app.py

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
from datetime import datetime
import xml.etree.ElementTree as ET

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-saiga-cuda-internal.xml"
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(req: str = '', resp: str = ''):
    if req or resp:
        element = ET.Element("row", {"time": str(datetime.now()) })
        req_element = ET.SubElement(element, "request")
        req_element.text = req
        resp_element = ET.SubElement(element, "response")
        resp_element.text = resp
    
        with open(DATA_FILE, "ab+") as xml_file:
            xml_file.write(ET.tostring(element, encoding="utf-8"))
        
        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)

response_tokens = bytearray()
def generate_and_log_tokens(user_request, model, generator):
    global response_tokens
    for token in generate_tokens(model, generator):
        if token == b'': # or (max_new_tokens is not None and i >= max_new_tokens):
            log(user_request, response_tokens.decode("utf-8", errors="ignore"))
            response_tokens = bytearray()
            break
        response_tokens.extend(token)
        yield token
            
@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:')
    user_request = model.detokenize(tokens[:CONTEXT_SIZE]).decode("utf-8", errors="ignore")
    app.logger.info(user_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')


    

    # Use Response to stream tokens
    return Response(generate_and_log_tokens(user_request, 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)