model

name: str = Field(default='Model', description='Name of the model')

Name of the model.

version: str = Field(default='1.0.0', description='Version of the model')

Version of the model.

description: str = Field(default='', description='Description of the model')

Description of the model.

tensors: dict[str, AirTensor] = Field(default_factory=dict, description='Mapping from tensor ID to its AirTensor metadata')

Mapping from tensor ID to its AirTensor metadata.

operators: list[AirOperator] = Field(default_factory=list, description='Topologically ordered list of operators (nodes) in the graph')

Topologically ordered list of operators (nodes) in the graph.

input_ids: list[str] = Field(default_factory=list, description='IDs of tensors that serve as model inputs')

IDs of tensors that serve as model inputs.

output_ids: list[str] = Field(default_factory=list, description='IDs of tensors that serve as model outputs')

IDs of tensors that serve as model outputs.

inputs: list[AirTensor]

List of AirTensor objects corresponding to model inputs.

outputs: list[AirTensor]

List of AirTensor objects corresponding to model outputs.

add_tensor(tensor: AirTensor) -> None

Register a new tensor in the model.

Raises

Errors raised by add_tensor
Type	Description
`ValueError`	If a tensor with the same ID already exists.

remove_tensor(tid: TensorId) -> None

Remove a tensor from the model by its ID.

Raises

Errors raised by remove_tensor
Type	Description
`KeyError`	If the tensor ID is not found.

add_operator(op: AirOperator) -> None

Append a new operator to the execution list.

Raises

Errors raised by add_operator
Type	Description
`ValueError`	If an operator with the same ID already exists.

has_tensor(tid: TensorId) -> bool

Check if a tensor exists in the model by its ID.

get_tensor(tid: TensorId) -> AirTensor

Lookup a tensor by its ID.

Raises

Errors raised by get_tensor
Type	Description
`KeyError`	If the tensor ID is not found.

tensor_is_shared(tid: TensorId, reader: AirOperator | None = None) -> bool

Whether anything other than reader uses tensor tid.

Uses are other operators, aliases in either direction (following alias chains transitively), and the model’s inputs and outputs. Rewriting a tensor in place is safe only when this is False.

Parameters

Parameters of tensor_is_shared
Name	Type	Default	Description
`tid`	`TensorId`	Required	The tensor to check.
`reader`	`AirOperator \| None`	None	The operator asking; its own references do not count.

Returns

Returns of tensor_is_shared
Value	Type	Description
`bool`	`bool`	``True`` when some other use exists.

rewrite_constant(reader: AirOperator, name: str | int, data) -> AirTensor

Bind reader’s tensor name to data, rewriting it only where that is safe.

The tensor is rewritten in place when nothing else uses it. Otherwise reader is rebound to a copy under <id>_rewritten (or the first free <id>_rewritten_N) whose _packed_from names the source, so memory.tensors rules on the source id still place it.

Parameters

Parameters of rewrite_constant
Name	Type	Default	Description
`reader`	`AirOperator`	Required	The operator that owns the rewrite.
`name`	`str \| int`	Required	Its local name for the tensor, or the tensor's position in its inputs.
`data`		Required	The rewritten data.

Returns

Returns of rewrite_constant
Value	Type	Description
`AirTensor`	`AirTensor`	The tensor ``reader`` now reads.

topo_sort() -> list[AirOperator]

Return operators in execution order.

Assumes operators was populated in valid topological order.

order_violations() -> list[str]

Describe every read of a tensor that a later operator produces.

operators is the execution order the planner and the emitted schedule follow, so an operator may not reference, through its inputs or named tensors, a tensor that only a later operator writes. Constant and persistent tensors and model inputs hold a value before any operator runs and are exempt. A read through an alias is checked against every tensor on its alias chain down to the root, because an operator that writes an alias writes the root’s storage.

Returns

Returns of order_violations
Type	Description
`list[str]`	list[str]: One description per violation; empty when the order is valid.

prune_unused_tensors() -> list[TensorId]

Remove dangling tensors that are not referenced by any operator or model input/output.