Use maxint to bound integers. (#96121)
We don't actually support arbitrary precision integers.
Signed-off-by: Edward Z. Yang <[email protected]>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96121
Approved by: https://github.com/tugsbayasgalan, https://github.com/lezcano
diff --git a/test/test_proxy_tensor.py b/test/test_proxy_tensor.py
index d1f5de6..d8f2d6f 100644
--- a/test/test_proxy_tensor.py
+++ b/test/test_proxy_tensor.py
@@ -970,6 +970,16 @@
index_put_ = torch.ops.aten.index_put_.default(crop_camera_1, [mask_1], view_2); crop_camera_1 = mask_1 = view_2 = None
return None""")
+ def test_unbacked_slice(self):
+ def f(x, m):
+ x = x[m]
+ return x[slice(None, None, None), slice(None, None, None), slice(None, 2, None)]
+
+ make_fx(f, tracing_mode="symbolic")(
+ torch.randn((12, 3, 3)),
+ torch.randint(0, 2, (12,), dtype=torch.bool)
+ )
+
@unittest.skipIf(not USE_TORCHVISION, "test requires torchvision")
def test_unbacked_batch_resnet(self):
mod = torchvision.models.resnet18()
