[AMD] Enable share expert fusion with router experts for Qwen3.5 BF16 & FP8

python/sglang/srt/models/qwen2_moe.py

@@ -62,7 +62,7 @@
 )
 from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
 from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
-from sglang.srt.layers.moe.topk import TopK
+from sglang.srt.layers.moe.topk import StandardTopKOutput, TopK, TopKOutputChecker
 from sglang.srt.layers.moe.utils import (
     RoutingMethodType,
     filter_moe_weight_param_global_expert,
@@ -88,8 +88,10 @@
 from sglang.srt.utils import (
     add_prefix,
     cpu_has_amx_support,
+    get_bool_env_var,
     is_cpu,
     is_cuda,
+    is_hip,
     make_layers,
     use_intel_amx_backend,
 )
@@ -100,6 +102,25 @@
 _is_cuda = is_cuda()
 _is_cpu = is_cpu()
 _is_cpu_amx_available = cpu_has_amx_support()
+_is_hip = is_hip()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
+
+
+def can_fuse_shared_expert(
+    config: PretrainedConfig,
+) -> bool:
+    """Whether the shared expert may be fused as an extra MoE expert (Qwen3.5 + Aiter).
+
+    Caller must still gate on ``support_shared_expert_fusion`` and ``_use_aiter``.
+    """
+    if (
+        get_global_server_args().disable_shared_experts_fusion is True
+        or getattr(config, "shared_expert_intermediate_size", 0) <= 0
+        or config.shared_expert_intermediate_size != config.moe_intermediate_size
+        or get_moe_a2a_backend().is_deepep()
+    ):
+        return False
+    return True
 
 
 class Qwen2MoeMLP(nn.Module):
@@ -163,6 +184,7 @@ def __init__(
         alt_stream: Optional[torch.cuda.Stream] = None,
         prefix: str = "",
         is_nextn: bool = False,
+        support_shared_expert_fusion: bool = False,
     ):
         super().__init__()
         self.tp_size = get_tensor_model_parallel_world_size()
@@ -173,6 +195,27 @@ def __init__(
                 f"Tensor parallel size {self.tp_size} is greater than "
                 f"the number of experts {config.num_experts}."
             )
+        self.num_experts = config.num_experts
+        self.num_shared_experts = 0
+        self.num_fused_shared_experts = 0
+        if hasattr(config, "n_shared_experts"):
+            # config defines the number of shared experts
+            self.num_shared_experts = config.n_shared_experts
+        elif (
+            hasattr(config, "shared_expert_intermediate_size")
+            and config.shared_expert_intermediate_size > 0
+        ):
+            # n_shared_experts is not defined, but shared_expert_intermediate_size is defined, so we use 1 as the number of shared experts
+            self.num_shared_experts = 1
+
+        self.enable_shared_expert_fusion = False  # default to False
+        if _use_aiter:
+            # enable shared expert fusion when use aiter
+            self.enable_shared_expert_fusion = (
+                support_shared_expert_fusion and can_fuse_shared_expert(config)
+            )
+        if self.enable_shared_expert_fusion:
+            self.num_fused_shared_experts = self.num_shared_experts
 
         self.topk = TopK(
             top_k=config.num_experts_per_tok,
@@ -182,14 +225,24 @@ def __init__(
 
         self.experts = get_moe_impl_class(quant_config)(
             layer_id=self.layer_id,
-            top_k=config.num_experts_per_tok,
-            num_experts=config.num_experts
-            + get_global_server_args().ep_num_redundant_experts,
+            top_k=(
+                config.num_experts_per_tok
+                if not self.enable_shared_expert_fusion
+                else config.num_experts_per_tok + self.num_fused_shared_experts
+            ),
+            num_experts=(
+                config.num_experts + get_global_server_args().ep_num_redundant_experts
+                if not self.enable_shared_expert_fusion
+                else config.num_experts
+                + get_global_server_args().ep_num_redundant_experts
+                + self.num_fused_shared_experts
+            ),
             hidden_size=config.hidden_size,
             intermediate_size=config.moe_intermediate_size,
             quant_config=quant_config,
             prefix=add_prefix("experts", prefix),
             routing_method_type=RoutingMethodType.RenormalizeNaive,
+            num_fused_shared_experts=self.num_fused_shared_experts,
         )
 
         self.gate = ReplicatedLinear(
@@ -199,7 +252,13 @@ def __init__(
             quant_config=None,
             prefix=add_prefix("gate", prefix),
         )
-        if config.shared_expert_intermediate_size > 0:
+        # When enable_shared_expert_fusion, the shared expert runs inside the MoE kernel
+        # (via _append_shared_to_topk_output); a separate shared_expert MLP would
+        # double-count. If fusion is off (num_fused_shared_experts == 0), keep shared_expert.
+        if (
+            config.shared_expert_intermediate_size > 0
+            and not self.enable_shared_expert_fusion
+        ):
             self.shared_expert = Qwen2MoeMLP(
                 hidden_size=config.hidden_size,
                 intermediate_size=config.shared_expert_intermediate_size,
@@ -245,6 +304,46 @@ def get_moe_weights(self):
             )
         ]
 
