2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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
******************************************************************************/
2020-03-02 10:49:41 +01:00
#include <legacy/NativeOpExecutioner.h>
#include <legacy/NativeOps.h>
2019-06-06 14:21:15 +02:00
#include <cuda.h>
2020-03-02 10:49:41 +01:00
#include <system/buffer.h>
2019-06-06 14:21:15 +02:00
#include <loops/transform_any.h>
#include <loops/reduce_bool.h>
#include <loops/reduce_long.h>
2019-11-13 15:04:59 +01:00
#include <loops/scalar.h>
2019-06-06 14:21:15 +02:00
#include <helpers/threshold.h>
#include <ops/specials_cuda.h>
#include <helpers/DebugHelper.h>
2020-03-02 10:49:41 +01:00
#include <execution/AffinityManager.h>
2019-06-06 14:21:15 +02:00
#include <exceptions/datatype_exception.h>
2019-08-20 17:52:41 +02:00
#include <exceptions/cuda_exception.h>
2019-06-06 14:21:15 +02:00
#include <helpers/CudaLaunchHelper.h>
2020-03-02 10:49:41 +01:00
#include <graph/GraphExecutioner.h>
2019-11-13 15:04:59 +01:00
#include <helpers/BlasHelper.h>
2019-06-06 14:21:15 +02:00
#include <graph/GraphHolder.h>
#include <ops/declarable/CustomOperations.h>
2020-03-02 10:49:41 +01:00
#include <helpers/PointersManager.h>
2019-06-06 14:21:15 +02:00
//#include <sys/time.h>
#include <curand.h>
2020-03-02 10:49:41 +01:00
#include <graph/Status.h>
2019-06-06 14:21:15 +02:00
#include <helpers/DebugHelper.h>
2020-03-02 10:49:41 +01:00
using namespace sd;
2019-06-06 14:21:15 +02:00
#include <loops/special_kernels.h>
2019-07-12 07:21:15 +02:00
#include <performance/benchmarking/FullBenchmarkSuit.h>
#include <performance/benchmarking/LightBenchmarkSuit.h>
2019-06-06 14:21:15 +02:00
cudaDeviceProp *deviceProperties;
cudaFuncAttributes *funcAttributes = new cudaFuncAttributes[64];
int blockLimit = 128;
int maxThreads = 512;
bool allowedP2P = false;
bool supportedP2P = false;
#ifdef __ND4J_EXPERIMENTAL__
bool experimentalSupport = true;
#else
bool experimentalSupport = false;
#endif
int minThreads = 32;
__constant__ char deviceConstantMemory[49152];
// this method just does type conversion in fancy way
int getDeviceId(Nd4jPointer ptrToDeviceId) {
return (int)(Nd4jLong)ptrToDeviceId;
}
/*
* Basic CUDA constants here: number of blocks per MP
*/
int getDeviceBlockThreshold(int deviceId) {
int ccMinor = deviceProperties[deviceId].minor;
int ccMajor = deviceProperties[deviceId].major;
int blockThreshold = 8;
if (ccMajor >= 5)
blockThreshold = 32;
else if (ccMajor == 3)
blockThreshold = 16;
else if (ccMajor < 3)
blockThreshold = 8;
return blockThreshold;
}
/*
* This message returns shared memory threshold value. default overflow ratio is 0.3
*/
int getDeviceSharedThreshold(int deviceId) {
int ccMinor = deviceProperties[deviceId].minor;
int ccMajor = deviceProperties[deviceId].major;
// please note threshold isn't multiple of 32, and that's NOT a mistake
int shmemThreshold;
if (ccMajor == 6 && ccMinor == 0)
shmemThreshold = 65536;
else if (ccMajor == 6 && ccMinor == 1)
shmemThreshold = 49152;
else if (ccMajor == 5 && ccMinor == 2)
shmemThreshold = 98304;
else if (ccMajor == 5)
shmemThreshold = 65536;
else if (ccMajor == 3 && ccMinor == 7)
shmemThreshold = 114688;
else shmemThreshold = 49152;
return shmemThreshold / 0.3;
}
2020-03-02 10:49:41 +01:00
sd::buffer::Buffer<Nd4jLong> * createScalarBuffer(cudaStream_t stream) {
2020-05-09 07:06:14 +02:00
auto scalarShapeInfo = shape::createScalarShapeInfo();
auto buff = sd::buffer::createBuffer(scalarShapeInfo,shape::shapeInfoLength(2), stream);
2020-03-02 10:49:41 +01:00
sd::buffer::copyDataToGpu(&buff, stream);
2019-06-06 14:21:15 +02:00
return buff;
}
class ScalarShapeInformation {
private:
2020-03-02 10:49:41 +01:00
sd::buffer::Buffer<Nd4jLong> *scalarDimension;
sd::buffer::Buffer<Nd4jLong> *scalarShapeInfo;
2019-06-06 14:21:15 +02:00
// std::thread::id threadId;
public:
ScalarShapeInformation(cudaStream_t stream) {
auto scalarDimensionBuff = reinterpret_cast<Nd4jLong *>(malloc(sizeof(Nd4jLong)));
CHECK_ALLOC(scalarDimensionBuff, "Failed to allocate ShapeInfoBuffer", sizeof(Nd4jLong));
scalarDimensionBuff[0] = MAX_DIMENSION;
2020-03-02 10:49:41 +01:00
scalarDimension = sd::buffer::createBuffer(scalarDimensionBuff,1, stream);
2019-06-06 14:21:15 +02:00
scalarShapeInfo = createScalarBuffer(stream);
// threadId = std::this_thread::get_id();
}
~ScalarShapeInformation() {
2020-03-02 10:49:41 +01:00
sd::buffer::freeBuffer(&scalarShapeInfo);
sd::buffer::freeBuffer(&scalarDimension);
2019-06-06 14:21:15 +02:00
}
Nd4jLong *getShapeInfoHostPointer() {
return scalarShapeInfo->data;
}
Nd4jLong * getShapeInfoGpuPointer() {
return scalarShapeInfo->gData;
}
Nd4jLong * getDimensionHostPointer() {
return scalarDimension->data;
}
Nd4jLong * getDimensionGpuPointer() {
return scalarDimension->gData;
}
};
template <typename T>
class ScalarInfo {
2020-03-02 10:49:41 +01:00
sd::buffer::Buffer<T> *scalarData;
2019-06-06 14:21:15 +02:00
ScalarShapeInformation *shapeInfo;
T finalResult;
cudaStream_t streamRef;
public:
ScalarInfo(cudaStream_t stream) {
T *scalarResult = reinterpret_cast<T*>(malloc(sizeof(T)));
CHECK_ALLOC(scalarResult, "Failed to allocate new scalar buffer", sizeof(T));
shapeInfo = new ScalarShapeInformation(stream);
2020-03-02 10:49:41 +01:00
scalarData = sd::buffer::createBuffer(scalarResult,1, stream);
2019-06-06 14:21:15 +02:00
streamRef = stream;
2020-03-02 10:49:41 +01:00
sd::buffer::copyDataToGpu(&scalarData, stream);
2019-06-06 14:21:15 +02:00
}
T getFinalResultFromDevice() {
2020-03-02 10:49:41 +01:00
sd::buffer::copyDataFromGpu(&scalarData, streamRef);
2019-06-06 14:21:15 +02:00
return scalarData->data[0];
}
/**
* Get the device shape information
* representing a scalar
*/
Nd4jLong *getDeviceShapeInfo() {
return shapeInfo->getShapeInfoGpuPointer();
}
/**
* Get the dZ pointers
*/
T *getDevicePointer() {
return scalarData->gData;
}
/**
* Get the infinite dimension device pointer
*/
Nd4jLong *getDimensionDevicePointer() {
return shapeInfo->getDimensionGpuPointer();
}
~ScalarInfo() {
2020-03-02 10:49:41 +01:00
sd::buffer::freeBuffer(&scalarData);
2019-06-06 14:21:15 +02:00
delete shapeInfo;
}
};
2019-07-22 13:34:08 +02:00
void execPairwiseTransform( Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execPairwiseTransform(&lc, opNum, dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(), extraParams);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execPairwiseTransformBool(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execPairwiseBoolTransform(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraParams);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execSummaryStatsScalar(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
bool biasCorrected) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execSummaryStatsScalar(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
biasCorrected);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execBroadcastBool(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2020-01-04 11:27:50 +01:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
2019-08-26 18:57:51 +02:00
2020-01-04 11:27:50 +01:00
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
auto hTADShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[9]);
auto tadOnlyShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[10]);
auto tadOffsets = reinterpret_cast<Nd4jLong *>(extraPointers[11]);
auto tadOnlyShapeInfoZ = reinterpret_cast<Nd4jLong *>(extraPointers[12]);
auto tadOffsetsZ = reinterpret_cast<Nd4jLong *>(extraPointers[13]);
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execBroadcastBool(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dimension, dimensionLength,
tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
/**
*
* @param opNum
* @param dX
* @param dXShapeInfo
* @param dY
* @param dYShapeInfo
* @param dZ
* @param dZShapeInfo
* @param dimension
* @param dimensionLength
*/
2019-07-22 13:34:08 +02:00
void execBroadcast(
2019-06-06 14:21:15 +02:00
Nd4jPointer *extraPointers,
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape) {
2020-01-04 11:27:50 +01:00
try {
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
auto hTADShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[9]);
auto tadOnlyShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[10]);
auto tadOffsets = reinterpret_cast<Nd4jLong *>(extraPointers[11]);
auto tadOnlyShapeInfoZ = reinterpret_cast<Nd4jLong *>(extraPointers[12]);
auto tadOffsetsZ = reinterpret_cast<Nd4jLong *>(extraPointers[13]);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(hXShapeInfo);
auto yType = sd::ArrayOptions::dataType(hYShapeInfo);
auto zType = sd::ArrayOptions::dataType(hZShapeInfo);
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execBroadcast(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadOnlyShapeInfo, tadOffsets, tadOnlyShapeInfoZ, tadOffsetsZ);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
/**
*
* @param opNum
* @param dX
* @param dXShapeInfo
* @param extraParams
* @param dZ
* @param dZShapeInfo
*/
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduceFloat(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduceFloatScalar(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduceSame(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduceSameScalar(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduceSame2(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const*hXShapeInfo, Nd4jLong const*dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const*hZShapeInfo, Nd4jLong const*dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const*hDimensionShape, Nd4jLong const*dDimensionShape) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo,
2020-01-04 11:27:50 +01:00
dimension,
2019-08-26 18:57:51 +02:00
shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduceSame(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadPack.specialShapeInfo(), tadPack.specialOffsets());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduceLong2(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const*hXShapeInfo, Nd4jLong const*dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const*hZShapeInfo, Nd4jLong const*dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const*hDimensionShape, Nd4jLong const*dDimensionShape) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo,
2020-01-04 11:27:50 +01:00
dimension,
2019-08-26 18:57:51 +02:00
shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduceLong(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadPack.specialShapeInfo(), tadPack.specialOffsets());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduceLong(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const*hXShapeInfo, Nd4jLong const*dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const*hZShapeInfo, Nd4jLong const*dZShapeInfo) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
auto hTADShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[9]);
auto dTADShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[10]);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
auto reductionPointer = reinterpret_cast<void *>(extraPointers[4]);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(hXShapeInfo);
auto zType = sd::ArrayOptions::dataType(hZShapeInfo);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
if (zType != sd::DataType::INT64)
throw datatype_exception::build("execReduceLong wrong Z data type", sd::DataType::INT64, zType);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
auto xLength = shape::length(hXShapeInfo);
auto blockWidth = 256;
auto numBlocks = CudaLaunchHelper::getReductionBlocks(xLength, blockWidth);
dim3 launchDims(numBlocks, blockWidth, 32768);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceLongFunction,
2020-01-04 11:27:50 +01:00
