fork file replace import

pilot/model/inference.py

@@ -3,6 +3,71 @@
 
 import torch
 
+@torch.inference_mode()
+def generate_stream(model, tokenizer, params, device,
+                    context_len=2048, stream_interval=2):
+
+    """Fork from fastchat: https://github.com/lm-sys/FastChat/blob/main/fastchat/serve/inference.py """
+    prompt = params["prompt"]
+    l_prompt = len(prompt)
+    temperature = float(params.get("temperature", 1.0))
+    max_new_tokens = int(params.get("max_new_tokens", 256))
+    stop_str = params.get("stop", None)
+
+    input_ids = tokenizer(prompt).input_ids
+    output_ids = list(input_ids)
+
+    max_src_len = context_len - max_new_tokens - 8
+    input_ids = input_ids[-max_src_len:]
+
+    for i in range(max_new_tokens):
+        if i == 0:
+            out = model(
+                torch.as_tensor([input_ids], device=device), use_cache=True)
+            logits = out.logits
+            past_key_values = out.past_key_values
+        else:
+            attention_mask = torch.ones(
+                1, past_key_values[0][0].shape[-2] + 1, device=device)
+            out = model(input_ids=torch.as_tensor([[token]], device=device),
+                        use_cache=True,
+                        attention_mask=attention_mask,
+                        past_key_values=past_key_values)
+            logits = out.logits
+            past_key_values = out.past_key_values
+
+        last_token_logits = logits[0][-1]
+
+        if device == "mps":
+            # Switch to CPU by avoiding some bugs in mps backend.
+            last_token_logits = last_token_logits.float().to("cpu")
+
+        if temperature < 1e-4:
+            token = int(torch.argmax(last_token_logits))
+        else:
+            probs = torch.softmax(last_token_logits / temperature, dim=-1)
+            token = int(torch.multinomial(probs, num_samples=1))
+
+        output_ids.append(token)
+
+        if token == tokenizer.eos_token_id:
+            stopped = True
+        else:
+            stopped = False
+
+        if i % stream_interval == 0 or i == max_new_tokens - 1 or stopped:
+            output = tokenizer.decode(output_ids, skip_special_tokens=True)
+            pos = output.rfind(stop_str, l_prompt)
+            if pos != -1:
+                output = output[:pos]
+                stopped = True
+            yield output
+
+        if stopped:
+            break
+
+    del past_key_values
+
 @torch.inference_mode()
 def generate_output(model, tokenizer, params, device, context_len=2048, stream_interval=2):
     prompt = params["prompt"]

pilot/model/loader.py

@@ -5,6 +5,7 @@ import torch
 from transformers import (
     AutoTokenizer,
     AutoModelForCausalLM,
+    AutoModel
 )
 
 from fastchat.serve.compression import compress_module
@@ -23,20 +24,39 @@ class ModerLoader:
             "device_map": "auto",
         }
 
-    def loader(self, load_8bit=False, debug=False):
-       
-        tokenizer = AutoTokenizer.from_pretrained(self.model_path, use_fast=False)
-        model = AutoModelForCausalLM.from_pretrained(self.model_path, low_cpu_mem_usage=True, **self.kwargs)
+    def loader(self, num_gpus, load_8bit=False, debug=False):
+        if self.device == "cpu":
+            kwargs = {}
+        elif self.device == "cuda":
+            kwargs = {"torch_dtype": torch.float16}
+            if num_gpus == "auto":
+                kwargs["device_map"] = "auto"
+            else:
+                num_gpus = int(num_gpus)
+                if num_gpus != 1:
+                    kwargs.update({
+                        "device_map": "auto",
+                        "max_memory": {i: "13GiB" for i in range(num_gpus)},
+                    })
+        else:
+            raise ValueError(f"Invalid device: {self.device}")
+
+        if "chatglm" in self.model_path:
+            tokenizer = AutoTokenizer.from_pretrained(self.model_path, trust_remote_code=True)
+            model = AutoModel.from_pretrained(self.model_path, trust_remote_code=True).half().cuda()
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(self.model_path, use_fast=False)
+            model = AutoModelForCausalLM.from_pretrained(self.model_path,
+                low_cpu_mem_usage=True, **kwargs)
+
+        if load_8bit:
+            compress_module(model, self.device)
+
+        if (self.device == "cuda" and num_gpus == 1):
+            model.to(self.device)
 
         if debug:
             print(model)
 
-        if load_8bit:
-            compress_module(model, self.device) 
-
-        # if self.device == "cuda":
-        #     model.to(self.device)
-
         return model, tokenizer
-
-
+ 

pilot/server/vicuna_server.py

@@ -7,10 +7,12 @@ import json
 from typing import Optional, List
 from fastapi import FastAPI, Request, BackgroundTasks
 from fastapi.responses import StreamingResponse
-from fastchat.serve.inference import generate_stream
+from pilot.model.inference import generate_stream
 from pydantic import BaseModel
 from pilot.model.inference import generate_output, get_embeddings
 from fastchat.serve.inference import load_model
+
+
 from pilot.model.loader import ModerLoader
 from pilot.configs.model_config import *
 
@@ -20,9 +22,9 @@ model_path = LLM_MODEL_CONFIG[LLM_MODEL]
 global_counter = 0
 model_semaphore = None
 
-# ml = ModerLoader(model_path=model_path)
-# model, tokenizer = ml.loader(load_8bit=isload_8bit, debug=isdebug)
-model, tokenizer = load_model(model_path=model_path, device=DEVICE, num_gpus=1, load_8bit=True, debug=False)
+ml = ModerLoader(model_path=model_path)
+model, tokenizer = ml.loader(num_gpus=1, load_8bit=ISLOAD_8BIT, debug=ISDEBUG)
+#model, tokenizer = load_model(model_path=model_path, device=DEVICE, num_gpus=1, load_8bit=True, debug=False)
 
 class ModelWorker:
     def __init__(self):

pilot/vector_store/file_loader.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+from langchain.prompts import PromptTemplate
+from langchain.vectorstores import Chroma 
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import UnstructuredFileLoader, UnstructuredPDFLoader
+
+VECTOR_SEARCH_TOP_K = 5
+
+class BaseKnownLedgeQA:
+    
+    llm: object = None
+    embeddings: object = None
+
+    top_k: int = VECTOR_SEARCH_TOP_K
+
+    def __init__(self) -> None:
+        pass
+        
+    def init_vector_store(self):
+        pass
+
+    def load_knownlege(self):
+        pass
+
+    def _load_file(self, filename):
+        # 加载文件
+        if filename.lower().endswith(".pdf"):
+            loader = UnstructuredFileLoader(filename) 
+            text_splitor = CharacterTextSplitter()
+            docs = loader.load_and_split(text_splitor)
+        else:
+            loader = UnstructuredFileLoader(filename, mode="elements")
+            text_splitor = CharacterTextSplitter()
+            docs = loader.load_and_split(text_splitor)
+        return docs
+
+    def _load_from_url(self, url):
+        pass
+