Map Operator

Apply functions element-wise across data.

See Also

• Operators Overview - All operator types
• Element Operator - Element-level transforms
• Field Operators - Field-specific transforms
• Operators Tutorial

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datarax.operators.map_operator

MapOperator - operator for applying functions to array leaves.

This module provides MapOperator, which applies user-provided array transformation functions to leaves in element data PyTree.

Key Features:

• Unified function signature: fn(x: Array, key: Array) -> Array
• Deterministic mode: key parameter ignored
• Stochastic mode: key parameter provides per-leaf randomness
• Full-tree mode: Apply fn to all array leaves
• Subtree mode: Apply fn only to specified subtree leaves
• Uses jax.tree.map_with_path for unified implementation

BREAKING CHANGE: User functions MUST accept key parameter even in deterministic mode.

logger module-attribute

logger = getLogger(__name__)

MapOperator

MapOperator(config: MapOperatorConfig, fn: Callable[[Array, Array], Array], *, rngs: Rngs | None = None, name: str | None = None)

Bases: OperatorModule

Unified operator for mapping functions over array leaves in data.

Applies user-provided array transformation function to leaves in element.data PyTree. Supports both full-tree and subtree transformations, both deterministic and stochastic modes.

User Function Signature (ALWAYS required):

fn(x: jax.Array, key: jax.Array) -> jax.Array

• Deterministic mode (stochastic=False): Ignore key parameter
• Stochastic mode (stochastic=True): Use key for randomness

Two operational modes: 1. Full-tree mode (subtree=None): Apply fn to all array leaves - Unified implementation with jax.tree.map_with_path

1. Subtree mode (subtree specified): Apply fn only to subtree leaves
2. Path-based filtering via keypath matching
3. Other leaves pass through unchanged

Examples:

Deterministic full-tree (ignore key)

config = MapOperatorConfig(subtree=None, stochastic=False) op = MapOperator(config, fn=lambda x, key: (x - 0.5) / 0.5, rngs=rngs)

Stochastic full-tree (use key for noise)

config = MapOperatorConfig(subtree=None, stochastic=True, stream_name="augment") op = MapOperator( config, fn=lambda x, key: x + jax.random.normal(key, x.shape) * 0.1, rngs=rngs )

Stochastic subtree (only augment image)

config = MapOperatorConfig( subtree={"image": None}, stochastic=True, stream_name="augment" ) op = MapOperator( config, fn=lambda x, key: x + jax.random.normal(key, x.shape) * 0.1, rngs=rngs )

Parameters:

Name	Type	Description	Default
config	MapOperatorConfig	Operator configuration	required
fn	Callable[[Array, Array], Array]	User function with signature: fn(x: Array, key: Array) -> Array BREAKING CHANGE: Must accept key parameter even for deterministic mode - Deterministic: ignore key parameter - Stochastic: use key for randomness	required
rngs	Rngs | None	Random number generators (required if stochastic=True, optional otherwise)	None
name	str | None	Optional name for the operator	None

config instance-attribute

config: MapOperatorConfig = config

fn instance-attribute

fn = fn

rngs instance-attribute

rngs = rngs

name instance-attribute

name = static(name)

stochastic instance-attribute

stochastic = static(stochastic)

stream_name instance-attribute

stream_name = static(stream_name)

generate_random_params

generate_random_params(rng: Array, data_shapes: PyTree) -> PyTree | None

Generate random parameters for batch transformation.

Generates PyTree of RNG keys matching data structure, with one key per batch element for each leaf. This enables per-leaf, per-element randomness.

Parameters:

Name	Type	Description	Default
rng	Array	JAX random key (single key for entire batch)	required
data_shapes	PyTree	PyTree with same structure as batch.data, containing shapes Examples: {"image": (batch_size, H, W, C)}	required

Returns:

Name	Type	Description
	PyTree | None	PyTree of keys matching data structure, each leaf is Array[batch_size, 2]
Examples	PyTree | None	{"image": Array[batch_size, 2]} where 2 is PRNGKey shape,
	PyTree | None	or None for deterministic operators.