::execReduceScalar(launchDims, stream, opNum,
dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(), hXShapeInfo,
extraParams,
dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(), hXShapeInfo,
nullptr, 0, reductionPointer, dTADShapeInfo), LIBND4J_TYPES, LONG_TYPES);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "execReduceLong(...) failed");
2020-01-04 11:27:50 +01:00
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-23 07:36:48 +02:00
void execReduceBool2(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const*hXShapeInfo, Nd4jLong const*dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const*hZShapeInfo, Nd4jLong const*dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const*hDimensionShape, Nd4jLong const*dDimensionShape) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo,
2020-01-04 11:27:50 +01:00
dimension,
2019-08-26 18:57:51 +02:00
shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduceBool(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadPack.specialShapeInfo(), tadPack.specialOffsets());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduceBool(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
auto hTADShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[9]);
auto dTADShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers[10]);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
auto reductionPointer = reinterpret_cast<void *>(extraPointers[4]);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(hXShapeInfo);
auto zType = sd::ArrayOptions::dataType(hZShapeInfo);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
if (zType != sd::DataType::BOOL)
2019-08-26 18:57:51 +02:00
throw std::runtime_error("execReduceBool requires Z operand to have BOOL type");
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
auto xLength = shape::length(hXShapeInfo);
auto blockWidth = 256;
auto numBlocks = CudaLaunchHelper::getReductionBlocks(xLength, blockWidth);
dim3 launchDims(numBlocks, blockWidth, 32768);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
BUILD_DOUBLE_SELECTOR(xType, zType, functions::reduce::ReduceBoolFunction,
2020-01-04 11:27:50 +01:00
::execReduceScalar(launchDims, stream, opNum,
dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(), hXShapeInfo,
extraParams,
dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(), hZShapeInfo,
nullptr, 0, reductionPointer, dTADShapeInfo), LIBND4J_TYPES, BOOL_TYPES);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "execReduceBool(...) failed");
2020-01-04 11:27:50 +01:00
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
/**
*
* @param opNum
* @param dX
* @param dXShapeInfo
* @param extraParams
* @param dZ
* @param dZShapeInfo
* @param dimension
* @param dimensionLength
*/
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execIndexReduce(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo,
2020-01-04 11:27:50 +01:00
dimension,
2019-08-26 18:57:51 +02:00
shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execIndexReduce(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
(int *) dbDimension->special(), dimensionLength,
tadPack.specialShapeInfo(), tadPack.specialOffsets());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
/**
*
* @param opNum
* @param dX
* @param dXShapeInfo
* @param extraParams
* @param dZ
* @param dZShapeInfo
*/
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduceFloat2(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo,
2020-01-04 11:27:50 +01:00
dimension,
2019-08-26 18:57:51 +02:00
shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduceFloat(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadPack.specialShapeInfo(), tadPack.specialOffsets());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
/**
*
* @param opNum
* @param dX
* @param dXShapeInfo
* @param extraParams
*/
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execIndexReduceScalar(
2019-06-06 14:21:15 +02:00
Nd4jPointer *extraPointers,
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo){
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execIndexReduceScalar(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execTransformSame(Nd4jPointer *extraPointers,int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
auto tadShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[0] : nullptr);
auto tadOffsets = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[1] : nullptr);
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execTransformSame(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
tadShapeInfo, tadOffsets);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execTransformBool(Nd4jPointer *extraPointers,int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
auto tadShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[0] : nullptr);
auto tadOffsets = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[1] : nullptr);
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execTransformBool(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
tadShapeInfo, tadOffsets);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execTransformAny(Nd4jPointer *extraPointers,int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2020-01-04 11:27:50 +01:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
auto streamSpecial = reinterpret_cast<cudaStream_t &>(extraPointers[4]);
LaunchContext lc(stream, streamSpecial, extraPointers[5], extraPointers[3],
reinterpret_cast<int *>(extraPointers[6]));
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execTransformAny(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
nullptr, nullptr);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execTransformStrict(Nd4jPointer *extraPointers,int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2020-01-04 11:27:50 +01:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
auto tadShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[10] : nullptr);
auto tadOffsets = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[11] : nullptr);
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execTransformStrict(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
tadShapeInfo, tadOffsets);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execTransformFloat(Nd4jPointer *extraPointers,int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
auto tadShapeInfo = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[10] : nullptr);
auto tadOffsets = reinterpret_cast<Nd4jLong *>(extraPointers != nullptr ? extraPointers[11] : nullptr);
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execTransformFloat(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
tadShapeInfo, tadOffsets);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void checkP2P() {
2019-06-06 14:21:15 +02:00
int curDevice = 0;
cudaGetDevice(&curDevice);
int devCnt = 0;
cudaGetDeviceCount(&devCnt);
if (curDevice < 0 && curDevice > devCnt)
curDevice = 0;
bool tempSupport = true;
if (devCnt > 1) {
for (int dX = 0; dX < devCnt; dX++) {
for (int dY = 0; dY < devCnt; dY++) {
if (dX == dY)
continue;
int canAccess = 0;
cudaSetDevice(dX);
cudaDeviceCanAccessPeer(&canAccess, dX , dY);
if (!canAccess) {
tempSupport = false;
break;
}
}
}
supportedP2P = tempSupport;
cudaSetDevice(curDevice);
} else {
// if we have only 1 device - we say that we support P2P, since all data will be on 1 device
supportedP2P = true;
}
}
2019-07-22 13:34:08 +02:00
void enableP2P(bool enable) {
2019-06-06 14:21:15 +02:00
if (enable == allowedP2P)
return;
int curDevice = 0;
cudaGetDevice(&curDevice);
int devCnt = 0;
cudaGetDeviceCount(&devCnt);
if (curDevice < 0 && curDevice > devCnt)
curDevice = 0;
if (devCnt > 1) {
for (int dX = 0; dX < devCnt; dX++) {
for (int dY = 0; dY < devCnt; dY++) {
if (dX == dY)
continue;
int canAccess = 0;
cudaSetDevice(dX);
cudaDeviceCanAccessPeer(&canAccess, dX , dY);
if (canAccess) {
if (enable) {
cudaDeviceEnablePeerAccess(dY, 0);
} else {
cudaDeviceDisablePeerAccess(dY);
}
} else {
2020-03-02 10:49:41 +01:00
if (sd::Environment::getInstance()->isVerbose()) printf("Peer access [%i] -> [%i] isn't possible\n", dX, dY);
2019-06-06 14:21:15 +02:00
}
}
}
cudaSetDevice(curDevice);
}
allowedP2P = enable;
cudaSetDevice(curDevice);
}
2019-07-22 13:34:08 +02:00
bool isP2PAvailable() {
2019-06-06 14:21:15 +02:00
return supportedP2P;
}
2019-07-22 13:34:08 +02:00
void initializeDevicesAndFunctions() {
2019-08-26 18:57:51 +02:00
try {
int devCnt = 0;
cudaGetDeviceCount(&devCnt);
deviceProperties = new cudaDeviceProp[devCnt];
for (int i = 0; i < devCnt; i++) {
cudaSetDevice(i);
cudaGetDeviceProperties(&deviceProperties[i], i);
cudaDeviceSetLimit(cudaLimitStackSize, 4096);
}
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
cudaSetDevice(0);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
checkP2P();
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
// enabling p2p gpu access if it's supported
if (supportedP2P && devCnt > 1)
enableP2P(allowedP2P);
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void initializeFunctions(Nd4jPointer *functions) {
2020-03-02 10:49:41 +01:00
sd::BlasHelper::getInstance()->initializeDeviceFunctions(functions);
2019-06-06 14:21:15 +02:00
/*
2019-07-22 13:34:08 +02:00
cublasSgemv = (CublasSgemv)functions[0];
cublasDgemv = (CublasDgemv)functions[1];
cublasHgemm = (CublasHgemm)functions[2];
cublasSgemm = (CublasSgemm)functions[3];
cublasDgemm = (CublasDgemm)functions[4];
cublasSgemmEx = (CublasSgemmEx)functions[5];
cublasHgemmBatched = (CublasHgemmBatched)functions[6];
cublasSgemmBatched = (CublasSgemmBatched)functions[7];
cublasDgemmBatched = (CublasDgemmBatched)functions[8];
2019-06-06 14:21:15 +02:00
*/
}
/**
* This method acquires memory chunk of requested size on host side
*
* @param pointer pointer that'll be used for allocation
* @param memorySize memory size, in bytes
* @param flags optional parameter
*/
2019-07-22 13:34:08 +02:00
Nd4jPointer mallocHost(Nd4jLong memorySize, int flags) {
2019-06-06 14:21:15 +02:00
Nd4jPointer pointer;
// cudaHostAllocMapped |cudaHostAllocPortable
2019-08-31 19:57:05 +02:00
auto res = cudaHostAlloc(reinterpret_cast<void **>(&pointer), memorySize + 8, cudaHostAllocDefault);
2019-08-26 18:57:51 +02:00
if (res != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaHostAlloc failed");
2019-08-26 18:57:51 +02:00
}
2019-08-24 08:20:57 +02:00
2019-08-31 19:57:05 +02:00
return reinterpret_cast<int8_t*>(pointer);
2019-06-06 14:21:15 +02:00
}
/**
* This method acquires memory chunk of requested size on specified device
*
* @param pointer pointer that'll be used for allocation
* @param memorySize memory size, in bytes
* @param ptrToDeviceId pointer to deviceId. For cuda that's just and int, for OpenCL that's pointer to device_id, etc
* @param flags optional parameter
*/
2019-07-22 13:34:08 +02:00
Nd4jPointer mallocDevice(Nd4jLong memorySize, int deviceId, int flags) {
2019-06-06 14:21:15 +02:00
Nd4jPointer pointer;
2019-08-31 19:57:05 +02:00
auto res = cudaMalloc(reinterpret_cast<void **>(&pointer), memorySize + 8);
2019-08-26 18:57:51 +02:00
if (res != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMalloc failed");
2019-08-26 18:57:51 +02:00
}
2019-08-31 19:57:05 +02:00
return reinterpret_cast<int8_t*>(pointer);
2019-06-06 14:21:15 +02:00
}
/**
* This method releases previously allocated host memory space
*
* @param pointer pointer that'll be freed
*/
2019-07-22 13:34:08 +02:00
int freeHost(Nd4jPointer pointer) {
2019-08-24 08:20:57 +02:00
auto res = cudaFreeHost(reinterpret_cast<void *>(pointer));
2019-08-26 18:57:51 +02:00
if (res != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaFreeHost failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1L;
}
/**
* This method releases previously allocated memory space on device
*
* @param pointer pointer that'll be freed
* @param ptrToDeviceId pointer to deviceId.
