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llmcompressor.metrics

Metrics logging and monitoring framework for compression workflows.

Provides comprehensive metrics collection, logging, and monitoring capabilities for model compression operations. Includes base loggers, frequency management, and specialized metrics tracking for training and inference performance during compression.

Modules:

  • logger –

    Contains code for loggers that help visualize the information from each

  • utils –

Classes:

  • BaseLogger –

    Base class that all modifier loggers must implement.

  • LambdaLogger –

    Logger that handles calling back to a lambda function with any logs.

  • LoggerManager –

    Wrapper around loggers that handles log scheduling and handing off logs to intended

  • PythonLogger –

    Modifier metrics that handles printing values into a python metrics instance.

  • SparsificationGroupLogger –

    Modifier metrics that handles outputting values to other supported systems.

  • TensorBoardLogger –

    Modifier metrics that handles outputting values into a TensorBoard log directory

  • WANDBLogger –

    Modifier metrics that handles outputting values to Weights and Biases.

BaseLogger 

BaseLogger(name: str, enabled: bool = True)

Bases: ABC

Base class that all modifier loggers must implement.

Parameters:

  • name

    (str) –

    name given to the metrics, used for identification

  • enabled

    (bool, default: True ) –

    True to log, False otherwise

Methods:

  • log_hyperparams –

    :param params: Each key-value pair in the dictionary is the name of the

  • log_scalar –

    :param tag: identifying tag to log the value with

  • log_scalars –

    :param tag: identifying tag to log the values with

  • log_string –

    :param tag: identifying tag to log the values with

  • save –

    :param file_path: path to a file to be saved

Attributes:

  • enabled (bool) –

    :return: True to log, False otherwise

  • name (str) –

    :return: name given to the metrics, used for identification

Source code in llmcompressor/metrics/logger.py 
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def __init__(self, name: str, enabled: bool = True):
    self._name = name
    self._enabled = enabled

enabled property writable 

enabled: bool

Returns:

  • bool –

    True to log, False otherwise

name property 

name: str

Returns:

  • str –

    name given to the metrics, used for identification

log_hyperparams 

log_hyperparams(params: Dict[str, float]) -> bool

Parameters:

  • params

    (Dict[str, float]) –

    Each key-value pair in the dictionary is the name of the hyper parameter and it's corresponding value.

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_hyperparams(self, params: Dict[str, float]) -> bool:
    """
    :param params: Each key-value pair in the dictionary is the name of the
        hyper parameter and it's corresponding value.
    :return: True if logged, False otherwise.
    """
    return False

log_scalar 

log_scalar(
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    **kwargs
) -> bool

Parameters:

  • tag

    (str) –

    identifying tag to log the value with

  • value

    (float) –

    value to save

  • step

    (Optional[int], default: None ) –

    global step for when the value was taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the value was taken

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_scalar(
    self,
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    **kwargs,
) -> bool:
    """
    :param tag: identifying tag to log the value with
    :param value: value to save
    :param step: global step for when the value was taken
    :param wall_time: global wall time for when the value was taken
    :param kwargs: additional logging arguments to support Python and custom loggers
    :return: True if logged, False otherwise.
    """
    return False

log_scalars 

log_scalars(
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    **kwargs
) -> bool

Parameters:

  • tag

    (str) –

    identifying tag to log the values with

  • values

    (Dict[str, float]) –

    values to save

  • step

    (Optional[int], default: None ) –

    global step for when the values were taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the values were taken

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_scalars(
    self,
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    **kwargs,
) -> bool:
    """
    :param tag: identifying tag to log the values with
    :param values: values to save
    :param step: global step for when the values were taken
    :param wall_time: global wall time for when the values were taken
    :param kwargs: additional logging arguments to support Python and custom loggers
    :return: True if logged, False otherwise.
    """
    return False

log_string 

log_string(
    tag: str,
    string: str,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    **kwargs
) -> bool

Parameters:

  • tag

    (str) –

    identifying tag to log the values with

  • values

    –

    values to save

  • step

    (Optional[int], default: None ) –

    global step for when the values were taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the values were taken

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_string(
    self,
    tag: str,
    string: str,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    **kwargs,
) -> bool:
    """
    :param tag: identifying tag to log the values with
    :param values: values to save
    :param step: global step for when the values were taken
    :param wall_time: global wall time for when the values were taken
    :param kwargs: additional logging arguments to support Python and custom loggers
    :return: True if logged, False otherwise.
    """
    return False

save 

save(file_path: str, **kwargs) -> bool

Parameters:

  • file_path

    (str) –

    path to a file to be saved

  • kwargs

    –

    additional arguments that a specific metrics might use

Returns:

  • bool –

    True if saved, False otherwise

Source code in llmcompressor/metrics/logger.py 
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def save(
    self,
    file_path: str,
    **kwargs,
) -> bool:
    """
    :param file_path: path to a file to be saved
    :param kwargs: additional arguments that a specific metrics might use
    :return: True if saved, False otherwise
    """
    return False

LambdaLogger 

LambdaLogger(
    lambda_func: Callable[
        [
            Optional[str],
            Optional[Union[float, str]],
            Optional[Dict[str, float]],
            Optional[int],
            Optional[float],
            Optional[int],
        ],
        bool,
    ],
    name: str = "lambda",
    enabled: bool = True,
)

Bases: BaseLogger

Logger that handles calling back to a lambda function with any logs.

Parameters:

  • lambda_func

    (Callable[[Optional[str], Optional[Union[float, str]], Optional[Dict[str, float]], Optional[int], Optional[float], Optional[int]], bool]) –

    the lambda function to call back into with any logs. The expected call sequence is (tag, value, values, step, wall_time) -> bool The return type is True if logged and False otherwise.

  • name

    (str, default: 'lambda' ) –

    name given to the metrics, used for identification; defaults to lambda

  • enabled

    (bool, default: True ) –

    True to log, False otherwise

Methods:

  • log_hyperparams –

    :param params: Each key-value pair in the dictionary is the name of the

  • log_scalar –

    :param tag: identifying tag to log the value with

  • log_scalars –

    :param tag: identifying tag to log the values with

Attributes:

  • lambda_func (Callable[[Optional[str], Optional[Union[float, str]], Optional[Dict[str, float]], Optional[int], Optional[float], Optional[int]], bool]) –

    :return: the lambda function to call back into with any logs.

Source code in llmcompressor/metrics/logger.py 
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def __init__(
    self,
    lambda_func: Callable[
        [
            Optional[str],
            Optional[Union[float, str]],
            Optional[Dict[str, float]],
            Optional[int],
            Optional[float],
            Optional[int],
        ],
        bool,
    ],
    name: str = "lambda",
    enabled: bool = True,
):
    super().__init__(name, enabled)
    self._lambda_func = lambda_func
    assert lambda_func, "lambda_func must be set to a callable function"

lambda_func property 

lambda_func: Callable[
    [
        Optional[str],
        Optional[Union[float, str]],
        Optional[Dict[str, float]],
        Optional[int],
        Optional[float],
        Optional[int],
    ],
    bool,
]

Returns:

  • Callable[[Optional[str], Optional[Union[float, str]], Optional[Dict[str, float]], Optional[int], Optional[float], Optional[int]], bool] –

    the lambda function to call back into with any logs. The expected call sequence is (tag, value, values, step, wall_time)

log_hyperparams 

log_hyperparams(
    params: Dict, level: Optional[Union[int, str]] = None
) -> bool

Parameters:

  • params

    (Dict) –

    Each key-value pair in the dictionary is the name of the hyper parameter and it's corresponding value.

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_hyperparams(
    self,
    params: Dict,
    level: Optional[Union[int, str]] = None,
) -> bool:
    """
    :param params: Each key-value pair in the dictionary is the name of the
        hyper parameter and it's corresponding value.
    :param level: minimum severity level for the log message
    :return: True if logged, False otherwise.
    """
    if not self.enabled:
        return False

    return self._lambda_func(
        tag=None,
        value=None,
        values=params,
        step=None,
        wall_time=None,
        level=level,
    )

log_scalar 

log_scalar(
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
) -> bool

Parameters:

  • tag

    (str) –

    identifying tag to log the value with

  • value

    (float) –

    value to save

  • step

    (Optional[int], default: None ) –

    global step for when the value was taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the value was taken, defaults to time.time()

