softmax as standalone compilation unit
Signed-off-by: raver119 <raver119@gmail.com>master
raver119
parent
4d81af9fe9
commit
ca96a13ed0
|
|
@ -29,52 +29,6 @@ namespace sd {
|
|||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
template <typename T>
|
||||
static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) {
|
||||
|
||||
T* inBuff = reinterpret_cast<T *>(input);
|
||||
T* outBuff = reinterpret_cast<T *>(output);
|
||||
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
T sum = 0.;
|
||||
int inEWS = shape::elementWiseStride(inShapeInfo);
|
||||
int outEWS = shape::elementWiseStride(outShapeInfo);
|
||||
int length = shape::length(inShapeInfo);
|
||||
|
||||
if (inEWS >= 1 && outEWS >= 1) {
|
||||
|
||||
if (inEWS == 1 && outEWS == 1) {
|
||||
|
||||
for (int i = 0; i < length; i++)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[i]);
|
||||
|
||||
for (int i = 0; i < length; i++) {
|
||||
outBuff[i] = sd::math::nd4j_exp<T, T>(inBuff[i] - max);
|
||||
sum += outBuff[i];
|
||||
}
|
||||
|
||||
PRAGMA_OMP_SIMD
|
||||
for (int i = 0; i < length; i++)
|
||||
outBuff[i] /= sum;
|
||||
}
|
||||
else {
|
||||
|
||||
for (int i = 0; i < length; i++)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[i * inEWS]);
|
||||
|
||||
for (int i = 0; i < length; i++) {
|
||||
T r = sd::math::nd4j_exp<T, T>(inBuff[i * inEWS] - max);
|
||||
outBuff[i * outEWS] = r;
|
||||
sum += r;
|
||||
}
|
||||
|
||||
PRAGMA_OMP_SIMD
|
||||
for (int i = 0; i < length; i++)
|
||||
outBuff[i * outEWS] /= sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
void static _softMaxDerivForVector(sd::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output) {
|
||||
|
|
@ -123,16 +77,6 @@ static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output,
|
|||
}
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
void softMaxForVector(sd::LaunchContext * context, const NDArray& input, NDArray& output) {
|
||||
|
||||
if(!input.isVector() || !output.isVector())
|
||||
throw std::runtime_error("ops::helpers::softMaxForVector function: input and output arrays must be vectors !");
|
||||
|
||||
auto xType = input.dataType();
|
||||
BUILD_SINGLE_SELECTOR(xType, softMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
void logSoftMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) {
|
||||
|
|
@ -191,148 +135,6 @@ void softMaxForVector(sd::LaunchContext * context, const NDArray& input, NDArray
|
|||
BUILD_SINGLE_SELECTOR(xType, logSoftMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen);
|
||||
|
||||
template <>
|
||||
FORCEINLINE void softmax_loop(float *input, float *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inBuff = input + offsets[i];
|
||||
auto outBuff = output + offsets[i];
|
||||
|
||||
float max = -DataTypeUtils::max<float>();
|
||||
float sum = 0.f;
|
||||
|
||||
#pragma omp simd reduction(max:max)
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
max = sd::math::nd4j_max<float>(max, inBuff[j]);
|
||||
|
||||
#pragma omp simd reduction(+:sum)
|
||||
for (uint j = 0; j < tadLen; ++j) {
|
||||
float temp = sd::math::nd4j_exp<float, float>(inBuff[j] - max);
|
||||
outBuff[j] = temp;
|
||||
sum += temp;
|
||||
}
|
||||
|
||||
#pragma omp simd
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
outBuff[j] /= sum;
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func,0, numOfSubArrs);
|
||||
}
|
||||
|
||||
|
||||
template <typename T>
|
||||
FORCEINLINE void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inBuff = input + offsets[i];
|
||||
auto outBuff = output + offsets[i];
|
||||
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
T sum(0.f);
|
||||
|
||||
#pragma omp simd reduction(maxT:max)
