Add activation parameters to fused_moe (#3170)

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

@@ -114,6 +114,7 @@ class EPMoE(torch.nn.Module):
         tp_size: Optional[int] = None,
         prefix: str = "",
         correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
     ):
         super().__init__()
 
@@ -140,6 +141,7 @@ class EPMoE(torch.nn.Module):
         self.num_expert_group = num_expert_group
         self.topk_group = topk_group
         self.correction_bias = correction_bias
+        self.activation = activation
 
         if quant_config is None:
             self.quant_method: Optional[QuantizeMethodBase] = UnquantizedEPMoEMethod()
@@ -166,6 +168,7 @@ class EPMoE(torch.nn.Module):
 
     def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
         assert self.quant_method is not None
+        assert self.activation == "silu"
 
         if self.grouped_gemm_runner is None:
             self.grouped_gemm_runner = GroupedGemmRunner(

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

@@ -8,7 +8,7 @@ from typing import Callable, Optional
 import torch
 from torch.nn import functional as F
 
-from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.activation import GeluAndMul, SiluAndMul
 from sglang.srt.layers.moe.topk import select_experts
 
 
@@ -23,6 +23,7 @@ def fused_moe_forward_native(
     num_expert_group: Optional[int] = None,
     custom_routing_function: Optional[Callable] = None,
     correction_bias: Optional[torch.Tensor] = None,
+    activation: str = "silu",
 ) -> torch.Tensor:
     topk_weights, topk_ids = select_experts(
         hidden_states=x,
@@ -41,7 +42,12 @@ def fused_moe_forward_native(
     w1_weights, w3_weights = torch.chunk(w13_weights, 2, dim=2)
     w2_weights = layer.w2_weight[topk_ids]
     x1 = torch.einsum("ti,taoi -> tao", x, w1_weights)
-    x1 = F.silu(x1)
+    if activation == "silu":
+        x1 = F.silu(x1)
+    elif activation == "gelu":
+        x1 = F.gelu(x1)
+    else:
+        raise ValueError(f"Unsupported activation: {activation=}")
     x3 = torch.einsum("ti, taoi -> tao", x, w3_weights)
     expert_outs = torch.einsum("tao, taio -> tai", (x1 * x3), w2_weights)
     return torch.einsum("tai,ta -> ti", expert_outs, topk_weights.to(expert_outs.dtype))
@@ -58,6 +64,7 @@ def moe_forward_native(
     num_expert_group: Optional[int] = None,
     custom_routing_function: Optional[Callable] = None,
     correction_bias: Optional[torch.Tensor] = None,
+    activation: str = "silu",
 ) -> torch.Tensor:
 
     topk_weights, topk_ids = select_experts(
@@ -84,6 +91,13 @@ def moe_forward_native(
     sorted_tokens = x[idxs // topk_ids.shape[1]]
     tokens_per_expert = tokens_per_expert.cpu().numpy()
 
+    if activation == "silu":
+        act = SiluAndMul()
+    elif activation == "gelu":
+        act = GeluAndMul()
+    else:
+        raise ValueError(f"Unsupported activation: {activation=}")
+
     outputs = []
     start_idx = 0
     for i, num_tokens in enumerate(tokens_per_expert):
@@ -96,7 +110,7 @@ def moe_forward_native(
         layer_w2_weight = layer.w2_weight[i]
 
         gate_up = F.linear(tokens_for_this_expert, layer_w13_weight)
-        gate_up = SiluAndMul()(gate_up)
+        gate_up = act(gate_up)
         expert_out = F.linear(gate_up, layer_w2_weight)
         outputs.append(expert_out)
         start_idx = end_idx

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

@@ -711,6 +711,7 @@ def inplace_fused_experts(
     w2: torch.Tensor,
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
+    activation: str = "silu",
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
@@ -726,6 +727,7 @@ def inplace_fused_experts(
         topk_weights,
         topk_ids,
         True,
+        activation,
         use_fp8_w8a8,
         use_int8_w8a16,
         w1_scale,
@@ -742,6 +744,7 @@ def inplace_fused_experts_fake(
     w2: torch.Tensor,
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
+    activation: str = "silu",
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
@@ -767,6 +770,7 @@ def outplace_fused_experts(
     w2: torch.Tensor,
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
+    activation: str = "silu",
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
@@ -782,6 +786,7 @@ def outplace_fused_experts(
         topk_weights,
         topk_ids,
         False,
+        activation,
         use_fp8_w8a8,
         use_int8_w8a16,
         w1_scale,
@@ -798,6 +803,7 @@ def outplace_fused_experts_fake(
     w2: torch.Tensor,
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
+    activation: str = "silu",
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
@@ -824,6 +830,7 @@ def fused_experts(
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
     inplace: bool = False,
+    activation: str = "silu",
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
@@ -839,6 +846,7 @@ def fused_experts(
             w2,
             topk_weights,
             topk_ids,
+            activation,
             use_fp8_w8a8,
             use_int8_w8a16,
             w1_scale,
@@ -855,6 +863,7 @@ def fused_experts(
             w2,
             topk_weights,
             topk_ids,
+            activation,
             use_fp8_w8a8,
             use_int8_w8a16,
             w1_scale,
@@ -872,6 +881,7 @@ def fused_experts_impl(
     topk_weights: torch.Tensor,
     topk_ids: torch.Tensor,
     inplace: bool = False,
+    activation: str = "silu",
     use_fp8_w8a8: bool = False,
     use_int8_w8a16: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
@@ -986,7 +996,12 @@ def fused_experts_impl(
             block_shape=block_shape,
         )
 