*/
2019-07-22 13:34:08 +02:00
int freeDevice(Nd4jPointer pointer, int deviceId) {
2019-08-24 08:20:57 +02:00
auto res = cudaFree(reinterpret_cast<void *>(pointer));
2019-08-26 18:57:51 +02:00
// we're intentionally skipping
if (res != 0 && res != 1) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(res);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaFree failed");
2019-08-26 18:57:51 +02:00
}
return res == 0 ? 1L : 0L;
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
Nd4jPointer createContext() {
2019-06-06 14:21:15 +02:00
return 0L;
}
2019-07-22 13:34:08 +02:00
Nd4jPointer createStream() {
2019-06-06 14:21:15 +02:00
auto stream = new cudaStream_t();
auto dZ = cudaStreamCreate(stream);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaStreamCreate failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return stream;
}
2019-07-22 13:34:08 +02:00
Nd4jPointer createEvent() {
2019-06-06 14:21:15 +02:00
Nd4jPointer nativeEvent= (Nd4jPointer) malloc(sizeof(cudaEvent_t));
CHECK_ALLOC(nativeEvent, "Failed to allocate new CUDA event buffer", sizeof(cudaEvent_t));
2019-08-24 08:20:57 +02:00
auto dZ = cudaEventCreateWithFlags(reinterpret_cast<cudaEvent_t *>(&nativeEvent), cudaEventDisableTiming);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventCreateWithFlags failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return nativeEvent;
}
2019-07-22 13:34:08 +02:00
int registerEvent(Nd4jPointer event, Nd4jPointer stream) {
2019-08-24 08:20:57 +02:00
auto pEvent = reinterpret_cast<cudaEvent_t *>(&event);
auto pStream = reinterpret_cast<cudaStream_t *>(stream);
2019-06-06 14:21:15 +02:00
2019-08-24 08:20:57 +02:00
auto dZ = cudaEventRecord(*pEvent, *pStream);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventRecord failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1;
}
2019-07-22 13:34:08 +02:00
int setDevice(int deviceId) {
2019-08-20 17:52:41 +02:00
AffinityManager::setCurrentDevice(deviceId);
2019-06-06 14:21:15 +02:00
return 1;
}
2019-07-23 07:36:48 +02:00
Nd4jLong getDeviceFreeMemoryDefault() {
2019-06-06 14:21:15 +02:00
size_t memFree = 0;
size_t memTotal = 0;
cudaMemGetInfo(&memFree, &memTotal);
return (Nd4jLong) memFree;
}
2019-07-22 13:34:08 +02:00
Nd4jLong getDeviceFreeMemory(int device) {
2019-06-06 14:21:15 +02:00
int orig = -1;
cudaGetDevice(&orig);
if (device >= 0 && device != orig) {
cudaSetDevice(device);
}
size_t memFree = 0;
size_t memTotal = 0;
cudaMemGetInfo(&memFree, &memTotal);
if (device >= 0 && device != orig) {
cudaSetDevice(orig);
}
return (Nd4jLong) memFree;
}
2019-07-22 13:34:08 +02:00
Nd4jLong getDeviceTotalMemory(int device) {
2019-06-06 14:21:15 +02:00
int orig = -1;
cudaGetDevice(&orig);
if (device >= 0 && device != orig) {
cudaSetDevice(device);
}
size_t memFree = 0;
size_t memTotal = 0;
cudaMemGetInfo(&memFree, &memTotal);
if (device >= 0 && device != orig) {
cudaSetDevice(orig);
}
return (Nd4jLong) memTotal;
}
2019-07-22 13:34:08 +02:00
int memcpySync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jPointer reserved) {
2020-01-04 11:27:50 +01:00
cudaMemcpyKind kind;
switch (flags) {
case 0: {
kind = cudaMemcpyHostToHost;
}
break;
case 1: {
kind = cudaMemcpyHostToDevice;
}
break;
case 2: {
kind = cudaMemcpyDeviceToHost;
}
break;
case 3: {
kind = cudaMemcpyDeviceToDevice;
}
break;
default: {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("UNDEFNED MEMCPY");
2020-01-04 11:27:50 +01:00
return 0;
}
}
auto dZ = cudaMemcpy(reinterpret_cast<void *>(dst), const_cast<const void *>(reinterpret_cast<void *>(src)), static_cast<size_t>(size), kind);
if (dZ != 0) {
printf("Failed on [%p] -> [%p], size: [%i], direction: [%i], dZ: [%i]\n", src, dst, size, flags, static_cast<int>(dZ));
fflush(stdout);
fflush(stderr);
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpy failed");
2020-01-04 11:27:50 +01:00
return 0;
}
return 1;
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
int memcpyAsync(Nd4jPointer dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jPointer reserved) {
2019-08-20 17:52:41 +02:00
auto pStream = reinterpret_cast<cudaStream_t *>(reserved);
2019-06-06 14:21:15 +02:00
cudaMemcpyKind kind;
2020-03-02 10:49:41 +01:00
//sd::DebugHelper::checkErrorCode(pStream, "Preliminary sync failed");
2019-06-06 14:21:15 +02:00
switch (flags) {
case 0: {
kind = cudaMemcpyHostToHost;
}
break;
case 1: {
kind = cudaMemcpyHostToDevice;
}
break;
case 2: {
kind = cudaMemcpyDeviceToHost;
}
2019-08-20 17:52:41 +02:00
break;
2019-06-06 14:21:15 +02:00
case 3: {
2019-08-20 17:52:41 +02:00
kind = cudaMemcpyDeviceToDevice;
}
2019-06-06 14:21:15 +02:00
break;
2019-08-26 18:57:51 +02:00
default: {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("UNDEFNED MEMCPY");
2019-08-26 18:57:51 +02:00
return 0;
}
2019-06-06 14:21:15 +02:00
}
2019-08-20 17:52:41 +02:00
auto dZ = cudaMemcpyAsync(reinterpret_cast<void *>(dst), const_cast<const void *>(reinterpret_cast<void *>(src)), static_cast<size_t>(size), kind, *pStream);
//auto dZ = cudaMemcpy(reinterpret_cast<void *>(dst), const_cast<const void *>(reinterpret_cast<void *>(src)), static_cast<size_t>(size), kind);
2019-06-06 14:21:15 +02:00
if (dZ != 0) {
2020-01-04 11:27:50 +01:00
printf("Failed on [%p] -> [%p], size: [%i], direction: [%i], dZ: [%i]\n", src, dst, size, flags, static_cast<int>(dZ));
2019-06-06 14:21:15 +02:00
fflush(stdout);
fflush(stderr);
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpyAsync failed");
2020-01-04 11:27:50 +01:00
return 0;
2019-06-06 14:21:15 +02:00
}
return 1;
}
2019-07-22 13:34:08 +02:00
int memsetSync(Nd4jPointer dst, int value, Nd4jLong size, int flags, Nd4jPointer reserved) {
2019-08-24 08:20:57 +02:00
auto dZ = cudaMemset(reinterpret_cast<void *>(dst), value, static_cast<size_t>(size));
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemset failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1;
}
2019-07-22 13:34:08 +02:00
int memsetAsync(Nd4jPointer dst, int value, Nd4jLong size, int flags, Nd4jPointer reserved) {
2019-08-24 08:20:57 +02:00
auto pStream = reinterpret_cast<cudaStream_t *>(reserved);
2019-06-06 14:21:15 +02:00
2019-08-24 08:20:57 +02:00
auto dZ = cudaMemsetAsync(reinterpret_cast<void *>(dst), value, static_cast<size_t>(size), *pStream);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemsetAsync failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1;
}
2019-07-22 13:34:08 +02:00
int destroyEvent(Nd4jPointer event) {
2019-08-24 08:20:57 +02:00
auto pEvent = reinterpret_cast<cudaEvent_t *>(&event);
auto dZ = cudaEventDestroy(*pEvent);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventDestroy failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1;
}
2019-07-22 13:34:08 +02:00
int streamSynchronize(Nd4jPointer stream) {
2019-08-24 08:20:57 +02:00
auto pStream = reinterpret_cast<cudaStream_t *>(stream);
2019-06-06 14:21:15 +02:00
2019-08-24 08:20:57 +02:00
auto dZ = cudaStreamSynchronize(*pStream);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaStreamSynchronize failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1L;
}
2019-07-22 13:34:08 +02:00
int eventSynchronize(Nd4jPointer event) {
2019-08-24 08:20:57 +02:00
auto pEvent = reinterpret_cast<cudaEvent_t *>(&event);
2019-06-06 14:21:15 +02:00
2019-08-24 08:20:57 +02:00
auto dZ = cudaEventSynchronize(*pEvent);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaEventSynchronize failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1L;
}
2019-07-22 13:34:08 +02:00
int getAvailableDevices() {
2019-06-06 14:21:15 +02:00
int devCnt = 0;
cudaGetDeviceCount(&devCnt);
return devCnt;
}
2019-07-22 13:34:08 +02:00
void enableDebugMode(bool reallyEnable) {
2020-03-02 10:49:41 +01:00
sd::Environment::getInstance()->setDebug(reallyEnable);
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void setGridLimit(int gridSize) {
2019-06-06 14:21:15 +02:00
if (gridSize > 8192)
gridSize = 8192;
if (gridSize < 1)
gridSize = 1;
blockLimit = gridSize;
}
2019-07-22 13:34:08 +02:00
int ompGetMaxThreads() {
2019-06-06 14:21:15 +02:00
return maxThreads;
}
2019-07-22 13:34:08 +02:00
int ompGetNumThreads() {
2019-06-06 14:21:15 +02:00
return maxThreads;
}
2019-07-22 13:34:08 +02:00
void setOmpNumThreads(int threads) {
2019-06-06 14:21:15 +02:00
if (threads > 1024)
threads = 1024;
if (threads < 32)
threads = 32;
maxThreads = threads;
}
2019-07-22 13:34:08 +02:00
void enableVerboseMode(bool reallyEnable) {
2020-03-02 10:49:41 +01:00
sd::Environment::getInstance()->setVerbose(reallyEnable);
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
int getDeviceMajor(int device) {
2019-06-06 14:21:15 +02:00
return deviceProperties[device].major;
}
2019-07-22 13:34:08 +02:00
int getDeviceMinor(int device) {
2019-06-06 14:21:15 +02:00
return deviceProperties[device].minor;
}
2019-07-22 13:34:08 +02:00
const char * getDeviceName(int device) {
2019-06-06 14:21:15 +02:00
return deviceProperties[device].name;
}
2019-07-22 13:34:08 +02:00
void specialConcat(
2019-06-06 14:21:15 +02:00
Nd4jPointer *extraPointers,
int dimension,
int numArrays,
Nd4jPointer *data,
Nd4jPointer *inputShapeInfo,
void *dZ,
2020-05-09 07:06:14 +02:00
Nd4jLong const* dZShapeInfo, Nd4jPointer *tadPointers, Nd4jPointer *offsetPointers) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
BUILD_SINGLE_SELECTOR(ArrayOptions::dataType(dZShapeInfo), sd::SpecialMethods,
2019-08-26 18:57:51 +02:00
::concatCpuGeneric(dimension, numArrays, data, inputShapeInfo, dZ, dZShapeInfo),
LIBND4J_TYPES);
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
/**
* This method saves
*/
2020-05-09 07:06:14 +02:00
sd::TadPack* tadOnlyShapeInfo(Nd4jLong const* dXShapeInfo, int *dimension, int dimensionLength) {
2019-08-26 18:57:51 +02:00
try {
auto pack = new TadPack();
2020-03-02 10:49:41 +01:00
*pack = sd::ConstantTadHelper::getInstance()->tadForDimensions(dXShapeInfo, dimension, dimensionLength);
2019-08-26 18:57:51 +02:00
return pack;
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-06-06 14:21:15 +02:00
}
2020-05-09 07:06:14 +02:00
Nd4jLong const* getPrimaryShapeInfo(sd::TadPack* pack) {
2019-07-24 14:14:54 +02:00
return pack->primaryShapeInfo();
}
2020-05-09 07:06:14 +02:00
Nd4jLong const* getPrimaryOffsets(sd::TadPack* pack) {
2019-07-24 14:14:54 +02:00
return pack->primaryOffsets();
}
2020-05-09 07:06:14 +02:00
Nd4jLong const* getSpecialShapeInfo(sd::TadPack* pack) {
2019-07-24 14:14:54 +02:00
return pack->specialShapeInfo();
}
2020-05-09 07:06:14 +02:00
Nd4jLong const* getSpecialOffsets(sd::TadPack* pack) {
2019-07-24 14:14:54 +02:00
return pack->specialOffsets();
}
2020-03-02 10:49:41 +01:00
Nd4jLong getNumberOfTads(sd::TadPack* pack) {
2019-07-24 14:14:54 +02:00
return pack->numberOfTads();
}
2020-03-02 10:49:41 +01:00
int getShapeInfoLength(sd::TadPack* pack) {
2019-07-24 14:14:54 +02:00
return pack->shapeInfoLength();
}
2019-07-22 13:34:08 +02:00
int memcpyConstantAsync(Nd4jLong dst, Nd4jPointer src, Nd4jLong size, int flags, Nd4jPointer reserved) {
2019-06-06 14:21:15 +02:00
cudaStream_t *pStream = reinterpret_cast<cudaStream_t *>(reserved);
cudaMemcpyKind kind;
DEBUG_KERNEL(pStream, -1);
switch (flags) {
case 0: {
kind = cudaMemcpyHostToHost;
}
break;
case 1: {
kind = cudaMemcpyHostToDevice;
}
break;
case 2: {
kind = cudaMemcpyDeviceToHost;
}
case 3: {
kind = cudaMemcpyDeviceToDevice;
}
break;
}
2019-08-26 18:57:51 +02:00
auto dZ = cudaMemcpyToSymbolAsync(deviceConstantMemory, const_cast<const void *>(src), size, dst, kind, *pStream);
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaMemcpyToSymbolAsync failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return 1;
}
2019-07-22 13:34:08 +02:00
Nd4jPointer getConstantSpace() {
2019-06-06 14:21:15 +02:00
Nd4jPointer dConstAddr;
cudaError_t dZ = cudaGetSymbolAddress(reinterpret_cast<void **>(&dConstAddr), deviceConstantMemory);
2019-08-26 18:57:51 +02:00
if (dZ != 0) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(dZ);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("cudaGetSymbolAddress failed");
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
