Update setup.py

* add rl qwen model support

* fix

* fix

* add_commit_config

* fix

fastdeploy/distributed/communication_op.py

@@ -17,13 +17,15 @@
 import paddle
 import paddle.distributed as dist
 
-
-@paddle.jit.marker.unified
-def tensor_model_parallel_all_reduce(input_: paddle.Tensor) -> paddle.Tensor:
-    """All-reduce the input tensor across model parallel group."""
-    if paddle.in_dynamic_mode():
-        hcg = dist.fleet.get_hybrid_communicate_group()
-        mp_group = hcg.get_model_parallel_group()
-        dist.all_reduce(input_, group=mp_group)
-    else:
-        dist.all_reduce(input_)
+try:
+    @paddle.jit.marker.unified
+    def tensor_model_parallel_all_reduce(input_: paddle.Tensor) -> paddle.Tensor:
+        """All-reduce the input tensor across model parallel group."""
+        if paddle.in_dynamic_mode():
+            hcg = dist.fleet.get_hybrid_communicate_group()
+            mp_group = hcg.get_model_parallel_group()
+            dist.all_reduce(input_, group=mp_group)
+        else:
+            dist.all_reduce(input_)
+except:
+    tensor_model_parallel_all_reduce=None

fastdeploy/engine/config.py

@@ -17,6 +17,7 @@
 import json
 import os
 from datetime import datetime
+from dataclasses import dataclass
 from typing import Any, Dict, List, Literal, Optional
 
 from fastdeploy import envs
@@ -467,7 +468,63 @@ class ParallelConfig:
         llm_logger.info("Parallel Configuration Information :")
         for k, v in self.__dict__.items():
             llm_logger.info("{:<20}:{:<6}{}".format(k, "", v))
-        llm_logger.info("==================")
+        llm_logger.info(
+            "=============================================================")
+
+
+@dataclass
+class CommitConfig:
+    """
+    Configuration for tracking version information from version.txt
+
+    Attributes:
+        fastdeploy_commit: Full FastDeploy git commit hash
+        paddle_version: PaddlePaddle version string
+        paddle_commit: PaddlePaddle git commit hash
+        cuda_version: CUDA version string
+        compiler_version: CXX compiler version string
+    """
+    fastdeploy_commit: str = ""
+    paddle_version: str = ""
+    paddle_commit: str = ""
+    cuda_version: str = ""
+    compiler_version: str = ""
+
+    def __post_init__(self):
+        """Automatically load version info when initialized"""
+        self._load_from_version_file()
+
+    def _load_from_version_file(self, file_path: str = "fastdeploy/version.txt"):
+        """Internal method to load version info from file"""
+        try:
+            with open(file_path, 'r') as f:
+                for line in f:
+                    line = line.strip()
+                    if line.startswith("fastdeploy GIT COMMIT ID:"):
+                        self.fastdeploy_commit = line.split(":")[1].strip()
+                    elif line.startswith("Paddle version:"):
+                        self.paddle_version = line.split(":")[1].strip()
+                    elif line.startswith("Paddle GIT COMMIT ID:"):
+                        self.paddle_commit = line.split(":")[1].strip()
+                    elif line.startswith("CUDA version:"):
+                        self.cuda_version = line.split(":")[1].strip()
+                    elif line.startswith("CXX compiler version:"):
+                        self.compiler_version = line.split(":")[1].strip()
+        except FileNotFoundError:
+            llm_logger.info(f"Warning: Version file not found at {file_path}")
+        except Exception as e:
+            llm_logger.info(f"Warning: Could not read version file - {str(e)}")
+
+    def print(self):
+        """
+        print all config
+
+        """
+        llm_logger.info("Fasedeploy Commit Information :")
+        for k, v in self.__dict__.items():
+            llm_logger.info("{:<20}:{:<6}{}".format(k, "", v))
+        llm_logger.info(
+            "=============================================================")
 
 
 class Config:
@@ -502,6 +559,7 @@ class Config:
         cache_config: CacheConfig,
         scheduler_config: SchedulerConfig,
         parallel_config: ParallelConfig,
+        commit_config: CommitConfig = CommitConfig(),
         model_name_or_path: str = None,
         tokenizer: str = None,
         tensor_parallel_size: int = 8,
@@ -561,6 +619,7 @@ class Config:
         self.cache_config = cache_config
         self.scheduler_config = scheduler_config
         self.parallel_config = parallel_config
+        self.commit_config = commit_config
         self.model_name_or_path = model_name_or_path
         self.tokenizer = tokenizer
         self.max_num_batched_tokens = max_num_batched_tokens
@@ -749,7 +808,11 @@ class Config:
             if k == "generation_config" and v is not None:
                 for gck, gcv in v.to_dict().items():
                     llm_logger.info("{:<20}:{:<6}{}".format(gck, "", gcv))
-            elif k == "cache_config" or k == "model_config" or k == "scheduler_config" or k == "parallel_config":
+            elif (k == "cache_config" or
+                  k == "model_config" or
+                  k == "scheduler_config" or
+                  k == "parallel_config" or
+                  k == "commit_config"):
                 v.print()
             else:
                 llm_logger.info("{:<20}:{:<6}{}".format(k, "", v))

fastdeploy/input/ernie_tokenizer.py

@@ -143,7 +143,7 @@ class ErnieBotTokenizer(PretrainedTokenizer):
 
     def convert_tokens_to_string(self, tokens):
         """Converts a sequence of tokens (string) in a single string."""
-        spec_init()
+        self.spec_init()
         current_sub_tokens = []
         out_string = ""
         # prev_is_special = False
@@ -216,7 +216,7 @@ class ErnieBotTokenizer(PretrainedTokenizer):
         #     if isinstance(t, AddedToken)
         # )
 
