rename ernie_xxx to ernie4_5_xxx (#3621)

* rename ernie_xxx to ernie4_5_xxx

* ci fix

docs/offline_inference.md

@@ -1,6 +1,7 @@
 # Offline Inference
 
 ## 1. Usage
+
 FastDeploy supports offline inference by loading models locally and processing user data. Usage examples:
 
 ### Chat Interface (LLM.chat)
@@ -91,10 +92,10 @@ from PIL import Image
 
 from fastdeploy.entrypoints.llm import LLM
 from fastdeploy.engine.sampling_params import SamplingParams
-from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 
 PATH = "baidu/ERNIE-4.5-VL-28B-A3B-Paddle"
-tokenizer = ErnieBotTokenizer.from_pretrained(PATH)
+tokenizer = Ernie4_5Tokenizer.from_pretrained(PATH)
 
 messages = [
     {
@@ -144,15 +145,16 @@ for output in outputs:
 
 ```
 
->Note: The `generate interface` does not currently support passing parameters to control the thinking function (on/off). It always uses the model's default parameters.
+> Note: The `generate interface` does not currently support passing parameters to control the thinking function (on/off). It always uses the model's default parameters.
 
 ## 2. API Documentation
 
 ### 2.1 fastdeploy.LLM
 
-For ```LLM``` configuration, refer to [Parameter Documentation](parameters.md).
+For ``LLM`` configuration, refer to [Parameter Documentation](parameters.md).
 
 > Configuration Notes:
+>
 > 1. `port` and `metrics_port` is only used for online inference.
 > 2. After startup, the service logs KV Cache block count (e.g. `total_block_num:640`). Multiply this by block_size (default 64) to get total cacheable tokens.
 > 3. Calculate `max_num_seqs` based on cacheable tokens. Example: avg input=800 tokens, output=500 tokens, blocks=640 → `kv_cache_ratio = 800/(800+500)=0.6`, `max_seq_len = 640*64/(800+500)=31`.

docs/zh/offline_inference.md

@@ -1,6 +1,7 @@
 # 离线推理
 
 ## 1. 使用方式
+
 通过FastDeploy离线推理，可支持本地加载模型，并处理用户数据，使用方式如下，
 
 ### 对话接口(LLM.chat)
@@ -32,9 +33,9 @@ for output in outputs:
     generated_text = output.outputs.text
 ```
 
-上述示例中```LLM```配置方式， `SamplingParams` ，`LLM.generate` ，`LLM.chat`以及输出output对应的结构体 `RequestOutput` 接口说明见如下文档说明。
+上述示例中 ``LLM``配置方式， `SamplingParams` ，`LLM.generate` ，`LLM.chat`以及输出output对应的结构体 `RequestOutput` 接口说明见如下文档说明。
 
-> 注： 若为思考模型, 加载模型时需要指定`resoning_parser` 参数，并在请求时, 可以通过配置`chat_template_kwargs` 中 `enable_thinking`参数, 进行开关思考。
+> 注： 若为思考模型, 加载模型时需要指定 `resoning_parser` 参数，并在请求时, 可以通过配置 `chat_template_kwargs` 中 `enable_thinking`参数, 进行开关思考。
 
 ```python
 from fastdeploy.entrypoints.llm import LLM
@@ -82,7 +83,7 @@ for output in outputs:
 > 注： 续写接口, 适应于用户自定义好上下文输入, 并希望模型仅输出续写内容的场景; 推理过程不会增加其他 `prompt`拼接。
 > 对于 `chat`模型, 建议使用对话接口(LLM.chat)。
 
-对于多模模型, 例如`baidu/ERNIE-4.5-VL-28B-A3B-Paddle`, 在调用`generate接口`时, 需要提供包含图片的prompt, 使用方式如下:
+对于多模模型, 例如 `baidu/ERNIE-4.5-VL-28B-A3B-Paddle`, 在调用 `generate接口`时, 需要提供包含图片的prompt, 使用方式如下:
 
 ```python
 import io
@@ -91,10 +92,10 @@ from PIL import Image
 
 from fastdeploy.entrypoints.llm import LLM
 from fastdeploy.engine.sampling_params import SamplingParams
-from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+from fastdeploy.input.ernie_tokenizer import Ernie4_5Tokenizer
 
