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Epoch Aware Sampler¤

Track epochs and reshuffle at epoch boundaries.

See Also¤

datarax.samplers.epoch_aware_sampler ¤

Epoch-aware sampler with per-epoch callbacks and index tracking.

logger module-attribute ¤

logger = getLogger(__name__)

EpochAwareSamplerConfig dataclass ¤

EpochAwareSamplerConfig(cacheable: bool = False, batch_stats_fn: Callable | Module | None = None, precomputed_stats: dict[str, Any] | None = None, stochastic: bool = False, stream_name: str | None = None, num_records: int | None = None, num_epochs: int = 1, shuffle: bool = True, seed: int = 42)

Bases: StructuralConfig

Configuration for EpochAwareSamplerModule.

Attributes:

Name Type Description
num_records int | None

Total number of records in the dataset (required)
num_epochs int

Number of epochs to iterate (default: 1, -1 for infinite)
shuffle bool

Whether to shuffle indices per epoch (default: True)
seed int

Base seed for shuffling (default: 42, only used if shuffle=True)

num_records class-attribute instance-attribute ¤

num_records: int | None = None

num_epochs class-attribute instance-attribute ¤

num_epochs: int = 1

shuffle class-attribute instance-attribute ¤

shuffle: bool = True

seed class-attribute instance-attribute ¤

seed: int = 42

cacheable class-attribute instance-attribute ¤

cacheable: bool = False

batch_stats_fn class-attribute instance-attribute ¤

batch_stats_fn: Callable | Module | None = None

precomputed_stats class-attribute instance-attribute ¤

precomputed_stats: dict[str, Any] | None = None

stochastic class-attribute instance-attribute ¤

stochastic: bool = False

stream_name class-attribute instance-attribute ¤

stream_name: str | None = None

EpochAwareSamplerModule ¤

EpochAwareSamplerModule(config: EpochAwareSamplerConfig, *, rngs: Rngs | None = None, name: str | None = None)

Bases: SamplerModule

Sampler with explicit epoch boundary handling.

Manages epochs with different shuffling per epoch while maintaining state for checkpointing.

Parameters:

Name Type Description Default
config EpochAwareSamplerConfig

Configuration specifying num_records, epochs, shuffle, and seed.

 required
rngs Rngs | None

Optional Flax NNX random number generators.

 None
name str | None

Optional module name for identification.

 None

num_records instance-attribute ¤

num_records = Variable(num_records)

num_epochs instance-attribute ¤

num_epochs = Variable(num_epochs)

shuffle instance-attribute ¤

shuffle = Variable(shuffle)

base_seed instance-attribute ¤

base_seed = Variable(seed)

current_epoch instance-attribute ¤

current_epoch = Variable(0)

current_index instance-attribute ¤

current_index = Variable(0)

epoch_indices instance-attribute ¤

epoch_indices: Variable[list[int] | None] = Variable(None)

epoch_complete_callbacks instance-attribute ¤

epoch_complete_callbacks: Variable[list[Callable[[int], None]]] = Variable([])

config instance-attribute ¤

config = static(config)

rngs instance-attribute ¤

rngs = rngs

name instance-attribute ¤

name = static(name)

stochastic instance-attribute ¤

stochastic = stochastic

stream_name instance-attribute ¤

stream_name = stream_name

add_epoch_callback ¤

add_epoch_callback(callback: Callable) -> None

Add callback for epoch completion.

get_epoch_progress ¤

get_epoch_progress() -> dict[str, Any]

Get current epoch progress.

reset ¤

reset(seed: int | None = None) -> None

Reset the sampler to initial state.

Parameters:

Name Type Description Default
seed int | None

Optional new base seed. If provided, changes the shuffle order for subsequent iterations.

 None

get_operation_stats ¤

get_operation_stats() -> dict[str, int]

Get operation statistics.

Note: This method converts JAX arrays to Python ints for introspection. It is intended for use outside of JIT-compiled functions.

Returns:

Type Description
dict[str, int]

Dictionary with 'applied_count' and 'skipped_count'

reset_operation_stats ¤

reset_operation_stats() -> None

Reset operation statistics to zero.

Note: Creates new JAX arrays to reset the counters.

compute_statistics ¤

compute_statistics(data: Any) -> dict[str, Any] | None

Compute statistics from data using batch_stats_fn.

If batch_stats_fn is not configured, returns None. Computed statistics are cached in _computed_stats.

Parameters:

Name Type Description Default
data Any

Input data to compute statistics from

 required

Returns:

Type Description
dict[str, Any] | None

Dictionary of statistics, or None if no batch_stats_fn configured

get_statistics ¤

get_statistics() -> dict[str, Any] | None

Get current statistics.

Returns precomputed_stats if configured (unless reset was called), otherwise returns cached computed statistics, or None if no statistics available.

Returns:

Type Description
dict[str, Any] | None

Dictionary of statistics, or None if no statistics available

set_statistics ¤

set_statistics(stats: dict[str, Any]) -> None

Manually set statistics.

This overwrites any previously computed statistics and clears reset flag.