+    def _get_shared_expert_weights(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """Return sigmoid(shared_expert_gate) for fused shared expert weights."""
+        if not self.enable_shared_expert_fusion or self.shared_expert_gate is None:
+            return None
+        shared_out = self.shared_expert_gate(hidden_states)
+        shared_logits = shared_out[0] if isinstance(shared_out, tuple) else shared_out
+        return F.sigmoid(shared_logits)
+
+    def _append_shared_to_topk_output(
+        self,
+        topk_output: StandardTopKOutput,
+        hidden_states: torch.Tensor,
+    ) -> StandardTopKOutput:
+        """Append shared expert ids and weights to topk output before fused MoE."""
+        if not self.enable_shared_expert_fusion:
+            return topk_output
+        shared_weights = self._get_shared_expert_weights(hidden_states)
+        if shared_weights is None:
+            return topk_output
+        M = topk_output.topk_ids.shape[0]
+        shared_expert_id = self.num_experts
+        shared_ids = torch.full(
+            (M, self.num_fused_shared_experts),
+            shared_expert_id,
+            dtype=topk_output.topk_ids.dtype,
+            device=topk_output.topk_ids.device,
+        )
+        shared_weights = shared_weights.expand(M, self.num_fused_shared_experts).to(
+            topk_output.topk_weights.dtype
+        )
+        fused_topk_ids = torch.cat([topk_output.topk_ids, shared_ids], dim=-1)
+        fused_topk_weights = torch.cat(
+            [topk_output.topk_weights, shared_weights], dim=-1
+        )
+        return StandardTopKOutput(
+            topk_weights=fused_topk_weights,
+            topk_ids=fused_topk_ids,
+            router_logits=topk_output.router_logits,
+        )
+
     def _forward_shared_experts(self, hidden_states: torch.Tensor):
         shared_output = None
         if self.shared_expert is not None:
@@ -300,6 +399,10 @@ def _forward_router_experts(self, hidden_states: torch.Tensor):
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
         topk_output = self.topk(hidden_states, router_logits)
+        if self.enable_shared_expert_fusion and TopKOutputChecker.format_is_standard(
+            topk_output
+        ):
+            topk_output = self._append_shared_to_topk_output(topk_output, hidden_states)
         return self.experts(hidden_states, topk_output)
 
     def forward_normal_dual_stream(

python/sglang/srt/models/qwen3_5.py

@@ -86,9 +86,11 @@
     LazyValue,
     add_prefix,
     cpu_has_amx_support,
+    get_bool_env_var,
     is_cpu,
     is_cuda,
     is_gfx95_supported,
+    is_hip,
     is_npu,
     make_layers,
     set_weight_attrs,
@@ -100,6 +102,8 @@
 _is_npu = is_npu()
 _is_cpu = is_cpu()
 _is_gfx95 = is_gfx95_supported()
+_is_hip = is_hip()
+_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
 _is_amx_available = cpu_has_amx_support()
 
 
@@ -528,6 +532,7 @@ def __init__(
                 alt_stream=alt_stream,
                 prefix=add_prefix("mlp", prefix.replace(".linear_attn", "")),
                 is_nextn=is_nextn,
+                support_shared_expert_fusion=True,
             )
             is_layer_sparse = True
             is_previous_layer_sparse = True
@@ -739,6 +744,7 @@ def __init__(
                 alt_stream=alt_stream,
                 prefix=add_prefix("mlp", prefix.replace(".self_attn", "")),
                 is_nextn=is_nextn,
+                support_shared_expert_fusion=True,
             )
             is_layer_sparse = True
             is_previous_layer_sparse = True
@@ -1494,6 +1500,20 @@ def __init__(
         self.is_mrope_enabled = "mrope_section" in rope_config
 
         self.deepstack_visual_indexes = self.visual.deepstack_visual_indexes
+        self.num_fused_shared_experts = 0
+        if _use_aiter:
+            self.num_fused_shared_experts = self._get_num_fused_shared_experts()
+
+        self.enable_shared_expert_fusion = self.num_fused_shared_experts > 0
+
+    def _get_num_fused_shared_experts(self):
+        if not (
+            hasattr(self.model, "layers")
+            and len(self.model.layers) > 0
+            and hasattr(self.model.layers[0].mlp, "num_fused_shared_experts")
+        ):
+            return 0
+        return self.model.layers[0].mlp.num_fused_shared_experts
 
     def get_embed_and_head(self):
         embed = self.model.embed_tokens.weight if self.pp_group.is_first_rank else None
@@ -1525,13 +1545,19 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
             ("in_proj_ba.", "in_proj_a.", 1),
         ]
 
