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Testing callbacks

AbstractStopAndGoCallback

Bases: ABC, BaseInterruptedVsContinuousCallback

Abstract base class for stop-and-go callback to compare metadata before pausing and after resuming training.

This base class provides utility methods to help streamline stop and go comparison.

Provided methods
  • init: initializes the callback with the given mode.
  • get_metadata: abstract method that should be overridden to get metadata from the trainer and pl_module.
Default behaviors
  • in stop mode, metadata is gotten and compared on_validation_epoch_end.
  • in go mode, metadata is gotten and saved on_train_epoch_start.

Override these behaviors if necessary.

Source code in bionemo/testing/testing_callbacks.py 
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class AbstractStopAndGoCallback(ABC, BaseInterruptedVsContinuousCallback):
    """Abstract base class for stop-and-go callback to compare metadata before pausing and after resuming training.

    This base class provides utility methods to help streamline stop and go comparison.

    Provided methods:
        - __init__: initializes the callback with the given mode.
        - get_metadata: abstract method that should be overridden to get metadata from the trainer and pl_module.

    Default behaviors:
        - in stop mode, metadata is gotten and compared on_validation_epoch_end.
        - in go mode, metadata is gotten and saved on_train_epoch_start.

    Override these behaviors if necessary.
    """

    def __init__(self, mode: Mode = Mode.STOP):
        """Initialize StopAndGoCallback.

        Args:
            mode (str, optional): Mode to run in. Must be either Mode.STOP or Mode.RESUME. Defaults to Mode.STOP.

        Notes:
            User must override get_metadata to get metadata from the trainer and pl_module.
        """
        if mode not in [Mode.STOP, Mode.RESUME]:
            raise ValueError(f"mode must be 'stop' or 'go', got {mode}")
        self.mode = mode
        super().__init__()

    @abstractmethod
    def get_metadata(self, trainer: Trainer, pl_module: LightningModule) -> Any:
        """Get metadata from trainer and pl_module."""
        raise NotImplementedError

    def on_train_epoch_start(self, trainer: Trainer, pl_module: LightningModule):  # noqa: D102
        if self.mode == Mode.RESUME:
            self.data = self.get_metadata(trainer, pl_module)

    def on_validation_epoch_end(self, trainer: Trainer, pl_module: LightningModule):  # noqa: D102
        if not trainer.sanity_checking and self.mode == Mode.STOP:
            self.data = self.get_metadata(trainer, pl_module)

__init__(mode=Mode.STOP)

Initialize StopAndGoCallback.

Parameters:

Name Type Description Default
mode str

Mode to run in. Must be either Mode.STOP or Mode.RESUME. Defaults to Mode.STOP.

 STOP
Notes

User must override get_metadata to get metadata from the trainer and pl_module.

Source code in bionemo/testing/testing_callbacks.py 
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def __init__(self, mode: Mode = Mode.STOP):
    """Initialize StopAndGoCallback.

    Args:
        mode (str, optional): Mode to run in. Must be either Mode.STOP or Mode.RESUME. Defaults to Mode.STOP.

    Notes:
        User must override get_metadata to get metadata from the trainer and pl_module.
    """
    if mode not in [Mode.STOP, Mode.RESUME]:
        raise ValueError(f"mode must be 'stop' or 'go', got {mode}")
    self.mode = mode
    super().__init__()

get_metadata(trainer, pl_module) abstractmethod

Get metadata from trainer and pl_module.

Source code in bionemo/testing/testing_callbacks.py 
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@abstractmethod
def get_metadata(self, trainer: Trainer, pl_module: LightningModule) -> Any:
    """Get metadata from trainer and pl_module."""
    raise NotImplementedError

BaseInterruptedVsContinuousCallback

Bases: Callback, CallbackMethods, IOMixin

Base class for serializable stop-and-go callback to compare continuous to interrupted training.

This class is used by extending a callback and collecting data into the self.data attribute. This data is then compared between continuous and interrupted training.

See nemo.lightning.megatron_parallel.CallbackMethods for the available callback methods.

Source code in bionemo/testing/testing_callbacks.py 
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class BaseInterruptedVsContinuousCallback(Callback, CallbackMethods, io.IOMixin):
    """Base class for serializable stop-and-go callback to compare continuous to interrupted training.

    This class is used by extending a callback and collecting data into the `self.data` attribute. This data is then
    compared between continuous and interrupted training.

    See nemo.lightning.megatron_parallel.CallbackMethods for the available callback methods.
    """

    def __init__(self):
        """Initializes the callback."""
        self.data = []

    def __deepcopy__(self, memo):
        """Don't actually attempt to copy this data when this callback is being serialized."""
        ...

