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[autoparallel] adapt autoparallel with new analyzer (#3261)

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* [autoparallel] adapt autoparallel with new analyzer

* fix all node handler tests

* polish

* polish
This commit is contained in:
YuliangLiu0306
2023-03-30 17:47:24 +08:00
committed by GitHub
parent e78a1e949a
commit fee2af8610
36 changed files with 481 additions and 386 deletions
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114
colossalai/_analyzer/fx/tracer/bias_addition.py
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@@ -21,111 +21,69 @@ def linear_impl(input, weight, bias=None):
@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):
def conv1d_impl(input, weight, **kwargs):
bias = getattr(kwargs, 'bias', None)
if bias is None:
return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups)
return F.conv1d(input, weight, **kwargs)
else:
return F.conv1d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape(
(-1, 1))
new_kwargs = kwargs
new_kwargs['bias'] = None
return F.conv1d(input, weight, **kwargs) + bias.reshape((-1, 1))
@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):
def conv2d_impl(input, weight, **kwargs):
bias = getattr(kwargs, 'bias', None)
if bias is None:
return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups)
return F.conv2d(input, weight, **kwargs)
else:
return F.conv2d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape(
(-1, 1, 1))
new_kwargs = kwargs
new_kwargs['bias'] = None
return F.conv2d(input, weight, **kwargs) + bias.reshape((-1, 1, 1))
@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):
def conv3d_impl(input, weight, **kwargs):
bias = getattr(kwargs, 'bias', None)
if bias is None:
return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups)
return F.conv3d(input, weight, **kwargs)
else:
return F.conv3d(input, weight, stride=stride, padding=padding, dilation=dilation, groups=groups) + bias.reshape(
(-1, 1, 1, 1))
new_kwargs = kwargs
new_kwargs['bias'] = None
return F.conv3d(input, weight, **new_kwargs) + bias.reshape((-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)):
def conv_transpose1d_impl(input, weight, **kwargs):
bias = getattr(kwargs, 'bias', None)
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, **kwargs)
else:
return F.conv_transpose1d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation) + bias.reshape((-1, 1))
new_kwargs = kwargs
new_kwargs['bias'] = None
return F.conv_transpose1d(input, weight, **new_kwargs) + 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)):
def conv_transpose2d_impl(input, weight, **kwargs):
bias = getattr(kwargs, 'bias', None)
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, **kwargs)
else:
return F.conv_transpose2d(input,
weight,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
dilation=dilation) + bias.reshape((-1, 1, 1))
new_kwargs = kwargs
new_kwargs['bias'] = None
return F.conv_transpose2d(input, weight, **new_kwargs) + 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)):
def conv_transpose3d_impl(input, weight, **kwargs):
bias = getattr(kwargs, 'bias', None)
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, **kwargs)
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))
new_kwargs = kwargs
new_kwargs['bias'] = None
return F.conv_transpose3d(input, weight, **new_kwargs) + bias.reshape((-1, 1, 1, 1))
@register_tracer_impl(torch.addmm, name='_bias_addition_impl')