mirror of https://github.com/hpcaitech/ColossalAI
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
import torch
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from torch.nn import functional as F
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from colossalai.fx.tracer.meta_patch import patched_function
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from colossalai.testing import clear_cache_before_run
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@clear_cache_before_run()
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def test_conv():
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# test F.conv_1d
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data_1d = torch.rand(3, 16, 10)
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weight_1d = torch.rand(3, 16, 3)
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out_1d = F.conv1d(data_1d, weight_1d)
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patched_out_1d = patched_function.torch_nn_functional_conv1d(data_1d, weight_1d)
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assert out_1d.shape == patched_out_1d.shape
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# test F.conv_transpose1d
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weight_1d = torch.transpose(weight_1d, 0, 1)
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out_transpose_1d = F.conv_transpose1d(data_1d, weight_1d)
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patched_out_transpose_1d = patched_function.torch_nn_functional_convtranspose1d(data_1d, weight_1d)
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assert out_transpose_1d.shape == patched_out_transpose_1d.shape
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# test F.conv2d
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data_2d = torch.rand(3, 16, 10, 10)
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weight_2d = torch.rand(3, 16, 3, 3)
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out_2d = F.conv2d(data_2d, weight_2d)
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patched_out_2d = patched_function.torch_nn_functional_conv2d(data_2d, weight_2d)
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assert out_2d.shape == patched_out_2d.shape
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# test F.conv_transpose2d
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weight_2d = torch.transpose(weight_2d, 0, 1)
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out_transpose_2d = F.conv_transpose2d(data_2d, weight_2d)
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patched_out_transpose_2d = patched_function.torch_nn_functional_convtranspose2d(data_2d, weight_2d)
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assert out_transpose_2d.shape == patched_out_transpose_2d.shape
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# test F.conv3d
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data_3d = torch.rand(3, 16, 10, 10, 10)
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weight_3d = torch.rand(3, 16, 3, 3, 3)
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out_3d = F.conv3d(data_3d, weight_3d)
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patched_out_3d = patched_function.torch_nn_functional_conv3d(data_3d, weight_3d)
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assert out_3d.shape == patched_out_3d.shape
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# test F.conv_transpose3d
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weight_3d = torch.transpose(weight_3d, 0, 1)
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out_transpose_3d = F.conv_transpose3d(data_3d, weight_3d)
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patched_out_transpose_3d = patched_function.torch_nn_functional_convtranspose3d(data_3d, weight_3d)
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assert out_transpose_3d.shape == patched_out_transpose_3d.shape
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if __name__ == "__main__":
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test_conv()
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