Rename servers and change port numbers to reduce confusion (#149)

benchmarks/benchmark_async_llm_server.py

@@ -52,7 +52,7 @@ def main(args: argparse.Namespace):
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="localhost")
-    parser.add_argument("--port", type=int, default=8001)
+    parser.add_argument("--port", type=int, default=8000)
     parser.add_argument("--max-tokens", type=int, default=128)
     parser.add_argument("--n-threads", type=int, default=128)
     args = parser.parse_args()

cacheflow/__init__.py

@@ -2,7 +2,7 @@ from cacheflow.entrypoints.llm import LLM
 from cacheflow.outputs import RequestOutput, CompletionOutput
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.server.arg_utils import ServerArgs
-from cacheflow.server.llm_server import LLMServer
+from cacheflow.server.llm_server import LLMEngine
 from cacheflow.server.ray_utils import initialize_cluster
 
 __version__ = "0.1.0"
@@ -12,7 +12,7 @@ __all__ = [
     "SamplingParams",
     "RequestOutput",
     "CompletionOutput",
-    "LLMServer",
+    "LLMEngine",
     "ServerArgs",
     "initialize_cluster",
 ]

cacheflow/entrypoints/api_server.py

@@ -8,7 +8,7 @@ import uvicorn
 
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.server.arg_utils import AsyncServerArgs
-from cacheflow.server.async_llm_server import AsyncLLMServer
+from cacheflow.server.async_llm_server import AsyncLLMEngine
 from cacheflow.utils import random_uuid
 
 TIMEOUT_KEEP_ALIVE = 5 # seconds.
@@ -18,7 +18,7 @@ app = FastAPI()
 
 @app.post("/generate")
 async def generate(request: Request) -> Response:
-    """ Stream the results of the generation request.
+    """Generate completion for the request.
 
     The request should be a JSON object with the following fields:
     - prompt: the prompt to use for the generation.
@@ -74,12 +74,12 @@ async def generate(request: Request) -> Response:
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="localhost")
-    parser.add_argument("--port", type=int, default=8001)
+    parser.add_argument("--port", type=int, default=8000)
     parser = AsyncServerArgs.add_cli_args(parser)
     args = parser.parse_args()
 
     server_args = AsyncServerArgs.from_cli_args(args)
-    server = AsyncLLMServer.from_server_args(server_args)
+    server = AsyncLLMEngine.from_server_args(server_args)
 
     uvicorn.run(app, host=args.host, port=args.port, log_level="debug",
                 timeout_keep_alive=TIMEOUT_KEEP_ALIVE)

cacheflow/entrypoints/llm.py

@@ -6,7 +6,7 @@ from tqdm import tqdm
 from cacheflow.outputs import RequestOutput
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.server.arg_utils import ServerArgs
-from cacheflow.server.llm_server import LLMServer
+from cacheflow.server.llm_server import LLMEngine
 from cacheflow.utils import Counter
 
 
@@ -20,7 +20,7 @@ class LLM:
     mechanism and efficient memory management.
 
     NOTE: This class is intended to be used for offline inference. For online
-    serving, use the `AsyncLLMServer` class instead.
+    serving, use the `AsyncLLMEngine` class instead.
     NOTE: For the comprehensive list of arguments, see `ServerArgs`.
 
     Args:
@@ -52,7 +52,7 @@ class LLM:
             seed=seed,
             **kwargs,
         )
-        self.llm_server = LLMServer.from_server_args(server_args)
+        self.llm_server = LLMEngine.from_server_args(server_args)
         self.request_counter = Counter()
 
     def get_tokenizer(

cacheflow/entrypoints/openai/api_server.py

@@ -15,7 +15,7 @@ import uvicorn
 
 from cacheflow.outputs import RequestOutput
 from cacheflow.server.arg_utils import AsyncServerArgs
-from cacheflow.server.async_llm_server import AsyncLLMServer
+from cacheflow.server.async_llm_server import AsyncLLMEngine
 from cacheflow.server.tokenizer_utils import get_tokenizer
 from cacheflow.logger import init_logger
 from cacheflow.sampling_params import SamplingParams
@@ -319,7 +319,7 @@ if __name__ == "__main__":
     served_model = args.served_model_name or args.model
 
     server_args = AsyncServerArgs.from_cli_args(args)
-    server = AsyncLLMServer.from_server_args(server_args)
+    server = AsyncLLMEngine.from_server_args(server_args)
 