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[j]);
|
||||
|
||||
#pragma omp simd reduction(sumT:sum)
|
||||
for (uint j = 0; j < tadLen; ++j) {
|
||||
T temp = sd::math::nd4j_exp<T, T>(inBuff[j] - max);
|
||||
outBuff[j] = temp;
|
||||
sum += temp;
|
||||
}
|
||||
|
||||
#pragma omp simd
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
outBuff[j] /= sum;
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func,0, numOfSubArrs);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static void softmax_(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
|
||||
|
||||
const int rank = input.rankOf();
|
||||
|
||||
if(input.isVector()) {
|
||||
|
||||
if(rank == 1 || input.sizeAt(dimension) != 1)
|
||||
softMaxForVector_<T>(input.getBuffer(), input.getShapeInfo(), output.buffer(), output.getShapeInfo());
|
||||
else
|
||||
output = 1.;
|
||||
}
|
||||
else if(input.isSameShapeStrict(output)) {
|
||||
|
||||
TadPack tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimension);
|
||||
Nd4jLong* tadShapeInfo = tadPack.primaryShapeInfo();
|
||||
Nd4jLong* tadOffsets = tadPack.primaryOffsets();
|
||||
const uint numOfSubArrs = tadPack.numberOfTads();
|
||||
const uint tadLen = shape::length(tadShapeInfo);
|
||||
|
||||
if(shape::elementWiseStride(tadShapeInfo) == 1){
|
||||
T *inBuff = input.bufferAsT<T>();
|
||||
T *outBuff = output.bufferAsT<T>();
|
||||
|
||||
softmax_loop(inBuff, outBuff, tadOffsets, numOfSubArrs, tadLen);
|
||||
}
|
||||
else {
|
||||
|
||||
uint inShapeInfoCast[MAX_RANK];
|
||||
bool canCast = sd::DataTypeUtils::castShapeInfo(tadShapeInfo, inShapeInfoCast);
|
||||
|
||||
auto offsets = new Nd4jLong[tadLen];
|
||||
shape::calcOffsets(tadShapeInfo, offsets);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inBuff = input.bufferAsT<T>() + tadOffsets[i];
|
||||
auto outBuff = output.bufferAsT<T>() + tadOffsets[i];
|
||||
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
T sum = 0.f;
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[offsets[j]]);
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j) {
|
||||
T temp = sd::math::nd4j_exp<T, T>(inBuff[offsets[j]] - max);
|
||||
outBuff[offsets[j]] = temp;
|
||||
sum += temp;
|
||||
}
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
outBuff[offsets[j]] /= sum;
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
||||
|
||||
delete []offsets;
|
||||
}
|
||||
}
|
||||
else {
|
||||
NDArray max = input.reduceAlongDimension(sd::reduce::Max, {dimension}, true);
|
||||
input.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), max, output, false);
|
||||
output.applyTransform(sd::transform::Exp, output);
|
||||
NDArray sum = output.reduceAlongDimension(sd::reduce::Sum, {dimension}, true);
|
||||
output /= sum;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
void softmax(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
|
||||
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), softmax_, (context, input, output, dimension), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
void prelu(sd::LaunchContext * context, const NDArray& input, const NDArray& alpha, NDArray& output) {
|
||||
|
|
@ -432,9 +234,8 @@ void preluBP(sd::LaunchContext * context, const NDArray& input, const NDArray& a
|
|||
}
|
||||
}
|
||||
|
||||
BUILD_SINGLE_TEMPLATE(template void thresholdReluDerivative_, (sd::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output), FLOAT_TYPES);
|
||||
BUILD_SINGLE_TEMPLATE(template void softmax_, (sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension), FLOAT_TYPES);
|
||||
BUILD_SINGLE_TEMPLATE(template void logSoftMaxForVector_, (void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo), FLOAT_TYPES);
|
||||
