IndexCallback

class lightning_ir.lightning_utils.callbacks.IndexCallback(index_dir: Path | str | None, index_config: IndexConfig, verbose: bool = False)[source]

Bases: Callback, GatherMixin

__init__(index_dir: Path | str | None, index_config: IndexConfig, verbose: bool = False) → None[source]

Methods

`__init__`(index_dir, index_config[, verbose])
`gather`(pl_module, dataclass)
`get_index_dir`(pl_module, dataset)
`get_indexer`(trainer, pl_module, dataset_idx)
`load_state_dict`(state_dict)	Called when loading a checkpoint, implement to reload callback state given callback's `state_dict`.
`log_to_pg`(info, trainer)
`on_after_backward`(trainer, pl_module)	Called after `loss.backward()` and before optimizers are stepped.
`on_before_backward`(trainer, pl_module, loss)	Called before `loss.backward()`.
`on_before_optimizer_step`(trainer, pl_module, ...)	Called before `optimizer.step()`.
`on_before_zero_grad`(trainer, pl_module, ...)	Called before `optimizer.zero_grad()`.
`on_exception`(trainer, pl_module, exception)	Called when any trainer execution is interrupted by an exception.
`on_fit_end`(trainer, pl_module)	Called when fit ends.
`on_fit_start`(trainer, pl_module)	Called when fit begins.
`on_load_checkpoint`(trainer, pl_module, ...)	Called when loading a model checkpoint, use to reload state.
`on_predict_batch_end`(trainer, pl_module, ...)	Called when the predict batch ends.
`on_predict_batch_start`(trainer, pl_module, ...)	Called when the predict batch begins.
`on_predict_end`(trainer, pl_module)	Called when predict ends.
`on_predict_epoch_end`(trainer, pl_module)	Called when the predict epoch ends.
`on_predict_epoch_start`(trainer, pl_module)	Called when the predict epoch begins.
`on_predict_start`(trainer, pl_module)	Called when the predict begins.
`on_sanity_check_end`(trainer, pl_module)	Called when the validation sanity check ends.
`on_sanity_check_start`(trainer, pl_module)	Called when the validation sanity check starts.
`on_save_checkpoint`(trainer, pl_module, ...)	Called when saving a checkpoint to give you a chance to store anything else you might want to save.
`on_test_batch_end`(trainer, pl_module, ...[, ...])
`on_test_batch_start`(trainer, pl_module, ...)	Called when the test batch begins.
`on_test_end`(trainer, pl_module)
`on_test_epoch_end`(trainer, pl_module)	Called when the test epoch ends.
`on_test_epoch_start`(trainer, pl_module)	Called when the test epoch begins.
`on_test_start`(trainer, pl_module)
`on_train_batch_end`(trainer, pl_module, ...)	Called when the train batch ends.
`on_train_batch_start`(trainer, pl_module, ...)	Called when the train batch begins.
`on_train_end`(trainer, pl_module)	Called when the train ends.
`on_train_epoch_end`(trainer, pl_module)	Called when the train epoch ends.
`on_train_epoch_start`(trainer, pl_module)	Called when the train epoch begins.
`on_train_start`(trainer, pl_module)	Called when the train begins.
`on_validation_batch_end`(trainer, pl_module, ...)	Called when the validation batch ends.
`on_validation_batch_start`(trainer, ...[, ...])	Called when the validation batch begins.
`on_validation_end`(trainer, pl_module)	Called when the validation loop ends.
`on_validation_epoch_end`(trainer, pl_module)	Called when the val epoch ends.
`on_validation_epoch_start`(trainer, pl_module)	Called when the val epoch begins.
`on_validation_start`(trainer, pl_module)	Called when the validation loop begins.
`setup`(trainer, pl_module, stage)
`state_dict`()	Called when saving a checkpoint, implement to generate callback's `state_dict`.
`teardown`(trainer, pl_module, stage)	Called when fit, validate, test, predict, or tune ends.

Attributes

state_key

Identifier for the state of the callback.

load_state_dict(state_dict: Dict[str, Any]) → None

Called when loading a checkpoint, implement to reload callback state given callback’s state_dict.