 PATH = "baidu/ERNIE-4.5-VL-28B-A3B-Paddle"
-tokenizer = ErnieBotTokenizer.from_pretrained(PATH)
+tokenizer = Ernie4_5Tokenizer.from_pretrained(PATH)
 
 messages = [
     {
@@ -153,7 +154,8 @@ for output in outputs:
 支持配置参数参考 [FastDeploy参数说明](./parameters.md)
 
 > 参数配置说明：
-> 1. 离线推理不需要配置 `port` 和`metrics_port` 参数。
+>
+> 1. 离线推理不需要配置 `port` 和 `metrics_port` 参数。
 > 2. 模型服务启动后，会在日志文件log/fastdeploy.log中打印如 `Doing profile, the total_block_num:640` 的日志，其中640即表示自动计算得到的KV Cache block数量，将它乘以block_size(默认值64)，即可得到部署后总共可以在KV Cache中缓存的Token数。
 > 3. `max_num_seqs` 用于配置decode阶段最大并发处理请求数，该参数可以基于第1点中缓存的Token数来计算一个较优值，例如线上统计输入平均token数800, 输出平均token数500，本次计>算得到KV Cache block为640， block_size为64。那么我们可以配置 `kv_cache_ratio = 800 / (800 + 500) = 0.6` , 配置 `max_seq_len = 640 * 64 / (800 + 500) = 31`。
 
@@ -163,12 +165,12 @@ for output in outputs:
 * sampling_params: 模型超参设置具体说明见2.4
 * use_tqdm: 是否打开推理进度可视化
 * chat_template_kwargs(dict): 传递给对话模板的额外参数，当前支持enable_thinking(bool)
-  *使用示例`chat_template_kwargs={"enable_thinking": False}`*
+  *使用示例 `chat_template_kwargs={"enable_thinking": False}`*
 
 ### 2.3 fastdeploy.LLM.generate
 
 * prompts(str, list[str], list[int], list[list[int]], dict[str, Any], list[dict[str, Any]]): 输入的prompt, 支持batch prompt 输入，解码后的token ids 进行输入
-  *dict 类型使用示例`prompts={"prompt": prompt, "multimodal_data": {"image": images}}`*
+  *dict 类型使用示例 `prompts={"prompt": prompt, "multimodal_data": {"image": images}}`*
 * sampling_params: 模型超参设置具体说明见2.4
 * use_tqdm: 是否打开推理进度可视化
 
@@ -193,7 +195,7 @@ for output in outputs:
 * outputs(fastdeploy.engine.request.CompletionOutput): 输出结果
 * finished(bool)：标识当前query 是否推理结束
 * metrics(fastdeploy.engine.request.RequestMetrics)：记录推理耗时指标
-* num_cached_tokens(int): 缓存的token数量, 仅在开启```enable_prefix_caching```时有效
+* num_cached_tokens(int): 缓存的token数量, 仅在开启 ``enable_prefix_caching``时有效
 * error_code(int): 错误码
 * error_msg(str): 错误信息
 

fastdeploy/input/ernie4_5_processor.py

@@ -19,14 +19,14 @@ import os
 import numpy as np
 from paddleformers.generation import GenerationConfig
 
-from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 from fastdeploy.input.text_processor import BaseDataProcessor
 from fastdeploy.utils import data_processor_logger
 
 _SAMPLING_EPS = 1e-5
 
 
-class ErnieProcessor(BaseDataProcessor):
+class Ernie4_5Processor(BaseDataProcessor):
     """
     初始化模型实例。
 
@@ -431,9 +431,9 @@ class ErnieProcessor(BaseDataProcessor):
         ]
         for i in range(len(vocab_file_names)):
             if os.path.exists(os.path.join(self.model_name_or_path, vocab_file_names[i])):
-                ErnieBotTokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
+                Ernie4_5Tokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
                 break
-        self.tokenizer = ErnieBotTokenizer.from_pretrained(self.model_name_or_path)
+        self.tokenizer = Ernie4_5Tokenizer.from_pretrained(self.model_name_or_path)
 
     def get_pad_id(self):
         """

fastdeploy/input/ernie4_5_tokenizer.py

@@ -27,7 +27,7 @@ from paddleformers.transformers.tokenizer_utils_base import PaddingStrategy, Tex
 from paddleformers.utils.log import logger
 