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_scalar(
    self,
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
) -> bool:
    """
    :param tag: identifying tag to log the value with
    :param value: value to save
    :param step: global step for when the value was taken
    :param wall_time: global wall time for when the value was taken,
        defaults to time.time()
    :param level: minimum severity level for the log message
    :param kwargs: additional logging arguments to support Python and custom loggers
    :return: True if logged, False otherwise.
    """
    if not wall_time:
        wall_time = time.time()

    return self._lambda_func(
        tag=tag,
        value=value,
        values=None,
        step=step,
        wall_time=wall_time,
        level=level,
    )

log_scalars 

log_scalars(
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
) -> bool

Parameters:

  • tag

    (str) –

    identifying tag to log the values with

  • values

    (Dict[str, float]) –

    values to save

  • step

    (Optional[int], default: None ) –

    global step for when the values were taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the values were taken, defaults to time.time()

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_scalars(
    self,
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
) -> bool:
    """
    :param tag: identifying tag to log the values with
    :param values: values to save
    :param step: global step for when the values were taken
    :param wall_time: global wall time for when the values were taken,
        defaults to time.time()
    :param level: minimum severity level for the log message
    :param kwargs: additional logging arguments to support Python and custom loggers
    :return: True if logged, False otherwise.
    """
    if not wall_time:
        wall_time = time.time()

    return self._lambda_func(
        tag=tag,
        value=None,
        values=values,
        step=step,
        wall_time=wall_time,
        level=level,
    )

LoggerManager 

LoggerManager(
    loggers: Optional[List[BaseLogger]] = None,
    log_frequency: Union[float, None] = 0.1,
    log_python: bool = True,
    name: str = "manager",
    mode: LoggingModeType = "exact",
    frequency_type: FrequencyType = "epoch",
)

Bases: ABC

Wrapper around loggers that handles log scheduling and handing off logs to intended loggers.

Parameters:

  • loggers

    (Optional[List[BaseLogger]], default: None ) –

    list of loggers assigned to this manager

  • log_frequency

    (Union[float, None], default: 0.1 ) –

    number of stes or fraction of steps to wait between logs

  • mode

    (LoggingModeType, default: 'exact' ) –

    The logging mode to use, either "on_change" or "exact", "on_change" will log when the model has been updated since the last log, "exact" will log at the given frequency regardless of model updates. Defaults to "exact"

  • frequency_type

    (FrequencyType, default: 'epoch' ) –

    The frequency type to use, either "epoch", "step", or "batch" controls what the frequency manager is tracking, e.g. if the frequency type is "epoch", then the frequency manager will track the number of epochs that have passed since the last log, if the frequency type is "step", then the frequency manager will track the number of optimizer steps

Methods:

  • add_logger –

    add a BaseLogger implementation to the loggers of this manager

  • log_hyperparams –

    (Note: this method is deprecated and will be removed in a future version,

  • log_ready –

    Check if there is a metrics that is ready to accept a log

  • log_scalar –

    (Note: this method is deprecated and will be removed in a future version,

  • log_scalars –

    (Note: this method is deprecated and will be removed in a future version,

  • log_string –

    (Note: this method is deprecated and will be removed in a future version,

  • log_written –

    Update the frequency manager with the last log step written

  • model_updated –

    Update the frequency manager with the last model update step

  • save –

    :param file_path: path to a file to be saved

  • time –

    Context manager to log the time it takes to run the block of code

Attributes:

  • log_frequency (Union[str, float, None]) –

    :return: number of epochs or fraction of epochs to wait between logs

  • loggers (List[BaseLogger]) –

    :return: list of loggers assigned to this manager

  • name (str) –

    :return: name given to the metrics, used for identification

  • wandb (Optional[ModuleType]) –

    :return: wandb module if initialized

Source code in llmcompressor/metrics/logger.py 
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def __init__(
    self,
    loggers: Optional[List[BaseLogger]] = None,
    log_frequency: Union[float, None] = 0.1,
    log_python: bool = True,
    name: str = "manager",
    mode: LoggingModeType = "exact",
    frequency_type: FrequencyType = "epoch",
):
    self._name = name
    self._loggers = (
        loggers
        or SparsificationGroupLogger(
            python=log_python,
            name=name,
            tensorboard=False,
            wandb_=False,
        ).loggers
    )

    self.frequency_manager = FrequencyManager(
        mode=mode,
        frequency_type=frequency_type,
        log_frequency=log_frequency,
    )

    self.system = SystemLoggingWraper(
        loggers=self._loggers, frequency_manager=self.frequency_manager
    )
    self.metric = MetricLoggingWrapper(
        loggers=self._loggers, frequency_manager=self.frequency_manager
    )

log_frequency property writable 

log_frequency: Union[str, float, None]

