cavis/libnd4j/include/ops/declarable/helpers/cpu/fake_quantization.cpp

123 lines
5.5 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author sgazeos@gmail.com
//
#include <ops/declarable/helpers/fake_quantization.h>
#include <NDArrayFactory.h>
namespace nd4j {
namespace ops {
namespace helpers {
//
// nudge - nudged min max over scale
// scale = (Max - Min) / (quantMax - quantMin)
// quantMin = 0 or 1, quantMax = 2^b - 1 == (1 << b) - 1
//
template <typename T>
static void nudge(T min, T max, int quantMin, int quantMax, T* scale, T* nudgedMin, T* nudgedMax) {
// floating point instead integers
T quantMaxF = static_cast<T>(quantMax);
T quantMinF = static_cast<T>(quantMin);
// compute scale
*scale = (max - min) / (quantMaxF - quantMinF);
// compute left bound point
auto zeroPointFromMin = quantMinF - min / *scale;
// bound zero point to conform with range [0 or 1, 2^b - 1]
uint16_t const nudged_zero_point = [zeroPointFromMin, quantMin, quantMax, quantMaxF, quantMinF] {
if (zeroPointFromMin < quantMinF) {
return static_cast<uint16_t>(quantMin);
}
if (zeroPointFromMin > quantMaxF) {
return static_cast<uint16_t>(quantMax);
}
return (uint16_t)nd4j::math::nd4j_round<T,int>(zeroPointFromMin);
}();
// compute nudged min and max with computed nudged zero point
*nudgedMin = (quantMinF - nudged_zero_point) * (*scale);
*nudgedMax = (quantMaxF - nudged_zero_point) * (*scale);
}
template <typename T>
void fakeQuantWithMinMaxVarsPerChannel_(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
int lowIntBound = narrowed ? 1 : 0; // 0 or 1
int upperIntBound = (1 << numBits) - 1; // 2^b - 1
auto channels = input->sizeAt(-1); // last dimension
PRAGMA_OMP_PARALLEL_FOR
for (auto i = 0; i < channels; i++) {
T scale, nudged_min, nudged_max;
// nudge min and max first, with scale computing
nudge<T>(min->t<T>(i), max->t<T>(i), lowIntBound, upperIntBound, &scale, &nudged_min, &nudged_max);
// slide using last dimension and process all for given channel
for (auto e = 0; e < input->lengthOf(); e += channels) {
T val = input->t<T>(e + i);
if ( val <= nudged_min)
val = nudged_min;
else if (val >= nudged_max)
val = nudged_max;
// quantization itself
output->t<T>(e + i) = math::nd4j_floor<T,T>((val - nudged_min)/scale + T(0.5)) * scale + nudged_min;
}
}
}
//
//const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
// const auto clamped_shifted = clamped - nudged_min;
// outputs.device(d) = (clamped_shifted / nudged_scale_repl + 0.5f).floor() *
// nudged_scale_repl +
// nudged_min;
//
template <typename T>
void fakeQuantWithMinMaxVars_(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
int lowIntBound = narrowed ? 1 : 0;
int upperIntBound = (1 << numBits) - 1;
T nudgedMin, nudgedMax, scale;
// nudge with given min and max and compute scale and nudged min and max
nudge<T>(min->t<T>(0), max->t<T>(0), lowIntBound, upperIntBound, &scale, &nudgedMin, &nudgedMax);
// quantization as one
auto fakeQuantizationWithMinMax = LAMBDA_T(x, nudgedMin, nudgedMax, scale) {
T val = x; // boundign value between nudged min and max
if (val < nudgedMin) {
val = nudgedMin;
}
else if (val > nudgedMax)
val = nudgedMax;
// converse value with scale and shifted with nudged min
val -= nudgedMin;
return (nd4j::math::nd4j_floor<T,T>(val / scale + T(0.5f)) * scale + nudgedMin);
};
input->applyLambda<T>(fakeQuantizationWithMinMax, output);
}
void fakeQuantWithMinMaxVars(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVars_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
}
void fakeQuantWithMinMaxVarsPerChannel(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
BUILD_SINGLE_SELECTOR(input->dataType(), fakeQuantWithMinMaxVarsPerChannel_, (input, min, max, numBits, narrowed, output), FLOAT_TYPES);
}
BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVars_, (NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
}
}
}