src/utils.py

import os
import logging
from http import HTTPStatus
from functools import wraps
from time import time
from vllm.entrypoints.openai.protocol import RequestResponseMetadata

try:
    from vllm.utils import random_uuid
    from vllm.entrypoints.openai.protocol import ErrorResponse
    from vllm import SamplingParams
except ImportError:
    logging.warning("Error importing vllm, skipping related imports. This is ONLY expected when baking model into docker image from a machine without GPUs")
    pass

logging.basicConfig(level=logging.INFO)

def count_physical_cores():
    with open('/proc/cpuinfo') as f:
        content = f.readlines()

    cores = set()
    current_physical_id = None
    current_core_id = None

    for line in content:
        if 'physical id' in line:
            current_physical_id = line.strip().split(': ')[1]
        elif 'core id' in line:
            current_core_id = line.strip().split(': ')[1]
            cores.add((current_physical_id, current_core_id))

    return len(cores)


class JobInput:
    def __init__(self, job):
        self.llm_input = job.get("messages", job.get("prompt"))
        self.stream = job.get("stream", False)
        self.max_batch_size = job.get("max_batch_size")
        self.apply_chat_template = job.get("apply_chat_template", False)
        self.use_openai_format = job.get("use_openai_format", False)
        self.sampling_params = SamplingParams(**job.get("sampling_params", {}))
        self.request_id = random_uuid()
        batch_size_growth_factor = job.get("batch_size_growth_factor")
        self.batch_size_growth_factor = float(batch_size_growth_factor) if batch_size_growth_factor else None 
        min_batch_size = job.get("min_batch_size")
        self.min_batch_size = int(min_batch_size) if min_batch_size else None 
        self.openai_route = job.get("openai_route")
        self.openai_input = job.get("openai_input")
class DummyState:
    def __init__(self):
        self.request_metadata = None
        
class DummyRequest:
    def __init__(self):
        self.headers = {}
        self.state = DummyState()
    async def is_disconnected(self):
        return False

class BatchSize:
    def __init__(self, max_batch_size, min_batch_size, batch_size_growth_factor):
        self.max_batch_size = max_batch_size
        self.batch_size_growth_factor = batch_size_growth_factor
        self.min_batch_size = min_batch_size
        self.is_dynamic = batch_size_growth_factor > 1 and min_batch_size >= 1 and max_batch_size > min_batch_size
        if self.is_dynamic:
            self.current_batch_size = min_batch_size
        else:
            self.current_batch_size = max_batch_size
        
    def update(self):
        if self.is_dynamic:
            self.current_batch_size = min(self.current_batch_size*self.batch_size_growth_factor, self.max_batch_size)
        
def create_error_response(message: str, err_type: str = "BadRequestError", status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> ErrorResponse:
    return ErrorResponse(message=message,
                            type=err_type,
                            code=status_code.value)
    
def get_int_bool_env(env_var: str, default: bool) -> bool:
    return int(os.getenv(env_var, int(default))) == 1

def timer_decorator(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time()
        result = func(*args, **kwargs)
        end = time()
        logging.info(f"{func.__name__} completed in {end - start:.2f} seconds")
        return result
    return wrapper