[NVIDIA] Add Low Latency NVFP4 decode kernels from Flashinfer (#8552)

Co-authored-by: Cheng Wan <cwan@x.ai>

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -14,13 +14,9 @@ from sglang.srt.layers.moe.ep_moe.kernels import (
     silu_and_mul_masked_post_quant_fwd,
     tma_align_input_scale,
 )
-from sglang.srt.layers.moe.fused_moe_triton.layer import (
-    FlashInferFusedMoE,
-    FusedMoE,
-    should_use_flashinfer_trtllm_moe,
-)
+from sglang.srt.layers.moe.fused_moe_triton.layer import FlashInferFusedMoE, FusedMoE
 from sglang.srt.layers.moe.topk import TopKOutput
-from sglang.srt.layers.moe.utils import DeepEPMode
+from sglang.srt.layers.moe.utils import DeepEPMode, should_use_flashinfer_trtllm_moe
 from sglang.srt.layers.quantization import deep_gemm_wrapper
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.fp8 import (
@@ -48,7 +44,6 @@ _is_npu = is_npu()
 _is_fp8_fnuz = is_fp8_fnuz()
 _use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 
-
 if not (_is_npu or _is_hip):
     from sgl_kernel import silu_and_mul
 
@@ -741,6 +736,22 @@ class FlashInferEPMoE(EPMoE):
 def get_moe_impl_class():
     if global_server_args_dict["moe_a2a_backend"].is_deepep():
         return DeepEPMoE
+
+    # NEW: Direct FP4 detection (bypasses EP requirements)
+    # Check for FP4 quantization with TRTLLM flag, regardless of EP
+    if global_server_args_dict.get("enable_flashinfer_trtllm_moe", False):
+        try:
+            # Check the quantization argument directly
+            quantization = global_server_args_dict.get("quantization")
+            if quantization == "modelopt_fp4":
+                from sglang.srt.layers.moe.fused_moe_triton.layer import (
+                    FlashInferFP4MoE,
+                )
+
+                return FlashInferFP4MoE
+        except:
+            pass
+
     if global_server_args_dict["enable_flashinfer_cutlass_moe"]:
         return FusedMoE
     if get_moe_expert_parallel_world_size() > 1:

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

 # Adapted from https://github.com/vllm-project/vllm/blob/a6221a144af772fd1a68fe7e627935dc53e81738/vllm/model_executor/layers/fused_moe/layer.py
 
-import importlib.util
+import datetime
+import glob
 import logging
+import os
+import sys
 from enum import Enum
-from functools import lru_cache
 from typing import List, Optional, Tuple
 
 import torch
-from packaging import version as pkg_version
 
 from sglang.srt.distributed import (
     get_moe_expert_parallel_rank,
@@ -22,6 +23,7 @@ from sglang.srt.distributed.device_communicators.pynccl_allocator import (
 )
 from sglang.srt.eplb.expert_location import get_global_expert_location_metadata
 from sglang.srt.layers.moe.topk import StandardTopKOutput
+from sglang.srt.layers.moe.utils import should_use_flashinfer_trtllm_moe
 from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
@@ -29,22 +31,58 @@ from sglang.srt.layers.quantization.base_config import (
 from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_loader.weight_utils import narrow_padded_param_and_loaded_weight
-from sglang.srt.utils import cpu_has_amx_support, get_bool_env_var, is_cpu, is_hip
+from sglang.srt.utils import (
+    cpu_has_amx_support,
+    get_bool_env_var,
+    is_cpu,
+    is_flashinfer_available,
+    is_hip,
+    next_power_of_2,
+)
+
+if is_flashinfer_available():
+    from flashinfer import (
+        RoutingMethodType,
+        fp4_quantize,
+        reorder_rows_for_gated_act_gemm,
+        shuffle_matrix_a,
+        shuffle_matrix_sf_a,
+    )
 
 _is_hip = is_hip()
 _is_cpu_amx_available = cpu_has_amx_support()
 _is_cpu = is_cpu()
 
+
+# Try to import FP4 TRTLLM function if flashinfer is available
+trtllm_fp4_block_scale_moe = None
+if should_use_flashinfer_trtllm_moe():
+    try:
+        from flashinfer.fused_moe import trtllm_fp4_block_scale_moe
+    except ImportError:
+        trtllm_fp4_block_scale_moe = None
+
 logger = logging.getLogger(__name__)
 
