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Sampler Protocol¤

Core protocol for data sampling.

See Also¤

datarax.core.sampler ¤

Base module for sampler components in Datarax.

This module defines the base class for all Datarax sampler components that use flax.nnx.Module for state management and JAX transformation compatibility.

logger module-attribute ¤

logger = getLogger(__name__)

SamplerModule ¤

SamplerModule(config: StructuralConfig, *, rngs: Rngs | None = None, name: str | None = None)

Bases: StructuralModule

Enhanced base module for all Datarax sampler components.

A SamplerModule determines the order in which records are accessed and processed. It handles global data transformations like shuffling and epoch management.

This class extends StructuralModule for non-parametric structural processing. Concrete samplers define their own config classes extending StructuralConfig.

Parameters:

Name Type Description Default
config StructuralConfig

StructuralConfig or subclass with sampler-specific parameters

 required
rngs Rngs | None

Random number generators (required if stochastic=True)

 None
name str | None

Optional name for the sampler

 None

Examples:

from dataclasses import dataclass from datarax.core.config import StructuralConfig from datarax.core.sampler import SamplerModule from flax import nnx

class SequentialSamplerConfig(StructuralConfig): num_records: int = 100 num_epochs: int = 1 SequentialSamplerConfig = dataclass(frozen=True)(SequentialSamplerConfig)

class SequentialSamplerModule(SamplerModule): def process(self, dataset_size): return list(range(min(self.config.num_records, dataset_size))) def iter(self): yield from self.process(100)

config = SequentialSamplerConfig(stochastic=False, num_records=10) sampler = SequentialSamplerModule(config, rngs=nnx.Rngs(0))

Parameters:

Name Type Description Default
config StructuralConfig

Sampler configuration (already validated)

 required
rngs Rngs | None

Random number generators (required if stochastic=True)

 None
name str | None

Optional sampler name

 None

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

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.

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.

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

reset ¤

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

Reset the sampler state, typically used to start a new epoch.

Parameters:

Name Type Description Default
seed int | None

Optional seed to use for shuffling or other random operations. If None, the sampler should use its default or previously set seed.

 None

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.

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().

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.

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()