support qwen3moe name_mapping (#2820)

fastdeploy/model_executor/models/model_base.py

@@ -56,6 +56,7 @@ class ModelForCasualLM(nn.Layer, ABC):
                 ori_vocab_size, use_topp_sampling, etc.
         """
         super(ModelForCasualLM, self).__init__()
+        self.fd_config = configs
 
     @abstractmethod
     def set_state_dict(self, state_dict: Dict[str, Union[np.ndarray,

fastdeploy/rl/rollout_model.py

@@ -303,7 +303,103 @@ class Qwen3MoeForCausalLMRL(Qwen3MoeForCausalLM):
 
     def get_name_mappings_to_training(self):
         """Generate mapping between inference and training parameter for RL(donot delete!)."""
-        pass
+        # Prepare placeholders
+        place_holders = ["weight"]
+
+        # Initialize mapping dictionary
+        infer_to_train = {}
+
+        infer_base_name = "model"
+        train_base_name = "model"
+        # Static mappings (non-layer specific)
+        static_mappings = {
+            f"{infer_base_name}.embeddings.word_embeddings.weight":
+            f"{train_base_name}.embed_tokens.weight",
+            f"{infer_base_name}.norm.ln_weight": f"{train_base_name}.norm.weight",
+            "lm_head.out_linear.weight": "lm_head.weight"
+        }
+        infer_to_train.update(static_mappings)
+
+        infer_base_name = infer_base_name + ".layers"
+        train_base_name = train_base_name + ".layers"
+
+        # Helper function to add layer mappings
+        def _add_layer_mappings(layer_idx, is_moe_layer=False):
+            # Handle special case for layer 0's input layernorm and attn o_proj
+            for ph in place_holders:
+                infer_key = f"{infer_base_name}.{layer_idx}.input_layernorm.ln_{ph}"
+                train_key = f"{train_base_name}.{layer_idx}.input_layernorm.{ph}"
+                infer_to_train[infer_key] = train_key
+
+                infer_to_train[f"{infer_base_name}.{layer_idx}.self_attn.o_proj.linear_{ph}"] = \
+                    f"{train_base_name}.{layer_idx}.self_attn.o_proj.{ph}"
+
+            # qwen q_norm/k_norm
+            for ph in place_holders:
+                infer_to_train[f"{infer_base_name}.{layer_idx}.self_attn.q_norm.ln_{ph}"] = \
+                    f"{train_base_name}.{layer_idx}.self_attn.q_norm.{ph}"
+                infer_to_train[f"{infer_base_name}.{layer_idx}.self_attn.k_norm.ln_{ph}"] = \
+                    f"{train_base_name}.{layer_idx}.self_attn.k_norm.{ph}"
+
+            # qwen qkv proj
+            for ph in place_holders:
+                infer_to_train[f"{infer_base_name}.{layer_idx}.self_attn.qkv_proj.linear_{ph}"] = \
+                    f"{train_base_name}.{layer_idx}.self_attn.qkv_proj.{ph}"
+
+            # Post-attention layernorm
+            for ph in place_holders:
+                infer_to_train[f"{infer_base_name}.{layer_idx}.post_attention_layernorm.ln_{ph}"] = \
+                    f"{train_base_name}.{layer_idx}.post_attention_layernorm.{ph}"
+
+            if not is_moe_layer:
+                # FFN mappings
+                for ph in place_holders:
+                    infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.gate_up_proj.linear_{ph}"] = \
+                        f"{train_base_name}.{layer_idx}.mlp.gate_up_fused_proj.{ph}"
+
+                    infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.down_proj.linear_{ph}"] = \
+                        f"{train_base_name}.{layer_idx}.mlp.down_proj.{ph}"
+            else:
+                # MoE specific mappings
+                infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.gate_weight"] = \
+                    f"{train_base_name}.{layer_idx}.mlp.gate.weight"
+
+                if self.fd_config.moe_config.moe_use_aux_free:
+                    infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.fused_moe.gate_correction_bias"] = \
+                        f"{train_base_name}.{layer_idx}.mlp.moe_statics.e_score_correction_bias"
+
+                # Support shared experts
+                if self.fd_config.model_config.get(
+                        "moe_num_shared_experts", 0) > 0:
+                    infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.shared_experts.gate_up_proj.linear_weight"] = \
+                        f"{train_base_name}.{layer_idx}.mlp.shared_experts.up_gate_proj.weight"
+                    infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.shared_experts.down_proj.linear_weight"] = \
+                        f"{train_base_name}.{layer_idx}.mlp.shared_experts.down_proj.weight"
+
+                # MoE experts mappings
+                for expert_idx in range(self.fd_config.moe_config.num_experts):
+                    for ph in place_holders:
+                        # FFN1 (up_gate_proj)
+                        ffn1_key = f"{infer_base_name}.{layer_idx}.mlp.moe_ffn1_weight"
+                        if ffn1_key not in infer_to_train:
+                            infer_to_train[ffn1_key] = []
+                        infer_to_train[ffn1_key].append(
+                            f"{train_base_name}.{layer_idx}.mlp.experts.{expert_idx}.up_gate_proj.{ph}"
+                        )
+
+                        # FFN2 (down_proj)
+                        ffn2_key = f"{infer_base_name}.{layer_idx}.mlp.moe_ffn2_weight"
+                        if ffn2_key not in infer_to_train:
+                            infer_to_train[ffn2_key] = []
+                        infer_to_train[ffn2_key].append(
+                            f"{train_base_name}.{layer_idx}.mlp.experts.{expert_idx}.down_proj.{ph}"
+                        )
+
+        # Process MoE layers
+        for layer_idx in range(self.fd_config.model_config.num_layers):
+            _add_layer_mappings(layer_idx, is_moe_layer=True)
+
+        return infer_to_train
 
 
 class Qwen3ForCausalLMRL(Qwen3ForCausalLM):