 
-@lru_cache(maxsize=1)
-def should_use_flashinfer_trtllm_moe():
-    return global_server_args_dict["enable_flashinfer_trtllm_moe"] and (
-        not importlib.util.find_spec("flashinfer")
-        or pkg_version.parse(__import__("flashinfer").__version__)
-        >= pkg_version.parse("0.2.9rc1")
-    )
+def _is_fp4_quantization_enabled():
+    """Check if ModelOpt FP4 quantization is enabled."""
+    try:
+        # Use the same simple check that works for class selection
+        quantization = global_server_args_dict.get("quantization")
+        return quantization == "modelopt_fp4"
+    except:
+        return False
+
+
+def _get_tile_tokens_dim(num_tokens, top_k, num_experts):
+    # 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_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)
+    return tile_tokens_dim
 
 
 class FusedMoeWeightScaleSupported(Enum):
@@ -157,10 +195,6 @@ class FusedMoE(torch.nn.Module):
             )
         else:
             self.quant_method = quant_config.get_quant_method(self, prefix)
-            if self.quant_method.__class__.__name__ == "ModelOptNvFp4FusedMoEMethod":
-                self.quant_method.enable_flashinfer_cutlass_moe = (
-                    self.enable_flashinfer_cutlass_moe
-                )
         assert self.quant_method is not None
 
         self.quant_config = quant_config
@@ -747,7 +781,130 @@ class FlashInferFusedMoE(FusedMoE):
             routed_scaling_factor=self.routed_scaling_factor,
         )
 
-        if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1):
+        if self.reduce_results and (self.moe_tp_size > 1 or self.moe_ep_size > 1):
             final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
 
         return final_hidden_states
+
+
+class FlashInferFP4MoE(FusedMoE):
+    """FP4 TRTLLM MoE implementation using FlashInfer."""
+
+    def __init__(self, *args, **kwargs):
+        # Extract DeepSeek-specific parameters
+        renormalize = kwargs.pop("renormalize", True)
+        num_fused_shared_experts = kwargs.pop("num_fused_shared_experts", 0)
+        use_grouped_topk = kwargs.pop("use_grouped_topk", False)
+        num_expert_group = kwargs.pop("num_expert_group", None)
+        topk_group = kwargs.pop("topk_group", None)
+        correction_bias = kwargs.pop("correction_bias", None)
+
+        # Extract additional TopK parameters that were previously extracted in forward
+        routed_scaling_factor = kwargs.pop("routed_scaling_factor", None)
+
+        super().__init__(*args, **kwargs)
+
+        # Store DeepSeek parameters
+        self.renormalize = renormalize
+        self.num_fused_shared_experts = num_fused_shared_experts
+        self.use_grouped_topk = use_grouped_topk
+        self.num_expert_group = num_expert_group
+        self.topk_group = topk_group
+        self.correction_bias = correction_bias
+        self.routed_scaling_factor = routed_scaling_factor
+
+    # ---------------------------------------------------------------------
+    # Helper: quantize hidden states to FP4 each forward pass
+    # ---------------------------------------------------------------------
+    def _quantize_hidden_states_fp4(self, hidden_states: torch.Tensor):
+        """
+        Quantize hidden states using global scale factor from quantization method.
+
+        Global scale factor is set by ModelOptNvFp4FusedMoEMethod during weight loading.
+        Only block scales are computed at runtime for efficiency.
+
+        Returns (packed_fp4_uint8, scale_float8_e4m3fn_runtime, global_scale_float32)
+        """
+
+        # flashinfer.fp4_quantize returns (packed_uint8, scale_fp8)
+        # Only the block scales are computed at runtime
+        hs_fp4_bytes, hs_sf_bytes = fp4_quantize(
+            hidden_states,
+            self.w13_input_scale_quant,
+            16,  # sf_vec_size
+            False,  # use_ue8m0
+            False,  # is_sf_swizzled_layout
+        )
+
+        hs_fp4 = hs_fp4_bytes.reshape(
+            hidden_states.shape[0], hidden_states.shape[1] // 2
+        )
+        hs_sf = hs_sf_bytes.view(torch.float8_e4m3fn).reshape(-1)
+
+        return hs_fp4, hs_sf
+
+    def forward(self, hidden_states: torch.Tensor, topk_output):
+        """Forward pass using FP4 TRTLLM kernel.
+
+        Args:
+            hidden_states: Input tensor
+            topk_output: Should be tuple of (TopK_config, router_logits) for TRTLLM mode
+        """
+
+        # TRTLLM mode expects (TopK_config, router_logits) tuple
+        if not isinstance(topk_output, tuple) or len(topk_output) != 2:
+            raise ValueError(
+                f"FlashInferFP4MoE expects (TopK_config, router_logits) tuple, got {type(topk_output)}"
+            )
+
+        _, router_logits = topk_output
+
+        hs_fp4, hs_scale_linear = self._quantize_hidden_states_fp4(hidden_states)
+
+        router_logits = router_logits.to(torch.float32)
+
+        result = trtllm_fp4_block_scale_moe(
+            routing_logits=router_logits,
+            routing_bias=self.correction_bias.to(hidden_states.dtype),
+            hidden_states=hs_fp4,
+            hidden_states_scale=hs_scale_linear.view(torch.float8_e4m3fn).flatten(),
+            gemm1_weights=self.gemm1_weights_fp4_shuffled.data,
+            gemm1_weights_scale=self.gemm1_scales_fp4_shuffled.data.view(
+                torch.float8_e4m3fn
+            ),
+            gemm2_weights=self.gemm2_weights_fp4_shuffled.data,
+            gemm2_weights_scale=self.gemm2_scales_fp4_shuffled.data.view(
+                torch.float8_e4m3fn
+            ),
+            output1_scale_scalar=self.g1_scale_c.data,
+            output1_scale_gate_scalar=self.g1_alphas.data,
+            output2_scale_scalar=self.g2_alphas.data,
+            num_experts=self.num_experts,
+            top_k=self.top_k,
+            n_group=self.num_expert_group,
+            topk_group=self.topk_group,
+            intermediate_size=self.intermediate_size_per_partition,
+            local_expert_offset=self.moe_ep_rank * self.num_local_experts,
+            local_num_experts=self.num_local_experts,
+            routed_scaling_factor=self.routed_scaling_factor,
+            tile_tokens_dim=_get_tile_tokens_dim(
+                hidden_states.shape[0], self.top_k, self.num_local_experts
+            ),
+            routing_method_type=RoutingMethodType.DeepSeekV3,
+            do_finalize=True,
+        )[0]
+
+        return result
+
+
+def get_fused_moe_impl_class():
+    """Factory function to get the appropriate FusedMoE implementation class."""
+    if should_use_flashinfer_trtllm_moe() and _is_fp4_quantization_enabled():
+        # Use FP4 variant when FP4 quantization is enabled
+        return FlashInferFP4MoE
+    elif should_use_flashinfer_trtllm_moe():
+        # Use regular FlashInfer variant for non-FP4 FlashInfer cases
+        return FlashInferFusedMoE
+    else:
+        # Default case
+        return FusedMoE

