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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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45
tests/test_fx/test_meta/test_aten.py
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@@ -13,35 +13,41 @@ if is_compatible_with_meta():
aten = torch.ops.aten
registered_meta = {
('aten.convolution.default', True): [ # (aten ops, requires_backward)
("aten.convolution.default", True): [ # (aten ops, requires_backward)
(nn.Conv1d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4)),
(nn.Conv2d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4, 4)),
(nn.Conv3d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4, 4, 4)),
(nn.ConvTranspose1d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2), torch.rand(2, 3, 4)),
(nn.ConvTranspose2d(in_channels=3, out_channels=4, kernel_size=2, padding=1,
dilation=2), torch.rand(2, 3, 4, 4)),
(nn.ConvTranspose3d(in_channels=3, out_channels=4, kernel_size=2, padding=1,
dilation=2), torch.rand(2, 3, 4, 4, 4)),
(
nn.ConvTranspose2d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2),
torch.rand(2, 3, 4, 4),
),
(
nn.ConvTranspose3d(in_channels=3, out_channels=4, kernel_size=2, padding=1, dilation=2),
torch.rand(2, 3, 4, 4, 4),
),
],
('aten.native_batch_norm.default', True): [
("aten.native_batch_norm.default", True): [
(nn.BatchNorm1d(4), torch.rand(2, 4)),
(nn.BatchNorm2d(4), torch.rand(1, 4, 4, 4)),
(nn.BatchNorm3d(4), torch.rand(1, 4, 4, 4, 4)),
],
('aten.native_layer_norm.default', True): [(nn.LayerNorm(4), torch.rand(1, 2, 3, 4)),],
('aten.avg_pool1d.default', True): [
("aten.native_layer_norm.default", True): [
(nn.LayerNorm(4), torch.rand(1, 2, 3, 4)),
],
("aten.avg_pool1d.default", True): [
(nn.MaxPool1d(3, stride=2), torch.rand(4, 5, 5)),
(nn.AvgPool1d(3, stride=2), torch.rand(4, 5, 5)),
(nn.AdaptiveMaxPool1d(3), torch.rand(4, 5, 5)),
(nn.AdaptiveAvgPool1d(3), torch.rand(4, 5, 5)),
],
('aten.avg_pool2d.default', True): [
("aten.avg_pool2d.default", True): [
(nn.MaxPool2d((3, 2), stride=(2, 1)), torch.rand(2, 4, 5, 5)),
(nn.AvgPool2d((3, 2), stride=(2, 1)), torch.rand(2, 4, 5, 5)),
(nn.AdaptiveMaxPool2d((3, 2)), torch.rand(2, 4, 5, 5)),
(nn.AdaptiveAvgPool2d((3, 2)), torch.rand(2, 4, 5, 5)),
],
('aten.relu.default', True): [
("aten.relu.default", True): [
(nn.ReLU(), torch.rand(4, 3, 1, 2)),
(nn.LeakyReLU(), torch.rand(4, 3, 1, 2)),
(nn.SiLU(), torch.rand(4, 3, 1, 2)),
@@ -50,15 +56,20 @@ registered_meta = {
(nn.Sigmoid(), torch.rand(4, 3, 1, 2)),
(nn.Tanh(), torch.rand(4, 3, 1, 2)),
(nn.Hardswish(), torch.rand(4, 3, 1, 2)),
]
],
}
def compare_all(tensor: torch.Tensor, meta_tensor: torch.Tensor) -> Any:
assert tensor.shape == meta_tensor.shape, f'the shape of tensor ({tensor.shape}) and meta tensor ({meta_tensor.shape}) does not match.'
assert tensor.dtype == meta_tensor.dtype, f'the dtype of tensor ({tensor.dtype}) and meta tensor ({meta_tensor.dtype}) does not match.'
assert tensor.stride() == meta_tensor.stride(
), f'the stride of tensor ({tensor.stride()}) and meta tensor ({meta_tensor.stride()}) does not match.'
assert (
tensor.shape == meta_tensor.shape
), f"the shape of tensor ({tensor.shape}) and meta tensor ({meta_tensor.shape}) does not match."
assert (
tensor.dtype == meta_tensor.dtype
), f"the dtype of tensor ({tensor.dtype}) and meta tensor ({meta_tensor.dtype}) does not match."
assert (
tensor.stride() == meta_tensor.stride()
), f"the stride of tensor ({tensor.stride()}) and meta tensor ({meta_tensor.stride()}) does not match."
def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_backward=False) -> Any:
@@ -72,7 +83,7 @@ def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_bac
compare_all(x.grad, meta_x.grad)
@pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not is_compatible_with_meta(), reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_meta_aten():
for (aten_op, requires_backward), v in registered_meta.items():
@@ -80,5 +91,5 @@ def test_meta_aten():
run_and_compare(f, x, requires_backward)
if __name__ == '__main__':
if __name__ == "__main__":
test_meta_aten()

31
tests/test_fx/test_meta/test_backward.py
View File

@@ -23,31 +23,40 @@ tm_models = [
]
tmm_models = [
tmm.resnest.resnest50d, tmm.beit.beit_base_patch16_224, tmm.cait.cait_s24_224, tmm.efficientnet.efficientnetv2_m,
tmm.resmlp_12_224, tmm.vision_transformer.vit_base_patch16_224, tmm.deit_base_distilled_patch16_224,
tmm.convnext.convnext_base, tmm.vgg.vgg11, tmm.dpn.dpn68, tmm.densenet.densenet121, tmm.rexnet.rexnet_100,
tmm.swin_transformer.swin_base_patch4_window7_224
tmm.resnest.resnest50d,
tmm.beit.beit_base_patch16_224,
tmm.cait.cait_s24_224,
tmm.efficientnet.efficientnetv2_m,
tmm.resmlp_12_224,
tmm.vision_transformer.vit_base_patch16_224,
tmm.deit_base_distilled_patch16_224,
tmm.convnext.convnext_base,
tmm.vgg.vgg11,
tmm.dpn.dpn68,
tmm.densenet.densenet121,
tmm.rexnet.rexnet_100,
tmm.swin_transformer.swin_base_patch4_window7_224,
]
@pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not is_compatible_with_meta(), reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_torchvision_models():
for m in tm_models:
model = m()
data = torch.rand(100000, 3, 224, 224, device='meta')
model(MetaTensor(data, fake_device=torch.device('cpu'))).sum().backward()
data = torch.rand(100000, 3, 224, 224, device="meta")
model(MetaTensor(data, fake_device=torch.device("cpu"))).sum().backward()
@pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not is_compatible_with_meta(), reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_timm_models():
for m in tmm_models:
model = m()
data = torch.rand(100000, 3, 224, 224, device='meta')
model(MetaTensor(data, fake_device=torch.device('cpu'))).sum().backward()
data = torch.rand(100000, 3, 224, 224, device="meta")
model(MetaTensor(data, fake_device=torch.device("cpu"))).sum().backward()
if __name__ == '__main__':
if __name__ == "__main__":
test_torchvision_models()
test_timm_models()

31
tests/test_fx/test_meta/test_meta_trace.py
View File

@@ -23,31 +23,40 @@ tm_models = [
]
tmm_models = [
tmm.resnest.resnest50d, tmm.beit.beit_base_patch16_224, tmm.cait.cait_s24_224, tmm.efficientnet.efficientnetv2_m,
tmm.resmlp_12_224, tmm.vision_transformer.vit_base_patch16_224, tmm.deit_base_distilled_patch16_224,
tmm.convnext.convnext_base, tmm.vgg.vgg11, tmm.dpn.dpn68, tmm.densenet.densenet121, tmm.rexnet.rexnet_100,
tmm.swin_transformer.swin_base_patch4_window7_224
tmm.resnest.resnest50d,
tmm.beit.beit_base_patch16_224,
tmm.cait.cait_s24_224,
tmm.efficientnet.efficientnetv2_m,
tmm.resmlp_12_224,
tmm.vision_transformer.vit_base_patch16_224,
tmm.deit_base_distilled_patch16_224,
tmm.convnext.convnext_base,
tmm.vgg.vgg11,
tmm.dpn.dpn68,
tmm.densenet.densenet121,
tmm.rexnet.rexnet_100,
tmm.swin_transformer.swin_base_patch4_window7_224,
]
@pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not is_compatible_with_meta(), reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_torchvision_models_trace():
for m in tm_models:
model = m()
data = torch.rand(1000, 3, 224, 224, device='meta')
graph = meta_trace(model, torch.device('cpu'), data)
data = torch.rand(1000, 3, 224, 224, device="meta")
meta_trace(model, torch.device("cpu"), data)
@pytest.mark.skipif(not is_compatible_with_meta(), reason='torch version is lower than 1.12.0')
@pytest.mark.skipif(not is_compatible_with_meta(), reason="torch version is lower than 1.12.0")
@clear_cache_before_run()
def test_timm_models_trace():
for m in tmm_models:
model = m()
data = torch.rand(1000, 3, 224, 224, device='meta')
graph = meta_trace(model, torch.device('cpu'), data)
data = torch.rand(1000, 3, 224, 224, device="meta")
meta_trace(model, torch.device("cpu"), data)
if __name__ == '__main__':
if __name__ == "__main__":
test_torchvision_models_trace()
test_timm_models_trace()