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[BE] Enable ruff's UP rules and autoformat test/ (#105434)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/105434
Approved by: https://github.com/albanD
This commit is contained in:
Justin Chu
2023-07-19 07:40:18 -07:00
committed by PyTorch MergeBot
parent 7b56238551
commit 73e1455327
88 changed files with 427 additions and 451 deletions
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36
test/test_xnnpack_integration.py
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@ -31,7 +31,7 @@ class TestXNNPACKOps(TestCase):
input_data = torch.rand(data_shape)
weight = torch.rand((weight_output_dim, data_shape[-1]))
if use_bias:
bias = torch.rand((weight_output_dim))
bias = torch.rand(weight_output_dim)
else:
bias = None
ref_result = F.linear(input_data, weight, bias)
@ -46,7 +46,7 @@ class TestXNNPACKOps(TestCase):
input_data = torch.rand(input_size)
weight = torch.rand((weight_output_dim, input_data.shape[-1]))
if use_bias:
bias = torch.rand((weight_output_dim))
bias = torch.rand(weight_output_dim)
else:
bias = None
ref_result = F.linear(input_data, weight, bias)
@ -102,7 +102,7 @@ class TestXNNPACKOps(TestCase):
weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
bias = None
if use_bias:
bias = torch.rand((output_channels))
bias = torch.rand(output_channels)
ref_result = F.conv2d(input_data, weight, bias,
strides, paddings, dilations, groups)
@ -166,7 +166,7 @@ class TestXNNPACKOps(TestCase):
weight = torch.rand((input_channels, output_channels_per_group, kernel_h, kernel_w))
bias = None
if use_bias:
bias = torch.rand((output_channels))
bias = torch.rand(output_channels)
# Note that groups/dilation is in reverse order from conv2d
ref_result = F.conv_transpose2d(input_data, weight, bias,
@ -209,7 +209,7 @@ class TestXNNPACKSerDes(TestCase):
data_shape = [batch_size] + list(data_shape)
weight = torch.rand((weight_output_dim, data_shape[-1]))
if use_bias:
bias = torch.rand((weight_output_dim))
bias = torch.rand(weight_output_dim)
else:
bias = None
scripted_linear = torch.jit.script(Linear(weight, bias))
@ -304,7 +304,7 @@ class TestXNNPACKSerDes(TestCase):
weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
bias = None
if use_bias:
bias = torch.rand((output_channels))
bias = torch.rand(output_channels)
scripted_conv2d = torch.jit.script(Conv2D(weight, bias,
strides, paddings, dilations, groups))
@ -411,7 +411,7 @@ class TestXNNPACKSerDes(TestCase):
weight = torch.rand((input_channels, output_channels_per_group, kernel_h, kernel_w))
bias = None
if use_bias:
bias = torch.rand((output_channels))
bias = torch.rand(output_channels)
scripted_conv2d = torch.jit.script(Conv2DT(weight, bias,
strides, paddings,
@ -525,7 +525,7 @@ class TestXNNPACKSerDes(TestCase):
conv_weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
conv_bias = None
if use_bias:
conv_bias = torch.rand((output_channels))
conv_bias = torch.rand(output_channels)
# This is done just to find the output shape of the result
# so that the shape of weight for the following linear layer
@ -537,7 +537,7 @@ class TestXNNPACKSerDes(TestCase):
linear_weight = torch.rand((linear_weight_output_dim, linear_input_shape))
linear_bias = None
if use_bias:
linear_bias = torch.rand((linear_weight_output_dim))
linear_bias = torch.rand(linear_weight_output_dim)
scripted_m = torch.jit.script(M(conv_weight, conv_bias, linear_weight,
linear_bias, strides, paddings, dilations, groups))
@ -625,7 +625,7 @@ class TestXNNPACKRewritePass(TestCase):
def __init__(self):
super().__init__()
self.weight = torch.nn.Parameter(torch.rand(weight_shape), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand((weight_output_dim)), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand(weight_output_dim), requires_grad=False)
def forward(self, x):
return F.linear(x, self.weight, self.bias)
@ -712,7 +712,7 @@ class TestXNNPACKRewritePass(TestCase):
input_data = torch.rand((batch_size, input_channels, height, width))
conv_weight = torch.rand((output_channels, input_channels_per_group, kernel_h, kernel_w))
conv_bias = torch.rand((output_channels))
conv_bias = torch.rand(output_channels)
result = F.conv2d(input_data, conv_weight, conv_bias,
strides, paddings, dilations, groups)
linear_input_shape = result.shape[1]
@ -722,9 +722,9 @@ class TestXNNPACKRewritePass(TestCase):
def __init__(self, activation_fn=F.relu):
super().__init__()
self.conv_weight = torch.nn.Parameter(torch.rand(conv_weight_shape), requires_grad=False)
self.conv_bias = torch.nn.Parameter(torch.rand((conv_bias_shape)), requires_grad=False)
self.conv_bias = torch.nn.Parameter(torch.rand(conv_bias_shape), requires_grad=False)
self.linear_weight = torch.nn.Parameter(torch.rand(linear_weight_shape), requires_grad=False)
self.linear_bias = torch.nn.Parameter(torch.rand((weight_output_dim)), requires_grad=False)
self.linear_bias = torch.nn.Parameter(torch.rand(weight_output_dim), requires_grad=False)
self.strides = strides
self.paddings = paddings
self.dilations = dilations
@ -834,7 +834,7 @@ class TestXNNPACKRewritePass(TestCase):
def __init__(self):
super().__init__()
self.linear_weight = torch.nn.Parameter(torch.rand(linear_weight_shape), requires_grad=False)
self.linear_bias = torch.nn.Parameter(torch.rand((weight_output_dim)), requires_grad=False)
self.linear_bias = torch.nn.Parameter(torch.rand(weight_output_dim), requires_grad=False)
self.strides = strides
self.paddings = paddings
self.dilations = dilations
@ -862,7 +862,7 @@ class TestXNNPACKRewritePass(TestCase):
def __init__(self):
super().__init__()
self.linear_weight = torch.nn.Parameter(torch.rand(linear_weight_shape), requires_grad=False)
self.linear_bias = torch.nn.Parameter(torch.rand((weight_output_dim)), requires_grad=False)
self.linear_bias = torch.nn.Parameter(torch.rand(weight_output_dim), requires_grad=False)
self.strides = strides
self.paddings = paddings
self.dilations = dilations
@ -895,7 +895,7 @@ class TestXNNPACKRewritePass(TestCase):
def __init__(self):
super().__init__()
self.weight = torch.nn.Parameter(torch.rand(weight_shape), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand((weight_output_dim)), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand(weight_output_dim), requires_grad=False)
def forward(self, x):
weight_t = self.weight.t()
@ -905,7 +905,7 @@ class TestXNNPACKRewritePass(TestCase):
def __init__(self):
super().__init__()
self.weight = torch.nn.Parameter(torch.rand(weight_shape), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand((weight_output_dim)), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand(weight_output_dim), requires_grad=False)
def forward(self, x):
weight_t = self.weight.t()
@ -917,7 +917,7 @@ class TestXNNPACKRewritePass(TestCase):
def __init__(self):
super().__init__()
self.weight = torch.nn.Parameter(torch.rand(weight_shape), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand((weight_output_dim)), requires_grad=False)
self.bias = torch.nn.Parameter(torch.rand(weight_output_dim), requires_grad=False)
def forward(self, x):
weight_t = self.weight.t()