tf.raw_ops.UniformQuantizedDotHybrid
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Perform hybrid quantized dot of float Tensor lhs and quantized Tensor rhs.
tf.raw_ops.UniformQuantizedDotHybrid(
lhs,
rhs,
rhs_scales,
rhs_zero_points,
Tout,
rhs_quantization_min_val,
rhs_quantization_max_val,
rhs_quantization_axis=-1,
name=None
)
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).
Args |
|---|
lhs
|
A Tensor. Must be one of the following types: float32.
Must be a 2D Tensor of Tlhs.
|
rhs
|
A Tensor. Must be one of the following types: qint8.
Must be a 2D Tensor of Trhs.
|
rhs_scales
|
A Tensor of type float32.
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).
|
rhs_zero_points
|
A Tensor of type int32.
The int32 value(s) used as zero_point when quantizing original data that rhs represents.
Same shape condition as rhs_scales.
|
Tout
|
A tf.DType from: tf.float32. The type of output Tensor.
|
rhs_quantization_min_val
|
An int.
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.
|
rhs_quantization_max_val
|
An int.
The max value of the quantized data stored in rhs.
For example, if Trhs is qint8, this must be set to 127.
|
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 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.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type Tout.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.raw_ops.UniformQuantizedDotHybrid\n\n\u003cbr /\u003e\n\nPerform hybrid quantized dot of float Tensor `lhs` and quantized Tensor `rhs`.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.UniformQuantizedDotHybrid`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/UniformQuantizedDotHybrid)\n\n\u003cbr /\u003e\n\n tf.raw_ops.UniformQuantizedDotHybrid(\n lhs,\n rhs,\n rhs_scales,\n rhs_zero_points,\n Tout,\n rhs_quantization_min_val,\n rhs_quantization_max_val,\n rhs_quantization_axis=-1,\n name=None\n )\n\nGiven float `lhs` and quantized `rhs`, internally performs quantization on `lhs`, and then performs quantized dot on quantized lhs and `rhs`.\nThe 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\\]).\n`lhs` and `rhs` must be 2D Tensors and the lhs.dim_size(1) must match rhs.dim_size(0).\n`rhs` must be quantized Tensor, where its data value is quantized using the formula:\nquantized_data = clip(original_data / scale + zero_point, quantization_min_val, quantization_max_val).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `lhs` | A `Tensor`. Must be one of the following types: `float32`. Must be a 2D Tensor of Tlhs. |\n| `rhs` | A `Tensor`. Must be one of the following types: `qint8`. Must be a 2D Tensor of Trhs. |\n| `rhs_scales` | A `Tensor` of type `float32`. 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). |\n| `rhs_zero_points` | A `Tensor` of type `int32`. The int32 value(s) used as zero_point when quantizing original data that rhs represents. Same shape condition as rhs_scales. |\n| `Tout` | A [`tf.DType`](../../tf/dtypes/DType) from: [`tf.float32`](../../tf#float32). The type of output Tensor. |\n| `rhs_quantization_min_val` | An `int`. 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. |\n| `rhs_quantization_max_val` | An `int`. The max value of the quantized data stored in rhs. For example, if Trhs is qint8, this must be set to 127. |\n| `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 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. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `Tout`. ||\n\n\u003cbr /\u003e"]]
