Training runtime architecture

Sequifier has one weight-owning model type: ComposableTransformerNetwork. The network owns the shared backbone and named ingestion/decoder interfaces; it does not own datasets, objectives, optimizers, metrics, checkpoints, exports, random state, or run lifecycle state.

RunBuilder is the composition root for training. It builds a TrainingRun containing the network and its callable distributed/compiled view, a DatasetRuntimeRegistry, OptimizationRuntime, RunState, a distributed strategy, random and loader-state services, integrations, evaluation, metrics, export, and checkpoint services. TrainingEngine.run() only coordinates those services while traversing configured phases and sources.

Artifact contracts

Portable .pt model artifacts use artifact_type=sequifier_model and contain a ModelExecutionConfig, canonical model state, and trace/provenance metadata. Their state keys are limited to:

backbone.*
interfaces.<interface-name>.*

Exact run checkpoints use artifact_type=sequifier_run_checkpoint. They embed the same portable model payload plus optimizer/scheduler/scaler state, run state, per-rank random state, loader state, integration state, and the resolved training configuration. Only the current formats are accepted; historical checkpoint layouts are not migrated at load time.

Sibling packages should import model contracts from sequifier.api. Update-aware training integrations should import runtime primitives from sequifier.training_api.

Resume ordering

Restore is staged: load and validate the checkpoint, construct and prepare the network, restore model weights, build and restore optimization, construct data runtimes and restore loader/integration state, compile and warm up, then restore the rank-local random state. This keeps setup-time random consumption from changing the first resumed batch or update.