-        spec_init()
+        self.spec_init()
         text, kwargs = self.prepare_for_tokenization(text, **kwargs)
 
         # TODO: should this be in the base class?

fastdeploy/model_executor/layers/attention/flash_attn_backend.py

@@ -21,7 +21,11 @@ from dataclasses import dataclass, field
 from typing import List, Optional
 
 import paddle
-from paddle.nn.functional.flash_attention import flash_attention_v3_varlen
+
+try:
+    from paddle.nn.functional.flash_attention import flash_attention_v3_varlen
+except:
+    flash_attention_v3_varlen = None
 
 from fastdeploy.config import FDConfig
 from fastdeploy.model_executor.layers.attention.attention import Attention

fastdeploy/model_executor/layers/linear.py

@@ -293,7 +293,7 @@ class ColumnParallelLinear(LinearBase):
         )
         if self.nranks > 0:
             # col parallel
-            _set_var_distributed(self.linear_weight, split_axis=-1)
+            _set_var_distributed(self.linear_weight, split_axis=1)
 
         self.linear_bias = None
         if self.with_bias:
@@ -304,7 +304,7 @@ class ColumnParallelLinear(LinearBase):
             )
             if self.nranks > 0:
                 # col parallel
-                _set_var_distributed(self.linear_bias, split_axis=-1)
+                _set_var_distributed(self.linear_bias, split_axis=1)
 
         # smooth quant
         self.linear_shift = None

fastdeploy/model_executor/layers/moe/moe.py

@@ -89,6 +89,7 @@ class FusedMoE(nn.Layer):
         self.routed_scaling_factor = routed_scaling_factor
 
         moe_quant_config = fd_config.quant_config
+        self.moe_quant_type = None
         if moe_quant_config:
             self.quant_method = moe_quant_config.get_quant_method(self)
             self.moe_quant_type = moe_quant_config.name()
@@ -142,7 +143,7 @@ class FusedMoE(nn.Layer):
         if self.moe_quant_type == "fp8":
             #(TODO:gaoziyuan)
             pass
-        else:
+        elif self.moe_quant_type == "wint8":
             self.weight_dtype = "int8"
             self.init_weight_only_scale()
 

fastdeploy/model_executor/model_loader.py

@@ -91,8 +91,11 @@ class DefaultModelLoader(BaseModelLoader):
     def load_model(self, fd_config: FDConfig) -> nn.Layer:
         context = paddle.LazyGuard()
         architectures = fd_config.model_config.architectures[0]
-        # TODO(gongshaotian): Now, only support safetensor
-        model_class = MODEL_CLASSES[architectures]
+
+        if fd_config.load_config.dynamic_load_weight:
+            # register rl model
+            import fastdeploy.rl
+            architectures = architectures + "RL"
 
         with context:
             model_cls = ModelRegistry.get_class(architectures)
@@ -104,6 +107,8 @@ class DefaultModelLoader(BaseModelLoader):
         if fd_config.load_config.dynamic_load_weight:
             return model
 
+        # TODO(gongshaotian): Now, only support safetensor
+        model_class = MODEL_CLASSES[architectures]
         state_dict = load_composite_checkpoint(
             fd_config.parallel_config.model_name_or_path,
             model_class,

fastdeploy/model_executor/models/__init__.py

@@ -36,8 +36,7 @@ def _find_py_files(root_dir):
 
 
 def auto_models_registry(dir_path,
-                         register_path="fastdeploy.model_executor.models",
-                         suffix=""):
+                         register_path="fastdeploy.model_executor.models"):
     """
     auto registry all models in this folder
     """
@@ -49,7 +48,7 @@ def auto_models_registry(dir_path,
                 if inspect.isclass(attr) and issubclass(
                         attr,
                         ModelForCasualLM) and attr is not ModelForCasualLM:
-                    ModelRegistry.register(attr, suffix=suffix)
+                    ModelRegistry.register(attr)
         except ImportError:
             raise ImportError(f"{module_file=} import error")
 

fastdeploy/model_executor/models/model_base.py

@@ -28,12 +28,12 @@ class ModelRegistry:
     _registry = {}
 
     @classmethod
-    def register(cls, model_class, suffix=""):
+    def register(cls, model_class):
         """register model class"""
         if issubclass(
                 model_class,
                 ModelForCasualLM) and model_class is not ModelForCasualLM:
-            cls._registry[f"{model_class.name()}{suffix}"] = model_class
+            cls._registry[model_class.name()] = model_class
         return model_class
 
     @classmethod

fastdeploy/model_executor/models/qwen2.py

@@ -302,6 +302,7 @@ class Qwen2ForCausalLM(ModelForCasualLM):
         """
         super(Qwen2ForCausalLM, self).__init__(fd_config)
 
+        self.fd_config =fd_config
         self.model = Qwen2Model(fd_config=fd_config)
 
         self.ori_vocab_size = fd_config.model_config.ori_vocab_size

fastdeploy/model_executor/ops/gpu/__init__.py

@@ -13,10 +13,19 @@
 # limitations under the License.
 """fastdeploy gpu ops"""
 