return dConstAddr;
}
2019-07-22 13:34:08 +02:00
void pullRows(Nd4jPointer *extraPointers,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* xShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* zShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
Nd4jLong n,
Nd4jLong *indexes,
2020-05-09 07:06:14 +02:00
Nd4jLong const* tadShapeInfo,
Nd4jLong const* tadOffsets,
Nd4jLong const* zTadShapeInfo,
Nd4jLong const* zTadOffsets) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
dim3 launchDims(64, 256, 1024);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
2019-08-26 18:57:51 +02:00
BUILD_SINGLE_SELECTOR(xType, pullRowsKernelGeneric,
2020-01-04 11:27:50 +01:00
(launchDims, stream, dbX->special(), dbZ->special(), n, indexes, tadShapeInfo, tadOffsets, zTadShapeInfo, zTadOffsets),
2019-08-26 18:57:51 +02:00
LIBND4J_TYPES);
DEBUG_KERNEL(stream, -1);
2020-01-04 11:27:50 +01:00
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void average(Nd4jPointer *extras,
2020-05-09 07:06:14 +02:00
Nd4jPointer *x, Nd4jLong const* xShapeInfo,
Nd4jPointer *dx, Nd4jLong const* dXShapeInfo,
void *z, Nd4jLong const* zShapeInfo,
void *dz, Nd4jLong const* dzShapeInfo,
2019-06-06 14:21:15 +02:00
int n,
Nd4jLong length,
bool propagate) {
2019-08-26 18:57:51 +02:00
try {
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extras[1]);
int mode = getDeviceId(extras[3]);
auto dX = reinterpret_cast<void **>(dx);
2020-03-02 10:49:41 +01:00
if (sd::Environment::getInstance()->isDebugAndVerbose())
2019-08-26 18:57:51 +02:00
printf("averageFloat called\n");
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
2019-08-26 18:57:51 +02:00
// launching on gpu
if (mode == 0) {
dim3 launchDims(256, 256, 4096);
BUILD_SINGLE_SELECTOR(xType, averagingKernelGeneric, (launchDims, stream, dX, dz, n, length, propagate),
LIBND4J_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "AverageFloat(...) failed");
2019-08-26 18:57:51 +02:00
} else {
// launching on host memory
2020-03-02 10:49:41 +01:00
BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::averageGeneric(x, z, zShapeInfo, n, length, propagate),
2019-08-26 18:57:51 +02:00
LIBND4J_TYPES);
}
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void accumulate(Nd4jPointer *extras,
2020-05-09 07:06:14 +02:00
Nd4jPointer *x, Nd4jLong const* xShapeInfo,
Nd4jPointer *dx, Nd4jLong const* dXShapeInfo,
void *z, Nd4jLong const* zShapeInfo,
void *dz, Nd4jLong const* dzShapeInfo,
2019-06-06 14:21:15 +02:00
int n,
Nd4jLong length) {
2019-08-26 18:57:51 +02:00
try {
auto stream = reinterpret_cast<cudaStream_t *>(extras[1]);
int mode = getDeviceId(extras[3]);
auto dX = reinterpret_cast<void **>(dx);
2020-03-02 10:49:41 +01:00
if (sd::Environment::getInstance()->isDebugAndVerbose())
2019-08-26 18:57:51 +02:00
printf("accumulateFloat called\n");
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
2019-08-26 18:57:51 +02:00
// launching on gpu
if (mode == 0) {
dim3 launchDims(n, 256, 16384);
BUILD_SINGLE_SELECTOR(xType, accumulateKernelGeneric, (launchDims, stream, dX, dz, n, length),
LIBND4J_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "AccumulateFloat(...) failed");
2019-08-26 18:57:51 +02:00
} else {
// launching on host memory
2020-03-02 10:49:41 +01:00
BUILD_SINGLE_SELECTOR(xType, sd::SpecialMethods, ::accumulateGeneric(x, z, zShapeInfo, n, length),
2019-08-26 18:57:51 +02:00
LIBND4J_TYPES);
}
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void shuffle(Nd4jPointer *extras,
2019-06-06 14:21:15 +02:00
Nd4jPointer *x, Nd4jPointer *xShapeInfo,
Nd4jPointer *dx, Nd4jPointer *dXShapeInfo,
Nd4jPointer *z, Nd4jPointer *zShapeInfo,
Nd4jPointer *dz, Nd4jPointer *dZShapeInfo,
int N,
int *shuffleMap,
Nd4jPointer *tadShapeInfo,
Nd4jPointer *tadOffsets) {
2019-08-26 18:57:51 +02:00
try {
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extras[1]);
auto dX = reinterpret_cast<void **>(dx);
auto dZ = reinterpret_cast<void **>(dz);
2020-05-09 07:06:14 +02:00
auto xShape = reinterpret_cast<Nd4jLong**>(xShapeInfo);
auto dxShape = reinterpret_cast<Nd4jLong**>(dXShapeInfo);
2019-08-26 18:57:51 +02:00
auto tadOnlyShapeInfo = reinterpret_cast<Nd4jLong **>(tadShapeInfo);
auto tadOffset = reinterpret_cast<Nd4jLong **>(tadOffsets);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShape[0]);
2019-08-26 18:57:51 +02:00
dim3 launchDims(256, 512, 8192);
BUILD_SINGLE_SELECTOR(xType, shuffleKernelGeneric,
(launchDims, stream, dX, dxShape, dZ, N, shuffleMap, tadOnlyShapeInfo, tadOffset),
LIBND4J_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "shuffle(...) failed");
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
bool isExperimentalEnabled() {
2020-03-02 10:49:41 +01:00
return sd::Environment::getInstance()->isExperimentalBuild();
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void setOmpMinThreads(int threads) {
2020-03-02 10:49:41 +01:00
minThreads = sd::math::nd4j_max<int>(32, threads);
minThreads = sd::math::nd4j_min<int>(maxThreads, minThreads);
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
int getDevice() {
2020-03-02 10:49:41 +01:00
return sd::AffinityManager::currentDeviceId();
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void setElementThreshold(int num) {
2019-06-06 14:21:15 +02:00
// this is no-op for CUDA
}
2019-07-22 13:34:08 +02:00
void setTADThreshold(int num) {
2019-06-06 14:21:15 +02:00
// this is no-op for CUDA
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execSummaryStats(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
bool biasCorrected) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execSummaryStats(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
biasCorrected);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execSummaryStatsTad(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape,
2019-06-06 14:21:15 +02:00
bool biasCorrected,
2020-05-09 07:06:14 +02:00
Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbDimension});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execSummaryStats(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
reinterpret_cast<int *>(dbDimension->special()), dimensionLength,
tadShapeInfo, tadOffsets,
biasCorrected);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbDimension});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduce3(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduce3(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduce3Tad(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape,
Nd4jLong const* tadOnlyShapeInfo, Nd4jLong const* tadOffsets,
Nd4jLong const* yTadOnlyShapeInfo, Nd4jLong const* yTadOffsets) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(hXShapeInfo,
2020-01-04 11:27:50 +01:00
dimension,
2019-08-26 18:57:51 +02:00
shape::length(hDimensionShape));
auto tadLength = shape::length(tadPack.primaryShapeInfo());
auto yLength = shape::length(hYShapeInfo);
auto xLength = shape::length(hXShapeInfo);
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
if (tadLength == yLength || tadLength == xLength) {
// nd4j_printf("== way\n","");
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduce3(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadOnlyShapeInfo, tadOffsets, yTadOnlyShapeInfo, yTadOffsets);
2019-08-26 18:57:51 +02:00
} else
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduce3TAD(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadOnlyShapeInfo, yTadOffsets, yTadOnlyShapeInfo, yTadOffsets);
2019-08-26 18:57:51 +02:00
2020-01-04 11:27:50 +01:00
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduce3Scalar(Nd4jPointer *extraPointers,int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2020-01-04 11:27:50 +01:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduce3Scalar(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>());
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execScalarBool(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbScalar, Nd4jLong const* hScalarShapeInfo, Nd4jLong const* dScalarShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbScalar});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execScalarBool(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dbScalar->primary(), hScalarShapeInfo, dbScalar->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hScalarShapeInfo).specialAsT<Nd4jLong>(),
extraParams);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalar});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-23 07:36:48 +02:00
void execScalarBoolTad(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbScalars, Nd4jLong const* hScalarShapeInfo, Nd4jLong const* dScalarShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape,
Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets,
Nd4jLong const* tadShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbScalars});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execScalarBool(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParams,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dbScalars->primary(), hScalarShapeInfo, dbScalars->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hScalarShapeInfo).specialAsT<Nd4jLong>(),
dimension, dimensionLength,
tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalars});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execScalar(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbScalar, Nd4jLong const* hScalarShapeInfo, Nd4jLong const* dScalarShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbScalar});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execScalar(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
dbScalar->primary(), hScalarShapeInfo, dbScalar->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hScalarShapeInfo).specialAsT<Nd4jLong>(),
extraParams);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalar});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execScalarTad(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbScalars, Nd4jLong const* hScalarShapeInfo, Nd4jLong const* dScalarShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParams,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape,
Nd4jLong const* tadShapeInfo, Nd4jLong const* tadOffsets,
Nd4jLong const* tadShapeInfoZ, Nd4jLong const* tadOffsetsZ) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbScalars});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(hXShapeInfo);
auto yType = sd::ArrayOptions::dataType(hScalarShapeInfo);
auto zType = sd::ArrayOptions::dataType(hZShapeInfo);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
if (yType != xType && yType != sd::DataType::BOOL && !isExperimentalEnabled())
throw sd::datatype_exception::build("execScalar both operands must have same data type", xType, yType);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
dim3 launchDims(256, 256, 16384);
2019-06-06 14:21:15 +02:00
#ifdef __ND4J_EXPERIMENTAL__
2019-08-26 18:57:51 +02:00
BUILD_PAIRWISE_SELECTOR(xType, yType, zType, functions::scalar::ScalarTransform, ::executeCudaAlongDimension(launchDims, stream, opNum, dX, dXShapeInfo, dZ, dZShapeInfo, dScalars, extraParams, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ), LIBND4J_TYPES, LIBND4J_TYPES);
2019-06-06 14:21:15 +02:00
#else
2020-01-04 11:27:50 +01:00
BUILD_SINGLE_SELECTOR_THRICE(xType, functions::scalar::ScalarTransform, ::executeCudaAlongDimension(launchDims, stream, opNum, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(), dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(), dbScalars->special(), extraParams, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ), LIBND4J_TYPES);
2019-06-06 14:21:15 +02:00
#endif
2019-08-26 18:57:51 +02:00
DEBUG_KERNEL(stream, opNum);
2020-01-04 11:27:50 +01:00
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbScalars});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void execAggregate(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
void **arguments,
int numArguments,
Nd4jLong **shapes,
int numShapes,
int *indexArguments,
int numIndexArguments,
int **intArrays,
int numIntArrays,
void *realArguments,