 
-class ErnieBotTokenizer(PretrainedTokenizer):
+class Ernie4_5Tokenizer(PretrainedTokenizer):
     """
     一个更好用的 `ErnieBotToknizer`，
     能 encode 目前 sft/ppo 阶段的特殊token，也支持多模态。
@@ -164,7 +164,7 @@ class ErnieBotTokenizer(PretrainedTokenizer):
         """doc"""
         if "add_special_tokens" in kwargs:
             kwargs.pop("add_special_tokens")
-            # logger.warning(f'ErnieBotTokenizer v2 does not support `add_special_tokens`')
+            # logger.warning(f'Ernie4_5Tokenizer v2 does not support `add_special_tokens`')
         return super().prepare_for_model(*args, **kwargs)
 
     def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:

fastdeploy/input/ernie4_5_vl_processor/__init__.py

@@ -14,14 +14,15 @@
 # limitations under the License.
 """
 
-from .process import IDS_TYPE_FLAG, DataProcessor, fancy_print
+from .ernie4_5_vl_processor import Ernie4_5_VLProcessor
+from .process import DataProcessor, fancy_print
 from .process_video import read_video_decord
 from .utils.video_utils import VideoReaderWrapper
 
 __all__ = [
     "DataProcessor",
     "fancy_print",
-    "IDS_TYPE_FLAG",
     "VideoReaderWrapper",
     "read_video_decord",
+    "Ernie4_5_VLProcessor",
 ]

fastdeploy/input/ernie4_5_vl_processor/ernie4_5_vl_processor.py

@@ -20,12 +20,14 @@ import numpy as np
 from paddleformers.generation import GenerationConfig
 
 from fastdeploy.engine.request import Request
-from fastdeploy.input.ernie_processor import ErnieProcessor
+from fastdeploy.input.ernie4_5_processor import Ernie4_5Processor
-from fastdeploy.input.mm_processor import IDS_TYPE_FLAG, DataProcessor
+from fastdeploy.input.utils import IDS_TYPE_FLAG
 from fastdeploy.utils import data_processor_logger
 
+from .process import DataProcessor
 
-class ErnieMoEVLProcessor(ErnieProcessor):
+
+class Ernie4_5_VLProcessor(Ernie4_5Processor):
     """The processor class for ERNIE MoE VL models."""
 
     def __init__(
@@ -41,14 +43,14 @@ class ErnieMoEVLProcessor(ErnieProcessor):
         preprocessor_path = model_name_or_path
         processor_kwargs = self._parse_processor_kwargs(mm_processor_kwargs)
 
-        self.ernie_processor = DataProcessor(
+        self.ernie4_5_processor = DataProcessor(
             tokenizer_name=tokenizer_path,
             image_preprocessor_name=preprocessor_path,
             **processor_kwargs,
         )
-        self.ernie_processor.eval()
+        self.ernie4_5_processor.eval()
-        self.image_patch_id = self.ernie_processor.image_patch_id
+        self.image_patch_id = self.ernie4_5_processor.image_patch_id
-        self.spatial_conv_size = self.ernie_processor.spatial_conv_size
+        self.spatial_conv_size = self.ernie4_5_processor.spatial_conv_size
 
         self.tool_parser_dict = dict()
         self.decode_status = dict()
@@ -86,7 +88,7 @@ class ErnieMoEVLProcessor(ErnieProcessor):
         Returns:
             tokenizer (AutoTokenizer)
         """
-        self.tokenizer = self.ernie_processor.tokenizer
+        self.tokenizer = self.ernie4_5_processor.tokenizer
 
     def _apply_default_parameters(self, request):
         """
@@ -222,7 +224,7 @@ class ErnieMoEVLProcessor(ErnieProcessor):
             images = multimodal_data.get("image", None)
             videos = multimodal_data.get("video", None)
             request["text_after_process"] = request.get("prompt")
-            outputs = self.ernie_processor.text2ids(request["prompt"], images, videos)
+            outputs = self.ernie4_5_processor.text2ids(request["prompt"], images, videos)
         elif request.get("messages"):
             messages = request["messages"]
             self._check_mm_limits(messages)
@@ -235,7 +237,7 @@ class ErnieMoEVLProcessor(ErnieProcessor):
                 else:
                     raise ValueError("Invalid input: chat_template_kwargs must be a dict")
             request.setdefault("enable_thinking", True)
-            outputs = self.ernie_processor.request2ids(request)
+            outputs = self.ernie4_5_processor.request2ids(request)
         else:
             raise ValueError(f"Request must contain 'prompt', or 'messages': {request}")
 