-import os
+import sys
+
 from fastdeploy.import_ops import import_custom_ops
 
 PACKAGE = "fastdeploy.model_executor.ops.gpu"
 
 import_custom_ops(PACKAGE, "..base.fastdeploy_base_ops", globals())
 import_custom_ops(PACKAGE, ".fastdeploy_ops", globals())
+
+
+def tolerant_import_error():
+    class NoneModule:
+        def __getattr__(self, name):
+            return None
+
+    sys.modules[__name__] = NoneModule()

fastdeploy/rl/__init__.py

@@ -17,4 +17,4 @@ import os
 
 from fastdeploy.model_executor.models import auto_models_registry
 
-auto_models_registry(os.path.dirname(__file__), "fastdeploy.rl", suffix="RL")
+auto_models_registry(os.path.dirname(__file__), "fastdeploy.rl")

fastdeploy/rl/rollout_config.py

@@ -0,0 +1,108 @@
+"""
+# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+
+from fastdeploy.worker.worker_process import initialize_fd_config
+
+
+class RolloutModelConfig:
+    def __init__(
+        self,
+        model_name_or_path: str,
+        max_model_len: int = 32768,
+        tensor_parallel_size: int = 4,
+        dynamic_load_weight: bool = True,
+        load_strategy: str = "meta",
+        enable_mm: bool = False,
+        # Default values for all other parameters
+        max_num_seqs: int = 34,
+        total_block_num: int = 2000,
+        block_size: int = 64,
+        engine_worker_queue_port: int = 9923,
+        device_ids: str = "0",
+        dtype: str = "bfloat16",
+        enc_dec_block_num: int = 1,
+        kv_cache_ratio: float = 0.7,
+        first_token_id: int = 1,
+        gpu_memory_utilization: float = 0.9,
+        engine_pid: int = None,
+        do_profile: bool = False,
+        pad_token_id: int = -1,
+        eos_tokens_lens: int = 2,
+        enable_chunked_prefill: bool = False,
+        speculative_method: str = None,
+        speculative_max_draft_token_num: int = 1,
+        speculative_model_name_or_path: str = "",
+        speculative_model_quantization: str = "WINT8",
+        max_num_batched_tokens: int = 2048,
+        enable_prefix_caching: bool = False,
+        splitwise_role: str = "mixed",
+        expert_parallel_size: int = 1,
+        enable_expert_parallell: bool = False,
+        ori_vocab_size: int = None,
+        quantization: str = "None",
+        enable_static_graph_inference: bool = False,
+        use_cudagraph: bool = False,
+        max_capture_batch_size: int = 64,
+        guided_decoding_backend: str = "off",
+        disable_any_whitespace: bool = True,
+    ):
+        # Required parameters
+        self.model_name_or_path = model_name_or_path
+        self.max_model_len = max_model_len
+        self.tensor_parallel_size = tensor_parallel_size
+        self.dynamic_load_weight = dynamic_load_weight
+        self.load_strategy = load_strategy
+        self.enable_mm = enable_mm
+
+        # Optional parameters with defaults
+        self.max_num_seqs = max_num_seqs
+        self.total_block_num = total_block_num
+        self.block_size = block_size
+        self.engine_worker_queue_port = engine_worker_queue_port
+        self.device_ids = device_ids
+        self.dtype = dtype
+        self.enc_dec_block_num = enc_dec_block_num
+        self.kv_cache_ratio = kv_cache_ratio
+        self.first_token_id = first_token_id
+        self.gpu_memory_utilization = gpu_memory_utilization
+        self.engine_pid = engine_pid
+        self.do_profile = do_profile
+        self.pad_token_id = pad_token_id
+        self.eos_tokens_lens = eos_tokens_lens
+        self.enable_chunked_prefill = enable_chunked_prefill
+        self.speculative_method = speculative_method
+        self.speculative_max_draft_token_num = speculative_max_draft_token_num
+        self.speculative_model_name_or_path = speculative_model_name_or_path
+        self.speculative_model_quantization = speculative_model_quantization
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.enable_prefix_caching = enable_prefix_caching
+        self.splitwise_role = splitwise_role
+        self.expert_parallel_size = expert_parallel_size
+        self.enable_expert_parallell = enable_expert_parallell
+        self.ori_vocab_size = ori_vocab_size
+        self.quantization = quantization
+        self.enable_static_graph_inference = enable_static_graph_inference
+        self.use_cudagraph = use_cudagraph
+        self.max_capture_batch_size = max_capture_batch_size
+        self.guided_decoding_backend = guided_decoding_backend
+        self.disable_any_whitespace = disable_any_whitespace
+
+    def __str__(self):
+        return "\n".join(f"{k}: {v}" for k, v in self.__dict__.items())
+
+    def initialize(self):
+        """Initialize the final fd config"""
+        return initialize_fd_config(self)

fastdeploy/rl/rollout_model.py

@@ -24,25 +24,18 @@ from fastdeploy.config import FDConfig
 from fastdeploy.model_executor.model_loader import ModelRegistry
 from fastdeploy.model_executor.models.ernie4_5_moe import \
     Ernie4_5_MoeForCausalLM
-from fastdeploy.model_executor.models.qwen2 import Qwen2PretrainedModel
-from fastdeploy.model_executor.models.qwen3 import Qwen3PretrainedModel
-from fastdeploy.model_executor.models.qwen3moe import Qwen3MoePretrainedModel
+from fastdeploy.model_executor.models.qwen2 import Qwen2ForCausalLM
+from fastdeploy.model_executor.models.qwen3 import Qwen3ForCausalLM
+from fastdeploy.model_executor.models.qwen3moe import Qwen3MoeForCausalLM
+from fastdeploy.rl.rollout_config import RolloutModelConfig
 
-RL_MODEL_CLASSES = {
-    "Ernie4_5_MoeForCausalLMRL": Ernie4_5_MoeForCausalLM,
-    "Qwen2ForCausalLMRL": Qwen2PretrainedModel,
-    "Qwen3ForCausalLMRL": Qwen3PretrainedModel,
-    "Qwen3MoeForCausalLMRL": Qwen3MoePretrainedModel,
-}
-
-
-class RollOutModel(nn.Layer):
+class RolloutModel(nn.Layer):
     """Main model class for rollout operations, supports multimodal components for train."""
 