Parameters:: state_dict – the callback state returned by state_dict.

on_after_backward(trainer: Trainer, pl_module: LightningModule) → None: Called after loss.backward() and before optimizers are stepped.

on_before_backward(trainer: Trainer, pl_module: LightningModule, loss: Tensor) → None: Called before loss.backward().

on_before_optimizer_step(trainer: Trainer, pl_module: LightningModule, optimizer: Optimizer) → None: Called before optimizer.step().

on_before_zero_grad(trainer: Trainer, pl_module: LightningModule, optimizer: Optimizer) → None: Called before optimizer.zero_grad().

on_exception(trainer: Trainer, pl_module: LightningModule, exception: BaseException) → None: Called when any trainer execution is interrupted by an exception.

on_fit_end(trainer: Trainer, pl_module: LightningModule) → None: Called when fit ends.

on_fit_start(trainer: Trainer, pl_module: LightningModule) → None: Called when fit begins.

on_load_checkpoint(trainer: Trainer, pl_module: LightningModule, checkpoint: Dict[str, Any]) → None

Called when loading a model checkpoint, use to reload state.

Parameters:

trainer – the current Trainer instance.
pl_module – the current LightningModule instance.
checkpoint – the full checkpoint dictionary that got loaded by the Trainer.

on_predict_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None: Called when the predict batch ends.

on_predict_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None: Called when the predict batch begins.

on_predict_end(trainer: Trainer, pl_module: LightningModule) → None: Called when predict ends.

on_predict_epoch_end(trainer: Trainer, pl_module: LightningModule) → None: Called when the predict epoch ends.

on_predict_epoch_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the predict epoch begins.

on_predict_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the predict begins.

on_sanity_check_end(trainer: Trainer, pl_module: LightningModule) → None: Called when the validation sanity check ends.

on_sanity_check_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the validation sanity check starts.

on_save_checkpoint(trainer: Trainer, pl_module: LightningModule, checkpoint: Dict[str, Any]) → None

Called when saving a checkpoint to give you a chance to store anything else you might want to save.

Parameters:

trainer – the current Trainer instance.
pl_module – the current LightningModule instance.
checkpoint – the checkpoint dictionary that will be saved.

on_test_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None: Called when the test batch begins.

on_test_epoch_end(trainer: Trainer, pl_module: LightningModule) → None: Called when the test epoch ends.

on_test_epoch_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the test epoch begins.

on_train_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int) → None: Called when the train batch ends.

Note

The value outputs["loss"] here will be the normalized value w.r.t accumulate_grad_batches of the loss returned from training_step.

on_train_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int) → None: Called when the train batch begins.

on_train_end(trainer: Trainer, pl_module: LightningModule) → None: Called when the train ends.

on_train_epoch_end(trainer: Trainer, pl_module: LightningModule) → None

Called when the train epoch ends.

To access all batch outputs at the end of the epoch, you can cache step outputs as an attribute of the lightning.pytorch.core.LightningModule and access them in this hook:

class MyLightningModule(L.LightningModule):
    def __init__(self):
        super().__init__()
        self.training_step_outputs = []

    def training_step(self):
        loss = ...
        self.training_step_outputs.append(loss)
        return loss


class MyCallback(L.Callback):
    def on_train_epoch_end(self, trainer, pl_module):
        # do something with all training_step outputs, for example:
        epoch_mean = torch.stack(pl_module.training_step_outputs).mean()
        pl_module.log("training_epoch_mean", epoch_mean)
        # free up the memory
        pl_module.training_step_outputs.clear()

on_train_epoch_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the train epoch begins.

on_train_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the train begins.

on_validation_batch_end(trainer: Trainer, pl_module: LightningModule, outputs: Tensor | Mapping[str, Any] | None, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None: Called when the validation batch ends.

on_validation_batch_start(trainer: Trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int = 0) → None: Called when the validation batch begins.

on_validation_end(trainer: Trainer, pl_module: LightningModule) → None: Called when the validation loop ends.

on_validation_epoch_end(trainer: Trainer, pl_module: LightningModule) → None: Called when the val epoch ends.

on_validation_epoch_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the val epoch begins.

on_validation_start(trainer: Trainer, pl_module: LightningModule) → None: Called when the validation loop begins.

state_dict() → Dict[str, Any]

Called when saving a checkpoint, implement to generate callback’s state_dict.

Returns:: A dictionary containing callback state.

property state_key: str

Identifier for the state of the callback.

Used to store and retrieve a callback’s state from the checkpoint dictionary by checkpoint["callbacks"][state_key]. Implementations of a callback need to provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.

teardown(trainer: Trainer, pl_module: LightningModule, stage: str) → None: Called when fit, validate, test, predict, or tune ends.