Returns:

  • Union[str, float, None] –

    number of epochs or fraction of epochs to wait between logs

loggers property writable 

loggers: List[BaseLogger]

Returns:

  • List[BaseLogger] –

    list of loggers assigned to this manager

name property 

name: str

Returns:

  • str –

    name given to the metrics, used for identification

wandb property 

wandb: Optional[ModuleType]

Returns:

  • Optional[ModuleType] –

    wandb module if initialized

add_logger 

add_logger(logger: BaseLogger)

add a BaseLogger implementation to the loggers of this manager

Parameters:

Source code in llmcompressor/metrics/logger.py 
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def add_logger(self, logger: BaseLogger):
    """
    add a BaseLogger implementation to the loggers of this manager

    :param logger: metrics object to add
    """
    if not isinstance(logger, BaseLogger):
        raise ValueError(f"metrics {type(logger)} must be of type BaseLogger")
    self._loggers.append(logger)

log_hyperparams 

log_hyperparams(
    params: Dict,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
)

(Note: this method is deprecated and will be removed in a future version, use LoggerManager().metric.log_hyperparams instead)

Parameters:

  • params

    (Dict) –

    Each key-value pair in the dictionary is the name of the hyper parameter and it's corresponding value.

Source code in llmcompressor/metrics/logger.py 
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def log_hyperparams(
    self,
    params: Dict,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
):
    """
    (Note: this method is deprecated and will be removed in a future version,
    use LoggerManager().metric.log_hyperparams instead)

    :param params: Each key-value pair in the dictionary is the name of the
        hyper parameter and it's corresponding value.
    """

    self.metric.log_hyperparams(
        params=params,
        log_types=log_types,
        level=level,
    )

log_ready 

log_ready(
    current_log_step,
    last_log_step=None,
    check_model_update: bool = False,
)

Check if there is a metrics that is ready to accept a log

Parameters:

  • current_log_step

    –

    current step log is requested at

  • last_log_step

    –

    last time a log was recorder for this object. (Deprecated)

  • check_model_update

    (bool, default: False ) –

    if True, will check if the model has been updated, if False, will only check the log frequency

Returns:

  • –

    True if a metrics is ready to accept a log.

Source code in llmcompressor/metrics/logger.py 
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def log_ready(
    self, current_log_step, last_log_step=None, check_model_update: bool = False
):
    """
    Check if there is a metrics that is ready to accept a log

    :param current_log_step: current step log is requested at
    :param last_log_step: last time a log was recorder for this object. (Deprecated)
    :param check_model_update: if True, will check if the model has been updated,
        if False, will only check the log frequency
    :return: True if a metrics is ready to accept a log.
    """
    log_enabled = any(logger.enabled for logger in self.loggers)
    if last_log_step is not None:
        self.frequency_manager.log_written(step=last_log_step)

    return log_enabled and self.frequency_manager.log_ready(
        current_log_step=current_log_step,
        check_model_update=check_model_update,
    )

log_scalar 

log_scalar(
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
)

(Note: this method is deprecated and will be removed in a future version, use LoggerManager().metric.log_scalar instead)

Parameters:

  • tag

    (str) –

    identifying tag to log the value with

  • value

    (float) –

    value to save

  • step

    (Optional[int], default: None ) –

    global step for when the value was taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the value was taken

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

Returns:

  • –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_scalar(
    self,
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
):
    """
    (Note: this method is deprecated and will be removed in a future version,
    use LoggerManager().metric.log_scalar instead)

    :param tag: identifying tag to log the value with
    :param value: value to save
    :param step: global step for when the value was taken
    :param wall_time: global wall time for when the value was taken
    :param level: minimum severity level for the log message
    :param kwargs: additional logging arguments to support Python and custom loggers
    :return: True if logged, False otherwise.
    """

    self.metric.log_scalar(
        tag=tag,
        value=value,
        step=step,
        wall_time=wall_time,
        log_types=log_types,
        level=level,
    )

log_scalars 

log_scalars(
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
)

(Note: this method is deprecated and will be removed in a future version, use LoggerManager().metric.log_scalars instead)

Parameters:

  • tag

    (str) –

    identifying tag to log the values with

  • values

    (Dict[str, float]) –

    values to save

  • step

    (Optional[int], default: None ) –

    global step for when the values were taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the values were taken

