tf.raw_ops.UniformQuantizedAdd

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

tf.raw_ops.UniformQuantizedAdd(
    lhs,
    rhs,
    lhs_scales,
    lhs_zero_points,
    rhs_scales,
    rhs_zero_points,
    output_scales,
    output_zero_points,
    lhs_quantization_min_val,
    lhs_quantization_max_val,
    rhs_quantization_min_val,
    rhs_quantization_max_val,
    output_quantization_min_val,
    output_quantization_max_val,
    lhs_quantization_axis=-1,
    rhs_quantization_axis=-1,
    output_quantization_axis=-1,
    name=None
)

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.

lhs A Tensor. Must be one of the following types: qint32. Must be a quantized tensor.
rhs A Tensor. Must have the same type as lhs. Must be a quantized tensor.
lhs_scales A Tensor of type float32. The float value(s) used as scale factors when quantizing the original data that lhs represents.
lhs_zero_points A Tensor of type int32. The int32 value(s) used as zero points when quantizing original data that lhs represents. Must have same shape with lhs_scales.
rhs_scales A Tensor of type float32. The float value(s) used as scale factors when quantizing the original data that rhs represents.
rhs_zero_points A Tensor of type int32. The int32 value(s) used as zero points when quantizing original data that rhs represents. Must have same shape with rhs_scales.
output_scales A Tensor of type float32. The float value(s) to use as scale factors when quantizing original data that output represents.
output_zero_points A Tensor of type int32. The int32 value(s) used as zero points when quantizing original data that output represents. Must have same shape with output_scales.
lhs_quantization_min_val An int. 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.
lhs_quantization_max_val An int. The max value of the quantized data stored in lhs. For example, if Tin is qint8, this must be set to 127.
rhs_quantization_min_val An int. 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.
rhs_quantization_max_val An int. The max value of the quantized data stored in rhs. For example, if Tin is qint8, this must be set to 127.
output_quantization_min_val An int. 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.
output_quantization_max_val An int. The max value of the quantized data stored in output. For example, if Tout is qint8, this must be set to 127.
lhs_quantization_axis An optional int. Defaults to -1. 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.
rhs_quantization_axis An optional int. Defaults to -1. 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.
output_quantization_axis An optional int. Defaults to -1. 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.
name A name for the operation (optional).

A Tensor. Has the same type as lhs.