tensorflow::ops::QuantizeAndDequantizeV2
tensorflow::ops::QuantizeAndDequantizeV2
#include <array_ops.h>
Quantizes then dequantizes a tensor.
Summary
This op simulates the precision loss from the quantized forward pass by:
- Quantizing the tensor to fixed point numbers, which should match the target quantization method when it is used in inference.
- Dequantizing it back to floating point numbers for the following ops, most likely matmul.
There are different ways to quantize. This version does not use the full range of the output type, choosing to elide the lowest possible value for symmetry (e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to 0.
To perform this op, we first find the range of values in our tensor. The range we use is always centered on 0, so we find m such that
- m = max(abs(input_min), abs(input_max)) if range_given is true,
- m = max(abs(min_elem(input)), abs(max_elem(input))) otherwise.
Our input tensor range is then [-m, m].
Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed]. If signed_input is true, this is
[min_fixed, max_fixed ] = [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1].
Otherwise, if signed_input is false, the fixed-point range is
[min_fixed, max_fixed] = [0, (1 << num_bits) - 1].
From this we compute our scaling factor, s:
s = (max_fixed - min_fixed) / (2 * m).
Now we can quantize and dequantize the elements of our tensor. An element e is transformed into e':
e' = (e * s).round_to_nearest() / s.
Note that we have a different number of buckets in the signed vs. unsigned cases. For example, if num_bits == 8, we get 254 buckets in the signed case vs. 255 in the unsigned case.
For example, suppose num_bits = 8 and m = 1. Then
[min_fixed, max_fixed] = [-127, 127], and s = (127 + 127) / 2 = 127.
Given the vector {-1, -0.5, 0, 0.3}, this is quantized to {-127, -63, 0, 38}, and dequantized to {-1, -63.0/127, 0, 38.0/127}.
Arguments:
- scope: A Scope object
- input: Tensor to quantize and then dequantize.
- input_min: If range_given, this is the min of the range, otherwise this input will be ignored.
- input_max: If range_given, this is the max of the range, otherwise this input will be ignored.
Optional attributes (see Attrs
):
- signed_input: If the quantization is signed or unsigned.
- num_bits: The bitwidth of the quantization.
- range_given: If the range is given or should be computed from the tensor.
Returns:
-
Output
: The output tensor.
Constructors and Destructors | |
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QuantizeAndDequantizeV2(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input input_min, ::tensorflow::Input input_max) | |
QuantizeAndDequantizeV2(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input input_min, ::tensorflow::Input input_max, const QuantizeAndDequantizeV2::Attrs & attrs) |
Public attributes | |
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output |
Public functions | |
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node() const | ::tensorflow::Node * |
operator::tensorflow::Input() const | |
operator::tensorflow::Output() const |
Public static functions | |
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NumBits(int64 x) | |
RangeGiven(bool x) | |
SignedInput(bool x) |
Structs | |
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tensorflow::ops::QuantizeAndDequantizeV2::Attrs | Optional attribute setters for QuantizeAndDequantizeV2. |
Public attributes
output
::tensorflow::Output output
Public functions
QuantizeAndDequantizeV2
QuantizeAndDequantizeV2( const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input input_min, ::tensorflow::Input input_max )
QuantizeAndDequantizeV2
QuantizeAndDequantizeV2( const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input input_min, ::tensorflow::Input input_max, const QuantizeAndDequantizeV2::Attrs & attrs )
node
::tensorflow::Node * node() const
operator::tensorflow::Input
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
Public static functions
NumBits
Attrs NumBits( int64 x )
RangeGiven
Attrs RangeGiven( bool x )
SignedInput
Attrs SignedInput( bool x )
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/quantize-and-dequantize-v2.html