feat(core): APP use new SDK component (#1050)

dbgpt/model/adapter/base.py

@@ -1,19 +1,20 @@
-from abc import ABC, abstractmethod
-from typing import Dict, List, Optional, Any, Tuple, Type, Callable
 import logging
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Dict, List, Optional, Tuple, Type
+
 from dbgpt.core.interface.message import ModelMessage, ModelMessageRoleType
+from dbgpt.model.adapter.template import (
+    ConversationAdapter,
+    ConversationAdapterFactory,
+    get_conv_template,
+)
 from dbgpt.model.base import ModelType
 from dbgpt.model.parameter import (
     BaseModelParameters,
-    ModelParameters,
     LlamaCppModelParameters,
+    ModelParameters,
     ProxyModelParameters,
 )
-from dbgpt.model.adapter.template import (
-    get_conv_template,
-    ConversationAdapter,
-    ConversationAdapterFactory,
-)
 
 logger = logging.getLogger(__name__)
 

dbgpt/model/adapter/fschat_adapter.py

@@ -2,17 +2,17 @@
 
 You can import fastchat only in this file, so that the user does not need to install fastchat if he does not use it.
 """
+import logging
 import os
 import threading
-import logging
 from functools import cache
-from typing import TYPE_CHECKING, Callable, Tuple, List, Optional
+from typing import TYPE_CHECKING, Callable, List, Optional, Tuple
 
 try:
     from fastchat.conversation import (
         Conversation,
-        register_conv_template,
         SeparatorStyle,
+        register_conv_template,
     )
 except ImportError as exc:
     raise ValueError(
@@ -20,8 +20,8 @@ except ImportError as exc:
         "Please install fastchat by command `pip install fschat` "
     ) from exc
 
-from dbgpt.model.adapter.template import ConversationAdapter, PromptType
 from dbgpt.model.adapter.base import LLMModelAdapter
+from dbgpt.model.adapter.template import ConversationAdapter, PromptType
 
 if TYPE_CHECKING:
     from fastchat.model.model_adapter import BaseModelAdapter

dbgpt/model/adapter/hf_adapter.py

@@ -1,10 +1,10 @@
-from abc import ABC, abstractmethod
-from typing import Dict, Optional, List, Any
 import logging
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
 
 from dbgpt.core import ModelMessage
-from dbgpt.model.base import ModelType
 from dbgpt.model.adapter.base import LLMModelAdapter, register_model_adapter
+from dbgpt.model.base import ModelType
 
 logger = logging.getLogger(__name__)
 
@@ -42,7 +42,7 @@ class NewHFChatModelAdapter(LLMModelAdapter, ABC):
     def load(self, model_path: str, from_pretrained_kwargs: dict):
         try:
             import transformers
-            from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
+            from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
         except ImportError as exc:
             raise ValueError(
                 "Could not import depend python package "

dbgpt/model/adapter/model_adapter.py

@@ -1,21 +1,15 @@
 from __future__ import annotations
 
-from typing import (
-    List,
-    Type,
-    Optional,
-)
 import logging
-import threading
 import os
+import threading
 from functools import cache
+from typing import List, Optional, Type
+
+from dbgpt.model.adapter.base import LLMModelAdapter, get_model_adapter
+from dbgpt.model.adapter.template import ConversationAdapter, ConversationAdapterFactory
 from dbgpt.model.base import ModelType
 from dbgpt.model.parameter import BaseModelParameters
-from dbgpt.model.adapter.base import LLMModelAdapter, get_model_adapter
-from dbgpt.model.adapter.template import (
-    ConversationAdapter,
-    ConversationAdapterFactory,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -64,9 +58,9 @@ def get_llm_model_adapter(
     if use_fastchat and not must_use_old:
         logger.info("Use fastcat adapter")
         from dbgpt.model.adapter.fschat_adapter import (
-            _get_fastchat_model_adapter,
-            _fastchat_get_adapter_monkey_patch,
             FastChatLLMModelAdapterWrapper,
+            _fastchat_get_adapter_monkey_patch,
+            _get_fastchat_model_adapter,
         )
 
         adapter = _get_fastchat_model_adapter(
@@ -79,11 +73,11 @@ def get_llm_model_adapter(
             result_adapter = FastChatLLMModelAdapterWrapper(adapter)
 