-    def __init__(self, fd_config: FDConfig):
+    def __init__(self, rollout_model_config: RolloutModelConfig):
         """Initialize with FastDeploy configuration."""
-        super(RollOutModel, self).__init__()
-        self.fd_config = fd_config
+        super(RolloutModel, self).__init__()
+        self.fd_config = rollout_model_config.initialize()
         self._init_models()
 
     def _init_models(self):
@@ -90,9 +83,9 @@ class RollOutModel(nn.Layer):
         all_params = {}
         for model in self.rollout_models:
             for name, param in model.state_dict().items():
-                logger.debug(
-                    f"Model param: {name}, shape={param.shape}, dtype={param.dtype}"
-                )
+                # logger.debug(
+                #     f"Model param: {name}, shape={param.shape}, dtype={param.dtype}"
+                # )
                 all_params[name] = param
         return all_params
 
@@ -123,11 +116,13 @@ class Ernie4_5_MoeForCausalLMRL(Ernie4_5_MoeForCausalLM):
         # Initialize mapping dictionary
         infer_to_train = {}
 
+        infer_base_name = "model"
+        train_base_name = "ernie"
         # Static mappings (non-layer specific)
         static_mappings = {
-            "model.embeddings.word_embeddings.weight":
-            "ernie.embed_tokens.weight",
-            "model.norm.ln_weight": "ernie.norm.weight",
+            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"
         }
         if self.fd_config.model_config.get("weight_sharing", False):
@@ -135,53 +130,55 @@ class Ernie4_5_MoeForCausalLMRL(Ernie4_5_MoeForCausalLM):
             logger.debug("enable tie_word_embeddings")
             static_mappings.pop("lm_head.out_linear.weight")
         infer_to_train.update(static_mappings)
-        infer_base_name = "model.hidden_layers"
+
+        infer_base_name = infer_base_name + ".hidden_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
             for ph in place_holders:
                 infer_key = f"{infer_base_name}.{layer_idx}.input_layernorm.ln_{ph}"
-                train_key = f"ernie.layers.{layer_idx}.input_layernorm.{ph}"
+                train_key = f"{train_base_name}.{layer_idx}.input_layernorm.{ph}"
                 infer_to_train[infer_key] = train_key
 
             # Common attention mappings
             for ph in place_holders:
                 infer_to_train[f"{infer_base_name}.{layer_idx}.self_attn.qkv_proj.linear_{ph}"] = \
-                    f"ernie.layers.{layer_idx}.self_attn.qkv_proj.{ph}"
+                    f"{train_base_name}.{layer_idx}.self_attn.qkv_proj.{ph}"
 
                 infer_to_train[f"{infer_base_name}.{layer_idx}.self_attn.o_proj.linear_{ph}"] = \
-                    f"ernie.layers.{layer_idx}.self_attn.o_proj.{ph}"
+                    f"{train_base_name}.{layer_idx}.self_attn.o_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"ernie.layers.{layer_idx}.post_attention_layernorm.{ph}"
+                    f"{train_base_name}.{layer_idx}.post_attention_layernorm.{ph}"
 
             if not is_moe_layer:
                 # Dense FFN mappings
                 for ph in place_holders:
                     infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.gate_up_proj.linear_{ph}"] = \
-                        f"ernie.layers.{layer_idx}.mlp.up_gate_proj.{ph}"
+                        f"{train_base_name}.{layer_idx}.mlp.up_gate_proj.{ph}"
 
                     infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.down_proj.linear_{ph}"] = \
-                        f"ernie.layers.{layer_idx}.mlp.down_proj.{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.fused_moe.gate_weight"] = \
-                    f"ernie.layers.{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"ernie.layers.{layer_idx}.mlp.moe_statics.e_score_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:
                     infer_to_train[f"{infer_base_name}.{layer_idx}.mlp.shared_experts.gate_up_proj.linear_weight"] = \
-                        f"ernie.layers.{layer_idx}.mlp.shared_experts.up_gate_proj.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"ernie.layers.{layer_idx}.mlp.shared_experts.down_proj.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):
@@ -191,7 +188,7 @@ class Ernie4_5_MoeForCausalLMRL(Ernie4_5_MoeForCausalLM):
                         if ffn1_key not in infer_to_train:
                             infer_to_train[ffn1_key] = []
                         infer_to_train[ffn1_key].append(
-                            f"ernie.layers.{layer_idx}.mlp.experts.{expert_idx}.up_gate_proj.{ph}"
+                            f"{train_base_name}.{layer_idx}.mlp.experts.{expert_idx}.up_gate_proj.{ph}"
                         )
 
