Merge remote-tracking branch 'origin/main' into check-para

ktransformers/server/api/openai/endpoints/chat.py

@@ -14,15 +14,10 @@ from ktransformers.server.backend.base import BackendInterfaceBase
 from ktransformers.server.config.config import Config
 from ktransformers.server.config.log import logger
 from fastapi.responses import JSONResponse
-from ktransformers.server.schemas.endpoints.chat import ChatCompletionChunk
+from ktransformers.server.schemas.endpoints.chat import ChatCompletionChunk, CompletionUsage
 
 # Define own data structure instead of importing from OpenAI
-class CompletionUsage(BaseModel):
-    prompt_tokens: int
-    completion_tokens: int
-    total_tokens: int
-    prompt_tokens_details: Optional[Dict[str, Any]] = None
-    completion_tokens_details: Optional[Dict[str, Any]] = None
+
 
 class Choice(BaseModel):
     index: int
@@ -267,6 +262,12 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
                         completion_tokens=raw_usage.decode_count,
                         total_tokens=raw_usage.prefill_count + raw_usage.decode_count
                     )
+                    if create.return_speed:
+                        chunk.usage.prefill_time = res.prefill_time
+                        chunk.usage.decode_time = res.decode_time
+                    else:
+                        chunk.usage.__dict__.pop('prefill_time', None)
+                        chunk.usage.__dict__.pop('decode_time', None)
                     yield chunk
                 elif isinstance(res, tuple) and len(res) == 2:
                     token, finish_reason = res
@@ -427,8 +428,15 @@ async def chat_completion(request: Request, create: ChatCompletionCreate):
                 usage = CompletionUsage(
                     prompt_tokens=raw_usage.prefill_count,
                     completion_tokens=raw_usage.decode_count,
-                    total_tokens=raw_usage.prefill_count + raw_usage.decode_count
+                    total_tokens=raw_usage.prefill_count + raw_usage.decode_count,
                 )
+                if create.return_speed:
+                    usage.prefill_time = res.prefill_time
+                    usage.decode_time = res.decode_time
+                else:
+                    usage.__dict__.pop('prefill_time', None)
+                    usage.__dict__.pop('decode_time', None)
+
             elif isinstance(res, tuple) and len(res) == 2:
                 token, finish_reason = res
                 token = re.sub('|'.join(map(re.escape, too_calls_dict.keys())), lambda m: too_calls_dict[m.group(0)], token)

ktransformers/server/backend/interfaces/balance_serve.py

@@ -46,6 +46,8 @@ import pickle
 import subprocess
 import tempfile
 import atexit
+import signal
+
 
 ktransformer_rules_dir = (
     os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..", "..", "./optimize/optimize_rules/") 
@@ -55,6 +57,7 @@ default_optimize_rules = {
     "Qwen2MoeForCausalLM": ktransformer_rules_dir + "Qwen2-57B-A14B-Instruct-serve.yaml",
 }
 
+
 async def chat_stream(queue: asyncio.Queue, tokenizer: AutoTokenizer):
     streamer = TextStreamer(tokenizer)
     while True:
@@ -293,10 +296,6 @@ class BalanceServeInterface(BackendInterfaceBase):
         kvcache_event.wait()
 
 
-        def cleanup():
-            if sched_process.poll() is None:
-                sched_process.terminate()
-
         with tempfile.NamedTemporaryFile(delete=False) as temp_file:
             pickle.dump(args, temp_file)
             temp_file_path = temp_file.name
@@ -311,7 +310,27 @@ class BalanceServeInterface(BackendInterfaceBase):
             stderr=log
         )
         print("sched_rpc started with PID:", sched_process.pid)
-        atexit.register(cleanup)
+
+        def signal_handler(signum, frame):
+            print(f"Received signal {signum}, shutting down...")
+            cleanup()
+            os._exit(0) 
+
+        def cleanup():
+            print("Cleaning up...")
+
+            for p in processes:
+                if p.is_alive():
+                    print(f"Terminating subprocess {p.pid}")
+                    p.terminate()
+                    p.join()
+
+            if sched_process and sched_process.poll() is None:
+                print(f"Terminating sched_process {sched_process.pid}")
+                sched_process.terminate()
+                sched_process.wait()
+        signal.signal(signal.SIGINT, signal_handler)   
+        signal.signal(signal.SIGTERM, signal_handler)
 
         start_event.wait()
     

ktransformers/server/schemas/endpoints/chat.py

@@ -2,14 +2,22 @@ from typing import List, Optional, Union, Dict, Any
 from typing_extensions import Literal
 from enum import Enum
 from pydantic import BaseModel, Field
-
+from ktransformers.server.config.config import Config
 from ktransformers.server.schemas.base import Object
 
