Add stream client

llm_service/client.py

@@ -2,6 +2,7 @@ import json
 import argparse
 import requests
 import configparser
+
 from typing import Iterable, List
 
 
@@ -57,18 +58,24 @@ if __name__ == "__main__":
     api_url = f"http://localhost:8888/{func}"
 
     if stream_chat:
-        response = requests.get(api_url, headers=headers, data=json_str.encode(
-            "utf-8"), verify=False,  stream=stream_chat)
+        headers={
+            "Content-Type": "text/event-stream",
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive"
+        }
+        response = requests.post(api_url, headers=headers, data=json_str.encode(
+        "utf-8"), verify=False, stream=True)
         num_printed_lines = 0
         for h in get_streaming_response(response):
-            clear_line(num_printed_lines)
-            num_printed_lines = 0
+            # clear_line(num_printed_lines)
             for i, line in enumerate(h):
                 num_printed_lines += 1
-                print(f"Beam candidate {i}: {line!r}", flush=True)
+                print(f"{line!r}", flush=True)
+
     else:
-        response = requests.get(api_url, headers=headers, data=json_str.encode(
-            "utf-8"), verify=False,  stream=stream_chat)
+        headers = {"Content-Type": "application/json"}
+        response = requests.post(api_url, headers=headers, data=json_str.encode(
+            "utf-8"), verify=False)
         output = get_response(response)
         for i, line in enumerate(output):
             print(f"Beam candidate {i}: {line!r}", flush=True)
\ No newline at end of file

llm_service/client_stream.py

-"""Example Python client for vllm.entrypoints.api_server"""
-
-import argparse
-import json
-from typing import Iterable, List
-
-import requests
-
-
-def clear_line(n: int = 1) -> None:
-    LINE_UP = '\033[1A'
-    LINE_CLEAR = '\x1b[2K'
-    for _ in range(n):
-        print(LINE_UP, end=LINE_CLEAR, flush=True)
-
-
-def post_http_request(query: str, api_url: str, n: int = 1,
-                      stream: bool = False) -> requests.Response:
-    headers = {"User-Agent": "Test Client"}
-    pload = {
-        "query": query,
-        "n": n,
-        "use_beam_search": True,
-        "temperature": 0.0,
-        "max_tokens": 16,
-        "stream": stream,
-    }
-    response = requests.post(api_url, headers=headers, json=pload, stream=True)
-    return response
-
-
-def get_streaming_response(response: requests.Response) -> Iterable[List[str]]:
-    for chunk in response.iter_lines(chunk_size=8192, decode_unicode=False,
-                                     delimiter=b"\0"):
-        if chunk:
-            data = json.loads(chunk.decode("utf-8"))
-            output = data["text"]
-            yield output
-
-
-def get_response(response: requests.Response) -> List[str]:
-    data = json.loads(response.content)
-    output = data["text"]
-    return output
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--host", type=str, default="localhost")
-    parser.add_argument("--port", type=int, default=8888)
-    parser.add_argument("--n", type=int, default=4)
-    parser.add_argument("--query", type=str, default="San Francisco is a")
-    parser.add_argument("--stream", action="store_true")
-    args = parser.parse_args()
-    query = args.query
-    api_url = f"http://{args.host}:{args.port}/generate"
-    n = args.n
-    stream = args.stream
-
-    print(f"Prompt: {query!r}\n", flush=True)
-    response = post_http_request(query, api_url, n, stream)
-
-    if stream:
-        num_printed_lines = 0
-        for h in get_streaming_response(response):
-            clear_line(num_printed_lines)
-            num_printed_lines = 0
-            for i, line in enumerate(h):
-                num_printed_lines += 1
-                print(f"Beam candidate {i}: {line!r}", flush=True)
-    else:
-        output = get_response(response)
-        for i, line in enumerate(output):
-            print(f"Beam candidate {i}: {line!r}", flush=True)
\ No newline at end of file

llm_service/inferencer.py

@@ -11,7 +11,6 @@ from aiohttp import web
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 
-
 COMMON = {
     "<光合组织登记网址>": "https://www.hieco.com.cn/partner?from=timeline",
     "<官网>": "https://www.sugon.com/after_sale/policy?sh=1",
@@ -259,12 +258,10 @@ def vllm_inference(bind_port, model, tokenizer, sampling_params):
 
 def vllm_inference_stream(bind_port, model, tokenizer, sampling_params):
     '''启动 Web 服务器，接收 HTTP 请求，并通过调用本地的 LLM 推理服务生成响应. '''
-    from typing import AsyncGenerator
-    from fastapi.responses import StreamingResponse
 
     async def inference(request):
-        input_json = await request.json()
 
+        input_json = await request.json()
         prompt = input_json['query']
         # history = input_json['history']
 
@@ -279,21 +276,20 @@ def vllm_inference_stream(bind_port, model, tokenizer, sampling_params):
         results_generator = model.generate(input_text, sampling_params=sampling_params, request_id=request_id)
 
         # Streaming case
-        async def stream_results() -> AsyncGenerator[bytes, None]:
-            # final_output = None
-            logger.info("****************** in stream_results *****************")
+        logger.info("****************** in stream chat ******************")
+        response = web.StreamResponse()
+        await response.prepare(request)
+        text_outputs = None
         async for request_output in results_generator:
-                # final_output = request_output
+            prompt = request_output.prompt
             text_outputs = [output.text for output in request_output.outputs]
             ret = {"text": text_outputs}
-                print(ret)
-                yield (json.dumps(ret) + "\0").encode("utf-8")
-
-        logger.info("****************** in chat stream *****************")
-        return StreamingResponse(stream_results())
+            await response.write((json.dumps(ret)+"\0").encode("utf-8"))
+        response.write_eof()
+        return response
 
     app = web.Application()
-    app.add_routes([web.get('/vllm_inference_stream', inference)])
+    app.add_routes([web.post('/vllm_inference_stream', inference)])
     web.run_app(app, host='0.0.0.0', port=bind_port)