[feat]适配sgl moe_fused_gate kernel

CMakeLists.txt

@@ -621,7 +621,8 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/topk_softmax_kernels.cu")
+  "csrc/moe/topk_softmax_kernels.cu"
+  "csrc/moe/moe_fused_gate.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")

csrc/moe/moe_ops.h

@@ -29,3 +29,12 @@ torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                              int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
                              int64_t BLOCK_SIZE_K, int64_t bit);
 #endif
+
+std::vector<torch::Tensor> moe_fused_gate(
+    torch::Tensor& input,
+    torch::Tensor& bias,
+    int64_t num_expert_group,
+    int64_t topk_group,
+    int64_t topk,
+    int64_t n_share_experts_fusion,
+    double routed_scaling_factor);
\ No newline at end of file

csrc/moe/torch_bindings.cpp

@@ -31,6 +31,12 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "                         Tensor! num_tokens_post_pad) -> ()");
   m.impl("sgl_moe_align_block_size", torch::kCUDA, &sgl_moe_align_block_size);
 
+  m.def(
+      "moe_fused_gate(Tensor input, Tensor bias, int num_expert_group, int topk_group, int topk, int "
+      "n_share_experts_fusion, float routed_scaling_factor) -> "
+      "(Tensor[])");
+  m.impl("moe_fused_gate", torch::kCUDA, &moe_fused_gate);
+
 #ifndef USE_ROCM
   m.def(
       "moe_wna16_gemm(Tensor input, Tensor! output, Tensor b_qweight, "

vllm/_custom_ops.py

@@ -1979,3 +1979,31 @@ def flash_mla_with_kvcache(
 #     torch.ops._C.cutlass_mla_decode(out, q_nope, q_pe, kv_c_and_k_pe_cache,
 #                                     seq_lens, page_table, scale)
 #     return out
+
+
+def moe_fused_gate(
+    input_tensor,
+    bias,
+    num_expert_group,
+    topk_group,
+    topk,
+    n_share_experts_fusion=0,
+    routed_scaling_factor=0,
+):
+    # 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 exerpt 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 limitted for now.
+    # for non-supported case, we suggestion to use the biased_grouped_topk func in sglang.srt.layers.moe.topk
+    # n_share_experts_fusion: if > 0, the last expert will be replaced with a round-robin shared expert
+    # routed_scaling_factor: if > 0, the last expert will be scaled by this factor
+    return torch.ops._moe_C.moe_fused_gate(
+        input_tensor,
+        bias,
+        num_expert_group,
+        topk_group,
+        topk,
+        n_share_experts_fusion,
+        routed_scaling_factor,
+    )

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -3,6 +3,7 @@
 import functools
 import json
 import os
+import math
 from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import torch
@@ -1182,6 +1183,10 @@ def fused_topk(
     return topk_weights, topk_ids
 
 
+def is_power_of_two(n):
+    return n > 0 and math.log2(n).is_integer()
+
+
 # This is used by the Deepseek-V2 and Deepseek-V3 model
 @torch.compile(dynamic=True, backend=current_platform.simple_compile_backend)
 def grouped_topk(

vllm/model_executor/layers/fused_moe/layer.py

@@ -23,6 +23,7 @@ from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.platforms.interface import CpuArchEnum
 from vllm.utils import direct_register_custom_op
+from vllm import _custom_ops as ops
 
 if current_platform.is_cuda_alike():
     from .fused_moe import fused_experts
@@ -222,7 +223,8 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             num_expert_group=num_expert_group,
             custom_routing_function=custom_routing_function,
             scoring_func=scoring_func,
-            e_score_correction_bias=e_score_correction_bias)
+            e_score_correction_bias=e_score_correction_bias,
+            routed_scaling_factor=self.routed_scaling_factor if hasattr(self, "routed_scaling_factor") else None)
 
         return fused_experts(
             hidden_states=x,
@@ -436,6 +438,7 @@ class FusedMoE(torch.nn.Module):
         e_score_correction_bias: Optional[torch.Tensor] = None,
         apply_router_weight_on_input: bool = False,
         activation: str = "silu",
+        routed_scaling_factor: Optional[float] = None,
     ):
         super().__init__()
 
@@ -505,6 +508,7 @@ class FusedMoE(torch.nn.Module):
         self.scoring_func = scoring_func
         self.e_score_correction_bias = e_score_correction_bias
         self.activation = activation
+        self.routed_scaling_factor = routed_scaling_factor
 
         if self.scoring_func != "softmax" and not self.use_grouped_topk:
             raise ValueError("Only softmax scoring function is supported for "
@@ -554,6 +558,7 @@ class FusedMoE(torch.nn.Module):
 
 
         self.quant_method.create_weights(layer=self, **moe_quant_params)
+        setattr(self.quant_method, "routed_scaling_factor", self.routed_scaling_factor)
         from vllm.two_batch_overlap.two_batch_overlap import tbo_all_reduce
         self.tbo_all_reduce = tbo_all_reduce
 
@@ -839,14 +844,30 @@ class FusedMoE(torch.nn.Module):
                        num_expert_group: Optional[int] = None,
                        custom_routing_function: Optional[Callable] = None,
                        scoring_func: str = "softmax",
-                       e_score_correction_bias: Optional[torch.Tensor] = None):
+                       e_score_correction_bias: Optional[torch.Tensor] = None,
+                       routed_scaling_factor: Optional[float] = None,):
         from vllm.model_executor.layers.fused_moe.fused_moe import (
-            fused_topk, grouped_topk)
+            fused_topk, grouped_topk, is_power_of_two)
 
         # DeekSeekv2 uses grouped_top_k
         if use_grouped_topk:
             assert topk_group is not None
             assert num_expert_group is not None
+            if e_score_correction_bias is not None \
+                and router_logits.shape[1] // num_expert_group <= 32 \
+                    and is_power_of_two(e_score_correction_bias.shape[0]):
+                    
+                # moe_fused_gate kernel ensure that num_experts/num_expert_group does not exceed MAX_VPT=32 now. And when kernel can handle MAX_VPT > 32, we can remove this assertion.
+                topk_weights, topk_ids = ops.moe_fused_gate(
+                    router_logits,
+                    e_score_correction_bias,
+                    num_expert_group,
+                    topk_group,
+                    top_k,
+                    routed_scaling_factor=routed_scaling_factor,
+                    n_share_experts_fusion=0,
+                )
+            else:
                 topk_weights, topk_ids = grouped_topk(
                     hidden_states=hidden_states,
                     gating_output=router_logits,
@@ -926,7 +947,7 @@ class FusedMoE(torch.nn.Module):
             e_score_correction_bias=self.e_score_correction_bias,
             activation=self.activation,
             apply_router_weight_on_input=self.apply_router_weight_on_input,
-            use_nn_moe=self.use_nn_moe,
+            use_nn_moe=self.use_nn_moe
         )
 
         if self.dp_size > 1:

vllm/model_executor/models/deepseek_v2.py

@@ -142,7 +142,8 @@ class DeepseekV2MoE(nn.Module):
             topk_group=config.topk_group,
             prefix=f"{prefix}.experts",
             scoring_func=config.scoring_func,
-            e_score_correction_bias=self.gate.e_score_correction_bias,)
+            e_score_correction_bias=self.gate.e_score_correction_bias,
+            routed_scaling_factor=self.routed_scaling_factor)
 
         if config.n_shared_experts is not None:
             intermediate_size = (config.moe_intermediate_size *