add gpt4all

pilot/configs/model_config.py

@@ -37,6 +37,7 @@ LLM_MODEL_CONFIG = {
     "guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"),
     "falcon-40b": os.path.join(MODEL_PATH, "falcon-40b"),
     "gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"),
+    "ggml-gpt4all-j-v1.3-groovy": os.path.join(MODEL_PATH, "ggml-gpt4all-j-v1.3-groovy.bin"),
     "proxyllm": "proxyllm",
 }
 

pilot/model/adapter.py

@@ -2,6 +2,8 @@
 # -*- coding: utf-8 -*-
 
 import torch
+import os
+from functools import cache
 from typing import List
 from functools import cache
 from transformers import (
@@ -92,8 +94,8 @@ class ChatGLMAdapater(BaseLLMAdaper):
                 AutoModel.from_pretrained(
                     model_path, trust_remote_code=True, **from_pretrained_kwargs
                 )
-                .half()
-                .cuda()
+                    .half()
+                    .cuda()
             )
             return model, tokenizer
 
@@ -185,18 +187,26 @@ class RWKV4LLMAdapter(BaseLLMAdaper):
 
 
 class GPT4AllAdapter(BaseLLMAdaper):
-    """A light version for someone who want practise LLM use laptop."""
+    """
+        A light version for someone who want practise LLM use laptop.
+        All model names see: https://gpt4all.io/models/models.json
+    """
 
     def match(self, model_path: str):
         return "gpt4all" in model_path
 
     def loader(self, model_path: str, from_pretrained_kwargs: dict):
-        # TODO
-        pass
+        import gpt4all
+
+        if model_path is None and from_pretrained_kwargs.get('model_name') is None:
+            model = gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy")
+        else:
+            path, file = os.path.split(model_path)
+            model = gpt4all.GPT4All(model_path=path, model_name=file)
+        return model, None
 
 
 class ProxyllmAdapter(BaseLLMAdaper):
-
     """The model adapter for local proxy"""
 
     def match(self, model_path: str):
@@ -211,6 +221,7 @@ register_llm_model_adapters(ChatGLMAdapater)
 register_llm_model_adapters(GuanacoAdapter)
 register_llm_model_adapters(FalconAdapater)
 register_llm_model_adapters(GorillaAdapter)
+register_llm_model_adapters(GPT4AllAdapter)
 # TODO Default support vicuna, other model need to tests and Evaluate
 
 # just for test, remove this later

pilot/model/llm_out/gpt4all_llm.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+def gpt4all_generate_stream(model, tokenizer, params, device, max_position_embeddings):
+    stop = params.get("stop", "###")
+    prompt = params["prompt"]
+    role, query = prompt.split(stop)[1].split(":")
+    print(f"gpt4all, role: {role}, query: {query}")
+
+    messages = [{"role": "user", "content": query}]
+    res = model.chat_completion(messages)
+    if res.get('choices') and len(res.get('choices')) > 0 and res.get('choices')[0].get('message') and \
+            res.get('choices')[0].get('message').get('content'):
+        yield res.get('choices')[0].get('message').get('content')
+    else:
+        yield "error response"
+

pilot/out_parser/base.py

@@ -51,7 +51,7 @@ class BaseOutputParser(ABC):
 
         """ TODO Multi mode output handler,  rewrite this for multi model, use adapter mode.
         """
-        if data["error_code"] == 0:
+        if data.get('error_code', 0) == 0:
             if "vicuna" in CFG.LLM_MODEL:
                 # output = data["text"][skip_echo_len + 11:].strip()
                 output = data["text"][skip_echo_len:].strip()

pilot/server/chat_adapter.py

@@ -37,7 +37,6 @@ def get_llm_chat_adapter(model_path: str) -> BaseChatAdpter:
 
 
 class VicunaChatAdapter(BaseChatAdpter):
-
     """Model chat Adapter for vicuna"""
 
     def match(self, model_path: str):
@@ -60,7 +59,6 @@ class ChatGLMChatAdapter(BaseChatAdpter):
 
 
 class CodeT5ChatAdapter(BaseChatAdpter):
-
     """Model chat adapter for CodeT5"""
 
     def match(self, model_path: str):
@@ -72,7 +70,6 @@ class CodeT5ChatAdapter(BaseChatAdpter):
 
 
 class CodeGenChatAdapter(BaseChatAdpter):
-
     """Model chat adapter for CodeGen"""
 
     def match(self, model_path: str):
@@ -127,11 +124,23 @@ class GorillaChatAdapter(BaseChatAdpter):
         return generate_stream
 
 
+class GPT4AllChatAdapter(BaseChatAdpter):
+
+    def match(self, model_path: str):
+        return "gpt4all" in model_path
+
+    def get_generate_stream_func(self):
+        from pilot.model.llm_out.gpt4all_llm import gpt4all_generate_stream
+
+        return gpt4all_generate_stream
+
+
 register_llm_model_chat_adapter(VicunaChatAdapter)
 register_llm_model_chat_adapter(ChatGLMChatAdapter)
 register_llm_model_chat_adapter(GuanacoChatAdapter)
 register_llm_model_chat_adapter(FalconChatAdapter)
 register_llm_model_chat_adapter(GorillaChatAdapter)
+register_llm_model_chat_adapter(GPT4AllChatAdapter)
 
 # Proxy model for test and develop, it's cheap for us now.
 register_llm_model_chat_adapter(ProxyllmChatAdapter)

pilot/server/llmserver.py

@@ -39,9 +39,9 @@ class ModelWorker:
         )
 
         if not isinstance(self.model, str):
-            if hasattr(self.model.config, "max_sequence_length"):
+            if hasattr(self.model, "config") and hasattr(self.model.config, "max_sequence_length"):
                 self.context_len = self.model.config.max_sequence_length
-            elif hasattr(self.model.config, "max_position_embeddings"):
+            elif hasattr(self.model, "config") and hasattr(self.model.config, "max_position_embeddings"):
                 self.context_len = self.model.config.max_position_embeddings
 
         else:
@@ -66,6 +66,7 @@ class ModelWorker:
 
     def generate_stream_gate(self, params):
         try:
+            print(f"llmserver params: {params}, self: {self}")
             for output in self.generate_stream_func(
                 self.model, self.tokenizer, params, DEVICE, CFG.MAX_POSITION_EMBEDDINGS
             ):