[1/2][resubmit again] sgl-kernel: Fuse routed scaling factor into moe_fused_gate (#9088)

Author: trevor-m

sgl-kernel/csrc/common_extension.cc

@@ -175,7 +175,7 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
 
   m.def(
       "moe_fused_gate(Tensor input, Tensor bias, int num_expert_group, int topk_group, int topk, int "
-      "num_fused_shared_experts, float routed_scaling_factor) -> "
+      "num_fused_shared_experts, float routed_scaling_factor, bool apply_routed_scaling_factor_on_output) -> "
       "(Tensor[])");
   m.impl("moe_fused_gate", torch::kCUDA, &moe_fused_gate);
   m.def(

sgl-kernel/csrc/moe/moe_fused_gate.cu

@@ -59,6 +59,7 @@ __device__ void moe_fused_gate_impl(
     int64_t topk,
     int64_t num_fused_shared_experts,
     double routed_scaling_factor,
+    bool apply_routed_scaling_factor_on_output,
     Params params) {
   int tidx = threadIdx.x;
   int64_t thread_row =
@@ -248,6 +249,9 @@ __device__ void moe_fused_gate_impl(
     for (int ii = 0; ii < topk; ++ii) {
       int64_t const idx = topk * thread_row + ii;
       output_ptr[idx] = output_ptr[idx] / output_sum;
+      if (apply_routed_scaling_factor_on_output) {
+        output_ptr[idx] *= routed_scaling_factor;
+      }
     }
   }
 }
@@ -282,7 +286,8 @@ __global__ void moe_fused_gate_kernel(
     int64_t topk_group,
     int64_t topk,
     int64_t num_fused_shared_experts,
-    double routed_scaling_factor) {
+    double routed_scaling_factor,
+    bool apply_routed_scaling_factor_on_output) {
   KernelParams<VPT, NUM_EXPERTS, THREADS_PER_ROW, ROWS_PER_WARP, ROWS_PER_CTA, WARPS_PER_CTA> params;
   moe_fused_gate_impl<T>(
       input,
@@ -294,6 +299,7 @@ __global__ void moe_fused_gate_kernel(
       topk,
       num_fused_shared_experts,
       routed_scaling_factor,
+      apply_routed_scaling_factor_on_output,
       params);
 }
 
@@ -314,7 +320,8 @@ __global__ void moe_fused_gate_kernel(
             topk_group,                                                                                  \
             topk,                                                                                        \
             num_fused_shared_experts,                                                                    \
-            routed_scaling_factor);                                                                      \
+            routed_scaling_factor,                                                                       \
+            apply_routed_scaling_factor_on_output);                                                      \
     dispatched = true;                                                                                   \
   } while (0)
 
@@ -342,7 +349,8 @@ __global__ void moe_fused_gate_kernel_dynamic(
     int64_t topk_group,
     int64_t topk,
     int64_t num_fused_shared_experts,
-    double routed_scaling_factor) {
+    double routed_scaling_factor,
+    bool apply_routed_scaling_factor_on_output) {
   KernelParamsDynamic params;
   params.NUM_EXPERTS = num_experts;             // e.g, for deepseek v3, this is 256
   params.VPT = num_experts / num_expert_group;  // e.g., for deepseek v3, this is 256 / 8 = 32
@@ -361,6 +369,7 @@ __global__ void moe_fused_gate_kernel_dynamic(
       topk,
       num_fused_shared_experts,
       routed_scaling_factor,
+      apply_routed_scaling_factor_on_output,
       params);
 }
 
@@ -374,7 +383,8 @@ std::vector<at::Tensor> moe_fused_gate(
     int64_t topk_group,
     int64_t topk,
     int64_t num_fused_shared_experts,
-    double routed_scaling_factor) {
+    double routed_scaling_factor,
+    bool apply_routed_scaling_factor_on_output) {
   int64_t num_rows = input.size(0);
   int32_t num_experts = input.size(1);
   auto options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA);
@@ -473,7 +483,8 @@ std::vector<at::Tensor> moe_fused_gate(
           topk_group,
           topk,
           num_fused_shared_experts,
-          routed_scaling_factor);
+          routed_scaling_factor,
+          apply_routed_scaling_factor_on_output);
     } else if (input.scalar_type() == at::kHalf) {
       moe_fused_gate_kernel_dynamic<float16_t><<<num_blocks, block_dim, 0, stream>>>(
           input.data_ptr(),
@@ -486,7 +497,8 @@ std::vector<at::Tensor> moe_fused_gate(
           topk_group,
           topk,
           num_fused_shared_experts,
-          routed_scaling_factor);
+          routed_scaling_factor,
+          apply_routed_scaling_factor_on_output);
     } else if (input.scalar_type() == at::kFloat) {
       moe_fused_gate_kernel_dynamic<float32_t><<<num_blocks, block_dim, 0, stream>>>(
           input.data_ptr(),
@@ -499,7 +511,8 @@ std::vector<at::Tensor> moe_fused_gate(
           topk_group,
           topk,
           num_fused_shared_experts,
-          routed_scaling_factor);
+          routed_scaling_factor,
+          apply_routed_scaling_factor_on_output);
     } else {
       TORCH_CHECK(false, "Unsupported data type for moe_fused_gate");
     }

sgl-kernel/include/sgl_kernel_ops.h

@@ -247,7 +247,8 @@ std::vector<at::Tensor> moe_fused_gate(
     int64_t topk_group,
     int64_t topk,
     int64_t num_fused_shared_experts,
-    double routed_scaling_factor);
+    double routed_scaling_factor,
+    bool apply_routed_scaling_factor_on_output);
 
 void fp8_blockwise_scaled_grouped_mm(
     torch::Tensor& output,

sgl-kernel/python/sgl_kernel/moe.py

@@ -44,15 +44,21 @@ def moe_fused_gate(
     topk,
     num_fused_shared_experts=0,
     routed_scaling_factor=0,
+    apply_routed_scaling_factor_on_output=False,
 ):
     # This fused kernel function is used to select topk expert in a hierarchical 2-layer fashion
     # it split group of expert into num_expert_group, and use top2 expert weight sum in each group
     # as the group weight to select expert groups and then select topk experts within the selected groups
     # the #experts is decided by the input tensor shape and we currently only support power of 2 #experts
     # and #experts should be divisible by num_expert_group. #expert/num_expert_group <= 32 is limited for now.
     # for non-supported case, we suggest to use the biased_grouped_topk func in sglang.srt.layers.moe.topk
-    # num_fused_shared_experts: if > 0, the last several experts will be replaced with shared experts
-    # routed_scaling_factor: if > 0, the shared experts will be scaled by this factor
+    # num_fused_shared_experts: if > 0, the last several experts will be
+    #   replaced with shared experts. the shared experts will be divided by the
+    #   routed_scaling_factor - this is intended to cancel out later when routed+shared
+    #   output is scaled so that shared experts are not scaled.
+    # routed_scaling_factor: if > 0, the experts will be scaled by this factor
+    # apply_routed_scaling_factor_on_output: if true, output will be
+    #   scaled by the routed_scaling_factor
     return torch.ops.sgl_kernel.moe_fused_gate.default(
         input_tensor,
         bias,
@@ -61,6 +67,7 @@ def moe_fused_gate(
         topk,
         num_fused_shared_experts,
         routed_scaling_factor,
+        apply_routed_scaling_factor_on_output,
     )