-from openai.types.completion_usage import CompletionUsage
+
 from openai.types.chat.chat_completion_chunk import Choice
 
 from uuid import uuid4
 
+class CompletionUsage(BaseModel):
+    prompt_tokens: int
+    completion_tokens: int
+    total_tokens: int
+    prompt_tokens_details: Optional[Dict[str, Any]] = None
+    completion_tokens_details: Optional[Dict[str, Any]] = None
+    prefill_time: Optional[float] = None
+    decode_time: Optional[float] = None
 
 class Role(Enum):
     system = 'system'
@@ -58,16 +66,16 @@ class ChatCompletionCreate(BaseModel):
     messages: List[Message]
     model: str
     stream: bool = False
-    temperature: Optional[float] = Field(default=0.6)
-    top_p: Optional[float] = Field(default=1.0)
+    temperature: Optional[float] = Field(default=Config().temperature)
+    top_p: Optional[float] = Field(default=Config().top_p)
     tools: Optional[List[Tool]] = None
     tool_choice: Optional[Union[str, Dict[str, Any]]] = None
     stream_options: Optional[Dict[str, Any]] = None
     frequency_penalty: float = 0
     presence_penalty: float = 0
-    max_tokens: Optional[int] = Field(default=50)
-    max_completion_tokens: Optional[int] = Field(default=50)
-
+    max_tokens: Optional[int] = Field(default=Config().max_new_tokens)
+    max_completion_tokens: Optional[int] = Field(default=Config().max_new_tokens)
+    return_speed: Optional[bool] = Field(default=False)
     def get_tokenizer_messages(self):
         return [m.to_tokenizer_message() for m in self.messages]
 

ktransformers/server/schemas/legacy/completions.py

@@ -1,17 +1,17 @@
 from typing import List, Optional
 from enum import Enum
 from pydantic import BaseModel, Field
-
+from ktransformers.server.config.config import Config
 from ..base import Object
 
 class CompletionCreate(BaseModel):
     model: str
     prompt: str | List[str]
     stream: bool = False
-    temperature: Optional[float] = Field(default=0.6)
-    top_p: Optional[float] = Field(default=1)
-    max_tokens: Optional[int] = Field(default=50)
-    max_completion_tokens: Optional[int] = Field(default=50)
+    temperature: Optional[float] = Field(default=Config().temperature)
+    top_p: Optional[float] = Field(default=Config().top_p)
+    max_tokens: Optional[int] = Field(default=Config().max_new_tokens)
+    max_completion_tokens: Optional[int] = Field(default=Config().max_new_tokens)
     
     def get_tokenizer_messages(self):
         if isinstance(self.prompt,List):

ktransformers/tests/mmlu_test.py

@@ -25,19 +25,10 @@ class DataEvaluator:
         """
         # 读取 Parquet 文件
         # dataset = load_dataset('parquet', data_files=file_path)
-        ds = load_dataset(file_path,"all")
-        df = pd.DataFrame(ds['test'])
-        # print(ds)
-        # # ds_1 =  ds['train']
-        # ds_2 =  ds['validation']
-        # ds_3 =  ds['test']
-        # # 将数据集转换为 Pandas DataFrame
-        # df_test = pd.DataFrame(ds['test'])
-        # df_val = pd.DataFrame(ds['validation'])
-
-        # for _, row in df.iterrows():
-        #     self.data.append(row.to_dict())
-        # df = pd.read_parquet(file_path)
+        splits = {'test': 'all/test-00000-of-00001.parquet', 'validation': 'all/validation-00000-of-00001.parquet',
+                  'dev': 'all/dev-00000-of-00001.parquet',
+                  'auxiliary_train': 'all/auxiliary_train-00000-of-00001.parquet'}
+        df = pd.read_parquet("hf://datasets/cais/mmlu/" + splits["test"])
 
         for _, row in df.iterrows():
             self.data.append(row.to_dict())

ktransformers/tests/mmlu_test_multi.py

@@ -8,12 +8,57 @@ from datasets import load_dataset
 import os
 import concurrent.futures
 import threading
+import re
 
 os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
 os.environ['https_proxy'] = ''
 os.environ['http_proxy'] = ''
 hint = 'There is a single choice question. Answer the question by replying A, B, C, D. No other answers are accepted. Just the letter.'
 
