zeekay
e037e8f7ea
The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
73 KiB
module torus.ml.quantization.quantized_ops
Quantized versions of the ONNX operators for post training quantization.
class QuantizedSigmoid
Quantized sigmoid op.
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
class QuantizedHardSigmoid
Quantized HardSigmoid op.
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
class QuantizedRelu
Quantized Relu op.
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
class QuantizedPRelu
Quantized PRelu op.
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
class QuantizedLeakyRelu
Quantized LeakyRelu op.
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
class QuantizedHardSwish
Quantized Hardswish op.
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
class QuantizedElu
Quantized Elu op.
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
class QuantizedSelu
Quantized Selu op.
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
class QuantizedCelu
Quantized Celu op.
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
class QuantizedClip
Quantized clip op.
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
class QuantizedRound
Quantized round op.
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
class QuantizedPow
Quantized pow op.
Only works for a float constant power. This operation will be fused to a (potentially larger) TLU.
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
class QuantizedGemm
Quantized Gemm op.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Optional[Set[str]] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: Optional[QuantizationOptions] = None,
**attrs
) → None
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_analytical_output
calibrate_analytical_output(*inputs: QuantizedArray) → UniformQuantizer
Calibrate output quantization based on analytical formulas.
Args:
*inputs (QuantizedArray): quantized operation inputs. Quantized weights are stored in the op instance
Returns:
res(UniformQuantizer): the quantizer of the operation's output values that can be used to de-quantize these values.
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
classmethod supported_by_linear_backend
supported_by_linear_backend() → bool
class QuantizedMatMul
Quantized MatMul op.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Optional[Set[str]] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: Optional[QuantizationOptions] = None,
**attrs
) → None
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_analytical_output
calibrate_analytical_output(*inputs: QuantizedArray) → UniformQuantizer
Calibrate output quantization based on analytical formulas.
Args:
*inputs (QuantizedArray): quantized operation inputs. Quantized weights are stored in the op instance
Returns:
res(UniformQuantizer): the quantizer of the operation's output values that can be used to de-quantize these values.
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
classmethod supported_by_linear_backend
supported_by_linear_backend() → bool
class QuantizedAdd
Quantized Addition operator.
Can add either two variables (both encrypted) or a variable and a constant
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 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: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedTanh
Quantized Tanh op.
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
class QuantizedSoftplus
Quantized Softplus op.
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
class QuantizedExp
Quantized Exp op.
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
class QuantizedLog
Quantized Log op.
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
class QuantizedAbs
Quantized Abs op.
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
class QuantizedIdentity
Quantized Identity op.
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 q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedReshape
Quantized Reshape op.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Reshape operation can not be fused since it must be performed over integer tensors.
Returns:
bool: False, this operation can not be fused.
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Reshape the input integer encrypted tensor.
Args:
q_inputs: an encrypted integer tensor at index 0 and one constant shape at index 1attrs: additional optional reshape options
Returns:
result(QuantizedArray): reshaped encrypted integer tensor
class QuantizedConv
Quantized Conv op.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
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 operatorop_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: names of integer tensors that are taken as input for this operationconstant_inputs: the weights and activationsinput_quant_opts: options for the input quantizerattrs: convolution optionsdilations(Tuple[int]): dilation of the kernel. Default to 1 on all dimensions.group(int): number of convolution groups. Default to 1.kernel_shape(Tuple[int]): shape of the kernel. Should have 2 elements for 2d convpads(Tuple[int]): padding in ONNX format (begin, end) on each axisstrides(Tuple[int]): stride of the convolution on each axis
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 q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Compute the quantized convolution between two quantized tensors.
Allows an optional quantized bias.
Args:
q_inputs: input tuple, containsx(numpy.ndarray): input data. Shape is N x C x H x W for 2dw(numpy.ndarray): weights tensor. Shape is (O x I x Kh x Kw) for 2db(numpy.ndarray, Optional): bias tensor, Shape is (O,)calibrate_rounding(bool): Whether to calibrate roundingattrs: 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,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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 q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedMaxPool
Quantized Max Pooling op.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Max 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: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedPad
Quantized Padding op.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Pad operation cannot be fused since it must be performed over integer tensors.
