mirror of https://github.com/hpcaitech/ColossalAI
112 lines
4.6 KiB
Python
112 lines
4.6 KiB
Python
import math
|
|
import torch
|
|
from ..registry import meta_patched_module
|
|
|
|
|
|
@meta_patched_module.register(torch.nn.Conv1d)
|
|
def torch_nn_conv1d(self, input):
|
|
# the output shape is calculated using the formula stated
|
|
# at https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html#torch.nn.Conv1d
|
|
l_in = input.shape[-1]
|
|
c_out = self.out_channels
|
|
l_out = math.floor((l_in + 2 * self.padding[0] - self.dilation[0] *
|
|
(self.kernel_size[0] - 1) - 1) / self.stride[0] + 1)
|
|
result_shape = input.shape[:-2] + (
|
|
c_out,
|
|
l_out,
|
|
)
|
|
return torch.empty(result_shape, device='meta')
|
|
|
|
|
|
@meta_patched_module.register(torch.nn.Conv2d)
|
|
def torch_nn_conv2d(self, input):
|
|
# the output shape is calculated using the formula stated
|
|
# at https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html#torch.nn.Conv2d
|
|
h_in, w_in = input.shape[-2:]
|
|
c_out = self.out_channels
|
|
h_out = math.floor((h_in + 2 * self.padding[0] - self.dilation[0] *
|
|
(self.kernel_size[0] - 1) - 1) / self.stride[0] + 1)
|
|
w_out = math.floor((w_in + 2 * self.padding[1] - self.dilation[1] *
|
|
(self.kernel_size[1] - 1) - 1) / self.stride[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.Conv3d)
|
|
def torch_nn_conv3d(self, input):
|
|
# the output shape is calculated using the formula stated
|
|
# at https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html#torch.nn.Conv3d
|
|
d_in, h_in, w_in = input.shape[-3:]
|
|
c_out = self.out_channels
|
|
d_out = math.floor((d_in + 2 * self.padding[0] - self.dilation[0] *
|
|
(self.kernel_size[0] - 1) - 1) / self.stride[0] + 1)
|
|
h_out = math.floor((h_in + 2 * self.padding[1] - self.dilation[1] *
|
|
(self.kernel_size[1] - 1) - 1) / self.stride[1] + 1)
|
|
w_out = math.floor((w_in + 2 * self.padding[2] - self.dilation[2] *
|
|
(self.kernel_size[2] - 1) - 1) / self.stride[2] + 1)
|
|
result_shape = input.shape[:-4] + (
|
|
c_out,
|
|
d_out,
|
|
h_out,
|
|
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')
|