[Performance] Support FP8 flashinfer TRTLLM MOE on Qwen3 and Qwen-3next

vllm/model_executor/layers/fused_moe/config.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from dataclasses import dataclass
+from enum import IntEnum
 from typing import Optional, Union
 
 import torch
@@ -91,6 +92,26 @@ def _quant_flags_to_group_shape(
     return a_shape, w_shape
 
 
+# The type of method in top-K routing
+# Please keep this in sync with the counterpart defined in https://github.com/flashinfer-ai/flashinfer/blob/main/include/flashinfer/trtllm/fused_moe/runner.h
+class RoutingMethodType(IntEnum):
+    # Default: Softmax -> TopK
+    Default = (0,)
+    # Renormalize: TopK -> Softmax
+    Renormalize = (1,)
+    # DeepSeekV3: Sigmoid -> RoutingBiasAdd -> Top2 in group -> Top4 groups
+    # -> Top8 experts from the Top4 groups
+    DeepSeekV3 = (2,)
+    # Llama4: Top1 -> Sigmoid
+    Llama4 = (3,)
+    # RenormalizeNaive: Softmax -> TopK -> Renormalize
+    RenormalizeNaive = (4,)
+    # TopK: TopK (no softmax)
+    TopK = (5,)
+    # Unspecified
+    Unspecified = 6.0
+
+
 @dataclass
 class FusedMoEQuantDesc:
     """

vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py

@@ -3,6 +3,7 @@
 
 import torch
 
+from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
 from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
 from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
     calculate_tile_tokens_dim,
@@ -23,26 +24,24 @@ def flashinfer_fused_moe_blockscale_fp8(
     w2_weight_scale_inv: torch.Tensor,
     global_num_experts: int,
     top_k: int,
-    num_expert_group: int,
-    topk_group: int,
+    num_expert_group: int | None,
+    topk_group: int | None,
     intermediate_size: int,
     expert_offset: int,
     local_num_experts: int,
     block_shape: list[int],
-    routed_scaling: float = 1.0,
+    routing_method_type: int = RoutingMethodType.DeepSeekV3,
+    routed_scaling: float | None = 1.0,
 ) -> torch.Tensor:
     from vllm.utils.flashinfer import flashinfer_trtllm_fp8_block_scale_moe
 
+    topk_group = topk_group if topk_group is not None else 0
     assert top_k <= global_num_experts
-    assert top_k <= 8
-    assert topk_group <= 4
-    assert global_num_experts > num_expert_group
-    assert global_num_experts % num_expert_group == 0
+    assert top_k <= 10
     assert global_num_experts % 4 == 0
-    assert top_k < (topk_group * global_num_experts / num_expert_group)
     assert block_shape == [128, 128]
-    # Routing kernel expects #experts <= #threads 256
-    assert global_num_experts <= 256
+    # Routing kernel expects #experts <= #threads 512
+    assert global_num_experts <= 512
 
     a_q, a_sf = per_token_group_quant_fp8(x, block_shape[1])
     # NOTE: scales of hidden states have to be transposed!
@@ -64,10 +63,8 @@ def flashinfer_fused_moe_blockscale_fp8(
         local_expert_offset=expert_offset,
         local_num_experts=local_num_experts,
         routed_scaling_factor=routed_scaling,
-        tile_tokens_dim=calculate_tile_tokens_dim(
-            x.shape[0], top_k, global_num_experts
-        ),
-        routing_method_type=2,  # DeepSeek-styled routing method
+        tile_tokens_dim=None,
+        routing_method_type=routing_method_type,
         use_shuffled_weight=False,
     )
 
@@ -88,6 +85,7 @@ def flashinfer_fused_moe_blockscale_fp8_fake(
     expert_offset: int,
     local_num_experts: int,
     block_shape: list[int],
+    routing_method_type: int,
     routed_scaling: float = 1.0,
 ) -> torch.Tensor:
     return torch.empty_like(x)

vllm/model_executor/layers/fused_moe/layer.py

@@ -30,6 +30,7 @@
     FusedMoEConfig,
     FusedMoEParallelConfig,
     FusedMoEQuantConfig,
+    RoutingMethodType,
     biased_moe_quant_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_moe import zero_experts_compute_triton
@@ -1218,6 +1219,7 @@ def __init__(
         zero_expert_type: str | None = None,
         expert_mapping: list[tuple[str, str, int, str]] | None = None,
         n_shared_experts: int | None = None,
+        routing_method_type: int | None = None,
     ):
         super().__init__()
 
@@ -1397,6 +1399,24 @@ def __init__(
                 "Only softmax scoring function is supported for non-grouped topk."
             )
 