python/sglang/srt/layers/moe/utils.py

+import importlib.util
 from enum import Enum
+from functools import lru_cache
+
+from packaging import version as pkg_version
+
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+
+
+@lru_cache(maxsize=1)
+def should_use_flashinfer_trtllm_moe():
+    result = global_server_args_dict["enable_flashinfer_trtllm_moe"] and (
+        not importlib.util.find_spec("flashinfer")
+        or pkg_version.parse(__import__("flashinfer").__version__)
+        >= pkg_version.parse("0.2.9rc1")
+    )
+    return result
 
 
 class MoeA2ABackend(Enum):

python/sglang/srt/layers/quantization/modelopt_quant.py

 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py
 from __future__ import annotations
 
+import importlib.util
 import logging
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
 
 import torch
 from torch.nn.parameter import Parameter
 
 from sglang.srt.layers.moe.cutlass_moe_params import CutlassMoEParams, CutlassMoEType
+from sglang.srt.layers.moe.utils import should_use_flashinfer_trtllm_moe
 from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
 from sglang.srt.layers.quantization.base_config import (
     FusedMoEMethodBase,
@@ -29,6 +31,7 @@ from sglang.srt.layers.quantization.utils import (
     requantize_with_max_scale,
 )
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.utils import is_cuda, next_power_of_2
 
 if TYPE_CHECKING:
@@ -39,6 +42,11 @@ if is_cuda():
 
 try:
     from flashinfer import mm_fp4 as fp4_gemm
+    from flashinfer import (
+        reorder_rows_for_gated_act_gemm,
+        shuffle_matrix_a,
+        shuffle_matrix_sf_a,
+    )
 
     enable_flashinfer_fp4_gemm = True
 except ImportError:
@@ -47,6 +55,9 @@ except ImportError:
     else:
         fp4_gemm = None
     enable_flashinfer_fp4_gemm = False
+    reorder_rows_for_gated_act_gemm = None
+    shuffle_matrix_a = None
+    shuffle_matrix_sf_a = None
 
 try:
     from flashinfer.fused_moe import cutlass_fused_moe as flashinfer_cutlass_fused_moe
@@ -527,6 +538,7 @@ class ModelOptFp4Config(QuantizationConfig):
     ) -> Optional[QuantizeMethodBase]:
         from sglang.srt.layers.linear import LinearBase
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+        from sglang.srt.layers.moe.fused_moe_triton.layer import FlashInferFP4MoE
 
