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[fx] add torchaudio test (#1369)

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* [fx]add torchaudio test

* [fx]add torchaudio test

* [fx] add torchaudio test

* [fx] add torchaudio test

* [fx] add torchaudio test

* [fx] add torchaudio test

* [fx] add torchaudio test

* [fx] add torchaudio test and test patches

* Delete ~

* [fx] add patches and patches test

* [fx] add patches and patches test

* [fx] fix patches

* [fx] fix rnn patches

* [fx] fix rnn patches

* [fx] fix rnn patches

* [fx] fix rnn patches

* [fx] merge upstream

* [fx] fix import errors
This commit is contained in:
Super Daniel
2022-07-27 11:03:14 +08:00
committed by GitHub
parent fb6f085907
commit be229217ce
18 changed files with 609 additions and 16 deletions
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57
colossalai/fx/tracer/meta_patch/patched_module/convolution.py
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@@ -55,3 +55,60 @@ def torch_nn_conv3d(self, input):
w_out,
)
return torch.empty(result_shape, device='meta')
@meta_patched_module.register(torch.nn.ConvTranspose1d)
def torch_nn_convtranspose1d(self, input):
# the output shape is calculated using the formula stated
# at https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose1d.html
l_in = input.shape[-1]
c_out = self.out_channels
l_out = math.floor((l_in - 1) * self.stride[0] - 2 * self.padding[0] +
self.dilation[0] * (self.kernel_size[0] - 1) +
self.output_padding[0] + 1)
result_shape = input.shape[:-2] + (
c_out,
l_out,
)
return torch.empty(result_shape, device='meta')
@meta_patched_module.register(torch.nn.ConvTranspose2d)
def torch_nn_convtranspose2d(self, input):
# the output shape is calculated using the formula stated
# at https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d.html
h_in, w_in = input.shape[-2:]
c_out = self.out_channels
h_out = math.floor((h_in - 1) * self.stride[0] - 2 * self.padding[0] +
self.dilation[0] * (self.kernel_size[0] - 1) +
self.output_padding[0] + 1)
w_out = math.floor((w_in - 1) * self.stride[1] - 2 * self.padding[1] +
self.dilation[1] * (self.kernel_size[1] - 1) +
self.output_padding[1] + 1)
result_shape = input.shape[:-3] + (
c_out,
h_out,
w_out,
)
return torch.empty(result_shape, device='meta')
@meta_patched_module.register(torch.nn.ConvTranspose3d)
def torch_nn_convtranspose3d(self, input):
# the output shape is calculated using the formula stated
# at https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose3d.html
d_in, h_in, w_in = input.shape[-3:]
c_out = self.out_channels
d_out = math.floor((d_in - 1) * self.stride[0] - 2 * self.padding[0] +
self.dilation[0] * (self.kernel_size[0] - 1) +
self.output_padding[0] + 1)
h_out = math.floor((h_in - 1) * self.stride[1] - 2 * self.padding[1] +
self.dilation[1] * (self.kernel_size[1] - 1) +
self.output_padding[1] + 1)
w_out = math.floor((w_in - 1) * self.stride[2] - 2 * self.padding[2] +
self.dilation[2] * (self.kernel_size[2] - 1) +
self.output_padding[2] + 1)
result_shape = input.shape[:-4] + (
c_out,
d_out,
h_out,
w_out,
)
return torch.empty(result_shape, device='meta')