fastdeploy/input/ernie4_5_vl_processor/process.py

@@ -26,15 +26,14 @@ from paddleformers.transformers.image_utils import ChannelDimension
 from PIL import Image
 
 from fastdeploy.entrypoints.chat_utils import parse_chat_messages
-from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
+from fastdeploy.input.utils import IDS_TYPE_FLAG
 from fastdeploy.utils import data_processor_logger
 
 from .image_preprocessor.image_preprocessor_adaptive import AdaptiveImageProcessor
 from .process_video import read_frames_decord, read_video_decord
 from .utils.render_timestamp import render_frame_timestamp
 
-IDS_TYPE_FLAG = {"text": 0, "image": 1, "video": 2, "audio": 3}
-
 
 def fancy_print(input_ids, tokenizer, image_patch_id=None):
     """
@@ -477,9 +476,9 @@ class DataProcessor:
         ]
         for i in range(len(vocab_file_names)):
             if os.path.exists(os.path.join(self.model_name_or_path, vocab_file_names[i])):
-                ErnieBotTokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
+                Ernie4_5Tokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
                 break
-        self.tokenizer = ErnieBotTokenizer.from_pretrained(self.model_name_or_path)
+        self.tokenizer = Ernie4_5Tokenizer.from_pretrained(self.model_name_or_path)
 
     def apply_chat_template(self, request):
         """

fastdeploy/input/ernie4_5_vl_processor/tokenizer/__init__.py

@@ -14,6 +14,6 @@
 # limitations under the License.
 """
 
-from .tokenizer_vl import ErnieVLTokenizer
+from .ernie4_5_vl_tokenizer import Ernie4_5_VLTokenizer
 
-__all__ = ["ErnieVLTokenizer"]
+__all__ = ["Ernie4_5_VLTokenizer"]

fastdeploy/input/ernie4_5_vl_processor/tokenizer/ernie4_5_vl_tokenizer.py

@@ -14,9 +14,6 @@
 # limitations under the License.
 """
 
-"""
-ErnieVLTokenizer
-"""
 import os
 import re
 from shutil import copyfile
@@ -31,7 +28,7 @@ from paddleformers.transformers.tokenizer_utils_base import PaddingStrategy, Tex
 from fastdeploy.utils import console_logger as logger
 
 
-class ErnieVLTokenizer(PretrainedTokenizer):
+class Ernie4_5_VLTokenizer(PretrainedTokenizer):
     """doc"""
 
     resource_files_names = {
@@ -157,7 +154,7 @@ class ErnieVLTokenizer(PretrainedTokenizer):
         """doc"""
         if "add_special_tokens" in kwargs:
             kwargs.pop("add_special_tokens")
-            # logger.warning(f'ErnieBotTokenizer v2 does not support `add_special_tokens`')
+            # logger.warning(f'Ernie4_5Tokenizer v2 does not support `add_special_tokens`')
         return super().prepare_for_model(*args, **kwargs)
 
     def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:

fastdeploy/input/preprocess.py

@@ -89,18 +89,18 @@ class InputPreprocessor:
                     tool_parser_obj=tool_parser_obj,
                 )
             else:
-                from fastdeploy.input.ernie_processor import ErnieProcessor
+                from fastdeploy.input.ernie4_5_processor import Ernie4_5Processor
 
-                self.processor = ErnieProcessor(
+                self.processor = Ernie4_5Processor(
                     model_name_or_path=self.model_name_or_path,
                     reasoning_parser_obj=reasoning_parser_obj,
                     tool_parser_obj=tool_parser_obj,
                 )
         else:
             if ErnieArchitectures.contains_ernie_arch(architectures):
-                from fastdeploy.input.ernie_vl_processor import ErnieMoEVLProcessor
+                from fastdeploy.input.ernie4_5_vl_processor import Ernie4_5_VLProcessor
 
