mixle.experimental.typed_runtime.distributed module

Lower statistically typed update contracts into communication declarations.

class CollectiveNumericsEvidence(evidence_id, update_id, collective, mesh_axes, backend, dtype, deterministic, maximum_absolute_error, maximum_relative_error, sample_count, ordering_fingerprint)

Bases: object

Observed backend determinism and error for one exact collective identity.

Parameters:

• evidence_id (str)

• update_id (str)

• collective (CollectiveKind)

• mesh_axes (tuple[ParallelAxis, ...])

• backend (str)

• dtype (str)

• deterministic (bool)

• maximum_absolute_error (float)

• maximum_relative_error (float)

• sample_count (int)

• ordering_fingerprint (str)

plan_distributed_updates(graph, plan, *, numerics_evidence=None)

Produce an auditable collective plan for every compiled update node.

The result deliberately distinguishes additive sufficient statistics from gradients. It also emits model-axis transfers separately, so a scheduler cannot mistake an EP token exchange or PP activation send for gradient synchronization.

Parameters:

• graph (UpdateGraph)

• plan (ParallelPlan)

• numerics_evidence (Mapping[str, CollectiveNumericsEvidence] | None)

Return type:

tuple[DistributedUpdate, …]