Returns:
bool: False, this operation cannot be fused as it is manipulates integer tensors
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
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,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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
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 int_input_names
Get the names of encrypted integer tensors that are used by this op.
Returns:
Set[str]: the names of the tensors
class QuantizedGreater
Comparison operator >.
Only supports comparison with a constant.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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
class QuantizedGreaterOrEqual
Comparison operator >=.
Only supports comparison with a constant.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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
class QuantizedLess
Comparison operator <.
Only supports comparison with a constant.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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
class QuantizedLessOrEqual
Comparison operator <=.
Only supports comparison with a constant.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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
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 int_input_names
Get the names of encrypted integer tensors that are used by this op.
Returns:
Set[str]: the names of the tensors
class QuantizedDiv
Quantized Division operator.
Can divide either two variables (both encrypted) or a variable and a constant
method __init__
__init__(
*args,
rounding_threshold_bits: Union[NoneType, int, Dict[str, Union[str, int]]] = None,
**kwargs
) → None
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(*inputs: Union[QuantizedArray, ndarray]) → ndarray
Create corresponding QuantizedArray for the output of the activation function.
Args:
*inputs (Union[QuantizedArray, numpy.ndarray]): Calibration sample inputs.
Returns:
numpy.ndarray: the output values for the provided calibration samples.
method can_fuse
can_fuse() → bool
Determine if this op can be fused.
Div operation can be computed in float and fused if it operates over inputs produced by a single integer tensor.
Returns:
bool: Whether the number of integer input tensors allows computing this op as a TLU
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedMul
Quantized Multiplication operator.
Can multiply either two variables (both encrypted) or a variable and a constant
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Mul operation can be computed in float and fused if it operates over inputs produced by a single integer tensor.
Returns:
bool: Whether the number of integer input tensors allows computing this op as a TLU
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedSub
Subtraction operator.
This works the same as addition on both encrypted - encrypted and on encrypted - constant.
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 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: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedBatchNormalization
Quantized Batch normalization with encrypted input and in-the-clear normalization params.
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(*inputs: Union[QuantizedArray, 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.
class QuantizedFlatten
Quantized flatten for encrypted inputs.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Flatten operation cannot be fused since it must be performed over integer tensors.
Returns:
bool: False, this operation cannot be fused as it is manipulates integer tensors.
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Flatten the input integer encrypted tensor.
Args:
q_inputs: an encrypted integer tensor at index 0attrs: contains axis attribute
Returns:
result(QuantizedArray): reshaped encrypted integer tensor
class QuantizedReduceSum
ReduceSum with encrypted input.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
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.op_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(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 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(*inputs: Union[QuantizedArray, 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: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
calibrate_rounding: bool = False,
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Sum the encrypted tensor's values along the given axes.
Args:
q_inputs(QuantizedArray): An encrypted integer tensor at index 0.calibrate_rounding(bool): Whether to calibrate rounding or not.attrs(Dict): Options are handled in constructor.
Returns:
(QuantizedArray): The sum of all values along the given axes.
class QuantizedErf
Quantized erf op.
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
class QuantizedNot
Quantized Not op.
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
class QuantizedBrevitasQuant
Brevitas uniform quantization with encrypted input.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
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 ONNXop_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(Optional[Set[str]]): Names of input integer tensors. Default to None.constant_inputs(Optional[Dict]): Input constant tensor.scale(float): Quantizer scalezero_point(float): Quantizer zero-pointbit_width(int): Number of bits of the integer representationinput_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 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(*inputs: Union[QuantizedArray, ndarray]) → ndarray
Create corresponding QuantizedArray for the output of Quantization 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: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Quantize values.
Args:
q_inputs: an encrypted integer tensor at index 0, scale, zero_point, n_bits at indices 1,2,3attrs: additional optional attributes
Returns:
result(QuantizedArray): reshaped encrypted integer tensor
class QuantizedTranspose
Transpose operator for quantized inputs.