         if isinstance(layer, LinearBase):
             if is_layer_skipped(prefix, self.exclude_modules) or self.is_layer_excluded(
@@ -536,6 +548,9 @@ class ModelOptFp4Config(QuantizationConfig):
             return ModelOptFp4LinearMethod(self)
         if self.kv_cache_quant_algo and isinstance(layer, RadixAttention):
             return ModelOptFp8KVCacheMethod(self)
+        elif isinstance(layer, FlashInferFP4MoE):
+            # FlashInferFP4MoE needs the same quantization method but with compatible attribute handling
+            return ModelOptNvFp4FusedMoEMethod(self)
         elif isinstance(layer, FusedMoE):
             return ModelOptNvFp4FusedMoEMethod(self)
         return None
@@ -726,7 +741,12 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
                 " quantization. Please use Blackwell and"
                 " above."
             )
-        self.enable_flashinfer_cutlass_moe = False
+        self.enable_flashinfer_trtllm_moe = should_use_flashinfer_trtllm_moe()
+
+    @property
+    def enable_flashinfer_cutlass_moe(self) -> bool:
+        """Access the global enable_flashinfer_cutlass_moe setting."""
+        return global_server_args_dict.get("enable_flashinfer_cutlass_moe", False)
 
     def create_weights(
         self,
@@ -743,16 +763,20 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
                 " dynamic quantization is not supported."
             )
 
+        # TODO(ch-wan): check if this is needed
         layer.num_experts = num_experts
+        layer.num_local_experts = num_experts
+        layer.intermediate_size_per_partition = intermediate_size_per_partition
         layer.params_dtype = params_dtype
         layer.quant_config = self.quant_config
+
         weight_dtype = torch.uint8
         weight_scale_dtype = torch.float8_e4m3fn
         weight_loader = extra_weight_attrs.get("weight_loader")
         # GEMM 1
         w13_weight = ModelWeightParameter(
             data=torch.empty(
-                num_experts,
+                layer.local_num_experts,
                 2 * intermediate_size_per_partition,
                 # 2 fp4 items are packed in the input dimension
                 hidden_size // 2,
@@ -767,7 +791,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         # GEMM 2
         w2_weight = ModelWeightParameter(
             data=torch.empty(
-                num_experts,
+                layer.num_local_experts,
                 hidden_size,
                 # 2 fp4 items are packed in the input dimension
                 intermediate_size_per_partition // 2,
@@ -781,7 +805,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
 
         w13_weight_scale = ModelWeightParameter(
             data=torch.empty(
-                num_experts,
+                layer.num_local_experts,
                 2 * intermediate_size_per_partition,
                 # 2 fp4 items are packed in the input dimension
                 hidden_size // self.quant_config.group_size,
@@ -795,7 +819,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
 
         w2_weight_scale = ModelWeightParameter(
             data=torch.empty(
-                num_experts,
+                layer.num_local_experts,
                 hidden_size,
                 # 2 fp4 items are packed in the input dimension
                 intermediate_size_per_partition // self.quant_config.group_size,
@@ -814,13 +838,13 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         )
 
         w13_weight_scale_2 = PerTensorScaleParameter(
-            data=torch.empty(num_experts, 2, dtype=torch.float32),
+            data=torch.empty(layer.num_local_experts, 2, dtype=torch.float32),
             weight_loader=weight_loader,
         )
         layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)
 
         w2_weight_scale_2 = PerTensorScaleParameter(
-            data=torch.empty(num_experts, dtype=torch.float32),
+            data=torch.empty(layer.num_local_experts, dtype=torch.float32),
             weight_loader=weight_loader,
         )
         layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)
@@ -830,18 +854,18 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         )
 
         w13_input_scale = PerTensorScaleParameter(
-            data=torch.empty(num_experts, 2, dtype=torch.float32),
+            data=torch.empty(layer.num_local_experts, 2, dtype=torch.float32),
             weight_loader=weight_loader,
         )
         layer.register_parameter("w13_input_scale", w13_input_scale)
 
         w2_input_scale = PerTensorScaleParameter(
-            data=torch.empty(num_experts, dtype=torch.float32),
+            data=torch.empty(layer.num_local_experts, dtype=torch.float32),
             weight_loader=weight_loader,
         )
         layer.register_parameter("w2_input_scale", w2_input_scale)
 
-    def swizzle_blockscale(self, scale: torch.tensor):
+    def swizzle_blockscale(self, scale: torch.Tensor):
         assert scale.dtype == torch.float8_e4m3fn
         # Pad and blockwise interleave weight_scale
         scale_ndim = scale.ndim
@@ -866,9 +890,125 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
             else swizzled_scale.reshape(B, M, K)
         )
 
