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Revert "fix torch.sparse.log_softmax on CPU (#161959)"

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This reverts commit 002e59440afe8711019e68df500f5e18b9a43f3c.

Reverted https://github.com/pytorch/pytorch/pull/161959 on behalf of https://github.com/davidberard98 due to test failure: test_sparse.py::TestSparseMPS::test_log_softmax_float_mps_float32 [GH job link](https://github.com/pytorch/pytorch/actions/runs/17573794461/job/49915138287) [HUD commit link](002e59440a) ([comment](https://github.com/pytorch/pytorch/pull/161959#issuecomment-3270509418))
This commit is contained in:
PyTorch MergeBot
2025-09-09 12:33:25 +00:00
parent 0d9c95cd7e
commit 4dd73e659a
2 changed files with 2 additions and 12 deletions
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6
aten/src/ATen/native/sparse/SoftMax.cpp
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@ -2,7 +2,6 @@
#include <ATen/core/Tensor.h>
#include <ATen/Config.h>
#include <ATen/Dispatch.h>
#include <ATen/AccumulateType.h>
#include <ATen/NamedTensorUtils.h>
#include <ATen/native/sparse/ParamUtils.h>
#include <ATen/native/SparseTensorUtils.h>
@ -296,7 +295,6 @@ void cpu_sparse_coo_softmax(Tensor output, const Tensor& input, const int64_t di
to exp functions as well as reuse of softmax implementation for
log_softmax.
*/
using accscalar_t = at::acc_type<scalar_t, false>;
auto sparse_dim = input.sparse_dim();
auto indices = input._indices().contiguous();
auto values = input._values().contiguous();
@ -342,14 +340,14 @@ void cpu_sparse_coo_softmax(Tensor output, const Tensor& input, const int64_t di
continue;
/* Prepare scratch space */
std::vector<accscalar_t> mx_row(nvalues, -std::numeric_limits<accscalar_t>::infinity());
std::vector<scalar_t> mx_row(nvalues, -std::numeric_limits<scalar_t>::infinity());
std::vector<scalar_t> exp_sums_row(nvalues, 0);
/* Compute mx */
for (int64_t i : pool_indices) {
auto values_row = values_accessor[i];
for (const auto j : c10::irange(nvalues)) {
mx_row[j] = std::max(mx_row[j], accscalar_t(values_row[j]));
mx_row[j] = std::max(mx_row[j], values_row[j]);
}
}

8
test/test_sparse.py
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@ -3694,14 +3694,6 @@ class TestSparse(TestSparseBase):
self._check_zero_nnz_softmax_op(torch.sparse.log_softmax, 1, device, dtype)
self._check_zero_nnz_softmax_op(torch.sparse.log_softmax, 10, device, dtype)
@dtypes(torch.float)
def test_log_softmax_float(self, device, dtype):
x = (torch.rand(4, 3, dtype=dtype, device=device) - 10000000.0).to_sparse()
out = torch.sparse.log_softmax(x, dim=1).to_dense()
x_double = x.double()
out_double = torch.sparse.log_softmax(x_double, dim=1).to_dense()
self.assertEqual(out, out_double.to(dtype=dtype))
# TODO: Check after why ROCm's cusparseXcsrgemm2Nnz function doesn't return the same nnz value as CUDA
@coalescedonoff
@dtypes(*floating_and_complex_types())