This operator performs quantization and transposes the encrypted data. When the inputs are pre-computed QAT the input is only quantized if needed.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Transpose can not be fused since it must be performed over integer tensors as it moves around different elements of these input tensors.
Returns:
bool: False, this operation can not be fused as it copies encrypted integers
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Transpose the input integer encrypted tensor.
Args:
q_inputs: an encrypted integer tensor at index 0 and one constant shape at index 1attrs: additional optional reshape options
Returns:
result(QuantizedArray): transposed encrypted integer tensor
class QuantizedFloor
Quantized Floor op.
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
class QuantizedMax
Quantized Max op.
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
class QuantizedMin
Quantized Min op.
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
class QuantizedNeg
Quantized Neg op.
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
class QuantizedSign
Quantized Neg op.
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
class QuantizedUnsqueeze
Unsqueeze operator.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Unsqueeze can not be fused since it must be performed over integer tensors as it reshapes an encrypted tensor.
Returns:
bool: False, this operation can not be fused as it operates on encrypted tensors
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Unsqueeze the input tensors on a given axis.
Args:
q_inputs: an encrypted integer tensor at index 0, axes at index 1attrs: additional optional unsqueeze options
Returns:
result(QuantizedArray): unsqueezed encrypted integer tensor
class QuantizedConcat
Concatenate operator.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Concatenation can not be fused since it must be performed over integer tensors as it copies encrypted integers from one tensor to another.
Returns:
bool: False, this operation can not be fused as it copies encrypted integers
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Concatenate the input tensors on a given axis.
Args:
q_inputs: an encrypted integer tensorattrs: additional optional concatenate options
Returns:
result(QuantizedArray): concatenated encrypted integer tensor
class QuantizedSqueeze
Squeeze operator.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Squeeze can not be fused since it must be performed over integer tensors as it reshapes encrypted tensors.
Returns:
bool: False, this operation can not be fused as it reshapes encrypted tensors
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Squeeze the input tensors on a given axis.
Args:
q_inputs: an encrypted integer tensor at index 0, axes at index 1attrs: additional optional squeeze options
Returns:
result(QuantizedArray): squeezed encrypted integer tensor
class ONNXShape
Shape operator.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
This operation returns the shape of the tensor and thus can not be fused into a univariate TLU.
Returns:
bool: False, this operation can not be fused
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class ONNXConstantOfShape
ConstantOfShape operator.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
This operation returns a new encrypted tensor and thus can not be fused.
Returns:
bool: False, this operation can not be fused
class ONNXGather
Gather operator.
Returns values at requested indices from the input tensor.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
This operation returns values from a tensor and thus can not be fused into a univariate TLU.
Returns:
bool: False, this operation can not be fused
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class ONNXSlice
Slice operator.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
This operation returns values from a tensor and thus can not be fused into a univariate TLU.
Returns:
bool: False, this operation can not be fused
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
class QuantizedExpand
Expand operator for quantized tensors.
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 can_fuse
can_fuse() → bool
Determine if this op can be fused.
Unsqueeze can not be fused since it must be performed over integer tensors as it reshapes an encrypted tensor.
Returns:
bool: False, this operation can not be fused as it operates on encrypted tensors
method q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]
Expand the input tensor to a specified shape.
Args:
q_inputs: an encrypted integer tensor at index 0, shape at index 1attrs: additional optional expand options
Returns:
result(QuantizedArray): expanded encrypted integer tensor
class QuantizedEqual
Comparison operator ==.
Only supports comparison with a constant.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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
class QuantizedUnfold
Quantized Unfold op.
method __init__
__init__(
n_bits_output: int,
op_instance_name: str,
int_input_names: Set[str] = None,
constant_inputs: Optional[Dict[str, Any], Dict[int, Any]] = None,
input_quant_opts: QuantizationOptions = None,
**attrs
) → None
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 q_impl
q_impl(
*q_inputs: Union[ndarray, QuantizedArray, NoneType, bool, int, float],
**attrs
) → Union[ndarray, QuantizedArray, NoneType, bool, int, float]