int numRealArguments,
2020-03-02 10:49:41 +01:00
sd::DataType dtype) {
2019-08-26 18:57:51 +02:00
2019-06-06 14:21:15 +02:00
}
2019-07-26 09:22:44 +02:00
void batchExecutor(Nd4jPointer *extraPointers,
int numAggregates,
int opNum,
int maxArgs,
int maxShapes,
int maxIntArrays,
int maxIntArraySize,
int maxIdx,
int maxReals,
void *ptrToArguments,
2020-03-02 10:49:41 +01:00
sd::DataType dtype) {
2019-07-26 09:22:44 +02:00
}
2019-07-22 13:34:08 +02:00
void execAggregateBatch(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int numAggregates, int opNum,
int maxArgs, int maxShapes,
int maxIntArrays, int maxIntArraySize,
int maxIdx, int maxReals,
2020-03-02 10:49:41 +01:00
void *ptrToArguments, sd::DataType dtype) {
2019-08-26 18:57:51 +02:00
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execRandom(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
Nd4jPointer stateHost,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraArguments) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execRandom(&lc, opNum, stateHost,
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraArguments);
InteropDataBuffer::registerSpecialUse({dbZ}, {});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execRandom2(Nd4jPointer *extraPointers, int opNum, Nd4jPointer stateHost,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraArguments) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execRandom(&lc, opNum, stateHost,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraArguments);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execRandom3(Nd4jPointer *extraPointers, int opNum, Nd4jPointer stateHost,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraArguments) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execRandom(&lc, opNum, stateHost,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
extraArguments);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
Nd4jPointer initRandom(Nd4jPointer *extraPointers, long seed, long bufferSize, Nd4jPointer ptrToBuffer) {
2019-06-06 14:21:15 +02:00
unsigned long long *ptrHost = reinterpret_cast<unsigned long long *>(extraPointers[0]);
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
// we don't synchronize at random initialization, it's safe to go unsync here
// cudaStreamSynchronize(*stream);
auto ptrDev = reinterpret_cast<unsigned long long *>(ptrToBuffer);
2020-03-02 10:49:41 +01:00
auto buffer = new sd::random::RandomBuffer(seed, bufferSize, reinterpret_cast<uint64_t *>(ptrHost), reinterpret_cast<uint64_t *>(ptrDev));
2019-06-06 14:21:15 +02:00
buffer->propagateToDevice(buffer, *stream);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "initRandom(...) failed A");
2019-06-06 14:21:15 +02:00
// we generate sequence in the host memory
2020-03-02 10:49:41 +01:00
sd::random::Xoroshiro128 generator(buffer);
2019-06-06 14:21:15 +02:00
generator.refreshBuffer();
// and copy it to gpu
cudaMemcpyAsync(ptrDev, ptrHost, bufferSize * 8, cudaMemcpyHostToDevice, *stream);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "initRandom(...) failed B");
2019-06-06 14:21:15 +02:00
return buffer;
}
2019-07-22 13:34:08 +02:00
void destroyRandom(Nd4jPointer ptrBuffer) {
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
sd::random::RandomBuffer *buffer = reinterpret_cast<sd::random::RandomBuffer *> (ptrBuffer);
2019-06-06 14:21:15 +02:00
// FIXME: it's bad thing, but we can't know in advance, which stream(s) where using this generator in practice
cudaDeviceSynchronize();
delete buffer;
}
2019-07-22 13:34:08 +02:00
void refreshBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) {
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
sd::random::RandomBuffer *buffer = reinterpret_cast<sd::random::RandomBuffer *> (ptrRandom);
2019-06-06 14:21:15 +02:00
unsigned long long *ptrHost = reinterpret_cast<unsigned long long *>(extraPointers[0]);
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
cudaStreamSynchronize(*stream);
uint64_t *ptrDev = buffer->getDeviceBuffer();
// update rng state
buffer->setSeed(seed);
buffer->setOffset(0);
buffer->propagateToDevice(buffer, *stream);
// refresh buffer on host size
2020-03-02 10:49:41 +01:00
sd::random::Xoroshiro128 generator(buffer);
2019-06-06 14:21:15 +02:00
generator.refreshBuffer();
// copy back to gpu
cudaMemcpyAsync(ptrDev, ptrHost, buffer->getSize() * 8, cudaMemcpyHostToDevice, *stream);
}
2019-07-22 13:34:08 +02:00
void reSeedBuffer(Nd4jPointer *extraPointers, long seed, Nd4jPointer ptrRandom) {
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
sd::random::RandomBuffer *buffer = reinterpret_cast<sd::random::RandomBuffer *> (ptrRandom);
2019-06-06 14:21:15 +02:00
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
cudaStreamSynchronize(*stream);
// update rng state
buffer->reSeed(seed);
buffer->setOffset(0);
buffer->propagateToDevice(buffer, *stream);
}
/**
* Return the length of a shape buffer
* based on the pointer
* @param buffer the buffer pointer to check
* @return
*/
2019-07-22 13:34:08 +02:00
int lengthForShapeBufferPointer(Nd4jPointer buffer) {
2019-06-06 14:21:15 +02:00
auto shapeBuffer = reinterpret_cast<Nd4jLong *>(buffer);
return shape::shapeInfoLength(shape::rank(shapeBuffer));
}
/**
* The pointer to get the address for
*
* @param address the address to get the pointer
* @return the pointer for the given address
*/
2019-07-22 13:34:08 +02:00
Nd4jPointer pointerForAddress(Nd4jLong address) {
2019-06-06 14:21:15 +02:00
return reinterpret_cast<Nd4jPointer >(address);
}
2019-07-22 13:34:08 +02:00
void tear(Nd4jPointer *extras,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* xShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
Nd4jPointer *targets,
2020-05-09 07:06:14 +02:00
Nd4jLong const* zShapeInfo,
Nd4jLong const* tadShapeInfo,
Nd4jLong const* tadOffsets) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({}, {dbX});
2019-08-26 18:57:51 +02:00
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extras[1]);
dim3 launchDims(512, 512, 512);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
2019-08-26 18:57:51 +02:00
BUILD_SINGLE_SELECTOR(xType, tearKernelGeneric,
2020-01-04 11:27:50 +01:00
(launchDims, stream, dbX->special(), dXShapeInfo, targets, zShapeInfo, tadShapeInfo, tadOffsets),
2019-08-26 18:57:51 +02:00
LIBND4J_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "tearFloat(...) failed");
2020-01-04 11:27:50 +01:00
InteropDataBuffer::registerSpecialUse({}, {dbX});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
void prescanArrayRecursive(Nd4jPointer *extras, int *dZ, int *dX, int numElements, int level) {
auto stream = reinterpret_cast<cudaStream_t *>(extras[1]);
auto g_scanBlockSums = reinterpret_cast<int **>(extras[2]);
int blockSize = 512; // max size of the thread blocks
2020-03-02 10:49:41 +01:00
int numBlocks = sd::math::nd4j_max<int>(1, static_cast<int>(ceil(static_cast<float>(numElements) / (2.f * blockSize))));
2019-06-06 14:21:15 +02:00
int numThreads;
if (numBlocks > 1)
numThreads = blockSize;
2020-03-02 10:49:41 +01:00
else if (sd::isPowerOfTwo(numElements))
2019-06-06 14:21:15 +02:00
numThreads = numElements / 2;
else
2020-03-02 10:49:41 +01:00
numThreads = sd::floorPow2(numElements);
2019-06-06 14:21:15 +02:00
int numEltsPerBlock = numThreads * 2;
// if this is a non-power-of-2 array, the last block will be non-full
// compute the smallest power of 2 able to compute its scan.
int numEltsLastBlock =
numElements - (numBlocks-1) * numEltsPerBlock;
2020-03-02 10:49:41 +01:00
int numThreadsLastBlock = sd::math::nd4j_max<int>(1, numEltsLastBlock / 2);
2019-06-06 14:21:15 +02:00
int np2LastBlock = 0;
int sharedMemLastBlock = 0;
if (numEltsLastBlock != numEltsPerBlock) {
np2LastBlock = 1;
if(!isPowerOfTwo(numEltsLastBlock))
numThreadsLastBlock = floorPow2(numEltsLastBlock);
unsigned int extraSpace = (2 * numThreadsLastBlock) / NUM_BANKS;
sharedMemLastBlock = sizeof(int) * (2 * numThreadsLastBlock + extraSpace);
}
// padding space is used to avoid shared memory bank conflicts
int extraSpace = numEltsPerBlock / NUM_BANKS;
int sharedMemSize = sizeof(int) * (numEltsPerBlock + extraSpace);
// setup execution parameters
// if NP2, we process the last block separately
dim3 grid(max(1, numBlocks - np2LastBlock), 1, 1);
dim3 threads(numThreads, 1, 1);
dim3 gridOnes(1, 1, 1);
dim3 threadsOnes(numThreadsLastBlock, 1, 1);
if (sharedMemSize < 2048)
sharedMemSize = 2048;
if (sharedMemLastBlock < 2048)
sharedMemLastBlock = 2048;
// execute the scan
if (numBlocks > 1) {
2020-03-02 10:49:41 +01:00
sd::prescanLauncher<true, false>(grid, threads, sharedMemSize, stream, dZ, dX, g_scanBlockSums[level], numThreads * 2, 0, 0);
2019-06-06 14:21:15 +02:00
if (np2LastBlock) {
2020-03-02 10:49:41 +01:00
sd::prescanLauncher<true, true>(gridOnes, threadsOnes, sharedMemLastBlock, stream, dZ, dX, g_scanBlockSums[level], numEltsLastBlock, numBlocks - 1, numElements - numEltsLastBlock);
2019-06-06 14:21:15 +02:00
}
// After scanning all the sub-blocks, we are mostly done. But now we
// need to take all of the last values of the sub-blocks and scan those.
// This will give us a new value that must be sdded to each block to
// get the final results.
// recursive (CPU) call
prescanArrayRecursive(extras, g_scanBlockSums[level], g_scanBlockSums[level], numBlocks, level+1);
2020-03-02 10:49:41 +01:00
sd::uniformAdd<<<grid, threads, 1024, *stream>>>(dZ, g_scanBlockSums[level], numElements - numEltsLastBlock, 0, 0);
2019-06-06 14:21:15 +02:00
if (np2LastBlock) {
2020-03-02 10:49:41 +01:00
sd::uniformAdd<<<1, numThreadsLastBlock, 1024, *stream>>>(dZ, g_scanBlockSums[level], numEltsLastBlock, numBlocks - 1, numElements - numEltsLastBlock);
2019-06-06 14:21:15 +02:00
}
} else if (isPowerOfTwo(numElements)) {
2020-03-02 10:49:41 +01:00
sd::prescanLauncher<false, false>(grid, threads, sharedMemSize, stream, dZ, dX, 0, numThreads * 2, 0, 0);
2019-06-06 14:21:15 +02:00
} else {
2020-03-02 10:49:41 +01:00
sd::prescanLauncher<false, true>(grid, threads, sharedMemSize, stream, dZ, dX, 0, numElements, 0, 0);
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "prescanArray(...) failed");
2019-06-06 14:21:15 +02:00
}
////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void execReduce3All(Nd4jPointer *extraPointers,
2019-06-06 14:21:15 +02:00
int opNum,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbX, Nd4jLong const* hXShapeInfo, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
void *extraParamsVals,
2020-05-09 07:06:14 +02:00
OpaqueDataBuffer *dbY, Nd4jLong const* hYShapeInfo, Nd4jLong const* dYShapeInfo,
OpaqueDataBuffer *dbZ, Nd4jLong const* hZShapeInfo, Nd4jLong const* dZShapeInfo,
OpaqueDataBuffer *dbDimension, Nd4jLong const* hDimensionShape, Nd4jLong const* dDimensionShape,
Nd4jLong const* xTadShapeInfo, Nd4jLong const* xOffsets,
Nd4jLong const* yTadShapeInfo, Nd4jLong const* yOffsets) {
2019-08-26 18:57:51 +02:00
try {
2020-01-04 11:27:50 +01:00
InteropDataBuffer::prepareSpecialUse({dbZ}, {dbX, dbY, dbDimension});
InteropDataBuffer::preparePrimaryUse({}, {dbDimension});
auto dimension = reinterpret_cast<int *>(dbDimension->primary());
2019-08-26 18:57:51 +02:00
int dimensionLength = static_cast<int>(shape::length(hDimensionShape));
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-01-04 11:27:50 +01:00
NativeOpExecutioner::execReduce3All(&lc, opNum,
dbX->primary(), hXShapeInfo, dbX->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hXShapeInfo).specialAsT<Nd4jLong>(),
extraParamsVals,
dbY->primary(), hYShapeInfo, dbY->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hYShapeInfo).specialAsT<Nd4jLong>(),
dbZ->primary(), hZShapeInfo, dbZ->special(), ConstantShapeHelper::getInstance()->bufferForShapeInfo(hZShapeInfo).specialAsT<Nd4jLong>(),
reinterpret_cast<int *>(dbDimension->special()), dimensionLength,
xTadShapeInfo, xOffsets, yTadShapeInfo, yOffsets);
InteropDataBuffer::registerSpecialUse({dbZ}, {dbX, dbY});
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void sort(Nd4jPointer *extraPointers,
2020-05-09 07:06:14 +02:00
void *x, Nd4jLong const* xShapeInfo,
