8000 [MPS] Fix conv backward for channels last (cont) by malfet · Pull Request #143196 · pytorch/pytorch · GitHub
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[MPS] Fix conv backward for channels last (cont) #143196

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[MPS] Fix conv backward for channels last (cont) #143196

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[MPS] Fix conv backward for channels last (cont)
malfet Dec 13, 2024
437ccbe
Fix lint
malfet Dec 13, 2024
28f64d3
Delete more code that produces incorrect results
malfet Dec 13, 2024
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Fix compilation
malfet Dec 13, 2024
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[MPS] Fix conv backward for channels last (cont) 
This is a continuation of #140902 but extends the same logic to input

Fixes #142344
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@malfet
malfet committed Dec 13, 2024
commit 2f55cff569e9dc039c41bbf886187b4bdb5fee9e
5 changes: 3 additions & 2 deletions aten/src/ATen/native/mps/operations/Convolution.mm
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Expand Up @@ -372,6 +372,7 @@ static Tensor mps_convolution_backward_input(IntArrayRef input_size,
using namespace at::native::mps;
using namespace mps;
bool is3DConv = grad_output_t.dim() == 5;
const auto has_strided_api = is_macos_13_or_newer(MacOSVersion::MACOS_VER_15_0_PLUS);

if (!is_macos_13_or_newer(MacOSVersion::MACOS_VER_15_1_PLUS)) {
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@NripeshN NripeshN Dec 13, 2024 •
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Suggested change
if (!is_macos_13_or_newer(MacOSVersion::MACOS_VER_15_1_PLUS)) {
Tensor grad_output_contiguous = is_macos_13_or_newer(MacOSVersion::MACOS_VER_15_0_PLUS) ?
grad_output_t : grad_output_t.contiguous();

Then update to use grad_output_contiguous everywhere else in the code

This was why I was copying your code from your PR to mine, just wanted to keep your implementation at the same time try having a fallback for anything lower than macOS15. I am really sorry that straight away caused an error. I wanted the PR to get merged into the branch you are working, so when I did changed that it automatically caused a lot of issues.

p.s. really sorry for what happened

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Why do you think this is needed? I.e. looks at the test sanity? On both MacOS-13, 14 and 15, results of the backward path are now identical, so existing logic (in Placeholder constructor) works, because it's no longer told to do something weird in case of channels last. And as result, on MacOS-15 no copies will be needed for channels_last case, but on MacOS-14 there will be copies, but results will be correct

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Oh I was under the impression it was not working on older Mac devices
(#142344 (comment)). I do not have an older Mac to test this though, but again thank you for addressing this issue, and sorry for confusion.

// On macOS < 15.1, MPS convolution kernel does not support output channels > 2^16
Expand Down Expand Up @@ -417,7 +418,7 @@ static Tensor mps_convolution_backward_input(IntArrayRef input_size,
assert(0 && "Check should have been done earlier\n");
}

MPSShape* gradOutputShape = getMPSShape(grad_output_t, memory_format);
MPSShape* gradOutputShape = has_strided_api ? getMPSShape(grad_output_t) : getMPSShape(grad_output_t, memory_format);
MPSShape* mps_input_shape = getMPSShape(input_size);
NSString* ns_shape_key = [[gradOutputShape valueForKey:@"description"] componentsJoinedByString:@","];
string key;
Expand All @@ -440,7 +441,7 @@ static Tensor mps_convolution_backward_input(IntArrayRef input_size,
MPSGraphTensor* weightTensor = mpsGraphRankedPlaceHolder(mpsGraph, weight_t);

MPSGraphTensor* gradOutputTensorTranspose = gradOutputTensor;
if (is_channels_last) {
if (is_channels_last && !has_strided_api) {
gradOutputTensorTranspose = mps::convertNHWCtoNCHW(mpsGraph, gradOutputTensorTranspose);
}
MPSGraphTensor* gradInputTensor;
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8 changes: 5 additions & 3 deletions test/test_mps.py
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Expand Up @@ -10593,20 +10593,22 @@ def helper(shape, in_channels=1, out_channels=1, kernel_size=3, groups=1):
helper(shape=(1024, 376, 9), in_channels=9, out_channels=9, groups=3)

# Regression test for https://github.com/pytorch/pytorch/issues/140902
# And https://github.com/pytorch/pytorch/issues/142344 (adding grad for input)
ic, oc, ks, f = 2, 5, 3, 7
conv = torch.nn.Conv1d(ic, oc, kernel_size=ks, padding=1).to("mps")
inp = torch.rand(1, ic, f, device="mps")
inp = torch.rand(1, ic, f, device="mps", requires_grad=True)
out = conv(inp)
grad_in = torch.rand(1, oc, f, device="mps")
grad_in_cl = torch.empty(1, f, oc, device="mps").transpose(1, 2)
grad_in_cl[:] = grad_in

# It does not matter whether grad_in contigous, or channels last, results should equal to each other
grad_rc = torch.autograd.grad((out,), (conv.weight, conv.bias), (grad_in,), retain_graph=True)
grad_rc_cl = torch.autograd.grad((out,), (conv.weight, conv.bias), (grad_in_cl,), retain_graph=True)
grad_rc = torch.autograd.grad((out,), (inp, conv.weight, conv.bias), (grad_in,), retain_graph=True)
grad_rc_cl = torch.autograd.grad((out,), (inp, conv.weight, conv.bias), (grad_in_cl,), retain_graph=True)

self.assertEqual(grad_rc[0], grad_rc_cl[0])
self.assertEqual(grad_rc[1], grad_rc_cl[1])
self.assertEqual(grad_rc[2], grad_rc_cl[2])

def test_conv1d_contiguous(self):
model_cpu = torch.nn.Conv1d(1, 128, 3)
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