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

Returns:

  • –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_scalars(
    self,
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
):
    """
    (Note: this method is deprecated and will be removed in a future version,
    use LoggerManager().metric.log_scalars instead)

    :param tag: identifying tag to log the values with
    :param values: values to save
    :param step: global step for when the values were taken
    :param wall_time: global wall time for when the values were taken
    :param level: minimum severity level for the log message
    :param kwargs: additional logging arguments to support Python and custom loggers
    :return: True if logged, False otherwise.
    """

    self.metric.log_scalars(
        tag=tag,
        values=values,
        step=step,
        wall_time=wall_time,
        log_types=log_types,
        level=level,
    )

log_string 

log_string(
    tag: str,
    string: str,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
)

(Note: this method is deprecated and will be removed in a future version, use LoggerManager().system.log_string instead)

Parameters:

  • tag

    (str) –

    identifying tag to log the values with

  • values

    –

    values to save

  • step

    (Optional[int], default: None ) –

    global step for when the values were taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the values were taken

  • kwargs

    –

    additional logging arguments to support Python and custom loggers

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

Returns:

  • –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_string(
    self,
    tag: str,
    string: str,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    log_types: Union[str, List[str]] = ALL_TOKEN,
    level: Optional[Union[int, str]] = None,
):
    """
    (Note: this method is deprecated and will be removed in a future version,
    use LoggerManager().system.log_string instead)

    :param tag: identifying tag to log the values with
    :param values: values to save
    :param step: global step for when the values were taken
    :param wall_time: global wall time for when the values were taken
    :param kwargs: additional logging arguments to support Python and custom loggers
    :param level: minimum severity level for the log message
    :return: True if logged, False otherwise.
    """
    self.system.log_string(
        tag=tag,
        string=string,
        step=step,
        wall_time=wall_time,
        log_types=log_types,
        level=level,
    )

log_written 

log_written(step: LogStepType)

Update the frequency manager with the last log step written

Parameters:

  • step

    (LogStepType) –

    step that was last logged

Source code in llmcompressor/metrics/logger.py 
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def log_written(self, step: LogStepType):
    """
    Update the frequency manager with the last log step written

    :param step: step that was last logged
    """
    self.frequency_manager.log_written(step=step)

model_updated 

model_updated(step: LogStepType)

Update the frequency manager with the last model update step

Parameters:

  • step

    (LogStepType) –

    step that was last logged

Source code in llmcompressor/metrics/logger.py 
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def model_updated(self, step: LogStepType):
    """
    Update the frequency manager with the last model update step

    :param step: step that was last logged
    """
    self.frequency_manager.model_updated(step=step)

save 

save(file_path: str, **kwargs)

Parameters:

  • file_path

    (str) –

    path to a file to be saved

  • kwargs

    –

    additional arguments that a specific metrics might use

Source code in llmcompressor/metrics/logger.py 
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def save(
    self,
    file_path: str,
    **kwargs,
):
    """
    :param file_path: path to a file to be saved
    :param kwargs: additional arguments that a specific metrics might use
    """
    for log in self._loggers:
        if log.enabled:
            log.save(file_path, **kwargs)

time 

time(tag: Optional[str] = None, *args, **kwargs)

Context manager to log the time it takes to run the block of code

Usage:

with LoggerManager().time("my_block"): time.sleep(1)

Parameters:

  • tag

    (Optional[str], default: None ) –

    identifying tag to log the values with

Source code in llmcompressor/metrics/logger.py 
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@contextmanager
def time(self, tag: Optional[str] = None, *args, **kwargs):
    """
    Context manager to log the time it takes to run the block of code

    Usage:
    >>> with LoggerManager().time("my_block"):
    >>>    time.sleep(1)

    :param tag: identifying tag to log the values with
    """

    start = time.time()
    yield
    elapsed = time.time() - start
    if not tag:
        tag = f"{DEFAULT_TAG}_time_secs"
    self.log_scalar(tag=tag, value=float(f"{elapsed:.3f}"), *args, **kwargs)

PythonLogger 

PythonLogger(name: str = 'python', enabled: bool = True)

Bases: LambdaLogger

Modifier metrics that handles printing values into a python metrics instance.

