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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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23
tests/test_fx/test_tracer/test_bias_addition_module.py
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@@ -5,7 +5,6 @@ from colossalai.testing import clear_cache_before_run
class LinearModel(torch.nn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.linear = torch.nn.Linear(in_features, out_features)
@@ -18,13 +17,11 @@ class LinearModel(torch.nn.Module):
class ConvModel(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
bias=bias)
self.conv = torch.nn.Conv2d(
in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, bias=bias
)
def forward(self, x):
x = self.conv(x)
@@ -45,7 +42,7 @@ def test_linear_module():
# %add : [#users=1] = call_function[target=operator.add](args = (%linear, %linear_bias), kwargs = {})
# %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {})
# return mul
graph = tracer.trace(root=model, meta_args={'x': torch.rand(3, 3).to('meta')})
graph = tracer.trace(root=model, meta_args={"x": torch.rand(3, 3).to("meta")})
# def forward(self, x : torch.Tensor):
# linear_weight = self.linear.weight
# linear_bias = self.linear.bias
@@ -57,9 +54,9 @@ def test_linear_module():
gm.recompile()
node_list = list(graph.nodes)
for node in node_list:
if node.op == 'output':
if node.op == "output":
continue
assert hasattr(node, '_meta_data')
assert hasattr(node, "_meta_data")
weight_node = node_list[1]
bias_node = node_list[2]
linear_node = node_list[3]
@@ -83,7 +80,7 @@ def test_conv_module():
# %add : [#users=1] = call_function[target=operator.add](args = (%conv2d, %view), kwargs = {})
# %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {})
# return mul
graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 3, 64, 64).to('meta')})
graph = tracer.trace(root=model, meta_args={"x": torch.rand(4, 3, 64, 64).to("meta")})
# def forward(self, x : torch.Tensor):
# conv_weight = self.conv.weight
# conv_bias = self.conv.bias
@@ -97,9 +94,9 @@ def test_conv_module():
gm.recompile()
node_list = list(graph.nodes)
for node in node_list:
if node.op == 'output':
if node.op == "output":
continue
assert hasattr(node, '_meta_data')
assert hasattr(node, "_meta_data")
weight_node = node_list[1]
bias_node = node_list[2]
conv_node = node_list[3]
@@ -112,6 +109,6 @@ def test_conv_module():
assert add_node._meta_data.shape == (4, 6, 63, 63)
if __name__ == '__main__':
if __name__ == "__main__":
test_linear_module()
test_conv_module()