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Align max_pool1d Error Checking between CPU and CUDA/CPU requires_grad (#90211)

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Fixes https://github.com/pytorch/pytorch/issues/85712

Standardizes error checking for max_pool1d between CPU and CPU requires_grad/CUDA.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/90211
Approved by: https://github.com/mruberry
This commit is contained in:
Dean Yu 2022-12-14 20:12:06 +00:00 committed by PyTorch MergeBot
parent 3859aace20
commit df58020bb6
2 changed files with 47 additions and 36 deletions
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62
aten/src/ATen/native/MaxPooling.cpp
View File

@ -24,14 +24,13 @@ DEFINE_DISPATCH(max_pool1d_stub);
namespace {
Tensor max_pool1d_impl(
static void check_max_pool1d(
const Tensor& self,
IntArrayRef kernel_size,
IntArrayRef stride,
IntArrayRef padding,
IntArrayRef dilation,
bool ceil_mode) {
NoNamesGuard guard;
TORCH_CHECK(
self.dim() == 2 || self.dim() == 3,
@ -58,6 +57,45 @@ Tensor max_pool1d_impl(
stride = kernel_size;
}
TORCH_CHECK(
kernel_size[0] > 0,
"max_pool1d() kernel_size must be greater than zero, but got ",
kernel_size[0]);
TORCH_CHECK(
stride[0] > 0, "max_pool1d() stride must be greater than zero, but got ", stride[0]);
TORCH_CHECK(
padding[0] >= 0, "max_pool1d() padding must be non-negative, but got ", padding[0]);
TORCH_CHECK(
padding[0] <= kernel_size[0] / 2,
"max_pool1d() padding should be at most half of kernel size, but got padding=",
padding[0],
" and kernel_size=",
kernel_size[0]);
TORCH_CHECK(
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);
}
} // namespace
namespace {
Tensor max_pool1d_impl(
const Tensor& self,
IntArrayRef kernel_size,
IntArrayRef stride,
IntArrayRef padding,
IntArrayRef dilation,
bool ceil_mode) {
NoNamesGuard guard;
// If stride=None then set it to kernel_size
if (stride.empty()) {
stride = kernel_size;
}
const int64_t NB = self.dim() == 3 ? self.size(-3) : 1;
const int64_t NC = self.size(-2);
const int64_t IW = self.size(-1);
@ -66,25 +104,7 @@ Tensor max_pool1d_impl(
const int64_t PJ = padding[0];
const int64_t DJ = dilation[0];
TORCH_CHECK(
KW > 0,
"max_pool1d() kernel_size must be greater than zero, but got ",
KW);
TORCH_CHECK(
SJ > 0, "max_pool1d() stride must be greater than zero, but got ", SJ);
TORCH_CHECK(
PJ >= 0, "max_pool1d() padding must be non-negative, but got ", PJ);
TORCH_CHECK(
PJ <= KW / 2,
"max_pool1d() padding should be at most half of kernel size, but got padding=",
PJ,
" and kernel_size=",
KW);
TORCH_CHECK(
DJ > 0, "max_pool1d() dilation must be greater than zero, but got ", DJ);
const int64_t OW = pooling_output_shape(IW, KW, PJ, SJ, DJ, ceil_mode);
TORCH_CHECK(OW >= 0, "max_pool1d() Invalid computed output size: ", OW);
Tensor output = at::empty({NB, NC, OW}, self.options());
PoolingParams1D params{NB, NC, IW, OW, KW, SJ, PJ, DJ};
@ -121,6 +141,8 @@ Tensor max_pool1d(
return at::quantized_max_pool1d(
self, kernel_size, stride, padding, dilation, ceil_mode);
}
check_max_pool1d(self, kernel_size, stride, padding, dilation, ceil_mode);
if ((self.requires_grad() && at::GradMode::is_enabled()) ||
self._fw_grad(/*level */ 0).defined() ||
!self.device().is_cpu() ||

21
torch/testing/_internal/common_methods_invocations.py
View File

@ -3032,40 +3032,29 @@ def error_inputs_max_pool1d(op_info, device, **kwargs):
error_regex=error_msg)
# error inputs for empty input with stride=0
# NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
error_msg = 'stride must be greater than zero, but got 0' if torch.device(
device).type == 'cpu' and not requires_grad else 'stride should not be zero'
error_msg = 'stride must be greater than zero, but got 0'
yield ErrorInput(SampleInput(make_arg((3, 3, 3)), kwargs={'kernel_size': 1, 'stride': 0}),
error_regex=error_msg)
# error inputs for empty input with dilation=0
# NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
error_msg = 'dilation must be greater than zero, but got 0' if torch.device(
device).type == 'cpu' and not requires_grad else 'dilation should be greater than zero, but got dilation'
error_msg = 'dilation must be greater than zero, but got 0'
yield ErrorInput(SampleInput(make_arg((3, 3, 3)),
kwargs={'kernel_size': 1, 'stride': 1, 'padding': 0, 'dilation': 0}),
error_regex=error_msg)
# error inputs for invalid output size
# NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
error_msg = 'Invalid computed output size: -2' if torch.device(device).type == 'cpu' and not requires_grad \
else \
r'Given input size: \(2x1x2\). Calculated output size: \(2x1x-2\). Output size is too small'
error_msg = 'Invalid computed output size: -2'
yield ErrorInput(SampleInput(make_arg((2, 2, 2)),
kwargs={'kernel_size': 5, 'stride': 1, 'padding': 0, 'dilation': 1}),
error_regex=error_msg)
# error inputs when kernel_size=0
# NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
error_msg = 'kernel_size must be greater than zero' if torch.device(
device).type == 'cpu' and not requires_grad else r'stride should not be zero'
error_msg = 'kernel_size must be greater than zero'
yield ErrorInput(SampleInput(x, kwargs={'kernel_size': 0}),
error_regex=error_msg)
# error inputs for strides > 0
# NOTE: CPU vs (CPU with requires_grad and CUDA) error messages are different.
error_msg = 'stride must be greater than zero' if torch.device(
device).type == 'cpu' and not requires_grad else r'stride should not be zero'
error_msg = 'stride must be greater than zero'
yield ErrorInput(SampleInput(x, kwargs={'kernel_size': 2, 'stride': 0}),
error_regex=error_msg)