+    def prepare_static_weights_for_kernel(
+        self,
+        # args_dequant,
+        # args,
+        gemm1_weights,
+        gemm2_weights,
+        gemm1_scales_linear_fp4_bytes,
+        gemm2_scales_linear_fp4_bytes,
+        hidden_size,
+        intermediate_size,
+        num_experts,
+    ):
+        from flashinfer import (
+            RoutingMethodType,
+            e2m1_and_ufp8sf_scale_to_float,
+            fp4_quantize,
+            next_positive_power_of_2,
+            reorder_rows_for_gated_act_gemm,
+            shuffle_matrix_a,
+            shuffle_matrix_sf_a,
+        )
+
+        """Prepare quantized weights for kernel (done offline with weights)."""
+        epilogue_tile_m = 128  # FIXME: this depends on the kernel internals
+
+        # Convert quantized weights to proper formats
+        gemm1_weights_fp4 = gemm1_weights.view(torch.float8_e4m3fn).reshape(
+            num_experts, 2 * intermediate_size, hidden_size // 2
+        )  # packed fp4
+        gemm1_scales_linear_fp4 = gemm1_scales_linear_fp4_bytes.view(
+            torch.float8_e4m3fn
+        ).reshape(
+            num_experts, 2 * intermediate_size, hidden_size // 16
+        )  # fp8 scaling factors
+
+        gemm2_weights_fp4 = gemm2_weights.view(torch.float8_e4m3fn).reshape(
+            num_experts, hidden_size, intermediate_size // 2
+        )  # packed fp4
+        gemm2_scales_linear_fp4 = gemm2_scales_linear_fp4_bytes.view(
+            torch.float8_e4m3fn
+        ).reshape(
+            num_experts, hidden_size, intermediate_size // 16
+        )  # fp8 scaling factors
+
+        # Reorder rows of W1 and scales for fused gated activation
+        gemm1_weights_fp4_interleaved = []
+        gemm1_scales_fp4_interleaved = []
+        for i in range(num_experts):
+            gemm1_weights_fp4_interleaved.append(
+                reorder_rows_for_gated_act_gemm(gemm1_weights_fp4[i].clone())
+            )
+            gemm1_scales_fp4_interleaved.append(
+                reorder_rows_for_gated_act_gemm(gemm1_scales_linear_fp4[i].clone())
+            )
+
+        # Stack weights and scales for all experts
+        gemm1_weights_fp4_interleaved = torch.stack(
+            gemm1_weights_fp4_interleaved
+        ).reshape(num_experts, 2 * intermediate_size, hidden_size // 2)
+        gemm1_scales_fp4_interleaved = torch.stack(
+            gemm1_scales_fp4_interleaved
+        ).reshape(num_experts, 2 * intermediate_size, hidden_size // 16)
+
+        # Shuffle weights and scaling factors for transposed mma output
+        gemm1_weights_fp4_shuffled = []
+        gemm1_scales_fp4_shuffled = []
+        gemm2_weights_fp4_shuffled = []
+        gemm2_scales_fp4_shuffled = []
+        for i in range(num_experts):
+            gemm1_weights_fp4_shuffled.append(
+                shuffle_matrix_a(
+                    gemm1_weights_fp4_interleaved[i].view(torch.uint8), epilogue_tile_m
+                )
+            )
+            gemm1_scales_fp4_shuffled.append(
+                shuffle_matrix_sf_a(
+                    gemm1_scales_fp4_interleaved[i].view(torch.uint8), epilogue_tile_m
+                )
+            )
+
+            gemm2_weights_fp4_shuffled.append(
+                shuffle_matrix_a(
+                    gemm2_weights_fp4[i].view(torch.uint8), epilogue_tile_m
+                )
+            )
+            gemm2_scales_fp4_shuffled.append(
+                shuffle_matrix_sf_a(
+                    gemm2_scales_linear_fp4[i].view(torch.uint8), epilogue_tile_m
+                )
+            )
+
+        # Stack weights for all experts
+        gemm1_weights_fp4_shuffled = torch.stack(gemm1_weights_fp4_shuffled)
+        gemm1_scales_fp4_shuffled = (
+            torch.stack(gemm1_scales_fp4_shuffled)
+            .view(torch.float8_e4m3fn)
+            .reshape(num_experts, 2 * intermediate_size, hidden_size // 16)
+        )
+
+        gemm2_weights_fp4_shuffled = torch.stack(gemm2_weights_fp4_shuffled)
+        gemm2_scales_fp4_shuffled = (
+            torch.stack(gemm2_scales_fp4_shuffled)
+            .view(torch.float8_e4m3fn)
+            .reshape(num_experts, hidden_size, intermediate_size // 16)
+        )
+        return (
+            gemm1_weights_fp4_shuffled,
+            gemm1_scales_fp4_shuffled,
+            gemm2_weights_fp4_shuffled,
+            gemm2_scales_fp4_shuffled,
+        )
+
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        """Process FP4 MoE weights after loading from serialized checkpoint.
 
