3, 3, 3, 3)])
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def test_mod_dir(bias, bias_addition_split, shape):
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model = AModel(bias=bias)
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x = torch.rand(shape)
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gm = symbolic_trace(model, meta_args={'x': x}, bias_addition_split=bias_addition_split)
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for node in gm.graph.nodes:
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assert len(node.meta['info'].mod_dir), f"{node} should have non-trivial ``mod_dir``."
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print(node, node.meta['info'].mod_dir)
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if __name__ == '__main__':
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test_mod_dir(True, True, (3, 3, 3))
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