__deepcopy__(memo)

Don't actually attempt to copy this data when this callback is being serialized.

Source code in bionemo/testing/testing_callbacks.py 
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def __deepcopy__(self, memo):
    """Don't actually attempt to copy this data when this callback is being serialized."""
    ...

__init__()

Initializes the callback.

Source code in bionemo/testing/testing_callbacks.py 
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def __init__(self):
    """Initializes the callback."""
    self.data = []

ConsumedSamplesCallback

Bases: BaseInterruptedVsContinuousCallback

Stop-and-go callback to check consumed samples before pausing and after resuming training.

Source code in bionemo/testing/testing_callbacks.py 
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class ConsumedSamplesCallback(BaseInterruptedVsContinuousCallback):
    """Stop-and-go callback to check consumed samples before pausing and after resuming training."""

    def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
        """Get consumed samples as metadata."""
        if step.trainer.training:
            data_sampler = step.trainer.datamodule.data_sampler
            consumed_samples = data_sampler.compute_consumed_samples(
                step.trainer.global_step - step.trainer.datamodule.init_global_step
            )
            self.data.append(np.array(consumed_samples))
        return step

on_megatron_step_start(step)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
    """Get consumed samples as metadata."""
    if step.trainer.training:
        data_sampler = step.trainer.datamodule.data_sampler
        consumed_samples = data_sampler.compute_consumed_samples(
            step.trainer.global_step - step.trainer.datamodule.init_global_step
        )
        self.data.append(np.array(consumed_samples))
    return step

GlobalStepStateCallback

Bases: BaseInterruptedVsContinuousCallback

Stop-and-go callback for global_step before pausing and after resuming training.

Source code in bionemo/testing/testing_callbacks.py 
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class GlobalStepStateCallback(BaseInterruptedVsContinuousCallback):
    """Stop-and-go callback for global_step before pausing and after resuming training."""

    def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
        """Get learning rate as metadata."""
        if step.trainer.training:
            self.data.append(np.array(step.trainer.global_step))
        return step

on_megatron_step_start(step)

Get learning rate as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
    """Get learning rate as metadata."""
    if step.trainer.training:
        self.data.append(np.array(step.trainer.global_step))
    return step

LearningRateCallback

Bases: BaseInterruptedVsContinuousCallback

Stop-and-go callback for learning rate before pausing and after resuming training.

Source code in bionemo/testing/testing_callbacks.py 
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class LearningRateCallback(BaseInterruptedVsContinuousCallback):
    """Stop-and-go callback for learning rate before pausing and after resuming training."""

    def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
        """Get learning rate as metadata."""
        if step.trainer.training:
            self.data.append(np.array(step.trainer.optimizers[0].param_groups[0]["lr"]))
        return step

on_megatron_step_start(step)

Get learning rate as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
    """Get learning rate as metadata."""
    if step.trainer.training:
        self.data.append(np.array(step.trainer.optimizers[0].param_groups[0]["lr"]))
    return step

OptimizerStateCallback

Bases: BaseInterruptedVsContinuousCallback

Stop-and-go callback to check optimizer states before pausing and after resuming training.

Source code in bionemo/testing/testing_callbacks.py 
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class OptimizerStateCallback(BaseInterruptedVsContinuousCallback):
    """Stop-and-go callback to check optimizer states before pausing and after resuming training."""

    def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
        """Get optimizer states as metadata."""
        if step.trainer.training:
            self.data.append(
                recursive_detach(
                    [
                        optimizer.mcore_optimizer.optimizer.state_dict()["state"]
                        for optimizer in step.trainer.optimizers
                    ]
                )
            )
        return step

on_megatron_step_start(step)

Get optimizer states as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
    """Get optimizer states as metadata."""
    if step.trainer.training:
        self.data.append(
            recursive_detach(
                [
                    optimizer.mcore_optimizer.optimizer.state_dict()["state"]
                    for optimizer in step.trainer.optimizers
                ]
            )
        )
    return step

SignalAfterGivenStepCallback

Bases: Callback, CallbackMethods

A callback that emits a given signal to the current process at the defined step.

Use this callback for pytest based Stop and go tests.

Source code in bionemo/testing/testing_callbacks.py 
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class SignalAfterGivenStepCallback(Callback, CallbackMethods):
    """A callback that emits a given signal to the current process at the defined step.