                         # FFN2 (down_proj)
@@ -199,7 +196,7 @@ class Ernie4_5_MoeForCausalLMRL(Ernie4_5_MoeForCausalLM):
                         if ffn2_key not in infer_to_train:
                             infer_to_train[ffn2_key] = []
                         infer_to_train[ffn2_key].append(
-                            f"ernie.layers.{layer_idx}.mlp.experts.{expert_idx}.down_proj.{ph}"
+                            f"{train_base_name}.{layer_idx}.mlp.experts.{expert_idx}.down_proj.{ph}"
                         )
 
         # Process non-MoE layers
@@ -213,3 +210,118 @@ class Ernie4_5_MoeForCausalLMRL(Ernie4_5_MoeForCausalLM):
             _add_layer_mappings(layer_idx, is_moe_layer=True)
 
         return infer_to_train
+
+
+class Qwen2ForCausalLMRL(Qwen2ForCausalLM):
+    """
+    Qwen2ForCausalLMRL
+    """
+
+    def __init__(self, fd_config: FDConfig):
+        """
+        Args:
+            fd_config (FDConfig): Configurations for the LLM model.
+        """
+        super(Qwen2ForCausalLMRL, self).__init__(fd_config)
+
+    @classmethod
+    def name(self):
+        """name"""
+        return "Qwen2ForCausalLMRL"
+
+    def get_name_mappings_to_training(self):
+        """Generate mapping between inference and training parameter for RL(donot delete!)."""
+        # Prepare placeholders
+        place_holders = ["weight"]
+
+        # Initialize mapping dictionary
+        infer_to_train = {}
+
+        infer_base_name = "model"
+        train_base_name = "qwen2"
+        # 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):
+            # 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 qkv proj need bias
+            for ph in ["weight", "bias"]:
+                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}"
+
+            # 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}"
+
+        for layer_idx in range(
+                self.fd_config.model_config.num_layers):
+            _add_layer_mappings(layer_idx)
+
+        return infer_to_train
+
+
+class Qwen3MoeForCausalLMRL(Qwen3MoeForCausalLM):
+    """
+    Qwen3MoeForCausalLMRL
+    """
+
+    def __init__(self, fd_config: FDConfig):
+        """
+        Args:
+            fd_config (FDConfig): Configurations for the LLM model.
+        """
+        super(Qwen3MoeForCausalLMRL, self).__init__(fd_config)
+
+    @classmethod
+    def name(self):
+        """name"""
+        return "Qwen3MoeForCausalLMRL"
+
+    def get_name_mappings_to_training(self):
+        """Generate mapping between inference and training parameter for RL(donot delete!)."""
+        pass
+
+
+class Qwen3ForCausalLMRL(Qwen3ForCausalLM):
+    """
+    Qwen3ForCausalLMRL
+    """
+
+    def __init__(self, fd_config: FDConfig):
+        """
+        Args:
+            fd_config (FDConfig): Configurations for the LLM model.
+        """
+        super(Qwen3ForCausalLMRL, self).__init__(fd_config)
+
+    @classmethod
+    def name(self):
+        """name"""
+        return "Qwen3ForCausalLMRL"

fastdeploy/worker/worker_process.py

@@ -511,7 +511,7 @@ def parse_args():
 
     parser.add_argument("--quantization",
                         type=str,
-                        default="",
+                        default="None",
                         help="Quantization name for the model, currentlly support " \
                             "'wint4', 'wint8'," \
                             "default is None. The priority of this configuration "\
@@ -555,148 +555,169 @@ def parse_args():
     return args
 
 
-def initialize_fd_config(args: argparse.Namespace) -> FDConfig:
-    """Initialize FDConfig
-    TODO(gongshaotian): Unified all configs to FDConfig
+def initialize_fd_config(config) -> FDConfig:
+    """Initialize FDConfig from either RolloutModelConfig or argparse.Namespace
+
+    Args:
+        config: Configuration object containing all parameters (either RolloutModelConfig or argparse.Namespace)
+
+    Returns:
+        FDConfig: Initialized FastDeploy configuration object
     """
-    # NOTE(gongshaotian): From build stream line model
-    config, _ = ModelConfig.get_config_dict(args.model_name_or_path)
-    if 'num_experts' in config:
-        config['moe_num_experts'] = config.pop('num_experts')
+    # Get model config from model directory
+    model_config_dict, _ = ModelConfig.get_config_dict(config.model_name_or_path)
 
-    if 'num_experts_per_tok' in config:
-        config['moe_topk'] = config.pop('num_experts_per_tok')
-    config["head_dim"] = config.get(
-        "head_dim", config["hidden_size"] // config["num_attention_heads"])
-    config["rope_theta"] = config.get("rope_theta", 10000.0)
-    model_config = ModelConfig.from_dict(config)
-    # TODO Set `head_dim` again. Because `ModelConfig` class doesn't support feeding head_dim at all!
-    model_config.head_dim = config["head_dim"]
-    paddle.set_default_dtype(args.dtype)
+    # Handle MoE related configs
+    if 'num_experts' in model_config_dict:
+        model_config_dict['moe_num_experts'] = model_config_dict.pop('num_experts')
+    if 'num_experts_per_tok' in model_config_dict:
+        model_config_dict['moe_topk'] = model_config_dict.pop('num_experts_per_tok')
 
