[2/n] lora - Shared outer experts and support qwen3_30b_a3b_instruct

python/sglang/srt/lora/layers.py

@@ -711,6 +711,9 @@ def __init__(
         # initializes FusedMoE with its own moe_runner for base path
         super().__init__(base_layer, lora_backend)
 
+        self.experts_shared_outer_loras: bool = False
+        self.quant_method = base_layer.quant_method
+
         self.tp_size = getattr(base_layer, "moe_tp_size", 1)
         self.tp_rank = getattr(base_layer, "moe_tp_rank", 0)
         self.intermediate_size_per_partition = getattr(
@@ -782,6 +785,7 @@ def _get_lora_info(self):
             adapter_enabled=adapter_enabled,
             max_lora_rank=max_lora_rank,
             num_experts=self.base_layer.num_experts,
+            experts_shared_outer_loras=self.experts_shared_outer_loras,
             tp_size=self.tp_size,
             tp_rank=self.tp_rank,
             hidden_size=getattr(self.base_layer, "hidden_size", 0),
@@ -838,35 +842,75 @@ def slice_lora_a_weights(self, A: torch.Tensor, tp_rank: int):
     def slice_lora_b_weights(self, B: torch.Tensor, tp_rank: int):
         return B
 
-    def slice_moe_lora_a_weights(
-        self, A: torch.Tensor, tp_rank: int, target_module: str
-    ) -> torch.Tensor:
+    def slice_moe_lora_a_weights(self, A, tp_rank: int, target_module: str):
         """Slice LoRA A weights for MoE with TP.
 
+        Accepts:
+          - 2D tensor [rank, hidden] (single expert)
+          - 3D tensor [num_experts_or_1, rank, hidden]
+          - dict {expert_id: 2D tensor}
+
         Per-expert weight shapes:
           gate_up_proj_moe A: [rank, hidden_size]  — input is full hidden_states, no slice
           down_proj_moe A:    [rank, intermediate_size] — input is sharded intermediate
         """
         if self.tp_size <= 1:
             return A
+        if isinstance(A, dict):
+            return {
+                eid: self._slice_moe_a_2d(w, tp_rank, target_module)
+                for eid, w in A.items()
+            }
+        if isinstance(A, torch.Tensor) and A.dim() == 3:
+            return torch.stack(
+                [
+                    self._slice_moe_a_2d(A[i], tp_rank, target_module)
+                    for i in range(A.shape[0])
+                ]
+            )
+        return self._slice_moe_a_2d(A, tp_rank, target_module)
+
+    def _slice_moe_a_2d(
+        self, A: torch.Tensor, tp_rank: int, target_module: str
+    ) -> torch.Tensor:
         if target_module == "down_proj_moe":
             shard_size = self.intermediate_size_per_partition
             start = tp_rank * shard_size
             end = start + shard_size
             return A[:, start:end].contiguous()
         return A
 
-    def slice_moe_lora_b_weights(
-        self, B: torch.Tensor, tp_rank: int, target_module: str
-    ) -> torch.Tensor:
+    def slice_moe_lora_b_weights(self, B, tp_rank: int, target_module: str):
         """Slice LoRA B weights for MoE with TP.
 
+        Accepts:
+          - 2D tensor [output_dim, rank] (single expert)
+          - 3D tensor [num_experts_or_1, output_dim, rank]
+          - dict {expert_id: 2D tensor}
+
         Per-expert weight shapes:
           gate_up_proj_moe B: [intermediate_size*2, rank] — output matches sharded base w13
           down_proj_moe B:    [hidden_size, rank] — output is all-reduced, no slice
         """
         if self.tp_size <= 1:
             return B
+        if isinstance(B, dict):
+            return {
+                eid: self._slice_moe_b_2d(w, tp_rank, target_module)
+                for eid, w in B.items()
+            }
+        if isinstance(B, torch.Tensor) and B.dim() == 3:
+            return torch.stack(
+                [
+                    self._slice_moe_b_2d(B[i], tp_rank, target_module)
+                    for i in range(B.shape[0])
+                ]
+            )
+        return self._slice_moe_b_2d(B, tp_rank, target_module)
+
+    def _slice_moe_b_2d(
+        self, B: torch.Tensor, tp_rank: int, target_module: str
+    ) -> torch.Tensor:
         if target_module == "gate_up_proj_moe":
             shard_size = self.intermediate_size_per_partition
             start = tp_rank * shard_size

