concrete.ml.quantization.quantized_module

module concrete.ml.quantization.quantized_module

QuantizedModule API.

Global Variables

• DEFAULT_P_ERROR_PBS

---

class QuantizedModule

Inference for a quantized model.

method __init__

__init__(
    ordered_module_input_names: Iterable[str] = None,
    ordered_module_output_names: Iterable[str] = None,
    quant_layers_dict: Dict[str, Tuple[Tuple[str, ], QuantizedOp]] = None
)

---

property fhe_circuit

Get the FHE circuit.

Returns:

• Circuit: the FHE circuit

---

property is_compiled

Return the compiled status of the module.

Returns:

• bool: the compiled status of the module.

---

property onnx_model

Get the ONNX model.

.. # noqa: DAR201

Returns:

• _onnx_model (onnx.ModelProto): the ONNX model

---

property post_processing_params

Get the post-processing parameters.

Returns:

• Dict[str, Any]: the post-processing parameters

---

method compile

compile(
    q_inputs: Union[Tuple[ndarray, ], ndarray],
    configuration: Optional[Configuration] = None,
    compilation_artifacts: Optional[DebugArtifacts] = None,
    show_mlir: bool = False,
    use_virtual_lib: bool = False,
    p_error: Optional[float] = 6.3342483999973e-05
) → Circuit

Compile the forward function of the module.

Args:

• q_inputs (Union[Tuple[numpy.ndarray, ...], numpy.ndarray]): Needed for tracing and building the boundaries.

• configuration (Optional[Configuration]): Configuration object to use during compilation

• compilation_artifacts (Optional[DebugArtifacts]): Artifacts object to fill during

• show_mlir (bool): if set, the MLIR produced by the converter and which is going to be sent to the compiler backend is shown on the screen, e.g., for debugging or demo. Defaults to False.

• use_virtual_lib (bool): set to use the so called virtual lib simulating FHE computation. Defaults to False.

• p_error (Optional[float]): probability of error of a PBS.

Returns:

• Circuit: the compiled Circuit.

---

method dequantize_output

dequantize_output(qvalues: ndarray) → ndarray

Take the last layer q_out and use its dequant function.

Args:

• qvalues (numpy.ndarray): Quantized values of the last layer.

Returns:

• numpy.ndarray: Dequantized values of the last layer.

---

method forward

forward(*qvalues: ndarray) → ndarray

Forward pass with numpy function only.

Args:

• *qvalues (numpy.ndarray): numpy.array containing the quantized values.

Returns:

• (numpy.ndarray): Predictions of the quantized model

---

method forward_and_dequant

forward_and_dequant(*q_x: ndarray) → ndarray

Forward pass with numpy function only plus dequantization.

Args:

• *q_x (numpy.ndarray): numpy.ndarray containing the quantized input values. Requires the input dtype to be uint8.

Returns:

• (numpy.ndarray): Predictions of the quantized model

---

method post_processing

post_processing(qvalues: ndarray) → ndarray

Post-processing of the quantized output.

Args:

• qvalues (numpy.ndarray): numpy.ndarray containing the quantized input values.

Returns:

• (numpy.ndarray): Predictions of the quantized model

---

method quantize_input

quantize_input(*values: ndarray) → Union[ndarray, Tuple[ndarray, ]]

Take the inputs in fp32 and quantize it using the learned quantization parameters.

Args:

• *values (numpy.ndarray): Floating point values.

Returns:

• Union[numpy.ndarray, Tuple[numpy.ndarray, ...]]: Quantized (numpy.uint32) values.

---

method set_inputs_quantization_parameters

set_inputs_quantization_parameters(*input_q_params: UniformQuantizer)

Set the quantization parameters for the module's inputs.

Args:

• *input_q_params (UniformQuantizer): The quantizer(s) for the module.