Skip to content

Fix inconsistent torch.nn.MaxPool1d output on cpu and gpu #99843

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 5 commits into from
Closed

Fix inconsistent torch.nn.MaxPool1d output on cpu and gpu #99843

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
934208c
fixed check for output size
tfsingh Apr 23, 2023
2fb8fcc
fixed line deletion
tfsingh Apr 23, 2023
fae0343
add test
tfsingh May 5, 2023
799c88c
remove spaces
tfsingh May 5, 2023
7025c3d
change to assertRaises
tfsingh May 5, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion aten/src/ATen/native/MaxPooling.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -75,7 +75,7 @@ static void check_max_pool1d(
dilation[0] > 0, "max_pool1d() dilation must be greater than zero, but got ", dilation[0]);

const int64_t OW = pooling_output_shape(self.size(-1), kernel_size[0], padding[0], stride[0], dilation[0], ceil_mode);
TORCH_CHECK(OW >= 0, "max_pool1d() Invalid computed output size: ", OW);
TORCH_CHECK(OW > 0, "max_pool1d() Invalid computed output size: ", OW);
}

} // namespace
Expand Down
10 changes: 10 additions & 0 deletions test/test_nn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7228,6 +7228,16 @@ def test_fractional_max_pool2d_invalid_output_ratio(self):
"fractional_max_pool2d requires output_ratio to either be a single Int or tuple of Ints."):
res = arg_class(*arg_3)

def test_max_pool1d_invalid_output_size(self):
arg_1 = 3
arg_2 = 255
arg_3 = False
arg_class = torch.nn.MaxPool1d(kernel_size=arg_1, stride=arg_2, return_indices=arg_3)
arg_4_0 = torch.as_tensor([[0.3204]])
arg_4 = [arg_4_0,]

with self.assertRaises(RuntimeError):
res = arg_class(*arg_4)

class TestFusionEval(TestCase):
@given(X=hu.tensor(shapes=((5, 3, 5, 5),)),
Expand Down