    Use this callback for pytest based Stop and go tests.
    """

    def __init__(self, stop_step: int, signal_: signal.Signals = signal.SIGUSR2):
        """Initializes the callback with the given stop_step."""
        self.stop_step = stop_step
        self.signal = signal_

    def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
        """Stop training if the global step is greater than or equal to the stop_step."""
        if step.trainer.global_step >= self.stop_step:
            os.kill(os.getpid(), self.signal)
        return step

__init__(stop_step, signal_=signal.SIGUSR2)

Initializes the callback with the given stop_step.

Source code in bionemo/testing/testing_callbacks.py 
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def __init__(self, stop_step: int, signal_: signal.Signals = signal.SIGUSR2):
    """Initializes the callback with the given stop_step."""
    self.stop_step = stop_step
    self.signal = signal_

on_megatron_step_start(step)

Stop training if the global step is greater than or equal to the stop_step.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_step_start(self, step: MegatronStep) -> MegatronStep:
    """Stop training if the global step is greater than or equal to the stop_step."""
    if step.trainer.global_step >= self.stop_step:
        os.kill(os.getpid(), self.signal)
    return step

StopAfterValidEpochEndCallback

Bases: Callback, CallbackMethods

A callback that stops training after the validation epoch.

Use this callback for pytest based Stop and go tests.

Source code in bionemo/testing/testing_callbacks.py 
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class StopAfterValidEpochEndCallback(Callback, CallbackMethods):
    """A callback that stops training after the validation epoch.

    Use this callback for pytest based Stop and go tests.
    """

    def on_validation_epoch_end(self, trainer: Trainer, pl_module: LightningModule):  # noqa: D102
        if trainer.sanity_checking:
            return
        trainer.should_stop = True

TrainInputCallback

Bases: BaseInterruptedVsContinuousCallback

Collect training input samples for comparison.

Source code in bionemo/testing/testing_callbacks.py 
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class TrainInputCallback(BaseInterruptedVsContinuousCallback):
    """Collect training input samples for comparison."""

    def on_megatron_microbatch_end(
        self,
        step: MegatronStep,
        batch: DataT,
        forward_callback: "MegatronLossReduction",
        output: Any,
    ) -> None:
        """Get consumed samples as metadata."""
        if step.trainer.training:
            self.data.append(recursive_detach(batch))

on_megatron_microbatch_end(step, batch, forward_callback, output)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_microbatch_end(
    self,
    step: MegatronStep,
    batch: DataT,
    forward_callback: "MegatronLossReduction",
    output: Any,
) -> None:
    """Get consumed samples as metadata."""
    if step.trainer.training:
        self.data.append(recursive_detach(batch))

TrainLossCallback

Bases: BaseInterruptedVsContinuousCallback

Collect training loss samples for comparison.

Source code in bionemo/testing/testing_callbacks.py 
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class TrainLossCallback(BaseInterruptedVsContinuousCallback):
    """Collect training loss samples for comparison."""

    def on_megatron_step_end(
        self,
        step: MegatronStep,
        microbatch_outputs: List[Any],
        reduced: Optional[Union[torch.Tensor, Dict[str, torch.Tensor]]] = None,
    ) -> None:
        """Get consumed samples as metadata."""
        if step.trainer.training:
            self.data.append(recursive_detach(reduced))

on_megatron_step_end(step, microbatch_outputs, reduced=None)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_step_end(
    self,
    step: MegatronStep,
    microbatch_outputs: List[Any],
    reduced: Optional[Union[torch.Tensor, Dict[str, torch.Tensor]]] = None,
) -> None:
    """Get consumed samples as metadata."""
    if step.trainer.training:
        self.data.append(recursive_detach(reduced))

TrainOutputCallback

Bases: BaseInterruptedVsContinuousCallback

Collect training output samples for comparison.

Source code in bionemo/testing/testing_callbacks.py 
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class TrainOutputCallback(BaseInterruptedVsContinuousCallback):
    """Collect training output samples for comparison."""

    def on_megatron_microbatch_end(
        self,
        step: MegatronStep,
        batch: DataT,
        forward_callback: "MegatronLossReduction",
        output: Any,
    ) -> None:
        """Get consumed samples as metadata."""
        if step.trainer.training:
            self.data.append(recursive_detach(output))

on_megatron_microbatch_end(step, batch, forward_callback, output)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_microbatch_end(
    self,
    step: MegatronStep,
    batch: DataT,
    forward_callback: "MegatronLossReduction",
    output: Any,
) -> None:
    """Get consumed samples as metadata."""
    if step.trainer.training:
        self.data.append(recursive_detach(output))

TrainValInitConsumedSamplesStopAndGoCallback

Bases: AbstractStopAndGoCallback

Stop-and-go callback to check consumed samples before pausing and after resuming training.

This is currently the only callback that doesn't fit with the new pattern of directly comparing continuous and interrupted training, since the dataloaders don't track their consumed_samples before and after checkpoint resumption.

