concrete.ml.quantization.quantized_ops

module `concrete.ml.quantization.quantized_ops`

Quantized versions of the ONNX operators for post training quantization.

class `QuantizedSigmoid`

Quantized sigmoid op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedHardSigmoid`

Quantized HardSigmoid op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedRelu`

Quantized Relu op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedPRelu`

Quantized PRelu op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedLeakyRelu`

Quantized LeakyRelu op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedHardSwish`

Quantized Hardswish op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedElu`

Quantized Elu op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedSelu`

Quantized Selu op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedCelu`

Quantized Celu op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedClip`

Quantized clip op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedRound`

Quantized round op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedPow`

Quantized pow op.

Only works for a float constant power. This operation will be fused to a (potentially larger) TLU.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedGemm`

Quantized Gemm op.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse()

Determine if this op can be fused.

Gemm operation can not be fused since it must be performed over integer tensors and it combines different values of the input tensors.

Returns:

bool: False, this operation can not be fused as it adds different encrypted integers

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

class `QuantizedMatMul`

Quantized MatMul op.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse()

Determine if this op can be fused.

Gemm operation can not be fused since it must be performed over integer tensors and it combines different values of the input tensors.

Returns:

bool: False, this operation can not be fused as it adds different encrypted integers

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

class `QuantizedAdd`

Quantized Addition operator.

Can add either two variables (both encrypted) or a variable and a constant

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse() → bool

Determine if this op can be fused.

Add operation can be computed in float and fused if it operates over inputs produced by a single integer tensor. For example the expression x + x * 1.75, where x is an encrypted tensor, can be computed with a single TLU.

Returns:

bool: Whether the number of integer input tensors allows computing this op as a TLU

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

class `QuantizedTanh`

Quantized Tanh op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedSoftplus`

Quantized Softplus op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedExp`

Quantized Exp op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedLog`

Quantized Log op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedAbs`

Quantized Abs op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedIdentity`

Quantized Identity op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

class `QuantizedReshape`

Quantized Reshape op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

Reshape the input integer encrypted tensor.

Args:

q_inputs: an encrypted integer tensor at index 0 and one constant shape at index 1
attrs: additional optional reshape options

Returns:

result (QuantizedArray): reshaped encrypted integer tensor

class `QuantizedConv`

Quantized Conv op.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

Construct the quantized convolution operator and retrieve parameters.

Args:

n_bits_output: number of bits for the quantization of the outputs of this operator
int_input_names: names of integer tensors that are taken as input for this operation
constant_inputs: the weights and activations
input_quant_opts: options for the input quantizer
attrs: convolution options
dilations (Tuple[int]): dilation of the kernel, default 1 on all dimensions.
group (int): number of convolution groups, default 1
kernel_shape (Tuple[int]): shape of the kernel. Should have 2 elements for 2d conv
pads (Tuple[int]): padding in ONNX format (begin, end) on each axis
strides (Tuple[int]): stride of the convolution on each axis

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse() → bool

Determine if this op can be fused.

Conv operation can not be fused since it must be performed over integer tensors and it combines different elements of the input tensors.

Returns:

bool: False, this operation can not be fused as it adds different encrypted integers

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

Compute the quantized convolution between two quantized tensors.

Allows an optional quantized bias.

Args:

q_inputs: input tuple, contains
x (numpy.ndarray): input data. Shape is N x C x H x W for 2d
w (numpy.ndarray): weights tensor. Shape is (O x I x Kh x Kw) for 2d
b (numpy.ndarray, Optional): bias tensor, Shape is (O,)
attrs: convolution options handled in constructor

Returns:

res (QuantizedArray): result of the quantized integer convolution

class `QuantizedAvgPool`

Quantized Average Pooling op.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse() → bool

Determine if this op can be fused.

Avg Pooling operation can not be fused since it must be performed over integer tensors and it combines different elements of the input tensors.

Returns:

bool: False, this operation can not be fused as it adds different encrypted integers

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

class `QuantizedPad`

Quantized Padding op.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse() → bool

Determine if this op can be fused.

Pad operation can not be fused since it must be performed over integer tensors.

Returns:

bool: False, this operation can not be fused as it is manipulates integer tensors

class `QuantizedWhere`

Where operator on quantized arrays.

Supports only constants for the results produced on the True/False branches.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedCast`

Cast the input to the required data type.

In FHE we only support a limited number of output types. Booleans are cast to integers.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedGreater`

Comparison operator >.

Only supports comparison with a constant.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedGreaterOrEqual`

Comparison operator >=.

Only supports comparison with a constant.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedLess`

Comparison operator <.

Only supports comparison with a constant.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedLessOrEqual`

Comparison operator <=.