python/sglang/srt/lora/lora.py

@@ -137,6 +137,8 @@ def _normalize_weights(self):
         for layer in self.layers:
             weight_names = list(layer.weights.keys())
             self.normalize_qkv_proj(weight_names, layer.weights)
+            self._rename_expert_w_to_proj(layer.weights)
+            weight_names = list(layer.weights.keys())
             self.normalize_gate_up_proj(weight_names, layer.weights)
 
     def normalize_qkv_proj(
@@ -192,6 +194,23 @@ def normalize_qkv_proj(
                     weights[qkv_name] = weights[qkv_name].repeat(3, 1)
                 # else: no-op as LoRA B weight is already stacked.
 
+    def _rename_expert_w_to_proj(self, weights: Dict[str, torch.Tensor]):
+        """Rename w1 -> gate_proj, w3 -> up_proj, w2 -> down_proj so that
+        normalize_gate_up_proj can stack them into gate_up_proj."""
+        renames = {}
+        for name in list(weights.keys()):
+            new_name = name
+            if ".w1." in name:
+                new_name = name.replace(".w1.", ".gate_proj.")
+            elif ".w3." in name:
+                new_name = name.replace(".w3.", ".up_proj.")
+            elif ".w2." in name:
+                new_name = name.replace(".w2.", ".down_proj.")
+            if new_name != name:
+                renames[name] = new_name
+        for old_name, new_name in renames.items():
+            weights[new_name] = weights.pop(old_name)
+
     def normalize_gate_up_proj(
         self, weight_names: List[str], weights: Dict[str, torch.Tensor]
     ):
@@ -206,8 +225,9 @@ def normalize_gate_up_proj(
                         f"Received backend: {self.lora_backend.name}. Please verify your backend configuration "
                         f"or consider implementing custom initialization logic for other backends."
                     )
+                cat_dim = weights[weight_name].dim() - 2
                 weights[gate_up_name] = torch.cat(
-                    (weights[weight_name], weights[up_name]), 0
+                    (weights[weight_name], weights[up_name]), cat_dim
                 )
                 weights.pop(weight_name)
                 if up_name in weights:
@@ -216,7 +236,10 @@ def normalize_gate_up_proj(
                 # If gate_up_proj is already stacked, we normalize it following the SGL convention
                 gate_up_name = weight_name
                 if "lora_A" in weight_name:
-                    weights[gate_up_name] = weights[gate_up_name].repeat(2, 1)
+                    ndim = weights[gate_up_name].dim()
+                    repeat_dims = [1] * ndim
+                    repeat_dims[ndim - 2] = 2
+                    weights[gate_up_name] = weights[gate_up_name].repeat(*repeat_dims)
                 # else: no-op as LoRA B weight is already stacked.
 
     def pin_weights_in_cpu(self):

python/sglang/srt/lora/lora_manager.py

@@ -36,6 +36,7 @@
 from sglang.srt.lora.mem_pool import LoRAMemoryPool
 from sglang.srt.lora.utils import (
     LoRAType,
+    auto_detect_lora_target_modules,
     get_normalized_target_modules,
     get_target_module_name,
 )
@@ -77,8 +78,10 @@ def __init__(
             server_args.enable_lora_overlap_loading
         )
 
-        # Store eviction policy from server args
         self.eviction_policy = server_args.lora_eviction_policy
+        self._experts_shared_outer_override: Optional[bool] = (
+            server_args.experts_shared_outer_loras
+        )
 
         # LoRA backend for running sgemm kernels
         logger.info(f"Using {lora_backend} as backend of LoRA kernels.")
@@ -302,23 +305,33 @@ def update_lora_info(self):
                 if isinstance(module, FusedMoEWithLoRA) and all(
                     x in self.target_modules for x in ["gate_up_proj", "down_proj"]
                 ):
+                    gate_up_key = (
+                        "gate_up_proj_moe"
+                        if "gate_up_proj_moe" in self.memory_pool.A_buffer
+                        else "gate_up_proj"
+                    )
+                    down_key = (
+                        "down_proj_moe"
+                        if "down_proj_moe" in self.memory_pool.A_buffer
+                        else "down_proj"
+                    )
                     gate_up_a = self.memory_pool.get_tensor(
-                        target_module="gate_up_proj_moe",
+                        target_module=gate_up_key,
                         layer_id=layer_id,
                         lora_type=LoRAType.LORA_A,
                     )
                     gate_up_b = self.memory_pool.get_tensor(
-                        target_module="gate_up_proj_moe",
+                        target_module=gate_up_key,
                         layer_id=layer_id,
                         lora_type=LoRAType.LORA_B,
                     )
                     down_a = self.memory_pool.get_tensor(
-                        target_module="down_proj_moe",
+                        target_module=down_key,
                         layer_id=layer_id,
                         lora_type=LoRAType.LORA_A,
                     )
                     down_b = self.memory_pool.get_tensor(
-                        target_module="down_proj_moe",
+                        target_module=down_key,
                         layer_id=layer_id,
                         lora_type=LoRAType.LORA_B,
                     )
@@ -386,6 +399,16 @@ def init_state(
             target_modules=target_modules,
         )
 