+        # ToDo: Better logic to determine the routing method type
+        if routing_method_type is not None:
+            self.routing_method_type = routing_method_type
+        else:
+            if scoring_func == "sigmoid":
+                if self.use_grouped_topk:
+                    self.routing_method_type = RoutingMethodType.DeepSeekV3
+                elif self.top_k == 1:
+                    self.routing_method_type = RoutingMethodType.Llama4
+            elif self.scoring_func == "softmax":
+                self.routing_method_type = (
+                    RoutingMethodType.Renormalize
+                    if not self.renormalize
+                    else RoutingMethodType.RenormalizeNaive
+                )
+            else:
+                self.routing_method_type = RoutingMethodType.TopK
+
         self.moe_config: FusedMoEConfig = FusedMoEConfig(
             num_experts=self.global_num_experts,
             experts_per_token=top_k,

vllm/model_executor/layers/quantization/fp8.py

@@ -27,6 +27,7 @@
 )
 from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEQuantConfig,
+    RoutingMethodType,
     fp8_w8a8_moe_quant_config,
 )
 from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
@@ -1226,22 +1227,20 @@ def apply(
             assert activation == "silu", (
                 f"Expected 'silu' activation but got {activation}"
             )
-            assert scoring_func == "sigmoid", (
-                f"Expected 'sigmoid' scoring func but got {scoring_func}"
-            )
+
             if self.block_quant:
                 import vllm.model_executor.layers.fused_moe.flashinfer_trtllm_moe  # noqa: E501, F401
 
-                assert (
-                    renormalize and use_grouped_topk and custom_routing_function is None
-                )
                 e_score_correction_bias = (
                     e_score_correction_bias.to(x.dtype)
                     if e_score_correction_bias is not None
                     else None
                 )
+                routing_method_type = layer.routing_method_type
                 return torch.ops.vllm.flashinfer_fused_moe_blockscale_fp8(
-                    routing_logits=router_logits.to(torch.float32),
+                    routing_logits=router_logits.to(torch.float32)
+                    if routing_method_type == RoutingMethodType.DeepSeekV3
+                    else router_logits,
                     routing_bias=e_score_correction_bias,
                     x=x,
                     w13_weight=layer.w13_weight,
@@ -1256,6 +1255,7 @@ def apply(
                     expert_offset=layer.ep_rank * layer.local_num_experts,
                     local_num_experts=layer.local_num_experts,
                     block_shape=self.weight_block_size,
+                    routing_method_type=routing_method_type,
                     routed_scaling=routed_scaling_factor,
                 )
             else:

vllm/model_executor/layers/quantization/utils/flashinfer_utils.py

@@ -27,20 +27,25 @@ class FlashinferMoeBackend(Enum):
 
 
 def calculate_tile_tokens_dim(num_tokens, top_k, num_experts):
+    from flashinfer import next_positive_power_of_2
+
     # FlashInfer 0.2.10 has issues with larger tile sizes. Set to 8 for now.
     # TODO: Revert this to dynamic calculation once a new version of FlashInfer
     # with the necessary kernels is released.
     tile_tokens_dim = 8
 
-    # from flashinfer import next_positive_power_of_2
-
-    # # Guess tokens per expert assuming perfect expert distribution first.
-    # num_tokens_per_expert = (num_tokens * top_k) // num_experts
-    # # And pad the number to the next power of 2.
-    # tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert)
-    # # Cap to 8-64 tokens per CTA tile as it's the range supported by the
-    # # kernel.
-    # tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
+    # A factor considering tokens are not perfectly balanced among experts.
+    imbalance_factor = 1.3
+    # Calculate the number of tokens per expert
+    # assuming perfect distribution.
+    num_tokens_per_expert = (num_tokens * top_k) // num_experts
+    # Apply the imbalance factor.
+    num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor)
+    # And pad the number to the next power of 2.
+    tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert)
+    # Cap to 8-max_tile_tokens_dim tokens per CTA tile
+    # as it's the range supported by the kernel.
+    tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
 
     return tile_tokens_dim
 

vllm/model_executor/models/qwen3_moe.py

@@ -43,6 +43,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (
     MergedColumnParallelLinear,
@@ -171,6 +172,7 @@ def __init__(
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
             is_sequence_parallel=self.is_sequence_parallel,
+            routing_method_type=RoutingMethodType.Renormalize,
         )
 
         self.gate = ReplicatedLinear(

vllm/model_executor/models/qwen3_next.py

@@ -34,6 +34,7 @@
     fused_recurrent_gated_delta_rule,
 )
 from vllm.model_executor.layers.fused_moe import SharedFusedMoE
+from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
 from vllm.model_executor.layers.layernorm import (
     GemmaRMSNorm as Qwen3NextRMSNorm,
 )
@@ -173,6 +174,7 @@ def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
             enable_eplb=self.enable_eplb,
             num_redundant_experts=self.n_redundant_experts,
             is_sequence_parallel=self.is_sequence_parallel,
+            routing_method_type=RoutingMethodType.Renormalize,
         )
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: