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Add support for negative dimensions in cumsum #238

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Merged
DenisVieriu97 merged 3 commits into from
Jan 28, 2023
Merged

Add support for negative dimensions in cumsum #238

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8f3d188
Add support for negative dimensions in cumsum
Jan 19, 2023
160e08a
Expand test with dtypes
Jan 20, 2023
78c2e44
Fix lint issues
abhudev Jan 27, 2023
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5 changes: 4 additions & 1 deletion aten/src/ATen/native/mps/operations/UnaryOps.mm
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Original file line number Diff line number Diff line change
Expand Up @@ -254,7 +254,10 @@ void unary_op(const Tensor& self, const Tensor& output, std::string op_name, Una
int64_t dim,
c10::optional<ScalarType> dtype,
const Tensor& result) {
TORCH_CHECK(dim >=0 && dim < std::max(1LL, self.ndimension()), "Expected dim to be between 0 and ", self.ndimension(), " but got ", dim);

auto nDims = self.dim();
auto wrapped_dim = maybe_wrap_dim(dim, nDims);
TORCH_CHECK(wrapped_dim >=0 && wrapped_dim < std::max(1LL, self.ndimension()), "Expected wrapped dim to be between 0 and ", self.ndimension(), " but got ", wrapped_dim , "(original dim is ", dim, ")");
if (!is_macos_13_or_newer()) {
TORCH_WARN_ONCE("torch.cumsum supported by MPS on MacOS 13+, please upgrade");
auto cpu_result = self.to(at::Device(kCPU)).cumsum(dim, dtype);
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17 changes: 17 additions & 0 deletions test/test_mps.py
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Expand Up @@ -2248,6 +2248,23 @@ def helper(shape, dtype):

helper((2, 8, 4, 5), torch.int16)

def test_cumsum_minus_one_axis(self):
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@kulinseth kulinseth Jan 19, 2023

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Can you test the TestConsistency test for cumsum.

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@kulinseth kulinseth Jan 19, 2023

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Also enable that for all types.

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@abhudev abhudev Jan 20, 2023

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TestConsistency passes, and expanded this test with datatypes (which all pass).

def helper(dtype):
# Test with axis -1
cpu_x = None
if(dtype == torch.float32):
cpu_x = torch.randn(10, 3, device='cpu', dtype=torch.float32)
else:
cpu_x = torch.randint(0, 20, (10, 3), device='cpu', dtype=torch.float32)
x = cpu_x.detach().clone().to('mps')

cpu_y = cpu_x.cumsum(-1)
y = x.cumsum(-1)

self.assertEqual(y, cpu_y)

[helper(dtype) for dtype in [torch.float32, torch.int16, torch.int32, torch.uint8]]

class TestLogical(TestCase):
def _wrap_tensor(self, x, device="cpu", dtype=None, requires_grad=False):
return torch.tensor(x, device=device, dtype=dtype, requires_grad=requires_grad)
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