-        # GEMM 1
+        Only supports pre-quantized checkpoints with FP8 weights and scales.
+        """
+
+        # GEMM 1 scale processing
         if not torch.allclose(
             layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1]
         ):
@@ -880,73 +1020,123 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
         w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0]
         layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2, requires_grad=False)
 
-        if self.enable_flashinfer_cutlass_moe:
+        # Calculate input scales based on strategy
+        if self.enable_flashinfer_cutlass_moe or self.enable_flashinfer_trtllm_moe:
             w13_input_scale = layer.w13_input_scale.max().to(torch.float32)
+            w2_input_scale = layer.w2_input_scale.max().to(torch.float32)
         else:
             w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(torch.float32)
+            w2_input_scale = layer.w2_input_scale
+
+        # Create shared parameters
         layer.g1_alphas = Parameter(
             (w13_input_scale * w13_weight_scale_2).to(torch.float32),
             requires_grad=False,
         )
-
-        assert (
-            layer.w13_weight_scale.shape[2] % 16 == 0
-        ), "Expected weight_scale.dim(1) to be divisible by 16"
-        assert (
-            layer.w13_weight_scale.dtype == torch.float8_e4m3fn
-        ), "Weight Blockscale must be represented as FP8-E4M3"
-        w13_blockscale_swizzled = self.swizzle_blockscale(layer.w13_weight_scale)
-
-        layer.w13_blockscale_swizzled = Parameter(
-            w13_blockscale_swizzled, requires_grad=False
+        layer.g2_alphas = Parameter(
+            (w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
+            requires_grad=False,
         )
-        del layer.w13_weight_scale
-
-        # This is for quantization, so we need to invert it.
         layer.w13_input_scale_quant = Parameter(
             (1 / w13_input_scale).to(torch.float32), requires_grad=False
         )
+        layer.w2_input_scale_quant = Parameter(
+            (1 / w2_input_scale).to(torch.float32), requires_grad=False
+        )
 
-        layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
+        # Validate weight scales
+        for name, weight_scale in [
+            ("w13", layer.w13_weight_scale),
+            ("w2", layer.w2_weight_scale),
+        ]:
+            assert (
+                weight_scale.shape[2] % 16 == 0
+            ), f"Expected {name}_weight_scale.dim(2) to be divisible by 16"
+            assert (
+                weight_scale.dtype == torch.float8_e4m3fn
+            ), f"{name} Weight Blockscale must be represented as FP8-E4M3"
+
+        # Weight processing based on strategy
+        if (
+            self.enable_flashinfer_trtllm_moe
+            and reorder_rows_for_gated_act_gemm is not None
+            and shuffle_matrix_sf_a is not None
+        ):
+            # FlashInfer TRTLLM processing - handles both w13 and w2
+            (
+                gemm1_weights_fp4_shuffled,
+                gemm1_scales_fp4_shuffled,
+                gemm2_weights_fp4_shuffled,
+                gemm2_scales_fp4_shuffled,
+            ) = self.prepare_static_weights_for_kernel(
+                layer.w13_weight,
+                layer.w2_weight,
+                layer.w13_weight_scale,
+                layer.w2_weight_scale,
+                layer.w2_weight.size(-2),  # hidden_size
+                layer.w13_weight.size(-2) // 2,  # intermediate_size
+                layer.w13_weight.size(0),  # num_experts
+            )
 
-        # GEMM 2
-        if self.enable_flashinfer_cutlass_moe:
-            w2_input_scale = layer.w2_input_scale.max().to(torch.float32)
-        else:
-            w2_input_scale = layer.w2_input_scale
+            # Set flashinfer parameters
+            layer.gemm1_weights_fp4_shuffled = Parameter(
+                gemm1_weights_fp4_shuffled, requires_grad=False
+            )
+            layer.gemm2_weights_fp4_shuffled = Parameter(
+                gemm2_weights_fp4_shuffled, requires_grad=False
+            )
+            layer.gemm1_scales_fp4_shuffled = Parameter(
+                gemm1_scales_fp4_shuffled, requires_grad=False
+            )
+            layer.gemm2_scales_fp4_shuffled = Parameter(
+                gemm2_scales_fp4_shuffled, requires_grad=False
+            )
 
-        layer.g2_alphas = Parameter(
-            (w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
-            requires_grad=False,
-        )
+            # Additional parameter needed for TRT-LLM
+            layer.g1_scale_c = Parameter(
+                (layer.w2_input_scale_quant * layer.g1_alphas).to(torch.float32),
+                requires_grad=False,
+            )
 
-        # This is for quantization, so we need to invert it.
-        layer.w2_input_scale_quant = Parameter(
-            (1 / w2_input_scale).to(torch.float32), requires_grad=False
-        )
+            # Clean up weights that won't be used by TRT-LLM
+            del (
+                layer.w2_weight,
+                layer.w2_weight_scale,
+                layer.w13_weight,
+                layer.w13_weight_scale,
+            )
 
