[Frontend] Add progress reporting to run_batch.py

vllm/entrypoints/openai/run_batch.py

@@ -1,9 +1,11 @@
 import asyncio
 from io import StringIO
-from typing import Awaitable, Callable, List
+from typing import Awaitable, Callable, List, Optional
 
 import aiohttp
+import torch
 from prometheus_client import start_http_server
+from tqdm import tqdm
 
 from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -78,6 +80,38 @@ def parse_args():
     return parser.parse_args()
 
 
+# explicitly use pure text format, with a newline at the end
+# this makes it impossible to see the animation in the progress bar
+# but will avoid messing up with ray or multiprocessing, which wraps
+# each line of output with some prefix.
+_BAR_FORMAT = "{desc}: {percentage:3.0f}% Completed | {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]\n"  # noqa: E501
+
+
+class BatchProgressTracker:
+
+    def __init__(self):
+        self._total = 0
+        self._pbar: Optional[tqdm] = None
+
+    def submitted(self):
+        self._total += 1
+
+    def completed(self):
+        if self._pbar:
+            self._pbar.update()
+
+    def pbar(self) -> tqdm:
+        enable_tqdm = not torch.distributed.is_initialized(
+        ) or torch.distributed.get_rank() == 0
+        self._pbar = tqdm(total=self._total,
+                          unit="req",
+                          desc="Running batch",
+                          mininterval=5,
+                          disable=not enable_tqdm,
+                          bar_format=_BAR_FORMAT)
+        return self._pbar
+
+
 async def read_file(path_or_url: str) -> str:
     if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
         async with aiohttp.ClientSession() as session, \
@@ -102,7 +136,8 @@ async def write_file(path_or_url: str, data: str) -> None:
 
 
 async def run_request(serving_engine_func: Callable,
-                      request: BatchRequestInput) -> BatchRequestOutput:
+                      request: BatchRequestInput,
+                      tracker: BatchProgressTracker) -> BatchRequestOutput:
     response = await serving_engine_func(request.body)
 
     if isinstance(response, (ChatCompletionResponse, EmbeddingResponse)):
@@ -125,6 +160,7 @@ async def run_request(serving_engine_func: Callable,
     else:
         raise ValueError("Request must not be sent in stream mode")
 
+    tracker.completed()
     return batch_output
 
 
@@ -164,6 +200,9 @@ async def main(args):
         request_logger=request_logger,
     )
 
+    tracker = BatchProgressTracker()
+    logger.info("Reading batch from %s...", args.input_file)
+
     # Submit all requests in the file to the engine "concurrently".
     response_futures: List[Awaitable[BatchRequestOutput]] = []
     for request_json in (await read_file(args.input_file)).strip().split("\n"):
@@ -178,16 +217,19 @@ async def main(args):
         if request.url == "/v1/chat/completions":
             response_futures.append(
                 run_request(openai_serving_chat.create_chat_completion,
-                            request))
+                            request, tracker))
+            tracker.submitted()
         elif request.url == "/v1/embeddings":
             response_futures.append(
-                run_request(openai_serving_embedding.create_embedding,
-                            request))
+                run_request(openai_serving_embedding.create_embedding, request,
+                            tracker))
+            tracker.submitted()
         else:
             raise ValueError("Only /v1/chat/completions and /v1/embeddings are"
                              "supported in the batch endpoint.")
 
-    responses = await asyncio.gather(*response_futures)
+    with tracker.pbar():
+        responses = await asyncio.gather(*response_futures)
 
     output_buffer = StringIO()
     for response in responses: