[fix] Fix NPU FusedMoE and RMSNorm (#10512)

src/llamafactory/v1/plugins/model_plugins/kernels/ops/mlp/npu_fused_moe.py

@@ -228,6 +228,30 @@ class NpuMoeFused:
         routed_out = self.experts(hidden_states, routing_weights, router_indices)
         return routed_out
 
+    @staticmethod
+    def npu_moe_experts_v5_forward(
+        self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
+    ) -> torch.Tensor:
+        """Forward pass for Transformers v5+ MoE experts using NPU fused operations.
+
+        Transformers v5 stores expert weights in F.linear layout:
+        gate_up_proj: [num_experts, 2 * intermediate_dim, hidden_dim]
+        down_proj: [num_experts, hidden_dim, intermediate_dim]
+        The NPU grouped matmul path expects matmul layout, so both weights are transposed.
+        """
+        hidden_states = hidden_states.reshape(-1, self.hidden_dim)
+        permuted_hidden_states, row_ids_map = torch_npu.npu_moe_token_permute(
+            hidden_states, top_k_index.to(torch.int32)
+        )
+        tokens_per_expert = torch.histc(top_k_index.float(), bins=self.num_experts, min=0, max=self.num_experts).long()
+
+        gate_up_proj = self.gate_up_proj.transpose(1, 2)
+        down_proj = self.down_proj.transpose(1, 2)
+        intermediate_hidden_states = GmmFunction.apply(permuted_hidden_states, gate_up_proj, tokens_per_expert)
+        intermediate_activations = torch_npu.npu_swiglu(intermediate_hidden_states, dim=-1)
+        output = GmmFunction.apply(intermediate_activations, down_proj, tokens_per_expert)
+        return torch_npu.npu_moe_token_unpermute(output, row_ids_map, probs=top_k_weights)
+
 
 class Qwen3NpuMoeFused:
     """Container for Qwen3 NPU fused MoE forward functions."""
@@ -283,16 +307,30 @@ class Qwen3NpuMoeFused:
 
 
 # moe patch config mapping
-kernel_moe_mapping = {
+if is_transformers_version_greater_than("5.0.0"):
+    kernel_moe_mapping = {
+        "Qwen3MoeForCausalLM": {
+            "Qwen3MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
+        "Qwen3VLMoeForConditionalGeneration": {
+            "Qwen3VLMoeTextExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
+        "Qwen3_5MoeForCausalLM": {
+            "Qwen3_5MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
+        "Qwen3_5MoeForConditionalGeneration": {
+            "Qwen3_5MoeExperts": NpuMoeFused.npu_moe_experts_v5_forward,
+        },
+    }
+else:
+    kernel_moe_mapping = {
+        "Qwen3MoeForCausalLM": {
+            "Qwen3MoeSparseMoeBlock": Qwen3NpuMoeFused.qwen3moe_sparse_moe_block_forward,
+        },
         "Qwen3VLMoeForConditionalGeneration": {
             "Qwen3VLMoeTextExperts": NpuMoeFused.npu_moe_experts_forward,
             "Qwen3VLMoeTextSparseMoeBlock": NpuMoeFused.npu_moe_sparse_block_forward,
-    }
+        },
-}
-
-if not is_transformers_version_greater_than("5.0.0"):
-    kernel_moe_mapping["Qwen3MoeForCausalLM"] = {
-        "Qwen3MoeSparseMoeBlock": Qwen3NpuMoeFused.qwen3moe_sparse_moe_block_forward
     }
 
 

src/llamafactory/v1/plugins/model_plugins/kernels/ops/rms_norm/npu_rms_norm.py

@@ -51,22 +51,17 @@ def _should_use_residual_rmsnorm(module):
         bool: ``True`` if the module uses residual parameterization, ``False`` otherwise.
 
     .. note::
-        This detection ensures compatibility with future model versions (e.g., Qwen3.6, Qwen4.0)
+        This must follow the module's forward semantics. Do not infer it from trained
-        without hardcoding version numbers. Two methods are used: weight value inspection
+        weight values because standard RMSNorm weights can also be close to zero.
-        (most reliable) and class name pattern matching (backward compatibility).
     """
-    if hasattr(module, "weight") and module.weight is not None:
+    residual_rmsnorm_classes = {
-        weight_mean = module.weight.data.mean().item()
+        "Qwen3_5RMSNorm",
-        if abs(weight_mean) < 0.3:
+        "Qwen3_5MoeRMSNorm",
-            return True
+        "Qwen3NextRMSNorm",
+    }
 
     class_name = module.__class__.__name__
-    residual_patterns = ["Qwen3_5", "Qwen3_6", "Qwen4"]
+    return class_name in residual_rmsnorm_classes
-    for pattern in residual_patterns:
-        if pattern in class_name:
-            return True
-
-    return False
 
 
 def npu_rms_norm_forward(self, hidden_states):
@@ -82,7 +77,7 @@ def npu_rms_norm_forward(self, hidden_states):
     _eps = getattr(self, "variance_epsilon", None) or getattr(self, "eps", 1e-6)
 
     if hasattr(self, "weight") and self.weight is not None:
-        if _should_use_residual_rmsnorm(self):
+        if getattr(self, "_npu_use_residual_rmsnorm", False):
             effective_weight = 1.0 + self.weight.float()
         else:
             effective_weight = self.weight.float()
@@ -162,6 +157,7 @@ class NpuRMSNormKernel(BaseKernel):
                 if "Gated" in module.__class__.__name__:
                     module.forward = types.MethodType(npu_gated_rms_norm_forward, module)
                 else:
+                    module._npu_use_residual_rmsnorm = _should_use_residual_rmsnorm(module)
                     module.forward = types.MethodType(npu_rms_norm_forward, module)
 
         return model