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IPEX dupe conv2d fix for conv1d and conv3d too

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Disty0
2024-12-21 01:25:49 +03:00
parent b5463a52fd
commit 1649e4e3c4
1 changed files with 21 additions and 0 deletions
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modules/intel/ipex/hijacks.py
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@@ -149,6 +149,15 @@ def functional_linear(input, weight, bias=None):
bias.data = bias.data.to(dtype=weight.data.dtype)
return original_functional_linear(input, weight, bias=bias)
original_functional_conv1d = torch.nn.functional.conv1d
@wraps(torch.nn.functional.conv1d)
def functional_conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
if input.dtype != weight.data.dtype:
input = input.to(dtype=weight.data.dtype)
if bias is not None and bias.data.dtype != weight.data.dtype:
bias.data = bias.data.to(dtype=weight.data.dtype)
return original_functional_conv1d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
original_functional_conv2d = torch.nn.functional.conv2d
@wraps(torch.nn.functional.conv2d)
def functional_conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
@@ -158,6 +167,16 @@ def functional_conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
bias.data = bias.data.to(dtype=weight.data.dtype)
return original_functional_conv2d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
# LTX Video
original_functional_conv3d = torch.nn.functional.conv3d
@wraps(torch.nn.functional.conv3d)
def functional_conv3d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
if input.dtype != weight.data.dtype:
input = input.to(dtype=weight.data.dtype)
if bias is not None and bias.data.dtype != weight.data.dtype:
bias.data = bias.data.to(dtype=weight.data.dtype)
return original_functional_conv3d(input, weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
# SwinIR BF16:
original_functional_pad = torch.nn.functional.pad
@wraps(torch.nn.functional.pad)
@@ -320,7 +339,9 @@ def ipex_hijacks(legacy=True):
torch.nn.functional.group_norm = functional_group_norm
torch.nn.functional.layer_norm = functional_layer_norm
torch.nn.functional.linear = functional_linear
torch.nn.functional.conv1d = functional_conv1d
torch.nn.functional.conv2d = functional_conv2d
torch.nn.functional.conv3d = functional_conv3d
torch.nn.functional.pad = functional_pad
torch.bmm = torch_bmm