+    # Set default values for model config
+    model_config_dict["head_dim"] = model_config_dict.get(
+        "head_dim", model_config_dict["hidden_size"] // model_config_dict["num_attention_heads"])
+    model_config_dict["rope_theta"] = model_config_dict.get("rope_theta", 10000.0)
+
+    # Create model config object
+    model_config = ModelConfig.from_dict(model_config_dict)
+    model_config.head_dim = model_config_dict["head_dim"]
+    paddle.set_default_dtype(config.dtype)
+
+    # Initialize all config components
     device_config = DeviceConfig()
-    # model_config = ModelConfig()
-
     decoding_config = DecodingConfig()
-
     speculative_config = SpeculativeConfig()
     parallel_config = ParallelConfig()
     load_config = LoadConfig()
     moe_config = MoEConfig()
-    graph_opt_config = GraphOptimizationConfig(
-        args.enable_static_graph_inference, args.use_cudagraph,
-        args.max_capture_batch_size)
-    model_config.quantization = args.quantization
 
-    # Update speculate config
-    speculative_config.method = args.speculative_method
-    speculative_config.num_speculative_tokens = args.speculative_max_draft_token_num
-    speculative_config.model_name_or_path = args.speculative_model_name_or_path
-    speculative_config.quantization = args.speculative_model_quantization
+    # Handle graph optimization config (check for attribute existence for backward compatibility)
+    enable_static_graph_inference = getattr(config, 'enable_static_graph_inference', False)
+    use_cudagraph = getattr(config, 'use_cudagraph', False)
+    max_capture_batch_size = getattr(config, 'max_capture_batch_size', 0)
+
+    graph_opt_config = GraphOptimizationConfig(
+        enable_static_graph_inference,
+        use_cudagraph,
+        max_capture_batch_size
+    )
+
+    # Handle quantization (check for attribute existence)
+    model_config.quantization = getattr(config, 'quantization', None)
+
+    # Update speculative config
+    speculative_config.method = getattr(config, 'speculative_method', None)
+    speculative_config.num_speculative_tokens = getattr(config, 'speculative_max_draft_token_num', 0)
+    speculative_config.model_name_or_path = getattr(config, 'speculative_model_name_or_path', None)
+    speculative_config.quantization = getattr(config, 'speculative_model_quantization', None)
 
     # Update parallel config
-    parallel_config.engine_pid = args.engine_pid
-    parallel_config.model_name_or_path = args.model_name_or_path
-    parallel_config.max_num_seqs = args.max_num_seqs
-    parallel_config.max_block_num = args.total_block_num
-    parallel_config.block_size = args.block_size
-    parallel_config.engine_worker_queue_port = args.engine_worker_queue_port
-    parallel_config.max_model_len = args.max_model_len
-    model_config.max_seq_len = args.max_model_len
-    model_config.max_length = args.max_model_len
-    parallel_config.device_ids = args.device_ids
-    parallel_config.dtype = args.dtype
-    parallel_config.enc_dec_block_num = args.enc_dec_block_num
-    parallel_config.kv_cache_ratio = args.kv_cache_ratio
-    parallel_config.first_token_id = args.first_token_id
-    parallel_config.gpu_memory_utilization = args.gpu_memory_utilization
-    parallel_config.engine_pid = args.engine_pid
-    parallel_config.do_profile = args.do_profile
-    parallel_config.dynamic_load_weight = args.dynamic_load_weight
-    parallel_config.pad_token_id = args.pad_token_id
-    parallel_config.eos_tokens_lens = args.eos_tokens_lens
-    parallel_config.enable_chunked_prefill = args.enable_chunked_prefill
-    parallel_config.max_num_batched_tokens = args.max_num_batched_tokens
-    parallel_config.enable_prefix_caching = args.enable_prefix_caching
+    parallel_config.engine_pid = getattr(config, 'engine_pid', None)
+    parallel_config.model_name_or_path = config.model_name_or_path
+    parallel_config.max_num_seqs = getattr(config, 'max_num_seqs', 0)
+    parallel_config.max_block_num = getattr(config, 'total_block_num', 0)
+    parallel_config.block_size = getattr(config, 'block_size', 0)
+    parallel_config.engine_worker_queue_port = getattr(config, 'engine_worker_queue_port', 0)
+    parallel_config.max_model_len = getattr(config, 'max_model_len', 0)
+    model_config.max_seq_len = getattr(config, 'max_model_len', 0)
+    model_config.max_length = getattr(config, 'max_model_len', 0)
+    parallel_config.device_ids = getattr(config, 'device_ids', [])
+    parallel_config.dtype = config.dtype
+    parallel_config.enc_dec_block_num = getattr(config, 'enc_dec_block_num', 0)
+    parallel_config.kv_cache_ratio = getattr(config, 'kv_cache_ratio', 1.0)
+    parallel_config.first_token_id = getattr(config, 'first_token_id', None)
+    parallel_config.gpu_memory_utilization = getattr(config, 'gpu_memory_utilization', 0.9)
+    parallel_config.engine_pid = getattr(config, 'engine_pid', None)
+    parallel_config.do_profile = getattr(config, 'do_profile', False)
+    parallel_config.dynamic_load_weight = getattr(config, 'dynamic_load_weight', False)
+    parallel_config.pad_token_id = getattr(config, 'pad_token_id', None)
+    parallel_config.eos_tokens_lens = getattr(config, 'eos_tokens_lens', 0)
+    parallel_config.enable_chunked_prefill = getattr(config, 'enable_chunked_prefill', False)
+    parallel_config.max_num_batched_tokens = getattr(config, 'max_num_batched_tokens', 0)
+    parallel_config.enable_prefix_caching = getattr(config, 'enable_prefix_caching', False)
+    parallel_config.use_ep = getattr(config, 'enable_expert_parallell', False)
+    parallel_config.tensor_parallel_degree = getattr(config, 'tensor_parallel_size', 1)
+    parallel_config.expert_parallel_degree = getattr(config, 'expert_parallel_size', 1)
+    parallel_config.splitwise_role = getattr(config, 'splitwise_role', None)
+    parallel_config.guided_decoding_backend = getattr(config, 'guided_decoding_backend', None)
+    parallel_config.disable_any_whitespace = getattr(config, 'disable_any_whitespace', False)
 
