concrete.ml.quantization.base_quantized_op.md
module concrete.ml.quantization.base_quantized_op
concrete.ml.quantization.base_quantized_op
Base Quantized Op class that implements quantization for a float numpy op.
Global Variables
ONNX_OPS_TO_NUMPY_IMPL
ALL_QUANTIZED_OPS
ONNX_OPS_TO_QUANTIZED_IMPL
DEFAULT_MODEL_BITS
class QuantizedOp
QuantizedOp
Base class for quantized ONNX ops implemented in numpy.
Args:
n_bits_output
(int): The number of bits to use for the quantization of the outputop_instance_name
(str): The name that should be assigned to this operation, used to retrieve it later or get debugging information about this op (bit-width, value range, integer intermediary values, op-specific error messages). Usually this name is the same as the ONNX operation name for which this operation is constructed.int_input_names
(Set[str]): The set of names of integer tensors that are inputs to this opconstant_inputs
(Optional[Union[Dict[str, Any], Dict[int, Any]]]): The constant tensors that are inputs to this opinput_quant_opts
(QuantizationOptions): Input quantizer options, determine the quantization that is applied to input tensors (that are not constants)
method __init__
__init__
property int_input_names
Get the names of encrypted integer tensors that are used by this op.
Returns:
Set[str]
: the names of the tensors
method calibrate
calibrate
Create corresponding QuantizedArray for the output of the activation function.
Args:
*inputs (numpy.ndarray)
: Calibration sample inputs.
Returns:
numpy.ndarray
: the output values for the provided calibration samples.
method call_impl
call_impl
Call self.impl to centralize mypy bug workaround.
Args:
*inputs (numpy.ndarray)
: real valued inputs.**attrs
: the QuantizedOp attributes.
Returns:
numpy.ndarray
: return value of self.impl
method can_fuse
can_fuse
Determine if the operator impedes graph fusion.
This function shall be overloaded by inheriting classes to test self._int_input_names, to determine whether the operation can be fused to a TLU or not. For example an operation that takes inputs produced by a unique integer tensor can be fused to a TLU. Example: f(x) = x * (x + 1) can be fused. A function that does f(x) = x * (x @ w + 1) can't be fused.
Returns:
bool
: whether this QuantizedOp instance produces Concrete code that can be fused to TLUs
method dump
dump
Dump itself to a file.
Args:
file
(TextIO): The file to dump the serialized object into.
method dump_dict
dump_dict
Dump itself to a dict.
Returns:
metadata
(Dict): Dict of serialized objects.
method dumps
dumps
Dump itself to a string.
Returns:
metadata
(str): String of the serialized object.
method load_dict
load_dict
Load itself from a string.
Args:
metadata
(Dict): Dict of serialized objects.
Returns:
QuantizedOp
: The loaded object.
classmethod must_quantize_input
must_quantize_input
Determine if an input must be quantized.
Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be either constant or variable (encrypted). Note that this does not handle attributes, which are handled by QuantizedOp classes separately in their constructor.
Args:
input_name_or_idx
(int): Index of the input to check.
Returns:
result
(bool): Whether the input must be quantized (must be aQuantizedArray
) or if it stays as a rawnumpy.array
read from ONNX.
classmethod op_type
op_type
Get the type of this operation.
Returns:
op_type
(str): The type of this operation, in the ONNX referential
method prepare_output
prepare_output
Quantize the output of the activation function.
The calibrate method needs to be called with sample data before using this function.
Args:
qoutput_activation
(numpy.ndarray): Output of the activation function.
Returns:
QuantizedArray
: Quantized output.
method q_impl
q_impl
Execute the quantized forward.
Args:
*q_inputs (ONNXOpInputOutputType)
: Quantized inputs.**attrs
: the QuantizedOp attributes.
Returns:
ONNXOpInputOutputType
: The returned quantized value.
class QuantizedOpUnivariateOfEncrypted
QuantizedOpUnivariateOfEncrypted
An univariate operator of an encrypted value.
This operation is not really operating as a quantized operation. It is useful when the computations get fused into a TLU, as in e.g., Act(x) = x || (x + 42)).
method __init__
__init__
property int_input_names
Get the names of encrypted integer tensors that are used by this op.
Returns:
Set[str]
: the names of the tensors
method calibrate
calibrate
Create corresponding QuantizedArray for the output of the activation function.
Args:
*inputs (numpy.ndarray)
: Calibration sample inputs.
Returns:
numpy.ndarray
: the output values for the provided calibration samples.
method call_impl
call_impl
Call self.impl to centralize mypy bug workaround.
Args:
*inputs (numpy.ndarray)
: real valued inputs.**attrs
: the QuantizedOp attributes.
Returns:
numpy.ndarray
: return value of self.impl
method can_fuse
can_fuse
Determine if this op can be fused.
This operation can be fused and computed in float when a single integer tensor generates both the operands. For example in the formula: f(x) = x || (x + 1) where x is an integer tensor.
Returns:
bool
: Can fuse
method dump
dump
Dump itself to a file.
Args:
file
(TextIO): The file to dump the serialized object into.
method dump_dict
dump_dict
Dump itself to a dict.
