mirror of https://github.com/hpcaitech/ColossalAI
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
import pytest
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import torch
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from colossalai.testing import clear_cache_before_run, parameterize
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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(
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in_channel, out_channels, kernel_size, bias=bias, padding=1, stride=2, dilation=2, groups=3
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)
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self.conv_transpose = torch.nn.ConvTranspose2d(
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out_channels, out_channels, kernel_size, bias=bias, padding=1, stride=2, dilation=2, groups=3
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)
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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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@clear_cache_before_run()
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@parameterize("bias", [True, False])
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@parameterize("bias_addition_split", [True, False])
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@parameterize("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(bias=True, bias_addition_split=True, shape=(3, 3, 3))
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