     else:
+        from dbgpt.app.chat_adapter import get_llm_chat_adapter
+        from dbgpt.model.adapter.old_adapter import OldLLMModelAdapterWrapper
         from dbgpt.model.adapter.old_adapter import (
             get_llm_model_adapter as _old_get_llm_model_adapter,
-            OldLLMModelAdapterWrapper,
         )
-        from dbgpt.app.chat_adapter import get_llm_chat_adapter
 
         logger.info("Use DB-GPT old adapter")
         result_adapter = OldLLMModelAdapterWrapper(
@@ -139,12 +133,12 @@ def _dynamic_model_parser() -> Optional[List[Type[BaseModelParameters]]]:
     Returns:
         Optional[List[Type[BaseModelParameters]]]: The model parameters class list.
     """
-    from dbgpt.util.parameter_utils import _SimpleArgParser
     from dbgpt.model.parameter import (
+        EMBEDDING_NAME_TO_PARAMETER_CLASS_CONFIG,
         EmbeddingModelParameters,
         WorkerType,
-        EMBEDDING_NAME_TO_PARAMETER_CLASS_CONFIG,
     )
+    from dbgpt.util.parameter_utils import _SimpleArgParser
 
     pre_args = _SimpleArgParser("model_name", "model_path", "worker_type", "model_type")
     pre_args.parse()

dbgpt/model/adapter/old_adapter.py

@@ -5,31 +5,26 @@ We have integrated fastchat. For details, see: dbgpt/model/model_adapter.py
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
+import logging
 import os
 import re
-import logging
-from pathlib import Path
-from typing import List, Tuple, TYPE_CHECKING, Optional
 from functools import cache
-from transformers import (
-    AutoModel,
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    LlamaTokenizer,
-)
+from pathlib import Path
+from typing import TYPE_CHECKING, List, Optional, Tuple
 
+from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
+
+from dbgpt._private.config import Config
+from dbgpt.configs.model_config import get_device
 from dbgpt.model.adapter.base import LLMModelAdapter
 from dbgpt.model.adapter.template import ConversationAdapter, PromptType
 from dbgpt.model.base import ModelType
-
+from dbgpt.model.conversation import Conversation
 from dbgpt.model.parameter import (
-    ModelParameters,
     LlamaCppModelParameters,
+    ModelParameters,
     ProxyModelParameters,
 )
-from dbgpt.model.conversation import Conversation
-from dbgpt.configs.model_config import get_device
-from dbgpt._private.config import Config
 
 if TYPE_CHECKING:
     from dbgpt.app.chat_adapter import BaseChatAdpter

dbgpt/model/adapter/template.py

@@ -1,6 +1,6 @@
 from abc import ABC, abstractmethod
 from enum import Enum
-from typing import TYPE_CHECKING, Optional, Tuple, Union, List
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 
 if TYPE_CHECKING:
     from fastchat.conversation import Conversation
@@ -124,6 +124,7 @@ def get_conv_template(name: str) -> ConversationAdapter:
         Conversation: The conversation template.
     """
     from fastchat.conversation import get_conv_template
+
     from dbgpt.model.adapter.fschat_adapter import FschatConversationAdapter
 
     conv_template = get_conv_template(name)

dbgpt/model/adapter/vllm_adapter.py

@@ -1,12 +1,13 @@
 import dataclasses
 import logging
-from dbgpt.model.base import ModelType
+
 from dbgpt.model.adapter.base import LLMModelAdapter
 from dbgpt.model.adapter.template import ConversationAdapter, ConversationAdapterFactory
+from dbgpt.model.base import ModelType
 from dbgpt.model.parameter import BaseModelParameters
 from dbgpt.util.parameter_utils import (
-    _extract_parameter_details,
     _build_parameter_class,
+    _extract_parameter_details,
     _get_dataclass_print_str,
 )
 
@@ -27,6 +28,7 @@ class VLLMModelAdapterWrapper(LLMModelAdapter):
 
     def model_param_class(self, model_type: str = None) -> BaseModelParameters:
         import argparse
+
         from vllm.engine.arg_utils import AsyncEngineArgs
 
         parser = argparse.ArgumentParser()
@@ -56,9 +58,9 @@ class VLLMModelAdapterWrapper(LLMModelAdapter):
         return _build_parameter_class(descs)
 
     def load_from_params(self, params):
+        import torch
         from vllm import AsyncLLMEngine
         from vllm.engine.arg_utils import AsyncEngineArgs
-        import torch
 
         num_gpus = torch.cuda.device_count()
         if num_gpus > 1 and hasattr(params, "tensor_parallel_size"):