diff --git a/transformer_engine/pytorch/ops/_common.py b/transformer_engine/pytorch/ops/_common.py
index e21915a5a6..c86cc8cb15 100644
--- a/transformer_engine/pytorch/ops/_common.py
+++ b/transformer_engine/pytorch/ops/_common.py
@@ -6,6 +6,7 @@
 
 from __future__ import annotations
 import functools
+import inspect
 from importlib.metadata import PackageNotFoundError, version as get_pkg_version
 from typing import Optional
 
diff --git a/transformer_engine/pytorch/ops/fused/backward_grouped_mlp.py b/transformer_engine/pytorch/ops/fused/backward_grouped_mlp.py
index 389dfbc838..4cbc5cb620 100644
--- a/transformer_engine/pytorch/ops/fused/backward_grouped_mlp.py
+++ b/transformer_engine/pytorch/ops/fused/backward_grouped_mlp.py
@@ -44,6 +44,7 @@ def _cudnn_compute_wgrad(
     accumulate: bool,
     wgrad_kernel_fn,
     single_grouped_weight: bool,
+    current_stream=None,
 ):
     """Compute wgrad using the cuDNN CuTe DSL grouped GEMM wgrad kernel.
 
@@ -88,6 +89,7 @@ def _cudnn_compute_wgrad(
             wgrad_dtype=wgrad_tensor.dtype,
             sf_vec_size=MXFP8_BLOCK_SCALING_SIZE,
             accumulate_on_output=accumulate,
+            current_stream=current_stream,
         )
     else:
         # Discrete mode: per-expert wgrad device pointers
@@ -104,6 +106,7 @@ def _cudnn_compute_wgrad(
             wgrad_dtype=wgrad_output[0].dtype,
             sf_vec_size=MXFP8_BLOCK_SCALING_SIZE,
             accumulate_on_output=accumulate,
+            current_stream=current_stream,
         )
 
 
@@ -214,6 +217,7 @@ def _compute_grad_params(
                 accumulate=accumulate_into_main_grad,
                 wgrad_kernel_fn=cudnn_wgrad_kernel_fn,
                 single_grouped_weight=fc_op.single_grouped_weight,
+                current_stream=torch.cuda.current_stream().cuda_stream,
             )
         else:
             gemm_fn = functools.partial(
@@ -295,11 +299,9 @@ def grouped_gemm_quant_kernel(cls) -> Callable:
     @functools.lru_cache(maxsize=None)
     def grouped_gemm_wgrad_kernel(cls) -> Optional[Callable]:
         """CuTe DSL kernel for grouped GEMM wgrad on SM100+.
-        Returns ``None`` when the cuDNN front-end package is older than
-        1.23.0.
+        Returns ``None`` when the cuDNN front-end wgrad API is not
+        available or lacks the required wgrad_tensor/wgrad_ptrs params.
         """
-        if not _nvidia_cudnn_frontend_supports_wgrad():
-            return None
         from cudnn import grouped_gemm_wgrad_wrapper_sm100  # pylint: disable=no-name-in-module
 
         return grouped_gemm_wgrad_wrapper_sm100