Parameters:

  • name

    (str, default: 'python' ) –

    name given to the metrics, used for identification; defaults to python

  • enabled

    (bool, default: True ) –

    True to log, False otherwise

Methods:

  • log_string –

    :param tag: identifying tag to log the values with

Source code in llmcompressor/metrics/logger.py 
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def __init__(
    self,
    name: str = "python",
    enabled: bool = True,
):
    self._create_default_logger()

    super().__init__(
        lambda_func=self._log_lambda,
        name=name,
        enabled=enabled,
    )

log_string 

log_string(
    tag: Optional[str],
    string: Optional[str],
    step: Optional[int],
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
) -> bool

Parameters:

  • tag

    (Optional[str]) –

    identifying tag to log the values with

  • string

    (Optional[str]) –

    string to log

  • step

    (Optional[int]) –

    global step for when the values were taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the values were taken, defaults to time.time()

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

Returns:

  • bool –

    True if logged, False otherwise.

Source code in llmcompressor/metrics/logger.py 
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def log_string(
    self,
    tag: Optional[str],
    string: Optional[str],
    step: Optional[int],
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
) -> bool:
    """
    :param tag: identifying tag to log the values with
    :param string: string to log
    :param step: global step for when the values were taken
    :param wall_time: global wall time for when the values were taken,
        defaults to time.time()
    :param level: minimum severity level for the log message
    :return: True if logged, False otherwise.
    """
    if not wall_time:
        wall_time = time.time()

    return self._lambda_func(
        tag=tag,
        value=string,
        values=None,
        step=step,
        level=level,
        wall_time=wall_time,
    )

SparsificationGroupLogger 

SparsificationGroupLogger(
    lambda_func: Optional[
        Callable[
            [
                Optional[str],
                Optional[float],
                Optional[Dict[str, float]],
                Optional[int],
                Optional[float],
            ],
            bool,
        ]
    ] = None,
    python: bool = False,
    python_log_level: Optional[Union[int, str]] = "INFO",
    tensorboard: Optional[
        Union[bool, str, SummaryWriter]
    ] = None,
    wandb_: Optional[Union[bool, Dict]] = None,
    name: str = "sparsification",
    enabled: bool = True,
)

Bases: BaseLogger

Modifier metrics that handles outputting values to other supported systems. Supported ones include: - Python logging - Tensorboard - Weights and Biases - Lambda callback

All are optional and can be bulk disabled and enabled by this root.

Parameters:

  • lambda_func

    (Optional[Callable[[Optional[str], Optional[float], Optional[Dict[str, float]], Optional[int], Optional[float]], bool]], default: None ) –

    an optional lambda function to call back into with any logs. The expected call sequence is (tag, value, values, step, wall_time) -> bool The return type is True if logged and False otherwise.

  • python

    (bool, default: False ) –

    an bool argument for logging to a python metrics. True to create a metrics instance, or False to not log anything

  • python_log_level

    (Optional[Union[int, str]], default: 'INFO' ) –

    if python, the level to log any incoming data at on the loguru.logger instance

  • tensorboard

    (Optional[Union[bool, str, SummaryWriter]], default: None ) –

    an optional argument for logging to a tensorboard writer. May be a SummaryWriter instance to log to, a string representing the directory to create a new SummaryWriter to log to, True to create a new SummaryWriter, or non truthy to not log anything (False, None)

  • wandb_

    (Optional[Union[bool, Dict]], default: None ) –

    an optional argument for logging to wandb. May be a dictionary to pass to the init call for wandb, True to log to wandb (will not call init), or non truthy to not log anything (False, None)

  • name

    (str, default: 'sparsification' ) –

    name given to the metrics, used for identification; defaults to sparsification

  • enabled

    (bool, default: True ) –

    True to log, False otherwise

Methods:

  • enabled –

    :param value: True to log, False otherwise

  • log_hyperparams –

    :param params: Each key-value pair in the dictionary is the name of the

  • log_scalar –

    :param tag: identifying tag to log the value with

  • log_scalars –

    :param tag: identifying tag to log the values with

Attributes:

