# concrete.ml.quantization.quantized_ops.md

## module `concrete.ml.quantization.quantized_ops`

`concrete.ml.quantization.quantized_ops`

Quantized versions of the ONNX operators for post training quantization.

### class `QuantizedSigmoid`

`QuantizedSigmoid`

Quantized sigmoid op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedHardSigmoid`

`QuantizedHardSigmoid`

Quantized HardSigmoid op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedRelu`

`QuantizedRelu`

Quantized Relu op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedPRelu`

`QuantizedPRelu`

Quantized PRelu op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedLeakyRelu`

`QuantizedLeakyRelu`

Quantized LeakyRelu op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedHardSwish`

`QuantizedHardSwish`

Quantized Hardswish op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedElu`

`QuantizedElu`

Quantized Elu op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedSelu`

`QuantizedSelu`

Quantized Selu op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedCelu`

`QuantizedCelu`

Quantized Celu op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedClip`

`QuantizedClip`

Quantized clip op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedRound`

`QuantizedRound`

Quantized round op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedPow`

`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:**

`List[str]`

: the names of the tensors

### class `QuantizedGemm`

`QuantizedGemm`

Quantized Gemm op.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

### class `QuantizedMatMul`

`QuantizedMatMul`

Quantized MatMul op.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

### class `QuantizedAdd`

`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:**

`List[str]`

: the names of the tensors

#### method `can_fuse`

`can_fuse`

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`

### class `QuantizedTanh`

`QuantizedTanh`

Quantized Tanh op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedSoftplus`

`QuantizedSoftplus`

Quantized Softplus op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedExp`

`QuantizedExp`

Quantized Exp op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedLog`

`QuantizedLog`

Quantized Log op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedAbs`

`QuantizedAbs`

Quantized Abs op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedIdentity`

`QuantizedIdentity`

Quantized Identity op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

### class `QuantizedReshape`

`QuantizedReshape`

Quantized Reshape op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

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`

`QuantizedConv`

Quantized Conv op.

#### method `__init__`

`__init__`

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 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 conv`pads`

(Tuple[int]): padding in ONNX format (begin, end) on each axis`strides`

(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:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

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`

`QuantizedAvgPool`

Quantized Average Pooling op.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

### class `QuantizedMaxPool`

`QuantizedMaxPool`

Quantized Max Pooling op.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `can_fuse`

`can_fuse`

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`

### class `QuantizedPad`

`QuantizedPad`

Quantized Padding op.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `can_fuse`

`can_fuse`

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

### class `QuantizedWhere`

`QuantizedWhere`

Where operator on quantized arrays.

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

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedCast`

`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:**

`List[str]`

: the names of the tensors

### class `QuantizedGreater`

`QuantizedGreater`

Comparison operator >.

Only supports comparison with a constant.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedGreaterOrEqual`

`QuantizedGreaterOrEqual`

Comparison operator >=.

Only supports comparison with a constant.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedLess`

`QuantizedLess`

Comparison operator <.

Only supports comparison with a constant.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedLessOrEqual`

`QuantizedLessOrEqual`

Comparison operator <=.

Only supports comparison with a constant.

#### method `__init__`

`__init__`

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedOr`

`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:**

`List[str]`

: the names of the tensors

### class `QuantizedDiv`

`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 int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedMul`

`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 int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedSub`

`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:**

`List[str]`

: the names of the tensors

#### method `can_fuse`

`can_fuse`

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`

### class `QuantizedBatchNormalization`

`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:**

`List[str]`

: the names of the tensors

### class `QuantizedFlatten`

`QuantizedFlatten`

Quantized flatten for encrypted inputs.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `can_fuse`

`can_fuse`

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`

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`

`QuantizedReduceSum`

ReduceSum with encrypted input.

#### method `__init__`

`__init__`

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 int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[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 `q_impl`

`q_impl`

Sum the encrypted tensor's values along the given axes.

**Args:**

`q_inputs`

(QuantizedArray): An encrypted integer tensor at index 0.`attrs`

(Dict): Options are handled in constructor.

**Returns:**

`(QuantizedArray)`

: The sum of all values along the given axes.

### class `QuantizedErf`

`QuantizedErf`

Quantized erf op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedNot`

`QuantizedNot`

Quantized Not op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedBrevitasQuant`

`QuantizedBrevitasQuant`

Brevitas uniform quantization with encrypted input.

#### method `__init__`

`__init__`

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. [-2**n_bits-1 .. 2**n_bits-1] (default 0),

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `calibrate`

`calibrate`

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`

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`

`QuantizedTranspose`

Transpose operator for quantized inputs.

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

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

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`

`QuantizedFloor`

Quantized Floor op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedMax`

`QuantizedMax`

Quantized Max op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedMin`

`QuantizedMin`

Quantized Min op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedNeg`

`QuantizedNeg`

Quantized Neg op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedSign`

`QuantizedSign`

Quantized Neg op.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

### class `QuantizedUnsqueeze`

`QuantizedUnsqueeze`

Unsqueeze operator.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

Unsqueeze the input tensors on a given axis.

**Args:**

`q_inputs`

: an encrypted integer tensor`attrs`

: additional optional unsqueeze options

**Returns:**

`result`

(QuantizedArray): unsqueezed encrypted integer tensor

### class `QuantizedConcat`

`QuantizedConcat`

Concatenate operator.

**property int_input_names**

Get the names of encrypted integer tensors that are used by this op.

**Returns:**

`List[str]`

: the names of the tensors

#### method `q_impl`

`q_impl`

Concatenate the input tensors on a giver axis.

**Args:**

`q_inputs`

: an encrypted integer tensor`attrs`

: additional optional concatenate options

**Returns:**

`result`

(QuantizedArray): concatenated encrypted integer tensor

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