-        assert (
-            layer.w2_weight_scale.shape[2] % 16 == 0
-        ), "Expected weight_scale.dim(1) to be divisible by 16"
-        assert (
-            layer.w2_weight_scale.dtype == torch.float8_e4m3fn
-        ), "Weight Blockscale must be represented as FP8-E4M3"
-        w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)
+            print("Applied flashinfer weight processing for both w13 and w2")
 
-        layer.w2_blockscale_swizzled = Parameter(
-            w2_blockscale_swizzled, requires_grad=False
-        )
-        del layer.w2_weight_scale
-        layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
+        else:
+            # CUTLASS processing - handle w13 and w2 separately
+
+            # Process w13 weights
+            w13_blockscale_swizzled = self.swizzle_blockscale(layer.w13_weight_scale)
+            layer.w13_blockscale_swizzled = Parameter(
+                w13_blockscale_swizzled, requires_grad=False
+            )
+            layer.w13_weight = Parameter(layer.w13_weight.data, requires_grad=False)
+
+            # Process w2 weights
+            w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)
+            layer.w2_blockscale_swizzled = Parameter(
+                w2_blockscale_swizzled, requires_grad=False
+            )
+            layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
+
+            # Both flashinfer cutlass and regular cutlass use same processing for w2
+            print("Applied weight processing for both w13 and w2")
 
-        device = layer.w13_weight.device
-        layer.cutlass_moe_params = CutlassMoEParams(
-            CutlassMoEType.BlockscaledFP4,
-            device,
-            num_experts=layer.num_experts,  # global num experts
-            intermediate_size_per_partition=layer.w2_weight.shape[2] * 2,  # n
-            hidden_size=layer.w13_weight.shape[2] * 2,
-        )  # k
+            # Set up CUTLASS MoE parameters
+            device = layer.w13_weight.device
+            layer.cutlass_moe_params = CutlassMoEParams(
+                CutlassMoEType.BlockscaledFP4,
+                device,
+                num_experts=layer.num_experts,  # global num experts
+                intermediate_size_per_partition=layer.w2_weight.shape[2] * 2,  # n
+                hidden_size=layer.w13_weight.shape[2] * 2,
+            )  # k
 
     @property
     def load_up_proj_weight_first(self) -> bool:
@@ -971,13 +1161,20 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
     ) -> torch.Tensor:
         assert activation == "silu", "Only SiLU activation is supported."
 
+        # Check if this is a FlashInferFP4MoE layer that should handle its own forward
+        if hasattr(layer, "gemm1_weights_fp4_shuffled"):
+            # This layer was processed with flashinfer TRTLLM - delegate to its own forward
+            return layer.forward(x, topk_output)
+
         if self.enable_flashinfer_cutlass_moe:
             assert (
                 not apply_router_weight_on_input
             ), "apply_router_weight_on_input is not supported for Flashinfer"
             # TRTLLM Cutlass moe takes in activations in BF16/Half/nvfp4 precision
             # and fp4 quantized weights loaded from the checkpoint
-            topk_weights, topk_ids, _ = topk_output
+
+            topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids
+
             output = flashinfer_cutlass_fused_moe(
                 x,
                 topk_ids.to(torch.int),
@@ -1005,7 +1202,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase):
 
         from sglang.srt.layers.moe.cutlass_moe import cutlass_moe_fp4
 
-        topk_weights, topk_ids, _ = topk_output
+        topk_weights, topk_ids = topk_output.topk_weights, topk_output.topk_ids
         output = cutlass_moe_fp4(
             a=x,
             a1_gscale=layer.w13_input_scale_quant,

python/sglang/srt/managers/schedule_batch.py

@@ -51,7 +51,6 @@ from sglang.srt.disaggregation.decode_schedule_batch_mixin import (
     ScheduleBatchDisaggregationDecodeMixin,
 )
 from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_rank
-from sglang.srt.layers.moe.utils import DeepEPMode, MoeA2ABackend
 from sglang.srt.mem_cache.allocator import (
     BaseTokenToKVPoolAllocator,
     SWATokenToKVPoolAllocator,
@@ -109,6 +108,7 @@ GLOBAL_SERVER_ARGS_KEYS = [
     "enable_triton_kernel_moe",
     "enable_multimodal",
     "enable_symm_mem",
+    "quantization",
 ]
 
