cavis/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu

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/*******************************************************************************
* 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 {
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// fakeQuantWithMinMaxVars_
// input - input tensor
// min - min scalar tensor
// max - max scalar tensor
// numBits - (default 16bit)
// narrowed - shrink is true
// output - output tensor
//
template <typename T>
static __host__ __device__ void
nudge(T min, T max, int quantMin, int quantMax, T* scale, T* nudgedMin, T* nudgedMax) {
T quantMaxF = static_cast<T>(quantMax);
T quantMinF = static_cast<T>(quantMin);
*scale = (max - min) / (quantMaxF - quantMinF);
auto zeroPointFromMin = quantMinF - min / *scale;
uint16_t const nudgedZeroPoint = [zeroPointFromMin, quantMin, quantMax, quantMaxF, quantMinF] {
if (zeroPointFromMin < quantMinF) {
return static_cast<uint16_t>(quantMin);
}
if (zeroPointFromMin > quantMaxF) {
return static_cast<uint16_t>(quantMax);
}
return nd4j::math::nd4j_round<T,uint16_t>(zeroPointFromMin);
}();
*nudgedMin = (quantMinF - nudgedZeroPoint) * (*scale);
*nudgedMax = (quantMaxF - nudgedZeroPoint) * (*scale);
}
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;
min->syncToHost(); // these are scalars, so nothing much happened
max->syncToHost();
T scale, nudgedMin, nudgedMax;
nudge(min->t<T>(0), max->t<T>(0), lowIntBound, upperIntBound, &scale, &nudgedMin, &nudgedMax);
auto wiseMinMaxAndSoOn = LAMBDA_T(x, nudgedMin, nudgedMax, scale) {
T val = x;
if (x < nudgedMin) {
val = nudgedMin;
}
else if (x > nudgedMax) {
val = nudgedMax;
}
else
val = x;
return (math::nd4j_floor<T,T>((val - nudgedMin) / scale + T(0.5)) * scale + nudgedMin);
};
input->applyLambda(wiseMinMaxAndSoOn, output);
}
template <typename T>
static __global__ void fakeQuantWithMinMaxKernel(T* input, Nd4jLong* inputShape, T* min, T* max,
int lowIntBound, int upperIntBound, Nd4jLong channels,
T* output, Nd4jLong* outputShape, Nd4jLong length) {
__shared__ int block;
if (threadIdx.x == 0) {
block = length / channels; // to loop with last dimension as block
}
__syncthreads();
for (auto i = blockIdx.x; i < (int)channels; i += gridDim.x) {
T scale, nudgedMin, nudgedMax;
nudge(min[i], max[i], lowIntBound, upperIntBound, &scale, &nudgedMin, &nudgedMax);
// loop over blocks to quantization between nudged min and max
for (auto b = threadIdx.x; b < block; b += blockDim.x) {
T val = input[shape::getIndexOffset(b * channels + i, inputShape)];
if (val < nudgedMin) {
val = nudgedMin;
} else if (val > nudgedMax) {
val = nudgedMax;
}
output[shape::getIndexOffset(b * channels + i, outputShape)] =
(math::nd4j_floor<T, T>((val - nudgedMin) / scale + T(0.5)) * scale + nudgedMin);
};
}
}
template <typename T>
void fakeQuantWithMinMaxVarsPerChannel_(LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
int lowIntBound = narrowed?1:0;
int upperIntBound = (1 << numBits) - 1;
auto channels = min->lengthOf();
auto length = input->lengthOf();
NDArray::prepareSpecialUse({output}, {min, max, input});
auto stream = context->getCudaStream();
T* inputBuf = input->dataBuffer()->specialAsT<T>();
T* outputBuf = output->dataBuffer()->specialAsT<T>();
T* minBuf = min->dataBuffer()->specialAsT<T>();
T* maxBuf = max->dataBuffer()->specialAsT<T>();
fakeQuantWithMinMaxKernel<<<128, 256, 256, *stream>>>(inputBuf, input->specialShapeInfo(),
minBuf, maxBuf, lowIntBound, upperIntBound, channels, outputBuf, output->specialShapeInfo(), length);
NDArray::registerSpecialUse({output}, {min, max, input});
}
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_, (context, 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);
BUILD_SINGLE_TEMPLATE(template void fakeQuantWithMinMaxVarsPerChannel_, (LaunchContext* context, NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output), FLOAT_TYPES);
}
}
}