-                self.processor = ErnieMoEVLProcessor(
+                self.processor = Ernie4_5_VLProcessor(
                     model_name_or_path=self.model_name_or_path,
                     limit_mm_per_prompt=self.limit_mm_per_prompt,
                     mm_processor_kwargs=self.mm_processor_kwargs,

fastdeploy/input/qwen_vl_processor/__init__.py

@@ -14,9 +14,10 @@
 # limitations under the License.
 """
 
-from .process import IDS_TYPE_FLAG, DataProcessor
+from .process import DataProcessor
+from .qwen_vl_processor import QwenVLProcessor
 
 __all__ = [
     "DataProcessor",
-    "IDS_TYPE_FLAG",
+    "QwenVLProcessor",
 ]

fastdeploy/input/qwen_vl_processor/process.py

@@ -21,7 +21,7 @@ import numpy as np
 from paddleformers.transformers import AutoTokenizer
 
 from fastdeploy.entrypoints.chat_utils import parse_chat_messages
-from fastdeploy.input.mm_processor import IDS_TYPE_FLAG
+from fastdeploy.input.utils import IDS_TYPE_FLAG
 from fastdeploy.utils import data_processor_logger
 
 from .image_processor import ImageProcessor

fastdeploy/input/qwen_vl_processor/process_video.py

@@ -20,7 +20,7 @@ from typing import Optional, Union
 import numpy as np
 from PIL import Image
 
-from fastdeploy.input.mm_processor import read_video_decord
+from fastdeploy.input.ernie4_5_vl_processor import read_video_decord
 
 
 def read_frames(video_path):

fastdeploy/input/qwen_vl_processor/qwen_vl_processor.py

@@ -17,10 +17,11 @@
 import numpy as np
 
 from fastdeploy.engine.request import Request
-from fastdeploy.input.qwen_mm_processor import DataProcessor
 from fastdeploy.input.text_processor import DataProcessor as TextProcessor
 from fastdeploy.utils import data_processor_logger
 
+from .process import DataProcessor
+
 
 class QwenVLProcessor(TextProcessor):
     """

fastdeploy/input/utils.py

@@ -0,0 +1,21 @@
+"""
+# 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.
+"""
+
+__all__ = [
+    "IDS_TYPE_FLAG",
+]
+
+IDS_TYPE_FLAG = {"text": 0, "image": 1, "video": 2, "audio": 3}

fastdeploy/model_executor/guided_decoding/base_guided_decoding.py

@@ -279,7 +279,7 @@ class BackendBase:
                     tokenizer = PreTrainedTokenizerFast(__slow_tokenizer=tokenizer)
             else:
                 from fastdeploy.model_executor.guided_decoding.ernie_tokenizer import (
-                    ErnieBotTokenizer,
+                    Ernie4_5Tokenizer,
                 )
 
                 vocab_file_names = [
@@ -294,10 +294,10 @@ class BackendBase:
                             vocab_file_names[i],
                         )
                     ):
-                        ErnieBotTokenizer.vocab_files_names["vocab_file"] = vocab_file_names[i]
+                        Ernie4_5Tokenizer.vocab_files_names["vocab_file"] = vocab_file_names[i]
                         break
 
-                tokenizer = ErnieBotTokenizer.from_pretrained(self.fd_config.model_config.model)
+                tokenizer = Ernie4_5Tokenizer.from_pretrained(self.fd_config.model_config.model)
 
             return tokenizer
         except Exception as e:

fastdeploy/model_executor/guided_decoding/ernie_tokenizer.py

@@ -30,7 +30,7 @@ PRETRAINED_VOCAB_FILES_MAP = {
 PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
 
 
-class ErnieBotTokenizer(PreTrainedTokenizer):
+class Ernie4_5Tokenizer(PreTrainedTokenizer):
     """
     Construct a ErnieBot tokenizer. Based on byte-level Byte-Pair-Encoding.
     Args:

fastdeploy/worker/gpu_model_runner.py

@@ -66,7 +66,7 @@ if not (current_platform.is_dcu() or current_platform.is_iluvatar()):
     from fastdeploy.spec_decode import MTPProposer, NgramProposer
 
 from fastdeploy import envs
-from fastdeploy.input.mm_processor import DataProcessor
+from fastdeploy.input.ernie4_5_vl_processor import DataProcessor
 from fastdeploy.model_executor.forward_meta import ForwardMeta
 from fastdeploy.model_executor.models.ernie4_5_vl.modeling_resampler import ScatterOp
 from fastdeploy.worker.model_runner_base import ModelRunnerBase