Only supports comparison with a constant.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: QuantizationOptions = None,
    **attrs
) → None

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedOr`

Or operator ||.

This operation is not really working as a quantized operation. It just works when things got fused, as in e.g. Act(x) = x || (x + 42))

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedDiv`

Div operator /.

This operation is not really working as a quantized operation. It just works when things got fused, as in e.g. Act(x) = 1000 / (x + 42))

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedMul`

Multiplication operator.

Only multiplies an encrypted tensor with a float constant for now. This operation will be fused to a (potentially larger) TLU.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedSub`

Subtraction operator.

This works the same as addition on both encrypted - encrypted and on encrypted - constant.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse() → bool

Determine if this op can be fused.

Returns:

bool: Whether the number of integer input tensors allows computing this op as a TLU

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

class `QuantizedBatchNormalization`

Quantized Batch normalization with encrypted input and in-the-clear normalization params.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedFlatten`

Quantized flatten for encrypted inputs.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `can_fuse`

can_fuse() → bool

Determine if this op can be fused.

Flatten operation can not be fused since it must be performed over integer tensors.

Returns:

bool: False, this operation can not be fused as it is manipulates integer tensors.

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

Flatten the input integer encrypted tensor.

Args:

q_inputs: an encrypted integer tensor at index 0
attrs: contains axis attribute

Returns:

result (QuantizedArray): reshaped encrypted integer tensor

class `QuantizedReduceSum`

ReduceSum with encrypted input.

This operator is currently an experimental feature.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: Optional[QuantizationOptions] = None,
    **attrs
) → None

Construct the quantized ReduceSum operator and retrieve parameters.

Args:

n_bits_output (int): Number of bits for the operator's quantization of outputs.
int_input_names (Optional[Set[str]]): Names of input integer tensors. Default to None.
constant_inputs (Optional[Dict]): Input constant tensor.
axes (Optional[numpy.ndarray]): Array of integers along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data). Default to None.
input_quant_opts (Optional[QuantizationOptions]): Options for the input quantizer. Default to None.
attrs (dict): RecuseSum options.
keepdims (int): Keep the reduced dimension or not, 1 means keeping the input dimension, 0 will reduce it along the given axis. Default to 1.
noop_with_empty_axes (int): Defines behavior if 'axes' is empty or set to None. Default behavior with 0 is to reduce all axes. When axes is empty and this attribute is set to true 1, input tensor will not be reduced, and the output tensor would be equivalent to input tensor. Default to 0.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `calibrate`

calibrate(*inputs: ndarray) → ndarray

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 `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

Sum the encrypted tensor's values over axis 1.

Args:

q_inputs (QuantizedArray): An encrypted integer tensor at index 0.
attrs (Dict): Contains axis attribute.

Returns:

(QuantizedArray): The sum of all values along axis 1 as an encrypted integer tensor.

method `tree_sum`

tree_sum(input_qarray, is_calibration=False)

Large sum without overflow (only MSB remains).

Args:

input_qarray: Enctyped integer tensor.
is_calibration: Whether we are calibrating the tree sum. If so, it will create all the quantizers for the downscaling.

Returns:

(numpy.ndarray): The MSB (based on the precision self.n_bits) of the integers sum.

class `QuantizedErf`

Quantized erf op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedNot`

Quantized Not op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedBrevitasQuant`

Brevitas uniform quantization with encrypted input.

method `init`

__init__(
    n_bits_output: int,
    int_input_names: Set[str] = None,
    constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
    input_quant_opts: Optional[QuantizationOptions] = None,
    **attrs
) → None

Construct the Brevitas quantization operator.

Args:

n_bits_output (int): Number of bits for the operator's quantization of outputs. Not used, will be overridden by the bit_width in ONNX
int_input_names (Optional[Set[str]]): Names of input integer tensors. Default to None.
constant_inputs (Optional[Dict]): Input constant tensor.
scale (float): Quantizer scale
zero_point (float): Quantizer zero-point
bit_width (int): Number of bits of the integer representation
input_quant_opts (Optional[QuantizationOptions]): Options for the input quantizer. Default to None. attrs (dict):
rounding_mode (str): Rounding mode (default and only accepted option is "ROUND")
signed (int): Whether this op quantizes to signed integers (default 1),
narrow (int): Whether this op quantizes to a narrow range of integers e.g. [-2n_bits-1 .. 2n_bits-1] (default 0),

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

Quantize values.

Args:

q_inputs: an encrypted integer tensor at index 0 and one constant shape at index 1
attrs: additional optional reshape options

Returns:

result (QuantizedArray): reshaped encrypted integer tensor

class `QuantizedTranspose`

Transpose operator for quantized inputs.