void *dX, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
bool descending) {
2019-08-26 18:57:51 +02:00
try {
cudaStream_t *stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
auto xLength = shape::length(xShapeInfo);
auto xEWS = shape::elementWiseStride(xShapeInfo);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
// check if xLength is a power of 2, and use bitonic sort, if that's the case
if ((xLength != 0) && ((xLength & (xLength - 1)) == 0) && (xLength <= 1024 * 1024 * 10)) {
2020-03-02 10:49:41 +01:00
int numThreads = sd::math::nd4j_min<int>(512, xLength);
2019-08-26 18:57:51 +02:00
int numBlocks = xLength / numThreads;
if (xLength % numThreads > 0 || numBlocks == 0)
numBlocks++;
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
dim3 launchDims(numBlocks, numThreads, 32768);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
for (int k = 2; k <= xLength; k = 2 * k) {
for (int j = k >> 1; j > 0; j = j >> 1) {
BUILD_SINGLE_SELECTOR(xType, bitonicSortStepGeneric,
(launchDims, stream, dX, dXShapeInfo, j, k, xLength, descending),
LIBND4J_TYPES);
}
}
} else {
2020-03-02 10:49:41 +01:00
int numThreads = sd::math::nd4j_min<int>(512, xLength);
2019-08-26 18:57:51 +02:00
int numBlocks = xLength / numThreads;
if (xLength % numThreads > 0 || numBlocks == 0)
numBlocks++;
2020-03-02 10:49:41 +01:00
numBlocks = sd::math::nd4j_min<int>(512, numBlocks);
2019-08-26 18:57:51 +02:00
dim3 launchDims(numBlocks, numThreads, 32768);
int max = 2, dg = 0;
while (max < xLength) {
max <<= 1;
dg++;
}
max <<= 1;
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
for (int window = 2; window < max; window <<= 1) {
int n = window;
int rev = 0;
do {
int half = n >> 1;
BUILD_SINGLE_SELECTOR(xType, bitonicArbitraryStepGeneric,
(launchDims, stream, dX, dXShapeInfo, n, xLength, rev, descending),
LIBND4J_TYPES);
n >>= 1;
rev = 1;
} while (n > 1);
}
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "sort(...) failed");
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-06-06 14:21:15 +02:00
}
}
2019-07-22 13:34:08 +02:00
void sortByKey(Nd4jPointer *extraPointers,
2020-05-09 07:06:14 +02:00
void *x, Nd4jLong const* xShapeInfo,
void *dX, Nd4jLong const* dXShapeInfo,
void *y, Nd4jLong const* yShapeInfo,
void *dy, Nd4jLong const* dyShapeInfo,
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
bool descending) {
2019-08-26 18:57:51 +02:00
try {
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
auto xLength = shape::length(xShapeInfo);
2019-11-28 19:08:51 +01:00
auto yLength = shape::length(yShapeInfo);
2019-08-26 18:57:51 +02:00
auto xEWS = shape::elementWiseStride(xShapeInfo);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
auto yType = sd::ArrayOptions::dataType(yShapeInfo);
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-11-28 19:08:51 +01:00
if (shape::isEmpty(xShapeInfo) || shape::isEmpty(yShapeInfo))
return;
if (xLength != yLength)
throw std::runtime_error("sortByKey: keys and values must have the same size");
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
// check if xLength is a power of 2, and use bitonic sort, if that's the case
if ((xLength != 0) && ((xLength & (xLength - 1)) == 0) && (xLength <= 1024 * 1024 * 10)) {
2020-03-02 10:49:41 +01:00
int numThreads = sd::math::nd4j_min<int>(512, xLength);
2019-08-26 18:57:51 +02:00
int numBlocks = xLength / numThreads;
if (xLength % numThreads > 0 || numBlocks == 0)
numBlocks++;
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
dim3 launchDims(numBlocks, numThreads, 32768);
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
for (int k = 2; k <= xLength; k = 2 * k) {
for (int j = k >> 1; j > 0; j = j >> 1) {
BUILD_DOUBLE_SELECTOR(xType, yType, bitonicSortStepGenericKey,
(launchDims, stream, dX, dXShapeInfo, dy, dyShapeInfo, j, k, xLength, descending),
LIBND4J_TYPES, LIBND4J_TYPES);
}
}
} else {
2020-03-02 10:49:41 +01:00
int numThreads = sd::math::nd4j_min<int>(512, xLength);
2019-08-26 18:57:51 +02:00
int numBlocks = xLength / numThreads;
if (xLength % numThreads > 0 || numBlocks == 0)
numBlocks++;
2020-03-02 10:49:41 +01:00
numBlocks = sd::math::nd4j_min<int>(512, numBlocks);
2019-08-26 18:57:51 +02:00
dim3 launchDims(numBlocks, numThreads, 32768);
int max = 2, dg = 0;
while (max < xLength) {
max <<= 1;
dg++;
}
max <<= 1;
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
for (int window = 2; window < max; window <<= 1) {
int n = window;
int rev = 0;
do {
int half = n >> 1;
BUILD_DOUBLE_SELECTOR(xType, yType, bitonicArbitraryStepGenericKey,
(launchDims, stream, dX, dXShapeInfo, dy, dyShapeInfo, n, xLength, rev, descending),
LIBND4J_TYPES, LIBND4J_TYPES);
n >>= 1;
rev = 1;
} while (n > 1);
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
}
}
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
}
}
2019-07-22 13:34:08 +02:00
void sortByValue(Nd4jPointer *extraPointers,
2020-05-09 07:06:14 +02:00
void *x, Nd4jLong const* xShapeInfo,
void *dX, Nd4jLong const* dXShapeInfo,
void *y, Nd4jLong const* yShapeInfo,
void *dy, Nd4jLong const* dyShapeInfo,
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
bool descending) {
2019-08-26 18:57:51 +02:00
try {
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
auto xLength = shape::length(xShapeInfo);
2019-11-28 19:08:51 +01:00
auto yLength = shape::length(yShapeInfo);
2019-08-26 18:57:51 +02:00
auto xEWS = shape::elementWiseStride(xShapeInfo);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(yShapeInfo);
auto yType = sd::ArrayOptions::dataType(xShapeInfo);
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-11-28 19:08:51 +01:00
if (shape::isEmpty(xShapeInfo) || shape::isEmpty(yShapeInfo))
return;
if (xLength != yLength)
throw std::runtime_error("sortByValue: keys and values must have the same size");
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
// check if xLength is a power of 2, and use bitonic sort, if that's the case
if ((xLength != 0) && ((xLength & (xLength - 1)) == 0) && (xLength <= 1024 * 1024 * 10)) {
2020-03-02 10:49:41 +01:00
int numThreads = sd::math::nd4j_min<int>(512, xLength);
2019-08-26 18:57:51 +02:00
int numBlocks = xLength / numThreads;
if (xLength % numThreads > 0 || numBlocks == 0)
numBlocks++;
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
dim3 launchDims(numBlocks, numThreads, 32768);
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
2019-08-26 18:57:51 +02:00
for (int k = 2; k <= xLength; k = 2 * k) {
for (int j = k >> 1; j > 0; j = j >> 1) {
BUILD_DOUBLE_SELECTOR(xType, yType, bitonicSortStepGenericKey,
(launchDims, stream, dy, dyShapeInfo, dX, dXShapeInfo, j, k, xLength, descending),
LIBND4J_TYPES, LIBND4J_TYPES);
}
}
} else {
2020-03-02 10:49:41 +01:00
int numThreads = sd::math::nd4j_min<int>(512, xLength);
2019-08-26 18:57:51 +02:00
int numBlocks = xLength / numThreads;
if (xLength % numThreads > 0 || numBlocks == 0)
numBlocks++;
2020-03-02 10:49:41 +01:00
numBlocks = sd::math::nd4j_min<int>(512, numBlocks);
2019-08-26 18:57:51 +02:00
dim3 launchDims(numBlocks, numThreads, 32768);
int max = 2, dg = 0;
while (max < xLength) {
max <<= 1;
dg++;
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
}
max <<= 1;
2019-08-26 18:57:51 +02:00
for (int window = 2; window < max; window <<= 1) {
int n = window;
int rev = 0;
do {
int half = n >> 1;
BUILD_DOUBLE_SELECTOR(xType, yType, bitonicArbitraryStepGenericKey,
(launchDims, stream, dy, dyShapeInfo, dX, dXShapeInfo, n, xLength, rev, descending),
LIBND4J_TYPES, LIBND4J_TYPES);
n >>= 1;
rev = 1;
} while (n > 1);
}
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
}
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
}
}
2019-07-22 13:34:08 +02:00
void sortTadByKey(Nd4jPointer *extraPointers,
2020-05-09 07:06:14 +02:00
void *x, Nd4jLong const* xShapeInfo,
void *dX, Nd4jLong const* dXShapeInfo,
void *y, Nd4jLong const* yShapeInfo,
void *dy, Nd4jLong const* dyShapeInfo,
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
int *dimension,
int dimensionLength,
bool descending) {
2019-08-26 18:57:51 +02:00
try {
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
auto context = extraPointers[0] == 0 ? LaunchContext::defaultContext()
: reinterpret_cast<LaunchContext *>(extraPointers[0]);
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
2019-08-26 18:57:51 +02:00
dim3 launchDims((int) tadPack.numberOfTads(), 256, 2048);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
auto yType = sd::ArrayOptions::dataType(yShapeInfo);
2019-08-26 18:57:51 +02:00
BUILD_DOUBLE_SELECTOR(xType, yType, oesTadGenericKey,
(launchDims, stream, dX, dXShapeInfo, dy, dyShapeInfo, nullptr, dimensionLength, tadPack.platformShapeInfo(), tadPack.platformOffsets(), descending),
LIBND4J_TYPES, LIBND4J_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "sortTadKey(...) failed");
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
}
2019-07-22 13:34:08 +02:00
void sortTadByValue(Nd4jPointer *extraPointers,
2020-05-09 07:06:14 +02:00
void *x, Nd4jLong const* xShapeInfo,
void *dX, Nd4jLong const* dXShapeInfo,
void *y, Nd4jLong const* yShapeInfo,
void *dy, Nd4jLong const* dyShapeInfo,
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
int *dimension,
int dimensionLength,
bool descending) {
2019-08-26 18:57:51 +02:00
try {
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
auto context = extraPointers[0] == 0 ? LaunchContext::defaultContext()
: reinterpret_cast<LaunchContext *>(extraPointers[0]);
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
2019-08-26 18:57:51 +02:00
dim3 launchDims((int) tadPack.numberOfTads(), 256, 2048);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(yShapeInfo);
auto yType = sd::ArrayOptions::dataType(xShapeInfo);
2019-08-26 18:57:51 +02:00
BUILD_DOUBLE_SELECTOR(xType, yType, oesTadGenericKey,
(launchDims, stream, dy, dyShapeInfo, dX, dXShapeInfo, nullptr, dimensionLength, tadPack.platformShapeInfo(), tadPack.platformOffsets(), descending),
LIBND4J_TYPES, LIBND4J_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "sortTadValue(...) failed");
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
Merge master to upstream (#7945)
* Shugeo strided slice zeros (#14)
* Modified strided_slice op to properly work with empty-like shapes.
* Fixed test for reduce_mean with empty-like input.
* [WIP] Last merge (#15)
* correct logsoftmax looss (#2)
* Small SameDiff listener fix (#4)
* Various fixes (#6)
* #7839 Fix for asXMatrix and tests
* #7866 EmbeddingSequenceLayer dtype fix + test
* #7856 SameDiff save/load stream methods
* #7859 RegressionEvaluation rank 4 fix + tests + axis configuration
* EvaluationBinary 3d/4d
* More evaluation 3d/4d tests
* #7847 Evaluation empty checks
* Small test ifx
* #7848 Fix median edge case
* Improve DL4J samediff layer tests
* [WIP] FastText wrapper implemented (#8)
* FastText implemented
* Some fixes
* Fix shapes for wordsNearest
* Validation of input vectors
* Fixes
* Fixed test
* Thread tagged
* Some tweaks
* setContextClassLoader for DeallocatorServiceThread
* Numpy format tests (#1)
* Various fixes (#11)
* #7852 SameDiff gather fix
* #7892 SameDiff placeholder to constant conversion
* #7890 validate input rank for MLN/CG init methods
* Fix broken permute shape calculation
* Permute and gather fixes
* Tests
* #7850 LogSumExp fix + test
* Handful of test fixes
* Empty arrays with non-scalar shapes (#10)
* minor rearrangements for lambdas
* empty tensors with non-scalar shapes
* numpy empty tensors with non-scalar shapes
* few more empty tweaks
* Small fixes
* conv3d signature update
* micro fix in batchnorm mkldnn
* Import fixes
* Fix
* MKL-DNN update
* Small fill fix
* fill with empty input + test
* Fixes
* Small error improvement
* Fix
* one special test
* couple of fixes for lstm
* Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone
* Fixes
* FP16
* Unsigned
* BFloat16
* Fill op - empty tweaks
* - couple of fixes for empty arrays construction
- stack updated
* strided slice fix
* one transform test
* provide method for reducing shapeInfo in case of input array is empty
* Fixed reduceAlongDimensions to use empty input properly.
