UniformQuantizedDotHybrid

public final class UniformQuantizedDotHybrid

Perform hybrid quantized dot of float Tensor `lhs` and quantized Tensor `rhs`.

Given float `lhs` and quantized `rhs`, internally performs quantization on `lhs`, and then performs quantized dot on quantized lhs and `rhs`. The internal quantization on `lhs` is a quantization to qint8, dynamic range, per-batch (per-axis along axis 0), asymmetric, and not narrow range (the range is [-128, 127]). `lhs` and `rhs` must be 2D Tensors and the lhs.dim_size(1) must match rhs.dim_size(0). `rhs` must be quantized Tensor, where its data value is quantized using the formula: quantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val).

Nested Classes

class

UniformQuantizedDotHybrid.Options

Optional attributes for UniformQuantizedDotHybrid

Public Methods

Output<V>	asOutput() Returns the symbolic handle of a tensor.
static <V extends Number, T extends Number, U> UniformQuantizedDotHybrid<V>	create(Scope scope, Operand<T> lhs, Operand<U> rhs, Operand<Float> rhsScales, Operand<Integer> rhsZeroPoints, Class<V> Tout, Long rhsQuantizationMinVal, Long rhsQuantizationMaxVal, Options... options) Factory method to create a class wrapping a new UniformQuantizedDotHybrid operation.
Output<V>	output() The output 2D Tensor of Tout, whose shape is (lhs.dim_size(0), rhs.dim_size(1)).
static UniformQuantizedDotHybrid.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<V extends Number>

asOutput() Returns the symbolic handle of a tensor.

Public Methods

public Output<V> 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 UniformQuantizedDotHybrid<V> create (Scope scope, Operand<T> lhs, Operand<U> rhs, Operand<Float> rhsScales, Operand<Integer> rhsZeroPoints, Class<V> Tout, Long rhsQuantizationMinVal, Long rhsQuantizationMaxVal, Options... options)

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

Parameters

scope	current scope
lhs	Must be a 2D Tensor of Tlhs.
rhs	Must be a 2D Tensor of Trhs.
rhsScales	The float value(s) used as scale when quantizing original data that rhs represents. Must be a scalar Tensor (per-tensor quantization) or 1D Tensor of size (rhs.dim_size(1),) (per-channel quantization).
rhsZeroPoints	The int32 value(s) used as zero_point when quantizing original data that rhs represents. Same shape condition as rhs_scales.
Tout	The type of output Tensor.
rhsQuantizationMinVal	The min value of the quantized data stored in rhs. For example, if Trhs 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 Trhs is qint8, this must be set to 127.
options	carries optional attributes values

Returns

• a new instance of UniformQuantizedDotHybrid

public Output<V> output ()

The output 2D Tensor of Tout, whose shape is (lhs.dim_size(0), rhs.dim_size(1)). The output data is the original output data itself (Not quantized).

public static UniformQuantizedDotHybrid.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 dot op rhs, only per-tensor quantization or per-channel quantization along dimension 1 is supported. Thus, this attribute must be set to -1 or 1. Other values are rejected.