-    parallel_config.use_ep = args.enable_expert_parallell
-    parallel_config.tensor_parallel_degree = args.tensor_parallel_size
-    parallel_config.expert_parallel_degree = args.expert_parallel_size
-    parallel_config.splitwise_role = args.splitwise_role
+    # Handle load config (check for environment variable)
     load_config.use_fastsafetensor = int(envs.FD_USE_FASTSAFETENSOR) == 1
 
-    parallel_config.guided_decoding_backend = args.guided_decoding_backend
-    parallel_config.disable_any_whitespace = args.disable_any_whitespace
-
+    # Log parallel config info
     logger.info(f"parallel_config.use_ep {parallel_config.use_ep}")
-    logger.info(
-        f"parallel_config.tensor_parallel_degree {parallel_config.tensor_parallel_degree}"
-    )
-    logger.info(f"args.splitwise_role {args.splitwise_role}")
+    logger.info(f"parallel_config.tensor_parallel_degree {parallel_config.tensor_parallel_degree}")
+    logger.info(f"splitwise_role {parallel_config.splitwise_role}")
 
-    if args.splitwise_role == "mixed":
+    # Set MoE phase based on splitwise role
+    if parallel_config.splitwise_role == "mixed":
         parallel_config.moe_phase = MoEPhase.PREFILL
-    elif args.splitwise_role == "prefill":
+    elif parallel_config.splitwise_role == "prefill":
         parallel_config.moe_phase = MoEPhase.PREFILL
-    elif args.splitwise_role == "decode":
+    elif parallel_config.splitwise_role == "decode":
         parallel_config.moe_phase = MoEPhase.DECODER
-    else:
+    elif parallel_config.splitwise_role is not None:
         raise NotImplementedError
 
-    num_key_value_heads = config.get("num_key_value_heads", -1)
+    # Handle model architecture specific configurations
+    num_key_value_heads = model_config_dict.get("num_key_value_heads", -1)
     if num_key_value_heads is None:
         num_key_value_heads = -1
 
-    if config.get("ffn_hidden_size", None) is not None:
-        ffn_hidden_size = config["ffn_hidden_size"]
-    elif config.get("intermediate_size", None) is not None:
-        ffn_hidden_size = config["intermediate_size"]
+    # Calculate FFN hidden size
+    if model_config_dict.get("ffn_hidden_size", None) is not None:
+        ffn_hidden_size = model_config_dict["ffn_hidden_size"]
+    elif model_config_dict.get("intermediate_size", None) is not None:
+        ffn_hidden_size = model_config_dict["intermediate_size"]
     else:
-        ffn_hidden_size = 4 * config["hidden_size"]
-        if config["hidden_act"].lower() == "swiglu":
+        ffn_hidden_size = 4 * model_config_dict["hidden_size"]
+        if model_config_dict["hidden_act"].lower() == "swiglu":
             if paddle.distributed.get_world_size() > 1:
-                multiple_of = 8 * config["num_attention_heads"]
+                multiple_of = 8 * model_config_dict["num_attention_heads"]
             else:
-                multiple_of = 4 * config["num_attention_heads"]
+                multiple_of = 4 * model_config_dict["num_attention_heads"]
             ffn_hidden_size = multiple_of * (
                 (int(2 * ffn_hidden_size / 3) + multiple_of - 1) //
                 multiple_of)
 
-    num_layers = config.get("num_layers", None) or config.get(
+    # Get number of layers
+    num_layers = model_config_dict.get("num_layers", None) or model_config_dict.get(
         "num_hidden_layers", None)
     if num_layers is None:
         raise ValueError(f"num_layers<{num_layers}> is invalid")
 
-    use_moe = config.get("moe_layer_start_index", num_layers) < num_layers
+    use_moe = model_config_dict.get("moe_layer_start_index", num_layers) < num_layers
 
+    # Update model config
     model_config.ffn_hidden_size = ffn_hidden_size
     model_config.num_layers = num_layers
-
     model_config.num_key_value_heads = num_key_value_heads
-    model_config.start_layer_index = config.get("start_layer_index", 0)
-    moe_config.num_experts = config.get("moe_num_experts", None)
-    moe_config.moe_intermediate_size = config.get("moe_intermediate_size",
-                                                  None)
-    moe_config.top_k = config.get("moe_k", config.get("moe_topk", 8))
-    moe_config.moe_num_shared_experts = config.get("moe_num_shared_experts", 0)
-    moe_config.moe_layer_start_index = config.get("moe_layer_start_index", 0)
+    model_config.start_layer_index = model_config_dict.get("start_layer_index", 0)
 