* couple of broadcast tests
* couple of tests broadcast tests + tweak to make them pass
* add check of non-empty to methods producing sub-arrays
* Fixed reshapeC with zeros in shape.
* complete empty check in reduce_... legacy ops
* Concat and cumsum/prod
* Tweak to empty shape inference on import
* add empty check to the rest of reduce legacy ops
* one more test
* correct typo in evalReduceShapeInfoEmpty
* Added tests for reduce_* ops to tests with zero shapes.
* few more tests for empty reductions
* Fixed strided_slice op with empty case and tests.
* one more empty reduction test
* Fixed strided_slice test.
* add empty check to NDArray::reshapei
* infOrMax
* empty min/max with infinity tests
* made unstack working correctly with empty arrays
* few IndexReduce tests + tweaks for empty shapes
* add test for empty concat
* few tests fixed
* Validation fix for reductions on empty shapes
* Reverse fix
* Reduction shape calc fixes
* SameDiff.generateOutputVariable: don't use shape function to determine number of outputs
* Range fix
* - NDArray constructor updated for scalars/empty arrays
- few tests fixed
* More fixes
* Empty creator fixes
* concat fix
* concat fix
* TF import tests: allow 'both all NaN' and 'both all inf' to pass
* Slice, zero fraction, and reshape fixes
* transpose, gather
* Zero fraction
* scalar cast fix
* Empty reduction axis support
* few more tests fixed
* Fixed input checks conforming with TF for concat op and tests.
* few tests fixed
* matmul scalar shape fix
* Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats.
* broadcast bool fix
* few more tests
* few more tests
* correct evalReduceShapeInfoEmpty
* argmax/argmin + tests
* one more empty edge case + one more test
* argmax/argmin/realdiv_bp tweaks
* empty reshape test + fix
* Helper fixes
* Small fixes
* Gather test fix
* Gather test fix
* Small fixes
* reduce scalar zero values
* scalar mean workaround
* Remove debug code
* along dim mean workaround
* one more test
* - equalsTo() tweak for empty arrays
- one more test
* broadcast tweaks
* [WIP] Fixing outstanding issues for NLP (#9)
* Avoid using not-inited objects
* Test fixed.
* Redundant method avoided for models like FastText
* KMeans++ implementation
* KMeans++ implementation
* Disable parallel execution
* KMeans++
* Tests
* Dev branch merge (#16)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Fix some issues on master (#17)
* Fix DataVec test issue
* Fix issue with dl4j SameDiff output layer
* Dtype fix for lambda layers
* #7912 BertIterator dtype fix (use float32 not global default)
* [WIP] Next set of CUDA stuff (#7)
New CUDA implementations and improvements
* bad file
* Dev branch master merge (#23)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* SameDiff ops, TF import and fixes (#24)
* CheckNumerics tests + fixes + misc fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fake quant
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* FakeQuantWithMinMaxArgs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* CheckNumerics fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix libnd4j ALL_INTS and ALL_FLOATS declaration (uint and bfloat types)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Javadoc
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Exception tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix for out of scope stack allocated var use
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignores
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Ignore for known failing test (already logged issue)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Merge upstream to fork (#25)
* Add thousand-separator commas to TotalParams (#7915)
* Add thousand-separator commas to TotalParams
The number of parameters can be quite large, and it would help the reading of the summary printout to have the TotalParams column & values at the bottom have thousand-separator-commas in them.
* Add thousand-separator commas to MultiLayerNetwork
Corresponding change to MultiLayerNetwork
Signed-off-by: Jxtps Jxtps <jxtps435@gmail.com>
* Update contributing and issue/PR templates (#7934)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Fix link to AdaDelta paper (#7942)
Fix link to AdaDelta paper hosted on matthewzeiler.com
Signed-off-by: Jxtps
* Fixes, and ignores for known/logged failing issues (#7943)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* SameDiff + DL4J/SameDiff: Multiple fixes (#28)
* #7919 HDF5 attribute buffer length fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7909 Arbiter constructor exception ux improvements
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7925 RNN output layer length checks
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Add listener for validating inputs are not incorrectly modified
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7939 Integrate NonInplaceValidationListener into tests
* #7844 DL4J SameDiff fixes for variable minibatch size
* DL4J SameDiff fixes - ensure gradient for input placeholder is available
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tweaks to ExternalErrorsFunction - use placeholders, make more robust
* Another fix
* More fixes
* More SameDiff/DL4J fixes
* Scope out scalar array creation in BaseScalarOp
* Remove debug code
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Final dev branch merge (#29)
* SameDiff: convertDataType and gradient check util improvements (#12)
* GradCheck util improvements
* StopGradient constructor + test
* SameDiff: Add datatype conversion
* Javadoc and add DataType.isNumerical()
* Small fix
* Fix SameDiff TF import test cases intermediate naming (workaround for bad default)
* TFGraphTestAllHelper: check intermediates in execution order
* Add missing debug listener
* [WIP] lstmBlock fix + other changes (#13)
- fixes lstmBlock issue
- changes NDArray method reshape(), permute(), transpose() by making them return instance instead of pointer
- CheckNumerics op
- fixes for ReduceBool IsInfOrNan & IsFinite
* Small test fix
* CheckNumerics op wrapper
* Compatibility of deserialization (#18)
Signed-off-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
* SameDiff: add activation gradient checking support for debugging (#19)
* SameDiff gradient checker: first pass on activation gradient checks
* Fixes + tests for activation gradient checking
* Javadoc
* [WIP] Some nd4j data type corrections (#20)
* Adjust data type
* Set correct Data type.
* Size of proper data type.
* fix averaged cpu load (#22)
* [WIP] Multiple dataset iterators (#27)
* Splitting dataset into arbitrary number
* Fixes
* Multiple split of iterator
* Test
* Test
* Some fixes
* signature change
* one more tweak
Signed-off-by: raver119 <raver119@gmail.com>
* one more test for sequential use of DataSetIteratorSplitter
Signed-off-by: raver119 <raver119@gmail.com>
* Fixes
* Fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* one more test for Alexander
Signed-off-by: raver119 <raver119@gmail.com>
* minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Some fixes
* Some fixes
* couple of assertions tweaked
Signed-off-by: raver119 <raver119@gmail.com>
* MDS splitter test :/
Signed-off-by: raver119 <raver119@gmail.com>
* Minor refactoring
* Multi dataset
* Some fixes
* More tests
* Small number of test fixes/improvements (failures on CI) (#31)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] More CUDA stuff (#26)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* LRN BP CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* less memory
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed bug with crop_and_resize op helper.
* get rid of unnecessary index-calculation dunction
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed sort with nth_element cuda-based helper.
* Refactored nth_element.
* Refactored nth_element op and tests.
* Modified usage of dim array with sortTad routine.
* Refactored main routine of helper for non_max_image_suppression op.
* non_max_image_suppression op helper with cuda kernel implementation. Initial revision.
* fix vol2col cuda kernel
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* topK concept
Signed-off-by: raver119 <raver119@gmail.com>
* unsorted topK with scanWitdh of 1
Signed-off-by: raver119 <raver119@gmail.com>
* correct vol2col tests
* sorted/unsorted topK
Signed-off-by: raver119 <raver119@gmail.com>
* implementation and fixing col2im/col2vol
* Corrected usage flags with input/output with reverse op.
* dup is const now
Signed-off-by: raver119 <raver119@gmail.com>
* percentile op
Signed-off-by: raver119 <raver119@gmail.com>
* group tests for mapool2d
Signed-off-by: Yurii <yurii@skymind.io>
* special test for george
Signed-off-by: raver119 <raver119@gmail.com>
* less threads for sortTad
Signed-off-by: raver119 <raver119@gmail.com>
* provide conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* remove auther in sort tad kernel code
Signed-off-by: Yurii <yurii@skymind.io>
* provide depthwise_conv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* - max_pooling_with_argmax
- null check for special use
Signed-off-by: raver119 <raver119@gmail.com>
* dts cuda
Signed-off-by: raver119 <raver119@gmail.com>
* provide sconv2d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* std cuda
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op to conform TF implementation.
* Improved suppression helper.
* provide pooling3d for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* more of minor lstm rearrangements
Signed-off-by: raver119 <raver119@gmail.com>
* (bi)dynamic_rnn
Signed-off-by: raver119 <raver119@gmail.com>
* templates init order
Signed-off-by: raver119 <raver119@gmail.com>
* Refactored non_max_suppression op.
* Added cuda kernel for non_max_suppression.
* CPU sort by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value
Signed-off-by: raver119 <raver119@gmail.com>
* CPU sort TAD by key/value tests
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminate compiler error with cuda implementation.
* - repaired gradCheck in cuda
- provide conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* missed signature
Signed-off-by: raver119 <raver119@gmail.com>
* provide depthwise_conv2d_bp for cuda
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of lup helper with cuda kernel. Initial commit.
* further work on backprops for convolutions
Signed-off-by: Yurii <yurii@skymind.io>
* CUDA linear sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA tad sort by key/val
Signed-off-by: raver119 <raver119@gmail.com>
* start providing of backprop for pooling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* Added atomicAdd for bool datatype.
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic partition scalar CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* important comment
Signed-off-by: raver119 <raver119@gmail.com>
* fix pooling2d/3d backprop helpers
Signed-off-by: Yurii <yurii@skymind.io>
* Added non-linear test with dynamic_partition.
* Improved test for dynamic_partition.
* dynamic_partition TAD concept
Signed-off-by: raver119 <raver119@gmail.com>
* - dynamic_partition TAD CUDA impl
- dynamic_partition TAD CPU fix
Signed-off-by: raver119 <raver119@gmail.com>
* - rewrite cpu code for usampling2d/3d
- write cuda code for usampling2d/3d
Signed-off-by: Yurii <yurii@skymind.io>
* dynamic_stitch CUDA vector case
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case concept
Signed-off-by: raver119 <raver119@gmail.com>
* dynamic_stitch CUDA TAD case impl
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for dynamic_stitch 3D-4D cases.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed type check for dynamic stitch.
* min/max bp
Signed-off-by: raver119 <raver119@gmail.com>
* rewrite code for upsampling2d/3d cpu
Signed-off-by: Yurii <yurii@skymind.io>
* reduce min/max/norm_max bp
Signed-off-by: raver119 <raver119@gmail.com>
* lup implementation. Additional enhancements.
* provide code for upsamling2d/3d backprop
Signed-off-by: Yurii <yurii@skymind.io>
* weightedCrossEntropyWithLogits
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed template math atomicMul for 64bit ints.
* Refactored dynamic_partition_bp op.
* inverseBroadcast fix
Signed-off-by: raver119 <raver119@gmail.com>
* DynamicPartitionBP test datatype fixed.
