Optimizer Checkpoint Schemas

An optimizer checkpoint is meaningful only for the parameter layout that produced it. This module stores that shared layout: parameter shapes in module order and the corresponding trainability flags. Optimizer-specific codecs validate this schema before installing backend state.

structure Runtime.Autograd.Torch.Internal.OptimizerCheckpoint.ParameterSchema : Type

Ordered parameter metadata shared by backend-owned optimizer checkpoints.

• shapes : List Spec.Shape

  Parameter shapes in module order.

• requiresGrad : List Bool

  Whether each corresponding parameter is trainable.

def Runtime.Autograd.Torch.Internal.OptimizerCheckpoint.ParameterSchema.isWellFormed (schema : ParameterSchema) : Bool

Whether the shape list and mutability mask describe the same number of parameters.

def Runtime.Autograd.Torch.Internal.OptimizerCheckpoint.ParameterSchema.trainableCount (schema : ParameterSchema) : ℕ

Number of parameters for which an optimizer state entry is expected.

def Runtime.Autograd.Torch.Internal.OptimizerCheckpoint.ParameterSchema.write (format : CheckpointIO.Format) (handle : IO.FS.Handle) (schema : ParameterSchema) : IO Unit

Write the ordered parameter schema for an optimizer-specific checkpoint format.

def Runtime.Autograd.Torch.Internal.OptimizerCheckpoint.ParameterSchema.readAndCheck (format : CheckpointIO.Format) (handle : IO.FS.Handle) (expected : ParameterSchema) : IO Unit

Read a parameter schema and reject any difference from the expected module layout.