  • loggers (List[BaseLogger]) –

    :return: the created metrics sub instances for this metrics

Source code in llmcompressor/metrics/logger.py 
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def __init__(
    self,
    lambda_func: Optional[
        Callable[
            [
                Optional[str],
                Optional[float],
                Optional[Dict[str, float]],
                Optional[int],
                Optional[float],
            ],
            bool,
        ]
    ] = None,
    python: bool = False,
    python_log_level: Optional[Union[int, str]] = "INFO",
    tensorboard: Optional[Union[bool, str, SummaryWriter]] = None,
    wandb_: Optional[Union[bool, Dict]] = None,
    name: str = "sparsification",
    enabled: bool = True,
):
    super().__init__(name, enabled)
    self._loggers: List[BaseLogger] = []

    if lambda_func:
        self._loggers.append(
            LambdaLogger(lambda_func=lambda_func, name=name, enabled=enabled)
        )

    if python:
        self._loggers.append(
            PythonLogger(
                name=name,
                enabled=enabled,
            )
        )

    if tensorboard and TensorBoardLogger.available():
        self._loggers.append(
            TensorBoardLogger(
                log_path=tensorboard if isinstance(tensorboard, str) else None,
                writer=(
                    tensorboard if isinstance(tensorboard, SummaryWriter) else None
                ),
                name=name,
                enabled=enabled,
            )
        )

    if wandb_ and WANDBLogger.available():
        self._loggers.append(
            WANDBLogger(
                init_kwargs=wandb_ if isinstance(wandb_, Dict) else None,
                name=name,
                enabled=enabled,
            )
        )

loggers property 

loggers: List[BaseLogger]

Returns:

  • List[BaseLogger] –

    the created metrics sub instances for this metrics

enabled 

enabled(value: bool)

Parameters:

  • value

    (bool) –

    True to log, False otherwise

Source code in llmcompressor/metrics/logger.py 
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@BaseLogger.enabled.setter
def enabled(self, value: bool):
    """
    :param value: True to log, False otherwise
    """
    self._enabled = value

    for log in self._loggers:
        log.enabled = value

log_hyperparams 

log_hyperparams(
    params: Dict, level: Optional[Union[int, str]] = None
)

Parameters:

  • params

    (Dict) –

    Each key-value pair in the dictionary is the name of the hyper parameter and it's corresponding value.

Source code in llmcompressor/metrics/logger.py 
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def log_hyperparams(self, params: Dict, level: Optional[Union[int, str]] = None):
    """
    :param params: Each key-value pair in the dictionary is the name of the
        hyper parameter and it's corresponding value.
    """
    for log in self._loggers:
        log.log_hyperparams(params, level)

log_scalar 

log_scalar(
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
)

Parameters:

  • tag

    (str) –

    identifying tag to log the value with

  • value

    (float) –

    value to save

  • step

    (Optional[int], default: None ) –

    global step for when the value was taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the value was taken, defaults to time.time()

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

Source code in llmcompressor/metrics/logger.py 
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def log_scalar(
    self,
    tag: str,
    value: float,
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
):
    """
    :param tag: identifying tag to log the value with
    :param value: value to save
    :param step: global step for when the value was taken
    :param wall_time: global wall time for when the value was taken,
        defaults to time.time()
    :param level: minimum severity level for the log message
    """
    for log in self._loggers:
        log.log_scalar(tag, value, step, wall_time, level)

log_scalars 

log_scalars(
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
)

Parameters:

  • tag

    (str) –

    identifying tag to log the values with

  • values

    (Dict[str, float]) –

    values to save

  • step

    (Optional[int], default: None ) –

    global step for when the values were taken

  • wall_time

    (Optional[float], default: None ) –

    global wall time for when the values were taken, defaults to time.time()

  • level

    (Optional[Union[int, str]], default: None ) –

    minimum severity level for the log message

Source code in llmcompressor/metrics/logger.py 
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def log_scalars(
    self,
    tag: str,
    values: Dict[str, float],
    step: Optional[int] = None,
    wall_time: Optional[float] = None,
    level: Optional[Union[int, str]] = None,
):
    """
    :param tag: identifying tag to log the values with
    :param values: values to save
    :param step: global step for when the values were taken
    :param wall_time: global wall time for when the values were taken,
        defaults to time.time()
    :param level: minimum severity level for the log message
    """
    for log in self._loggers:
        log.log_scalars(tag, values, step, wall_time, level)

TensorBoardLogger 

TensorBoardLogger(
    log_path: str = None,
    writer: SummaryWriter = None,
    name: str = "tensorboard",
    enabled: bool = True,
)

Bases: LambdaLogger

Modifier metrics that handles outputting values into a TensorBoard log directory for viewing in TensorBoard.