+
+def extract_final_answer(text):
+    """
+    提取模型预测的最终选项（如 A/B/C/D）
+    支持自然语言、多行、markdown、高亮、非末尾结论等格式
+    """
+    text = text.strip()
+
+    # 1. 显式语句匹配（优先）
+    explicit_patterns = [
+        r'Answer:\s*([A-D])\b',
+        r'Correct answer:\s*([A-D])\b',
+        r'The correct answer is\s*\*?\*?\s*([A-D])\b',
+        r'Answer is\s*([A-D])\b',
+        r'Therefore,\s*answer is\s*([A-D])\b',
+        r'Therefore,\s*the answer should be\s*(?:Option\s*)?([A-D])\b',
+        r'The answer should be\s*(?:Option\s*)?([A-D])\b',
+        r'Option\s+([A-D])\s+is correct',
+    ]
+    for pat in explicit_patterns:
+        match = re.search(pat, text, re.IGNORECASE)
+        if match:
+            return match.group(1).upper()
+
+    # 2. markdown 强调 **C**, **C. something**
+    markdown_match = re.findall(r'\*\*\s*([A-D])[\.\s]?', text)
+    if markdown_match:
+        return markdown_match[-1].upper()
+
+    # 3. 查找单引号中的 'C' 或 "C"
+    quote_match = re.findall(r"['\"]([A-D])['\"]", text)
+    if quote_match:
+        return quote_match[-1].upper()
+
+    # 4. 倒数几行是否以 "C." 或 "C" 开头
+    lines = text.splitlines()
+    for line in reversed(lines[-5:]):
+        line = line.strip()
+        match = re.match(r'^([A-D])([.\s]|$)', line)
+        if match:
+            return match.group(1).upper()
+    
+    # 再不行就返回 None
+    return None
 class DataEvaluator:
     def __init__(self):
         self.data = []
@@ -22,8 +67,10 @@ class DataEvaluator:
         """
         从数据文件中加载数据，每条记录对应一个实例
         """
-        ds = load_dataset(file_path, "all")
-        df = pd.DataFrame(ds['test'])
+        splits = {'test': 'all/test-00000-of-00001.parquet', 'validation': 'all/validation-00000-of-00001.parquet',
+                  'dev': 'all/dev-00000-of-00001.parquet',
+                  'auxiliary_train': 'all/auxiliary_train-00000-of-00001.parquet'}
+        df = pd.read_parquet("hf://datasets/cais/mmlu/" + splits["test"])
         for _, row in df.iterrows():
             self.data.append(row.to_dict())
 
@@ -73,6 +120,7 @@ def generate_text(api_url, question, model_name, stream=False):
 def main(concurrent_requests, data_evaluator: DataEvaluator, result_file, log_file, api_url, model_name):
     start_total_time = time.time()
     total_score = 0
+    total_exact_score = 0
     results = []
     file_lock = threading.Lock()
     
@@ -85,6 +133,7 @@ def main(concurrent_requests, data_evaluator: DataEvaluator, result_file, log_fi
 
     def worker(index, data_item):
         nonlocal total_score
+        nonlocal total_exact_score
         question = data_evaluator.get_prompt(data_item)
         start_time = time.time()
         try:
@@ -95,13 +144,15 @@ def main(concurrent_requests, data_evaluator: DataEvaluator, result_file, log_fi
             answer = chr(data_item['answer'] + 65)
             processed_prediction = data_evaluator.post_processing(prediction)
             score = data_evaluator.score(processed_prediction, answer)
+            exact_score = data_evaluator.score(extract_final_answer(prediction), answer)
             elapsed_time = time.time() - start_time
             result_data = {
                 "question_id": index,
                 "answer": answer,
                 "prediction": processed_prediction,
-                "real_prediction": prediction,
+                "full_prediction": prediction,
                 "score": score,
+                "exact_score": exact_score,
                 "time": elapsed_time
             }
             # 写入结果时加锁保证线程安全
@@ -124,6 +175,7 @@ def main(concurrent_requests, data_evaluator: DataEvaluator, result_file, log_fi
                 if res is not None:
                     results.append(res)
                     total_score += res['score']
+                    total_exact_score += res['exact_score']
     
     total_time = time.time() - start_total_time
     throughput = len(data_subset) / total_time if total_time > 0 else 0
@@ -133,6 +185,8 @@ def main(concurrent_requests, data_evaluator: DataEvaluator, result_file, log_fi
         log_f.write(f"Throughput: {throughput:.2f} requests per second\n")
         average_score = total_score / len(data_subset) if data_subset else 0
         log_f.write(f"Average Score: {average_score}\n")
+        average_exact_score = total_exact_score / len(data_subset) if data_subset else 0
+        log_f.write(f"Average Exact Score: {average_exact_score}\n")
         log_f.write('-' * 40 + '\n')
     
     print(f"Results saved to {result_file}")
@@ -152,4 +206,4 @@ if __name__ == "__main__":
     data_evaluator = DataEvaluator()
     data_evaluator.load_data(args.file)
     
-    main(args.concurrent, data_evaluator, args.result, args.log, args.api_url, args.model)
+    main(args.concurrent, data_evaluator, args.result, args.log, args.api_url, args.model)

ktransformers/tests/test_client.py

@@ -2,23 +2,18 @@ import asyncio
 import json
 import sys
 import aiohttp
-import random
 import argparse
-import yaml
-import os
-import time
-from time import sleep
 
-decodesz = 128
-# Server URL (replace with your server URL)
-SERVER_URL = "http://localhost:10002/v1/chat/completions"
-bf_list = [1]
-decodesz_list = [128]
-prompt_list = ['Please elaborate on modern world history.', 'Please introduce Harry Potter.', 'I want to learn Python. Please give me some advice.', 'Please tell me a joke ']
-async def fetch_event_stream(session, payload, request_id):
+prompt_list = [
+    'Please elaborate on modern world history.',
+    'Please introduce Harry Potter.',
+    'I want to learn Python. Please give me some advice.',
+    'Please tell me a joke '
+]
+
+
+async def fetch_event_stream(session, payload, request_id, stream):
     try:
-        
-
         headers = {
             'accept': 'application/json',
             'Content-Type': 'application/json'
@@ -31,104 +26,80 @@ async def fetch_event_stream(session, payload, request_id):
                 print(f"Request {request_id}: Error, status {response.status}")
                 return
 
-            output_text = ""  # 存储当前 response 的所有 token
-            total_tokens = 0  # 统计总 tokens 数
-            decode_start_time = None  # 记录 decode 阶段开始时间
-            decode_end_time = None  # 记录 decode 结束时间
+            output_text = ""
 
-            async for line in response.content:
-                try:
-                    decoded_line = line.decode("utf-8").strip()
+            if stream:
+                async for line in response.content:
+                    try:
+                        decoded_line = line.decode("utf-8").strip()
+                        if not decoded_line or not decoded_line.startswith("data: "):
+                            continue
 
-                    # 过滤空行
-                    if not decoded_line or not decoded_line.startswith("data: "):
-                        continue
+                        decoded_line = decoded_line[6:].strip()
+                        if not decoded_line:
+                            continue
 
-                    decoded_line = decoded_line[6:].strip()  # 去掉 `data: `
+                        response_data = json.loads(decoded_line)
+                        choices = response_data.get("choices", [])
+                        if not choices:
+                            continue
 
-                    # 确保 JSON 数据是合法的
-                    if not decoded_line:
-                        continue
+                        delta = choices[0].get("delta", {})
+                        token = delta.get("content", "")
 
-                    response_data = json.loads(decoded_line)  # 解析 JSON
+                        if token:
+                            output_text += token
+                            sys.stdout.write(token)
+                            sys.stdout.flush()
 
-                    # 确保 choices 存在
-                    choices = response_data.get("choices", [])
-                    if not choices:
-                        continue
+                        finish_reason = choices[0].get("finish_reason", None)
+                        if finish_reason:
+                            break
 
-                    delta = choices[0].get("delta", {})
-                    token = delta.get("content", "")
-
-                    if token:
-                        if decode_start_time is None:
-                            decode_start_time = time.time()  # 记录 decode 开始时间
-                        
-                        output_text += token  # 追加 token
-                        sys.stdout.write(token)  # 直接输出 token
-                        sys.stdout.flush()  # 立即刷新，确保 token 立刻出现在终端
-                        total_tokens += 1  # 增加 token 计数
-                        decode_end_time = time.time()  # 每次收到 token，更新 decode 结束时间
-
-                    # 检查是否完成
-                    finish_reason = choices[0].get("finish_reason", None)
-                    if finish_reason:
-                        # print(f"\nRequest {request_id}: Done")
-                        break  # 结束流式处理
-
-                except json.JSONDecodeError as e:
-                    print(f"\nRequest {request_id}: JSON Decode Error - {e}")
-                except IndexError:
-                    print(f"\nRequest {request_id}: List Index Error - choices is empty")
-                except Exception as e:
-                    print(f"\nRequest {request_id}: Error parsing stream - {e}")
-
-            # 计算 decode 速度
-            if decode_start_time and decode_end_time and total_tokens > 0:
-                decode_time = decode_end_time - decode_start_time
-                decode_speed = total_tokens / decode_time if decode_time > 0 else 0
-                # print(f"Request {request_id}: Decode Speed = {decode_speed:.2f} tokens/s")
+                    except json.JSONDecodeError as e:
+                        print(f"\nRequest {request_id}: JSON Decode Error - {e}")
+                    except IndexError:
+                        print(f"\nRequest {request_id}: List Index Error - choices is empty")
+                    except Exception as e:
+                        print(f"\nRequest {request_id}: Error parsing stream - {e}")
+            else:
+                # 非 stream 模式下，一次性接收完整 json
+                response_data = await response.json()
+                choices = response_data.get("choices", [])
+                if choices:
+                    content = choices[0].get("message", {}).get("content", "")
+                    print(f"Request {request_id} Output:\n{content}")
+                    output_text += content
 
     except Exception as e:
         print(f"\nRequest {request_id}: Exception - {e}")
 
-async def main(prompt_id):
+async def main(prompt_id, model, stream, max_tokens, temperature, top_p):
     async with aiohttp.ClientSession() as session:
         payload = {
             "messages": [
                 {"role": "system", "content": ""},
                 {"role": "user", "content": prompt_list[prompt_id]}
             ],
-            "model": "DeepSeek-V3",
-            "stream": True,
-            "max_completion_tokens": 2,
-            # "temperature": 0.3,
-            # "top_p": 1.0,     
-            # "max_tokens" : 20,                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 
+            "model": model,
+            "stream": stream,
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "top_p": top_p
         }
-        tasks = [fetch_event_stream(session, payload, prompt_id)]
-        await asyncio.gather(*tasks)
-
-        payload["temperature"] = 0.3
-        tasks = [fetch_event_stream(session, payload, prompt_id)]
-        await asyncio.gather(*tasks)
-
-        payload["top_p"] = 1
-        tasks = [fetch_event_stream(session, payload, prompt_id)]
-        await asyncio.gather(*tasks)
-
-        payload["max_tokens"] = 200
-        tasks = [fetch_event_stream(session, payload, prompt_id)]
-        await asyncio.gather(*tasks)
-        
-        payload["stream"] = False
-        tasks = [fetch_event_stream(session, payload, prompt_id)]
+        tasks = [fetch_event_stream(session, payload, prompt_id, stream)]
         await asyncio.gather(*tasks)
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser(description="Event Stream Request Tester")
-    parser.add_argument("--question_id", type=int, default=0, required=False)
+    parser.add_argument("--question_id", type=int, default=0)
+    parser.add_argument("--model", type=str, required=True)
+    parser.add_argument("--stream", type=bool, default=True)  
+    parser.add_argument("--max_tokens", type=int, default=500)
+    parser.add_argument("--temperature", type=float, default=0.8)
+    parser.add_argument("--top_p", type=float, default=1)
+    parser.add_argument("--api_url", type=str, default="http://localhost:10006/v1/chat/completions", help="API URL")
+
     args = parser.parse_args()
-    output_file = "ktransformer_test_results.txt"
-    asyncio.run(main(args.question_id))
+    SERVER_URL = args.api_url
+    asyncio.run(main(args.question_id, args.model, args.stream, args.max_tokens, args.temperature, args.top_p))

