126 lines
5.8 KiB
Python
126 lines
5.8 KiB
Python
import json
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import time
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import traceback
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from flask import Response, jsonify, request
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from llm_server.custom_redis import redis
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from . import openai_bp
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from ..helpers.http import validate_json
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from ..openai_request_handler import OpenAIRequestHandler
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from ... import opts
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from ...database.database import log_prompt
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from ...llm.generator import generator
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from ...llm.openai.oai_to_vllm import oai_to_vllm
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from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit
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# TODO: add rate-limit headers?
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@openai_bp.route('/chat/completions', methods=['POST'])
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def openai_chat_completions():
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request_valid_json, request_json_body = validate_json(request)
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if not request_valid_json or not request_json_body.get('messages') or not request_json_body.get('model'):
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return jsonify({'code': 400, 'msg': 'invalid JSON'}), 400
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else:
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handler = OpenAIRequestHandler(incoming_request=request, incoming_json=request_json_body)
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if handler.cluster_backend_info['mode'] != 'vllm':
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# TODO: implement other backends
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raise NotImplementedError
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if not request_json_body.get('stream'):
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try:
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return handler.handle_request()
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except Exception:
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traceback.print_exc()
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return 'Internal server error', 500
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else:
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if not opts.enable_streaming:
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# TODO: return a proper OAI error message
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return 'disabled', 401
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if opts.openai_silent_trim:
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handler.request_json_body['messages'] = trim_messages_to_fit(request_json_body['messages'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
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response_status_code = 0
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start_time = time.time()
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request_valid, invalid_response = handler.validate_request()
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if not request_valid:
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return invalid_response
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else:
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if opts.openai_silent_trim:
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oai_messages = trim_messages_to_fit(handler.request.json['messages'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
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else:
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oai_messages = handler.request.json['messages']
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handler.prompt = transform_messages_to_prompt(oai_messages)
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handler.parameters = oai_to_vllm(handler.parameters, hashes=True, mode=handler.cluster_backend_info['mode'])
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msg_to_backend = {
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**handler.parameters,
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'prompt': handler.prompt,
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'stream': True,
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}
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try:
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response = generator(msg_to_backend, handler.backend_url)
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r_headers = dict(request.headers)
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r_url = request.url
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model = redis.get('running_model', 'ERROR', dtype=str) if opts.openai_expose_our_model else request_json_body.get('model')
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oai_string = generate_oai_string(30)
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def generate():
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generated_text = ''
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partial_response = b''
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for chunk in response.iter_content(chunk_size=1):
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partial_response += chunk
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if partial_response.endswith(b'\x00'):
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json_strs = partial_response.split(b'\x00')
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for json_str in json_strs:
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if json_str:
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try:
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json_obj = json.loads(json_str.decode())
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new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
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generated_text = generated_text + new
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except IndexError:
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# ????
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continue
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data = {
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"id": f"chatcmpl-{oai_string}",
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"object": "chat.completion.chunk",
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"created": int(time.time()),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {
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"content": new
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},
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"finish_reason": None
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}
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]
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}
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yield f'data: {json.dumps(data)}\n\n'
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yield 'data: [DONE]\n\n'
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end_time = time.time()
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elapsed_time = end_time - start_time
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log_prompt(
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handler.client_ip,
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handler.token,
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handler.prompt,
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generated_text,
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elapsed_time,
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handler.parameters,
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r_headers,
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response_status_code,
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r_url,
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handler.backend_url,
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)
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return Response(generate(), mimetype='text/event-stream')
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except:
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# TODO: simulate OAI here
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raise Exception
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