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Add padding='same' mode to conv{1,2,3}d (#45667)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45667

First part of #3867 (Pooling operators still to do)

This adds a `padding='same'` mode to the interface of `conv{n}d`and `nn.Conv{n}d`. This should match the behaviour of `tensorflow`. I couldn't find it explicitly documented but through experimentation I found `tensorflow` returns the shape `ceil(len/stride)` and always adds any extra asymmetric padding onto the right side of the input.

Since the `native_functions.yaml` schema doesn't seem to support strings or enums, I've moved the function interface into python and it now dispatches between the numerically padded `conv{n}d` and the `_conv{n}d_same` variant. Underscores because I couldn't see any way to avoid exporting a function into the `torch` namespace.

A note on asymmetric padding. The total padding required can be odd if both the kernel-length is even  and the dilation is odd. mkldnn has native support for asymmetric padding, so there is no overhead there, but for other backends I resort to padding the input tensor by 1 on the right hand side to make the remaining padding symmetrical. In these cases, I use `TORCH_WARN_ONCE` to notify the user of the performance implications.

Test Plan: Imported from OSS

Reviewed By: ejguan

Differential Revision: D27170744

Pulled By: jbschlosser

fbshipit-source-id: b3d8a0380e0787ae781f2e5d8ee365a7bfd49f22
This commit is contained in:
Peter Bell
2021-03-18 16:16:18 -07:00
committed by Facebook GitHub Bot
parent a8a1090324
commit 04e0cbf5a9
18 changed files with 892 additions and 65 deletions
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33
test/cpp/api/modules.cpp
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@ -55,6 +55,15 @@ TEST_F(ModulesTest, Conv1d) {
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3);
}
TEST_F(ModulesTest, Conv1dSameStrided) {
auto options = Conv1dOptions(3, 2, 3);
options.stride(1).padding(torch::kSame);
Conv1d model_valid(options);
ASSERT_THROWS_WITH(
[&]{ Conv1d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
}
TEST_F(ModulesTest, Conv2dEven) {
Conv2d model(Conv2dOptions(3, 2, 3).stride(1).bias(false));
model->weight.set_data(torch::arange(54, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 3}));
@ -95,6 +104,18 @@ TEST_F(ModulesTest, Conv2dUneven) {
ASSERT_EQ(model->weight.grad().numel(), 3 * 2 * 3 * 2);
}
TEST_F(ModulesTest, Conv2dSameStrided) {
auto options = Conv2dOptions(3, 2, {3, 4});
options.stride(1).padding(torch::kSame);
Conv2d model_valid(options);
ASSERT_THROWS_WITH(
[&]{ Conv2d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
ASSERT_THROWS_WITH(
[&]{ Conv2d model_invalid(options.stride({1, 2})); }(),
"padding='same' is not supported for strided convolutions");
}
TEST_F(ModulesTest, Conv3d) {
Conv3d model(Conv3dOptions(3, 2, 3).stride(1).bias(false));
model->weight.set_data(torch::arange(162, torch::dtype(torch::kFloat)).reshape({2, 3, 3, 3, 3}));
@ -131,6 +152,18 @@ TEST_F(ModulesTest, Conv3d) {
ASSERT_TRUE(model->weight.grad().numel() == 3 * 2 * 3 * 3 * 3);
}
TEST_F(ModulesTest, Conv3dSameStrided) {
auto options = Conv3dOptions(3, 2, {3, 4, 5});
options.stride(1).padding(torch::kSame);
Conv3d model_valid(options);
ASSERT_THROWS_WITH(
[&]{ Conv3d model_invalid(options.stride(2)); }(),
"padding='same' is not supported for strided convolutions");
ASSERT_THROWS_WITH(
[&]{ Conv3d model_invalid(options.stride({1, 2, 1})); }(),
"padding='same' is not supported for strided convolutions");
}
TEST_F(ModulesTest, ConvTranspose1d) {
ConvTranspose1d model(ConvTranspose1dOptions(3, 2, 3).stride(1).bias(false));
model->weight.set_data(torch::arange(18.).view({2, 3, 3}));