diff --git a/ggml/src/ggml-cuda/fwht.cu b/ggml/src/ggml-cuda/fwht.cu
index 74e94d844..184dc254c 100644
--- a/ggml/src/ggml-cuda/fwht.cu
+++ b/ggml/src/ggml-cuda/fwht.cu
@@ -19,6 +19,7 @@ __global__ void fwht_cuda(const float * src, float * dst, const int64_t n_rows,
     float     reg[el_w];
     const int lane = threadIdx.x;
 
+    ggml_cuda_pdl_sync();
 #pragma unroll
     for (int i = 0; i < el_w; ++i) {
         reg[i] = src[i * warp_size + lane] * scale;
@@ -57,10 +58,11 @@ __global__ void fwht_cuda(const float * src, float * dst, const int64_t n_rows,
     }
 }
 
-void ggml_cuda_op_fwht(ggml_backend_cuda_context & ctx, const ggml_tensor * src, ggml_tensor * dst) {
+bool ggml_cuda_op_fwht(ggml_backend_cuda_context & ctx, const ggml_tensor * src, ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_shape(src, dst));
-    GGML_ASSERT(ggml_is_contiguous(src));
-    GGML_ASSERT(ggml_is_contiguous(dst));
+    if (!ggml_is_contiguous(src) || !ggml_is_contiguous(dst)) {
+        return false;
+    }
     const int     n    = src->ne[0];
     const int64_t rows = ggml_nrows(src);
 
@@ -68,7 +70,6 @@ void ggml_cuda_op_fwht(ggml_backend_cuda_context & ctx, const ggml_tensor * src,
     float *       dst_d = (float *) dst->data;
 
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
-    GGML_ASSERT(n % warp_size == 0);
     const int rows_per_block = 4;
 
     const int64_t num_blocks = (rows + rows_per_block - 1) / rows_per_block;
@@ -83,26 +84,18 @@ void ggml_cuda_op_fwht(ggml_backend_cuda_context & ctx, const ggml_tensor * src,
 
     switch (n) {
         case 64:
-            {
-                ggml_cuda_kernel_launch(fwht_cuda<64>, launch_params, src_d, dst_d, rows, scale);
-                break;
-            }
+            ggml_cuda_kernel_launch(fwht_cuda<64>, launch_params, src_d, dst_d, rows, scale);
+            return true;
         case 128:
-            {
-                ggml_cuda_kernel_launch(fwht_cuda<128>, launch_params, src_d, dst_d, rows, scale);
-                break;
-            }
+            ggml_cuda_kernel_launch(fwht_cuda<128>, launch_params, src_d, dst_d, rows, scale);
+            return true;
         case 256:
-            {
-                ggml_cuda_kernel_launch(fwht_cuda<256>, launch_params, src_d, dst_d, rows, scale);
-                break;
-            }
+            ggml_cuda_kernel_launch(fwht_cuda<256>, launch_params, src_d, dst_d, rows, scale);
+            return true;
         case 512:
-            {
-                ggml_cuda_kernel_launch(fwht_cuda<512>, launch_params, src_d, dst_d, rows, scale);
-                break;
-            }
+            ggml_cuda_kernel_launch(fwht_cuda<512>, launch_params, src_d, dst_d, rows, scale);
+            return true;
         default:
-            GGML_ABORT("fatal error");
+            return false;
     }
 }
diff --git a/ggml/src/ggml-cuda/fwht.cuh b/ggml/src/ggml-cuda/fwht.cuh
index fa4c30477..cf3df94ca 100644
--- a/ggml/src/ggml-cuda/fwht.cuh
+++ b/ggml/src/ggml-cuda/fwht.cuh
@@ -1,3 +1,4 @@
 #include "common.cuh"
 
-void ggml_cuda_op_fwht(ggml_backend_cuda_context & ctx, const ggml_tensor * src, ggml_tensor * dst);
+// Returns whether the Fast Walsh-Hadamard transform could be used.
+bool ggml_cuda_op_fwht(ggml_backend_cuda_context & ctx, const ggml_tensor * src, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 1bb09ac80..23d1c0692 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2596,9 +2596,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     bool use_batched_cublas_f32  = src0->type == GGML_TYPE_F32;
 
     const int32_t hint = ggml_get_op_params_i32(dst, 1);
-    if (hint == GGML_HINT_SRC0_IS_HADAMARD) {
-        GGML_ASSERT(!split);
-        ggml_cuda_op_fwht(ctx, src1, dst);
+    if (hint == GGML_HINT_SRC0_IS_HADAMARD && !split && ggml_cuda_op_fwht(ctx, src1, dst)) {
         return;
     }