fastdeploy/worker/metax_model_runner.py

@@ -26,7 +26,7 @@ from paddleformers.utils.log import logger
 from fastdeploy import envs
 from fastdeploy.config import FDConfig
 from fastdeploy.engine.request import Request, RequestType
-from fastdeploy.input.mm_processor import DataProcessor
+from fastdeploy.input.ernie4_5_vl_processor import DataProcessor
 from fastdeploy.model_executor.forward_meta import ForwardMeta
 from fastdeploy.model_executor.graph_optimization.utils import (
     profile_run_guard,

fastdeploy/worker/worker_process.py

@@ -38,7 +38,7 @@ from fastdeploy.config import (
     ParallelConfig,
     SpeculativeConfig,
 )
-from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 from fastdeploy.inter_communicator import EngineWorkerQueue as TaskQueue
 from fastdeploy.inter_communicator import IPCSignal
 from fastdeploy.model_executor.layers.quantization import get_quantization_config
@@ -106,7 +106,7 @@ def init_distributed_environment(seed: int = 20) -> Tuple[int, int]:
 
 def update_fd_config_for_mm(fd_config: FDConfig) -> None:
     if fd_config.model_config.enable_mm:
-        tokenizer = ErnieBotTokenizer.from_pretrained(
+        tokenizer = Ernie4_5Tokenizer.from_pretrained(
             fd_config.model_config.model,
             model_max_length=fd_config.parallel_config.max_model_len,
             padding_side="right",

scripts/offline_w4a8.py

@@ -12,7 +12,7 @@ from paddleformers.utils.env import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME
 from paddleformers.utils.log import logger
 from safetensors.numpy import save_file as safe_save_file
 
-from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 from fastdeploy.model_executor.layers.utils import get_tensor
 from fastdeploy.model_executor.load_weight_utils import (
     get_all_safetensors,
@@ -140,9 +140,9 @@ def main():
     ]
     for i in range(len(vocab_file_names)):
         if os.path.exists(os.path.join(args.model_name_or_path, vocab_file_names[i])):
-            ErnieBotTokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
+            Ernie4_5Tokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
             break
-    tokenizer = ErnieBotTokenizer.from_pretrained(args.model_name_or_path)
+    tokenizer = Ernie4_5Tokenizer.from_pretrained(args.model_name_or_path)
     _, safetensor_files = get_all_safetensors(args.model_name_or_path)
     weights_iterator = safetensors_weights_iterator(safetensor_files)
     state_dict = {}

setup.py

@@ -211,7 +211,7 @@ setup(
             "model_executor/ops/iluvatar/*",
             "model_executor/models/*",
             "model_executor/layers/*",
-            "input/mm_processor/utils/*",
+            "input/ernie4_5_vl_processor/utils/*",
             "model_executor/ops/gcu/*",
             "version.txt",
         ]

tests/ci_use/EB_Lite/test_EB_Lite_serving.py

@@ -738,8 +738,8 @@ def test_non_streaming_chat_with_disable_chat_template(openai_client, capsys):
     assert hasattr(enabled_response, "choices")
     assert len(enabled_response.choices) > 0
 
-    # from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+    # from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
-    # tokenizer = ErnieBotTokenizer.from_pretrained("PaddlePaddle/ERNIE-4.5-0.3B-Paddle", trust_remote_code=True)
+    # tokenizer = Ernie4_5Tokenizer.from_pretrained("PaddlePaddle/ERNIE-4.5-0.3B-Paddle", trust_remote_code=True)
     # prompt = tokenizer.apply_chat_template([{"role": "user", "content": "Hello, how are you?"}], tokenize=False)
     prompt = "<|begin_of_sentence|>User: Hello, how are you?\nAssistant: "
     disabled_response = openai_client.chat.completions.create(
@@ -821,9 +821,9 @@ def test_non_streaming_chat_with_bad_words(openai_client, capsys):
     assert hasattr(response_0.choices[0].message, "completion_token_ids")
     assert isinstance(response_0.choices[0].message.completion_token_ids, list)
 
