Mark bincount CUDA deterministic if weights are not given (#105244)

Fixes #98316

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105244
Approved by: https://github.com/mikaylagawarecki

aten/src/ATen/native/cuda/SummaryOps.cu

@@ -366,9 +366,12 @@ Tensor _bincount_cuda(
   c10::MaybeOwned<Tensor> weights_maybe_owned = at::borrow_from_optional_tensor(weights_opt);
   const Tensor& weights = *weights_maybe_owned;
 
-  // See Note [Writing Nondeterministic Operations]
-  // Nondeterministic because of atomicAdd usage
-  globalContext().alertNotDeterministic("_bincount_cuda");
+  if (weights_opt.has_value()) {
+    // See Note [Writing Nondeterministic Operations]
+    // Nondeterministic if weights are given, because of floating point
+    // atomicAdd usage
+    globalContext().alertNotDeterministic("_bincount_cuda");
+  }
   return AT_DISPATCH_INTEGRAL_TYPES(self.scalar_type(), "bincount_cuda", [&] {
     const auto scalar = weights.scalar_type();
     if (scalar == ScalarType::Undefined || scalar == ScalarType::Float)

test/test_torch.py

@@ -1746,11 +1746,20 @@ else:
     @skipIfMps
     def test_nondeterministic_alert_bincount(self, device):
         a = torch.tensor([], device=device, dtype=torch.long)
+        weights = torch.tensor([], device=device)
+
         for op_call in [torch.bincount, torch.Tensor.bincount]:
+            # Error should only be raised when device is CUDA and weights are
+            # given
+            self.check_nondeterministic_alert(
+                lambda: op_call(a, weights),
+                '_bincount_cuda',
+                torch.device(device).type == 'cuda')
+
             self.check_nondeterministic_alert(
                 lambda: op_call(a),
                 '_bincount_cuda',
-                torch.device(device).type == 'cuda')
+                False)
 
     # Ensures that kthvalue throws nondeterministic alerts in the correct cases
     @dtypes(torch.double)

torch/__init__.py

@@ -743,7 +743,8 @@ def use_deterministic_algorithms(mode, *, warn_only=False):
         * :func:`torch.Tensor.put_` when ``accumulate=False``
         * :func:`torch.Tensor.put_` when ``accumulate=True`` and called on a CUDA tensor
         * :func:`torch.histc` when called on a CUDA tensor
-        * :func:`torch.bincount` when called on a CUDA tensor
+        * :func:`torch.bincount` when called on a CUDA tensor and ``weights``
+          tensor is given
         * :func:`torch.kthvalue` with called on a CUDA tensor
         * :func:`torch.median` with indices output when called on a CUDA tensor
         * :func:`torch.nn.functional.grid_sample` when attempting to differentiate a CUDA tensor