BUILD_SINGLE_TEMPLATE(template void thresholdReluDerivative_, (sd::LaunchContext * context, NDArray* input, double threshold, NDArray* dLdO, NDArray* output), FLOAT_TYPES);
|
||||
BUILD_SINGLE_TEMPLATE(template void logSoftMaxForVector_, (void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo), FLOAT_TYPES);
|
||||
BUILD_SINGLE_TEMPLATE(template void _softMaxDerivForVector, (sd::LaunchContext * context, const void *input, const Nd4jLong *inShapeInfo, void *output), FLOAT_TYPES);
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,228 @@
|
|||
/*******************************************************************************
|
||||
* 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 Yurii Shyrma (iuriish@yahoo.com), created on 19.04.2018
|
||||
// @author raver119@gmail.com
|
||||
//
|
||||
|
||||
#include <ops/declarable/helpers/activations.h>
|
||||
#include <helpers/ShapeUtils.h>
|
||||
#include <numeric>
|
||||
#include <helpers/ConstantTadHelper.h>
|
||||
#include <execution/Threads.h>
|
||||
|
||||
namespace sd {
|
||||
namespace ops {
|
||||
namespace helpers {
|
||||
|
||||
template <typename T>
|
||||
static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output, Nd4jLong *outShapeInfo) {
|
||||
|
||||
T* inBuff = reinterpret_cast<T *>(input);
|
||||
T* outBuff = reinterpret_cast<T *>(output);
|
||||
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
T sum = 0.;
|
||||
int inEWS = shape::elementWiseStride(inShapeInfo);
|
||||
int outEWS = shape::elementWiseStride(outShapeInfo);
|
||||
int length = shape::length(inShapeInfo);
|
||||
|
||||
if (inEWS >= 1 && outEWS >= 1) {
|
||||
|
||||
if (inEWS == 1 && outEWS == 1) {
|
||||
|
||||
for (int i = 0; i < length; i++)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[i]);
|
||||
|
||||
for (int i = 0; i < length; i++) {
|
||||
outBuff[i] = sd::math::nd4j_exp<T, T>(inBuff[i] - max);
|
||||
sum += outBuff[i];
|
||||
}
|
||||
|
||||
for (int i = 0; i < length; i++)
|
||||
outBuff[i] /= sum;
|
||||
}
|
||||
else {
|
||||
|
||||
for (int i = 0; i < length; i++)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[i * inEWS]);
|
||||
|
||||
for (int i = 0; i < length; i++) {
|
||||
T r = sd::math::nd4j_exp<T, T>(inBuff[i * inEWS] - max);
|
||||
outBuff[i * outEWS] = r;
|
||||
sum += r;
|
||||
}
|
||||
|
||||
for (int i = 0; i < length; i++)
|
||||
outBuff[i * outEWS] /= sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
void softMaxForVector(sd::LaunchContext * context, const NDArray& input, NDArray& output) {
|
||||
|
||||
if(!input.isVector() || !output.isVector())
|
||||
throw std::runtime_error("ops::helpers::softMaxForVector function: input and output arrays must be vectors !");
|
||||
|
||||
auto xType = input.dataType();
|
||||
BUILD_SINGLE_SELECTOR(xType, softMaxForVector_, (input.getBuffer(), input.getShapeInfo(), output.buffer(), output.shapeInfo()), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen);
|
||||
|
||||
template <>
|
||||
FORCEINLINE void softmax_loop(float *input, float *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inBuff = input + offsets[i];
|
||||
auto outBuff = output + offsets[i];
|
||||
|
||||
float max = -DataTypeUtils::max<float>();
|
||||
float sum = 0.f;
|
||||
|
||||
#pragma omp simd reduction(max:max)
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
max = sd::math::nd4j_max<float>(max, inBuff[j]);
|
||||
|
||||
#pragma omp simd reduction(+:sum)
|
||||
for (uint j = 0; j < tadLen; ++j) {
|
||||
float temp = sd::math::nd4j_exp<float, float>(inBuff[j] - max);
|
||||
outBuff[j] = temp;
|
||||
sum += temp;
|
||||
}
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
outBuff[j] /= sum;
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func,0, numOfSubArrs);
|
||||
}
|
