Optimization of elementwise CUDA kernel (#30801)

paddle/fluid/operators/elementwise/elementwise_op_function.h

@@ -99,6 +99,7 @@ inline void get_mid_dims(const framework::DDim &x_dims,
     (*post) *= x_dims[i];
   }
 }
+
 inline int GetElementwiseIndex(const int *x_dims_array, const int max_dim,
                                const int *index_array) {
   int index_ = 0;
@@ -202,12 +203,16 @@ void CommonForwardBroadcastCPU(const framework::Tensor *x,
 
 #if defined(__NVCC__) || defined(__HIPCC__)
 template <typename Functor, typename T, typename OutType>
-__global__ void ElementwiseKernel(const T *x, const T *y, OutType *out, int pre,
+__global__ void ElementwiseKernel(const T *__restrict__ x_data,
-                                  int n, int post, int total, Functor func) {
+                                  const T *__restrict__ y_data,
+                                  OutType *__restrict__ out_data, int n,
+                                  int post, const size_t total, Functor func) {
   int tid = threadIdx.x + blockDim.x * blockIdx.x;
-  int idx = tid / post % n;
+  int stride = blockDim.x * gridDim.x;
-  if (tid < total) {
+
-    out[tid] = func(x[tid], y[idx]);
+  for (int i = tid; i < total; i += stride) {
+    int idx = i / post % n;
+    out_data[i] = func(x_data[i], y_data[idx]);
   }
 }
 
@@ -224,14 +229,16 @@ void ComputeElementwiseCUDA(const framework::Tensor *x,
   int numel = pre * n * post;
   int threads = 256;
   int blocks = (numel + threads - 1) / threads;
+
   if (is_xsize_larger) {
     ElementwiseKernel<Functor, T,
                       OutType><<<blocks, threads, 0, ctx.stream()>>>(
-        x_data, y_data, out_data, pre, n, post, numel, func);
+        x_data, y_data, out_data, n, post, numel, func);
+
   } else {
     ElementwiseKernel<Functor, T,
                       OutType><<<blocks, threads, 0, ctx.stream()>>>(
-        y_data, x_data, out_data, pre, n, post, numel, func);
+        y_data, x_data, out_data, n, post, numel, func);
   }
 }