[Test]add glm45_air logprob test and rollout model (#4175)

* add glm45_air logprob test

* add glm rollout model and pretrainedmodel for rl

* add glm rollout model and test

* check

* delete cudagraph in glm45

* add UT for glm rollout model

* revert glm UT

fastdeploy/rl/rollout_model.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 """
 
+import copy
 from typing import Dict
 
 import paddle
@@ -28,6 +29,10 @@ from fastdeploy.model_executor.models.ernie4_5_vl.ernie4_5_vl_moe import (
     Ernie4_5_VLMoeForConditionalGeneration,
     Ernie4_5_VLPretrainedModel,
 )
+from fastdeploy.model_executor.models.glm4_moe import (
+    Glm4MoeForCausalLM,
+    Glm4MoePretrainedModel,
+)
 from fastdeploy.model_executor.models.model_base import ModelRegistry
 from fastdeploy.model_executor.models.qwen2 import (
     Qwen2ForCausalLM,
@@ -529,3 +534,83 @@ class Qwen2_5_VLForConditionalGenerationRL(Qwen2_5_VLForConditionalGeneration, B
         self._complete_missing_mappings()
 
         return self.infer_to_train_mapping
+
+
+class Glm4MoeForCausalLMRL(Glm4MoeForCausalLM, BaseRLModel):
+    """
+    Glm4MoeForCausalLMRL
+    """
+
+    _get_tensor_parallel_mappings = Glm4MoePretrainedModel._get_tensor_parallel_mappings
+
+    def __init__(self, fd_config: FDConfig):
+        """
+        Args:
+            fd_config (FDConfig): Configurations for the LLM model.
+        """
+        super(Glm4MoeForCausalLMRL, self).__init__(fd_config)
+
+    @classmethod
+    def name(self) -> str:
+        """name"""
+        return "Glm4MoeForCausalLMRL"
+
+    def get_name_mappings_to_training(self, trainer_degree=None) -> Dict[str, str]:
+        """Generate mapping between inference and training parameter for RL(donot delete!)."""
+        if self._mappings_built:
+            return self.infer_to_train_mapping
+
+        self.infer_to_train_mapping = {}
+        self._mappings_built = True
+        # Prepare placeholders
+        place_holders = ["weight"]
+
+        # Initialize mapping dictionary
+        self._update_base_mappings("model")
+
+        base_name = "model.layers"
+
+        # Helper function to add layer mappings
+        def _add_layer_mappings(layer_idx: int):
+            # MoE specific mappings
+            self.infer_to_train_mapping[f"{base_name}.{layer_idx}.mlp.gate.weight"] = (
+                f"{base_name}.{layer_idx}.mlp.gate.weight"
+            )
+
+            self.infer_to_train_mapping[f"{base_name}.{layer_idx}.mlp.gate.e_score_correction_bias"] = (
+                f"{base_name}.{layer_idx}.mlp.gate.e_score_correction_bias"
+            )
+
+            # MoE experts mappings
+            for expert_idx in range(self.fd_config.model_config.n_routed_experts):
+                for ph in place_holders:
+                    # up_gate_proj (up_gate_proj)
+                    up_gate_proj_key = f"{base_name}.{layer_idx}.mlp.experts.up_gate_proj_weight"
+                    if up_gate_proj_key not in self.infer_to_train_mapping:
+                        self.infer_to_train_mapping[up_gate_proj_key] = []
+                    self.infer_to_train_mapping[up_gate_proj_key].append(
+                        f"{base_name}.{layer_idx}.mlp.experts.{expert_idx}.up_gate_proj.{ph}"
+                    )
+
+                    # down_proj (down_proj)
+                    down_proj_key = f"{base_name}.{layer_idx}.mlp.experts.down_proj_weight"
+                    if down_proj_key not in self.infer_to_train_mapping:
+                        self.infer_to_train_mapping[down_proj_key] = []
+                    self.infer_to_train_mapping[down_proj_key].append(
+                        f"{base_name}.{layer_idx}.mlp.experts.{expert_idx}.down_proj.{ph}"
+                    )
+
+        # Process MoE layers
+        for layer_idx in range(
+            self.fd_config.model_config.first_k_dense_replace,
+            self.fd_config.model_config.num_hidden_layers,
+        ):
+            _add_layer_mappings(layer_idx)
+
+        self._complete_missing_mappings()
+        infer_to_train_mapping_copy = copy.deepcopy(self.infer_to_train_mapping)
+        for key in infer_to_train_mapping_copy.keys():
+            if "mlp.experts.gate_correction_bias" in key:
+                self.infer_to_train_mapping.pop(key)
+
+        return self.infer_to_train_mapping