 # Put some global args for easy access

python/sglang/srt/models/deepseek_v2.py

@@ -60,12 +60,9 @@ from sglang.srt.layers.linear import (
     RowParallelLinear,
 )
 from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.moe.ep_moe.layer import (
-    DeepEPMoE,
-    get_moe_impl_class,
-    should_use_flashinfer_trtllm_moe,
-)
+from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, get_moe_impl_class
 from sglang.srt.layers.moe.topk import TopK
+from sglang.srt.layers.moe.utils import should_use_flashinfer_trtllm_moe
 from sglang.srt.layers.quantization import deep_gemm_wrapper
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.fp8_kernel import (
@@ -307,19 +304,15 @@ class DeepseekV2MoE(nn.Module):
             config=config, prefix=add_prefix("gate", prefix), is_nextn=is_nextn
         )
 
-        self.topk = (
-            TopK(
-                top_k=config.num_experts_per_tok + self.num_fused_shared_experts,
-                renormalize=config.norm_topk_prob,
-                use_grouped_topk=True,
-                num_expert_group=config.n_group,
-                num_fused_shared_experts=self.num_fused_shared_experts,
-                topk_group=config.topk_group,
-                correction_bias=self.gate.e_score_correction_bias,
-                routed_scaling_factor=self.routed_scaling_factor,
-            )
-            if not should_use_flashinfer_trtllm_moe()
-            else None
+        self.topk = TopK(
+            top_k=config.num_experts_per_tok + self.num_fused_shared_experts,
+            renormalize=config.norm_topk_prob,
+            use_grouped_topk=True,
+            num_expert_group=config.n_group,
+            num_fused_shared_experts=self.num_fused_shared_experts,
+            topk_group=config.topk_group,
+            correction_bias=self.gate.e_score_correction_bias,
+            routed_scaling_factor=self.routed_scaling_factor,
         )
 
         self.experts = get_moe_impl_class()(
@@ -476,10 +469,14 @@ class DeepseekV2MoE(nn.Module):
             # router_logits: (num_tokens, n_experts)
             router_logits = self.gate(hidden_states)
             kwargs = {"hidden_states": hidden_states}
-            if self.topk is not None:
-                kwargs["topk_output"] = self.topk(hidden_states, router_logits)
+
+            # FlashInferFP4MoE (TRTLLM path) expects (TopK, router_logits) tuple
+            # Regular FusedMoE (CUTLASS path) expects StandardTopKOutput
+            if should_use_flashinfer_trtllm_moe():
+                kwargs["topk_output"] = (self.topk, router_logits)
             else:
-                kwargs["router_logits"] = router_logits
+                kwargs["topk_output"] = self.topk(hidden_states, router_logits)
+
             final_hidden_states = self.experts(**kwargs)
             if not _is_cuda:
                 final_hidden_states *= self.routed_scaling_factor
@@ -505,10 +502,14 @@ class DeepseekV2MoE(nn.Module):
         # router_logits: (num_tokens, n_experts)
         router_logits = self.gate(hidden_states)
         kwargs = {"hidden_states": hidden_states}
-        if self.topk is not None:
-            kwargs["topk_output"] = self.topk(hidden_states, router_logits)
+
+        # FlashInferFP4MoE (TRTLLM path) expects (TopK, router_logits) tuple
+        # Regular FusedMoE (CUTLASS path) expects StandardTopKOutput
+        if should_use_flashinfer_trtllm_moe():
+            kwargs["topk_output"] = (self.topk, router_logits)
         else:
-            kwargs["router_logits"] = router_logits
+            kwargs["topk_output"] = self.topk(hidden_states, router_logits)
+
         final_hidden_states = self.experts(**kwargs)
         if not _is_cuda and not _use_aiter:
             # fused in biased_grouped_topk so we can skip here

python/sglang/srt/models/glm4_moe.py

@@ -50,11 +50,9 @@ from sglang.srt.layers.linear import (
     RowParallelLinear,
 )
 from sglang.srt.layers.logits_processor import LogitsProcessor
-from sglang.srt.layers.moe.ep_moe.layer import (
-    get_moe_impl_class,
-    should_use_flashinfer_trtllm_moe,
-)
+from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
 from sglang.srt.layers.moe.topk import TopK
+from sglang.srt.layers.moe.utils import should_use_flashinfer_trtllm_moe
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.fp8_kernel import (
     is_fp8_fnuz,

python/sglang/srt/server_args.py

@@ -481,6 +481,13 @@ class ServerArgs:
                 self.tp_size,
             ], "The expert parallel size must be 1 or the same as the tensor parallel size"
 
+        if self.enable_flashinfer_trtllm_moe:
+            if not self.disable_shared_experts_fusion:
+                self.disable_shared_experts_fusion = True
+                logger.warning(
+                    "FlashInfer TRTLLM MoE is enabled. --disable-shared-experts-fusion is automatically set."
+                )
+
         # DeepEP MoE
         if self.moe_a2a_backend == "deepep":
             if self.deepep_mode == "normal":