from flask import Flask, request, Response import logging from llama_cpp import Llama import threading from huggingface_hub import snapshot_download, Repository import huggingface_hub import gc import os.path import csv from datetime import datetime SYSTEM_PROMPT = "Ты — русскоязычный автоматический ассистент. Ты максимально точно и отвечаешь на запросы пользователя, используя русский язык." SYSTEM_TOKEN = 1788 USER_TOKEN = 1404 BOT_TOKEN = 9225 LINEBREAK_TOKEN = 13 ROLE_TOKENS = { "user": USER_TOKEN, "bot": BOT_TOKEN, "system": SYSTEM_TOKEN } CONTEXT_SIZE = 4000 ENABLE_GPU = True GPU_LAYERS = 70 # Create a lock object lock = threading.Lock() app = Flask(__name__) # Configure Flask logging app.logger.setLevel(logging.DEBUG) # Set the desired logging level # Initialize the model when the application starts #model_path = "../models/model-q4_K.gguf" # Replace with the actual model path #model_name = "model/ggml-model-q4_K.gguf" #repo_name = "IlyaGusev/saiga2_13b_gguf" #model_name = "model-q4_K.gguf" repo_name = "IlyaGusev/saiga2_70b_gguf" model_name = "ggml-model-q4_1.gguf" #repo_name = "IlyaGusev/saiga2_7b_gguf" #model_name = "model-q4_K.gguf" local_dir = '.' if os.path.isdir('/data'): app.logger.info('Persistent storage enabled') model = None model_path = snapshot_download(repo_id=repo_name, allow_patterns=model_name) + '/' + model_name app.logger.info('Model path: ' + model_path) DATASET_REPO_URL = "https://huggingface.co/datasets/muryshev/saiga-chat" DATA_FILENAME = "data.csv" DATA_FILE = os.path.join("dataset", DATA_FILENAME) HF_TOKEN = os.environ.get("HF_TOKEN") app.logger.info("hfh: "+huggingface_hub.__version__) repo = Repository( local_dir="dataset", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN ) def log(request: str = '', response: str = ''): if request or response: with open(DATA_FILE, "a") as csvfile: writer = csv.DictWriter(csvfile, fieldnames=["request", "response", "time"]) writer.writerow( {"request": request, "response": response, "time": str(datetime.now())} ) commit_url = repo.push_to_hub() app.logger.info(commit_url) def init_model(context_size, enable_gpu=False, gpu_layer_number=35): global model if model is not None: del model gc.collect() if enable_gpu: model = Llama( model_path=model_path, n_ctx=context_size, n_parts=1, #n_batch=100, logits_all=True, #n_threads=12, verbose=True, n_gpu_layers=gpu_layer_number, n_gqa=8 #must be set for 70b models ) return model else: model = Llama( model_path=model_path, n_ctx=context_size, n_parts=1, #n_batch=100, logits_all=True, #n_threads=12, verbose=True, n_gqa=8 #must be set for 70b models ) return model init_model(CONTEXT_SIZE, ENABLE_GPU, GPU_LAYERS) def get_message_tokens(model, role, content): message_tokens = model.tokenize(content.encode("utf-8")) message_tokens.insert(1, ROLE_TOKENS[role]) message_tokens.insert(2, LINEBREAK_TOKEN) message_tokens.append(model.token_eos()) return message_tokens def get_system_tokens(model): system_message = { "role": "system", "content": SYSTEM_PROMPT } return get_message_tokens(model, **system_message) def get_system_tokens_for_preprompt(model, preprompt): system_message = { "role": "system", "content": preprompt } return get_message_tokens(model, **system_message) #app.logger.info('Evaluating system tokens start') #system_tokens = get_system_tokens(model) #model.eval(system_tokens) #app.logger.info('Evaluating system tokens end') stop_generation = False def generate_tokens(model, generator): global stop_generation app.logger.info('generate_tokens started') with lock: try: for token in generator: if token == model.token_eos() or stop_generation: stop_generation = False app.logger.info('End generating') yield b'' # End of chunk break token_str = model.detokenize([token])#.decode("utf-8", errors="ignore") yield token_str except Exception as e: app.logger.info('generator exception') app.logger.info(e) yield b'' # End of chunk @app.route('/change_context_size', methods=['GET']) def handler_change_context_size(): global