+        num_experts = self.config.num_experts
+
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
         expert_params_mapping = FusedMoE.make_expert_params_mapping(
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
             ckpt_up_proj_name="up_proj",
-            num_experts=self.config.num_experts,
+            num_experts=(
+                num_experts
+                if not self.enable_shared_expert_fusion
+                else num_experts + self.num_fused_shared_experts
+            ),
         )
 
         # Skip loading extra parameters for GPTQ/modelopt models.
@@ -1552,7 +1578,40 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
             ("experts.w2_weight", "experts.down_proj", 0, "w2"),
         ]
 
-        num_experts = self.config.num_experts
+        if self.enable_shared_expert_fusion:
+            """
+            When shared experts are fused, we need to map the shared experts to routed experts.
+
+            mlp.share_expert.gate_up_proj.weight  --> experts.512.gate_up_proj.weight -> experts.w13_weight, expert_id = 512
+            mlp.share_expert.down_proj.weight  --> experts.512.down_proj.weight -> experts.w2_weight, expert_id = 512
+            """
+            fused_expert_params_mapping += [
+                (
+                    "experts.w13_",
+                    f"experts.{num_experts}.gate_up_proj.",
+                    num_experts,
+                    "w1",
+                ),
+                (
+                    "experts.w2_",
+                    f"experts.{num_experts}.down_proj.",
+                    num_experts,
+                    "w2",
+                ),
+                ## shared experts may contain gate_proj and up_proj instead of gate_up_proj
+                (
+                    "experts.w13_",
+                    f"experts.{num_experts}.gate_proj.",
+                    num_experts,
+                    "w1",
+                ),
+                (
+                    "experts.w13_",
+                    f"experts.{num_experts}.up_proj.",
+                    num_experts,
+                    "w3",
+                ),
+            ]
 
         def load_fused_expert_weights(
             name: str,
@@ -1609,6 +1668,14 @@ def load_fused_expert_weights(
             ):
                 continue
 
+            if self.enable_shared_expert_fusion:
+                if "mlp.shared_expert." in name:
+                    # Firstly map mlp.shared_expert.xx_proj to mlp.experts.512.xx_proj
+                    name = name.replace(
+                        "mlp.shared_expert.",
+                        f"mlp.experts.{num_experts}.",
+                    )
+
             for param_name, weight_name, shard_id in stacked_params_mapping:
                 if name.endswith("experts.gate_up_proj") or name.endswith(
                     "experts.down_proj"
@@ -1657,7 +1724,10 @@ def load_fused_expert_weights(
                     is_expert_weight = True
                     name_mapped = name.replace(weight_name, param_name)
                     if is_fused_expert:
+                        # is_fused_expert is True, the checkpoint contains gate_up_proj and down_proj for each expert
                         if "experts.gate_up_proj" in name:
+                            # experts.gate_up_proj contains all 512 routed experts, excluding shared experts
+                            # split into w1 and w3
                             loaded_weight = loaded_weight.chunk(2, dim=-2)
                             load_fused_expert_weights(
                                 name_mapped,
@@ -1673,14 +1743,51 @@ def load_fused_expert_weights(
                                 "w3",
                                 num_experts,
                             )
-                        else:
+                        elif "experts.down_proj" in name:
+                            # experts.down_proj contains all 512 routed experts, excluding shared experts
                             load_fused_expert_weights(
                                 name_mapped,
                                 params_dict,
                                 loaded_weight,
                                 shard_id,
                                 num_experts,
                             )
+                        elif self.enable_shared_expert_fusion:
+                            # shared experts should be loaded to experts.w13_weight and experts.w2_weight
+                            param = params_dict[name_mapped]
+                            weight_loader = getattr(
+                                param, "weight_loader", default_weight_loader
+                            )
+                            param = params_dict[name_mapped]
+                            if f"{num_experts}.gate_up_proj" in name:
+                                # split into w1 and w3
+                                loaded_weight = loaded_weight.chunk(2, dim=-2)
+                                # load to experts.w13_weight, shard_id = w1, expert_id = 512
+                                weight_loader(
+                                    param,
+                                    loaded_weight[0],
+                                    name_mapped,
+                                    "w1",
+                                    expert_id,
+                                )
+                                # load to experts.w13_weight, shard_id = w3, expert_id = 512
+                                weight_loader(
+                                    param,
+                                    loaded_weight[1],
+                                    name_mapped,
+                                    "w3",
+                                    expert_id,
+                                )
+                            else:
+                                # load down_proj to experts.w2_weight, shard_id = w2, expert_id = 512
+                                # Or load gate_proj and up_proj to experts.w13_weight, shard_id = w1/w3, expert_id = 512
+                                weight_loader(
+                                    param,
+                                    loaded_weight,
+                                    name_mapped,
+                                    shard_id,
+                                    expert_id,
+                                )
                     else:
                         # Skip loading extra parameters for GPTQ models.
                         if (