更新VLLM的书写方式

demo/vllm_based_demo.py

@@ -1,36 +1,46 @@
-from typing import Dict
 from typing import List
-from typing import Tuple
-
 import argparse
 import gradio as gr
 from vllm import LLM, SamplingParams
-
+import torch
+from transformers import AutoTokenizer
 
 parser = argparse.ArgumentParser()
-parser.add_argument("--model_path", type=str, default="")
+parser.add_argument("--model_path", type=str, default="openbmb/MiniCPM-1B-sft-bf16")
 parser.add_argument("--torch_dtype", type=str, default="bfloat16", choices=["float32", "bfloat16"])
 parser.add_argument("--server_name", type=str, default="127.0.0.1")
 parser.add_argument("--server_port", type=int, default=7860)
-args = parser.parse_args()
+parser.add_argument("--max_tokens", type=int, default=2048)
+# for MiniCPM-1B and MiniCPM-2B  model, max_tokens should be set to 2048
 
+args = parser.parse_args()
 
 # init model torch dtype
 torch_dtype = args.torch_dtype
-if torch_dtype =="" or torch_dtype == "bfloat16":
-    torch_dtype = "bfloat16"
+if torch_dtype == "" or torch_dtype == "bfloat16":
+    torch_dtype = torch.bfloat16
 elif torch_dtype == "float32":
-    torch_dtype = "float32"
+    torch_dtype = torch.float32
+elif torch_dtype == "float16":
+    torch_dtype = torch.float16
 else:
     raise ValueError(f"Invalid torch dtype: {torch_dtype}")
 
 # init model and tokenizer
 path = args.model_path
-llm = LLM(model=path, tensor_parallel_size=1, dtype=torch_dtype)
+llm = LLM(
+    model=path,
+    tensor_parallel_size=1,
+    dtype=torch_dtype,
+    trust_remote_code=True,
+    gpu_memory_utilization=0.9,
+    max_model_len=args.max_tokens
+)
+tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
+
+server_name = args.server_name
+server_port = args.server_port
 
-# init gradio demo host and port
-server_name=args.server_name
-server_port=args.server_port
 
 def vllm_gen(dialog: List, top_p: float, temperature: float, max_dec_len: int):
     """generate model output with huggingface api
@@ -43,19 +53,13 @@ def vllm_gen(dialog: List, top_p: float, temperature: float, max_dec_len: int):
 
     Yields:
         str: real-time generation results of hf model
-    """    
-    prompt = ""
+    """
     assert len(dialog) % 2 == 1
-    for info in dialog:
-        if info["role"] == "user":
-            prompt += "<用户>" + info["content"]
-        else:
-            prompt += "<AI>" + info["content"]
-    prompt += "<AI>"
+    prompt = tokenizer.apply_chat_template(dialog, tokenize=False, add_generation_prompt=False)
     params_dict = {
         "n": 1,
         "best_of": 1,
-        "presence_penalty": 1.0,    
+        "presence_penalty": 1.0,
         "frequency_penalty": 0.0,
         "temperature": temperature,
         "top_p": top_p,
@@ -89,7 +93,7 @@ def generate(chat_history: List, query: str, top_p: float, temperature: float, m
 
     Yields:
         List: [[q_1, a_1], [q_2, a_2], ..., [q_n, a_n], [q_n+1, a_n+1]]. chat_history + QA of current round.
-    """    
+    """
     assert query != "", "Input must not be empty!!!"
     # apply chat template
     model_input = []
@@ -114,7 +118,7 @@ def regenerate(chat_history: List, top_p: float, temperature: float, max_dec_len
 
     Yields:
         List: [[q_1, a_1], [q_2, a_2], ..., [q_n, a_n]]. chat_history
-    """    
+    """
     assert len(chat_history) >= 1, "History is empty. Nothing to regenerate!!"
     # apply chat template
     model_input = []
@@ -133,7 +137,7 @@ def clear_history():
 
     Returns:
         List: empty chat history
-    """    
+    """
     return []
 
 
@@ -145,7 +149,7 @@ def reverse_last_round(chat_history):
 
     Returns:
         List: [[q_1, a_1], [q_2, a_2], ..., [q_n-1, a_n-1]]. chat_history without last round.
-    """    
+    """
     assert len(chat_history) >= 1, "History is empty. Nothing to reverse!!"
     return chat_history[:-1]
 
@@ -158,7 +162,7 @@ with gr.Blocks(theme="soft") as demo:
         with gr.Column(scale=1):
             top_p = gr.Slider(0, 1, value=0.8, step=0.1, label="top_p")
             temperature = gr.Slider(0.1, 2.0, value=0.5, step=0.1, label="temperature")
-            max_dec_len = gr.Slider(1, 1024, value=1024, step=1, label="max_dec_len")
+            max_dec_len = gr.Slider(1, args.max_tokens, value=args.max_tokens, step=1, label="max_tokens")
         with gr.Column(scale=5):
             chatbot = gr.Chatbot(bubble_full_width=False, height=400)
             user_input = gr.Textbox(label="User", placeholder="Input your query here!", lines=8)

requirements.txt

@@ -3,6 +3,9 @@ torch>=2.0.0
 transformers>=4.36.2
 gradio>=4.26.0
 
+# for vllm inference
+# vllm>=0.4.0.post1
+
 # for openai api inference
 openai>=1.17.1
 tiktoken>=0.6.0