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

101 lines
4.3 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 {
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;
const float quant_min_float = static_cast<float>(lowIntBound);
const float quant_max_float = static_cast<float>(upperIntBound);
T scale = (max->t<T>(0) - min->t<T>(0)) / (quant_max_float - quant_min_float);
const T zero_point_from_min = quant_min_float - min->e<T>(0) / scale;
const uint16_t nudged_zero_point = [zero_point_from_min, lowIntBound,
quant_min_float, upperIntBound,
quant_max_float] {
if (zero_point_from_min < quant_min_float) {
return static_cast<uint16_t>(lowIntBound);
}
if (zero_point_from_min > quant_max_float) {
return static_cast<uint16_t>(upperIntBound);
}
return static_cast<uint16_t>(roundf(zero_point_from_min));
}();
auto nudged_min = (quant_min_float - nudged_zero_point) * (scale);
auto nudged_max = (quant_max_float - nudged_zero_point) * (scale);
//input->applyScalar(scalar::CompareAndSet, nudged_max, clamped, nullptr); //.cwiseMin(nudged_max).cwiseMax(nudged_min);
//input->applyScalar(scalar::CompareAndSet, nudged_min, clamped, nullptr); //.cwiseMin(nudged_max).cwiseMax(nudged_min);
auto wiseMax = LAMBDA_T(x, nudged_min) {
if (x < nudged_min) {
return nudged_min;
}
return x;
};
auto wiseMin = LAMBDA_T(x, nudged_max) {
if (x > nudged_max) {
return nudged_max;
}
return x;
};
auto scaleTensor(*input); // = NDArrayFactory::create(input->ordering(), input->getShapeAsVector(), input->getWorkspace());
auto clamped(*input); // = NDArrayFactory::create(input->ordering(), input->getShapeAsVector(), input->getWorkspace());
scaleTensor.assign(scale);
input->applyLambda<T>(wiseMin, &clamped);
// const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
clamped.applyLambda<T>(wiseMax, output);
// const auto clamped_shifted = clamped - nudged_min;
*output -= nudged_min;
// auto nudgedScale = scale;
(*output) /= scaleTensor;
(*output) += T(0.5f);
output->applyTransform(transform::Floor, nullptr, nullptr);
(*output) *= scaleTensor;
(*output) += nudged_min;
//output->printIndexedBuffer("FAKE QUANTED");
/*
const auto nudged_scale_repl = inputs.constant(nudged_scale);
const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
const auto clamped_shifted = clamped - nudged_min;
*output = (clamped_shifted / nudged_scale_repl + 0.5f).floor() *
nudged_scale_repl +
nudged_min;
*/
}
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);
}
BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVars_, (NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
}
}
}