Source code in bionemo/testing/testing_callbacks.py 
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class TrainValInitConsumedSamplesStopAndGoCallback(AbstractStopAndGoCallback):
    """Stop-and-go callback to check consumed samples before pausing and after resuming training.

    This is currently the only callback that doesn't fit with the new pattern of directly comparing continuous and
    interrupted training, since the dataloaders don't track their consumed_samples before and after checkpoint
    resumption.
    """

    @override
    def get_metadata(self, trainer: Trainer, pl_module: LightningModule) -> Any:
        """Get consumed samples as metadata."""
        # return trainer.datamodule.state_dict()["consumed_samples"]  # TODO why state_dict can be empty despite working lines below
        train_data_sampler: MegatronPretrainingSampler = trainer.train_dataloader.batch_sampler
        val_data_sampler: MegatronPretrainingSampler = trainer.val_dataloaders.batch_sampler
        return train_data_sampler.consumed_samples, val_data_sampler.consumed_samples

get_metadata(trainer, pl_module)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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@override
def get_metadata(self, trainer: Trainer, pl_module: LightningModule) -> Any:
    """Get consumed samples as metadata."""
    # return trainer.datamodule.state_dict()["consumed_samples"]  # TODO why state_dict can be empty despite working lines below
    train_data_sampler: MegatronPretrainingSampler = trainer.train_dataloader.batch_sampler
    val_data_sampler: MegatronPretrainingSampler = trainer.val_dataloaders.batch_sampler
    return train_data_sampler.consumed_samples, val_data_sampler.consumed_samples

ValidInputCallback

Bases: BaseInterruptedVsContinuousCallback

Collect validation input samples for comparison.

Source code in bionemo/testing/testing_callbacks.py 
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class ValidInputCallback(BaseInterruptedVsContinuousCallback):
    """Collect validation input samples for comparison."""

    def on_megatron_microbatch_end(
        self,
        step: MegatronStep,
        batch: DataT,
        forward_callback: "MegatronLossReduction",
        output: Any,
    ) -> None:
        """Get consumed samples as metadata."""
        if step.trainer.validating:
            self.data.append(recursive_detach(batch))

on_megatron_microbatch_end(step, batch, forward_callback, output)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_microbatch_end(
    self,
    step: MegatronStep,
    batch: DataT,
    forward_callback: "MegatronLossReduction",
    output: Any,
) -> None:
    """Get consumed samples as metadata."""
    if step.trainer.validating:
        self.data.append(recursive_detach(batch))

ValidLossCallback

Bases: BaseInterruptedVsContinuousCallback

Collect training loss samples for comparison.

Source code in bionemo/testing/testing_callbacks.py 
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class ValidLossCallback(BaseInterruptedVsContinuousCallback):
    """Collect training loss samples for comparison."""

    def on_megatron_step_end(
        self,
        step: MegatronStep,
        microbatch_outputs: List[Any],
        reduced: Optional[Union[torch.Tensor, Dict[str, torch.Tensor]]] = None,
    ) -> None:
        """Get consumed samples as metadata."""
        if step.trainer.validating:
            self.data.append(recursive_detach(reduced))

on_megatron_step_end(step, microbatch_outputs, reduced=None)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_step_end(
    self,
    step: MegatronStep,
    microbatch_outputs: List[Any],
    reduced: Optional[Union[torch.Tensor, Dict[str, torch.Tensor]]] = None,
) -> None:
    """Get consumed samples as metadata."""
    if step.trainer.validating:
        self.data.append(recursive_detach(reduced))

ValidOutputCallback

Bases: BaseInterruptedVsContinuousCallback

Collect validation output samples for comparison.

Source code in bionemo/testing/testing_callbacks.py 
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class ValidOutputCallback(BaseInterruptedVsContinuousCallback):
    """Collect validation output samples for comparison."""

    def on_megatron_microbatch_end(
        self,
        step: MegatronStep,
        batch: DataT,
        forward_callback: "MegatronLossReduction",
        output: Any,
    ) -> None:
        """Get consumed samples as metadata."""
        if step.trainer.validating:
            self.data.append(recursive_detach(output))

on_megatron_microbatch_end(step, batch, forward_callback, output)

Get consumed samples as metadata.

Source code in bionemo/testing/testing_callbacks.py 
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def on_megatron_microbatch_end(
    self,
    step: MegatronStep,
    batch: DataT,
    forward_callback: "MegatronLossReduction",
    output: Any,
) -> None:
    """Get consumed samples as metadata."""
    if step.trainer.validating:
        self.data.append(recursive_detach(output))