+        if self._experts_shared_outer_override is not None:
+            self.experts_shared_outer_loras = self._experts_shared_outer_override
+        else:
+            self.experts_shared_outer_loras = self._detect_shared_outer_loras()
+        if self.experts_shared_outer_loras:
+            logger.info(
+                "Shared outer LoRA mode enabled: gate_up lora_A and "
+                "down lora_B will be shared across experts (expert_dim=1)."
+            )
+
         self.init_lora_modules()
         self.init_memory_pool()
         self.update_lora_info()
@@ -411,6 +434,26 @@ def init_lora_adapters(self, lora_paths: Optional[List[LoRARef]] = None):
                         f"Failed to load LoRA adapter {lora_ref.lora_name}: {result.error_message}"
                     )
 
+    def _detect_shared_outer_loras(self) -> bool:
+        """Auto-detect shared outer LoRA format from loaded adapter weights.
+
+        MoE adapters with shared outer experts store 3D tensors where
+        dim[0]=1 indicates weights shared across all experts, while
+        dim[0]=num_experts indicates per-expert weights.
+        Returns True if gate_up lora_A has expert_dim=1 (shared).
+        """
+        for adapter in self.loras.values():
+            for layer in adapter.layers:
+                for name, weight in layer.weights.items():
+                    if (
+                        "gate_up_proj" in name
+                        and "lora_A" in name
+                        and weight.dim() == 3
+                    ):
+                        return weight.shape[0] == 1
+            break
+        return False
+
     def init_lora_shapes(
         self,
         max_lora_rank: Optional[int] = None,
@@ -424,24 +467,27 @@ def init_lora_shapes(
 
         for lora_id, config in self.configs.items():
             # Handle PEFT shorthand strings like "all-linear" or "all".
-            # These cannot be resolved to concrete module names without
-            # inspecting the base model, so we require the user to specify
-            # --lora-target-modules explicitly when such shorthands are used.
             if isinstance(config.target_modules, str):
                 if config.target_modules in ("all-linear", "all"):
                     if target_modules is not None:
                         # CLI --lora-target-modules already provided; skip
                         # per-adapter inference for this adapter.
                         continue
                     else:
-                        lora_name = self.lora_refs[lora_id].lora_name
-                        raise ValueError(
-                            f"LoRA adapter '{lora_name}' uses "
-                            f"target_modules='{config.target_modules}' which cannot "
-                            "be resolved automatically. Please explicitly specify "
-                            "--lora-target-modules during server startup. You can "
-                            "specify 'all' to enable all supported module types."
+                        # Resolve by scanning the base model for all
+                        # LoRA-compatible linear modules.
+                        adapter_target_modules = auto_detect_lora_target_modules(
+                            self.base_model
+                        )
+                        logger.info(
+                            "LoRA adapter '%s' uses target_modules='%s'. "
+                            "Resolved to %s by inspecting the base model.",
+                            self.lora_refs[lora_id].lora_name,
+                            config.target_modules,
+                            sorted(adapter_target_modules),
                         )
+                        self.target_modules.update(adapter_target_modules)
+                        continue
                 else:
                     raise ValueError(
                         f"SGLang does not recognize target_modules="
@@ -585,6 +631,7 @@ def init_memory_pool(self):
             base_model=self.base_model,
             eviction_policy=self.eviction_policy,
             lora_added_tokens_size=self.lora_added_tokens_size,
+            experts_shared_outer_loras=self.experts_shared_outer_loras,
         )
 