-        ops.silu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N))
+        if activation == "silu":
+            ops.silu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N))
+        elif activation == "gelu":
+            ops.gelu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N))
+        else:
+            raise ValueError(f"Unsupported activation: {activation=}")
 
         invoke_fused_moe_kernel(
             intermediate_cache2,
@@ -1042,6 +1057,7 @@ def fused_moe(
     topk: int,
     renormalize: bool,
     inplace: bool = False,
+    activation: str = "silu",
     use_grouped_topk: bool = False,
     num_expert_group: Optional[int] = None,
     topk_group: Optional[int] = None,
@@ -1111,6 +1127,7 @@ def fused_moe(
         topk_weights,
         topk_ids,
         inplace=inplace,
+        activation=activation,
         use_fp8_w8a8=use_fp8_w8a8,
         use_int8_w8a16=use_int8_w8a16,
         w1_scale=w1_scale,

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

@@ -126,6 +126,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
         correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
     ) -> torch.Tensor:
         return self.forward(
             x=x,
@@ -138,6 +139,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             num_expert_group=num_expert_group,
             custom_routing_function=custom_routing_function,
             correction_bias=correction_bias,
+            activation=activation,
         )
 
     def forward_cuda(
@@ -152,6 +154,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
         correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
     ) -> torch.Tensor:
         topk_weights, topk_ids = select_experts(
             hidden_states=x,
@@ -169,6 +172,8 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             import ater
             from ater.fused_moe import fused_experts_ck
 
+            assert activation == "silu", f"{activation=} is not supported."
+
             return fused_experts_ck(
                 hidden_states=x,
                 w1=layer.w13_weight,
@@ -184,6 +189,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                 topk_weights=topk_weights,
                 topk_ids=topk_ids,
                 inplace=True,
+                activation=activation,
             )
 
     def forward_cpu(
@@ -256,6 +262,7 @@ class FusedMoE(torch.nn.Module):
         prefix: str = "",
         custom_routing_function: Optional[Callable] = None,
         correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
         use_presharded_weights: bool = False,
     ):
         super().__init__()
@@ -279,6 +286,7 @@ class FusedMoE(torch.nn.Module):
         self.topk_group = topk_group
         self.custom_routing_function = custom_routing_function
         self.correction_bias = correction_bias
+        self.activation = activation
 
         if quant_config is None:
             self.quant_method: Optional[QuantizeMethodBase] = (
@@ -589,6 +597,7 @@ class FusedMoE(torch.nn.Module):
             num_expert_group=self.num_expert_group,
             custom_routing_function=self.custom_routing_function,
             correction_bias=self.correction_bias,
+            activation=self.activation,
         )
 
         if self.reduce_results and self.tp_size > 1:

python/sglang/srt/layers/quantization/fp8.py

@@ -763,8 +763,8 @@ class Fp8MoEMethod:
         num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
         correction_bias: Optional[torch.Tensor] = None,
+        activation: str = "silu",
     ) -> torch.Tensor:
-        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
         from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
         from sglang.srt.layers.moe.topk import select_experts
 
@@ -785,6 +785,8 @@ class Fp8MoEMethod:
             import ater
             from ater.fused_moe import fused_experts_ck
 
+            assert activation == "silu", f"{activation=} is not supported."
+
             return fused_experts_ck(
                 x,
                 layer.w13_weight,
@@ -815,6 +817,7 @@ class Fp8MoEMethod:
                 topk_weights=topk_weights,
                 topk_ids=topk_ids,
                 inplace=True,
+                activation=activation,
                 use_fp8_w8a8=True,
                 w1_scale=(
                     layer.w13_weight_scale_inv

python/sglang/srt/models/grok.py

@@ -133,6 +133,7 @@ class Grok1MoE(nn.Module):
             renormalize=False,
             quant_config=quant_config,
             tp_size=tp_size,
+            activation="gelu",
             use_presharded_weights=use_presharded_weights,
         )
 

test/srt/test_fp8_kernel.py

@@ -2,8 +2,6 @@ import unittest
 
 import torch
 
-from sglang.srt.layers.activation import SiluAndMul
-from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_moe
 from sglang.srt.layers.quantization.fp8_kernel import (
     per_token_group_quant_fp8,
     w8a8_block_fp8_matmul,