-    moe_config.num_max_dispatch_tokens_per_rank = config.get(
+    # Update MoE config
+    moe_config.num_experts = model_config_dict.get("moe_num_experts", None)
+    moe_config.moe_intermediate_size = model_config_dict.get("moe_intermediate_size", None)
+    moe_config.top_k = model_config_dict.get("moe_k", model_config_dict.get("moe_topk", 8))
+    moe_config.moe_num_shared_experts = model_config_dict.get("moe_num_shared_experts", 0)
+    moe_config.moe_layer_start_index = model_config_dict.get("moe_layer_start_index", 0)
+    moe_config.num_max_dispatch_tokens_per_rank = model_config_dict.get(
         "num_max_dispatch_tokens_per_rank", 256)
-    moe_config.moe_use_aux_free = config.get("moe_use_aux_free", False)
+    moe_config.moe_use_aux_free = model_config_dict.get("moe_use_aux_free", False)
 
-    model_config.ori_vocab_size = config.get("vocab_size", -1)
-    if "Ernie4_5_ForCausalLM" in config.get("architectures"):
-        model_config.ori_vocab_size = args.ori_vocab_size
+    # Handle vocabulary size
+    model_config.ori_vocab_size = model_config_dict.get("vocab_size", -1)
+    if "Ernie4_5_ForCausalLM" in model_config_dict.get("architectures", []):
+        model_config.ori_vocab_size = getattr(config, 'ori_vocab_size', model_config.ori_vocab_size)
 
-    if "DeepseekV3ForCausalLM" in config.get("architectures"):
+    # Handle DeepseekV3 specific config
+    if "DeepseekV3ForCausalLM" in model_config_dict.get("architectures", []):
         from paddleformers.transformers import AutoConfig
         model_config.deepseekv3 = AutoConfig.from_pretrained(
-            args.model_name_or_path)
+            config.model_name_or_path)
 
-    #TODO(@yuanrisheng): kv_cache quant config can only be
-    # stored in model config file, which should be unified
-    quantization_config = config.get("quantization_config", None)
+    # Handle quantization config
+    quantization_config = model_config_dict.get("quantization_config", None)
     if not model_config.is_quantized:
         if quantization_config is not None:
             if "kv_cache_quant_type" not in quantization_config:
@@ -711,13 +732,13 @@ def initialize_fd_config(args: argparse.Namespace) -> FDConfig:
 
     if quantization_config is not None:
         quant_config_name = quantization_config["quantization"]
-    elif args.quantization != "None":
+    elif getattr(config, 'quantization', None) != "None":
         quantization_config = {}
-        quant_config_name = args.quantization
+        quant_config_name = getattr(config, 'quantization', None)
         quantization_config["quantization"] = quant_config_name
-        # use some trick code for ernie model and will unify it in future.
-        is_ernie = "Ernie4_5_ForCausalLM" in config.get("architectures") or \
-                    "Ernie4_5_MoeForCausalLM" in config.get("architectures")
+        # Special handling for Ernie models
+        is_ernie = "Ernie4_5_ForCausalLM" in model_config_dict.get("architectures", []) or \
+                   "Ernie4_5_MoeForCausalLM" in model_config_dict.get("architectures", [])
         if use_moe and quant_config_name == "wint4" and is_ernie:
             quantization_config["dense_quant_type"] = "wint8"
             quantization_config["moe_quant_type"] = "wint4"
@@ -732,6 +753,7 @@ def initialize_fd_config(args: argparse.Namespace) -> FDConfig:
         quant_cls = get_quantization_config(quant_config_name)
         quant_config = quant_cls.from_config(quantization_config)
 
+    # Log quantization info
     logger.info("===========quantization_config==============")
     if quant_config is not None:
         if model_config.is_quantized:
@@ -742,29 +764,33 @@ def initialize_fd_config(args: argparse.Namespace) -> FDConfig:
             logger.info(
                 "Model Status: Original (will apply online quantization)")
 
-        logger.info(f"Quantization Method: {args.quantization or 'None'}")
+        logger.info(f"Quantization Method: {getattr(config, 'quantization', 'None')}")
     else:
         logger.info(
             "No quantization config found and use original weight and act dtype."
         )
 
-    model_config.architectures = config.get("architectures")
+    model_config.architectures = model_config_dict.get("architectures")
 
+    # Update load config
     logger.info("===========load_config==============")
-    load_config.dynamic_load_weight = args.dynamic_load_weight
-    load_config.load_strategy = args.load_strategy
+    load_config.dynamic_load_weight = getattr(config, 'dynamic_load_weight', False)
+    load_config.load_strategy = getattr(config, 'load_strategy', None)
     logger.info(f"- Dynamic load weight: {load_config.dynamic_load_weight}")
     logger.info(f"- Load strategy: {load_config.load_strategy}")
 
-    fd_config = FDConfig(model_config=model_config,
-                         parallel_config=parallel_config,
-                         speculative_config=speculative_config,
-                         device_config=device_config,
-                         load_config=load_config,
-                         moe_config=moe_config,
-                         decoding_config=decoding_config,
-                         quant_config=quant_config,
-                         graph_opt_config=graph_opt_config)
+    # Create and return FDConfig
+    fd_config = FDConfig(
+        model_config=model_config,
+        parallel_config=parallel_config,
+        speculative_config=speculative_config,
+        device_config=device_config,
+        load_config=load_config,
+        moe_config=moe_config,
+        decoding_config=decoding_config,
+        quant_config=quant_config,
+        graph_opt_config=graph_opt_config
+    )
 
     return fd_config
 

setup.py

@@ -172,7 +172,7 @@ cmdclass_dict['build_ext'] = CMakeBuild
 
 setup(
     name=get_name(),
-    version="2.0.0",
+    version="2.0.1",
     author="PaddlePaddle",
     author_email="dltp@baidu.com",
     description="FastDeploy: Large Language Model Serving.",