stop_generation, model stop_generation = True new_size = int(request.args.get('size', CONTEXT_SIZE)) init_model(new_size, ENABLE_GPU, GPU_LAYERS) return Response('Size changed', content_type='text/plain') @app.route('/stop_generation', methods=['GET']) def handler_stop_generation(): global stop_generation stop_generation = True return Response('Stopped', content_type='text/plain') @app.route('/', methods=['GET', 'PUT', 'DELETE', 'PATCH']) def generate_unknown_response(): app.logger.info('unknown method: '+request.method) try: request_payload = request.get_json() app.logger.info('payload: '+request.get_json()) except Exception as e: app.logger.info('payload empty') return Response('What do you want?', content_type='text/plain') @app.route('/search_request', methods=['POST']) def generate_search_request(): global stop_generation stop_generation = True model.reset() data = request.get_json() app.logger.info(data) user_query = data.get("query", "") preprompt = data.get("preprompt", "") parameters = data.get("parameters", {}) # Extract parameters from the request temperature = parameters.get("temperature", 0.01) truncate = parameters.get("truncate", 1000) max_new_tokens = parameters.get("max_new_tokens", 1024) top_p = parameters.get("top_p", 0.85) repetition_penalty = parameters.get("repetition_penalty", 1.2) top_k = parameters.get("top_k", 30) return_full_text = parameters.get("return_full_text", False) tokens = get_system_tokens_for_preprompt(model, preprompt) tokens.append(LINEBREAK_TOKEN) tokens = get_message_tokens(model=model, role="user", content=user_query[:200]) + [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN] stop_generation = False generator = model.generate( tokens, top_k=top_k, top_p=top_p, temp=temperature, repeat_penalty=repetition_penalty ) # Use Response to stream tokens return Response(generate_tokens(model, generator), content_type='text/plain', status=200, direct_passthrough=True) @app.route('/', methods=['POST']) def generate_response(): global stop_generation stop_generation = True model.reset() data = request.get_json() app.logger.info(data) messages = data.get("messages", []) preprompt = data.get("preprompt", "") parameters = data.get("parameters", {}) # Extract parameters from the request temperature = parameters.get("temperature", 0.01) truncate = parameters.get("truncate", 1000) max_new_tokens = parameters.get("max_new_tokens", 1024) top_p = parameters.get("top_p", 0.85) repetition_penalty = parameters.get("repetition_penalty", 1.2) top_k = parameters.get("top_k", 30) return_full_text = parameters.get("return_full_text", False) tokens = get_system_tokens(model) tokens.append(LINEBREAK_TOKEN) tokens = [] for message in messages: if message.get("from") == "assistant": message_tokens = get_message_tokens(model=model, role="bot", content=message.get("content", "")) else: message_tokens = get_message_tokens(model=model, role="user", content=message.get("content", "")) tokens.extend(message_tokens) tokens.extend([model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]) app.logger.info('Prompt:') request = model.detokenize(tokens[:CONTEXT_SIZE]).decode("utf-8", errors="ignore") app.logger.info(request) stop_generation = False app.logger.info('Generate started') generator = model.generate( tokens[:CONTEXT_SIZE], top_k=top_k, top_p=top_p, temp=temperature, repeat_penalty=repetition_penalty ) app.logger.info('Generator created') response_tokens = [] def generate_and_log_tokens(model, generator): for token in generate_tokens(model, generator): if token == model.token_eos(): # or (max_new_tokens is not None and i >= max_new_tokens): log(request=request, response=model.detokenize(response_tokens).decode("utf-8", errors="ignore")) break response_tokens.append(token) yield token # Use Response to stream tokens return Response(generate_and_log_tokens(model, generator), content_type='text/plain', status=200, direct_passthrough=True) if __name__ == "__main__": app.run(host="0.0.0.0", port=7860, debug=False, threaded=False)