├── requirements.txt ├── images ├── model_endpoint.png └── config_template.png ├── README.md ├── api_server.py ├── .gitignore └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | git+https://github.com/vllm-project/vllm@main#egg=vllm -------------------------------------------------------------------------------- /images/model_endpoint.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangcx18/llm-vscode-inference-server/HEAD/images/model_endpoint.png -------------------------------------------------------------------------------- /images/config_template.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wangcx18/llm-vscode-inference-server/HEAD/images/config_template.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # `llm-vscode` Inference Server 2 | 3 | This repository serves as an alternative endpoint server for the [llm-vscode](https://github.com/huggingface/llm-vscode) extension (*formerly known as the Hugging Face VSCode extension*). In contrast to [LucienShui/huggingface-vscode-endpoint-server](https://github.com/LucienShui/huggingface-vscode-endpoint-server), the main objective here is to integrate support for **quantized** open-source LLMs tailored for coding tasks into the `llm-vscode` extension. Such an integration would make self-hosting a code completion service not only more accessible but also more cost-effective and faster, even on smaller GPUs and CPUs. 4 | 5 | 6 | Tested with : 7 | 8 | - [TheBloke/CodeLlama-7B-Python-AWQ](https://huggingface.co/TheBloke/CodeLlama-7B-Python-AWQ) 9 | 10 | I will test more models later. 11 | 12 | ## Usage 13 | ### Install requirements 14 | ```shell 15 | pip install -r requirements.txt 16 | ``` 17 | ### Serving 18 | ``` 19 | python api_server.py --trust-remote-code --model [/path/to/model/folder] 20 | ``` 21 | 22 | By default, the server runs on `localhost` using port `8000`. You can also specify a different port by using the `--port` flag. 23 | 24 | Since the `api_server.py` in this repository is adapted from [`api_server.py`](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/api_server.py), it inherits the same arguments. You can refer to [`arg_utils.py`](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py) to review all the supported command line arguments. 25 | 26 | For quantized models, you should append the following arguments: `--quantization awq --dtype half`. For example: 27 | 28 | ```shell 29 | python api_server.py --trust-remote-code --model [/path/to/model/folder] --quantization awq --dtype half 30 | ``` 31 | ### Config in VSCode 32 | 1. Open VSCode, go to `Preferences` -> `Settings`, navigate to `Hugging Face Code` section. 33 | 34 | 2. Set `Config Template` to `Custom`: 35 | 36 | ![](./images/config_template.png) 37 | 38 | 3. Set `Model ID or Endpoint` to `http://localhost:8000/generate`, and replace the port number if you are using a different one: 39 | 40 | ![](./images/model_endpoint.png) 41 | 42 | ## API 43 | 44 | - Request: 45 | 46 | ```shell 47 | curl http://localhost:8000/generate -d '{"inputs": "def quick_sort", "parameters": {"max_new_tokens": 64}}' 48 | ``` 49 | - Response: 50 | ```json 51 | { 52 | "generated_text": "def quick_sort(numbers):\n if len(numbers) < 2:\n return numbers\n else:\n pivot = numbers[-1]\n less = [\n el\n for ind, el in enumerate(numbers[:-1])\n if el <= pivot and ind != -1\n", 53 | "status": 200 54 | } 55 | ``` 56 | 57 | ## TODO 58 | 59 | - [ ] Test more models 60 | - [ ] Test distributed serving 61 | 62 | ## References 63 | 64 | - [huggingface/llm-vscode 65 | ](https://github.com/huggingface/llm-vscode) 66 | - [vllm-project/vllm](https://github.com/vllm-project/vllm) 67 | - [LucienShui/huggingface-vscode-endpoint-server](https://github.com/LucienShui/huggingface-vscode-endpoint-server) -------------------------------------------------------------------------------- /api_server.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | from typing import AsyncGenerator 4 | 5 | import uvicorn 6 | from fastapi import FastAPI, Request 7 | from fastapi.responses import JSONResponse, Response, StreamingResponse 8 | from vllm.engine.arg_utils import AsyncEngineArgs 9 | from vllm.engine.async_llm_engine import AsyncLLMEngine 10 | from vllm.sampling_params import SamplingParams 11 | from vllm.utils import random_uuid 12 | 13 | TIMEOUT_KEEP_ALIVE = 5 # seconds. 14 | TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds. 15 | app = FastAPI() 16 | engine = None 17 | 18 | 19 | @app.post("/generate") 20 | async def generate(request: Request) -> Response: 21 | """Generate completion for the request. 22 | 23 | The request should be a JSON object with the following fields: 24 | - prompt: the prompt to use for the generation. 25 | - stream: whether to stream the results or not. 26 | - other fields: the sampling parameters (See `SamplingParams` for details). 27 | """ 28 | request_dict = await request.json() 29 | prompt = request_dict.pop("inputs") 30 | parameters = request_dict.pop("parameters") 31 | max_new_tokens = parameters.pop("max_new_tokens") 32 | return_full_text = parameters.pop("return_full_text", False) 33 | do_sample = parameters.pop("do_sample", True) 34 | stream = request_dict.pop("stream", False) 35 | sampling_params = SamplingParams(max_tokens=max_new_tokens, 36 | use_beam_search=not do_sample, 37 | **parameters) 38 | request_id = random_uuid() 39 | 40 | results_generator = engine.generate(prompt, sampling_params, request_id) 41 | 42 | # Streaming case 43 | async def stream_results() -> AsyncGenerator[bytes, None]: 44 | async for request_output in results_generator: 45 | prompt = request_output.prompt 46 | text_outputs = [ 47 | prompt + output.text if return_full_text else output.text 48 | for output in request_output.outputs 49 | ] 50 | ret = {"text": text_outputs} 51 | yield (json.dumps(ret) + "\0").encode("utf-8") 52 | 53 | if stream: 54 | return StreamingResponse(stream_results()) 55 | 56 | # Non-streaming case 57 | final_output = None 58 | async for request_output in results_generator: 59 | if await request.is_disconnected(): 60 | # Abort the request if the client disconnects. 61 | await engine.abort(request_id) 62 | return Response(status_code=499) 63 | final_output = request_output 64 | 65 | assert final_output is not None 66 | prompt = final_output.prompt 67 | text_outputs = [ 68 | prompt + output.text if return_full_text else output.text 69 | for output in final_output.outputs 70 | ] 71 | ret = {"generated_text": text_outputs[0], "status": 200} 72 | return JSONResponse(ret) 73 | 74 | 75 | if __name__ == "__main__": 76 | parser = argparse.ArgumentParser() 77 | parser.add_argument("--host", type=str, default="localhost") 78 | parser.add_argument("--port", type=int, default=8000) 79 | parser = AsyncEngineArgs.add_cli_args(parser) 80 | args = parser.parse_args() 81 | 82 | engine_args = AsyncEngineArgs.from_cli_args(args) 83 | engine = AsyncLLMEngine.from_engine_args(engine_args) 84 | 85 | uvicorn.run(app, 86 | host=args.host, 87 | port=args.port, 88 | log_level="debug", 89 | timeout_keep_alive=TIMEOUT_KEEP_ALIVE) 90 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # 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