ktransformers/tests/test_speed.py

@@ -12,6 +12,8 @@ from time import sleep
 decodesz = 128
 # Server URL (replace with your server URL)
 decodesz_list = [128]
+prefill_speeds = []
+decode_speeds = []
 ktansformer_prompt1024="""Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to say that they were perfectly normal, thank you very much. 
 They were the last people you'd expect to be involved in anything strange or mysterious, because they just didn't hold with such nonsense.Mr. Dursley was the director of a firm called Grunnings, which made drills. 
 He was a big, beefy man with hardly any neck, although he did have a very large mustache. Mrs. 
@@ -43,17 +45,19 @@ They were whispering excitedly together. Mr. Dursley was enraged to see that a c
 The nerve of him! But then it struck Mr. Dursley that this was probably some silly stunt — these people were obviously collecting for something… yes, that would be it. 
 The traffic moved on and a few minutes later, Mr. Dursley arrived in the Grunnings parking lot, his mind back on drills.
 Mr. Dursley always sat with his back to the window in his office on the ninth floor."""
-async def fetch_event_stream(session, request_id, prompt):
+async def fetch_event_stream(session, request_id, prompt, max_tokens, model):
     try:
         payload = {
             "messages": [
                 {"role": "system", "content": ""},
                 {"role": "user", "content": prompt}
             ],
-            "model": "DeepSeek-V3",
+            "model": model,
             "temperature": 0.3,
             "top_p": 1.0,
-            "stream": True
+            "stream": True,
+            "return_speed": True,
+            "max_tokens": max_tokens,
         }
 
         headers = {
@@ -70,6 +74,7 @@ async def fetch_event_stream(session, request_id, prompt):
             total_tokens = 0
             decode_start_time = None
             decode_end_time = None
+            usage_info = None  
 
             async for line in response.content:
                 try:
@@ -82,6 +87,10 @@ async def fetch_event_stream(session, request_id, prompt):
                         continue
 
                     response_data = json.loads(decoded_line)
+                    
+                    if "usage" in response_data:
+                        usage_info = response_data["usage"]
+                    
                     choices = response_data.get("choices", [])
                     if not choices:
                         continue
@@ -107,34 +116,48 @@ async def fetch_event_stream(session, request_id, prompt):
                 except Exception as e:
                     print(f"[Request {request_id}] Stream Error: {e}")
 
-
             if buffer.strip():
                 print(f"[Request {request_id}] {buffer.strip()}")
 
-            if decode_start_time and decode_end_time and total_tokens > 0:
-                decode_time = decode_end_time - decode_start_time
-                decode_speed = total_tokens / decode_time if decode_time > 0 else 0
-                print(f"[Request {request_id}] Speed: {decode_speed:.2f} tokens/s")
+            if usage_info:
+                if "prefill_time" in usage_info:
+                    # print(f"[Request {request_id}] Usage:")
+                    # for key, value in usage_info.items():
+                    #     print(f"  {key}: {value}")
+                    prefill_speed = usage_info["prompt_tokens"] / usage_info["prefill_time"]
+                    decode_speed = usage_info["completion_tokens"] / usage_info["decode_time"]
+                    prefill_speeds.append(prefill_speed)
+                    decode_speeds.append(decode_speed)
+                    print(f'[Request {request_id}] prefill speed: {prefill_speed}')
+                    print(f'[Request {request_id}] decode speed: {decode_speed}')
 
     except Exception as e:
         print(f"[Request {request_id}] Exception: {e}")
 
-async def main(concurrent_requests , prompt ):
+async def main(concurrent_requests , prompt, max_tokens, model):
     async with aiohttp.ClientSession() as session:
-        tasks = [fetch_event_stream(session, i , prompt) for i in range(concurrent_requests)]
+        tasks = [fetch_event_stream(session, i , prompt, max_tokens, model) for i in range(concurrent_requests)]
         await asyncio.gather(*tasks)
+    if len(prefill_speeds) != 0:
+        import numpy as np
+        print(f"concurrency: {len(prefill_speeds)}")
+        print(f"total prefill speed: {np.sum(prefill_speeds)}\n total decode speed: {np.sum(decode_speeds)}")
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Event Stream Request Tester")
     parser.add_argument("--concurrent", type=int, default=1, help="Number of concurrent requests")
+    parser.add_argument("--model", type=str, default="DeepSeek-V3", help="Model name", required=True)
     parser.add_argument("--prompt_lens", type=int, default=1024, help="prefill prompt lens, 1024 or 2048")
     parser.add_argument("--api_url", type=str, default="http://localhost:10002/v1/chat/completions", help="API URL")
+    parser.add_argument("--max_tokens", type=int, default=50, help="max decode tokens")
     
     args = parser.parse_args()
     SERVER_URL = args.api_url
+    max_tokens = args.max_tokens
+    model = args.model
     if args.prompt_lens == 1024:
         prompt = ktansformer_prompt1024
     elif args.prompt_lens == 2048:
         prompt = ktansformer_prompt1024 * 2
-    asyncio.run(main(args.concurrent, prompt))
+    asyncio.run(main(args.concurrent, prompt, max_tokens, model))