litert

LiteRT (.tflite) conversion on the TensorFlow backend.

convert_litertfunctionConvert a Keras model to LiteRT bytes; exportmodel validates the arguments first.
tensor_recordsfunctionRead the main subgraph's input and output tensors from a .tflite flatbuffer.
operator_namesfunctionBuiltin operator names of every subgraph's operators, in execution order.
tie_state_scalesfunctionGive both tensors of each integer state pair one scale and zero point.
export_litertfunctionExport with an already validated spec and calibration array.

convert_litert(
    model: keras.Model,
    *,
    precision: Precision,
    io_dtype: IODType,
    mode: ConversionMode,
    strict: bool,
    calibration: npt.NDArray | Mapping[str, npt.NDArray] | None,
) -> bytes

Convert a Keras model to LiteRT bytes; export_model validates the arguments first.

A model with state inputs is converted with its outputs keyed by output name, so the signature names state_in_k and state_out_k.

Parameters

Parameters of convert_litert
Name	Type	Default	Description
`model`	`keras.Model`	Required	The Keras model, on the TensorFlow backend.
`precision`	`Precision`	Required	The numeric format; FP16 is TensorFlow's float16 weight storage rewritten to a native float16 graph (``to_native_fp16``).
`io_dtype`	`IODType`	Required	Inputs and outputs of the calibrated precisions. FP32 graphs have float32 and FP16 graphs float16 inputs and outputs whatever ``io_dtype`` says, so pass those.
`mode`	`ConversionMode`	Required	How the model is traced; SAVED_MODEL exports to a temporary directory removed after conversion.
`strict`	`bool`	Required	For calibrated precisions, refuse operators without an integer kernel.
`calibration`	`npt.NDArray \| Mapping[str, npt.NDArray] \| None`	Required	Samples for the calibrated precisions, used one at a time in stored order; for a model with several inputs, a mapping of each input name to its samples, fed by name because the converter orders inputs its own way.

Returns

Returns of convert_litert
Value	Type	Description
`bytes`	`bytes`	The LiteRT flatbuffer.

Raises

Errors raised by convert_litert
Type	Description
`ValueError`	If ``mode`` is CONCRETE and the model has several inputs or state inputs: a concrete function converts to a graph without a signature, which name-keyed calibration and state tensors need. Also if a model with state inputs repeats an output name, as two outputs of one layer do.

tensor_records(content: bytes) -> tuple[tuple[TensorRecord, ...], tuple[TensorRecord, ...]]

Read the main subgraph’s input and output tensors from a .tflite flatbuffer.

Inputs and outputs are in subgraph order. Tensors whose signature names are state_in_k and state_out_k, with equal shapes and types, are STATE tensors of pair k; the others, including unpaired state names of an imported model, are SIGNAL tensors.

operator_names(content: bytes) -> list[str]

Builtin operator names of every subgraph’s operators, in execution order.

tie_state_scales(content: bytes, tolerance: float) -> bytes

Give both tensors of each integer state pair one scale and zero point.

A runtime carries state_out_k back into state_in_k as raw integers, which keeps the state’s value only if both tensors have the same quantization. For each pair whose parameters differ, both tensors take the parameters of the tensor whose range covers the other’s (the larger scale of a symmetric int16 pair), or else parameters whose range covers both ranges. The kernels reading or writing either tensor recompute their scaling from the new parameters when prepared, and the bias of a FULLY_CONNECTED or convolution reading a state tensor is requantized to its new input scale. A RESHAPE or SQUEEZE reading a state tensor copies its bytes, so its output takes the same parameters, and its own readers are checked and requantized in turn.

Parameters

Parameters of tie_state_scales
Name	Type	Default	Description
`content`	`bytes`	Required	Calibrated ``.tflite`` flatbuffer.
`tolerance`	`float`	Required	Largest difference between the tied scale and either original scale, relative to that original.

Returns

Returns of tie_state_scales
Value	Type	Description
`bytes`	`bytes`	``content`` itself when every pair is already tied or float; otherwise the rewritten model.

Raises

Errors raised by tie_state_scales
Type	Description
`ValueError`	If state names do not pair up or a pair's tensors differ in shape or type, a pair mixes float and integer quantization, the tied scale differs from an original scale by more than ``tolerance``, an operator that reads or writes a state tensor needs its parameters unchanged, or a state tensor is a reader's weight or bias, or a reader's bias cannot be requantized (shared, of an unexpected type or scale count, or overflowing).

export_litert(
    model: keras.Model,
    spec: ExportSpec,
    calibration: npt.NDArray | Mapping[str, npt.NDArray] | None,
) -> ExportResult

Export with an already validated spec and calibration array.