Implementation

1. Flatten data_shapes to get list of shapes
2. Extract batch_size from first shape
3. Split rng into n_leaves keys (one per leaf type)
4. For each leaf key, split into batch_size keys
5. Unflatten into PyTree matching original structure

apply

apply(data: PyTree, state: PyTree, metadata: dict[str, Any] | None, random_params: Any = None, stats: dict[str, Any] | None = None) -> tuple[PyTree, PyTree, dict[str, Any] | None]

Apply array transformation to element (unified implementation).

Single method handles all four modes:

• Full-tree × deterministic
• Full-tree × stochastic
• Subtree × deterministic
• Subtree × stochastic

Uses jax.tree.map_with_path for unified traversal with keypath filtering.

Parameters:

Name	Type	Description	Default
data	PyTree	Element data PyTree	required
state	PyTree	Element state PyTree (unchanged)	required
metadata	dict[str, Any] | None	Element metadata dict (unchanged)	required
random_params	Any	PyTree of keys (stochastic) or dummy keys (deterministic)	None
stats	dict[str, Any] | None	Optional batch statistics (unused)	None

Returns:

Type	Description
PyTree	Tuple of (transformed_data, state, metadata)
PyTree	where state and metadata are unchanged

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]

Get module state for checkpointing.

This method implements the Checkpointable protocol using NNX state management. It extracts all state variables from the module and converts them to a serializable format.

Returns:

Type	Description
dict[str, Any]	A dictionary containing the internal state of the component.

set_state

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

Restore module state from a checkpoint.

This method implements the Checkpointable protocol using NNX state management. It restores the module state from a serialized format. Restoration is strict: checkpoint structure must match module state.

Parameters:

Name	Type	Description	Default
state	dict[str, Any]	A dictionary containing the internal state to restore.	required

Raises:

Type	Description
TypeError	If state is not a dictionary.
ValueError	If checkpoint structure does not match module state.

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 streams
list[str] | None	are 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.

get_output_structure

get_output_structure(sample_data: PyTree, sample_state: PyTree) -> tuple[PyTree, PyTree]

Declare output PyTree structure for vmap axis specification.

Default uses jax.eval_shape to discover structure automatically. Override for efficiency or when eval_shape doesn't work (e.g., data-dependent shapes).

Parameters:

Name	Type	Description	Default
sample_data	PyTree	Single element data (not batched)	required
sample_state	PyTree	Single element state (not batched)	required

Returns:

Type	Description
PyTree	Tuple of (output_data_structure, output_state_structure) with None leaves.
PyTree	The structure (keys/nesting) matters, leaf values are ignored.

Example override for operator that adds keys

def get_output_structure(self, sample_data, sample_state): out_data = { **jax.tree.map(lambda _: None, sample_data), "score": None, "alignment": None, } return out_data, sample_state

apply_batch

apply_batch(batch: Batch, stats: dict[str, Any] | None = None) -> Batch

Process entire batch with vmap and optional RNG generation.

This method implements the batch processing logic for both stochastic and deterministic modes. It uses static branching on self.stochastic for JIT compilation efficiency.

The implementation delegates to _vmap_apply() for the shared computational core, then wraps the result in a Batch object.

Parameters:

Name	Type	Description	Default
batch	Batch	Input batch (Batch[Element] structure)	required
stats	dict[str, Any] | None	Optional statistics (if None, uses get_statistics())	None

Returns:

Type	Description
Batch	Transformed batch with same structure

Note

This method is concrete (not abstract). Subclasses typically don't override it, but can if they need custom batch processing logic.

output_spec

output_spec(input_spec: PyTree) -> PyTree

Return the operator's output spec given an input spec.

Most operators (normalization, additive noise, simple element-wise transforms) do not change shape; the default returns input_spec unchanged. Shape-changing operators (Resize, Crop, Reshape) MUST override this method.

Parameters:

Name	Type	Description	Default
input_spec	PyTree	PyTree of jax.ShapeDtypeStruct describing the input element (matching the upstream DataSourceModule.element_spec() or another operator's output_spec).	required

Returns:

Type	Description
PyTree	PyTree of jax.ShapeDtypeStruct describing the operator's output.
PyTree	By default, equal to input_spec.