-    from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+    from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 
-    tokenizer = ErnieBotTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer = Ernie4_5Tokenizer.from_pretrained(model_path, trust_remote_code=True)
     output_tokens_0 = []
     output_ids_0 = []
     for ids in response_0.choices[0].message.completion_token_ids:
@@ -977,9 +977,9 @@ def test_non_streaming_completion_with_bad_words(openai_client, capsys):
     assert hasattr(response_0.choices[0], "completion_token_ids")
     assert isinstance(response_0.choices[0].completion_token_ids, list)
 
-    from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+    from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 
-    tokenizer = ErnieBotTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer = Ernie4_5Tokenizer.from_pretrained(model_path, trust_remote_code=True)
     output_tokens_0 = []
     output_ids_0 = []
     for ids in response_0.choices[0].completion_token_ids:

tests/e2e/test_EB_Lite_serving.py

@@ -733,8 +733,8 @@ def test_non_streaming_chat_completion_disable_chat_template(openai_client, caps
     assert hasattr(enabled_response, "choices")
     assert len(enabled_response.choices) > 0
 
-    # from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+    # from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
-    # tokenizer = ErnieBotTokenizer.from_pretrained("PaddlePaddle/ERNIE-4.5-0.3B-Paddle", trust_remote_code=True)
+    # tokenizer = Ernie4_5Tokenizer.from_pretrained("PaddlePaddle/ERNIE-4.5-0.3B-Paddle", trust_remote_code=True)
     # prompt = tokenizer.apply_chat_template([{"role": "user", "content": "Hello, how are you?"}], tokenize=False)
     prompt = "<|begin_of_sentence|>User: Hello, how are you?\nAssistant: "
     disabled_response = openai_client.chat.completions.create(
@@ -816,9 +816,9 @@ def test_non_streaming_chat_with_bad_words(openai_client, capsys):
     assert hasattr(response_0.choices[0].message, "completion_token_ids")
     assert isinstance(response_0.choices[0].message.completion_token_ids, list)
 
-    from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+    from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 
-    tokenizer = ErnieBotTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer = Ernie4_5Tokenizer.from_pretrained(model_path, trust_remote_code=True)
     output_tokens_0 = []
     output_ids_0 = []
     for ids in response_0.choices[0].message.completion_token_ids:
@@ -972,9 +972,9 @@ def test_non_streaming_completion_with_bad_words(openai_client, capsys):
     assert hasattr(response_0.choices[0], "completion_token_ids")
     assert isinstance(response_0.choices[0].completion_token_ids, list)
 
-    from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+    from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
 
-    tokenizer = ErnieBotTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer = Ernie4_5Tokenizer.from_pretrained(model_path, trust_remote_code=True)
     output_tokens_0 = []
     output_ids_0 = []
     for ids in response_0.choices[0].completion_token_ids:

tests/input/test_ernie_processor.py

@@ -1,14 +1,14 @@
 import unittest
 from unittest.mock import MagicMock, patch
 
-from fastdeploy.input.ernie_processor import ErnieProcessor
+from fastdeploy.input.ernie4_5_processor import Ernie4_5Processor
 
 
-class TestErnieProcessorProcessResponseDictStreaming(unittest.TestCase):
+class TestErnie4_5ProcessorProcessResponseDictStreaming(unittest.TestCase):
     def setUp(self):
-        # 创建 ErnieProcessor 实例的模拟对象
+        # 创建 Ernie4_5Processor 实例的模拟对象
-        with patch.object(ErnieProcessor, "__init__", return_value=None) as mock_init:
+        with patch.object(Ernie4_5Processor, "__init__", return_value=None) as mock_init:
-            self.processor = ErnieProcessor("model_path")
+            self.processor = Ernie4_5Processor("model_path")
             mock_init.side_effect = lambda *args, **kwargs: print(f"__init__ called with {args}, {kwargs}")
 
         # 设置必要的属性

tests/input/test_qwen_vl_processor.py

@@ -101,7 +101,7 @@ class TestQwenVLProcessor(unittest.TestCase):
         self.patcher_parse_video.start()
 
         self.patcher_read_frames = patch(
-            "fastdeploy.input.qwen_mm_processor.process.read_frames", return_value=mock_read_frames(480, 640, 5, 2)
+            "fastdeploy.input.qwen_vl_processor.process.read_frames", return_value=mock_read_frames(480, 640, 5, 2)
         )
         self.patcher_read_frames.start()
 

