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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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174
colossalai/_analyzer/fx/tracer/bias_addition.py
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@@ -12,7 +12,7 @@ from .tracer import register_tracer_impl
__all__ = []
@register_tracer_impl(F.linear, name='_bias_addition_impl')
@register_tracer_impl(F.linear, name="_bias_addition_impl")
def linear_impl(input, weight, bias=None):
if bias is None:
return F.linear(input, weight)
@@ -20,116 +20,130 @@ def linear_impl(input, weight, bias=None):
return F.linear(input, weight) + bias
@register_tracer_impl(F.conv1d, name='_bias_addition_impl')
@register_tracer_impl(F.conv1d, name="_bias_addition_impl")
def conv1d_impl(input, weight, bias=None, stride=_single(1), padding=_single(0), dilation=_single(1), groups=1):
if bias is None:
return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups)
else:
return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape(
(-1, 1))
(-1, 1)
)
@register_tracer_impl(F.conv2d, name='_bias_addition_impl')
@register_tracer_impl(F.conv2d, name="_bias_addition_impl")
def conv2d_impl(input, weight, bias=None, stride=_pair(1), padding=_pair(0), dilation=_pair(1), groups=1):
if bias is None:
return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups)
else:
return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape(
(-1, 1, 1))
(-1, 1, 1)
)
@register_tracer_impl(F.conv3d, name='_bias_addition_impl')
@register_tracer_impl(F.conv3d, name="_bias_addition_impl")
def conv3d_impl(input, weight, bias=None, stride=_triple(1), padding=_triple(0), dilation=_triple(1), groups=1):
if bias is None:
return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups)
else:
return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape(
(-1, 1, 1, 1))
(-1, 1, 1, 1)
)
@register_tracer_impl(F.conv_transpose1d, name='_bias_addition_impl')
def conv_transpose1d_impl(input,
weight,
bias=None,
stride=_single(1),
padding=_single(0),
output_padding=_single(0),
groups=1,
dilation=_single(1)):
@register_tracer_impl(F.conv_transpose1d, name="_bias_addition_impl")
def conv_transpose1d_impl(
input,
weight,
bias=None,
stride=_single(1),
padding=_single(0),
output_padding=_single(0),
groups=1,
dilation=_single(1),
):
if bias is None:
return F.conv_transpose1d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation)
return F.conv_transpose1d(
input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
)
else:
return F.conv_transpose1d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation) + bias.reshape((-1, 1))
return F.conv_transpose1d(
input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
) + bias.reshape((-1, 1))
@register_tracer_impl(F.conv_transpose2d, name='_bias_addition_impl')
def conv_transpose2d_impl(input,
weight,
bias=None,
stride=_pair(1),
padding=_pair(0),
output_padding=_pair(0),
groups=1,
dilation=_pair(1)):
@register_tracer_impl(F.conv_transpose2d, name="_bias_addition_impl")
def conv_transpose2d_impl(
input, weight, bias=None, stride=_pair(1), padding=_pair(0), output_padding=_pair(0), groups=1, dilation=_pair(1)
):
if bias is None:
return F.conv_transpose2d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation)
return F.conv_transpose2d(
input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
)
else:
return F.conv_transpose2d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation) + bias.reshape((-1, 1, 1))
return F.conv_transpose2d(
input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
) + bias.reshape((-1, 1, 1))
@register_tracer_impl(F.conv_transpose3d, name='_bias_addition_impl')
def conv_transpose3d_impl(input,
weight,
bias=None,
stride=_triple(1),
padding=_triple(0),
output_padding=_triple(0),
groups=1,
dilation=_triple(1)):
@register_tracer_impl(F.conv_transpose3d, name="_bias_addition_impl")
def conv_transpose3d_impl(
input,
weight,
bias=None,
stride=_triple(1),
padding=_triple(0),
output_padding=_triple(0),
groups=1,
dilation=_triple(1),
):
if bias is None:
return F.conv_transpose3d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation)
return F.conv_transpose3d(
input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
)
else:
return F.conv_transpose3d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation) + bias.reshape((-1, 1, 1, 1))
return F.conv_transpose3d(
input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation,
) + bias.reshape((-1, 1, 1, 1))
@register_tracer_impl(torch.addmm, name='_bias_addition_impl')
@register_tracer_impl(torch.Tensor.addmm, name='_bias_addition_impl')
@register_tracer_impl(torch.addmm, name="_bias_addition_impl")
@register_tracer_impl(torch.Tensor.addmm, name="_bias_addition_impl")
def addmm_impl(input, mat1, mat2, beta=1, alpha=1):
if alpha != 1 and beta != 1:
return F.linear(mat1, mat2.transpose(0, 1)) * alpha + input * beta
@@ -141,8 +155,8 @@ def addmm_impl(input, mat1, mat2, beta=1, alpha=1):
return F.linear(mat1, mat2.transpose(0, 1)) + input
@register_tracer_impl(torch.addbmm, name='_bias_addition_impl')
@register_tracer_impl(torch.Tensor.addbmm, name='_bias_addition_impl')
@register_tracer_impl(torch.addbmm, name="_bias_addition_impl")
@register_tracer_impl(torch.Tensor.addbmm, name="_bias_addition_impl")
def addbmm_impl(input, batch1, batch2, beta=1, alpha=1):
if alpha != 1 and beta != 1:
return torch.bmm(batch1, batch2.transpose(1, 2)) * alpha + input * beta