Parameters:

  • log_path

    (str, default: None ) –

    the path to create a SummaryWriter at. writer must be None to use if not supplied (and writer is None), will create a TensorBoard dir in cwd

  • writer

    (SummaryWriter, default: None ) –

    the writer to log results to, if none is given creates a new one at the log_path

  • name

    (str, default: 'tensorboard' ) –

    name given to the metrics, used for identification; defaults to tensorboard

  • enabled

    (bool, default: True ) –

    True to log, False otherwise

Methods:

  • available –

    :return: True if tensorboard is available and installed, False, otherwise

Attributes:

  • writer (SummaryWriter) –

    :return: the writer to log results to,

Source code in llmcompressor/metrics/logger.py 
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def __init__(
    self,
    log_path: str = None,
    writer: SummaryWriter = None,
    name: str = "tensorboard",
    enabled: bool = True,
):
    if tensorboard_import_error:
        raise tensorboard_import_error

    if writer and log_path:
        raise ValueError(
            (
                "log_path given:{} and writer object passed in, "
                "to create a writer at the log path set writer=None"
            ).format(log_path)
        )
    elif not writer and not log_path:
        log_path = os.path.join("", "tensorboard")

    if os.environ.get("NM_TEST_MODE"):
        test_log_root = os.environ.get("NM_TEST_LOG_DIR")
        log_path = (
            os.path.join(test_log_root, log_path) if log_path else test_log_root
        )

    if log_path:
        _create_dirs(log_path)

    self._writer = writer if writer is not None else SummaryWriter(log_path)
    super().__init__(
        lambda_func=self._log_lambda,
        name=name,
        enabled=enabled,
    )

writer property 

writer: SummaryWriter

Returns:

  • SummaryWriter –

    the writer to log results to, if none is given creates a new one at the log_path

available staticmethod 

available() -> bool

Returns:

  • bool –

    True if tensorboard is available and installed, False, otherwise

Source code in llmcompressor/metrics/logger.py 
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@staticmethod
def available() -> bool:
    """
    :return: True if tensorboard is available and installed, False, otherwise
    """
    return not tensorboard_import_error

WANDBLogger 

WANDBLogger(
    init_kwargs: Optional[Dict] = None,
    name: str = "wandb",
    enabled: bool = True,
    wandb_err: Optional[Exception] = wandb_err,
)

Bases: LambdaLogger

Modifier metrics that handles outputting values to Weights and Biases.

Parameters:

  • init_kwargs

    (Optional[Dict], default: None ) –

    the args to call into wandb.init with; ex: wandb.init(**init_kwargs). If not supplied, then init will not be called

  • name

    (str, default: 'wandb' ) –

    name given to the metrics, used for identification; defaults to wandb

  • enabled

    (bool, default: True ) –

    True to log, False otherwise

Methods:

  • available –

    :return: True if wandb is available and installed, False, otherwise

  • save –

    :param file_path: path to a file to be saved

Source code in llmcompressor/metrics/logger.py 
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def __init__(
    self,
    init_kwargs: Optional[Dict] = None,
    name: str = "wandb",
    enabled: bool = True,
    wandb_err: Optional[Exception] = wandb_err,
):
    if wandb_err:
        raise wandb_err

    super().__init__(
        lambda_func=self._log_lambda,
        name=name,
        enabled=enabled,
    )

    if os.environ.get("NM_TEST_MODE"):
        test_log_path = os.environ.get("NM_TEST_LOG_DIR")
        _create_dirs(test_log_path)
        if init_kwargs:
            init_kwargs["dir"] = test_log_path
        else:
            init_kwargs = {"dir": test_log_path}

    if wandb_err:
        raise wandb_err

    if init_kwargs:
        wandb.init(**init_kwargs)
    else:
        wandb.init()

    self.wandb = wandb

available staticmethod 

available() -> bool

Returns:

  • bool –

    True if wandb is available and installed, False, otherwise

Source code in llmcompressor/metrics/logger.py 
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@staticmethod
def available() -> bool:
    """
    :return: True if wandb is available and installed, False, otherwise
    """
    return wandb_available

save 

save(file_path: str) -> bool

Parameters:

  • file_path

    (str) –

    path to a file to be saved

Source code in llmcompressor/metrics/logger.py 
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def save(
    self,
    file_path: str,
) -> bool:
    """
    :param file_path: path to a file to be saved
    """
    wandb.save(file_path)
    return True