mirror of https://github.com/hpcaitech/ColossalAI
114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
import pytest
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import torch
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from torch.utils.checkpoint import checkpoint
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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(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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def forward(self, x, select=0):
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if select == 0:
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x = self.conv(x)
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else:
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x = self.conv_transpose(x)
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return x
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class SiuModel(torch.nn.Module):
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def __init__(self, bias) -> None:
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super().__init__()
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self.linear = LinearModel(3, 3, bias)
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self.conv = ConvModel(3, 6, 3, bias)
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def forward(self, x, select=0):
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x = self.linear(x)
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x = checkpoint(self.conv, x, select)
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return x
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class AddmmModel(torch.nn.Module):
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def __init__(self, alpha, beta) -> None:
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super().__init__()
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self.alpha = alpha
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self.beta = beta
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def forward(self, x):
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x = torch.addmm(x, x, x, alpha=self.alpha, beta=self.beta)
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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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@pytest.mark.parametrize("select", [0, 1])
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def test_siu_model(bias, bias_addition_split, shape, select):
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model = SiuModel(bias=bias)
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x = torch.rand(shape)
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gm = symbolic_trace(model,
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meta_args={'x': x},
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concrete_args={'select': select},
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trace_act_ckpt=True,
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bias_addition_split=bias_addition_split)
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assert torch.allclose(model(x, select), gm(x, select)), 'original model and traced model should be the same!'
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if bias and bias_addition_split:
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assert '+' in gm.code, 'bias addition should be split!'
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else:
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assert '+' not in gm.code, 'bias addition should not be split!'
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@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
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@pytest.mark.parametrize("alpha", [1, 2])
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@pytest.mark.parametrize("beta", [1, 2])
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@pytest.mark.parametrize("bias_addition_split", [True, False])
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@pytest.mark.parametrize("shape", [(3, 3), (5, 5)])
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def test_addmm_model(alpha, beta, bias_addition_split, shape):
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model = AddmmModel(alpha=alpha, beta=beta)
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x = torch.rand(shape)
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gm = symbolic_trace(model, meta_args={'x': x}, trace_act_ckpt=True, bias_addition_split=bias_addition_split)
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assert torch.allclose(model(x), gm(x)), 'original model and traced model should be the same!'
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if (alpha == 1 and beta == 1) or not bias_addition_split:
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assert '*' not in gm.code, 'bias addition should not be split!'
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elif bias_addition_split:
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assert '+' in gm.code, 'bias addition should be split!'
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if __name__ == '__main__':
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test_siu_model(True, True, (3, 3, 3))
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