UniformQuantizedAdd

public final class UniformQuantizedAdd

Perform quantized add of quantized Tensor `lhs` and quantized Tensor `rhs` to make quantized `output`.

Given quantized `lhs` and quantized `rhs`, performs quantized add on `lhs` and `rhs` to make quantized `output`.

`UniformQuantizedAdd` follows Numpy broadcasting rules. The two input array shapes are compared element-wise. Starting with the trailing dimensions, the two dimensions either have to be equal or one of them needs to be 1.

`lhs` and `rhs` must be quantized Tensor, where data value is quantized using the formula:

quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val)
 

`output` is also quantized, using the same formula.

If `lhs` and `output` is both per-axis quantized, the quantization axis must match. Also, if `rhs` and `output` is both per-axis quantized, the quantization axis must match. Match means the axis must match when adding, regarding the broadcasting. i.e. For both operands `lhs` and `rhs`, if `operand.quantization_axis` >= 0 and `output.quantization_axis` >= 0, `operand.dims` - `operand.quantization_axis` must be equal to `output.dims` - `output.quantization_axis`.

Nested Classes

class

UniformQuantizedAdd.Options

Optional attributes for UniformQuantizedAdd

Public Methods

Output<T>	asOutput() Returns the symbolic handle of a tensor.
static <T> UniformQuantizedAdd<T>	create(Scope scope, Operand<T> lhs, Operand<T> rhs, Operand<Float> lhsScales, Operand<Integer> lhsZeroPoints, Operand<Float> rhsScales, Operand<Integer> rhsZeroPoints, Operand<Float> outputScales, Operand<Integer> outputZeroPoints, Long lhsQuantizationMinVal, Long lhsQuantizationMaxVal, Long rhsQuantizationMinVal, Long rhsQuantizationMaxVal, Long outputQuantizationMinVal, Long outputQuantizationMaxVal, Options... options) Factory method to create a class wrapping a new UniformQuantizedAdd operation.
static UniformQuantizedAdd.Options	lhsQuantizationAxis(Long lhsQuantizationAxis)
Output<T>	output() The output quantized tensor.
static UniformQuantizedAdd.Options	outputQuantizationAxis(Long outputQuantizationAxis)
static UniformQuantizedAdd.Options	rhsQuantizationAxis(Long rhsQuantizationAxis)

Inherited Methods

From class org.tensorflow.op.PrimitiveOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Returns the underlying Operation
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.Operand

abstract Output<T>

asOutput() Returns the symbolic handle of a tensor.

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of a tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static UniformQuantizedAdd<T> create (Scope scope, Operand<T> lhs, Operand<T> rhs, Operand<Float> lhsScales, Operand<Integer> lhsZeroPoints, Operand<Float> rhsScales, Operand<Integer> rhsZeroPoints, Operand<Float> outputScales, Operand<Integer> outputZeroPoints, Long lhsQuantizationMinVal, Long lhsQuantizationMaxVal, Long rhsQuantizationMinVal, Long rhsQuantizationMaxVal, Long outputQuantizationMinVal, Long outputQuantizationMaxVal, Options... options)

Factory method to create a class wrapping a new UniformQuantizedAdd operation.

Parameters

scope	current scope
lhs	Must be a quantized tensor.
rhs	Must be a quantized tensor.
lhsScales	The float value(s) used as scale factors when quantizing the original data that `lhs` represents.
lhsZeroPoints	The int32 value(s) used as zero points when quantizing original data that `lhs` represents. Must have same shape with `lhs_scales`.
rhsScales	The float value(s) used as scale factors when quantizing the original data that `rhs` represents.
rhsZeroPoints	The int32 value(s) used as zero points when quantizing original data that `rhs` represents. Must have same shape with `rhs_scales`.
outputScales	The float value(s) to use as scale factors when quantizing original data that `output` represents.
outputZeroPoints	The int32 value(s) used as zero points when quantizing original data that output represents. Must have same shape with `output_scales`.
lhsQuantizationMinVal	The min value of the quantized data stored in `lhs`. For example, if `Tin` is `qint8`, this must be set to -127 if narrow range quantized or -128 if not.
lhsQuantizationMaxVal	The max value of the quantized data stored in `lhs`. For example, if `Tin` is `qint8`, this must be set to 127.
rhsQuantizationMinVal	The min value of the quantized data stored in `rhs`. For example, if `Tin` is `qint8`, this must be set to -127 if narrow range quantized or -128 if not.
rhsQuantizationMaxVal	The max value of the quantized data stored in `rhs`. For example, if `Tin` is `qint8`, this must be set to 127.
outputQuantizationMinVal	The min value of the quantized data stored in `output`. For example, if `Tout` is `qint8`, this must be set to -127 if narrow range quantized or -128 if not.
outputQuantizationMaxVal	The max value of the quantized data stored in `output`. For example, if `Tout` is `qint8`, this must be set to 127.
options	carries optional attributes values

Returns

• a new instance of UniformQuantizedAdd

public static UniformQuantizedAdd.Options lhsQuantizationAxis (Long lhsQuantizationAxis)

Parameters

lhsQuantizationAxis	Indicates the dimension index of the tensor where per-axis quantization is applied for the slices along that dimension. If set to -1 (default), this indicates per-tensor quantization. For the `lhs`, only per-tensor quantization is supported. Thus, this must be set to -1. Other values will raise error at OpKernel construction.

public Output<T> output ()

The output quantized tensor.

public static UniformQuantizedAdd.Options outputQuantizationAxis (Long outputQuantizationAxis)

Parameters

outputQuantizationAxis

Indicates the dimension index of the tensor where per-axis quantization is applied for the slices along that dimension. If set to -1 (default), this indicates per-tensor quantization. For the `output`, only per-tensor quantization or per-channel quantization along `output_feature_dimension` is supported. Thus, this must be set to -1 or `dimension_numbers.output_feature_dimension`. Other values will raise error at OpKernel construction.

public static UniformQuantizedAdd.Options rhsQuantizationAxis (Long rhsQuantizationAxis)

Parameters

rhsQuantizationAxis

Indicates the dimension index of the tensor where per-axis quantization is applied for the slices along that dimension. If set to -1 (default), this indicates per-tensor quantization. For the `rhs`, only per-tensor quantization or per-channel quantization along `kernel_output_feature_dimension` is supported. Thus, this must be set to -1 or `dimension_numbers.kernel_output_feature_dimension`. Other values will raise error at OpKernel construction.