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Revert "use sym_numel, to allow fake tensors to work (#163831)"

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This reverts commit e71c75680f2d6ce5f61ad4b2125f4934087762eb.

Reverted https://github.com/pytorch/pytorch/pull/163831 on behalf of https://github.com/isuruf due to test failure on mps introduced ([comment](https://github.com/pytorch/pytorch/pull/163831#issuecomment-3400131730))
This commit is contained in:
PyTorch MergeBot
2025-10-14 05:10:56 +00:00
parent f44935cc14
commit 33bfec27ff
2 changed files with 2 additions and 29 deletions
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4
aten/src/ATen/native/Itertools.cpp
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@ -21,7 +21,7 @@ namespace {
using namespace at; using namespace at;
Tensor _triu_mask(c10::SymInt n, int64_t dims, bool diagonal, TensorOptions opt) { Tensor _triu_mask(int64_t n, int64_t dims, bool diagonal, TensorOptions opt) {
// get a mask that has value 1 whose indices satisfies i < j < k < ... // get a mask that has value 1 whose indices satisfies i < j < k < ...
// or i <= j <= k <= ... (depending on diagonal) // or i <= j <= k <= ... (depending on diagonal)
Tensor range = at::arange(n, opt.dtype(kLong)); Tensor range = at::arange(n, opt.dtype(kLong));
@ -63,7 +63,7 @@ Tensor combinations(const Tensor& self, int64_t r, bool with_replacement) {
if (r == 0) { if (r == 0) {
return at::empty({0}, self.options()); return at::empty({0}, self.options());
} }
const auto num_elements = self.sym_numel(); int64_t num_elements = self.numel();
std::vector<Tensor> grids = at::meshgrid(std::vector<Tensor>(r, self), "ij"); std::vector<Tensor> grids = at::meshgrid(std::vector<Tensor>(r, self), "ij");
Tensor mask = _triu_mask(num_elements, r, with_replacement, self.options()); Tensor mask = _triu_mask(num_elements, r, with_replacement, self.options());
for(Tensor &t : grids) { for(Tensor &t : grids) {

27
test/inductor/test_torchinductor_dynamic_shapes.py
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@ -653,33 +653,6 @@ class TestInductorDynamic(TestCase):
self.assertEqual(foo_c(t, y), foobar(t, y)) self.assertEqual(foo_c(t, y), foobar(t, y))
@parametrize("with_replacement", [False, True])
def test_dynamic_shapes_r2_matches_eager(self, with_replacement):
def _eager(x, r):
out = torch.combinations(
x.flatten(), r=r, with_replacement=with_replacement
)
# Canonicalize for stable comparison
return out.to(torch.float32).sort(dim=0).values
def _compiled(r):
def fn(x):
return torch.combinations(
x.flatten(), r=r, with_replacement=with_replacement
)
# The original bug repro failed under aot_eager + dynamic=True
return torch.compile(fn, backend="aot_eager", dynamic=True)
def _assert_match(compiled, x, r):
out = compiled(x)
exp = _eager(x, r=r)
self.assertEqual(out.to(torch.float32).sort(dim=0).values, exp)
compiled = _compiled(r=2)
_assert_match(compiled, torch.tensor([1, 2, 3, 4], dtype=torch.int64), r=2)
_assert_match(compiled, torch.tensor([5, 6, 7], dtype=torch.int64), r=2)
def test_floor(self): def test_floor(self):
def fn(x): def fn(x):
n = x.size(-1) n = x.size(-1)