tests/utils/test_custom_chat_template.py

@@ -9,8 +9,8 @@ from fastdeploy.entrypoints.chat_utils import load_chat_template
 from fastdeploy.entrypoints.llm import LLM
 from fastdeploy.entrypoints.openai.protocol import ChatCompletionRequest
 from fastdeploy.entrypoints.openai.serving_chat import OpenAIServingChat
-from fastdeploy.input.ernie_processor import ErnieProcessor
+from fastdeploy.input.ernie4_5_processor import Ernie4_5Processor
-from fastdeploy.input.ernie_vl_processor import ErnieMoEVLProcessor
+from fastdeploy.input.ernie4_5_vl_processor import Ernie4_5_VLProcessor
 from fastdeploy.input.text_processor import DataProcessor
 
 
@@ -108,10 +108,10 @@ class TestLodChatTemplate(unittest.IsolatedAsyncioTestCase):
         chat_completion = await self.chat_completion_handler.create_chat_completion(request)
         self.assertEqual("hello", chat_completion["chat_template"])
 
-    @patch("fastdeploy.input.ernie_vl_processor.ErnieMoEVLProcessor.__init__")
+    @patch("fastdeploy.input.ernie4_5_vl_processor.Ernie4_5_VLProcessor.__init__")
-    def test_vl_processor(self, mock_class):
+    def test_ernie4_5_vl_processor(self, mock_class):
         mock_class.return_value = None
-        vl_processor = ErnieMoEVLProcessor()
+        ernie4_5_vl_processor = Ernie4_5_VLProcessor()
         mock_request = Request.from_dict({"request_id": "123"})
 
         def mock_apply_default_parameters(request):
@@ -120,9 +120,9 @@ class TestLodChatTemplate(unittest.IsolatedAsyncioTestCase):
         def mock_process_request(request, max_model_len):
             return request
 
-        vl_processor._apply_default_parameters = mock_apply_default_parameters
+        ernie4_5_vl_processor._apply_default_parameters = mock_apply_default_parameters
-        vl_processor.process_request_dict = mock_process_request
+        ernie4_5_vl_processor.process_request_dict = mock_process_request
-        result = vl_processor.process_request(mock_request, chat_template="hello")
+        result = ernie4_5_vl_processor.process_request(mock_request, chat_template="hello")
         self.assertEqual("hello", result.chat_template)
 
     @patch("fastdeploy.input.text_processor.DataProcessor.__init__")
@@ -149,10 +149,10 @@ class TestLodChatTemplate(unittest.IsolatedAsyncioTestCase):
         result = text_processor.process_request(mock_request, chat_template="hello")
         self.assertEqual("hello", result.chat_template)
 
-    @patch("fastdeploy.input.ernie_processor.ErnieProcessor.__init__")
+    @patch("fastdeploy.input.ernie4_5_processor.Ernie4_5Processor.__init__")
-    def test_ernie_processor_process(self, mock_class):
+    def test_ernie4_5_processor_process(self, mock_class):
         mock_class.return_value = None
-        ernie_processor = ErnieProcessor()
+        ernie4_5_processor = Ernie4_5Processor()
         mock_request = Request.from_dict(
             {"request_id": "123", "messages": ["hi"], "max_tokens": 128, "temperature": 1, "top_p": 1}
         )
@@ -166,12 +166,12 @@ class TestLodChatTemplate(unittest.IsolatedAsyncioTestCase):
         def mock_messages2ids(text):
             return [1]
 
-        ernie_processor._apply_default_parameters = mock_apply_default_parameters
+        ernie4_5_processor._apply_default_parameters = mock_apply_default_parameters
-        ernie_processor.process_request_dict = mock_process_request
+        ernie4_5_processor.process_request_dict = mock_process_request
-        ernie_processor.messages2ids = mock_messages2ids
+        ernie4_5_processor.messages2ids = mock_messages2ids
-        ernie_processor.eos_token_ids = [1]
+        ernie4_5_processor.eos_token_ids = [1]
-        ernie_processor.reasoning_parser = MagicMock()
+        ernie4_5_processor.reasoning_parser = MagicMock()
-        result = ernie_processor.process_request(mock_request, chat_template="hello")
+        result = ernie4_5_processor.process_request(mock_request, chat_template="hello")
         self.assertEqual("hello", result.chat_template)
 
     @patch("fastdeploy.entrypoints.llm.LLM.__init__")