fix(dnn/cuda): fix cutlass matmul splitk limit

GitOrigin-RevId: fc9a7c638ca8087fd42d5386163e6d820214484c

dnn/src/cuda/matrix_mul/cutlass_float32_simt_split_k.cpp

@@ -23,12 +23,20 @@ using namespace cutlass_wrapper;
 bool MatrixMulForwardImpl::AlgoFloat32SIMTSplitK::is_available(
         const SizeArgs& args) const {
     auto&& param = args.opr->param();
-    int n = args.layout_c.shape[1],
+    int m = args.layout_c.shape[0], n = args.layout_c.shape[1],
         k = args.layout_a.shape[param.transposeA ? 0 : 1];
-    return args.opr->param().format == param::MatrixMul::Format::DEFAULT &&
-           args.layout_a.dtype == dtype::Float32() &&
-           args.layout_b.dtype == dtype::Float32() &&
-           args.layout_c.dtype == dtype::Float32() && k > n;
+    bool available =
+            args.opr->param().format == param::MatrixMul::Format::DEFAULT &&
+            args.layout_a.dtype == dtype::Float32() &&
+            args.layout_b.dtype == dtype::Float32() &&
+            args.layout_c.dtype == dtype::Float32() && k > n;
+    auto&& device_prop = cuda::current_device_prop();
+    int y_grid_limit = device_prop.maxGridSize[1];
+    // limit y grid
+    available &= ((m + m_algo_param.threadblock_m - 1) /
+                          m_algo_param.threadblock_m <=
+                  y_grid_limit);
+    return available;
 }
 
 size_t MatrixMulForwardImpl::AlgoFloat32SIMTSplitK::get_workspace_in_bytes(
@@ -36,7 +44,7 @@ size_t MatrixMulForwardImpl::AlgoFloat32SIMTSplitK::get_workspace_in_bytes(
     auto&& param = args.opr->param();
     int m = args.layout_c.shape[0], n = args.layout_c.shape[1],
         k = args.layout_a.shape[param.transposeA ? 0 : 1];
-    int split_k_slices = k / n;
+    int split_k_slices = std::max(1, k / n);
     return args.layout_c.dtype.size(m * n * split_k_slices);
 }
 
@@ -49,7 +57,7 @@ void MatrixMulForwardImpl::AlgoFloat32SIMTSplitK::exec(
     int m = args.tensor_c.layout.shape[0], n = args.tensor_c.layout.shape[1],
         k = args.tensor_a.layout.shape[param.transposeA ? 0 : 1];
     GemmCoord problem_size{m, n, k};
-    int split_k_slices = k / n;
+    int split_k_slices = std::max(1, k / n);
     auto&& stream = cuda_stream(args.opr->handle());
     int* workspace = reinterpret_cast<int*>(args.workspace.raw_ptr);
     return cutlass_matrix_mul_float32_simt(