Follow logging best practices

Maintain high-quality logging throughout the codebase by following these best practices:

1. Use appropriate log levels: Reserve debug for detailed troubleshooting, info for general operational information, warning for potential issues, and error/critical for actual problems. Avoid excessive logging at higher levels.

   # For large object dumps or detailed diagnostics
   logger.debug("Initialized config %s", config)
   # Not: logger.info("Initialized config %s", config)
   

2. Remove temporary debugging logs: Debug statements with developer identifiers (e.g., "[Kourosh]") should be removed before merging code.

3. Optimize logging performance: Guard expensive parameter computations to avoid unnecessary work when the log level is disabled:

   if logger.isEnabledFor(logging.DEBUG):
       logger.debug("Complex calculation result: %s", 
                    ",".join(map(str, complex_calculation())))
   

4. Avoid logging in loops unless each iteration provides unique valuable information. Consider logging summaries before/after the loop instead.

5. Use standard logging methods: Use logger.warning() (not the deprecated logger.warn()), and use logger.exception() for exception logging to automatically include tracebacks:

   try:
       # Some operation
   except Exception:
       logger.exception("Failed to perform operation")
       # Not: logger.error("Failed: %s", str(e))
   

6. Initialize loggers consistently: Use the project’s standard logger initialization pattern:

   from vllm.logger import init_logger
   logger = init_logger(__name__)
   

7. Make log messages clear and actionable: Include relevant context and avoid ambiguous messages.