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Check inputs have same dtype in addmm_impl_cpu_ even if input has zero numel #100274

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Check inputs have same dtype in addmm_impl_cpu_ even if input has zero numel #100274

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331109a
Check dtype in addmm_impl_cpu_ even if input has zero numel
soulitzer Apr 28, 2023
ad898d7
Update on "Check inputs have same dtype in addmm_impl_cpu_ even if in…
soulitzer Apr 28, 2023
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5 changes: 5 additions & 0 deletions aten/src/ATen/native/LinearAlgebra.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1312,6 +1312,11 @@ Tensor outer(const Tensor& self, const Tensor& vec2) {
static void addmm_impl_cpu_(
Tensor &result, const Tensor &self, Tensor m1, Tensor m2, const Scalar& beta, const Scalar& alpha) {
TORCH_INTERNAL_ASSERT(self.dim() == 2 && m1.dim() == 2 && m2.dim() == 2);

TORCH_CHECK(
m1.dtype() == m2.dtype(),
"expected m1 and m2 to have the same dtype, but got: ", m1.dtype(), " != ", m2.dtype()
)
// Array access is faster than .size(n) and .stride(n)
const auto self_sizes = self.sizes();
auto m1_strides = m1.strides();
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4 changes: 4 additions & 0 deletions aten/src/ATen/native/cuda/Blas.cpp
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Expand Up @@ -151,6 +151,10 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
// preflights a check to try to avoid actually needing to call
// expand().
TORCH_CHECK(mat1.dim() == 2 && mat2.dim() == 2, "tensors must be 2-D");
TORCH_CHECK(
mat1.dtype() == mat2.dtype(),
"expected mat1 and mat2 to have the same dtype, but got: ", mat1.dtype(), " != ", mat2.dtype()
)

TensorArg args[]{{result, "out", 0}, {self, "self", 1}, {mat1, "mat1", 2}, {mat2, "mat2", 3}};
checkAllSameGPU(__func__, args);
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7 changes: 7 additions & 0 deletions test/test_linalg.py
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Expand Up @@ -5832,6 +5832,13 @@ def t_b(tensor):
self.assertEqual(torch.bmm(Ab_conj, Bb_, out=out_b), torch.bmm(Ab_conj_physical, Bb_, out=out_b))
self.assertEqual(torch.bmm(t_b(Ab_conj), Bb_, out=out_b), torch.bmm(t_b(Ab_conj_physical), Bb_, out=out_b))

@onlyNativeDeviceTypes
def test_mm_empty_inputs_mixed_dtype_errors(self, device):
a = torch.randint(0, 10, [1, 10], dtype=torch.int16, device=device)
b = torch.randn(10, 20, dtype=torch.float32, device=device)
with self.assertRaisesRegex(RuntimeError, "expected .* and .* to have the same dtype, but got:"):
torch.mm(a, b)

@onlyNativeDeviceTypes
@dtypes(torch.float32, torch.float64)
def test_strided_mm_bmm(self, device, dtype):
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