||||
|
||||
|
||||
template <typename T>
|
||||
FORCEINLINE void softmax_loop(T *input, T *output, Nd4jLong *offsets, Nd4jLong numOfSubArrs, uint32_t tadLen) {
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inBuff = input + offsets[i];
|
||||
auto outBuff = output + offsets[i];
|
||||
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
T sum(0.f);
|
||||
|
||||
#pragma omp simd reduction(maxT:max)
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[j]);
|
||||
|
||||
#pragma omp simd reduction(sumT:sum)
|
||||
for (uint j = 0; j < tadLen; ++j) {
|
||||
T temp = sd::math::nd4j_exp<T, T>(inBuff[j] - max);
|
||||
outBuff[j] = temp;
|
||||
sum += temp;
|
||||
}
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
outBuff[j] /= sum;
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func,0, numOfSubArrs);
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////
|
||||
template <typename T>
|
||||
static void softmax_(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
|
||||
|
||||
const int rank = input.rankOf();
|
||||
|
||||
if(input.isVector()) {
|
||||
|
||||
if(rank == 1 || input.sizeAt(dimension) != 1)
|
||||
softMaxForVector_<T>(input.getBuffer(), input.getShapeInfo(), output.buffer(), output.getShapeInfo());
|
||||
else
|
||||
output = 1.;
|
||||
}
|
||||
else if(input.isSameShapeStrict(output)) {
|
||||
|
||||
TadPack tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(input.getShapeInfo(), dimension);
|
||||
Nd4jLong* tadShapeInfo = tadPack.primaryShapeInfo();
|
||||
Nd4jLong* tadOffsets = tadPack.primaryOffsets();
|
||||
const uint numOfSubArrs = tadPack.numberOfTads();
|
||||
const uint tadLen = shape::length(tadShapeInfo);
|
||||
|
||||
if(shape::elementWiseStride(tadShapeInfo) == 1){
|
||||
T *inBuff = input.bufferAsT<T>();
|
||||
T *outBuff = output.bufferAsT<T>();
|
||||
|
||||
softmax_loop(inBuff, outBuff, tadOffsets, numOfSubArrs, tadLen);
|
||||
}
|
||||
else {
|
||||
|
||||
uint inShapeInfoCast[MAX_RANK];
|
||||
bool canCast = sd::DataTypeUtils::castShapeInfo(tadShapeInfo, inShapeInfoCast);
|
||||
|
||||
auto offsets = new Nd4jLong[tadLen];
|
||||
shape::calcOffsets(tadShapeInfo, offsets);
|
||||
|
||||
auto func = PRAGMA_THREADS_FOR {
|
||||
for (auto i = start; i < stop; i++) {
|
||||
auto inBuff = input.bufferAsT<T>() + tadOffsets[i];
|
||||
auto outBuff = output.bufferAsT<T>() + tadOffsets[i];
|
||||
|
||||
T max = -DataTypeUtils::max<T>();
|
||||
T sum = 0.f;
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
max = sd::math::nd4j_max<T>(max, inBuff[offsets[j]]);
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j) {
|
||||
T temp = sd::math::nd4j_exp<T, T>(inBuff[offsets[j]] - max);
|
||||
outBuff[offsets[j]] = temp;
|
||||
sum += temp;
|
||||
}
|
||||
|
||||
for (uint j = 0; j < tadLen; ++j)
|
||||
outBuff[offsets[j]] /= sum;
|
||||
}
|
||||
};
|
||||
|
||||
samediff::Threads::parallel_tad(func, 0, numOfSubArrs);
|
||||
|
||||
delete []offsets;
|
||||
}
|
||||
}
|
||||
else {
|
||||
NDArray max = input.reduceAlongDimension(sd::reduce::Max, {dimension}, true);
|
||||
input.applyTrueBroadcast(sd::BroadcastOpsTuple::Subtract(), max, output, false);
|
||||
output.applyTransform(sd::transform::Exp, output);
|
||||
NDArray sum = output.reduceAlongDimension(sd::reduce::Sum, {dimension}, true);
|
||||
output /= sum;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
///////////////////////////////////////////////////////////////////
|
||||
void softmax(sd::LaunchContext * context, const NDArray& input, NDArray& output, const int dimension) {
|
||||
|
||||
BUILD_SINGLE_SELECTOR(input.dataType(), softmax_, (context, input, output, dimension), FLOAT_TYPES);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue