local-llm-server/llm_server/routes/openai/completions.py

import time
import traceback

import simplejson as json
from flask import Response, jsonify, request

from llm_server.custom_redis import redis
from . import openai_bp
from ..helpers.http import validate_json
from ..ooba_request_handler import OobaRequestHandler
from ... import opts
from ...database.database import log_prompt
from ...llm import get_token_count
from ...llm.generator import generator
from ...llm.openai.oai_to_vllm import oai_to_vllm, validate_oai
from ...llm.openai.transform import generate_oai_string, trim_string_to_fit


# TODO: add rate-limit headers?

@openai_bp.route('/completions', methods=['POST'])
def openai_completions():
    request_valid_json, request_json_body = validate_json(request)
    if not request_valid_json or not request_json_body.get('prompt'):
        return jsonify({'code': 400, 'msg': 'Invalid JSON'}), 400
    else:
        try:
            handler = OobaRequestHandler(incoming_request=request)

            if handler.cluster_backend_info['mode'] != 'vllm':
                # TODO: implement other backends
                raise NotImplementedError

            invalid_oai_err_msg = validate_oai(handler.request_json_body)
            if invalid_oai_err_msg:
                return invalid_oai_err_msg
            handler.request_json_body = oai_to_vllm(handler.request_json_body, hashes=False, mode=handler.cluster_backend_info['mode'])

            # Convert parameters to the selected backend type
            if opts.openai_silent_trim:
                handler.request_json_body['prompt'] = trim_string_to_fit(request_json_body['prompt'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
            else:
                # The handle_request() call below will load the prompt so we don't have
                # to do anything else here.
                pass

            if not request_json_body.get('stream'):
                response, status_code = handler.handle_request()
                if status_code != 200:
                    return status_code
                output = response.json['results'][0]['text']

                # TODO: async/await
                prompt_tokens = get_token_count(request_json_body['prompt'], handler.backend_url)
                response_tokens = get_token_count(output, handler.backend_url)
                running_model = redis.get('running_model', 'ERROR', dtype=str)

                response = jsonify({
                    "id": f"cmpl-{generate_oai_string(30)}",
                    "object": "text_completion",
                    "created": int(time.time()),
                    "model": running_model if opts.openai_expose_our_model else request_json_body.get('model'),
                    "choices": [
                        {
                            "text": output,
                            "index": 0,
                            "logprobs": None,
                            "finish_reason": "stop"
                        }
                    ],
                    "usage": {
                        "prompt_tokens": prompt_tokens,
                        "completion_tokens": response_tokens,
                        "total_tokens": prompt_tokens + response_tokens
                    }
                })

                stats = redis.get('proxy_stats', dtype=dict)
                if stats:
                    response.headers['x-ratelimit-reset-requests'] = stats['queue']['estimated_wait_sec']
                return response, 200
            else:
                if not opts.enable_streaming:
                    # TODO: return a proper OAI error message
                    return 'disabled', 401

                response_status_code = 0
                start_time = time.time()

                request_valid, invalid_response = handler.validate_request()
                if not request_valid:
                    # TODO: simulate OAI here
                    raise Exception('TODO: simulate OAI here')
                else:
                    handler.prompt = handler.request_json_body['prompt']
                    msg_to_backend = {
                        **handler.parameters,
                        'prompt': handler.prompt,
                        'stream': True,
                    }
                    response = generator(msg_to_backend, handler.backend_url)
                    r_headers = dict(request.headers)
                    r_url = request.url
                    model = redis.get('running_model', 'ERROR', dtype=str) if opts.openai_expose_our_model else request_json_body.get('model')
                    oai_string = generate_oai_string(30)

                    def generate():
                        generated_text = ''
                        partial_response = b''
                        for chunk in response.iter_content(chunk_size=1):
                            partial_response += chunk
                            if partial_response.endswith(b'\x00'):
                                json_strs = partial_response.split(b'\x00')
                                for json_str in json_strs:
                                    if json_str:
                                        try:
                                            json_obj = json.loads(json_str.decode())
                                            new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
                                            generated_text = generated_text + new
                                        except IndexError:
                                            # ????
                                            continue

                                        data = {
                                            "id": f"chatcmpl-{oai_string}",
                                            "object": "text_completion",
                                            "created": int(time.time()),
                                            "model": model,
                                            "choices": [
                                                {
                                                    "index": 0,
                                                    "delta": {
                                                        "content": new
                                                    },
                                                    "finish_reason": None
                                                }
                                            ]
                                        }
                                        yield f'data: {json.dumps(data)}\n\n'

                        yield 'data: [DONE]\n\n'
                        end_time = time.time()
                        elapsed_time = end_time - start_time

                        log_prompt(
                            handler.client_ip,
                            handler.token,
                            handler.prompt,
                            generated_text,
                            elapsed_time,
                            handler.parameters,
                            r_headers,
                            response_status_code,
                            r_url,
                            handler.backend_url,
                        )

                    return Response(generate(), mimetype='text/event-stream')
        except Exception:
            traceback.print_exc()
            return 'Internal Server Error', 500