Returns:
metadata
(Dict): Dict of serialized objects.
method dumps
dumps
Dump itself to a string.
Returns:
metadata
(str): String of the serialized object.
method load_dict
load_dict
Load itself from a string.
Args:
metadata
(Dict): Dict of serialized objects.
Returns:
QuantizedOp
: The loaded object.
classmethod must_quantize_input
must_quantize_input
Determine if an input must be quantized.
Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be either constant or variable (encrypted). Note that this does not handle attributes, which are handled by QuantizedOp classes separately in their constructor.
Args:
input_name_or_idx
(int): Index of the input to check.
Returns:
result
(bool): Whether the input must be quantized (must be aQuantizedArray
) or if it stays as a rawnumpy.array
read from ONNX.
classmethod op_type
op_type
Get the type of this operation.
Returns:
op_type
(str): The type of this operation, in the ONNX referential
method prepare_output
prepare_output
Quantize the output of the activation function.
The calibrate method needs to be called with sample data before using this function.
Args:
qoutput_activation
(numpy.ndarray): Output of the activation function.
Returns:
QuantizedArray
: Quantized output.
method q_impl
q_impl
Execute the quantized forward.
Args:
*q_inputs (ONNXOpInputOutputType)
: Quantized inputs.**attrs
: the QuantizedOp attributes.
Returns:
ONNXOpInputOutputType
: The returned quantized value.
class QuantizedMixingOp
QuantizedMixingOp
An operator that mixes (adds or multiplies) together encrypted inputs.
Mixing operators cannot be fused to TLUs.
method __init__
__init__
Initialize quantized ops parameters plus specific parameters.
Args:
rounding_threshold_bits
(Optional[int]): Number of bits to round to.*args
: positional argument to pass to the parent class.**kwargs
: named argument to pass to the parent class.
property int_input_names
Get the names of encrypted integer tensors that are used by this op.
Returns:
Set[str]
: the names of the tensors
method calibrate
calibrate
Create corresponding QuantizedArray for the output of the activation function.
Args:
*inputs (numpy.ndarray)
: Calibration sample inputs.
Returns:
numpy.ndarray
: the output values for the provided calibration samples.
method call_impl
call_impl
Call self.impl to centralize mypy bug workaround.
Args:
*inputs (numpy.ndarray)
: real valued inputs.**attrs
: the QuantizedOp attributes.
Returns:
numpy.ndarray
: return value of self.impl
method can_fuse
can_fuse
Determine if this op can be fused.
Mixing operations cannot be fused since it must be performed over integer tensors and it combines different encrypted elements of the input tensors. Mixing operations are Conv, MatMul, etc.
Returns:
bool
: False, this operation cannot be fused as it adds different encrypted integers
method cnp_round
cnp_round
Round the input array to the specified number of bits.
Args:
x
(Union[numpy.ndarray, fhe.tracing.Tracer]): The input array to be rounded.calibrate_rounding
(bool): Whether to calibrate the rounding (compute the lsbs_to_remove)
Returns:
numpy.ndarray
: The rounded array.
method dump
dump
Dump itself to a file.
Args:
file
(TextIO): The file to dump the serialized object into.
method dump_dict
dump_dict
Dump itself to a dict.
Returns:
metadata
(Dict): Dict of serialized objects.
method dumps
dumps
Dump itself to a string.
Returns:
metadata
(str): String of the serialized object.
method load_dict
load_dict
Load itself from a string.
Args:
metadata
(Dict): Dict of serialized objects.
Returns:
QuantizedOp
: The loaded object.
method make_output_quant_parameters
make_output_quant_parameters
Build a quantized array from quantized integer results of the op and quantization params.
Args:
q_values
(Union[numpy.ndarray, Any]): the quantized integer values to wrap in the QuantizedArrayscale
(float): the pre-computed scale of the quantized valueszero_point
(Union[int, float, numpy.ndarray]): the pre-computed zero_point of the q_values
Returns:
QuantizedArray
: the quantized array that will be passed to the QuantizedModule output.
classmethod must_quantize_input
must_quantize_input
Determine if an input must be quantized.
Quantized ops and numpy onnx ops take inputs and attributes. Inputs can be either constant or variable (encrypted). Note that this does not handle attributes, which are handled by QuantizedOp classes separately in their constructor.
Args:
input_name_or_idx
(int): Index of the input to check.
Returns:
result
(bool): Whether the input must be quantized (must be aQuantizedArray
) or if it stays as a rawnumpy.array
read from ONNX.
classmethod op_type
op_type
Get the type of this operation.
Returns:
op_type
(str): The type of this operation, in the ONNX referential
method prepare_output
prepare_output
Quantize the output of the activation function.
The calibrate method needs to be called with sample data before using this function.
Args:
qoutput_activation
(numpy.ndarray): Output of the activation function.
Returns:
QuantizedArray
: Quantized output.
method q_impl
q_impl
Execute the quantized forward.
Args:
*q_inputs (ONNXOpInputOutputType)
: Quantized inputs.**attrs
: the QuantizedOp attributes.
Returns:
ONNXOpInputOutputType
: The returned quantized value.
Last updated