This operator performs quantization, transposes the encrypted data, then dequantizes again.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

method `q_impl`

q_impl(*q_inputs: QuantizedArray, **attrs) → QuantizedArray

Reshape the input integer encrypted tensor.

Args:

q_inputs: an encrypted integer tensor at index 0 and one constant shape at index 1
attrs: additional optional reshape options

Returns:

result (QuantizedArray): reshaped encrypted integer tensor

class `QuantizedFloor`

Quantized Floor op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedMax`

Quantized Max op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedMin`

Quantized Min op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedNeg`

Quantized Neg op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

class `QuantizedSign`

Quantized Neg op.

property op_type

Get the type of this operation.

Returns:

op_type (str): The type of this operation, in the ONNX referential

Previousconcrete.ml.quantization.quantized_module Nextconcrete.ml.quantization.quantizers

Last updated 1 year ago

module concrete.ml.quantization.quantized_ops

class QuantizedSigmoid

class QuantizedHardSigmoid

class QuantizedRelu

class QuantizedPRelu

class QuantizedLeakyRelu

class QuantizedHardSwish

class QuantizedElu

class QuantizedSelu

class QuantizedCelu

class QuantizedClip

class QuantizedRound

class QuantizedPow

class QuantizedGemm

method __init__

method can_fuse

method q_impl

class QuantizedMatMul

method __init__

method can_fuse

method q_impl

class QuantizedAdd

method can_fuse

method q_impl

class QuantizedTanh

class QuantizedSoftplus

class QuantizedExp

class QuantizedLog

class QuantizedAbs

class QuantizedIdentity

method q_impl

class QuantizedReshape

method q_impl

class QuantizedConv

method __init__

method can_fuse

method q_impl

class QuantizedAvgPool

method __init__

method can_fuse

method q_impl

class QuantizedPad

method __init__

method can_fuse

class QuantizedWhere

method __init__

class QuantizedCast

class QuantizedGreater

method __init__

class QuantizedGreaterOrEqual

method __init__

class QuantizedLess

method __init__

class QuantizedLessOrEqual

method __init__

class QuantizedOr

class QuantizedDiv

class QuantizedMul

class QuantizedSub

method can_fuse

method q_impl

class QuantizedBatchNormalization

class QuantizedFlatten

method can_fuse

method q_impl

class QuantizedReduceSum

method __init__

method calibrate

method q_impl

method tree_sum

class QuantizedErf

class QuantizedNot

class QuantizedBrevitasQuant

method __init__

method q_impl

class QuantizedTranspose

method q_impl

class QuantizedFloor

class QuantizedMax

class QuantizedMin

module `concrete.ml.quantization.quantized_ops`

class `QuantizedSigmoid`

class `QuantizedHardSigmoid`

class `QuantizedRelu`

class `QuantizedPRelu`

class `QuantizedLeakyRelu`

class `QuantizedHardSwish`

class `QuantizedElu`

class `QuantizedSelu`

class `QuantizedCelu`

class `QuantizedClip`

class `QuantizedRound`

class `QuantizedPow`

class `QuantizedGemm`

method `init`

method `can_fuse`

method `q_impl`

class `QuantizedMatMul`

method `init`

method `can_fuse`

method `q_impl`

class `QuantizedAdd`

method `can_fuse`

method `q_impl`

class `QuantizedTanh`

class `QuantizedSoftplus`

class `QuantizedExp`

class `QuantizedLog`

class `QuantizedAbs`

class `QuantizedIdentity`

method `q_impl`

class `QuantizedReshape`

method `q_impl`

class `QuantizedConv`

method `init`

method `can_fuse`

method `q_impl`

class `QuantizedAvgPool`

method `init`

method `can_fuse`

method `q_impl`

class `QuantizedPad`

method `init`

method `can_fuse`

class `QuantizedWhere`

method `init`

class `QuantizedCast`

class `QuantizedGreater`

method `init`

class `QuantizedGreaterOrEqual`

method `init`

class `QuantizedLess`

method `init`

class `QuantizedLessOrEqual`

method `init`

class `QuantizedOr`

class `QuantizedDiv`

class `QuantizedMul`

class `QuantizedSub`

method `can_fuse`

method `q_impl`

class `QuantizedBatchNormalization`

class `QuantizedFlatten`

method `can_fuse`

method `q_impl`

class `QuantizedReduceSum`

method `init`

method `calibrate`

method `q_impl`

method `tree_sum`

class `QuantizedErf`

class `QuantizedNot`

class `QuantizedBrevitasQuant`

method `init`

method `q_impl`

class `QuantizedTranspose`

method `q_impl`

class `QuantizedFloor`

class `QuantizedMax`

class `QuantizedMin`