     # A separate tokenizer to map token IDs to strings.
     tokenizer = get_tokenizer(args.model)

cacheflow/server/async_llm_server.py

@@ -6,7 +6,7 @@ from cacheflow.logger import init_logger
 from cacheflow.outputs import RequestOutput
 from cacheflow.sampling_params import SamplingParams
 from cacheflow.server.arg_utils import AsyncServerArgs
-from cacheflow.server.llm_server import LLMServer
+from cacheflow.server.llm_server import LLMEngine
 from cacheflow.server.ray_utils import ray, initialize_cluster
 
 logger = init_logger(__name__)
@@ -14,26 +14,26 @@ logger = init_logger(__name__)
 TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds
 
 
-class AsyncLLMServer:
-    """An asynchronous wrapper for LLMServer.
+class AsyncLLMEngine:
+    """An asynchronous wrapper for LLMEngine.
 
-    This class is used to wrap the LLMServer class to make it asynchronous. It
+    This class is used to wrap the LLMEngine class to make it asynchronous. It
     uses asyncio to create a background loop that keeps processing incoming
-    requests. The LLMServer is kicked by the generate method when there
+    requests. The LLMEngine is kicked by the generate method when there
     are requests in the waiting queue. The generate method yields the outputs
-    from the LLMServer to the caller.
+    from the LLMEngine to the caller.
 
-    NOTE: For the comprehensive list of arguments, see `LLMServer`.
+    NOTE: For the comprehensive list of arguments, see `LLMEngine`.
 
     Args:
         worker_use_ray: Whether to use Ray for model workers. Required for
             distributed execution. Should be the same as
             `parallel_config.worker_use_ray`.
-        server_use_ray: Whether to make LLMServer a Ray actor. If so, the
+        server_use_ray: Whether to make LLMEngine a Ray actor. If so, the
             async frontend will be executed in a separate process as the
             model workers.
         log_requests: Whether to log the requests.
-        *args, *kwargs: Arguments for LLMServer.
+        *args, *kwargs: Arguments for LLMEngine.
     """
     def __init__(self, worker_use_ray: bool, server_use_ray: bool,
                  log_requests: bool = True, *args, **kwargs) -> None:
@@ -41,11 +41,11 @@ class AsyncLLMServer:
         self.server_use_ray = server_use_ray
         self.log_requests = log_requests
         if not self.server_use_ray:
-            server_class = LLMServer
+            server_class = LLMEngine
         elif self.worker_use_ray:
-            server_class = ray.remote(num_cpus=0)(LLMServer).remote
+            server_class = ray.remote(num_cpus=0)(LLMEngine).remote
         else:
-            server_class = ray.remote(num_gpus=1)(LLMServer).remote
+            server_class = ray.remote(num_gpus=1)(LLMEngine).remote
         self.server = server_class(*args, **kwargs)
         # Request id -> request output.
         self.request_outputs: Dict[str, RequestOutput] = {}
@@ -85,8 +85,8 @@ class AsyncLLMServer:
         """Generate outputs for a request.
 
         Generate outputs for a request. This method is a coroutine. It adds the
-        request into the waiting queue of the LLMServer and streams the outputs
-        from the LLMServer to the caller.
+        request into the waiting queue of the LLMEngine and streams the outputs
+        from the LLMEngine to the caller.
 
         Args:
             prompt: The prompt string. Can be None if prompt_token_ids is
@@ -97,7 +97,7 @@ class AsyncLLMServer:
                 use the tokenizer to convert the prompts to token IDs.
 
