enhance: avoid unnecessary fallback in _bincount on CUDA with deterministic

CHANGELOG.md

@@ -33,7 +33,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
--
+- Fixed `_bincount` being less restrictive ([#3087](https://github.com/Lightning-AI/torchmetrics/pull/3087))
 
 
 ---

src/torchmetrics/utilities/data.py

@@ -20,7 +20,7 @@
 from torch import Tensor
 
 from torchmetrics.utilities.exceptions import TorchMetricsUserWarning
-from torchmetrics.utilities.imports import _TORCH_LESS_THAN_2_6, _XLA_AVAILABLE
+from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_2_1, _TORCH_LESS_THAN_2_6, _XLA_AVAILABLE
 from torchmetrics.utilities.prints import rank_zero_warn
 
 METRIC_EPS = 1e-6
@@ -178,10 +178,12 @@ def _squeeze_if_scalar(data: Any) -> Any:
 def _bincount(x: Tensor, minlength: Optional[int] = None) -> Tensor:
     """Implement custom bincount.
 
-    PyTorch currently does not support ``torch.bincount`` when running in deterministic mode on GPU or when running
-    MPS devices or when running on XLA device. This implementation therefore falls back to using a combination of
-    `torch.arange` and `torch.eq` in these scenarios. A small performance hit can expected and higher memory consumption
-    as `[batch_size, mincount]` tensor needs to be initialized compared to native ``torch.bincount``.
+    As of PyTorch v2.1, ``torch.bincount`` is supported in deterministic mode on CUDA
+    when no ``weights`` are provided and gradients are not required. However, this
+    operation remains unsupported or limited on some backends, such as MPS and XLA.
+    In those cases, we fall back to a manual implementation using `torch.arange` and `torch.eq`.
+    A small performance hit can expected and higher memory consumption as `[batch_size, mincount]`
+    tensor needs to be initialized compared to native ``torch.bincount``.
 
     Args:
         x: tensor to count
@@ -199,7 +201,7 @@ def _bincount(x: Tensor, minlength: Optional[int] = None) -> Tensor:
     if minlength is None:
         minlength = len(torch.unique(x))
 
-    if torch.are_deterministic_algorithms_enabled() or _XLA_AVAILABLE or x.is_mps:
+    if (not _TORCH_GREATER_EQUAL_2_1 and torch.are_deterministic_algorithms_enabled()) or _XLA_AVAILABLE or x.is_mps:
         mesh = torch.arange(minlength, device=x.device).repeat(len(x), 1)
         return torch.eq(x.reshape(-1, 1), mesh).sum(dim=0)