Parameters:

Name Type Description Default
stats dict[str, Any]

Dictionary of statistics to set

 required

reset_statistics ¤

reset_statistics() -> None

Reset all statistics to None.

This clears both computed statistics and marks that precomputed_stats should be ignored (via internal flag). After reset, get_statistics() will return None until new statistics are set or computed.

reset_cache ¤

reset_cache() -> None

Clear the cache.

Only has effect if cacheable=True in config.

copy ¤

copy(*, config: DataraxModuleConfig | None = None, rngs: Rngs | None = None, name: str | None = None) -> DataraxModule

Create a copy of this module with optional config/parameter changes.

This allows creating a new module instance with modified configuration while preserving other attributes. Useful for hyperparameter tuning.

Parameters:

Name Type Description Default
config DataraxModuleConfig | None

New config (if None, uses current config)

 None
rngs Rngs | None

New RNG state (if None, uses current rngs)

 None
name str | None

New name (if None, uses current name)

 None

Returns:

Type Description
DataraxModule

New module instance with updated parameters

Examples:

Change configuration¤

new_config = DataraxModuleConfig(cacheable=True) new_module = module.copy(config=new_config)

Change name only¤

renamed = module.copy(name="new_name")

Note

Subclasses can override this method to provide more fine-grained control over copying, such as allowing individual config field updates without requiring dataclass replace().

get_state ¤

get_state() -> dict[str, Any]

Return the current state for checkpointing purposes.

This extends the serializable state from DataraxModule with any additional state specific to this sampler.

Returns:

Type Description
dict[str, Any]

A dictionary containing the internal state of the Sampler.

set_state ¤

set_state(state: dict[str, Any]) -> None

Restore internal state from a checkpoint.

This restores both the DataraxModule state and any additional state specific to this sampler.

Parameters:

Name Type Description Default
state dict[str, Any]

A dictionary containing the internal state to restore.

 required

clone ¤

clone() -> DataraxModule

Create a new instance with the same state as this module.

Uses NNX's clone function for proper deep cloning of all state.

Returns:

Type Description
DataraxModule

A new module instance with the same state.

requires_rng_streams ¤

requires_rng_streams() -> list[str] | None

Get the list of RNG streams required by this module.

Returns:

Type Description
list[str] | None

A list of required RNG stream names, or None if no RNG is required.

ensure_rng_streams ¤

ensure_rng_streams(stream_names: list[str]) -> None

Ensure that the required RNG streams are available.

Parameters:

Name Type Description Default
stream_names list[str]

A list of available RNG stream names.

 required

Raises:

Type Description
ValueError

If a required RNG stream is not available.

process ¤

process(input: Any, *args: Any, **kwargs: Any) -> Any

Process input structure.

This method transforms the structure/organization of input data without modifying the data values themselves.

Subclasses MUST implement this method.

The input/output types depend on the specific structural processor:

  • Batcher: list[Element] -> list[Batch]
  • Sampler: int -> list[int]
  • Sharder: Batch -> Sharded[Batch]
  • Splitter: Dataset -> tuple[Dataset, Dataset]

Parameters:

Name Type Description Default
input Any

Input to process (type varies by processor)

 required
*args Any

Additional positional arguments (processor-specific)

 ()
**kwargs Any

Additional keyword arguments (processor-specific)

 {}

Returns:

Type Description
Any

Processed output (type varies by processor)

Examples:

Batcher implementation:

def process(self, elements: list[Element]) -> list[Batch]:
    batches = []
    for i in range(0, len(elements), self.config.batch_size):
        batch_elements = elements[i:i + self.config.batch_size]
        batches.append(Batch.from_elements(batch_elements))
    return batches

Sampler implementation (deterministic):

def process(self, dataset_size: int) -> list[int]:
    return list(range(min(self.config.num_samples, dataset_size)))

Sampler implementation (stochastic):

def process(self, dataset_size: int) -> list[int]:
    rng = self.rngs[self.config.stream_name]()
    indices = jax.random.choice(
        rng, dataset_size, shape=(self.config.num_samples,),
        replace=self.config.replacement
    )
    return indices.tolist()

sample ¤

sample(n: int) -> list[int]

Return a list of sampled indices.

This method returns all indices that would be yielded by the iterator, collected into a list. This is useful when you need all indices upfront rather than iterating through them one by one.

Parameters:

Name Type Description Default
n int

The number of indices to sample (typically the dataset size).

 required

Returns:

Type Description
list[int]

A list of sampled indices.
Note

The default implementation simply collects all indices from the iterator. Subclasses may override this for more efficient implementations.

index_spec ¤

index_spec() -> Any

Return a jax.ShapeDtypeStruct (or PyTree thereof) describing emitted indices.

The default implementation returns a scalar int32 spec, matching the common case of one-index-per-call samplers (sequential, shuffle, range). Specialized samplers (SlidingWindowSampler, BufferSampler) override this to declare windowed or vectorized index shapes.

Returns:

Type Description
Any

A jax.ShapeDtypeStruct (or PyTree thereof) describing the
Any

shape and dtype of one emitted index.