optimized sum kernel

csrc/moe/moe_align_sum_kernels.cu

@@ -363,6 +363,35 @@ __global__ void moe_sum_kernel(
   }
 }
 
+template <typename scalar_t, int TOPK, int SPLIT_D, int BLOCK_DIM>
+__global__ void moe_sum_sharedmem_topk8(
+    scalar_t* __restrict__ out,          
+    const scalar_t* __restrict__ input,  
+    const int d) {
+    const int token_idx = blockIdx.x / SPLIT_D;  
+    const int sub_block = blockIdx.x % SPLIT_D;  
+    const int d_per_block = (d + SPLIT_D - 1) / SPLIT_D;
+    const int64_t d_start = sub_block * d_per_block;
+    const int64_t token_offset = token_idx * TOPK * d;
+    const int64_t d_end = min(d_start + d_per_block, d);  
+    __shared__ __align__(16) scalar_t sem_input[TOPK][BLOCK_DIM];
+    for (int64_t idx = d_start + threadIdx.x; idx < d_end; idx += blockDim.x) {
+        sem_input[0][threadIdx.x] = input[token_offset + 0 * d + idx];
+        sem_input[1][threadIdx.x] = input[token_offset + 1 * d + idx];
+        sem_input[2][threadIdx.x] = input[token_offset + 2 * d + idx];
+        sem_input[3][threadIdx.x] = input[token_offset + 3 * d + idx];
+        sem_input[4][threadIdx.x] = input[token_offset + 4 * d + idx];
+        sem_input[5][threadIdx.x] = input[token_offset + 5 * d + idx];
+        sem_input[6][threadIdx.x] = input[token_offset + 6 * d + idx];
+        sem_input[7][threadIdx.x] = input[token_offset + 7 * d + idx];
+        __syncthreads();
+        scalar_t x = sem_input[0][threadIdx.x] + sem_input[1][threadIdx.x] + sem_input[2][threadIdx.x] + 
+          sem_input[3][threadIdx.x] + sem_input[4][threadIdx.x] + sem_input[5][threadIdx.x] + 
+          sem_input[6][threadIdx.x] + sem_input[7][threadIdx.x];
+        out[token_idx * d + idx] = x;
+    }
+}
+
 }  // namespace moe
 }  // namespace vllm
 
@@ -504,6 +533,12 @@ void moe_sum(torch::Tensor& input,   // [num_tokens, topk, hidden_size]
   const at::cuda::OptionalCUDAGuard device_guard(device_of(output));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
+  constexpr int splitD_ = 8;
+  const int TOPK8_GRID_DIM = num_tokens * splitD_;
+  constexpr int TOPK8_BLOCK_DIM = 256;
+  dim3 grid_8(TOPK8_GRID_DIM);
+  dim3 block_8(TOPK8_BLOCK_DIM);
+
   switch (topk) {
     case 2:
       VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe_sum_kernel", [&] {
@@ -530,15 +565,15 @@ void moe_sum(torch::Tensor& input,   // [num_tokens, topk, hidden_size]
       break;
 
     case 8:
-      VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe_sum_kernel", [&] {
-        vllm::moe::moe_sum_kernel<scalar_t, 8><<<grid, block, 0, stream>>>(
+      VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe_sum_sharedmem_topk8", [&]{
+        vllm::moe::moe_sum_sharedmem_topk8<scalar_t, 8, splitD_, TOPK8_BLOCK_DIM><<<grid_8, block_8, 0, stream>>>(
             output.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),
             hidden_size);
       });
       break;
-      
+
     default:
       at::sum_out(output, input, 1);
       break;
   }
-}
\ No newline at end of file
+}