         # Initializing memory pool with base model
@@ -672,16 +719,17 @@ def init_lora_modules(self):
             # The module should be converted if it is included in target_names
             if module_name.split(".")[-1] in self.target_modules:
                 layer_id = get_layer_id(module_name)
+                if layer_id is None:
+                    continue
                 self.lora_modules[layer_id][module_name] = self.set_lora_module(
                     module_name, module
                 )
                 continue
 
-            # Temporarily workaround for FusedMoE layer
             if isinstance(module, FusedMoE) and all(
                 x in self.target_modules for x in ["gate_up_proj", "down_proj"]
             ):
                 layer_id = get_layer_id(module_name)
-                self.lora_modules[layer_id][module_name] = self.set_lora_module(
-                    module_name, module
-                )
+                lora_module = self.set_lora_module(module_name, module)
+                lora_module.experts_shared_outer_loras = self.experts_shared_outer_loras
+                self.lora_modules[layer_id][module_name] = lora_module

python/sglang/srt/lora/lora_moe_runners.py

@@ -71,17 +71,22 @@
 class LoRAInfo:
     """LoRA weights and dispatch info for MoE computation."""
 
-    # LoRA weights: [num_loras, num_experts, dim1, dim2]
+    # LoRA weights: [num_loras, num_experts_or_1, dim1, dim2]
+    # When experts_shared_outer_loras=True:
+    #   gate_up_lora_a: [num_loras, 1, max_rank, hidden_dim] (shared)
+    #   down_lora_b: [num_loras, 1, hidden_dim, max_rank] (shared)
     gate_up_lora_a_weights: (
         torch.Tensor
-    )  # [num_loras, num_experts, max_rank, hidden_dim]
+    )  # [num_loras, num_experts_or_1, max_rank, hidden_dim]
     gate_up_lora_b_weights: (
         torch.Tensor
     )  # [num_loras, num_experts, gate_up_dim, max_rank]
     down_lora_a_weights: (
         torch.Tensor
     )  # [num_loras, num_experts, max_rank, intermediate_dim]
-    down_lora_b_weights: torch.Tensor  # [num_loras, num_experts, hidden_dim, max_rank]
+    down_lora_b_weights: (
+        torch.Tensor
+    )  # [num_loras, num_experts_or_1, hidden_dim, max_rank]
 
     # Indice pointers of each segment in shape (num_segments + 1, )
     seg_indptr: torch.Tensor
@@ -95,6 +100,7 @@ class LoRAInfo:
     max_lora_rank: int  # Maximum LoRA rank across all adapters
 
     num_experts: int
+    experts_shared_outer_loras: bool = False
 
     fully_sharded: bool = False
     tp_size: int = 1
@@ -469,16 +475,11 @@ def _add_lora_gate_up_delta(
 
         r = lora_info.max_lora_rank
         gate_up_a = lora_info.gate_up_lora_a_weights
+        if lora_info.experts_shared_outer_loras:
+            gate_up_a = gate_up_a.expand(-1, lora_info.num_experts, -1, -1)
         gate_up_b = lora_info.gate_up_lora_b_weights
         inter_size = gate_up_b.shape[2] // 2
 
-        # Split packed gate_up weights into separate gate and up slices.
-        # gate_up_lora_a has shape [max_loras, num_experts, 2*r, hidden_dim]
-        # where the first r rows are gate_lora_a and the next r are up_lora_a.
-        # gate_up_lora_b has shape [max_loras, num_experts, 2*inter_size, r]
-        # where the first inter_size rows are gate_lora_b and the rest up_lora_b.
-        # Using num_slices=2 lets the kernel handle gate and up independently,
-        # keeping the rank dimension at r so shrink and expand both match.
         lora_a_stacked = [gate_up_a[:, :, :r, :], gate_up_a[:, :, r : 2 * r, :]]
         lora_b_stacked = [
             gate_up_b[:, :, :inter_size, :],
@@ -542,8 +543,12 @@ def _add_lora_down_delta(
         if lora_info.max_lora_rank == 0:
             return
 
+        down_lora_b = lora_info.down_lora_b_weights
+        if lora_info.experts_shared_outer_loras:
+            down_lora_b = down_lora_b.expand(-1, lora_info.num_experts, -1, -1)
+
         lora_a_stacked = [lora_info.down_lora_a_weights]
-        lora_b_stacked = [lora_info.down_lora_b_weights]
+        lora_b_stacked = [down_lora_b]
 
         if lora_info.fully_sharded and lora_info.tp_size > 1:
             shard_size = lora_info.hidden_size // lora_info.tp_size