         Yields:
-            The output `RequestOutput` objects from the LLMServer for the
+            The output `RequestOutput` objects from the LLMEngine for the
             request.
         """
         # Preprocess the request.
@@ -200,7 +200,7 @@ class AsyncLLMServer:
             self.kicking_request_id = None
 
     @classmethod
-    def from_server_args(cls, server_args: AsyncServerArgs) -> "AsyncLLMServer":
+    def from_server_args(cls, server_args: AsyncServerArgs) -> "AsyncLLMEngine":
         """Creates an async LLM server from the server arguments."""
         # Create the server configs.
         server_configs = server_args.create_server_configs()

cacheflow/server/llm_server.py

@@ -18,7 +18,7 @@ from cacheflow.worker.worker import Worker
 logger = init_logger(__name__)
 
 
-class LLMServer:
+class LLMEngine:
     """An LLM server that receives requests and generates texts.
 
     This is the main class for the CacheFlow LLM server. It receives requests
@@ -29,7 +29,7 @@ class LLMServer:
     serving throughput.
 
     The `LLM` class wraps this class for offline batched inference and the
-    `AsyncLLMServer` class wraps this class for online serving.
+    `AsyncLLMEngine` class wraps this class for online serving.
 
     NOTE: The config arguments are derived from the `ServerArgs` class. For the
     comprehensive list of arguments, see `ServerArgs`.
@@ -135,7 +135,7 @@ class LLMServer:
         self._run_workers("init_cache_engine", cache_config=self.cache_config)
 
     @classmethod
-    def from_server_args(cls, server_args: ServerArgs) -> "LLMServer":
+    def from_server_args(cls, server_args: ServerArgs) -> "LLMEngine":
         """Creates an LLM server from the server arguments."""
         # Create the server configs.
         server_configs = server_args.create_server_configs()

examples/api_client.py

@@ -1,3 +1,5 @@
+"""Example Python client for cacheflow.entrypoints.api_server"""
+
 import argparse
 import json
 from typing import Iterable, List
@@ -45,7 +47,7 @@ def get_response(response: requests.Response) -> List[str]:
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="localhost")
-    parser.add_argument("--port", type=int, default=8001)
+    parser.add_argument("--port", type=int, default=8000)
     parser.add_argument("--n", type=int, default=4)
     parser.add_argument("--prompt", type=str, default="San Francisco is a")
     parser.add_argument("--stream", action="store_true")

examples/gradio_webserver.py

@@ -9,6 +9,7 @@ def http_bot(prompt):
     headers = {"User-Agent": "Cacheflow Client"}
     pload = {
         "prompt": prompt,
+        "stream": True,
         "max_tokens": 128,
     }
     response = requests.post(args.model_url, headers=headers, json=pload, stream=True)
@@ -34,8 +35,8 @@ def build_demo():
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="localhost")
-    parser.add_argument("--port", type=int, default=8002)
-    parser.add_argument("--model-url", type=str, default="http://localhost:8001/generate")
+    parser.add_argument("--port", type=int, default=8001)
+    parser.add_argument("--model-url", type=str, default="http://localhost:8000/generate")
     args = parser.parse_args()
 
     demo = build_demo()

examples/llmserver_example.py

@@ -1,12 +1,12 @@
 import argparse
 
-from cacheflow import ServerArgs, LLMServer, SamplingParams
+from cacheflow import ServerArgs, LLMEngine, SamplingParams
 
 
 def main(args: argparse.Namespace):
     # Parse the CLI argument and initialize the server.
     server_args = ServerArgs.from_cli_args(args)
-    server = LLMServer.from_server_args(server_args)
+    server = LLMEngine.from_server_args(server_args)
 
     # Test the following prompts.
     test_prompts = [
@@ -38,7 +38,8 @@ def main(args: argparse.Namespace):
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Simple CacheFlow server.')
+    parser = argparse.ArgumentParser(
+        description='Demo on using the LLMEngine class synchronously')
     parser = ServerArgs.add_cli_args(parser)
     args = parser.parse_args()
     main(args)