Added correct isinf handling for Integral tensors (#15489)

Summary:
Currently torch.isinf on integral tensor will raise RuntimeError: value cannot be converted to type int16_t without overflow: inf.
This pr will suppress the error and return false(0) for all integral tensors. The behavior will also be consistent with np.isinf
Pull Request resolved: pytorch/pytorch#15489

Reviewed By: zou3519

Differential Revision: D13540786

Pulled By: flashhack

fbshipit-source-id: e730dea849da6a59f3752d347bcfbadfd12c6483

test/test_cuda.py

@@ -938,6 +938,9 @@ def test_type_conversions_same_gpu(self):
     def test_neg(self):
         _TestTorchMixin._test_neg(self, lambda t: t.cuda())
 
+    def test_isinf(self):
+        _TestTorchMixin._test_isinf(self, lambda t: t.cuda())
+
     @unittest.skipIf(not TEST_LARGE_TENSOR, "not enough memory")
     def test_arithmetic_large_tensor(self):
         x = torch.empty(2**30, device='cuda')

test/test_torch.py

@@ -5316,9 +5316,23 @@ def test_isfinite_int(self):
         x = torch.tensor([1, 2, 3])
         self.assertEqual(torch.isfinite(x), torch.ByteTensor([1, 1, 1]))
 
+    @staticmethod
+    def _test_isinf(self, cast):
+        t1 = cast(torch.Tensor([1, inf, 2, -inf, nan]))
+        t2 = cast(torch.ByteTensor([1, 2, 3]))
+        t3 = cast(torch.CharTensor([1, 2, 3]))
+        t4 = cast(torch.ShortTensor([1, 2, 3]))
+        t5 = cast(torch.IntTensor([1, 2, 3]))
+        t6 = cast(torch.LongTensor([1, 2, 3]))
+        self.assertEqual(torch.isinf(t1), cast(torch.ByteTensor([0, 1, 0, 1, 0])))
+        self.assertEqual(torch.isinf(t2), cast(torch.ByteTensor([0, 0, 0])))
+        self.assertEqual(torch.isinf(t3), cast(torch.ByteTensor([0, 0, 0])))
+        self.assertEqual(torch.isinf(t4), cast(torch.ByteTensor([0, 0, 0])))
+        self.assertEqual(torch.isinf(t5), cast(torch.ByteTensor([0, 0, 0])))
+        self.assertEqual(torch.isinf(t6), cast(torch.ByteTensor([0, 0, 0])))
+
     def test_isinf(self):
-        x = torch.Tensor([1, inf, 2, -inf, nan])
-        self.assertEqual(torch.isinf(x), torch.ByteTensor([0, 1, 0, 1, 0]))
+        self._test_isinf(self, lambda t: t)
 
     def test_isnan(self):
         x = torch.Tensor([1, nan, 2])

torch/functional.py

@@ -240,6 +240,8 @@ def isinf(tensor):
     """
     if not isinstance(tensor, torch.Tensor):
         raise ValueError("The argument is not a tensor", str(tensor))
+    if tensor.dtype in [torch.uint8, torch.int8, torch.int16, torch.int32, torch.int64]:
+        return torch.zeros_like(tensor, dtype=torch.uint8)
     return tensor.abs() == inf