* - nd4j_atomicMul Windows fix
- cpu/NDArrayLambda.hpp excluded from CUDA
Signed-off-by: raver119 <raver119@gmail.com>
2019-06-27 17:37:04 +02:00
}
2019-07-22 13:34:08 +02:00
void sortTad(Nd4jPointer *extraPointers,
2020-05-09 07:06:14 +02:00
void *x, Nd4jLong const* xShapeInfo,
void *dX, Nd4jLong const* dXShapeInfo,
2019-06-06 14:21:15 +02:00
int *dimension,
int dimensionLength,
2020-05-09 07:06:14 +02:00
Nd4jLong const* tadShapeInfo,
Nd4jLong const* tadOffsets,
2019-06-06 14:21:15 +02:00
bool descending) {
2019-08-26 18:57:51 +02:00
try {
// to be implemented
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
auto context = extraPointers[0] == 0 ? LaunchContext::defaultContext()
: reinterpret_cast<LaunchContext *>(extraPointers[0]);
2020-03-02 10:49:41 +01:00
auto tadPack = sd::ConstantTadHelper::getInstance()->tadForDimensions(xShapeInfo, dimension, dimensionLength);
2019-08-26 18:57:51 +02:00
dim3 launchDims((int) tadPack.numberOfTads(), 512, 33768);
2020-03-02 10:49:41 +01:00
auto xType = sd::ArrayOptions::dataType(xShapeInfo);
2019-08-26 18:57:51 +02:00
BUILD_SINGLE_SELECTOR(xType, oesTadGeneric,
(launchDims, stream, dX, dXShapeInfo, nullptr, dimensionLength, tadShapeInfo, tadOffsets, descending),
LIBND4J_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "sortTad(...) failed");
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void sortCooIndices(Nd4jPointer *extraPointers, Nd4jLong *indices, void *values, Nd4jLong length, int rank) {
2019-06-06 14:21:15 +02:00
throw std::runtime_error("sortCooIndices:: Not implemented yet");
}
2019-07-22 13:34:08 +02:00
Nd4jLong* mmapFile(Nd4jPointer *extraPointers, const char *fileName, Nd4jLong length) {
2019-06-06 14:21:15 +02:00
return nullptr;
}
2019-07-22 13:34:08 +02:00
void munmapFile(Nd4jPointer *extraPointers, Nd4jLong* ptrMap, Nd4jLong length) {
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
sd::graph::ResultWrapper* executeFlatGraph(Nd4jPointer *extraPointers, Nd4jPointer flatBufferPointer) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
return sd::graph::GraphExecutioner::executeFlatBuffer(flatBufferPointer);
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
Nd4jLong getResultWrapperSize(sd::graph::ResultWrapper* ptr) {
2019-07-24 14:14:54 +02:00
return ptr->size();
}
2020-03-02 10:49:41 +01:00
Nd4jPointer getResultWrapperPointer(sd::graph::ResultWrapper* ptr) {
2019-07-24 14:14:54 +02:00
return ptr->pointer();
}
2019-06-06 14:21:15 +02:00
2019-07-22 13:34:08 +02:00
const char* getAllCustomOps() {
2020-03-02 10:49:41 +01:00
return sd::ops::OpRegistrator::getInstance()->getAllCustomOperations();
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
sd::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, sd::ops::DeclarableOp* op, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) {
sd::graph::VariableSpace varSpace;
2019-06-06 14:21:15 +02:00
Context block(2, &varSpace);
2020-03-02 10:49:41 +01:00
sd::ShapeList inShapes;
2019-06-06 14:21:15 +02:00
for (int e = 0; e < numIArgs; e++)
block.getIArguments()->push_back(iArgs[e]);
for (int e = 0; e < numTArgs; e++)
block.getTArguments()->push_back(tArgs[e]);
for (int e = 0; e < numBArgs; e++)
block.getBArguments()->push_back(bArgs[e]);
2020-01-30 16:46:12 +01:00
for (int e = 0; e < numDArgs; e++)
2020-03-02 10:49:41 +01:00
block.getDArguments()->push_back((sd::DataType) dArgs[e]);
2020-01-30 16:46:12 +01:00
2019-06-06 14:21:15 +02:00
for (int e = 0; e < numInputShapes; e++) {
auto shape_ = reinterpret_cast<Nd4jLong *>(inputShapes[e]);
// we shouldn't copy buffer if that's empty array
2020-03-02 10:49:41 +01:00
void *buffer_ = sd::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e];
void *bufferD_ = sd::ArrayOptions::arrayType(shape_) == ArrayType::EMPTY ? nullptr : inputBuffers[e + numInputShapes];
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
auto array = new sd::NDArray(buffer_, bufferD_, shape_);
2019-06-06 14:21:15 +02:00
// block should contain references to proper variable
varSpace.putVariable(1, e, array);
block.pickInput(1, e);
inShapes.push_back(shape_);
}
auto shapeList = op->calculateOutputShape(&inShapes, block);
if (varSpace.launchContext()->getWorkspace() != nullptr)
shapeList->detach();
return shapeList;
}
2020-03-02 10:49:41 +01:00
sd::ShapeList* calculateOutputShapes2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool *bArgs, int numBArgs, int *dArgs, int numDArgs) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash);
2019-08-26 18:57:51 +02:00
return _calculateOutputShapes(extraPointers, op, inputBuffers, inputShapes, numInputShapes, tArgs, numTArgs,
2020-01-30 16:46:12 +01:00
iArgs, numIArgs, bArgs, numBArgs, dArgs, numDArgs);
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
sd::ShapeList* _calculateOutputShapes(Nd4jPointer* extraPointers, sd::ops::DeclarableOp* op, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) {
2019-06-06 14:21:15 +02:00
Context block(1);
2020-03-02 10:49:41 +01:00
sd::ShapeList inShapes;
2019-06-06 14:21:15 +02:00
for (int e = 0; e < numIArgs; e++)
block.getIArguments()->push_back(iArgs[e]);
for (int e = 0; e < numTArgs; e++)
block.getTArguments()->push_back(tArgs[e]);
for (int e = 0; e < numInputShapes; e++)
inShapes.push_back(reinterpret_cast<Nd4jLong *>(inputShapes[e]));
auto shapeList = op->calculateOutputShape(&inShapes, block);
return shapeList;
}
2020-03-02 10:49:41 +01:00
sd::ShapeList* calculateOutputShapes(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputShapes, int numInputShapes, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash);
2019-08-26 18:57:51 +02:00
return _calculateOutputShapes(extraPointers, op, inputShapes, numInputShapes, tArgs, numTArgs, iArgs, numIArgs);
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
Nd4jLong getShapeListSize(sd::ShapeList* list) {
2019-07-24 14:14:54 +02:00
return list->size();
}
2020-05-09 07:06:14 +02:00
Nd4jLong const* getShape(sd::ShapeList* list, Nd4jLong i) {
2019-07-24 14:14:54 +02:00
return list->at(i);
}
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
static FORCEINLINE Nd4jStatus realExec(sd::ops::DeclarableOp* op, Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) {
2019-06-06 14:21:15 +02:00
if (op == nullptr)
nd4j_printf("Can't find requested operation: [%lld]\n", hash);
// we're using the same fake nodeId everywhere here
2020-03-02 10:49:41 +01:00
std::vector<sd::NDArray*> inputs(numInputs);
std::vector<sd::NDArray*> outputs(numOutputs);
2019-06-06 14:21:15 +02:00
std::vector<double> ttArgs(numTArgs);
std::vector<bool> bbArgs(numBArgs);
std::vector<Nd4jLong> iiArgs(numIArgs);
// filling block now with inputs
for (int e = 0; e < numInputs; e++) {
auto shape = reinterpret_cast<Nd4jLong *>(inputShapes[e]);
2020-03-02 10:49:41 +01:00
void *buffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e];
void *bufferD = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[e + numInputs];
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
inputs[e] = new sd::NDArray(buffer, bufferD, shape);
2019-06-06 14:21:15 +02:00
}
// if not inplace - transferring output arrays
if (!isInplace)
for (int e = 0; e < numOutputs; e++) {
// we want to keep original output shape intact
auto shape = shape::copyShape(reinterpret_cast<Nd4jLong *>(outputShapes[e]));
2020-03-02 10:49:41 +01:00
void *buffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e];
void *bufferD = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : outputBuffers[e + numOutputs];
2019-06-06 14:21:15 +02:00
// FIXME: revisit this.
bool canNullify = true;
for (int i = 0; i < numInputs; i++) {
2020-03-02 10:49:41 +01:00
void *ibuffer = sd::ArrayOptions::arrayType(shape) == ArrayType::EMPTY ? nullptr : inputBuffers[i];
2019-06-06 14:21:15 +02:00
if (ibuffer == buffer) {
canNullify = false;
break;
}
}
2019-08-02 19:01:03 +02:00
if (canNullify && buffer != nullptr)
2019-06-06 14:21:15 +02:00
memset((uint8_t *) buffer, '\0', shape::length(shape) * DataTypeUtils::sizeOfElement(ArrayOptions::dataType(shape)));
2020-03-02 10:49:41 +01:00
auto array = new sd::NDArray(buffer, bufferD, shape);
2019-06-06 14:21:15 +02:00
outputs[e] = array;
}
for (int e = 0; e < numIArgs; e++)
iiArgs[e] = iArgs[e];
for (int e = 0; e < numTArgs; e++)
ttArgs[e] = tArgs[e];
for (int e = 0; e < numBArgs; e++)
bbArgs[e] = bArgs[e];
// hypothetically at this point we have everything filled
2020-03-02 10:49:41 +01:00
auto dZ = op->execute(inputs, outputs, ttArgs, iiArgs, bbArgs, std::vector<sd::DataType>(), isInplace);
2019-06-06 14:21:15 +02:00
//auto dZ = op->execute(inputs, ttArgs, iiArgs, isInplace);
if (!isInplace)
for (int e = 0; e < numOutputs; e++) {
//shape::printShapeInfoLinear("JVM output shape", (int *) outputShapes[e]);
//shape::printShapeInfoLinear("C++ output shape", (int *) outputs[e]->shapeInfo());
//outputs[e]->printIndexedBuffer("C++ raw output");
//outputs[e]->printBuffer("C++ indexed output");
if (outputs[e]->ordering() != shape::order(reinterpret_cast<Nd4jLong *>(outputShapes[e])))
outputs[e]->streamline(shape::order(reinterpret_cast<Nd4jLong *>(outputShapes[e])));
}
for (auto v: inputs)
delete v;
for (auto v: outputs)
delete v;
return Status::OK();
}
2019-07-22 13:34:08 +02:00
int execCustomOp(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer* inputBuffers, Nd4jPointer* inputShapes, int numInputs, Nd4jPointer* outputBuffers, Nd4jPointer* outputShapes, int numOutputs, double* tArgs, int numTArgs, Nd4jLong *iArgs, int numIArgs, bool* bArgs, int numBArgs, bool isInplace) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash);
2019-08-26 18:57:51 +02:00
return realExec(op, extraPointers, hash, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes,
numOutputs, tArgs, numTArgs, iArgs, numIArgs, bArgs, numBArgs, isInplace);
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return 1;
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
int execCustomOp2(Nd4jPointer* extraPointers, Nd4jLong hash, Nd4jPointer opContext) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
auto op = sd::ops::OpRegistrator::getInstance()->getOperation(hash);
2019-08-26 18:57:51 +02:00
auto context = reinterpret_cast<Context *>(opContext);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
auto result = op->execute(context);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
auto res = cudaStreamSynchronize(*context->launchContext()->getCudaStream());
if (res != 0)
2020-03-02 10:49:41 +01:00
throw sd::cuda_exception::build("customOp execution failed", res);
2019-08-24 08:20:57 +02:00
2019-08-26 18:57:51 +02:00
for (auto v:context->fastpath_in()) {
2019-08-27 20:00:38 +02:00
if (!v->isEmpty())
v->syncToDevice();
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
for (auto v:context->fastpath_out()) {
2019-08-27 20:00:38 +02:00
if (!v->isEmpty())
v->syncToDevice();
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
return result;
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return 1;
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
int registerGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer flatBufferPointer) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
auto graph = sd::graph::GraphExecutioner::importFromFlatPointer(flatBufferPointer);
2019-06-06 14:21:15 +02:00
2020-03-02 10:49:41 +01:00
sd::graph::GraphHolder::getInstance()->registerGraph(graphId, graph);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
return ND4J_STATUS_OK;
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return 1;
}
2019-06-06 14:21:15 +02:00
}
static VariablesSet* executeStoredGraphT(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) {
2020-03-02 10:49:41 +01:00
auto graph = sd::graph::GraphHolder::getInstance()->pullGraph(graphId);
2019-06-06 14:21:15 +02:00
auto varSpace = graph->getVariableSpace()->clone();
2020-03-02 10:49:41 +01:00
std::vector<sd::NDArray*> handles;
2019-06-06 14:21:15 +02:00
for (int e = 0; e < numInputs; e++) {
auto idx = inputIndices[e];
// we'll delete this array later, together with cloned VariableSpace
2020-03-02 10:49:41 +01:00
auto array = new sd::NDArray(inputBuffers[e], reinterpret_cast<Nd4jLong *>(inputShapes[e]));
2019-06-06 14:21:15 +02:00
handles.emplace_back(array);
if (varSpace->hasVariable(idx)) {
auto var = varSpace->getVariable(idx);
if (var->hasNDArray())
delete var->getNDArray();
var->setNDArray(array);
} else
varSpace->putVariable(idx, array);
}
2020-03-02 10:49:41 +01:00
auto dZ = sd::graph::GraphExecutioner::execute(graph, varSpace);
auto varSet = new sd::graph::VariablesSet(dZ);
2019-06-06 14:21:15 +02:00
if (dZ == ND4J_STATUS_OK) {
// pull back results, and provide them
auto outputs = graph->fetchOutputs();
for (int e = 0; e < outputs->size(); e++) {
// we're only getting variable ID/Index from original grap. values will be taken from cloned workspace
std::pair<int, int> varId(outputs->at(e)->id(), outputs->at(e)->index());
auto var = varSpace->getVariable(varId);
varSet->push_back(var->clone());
}
delete outputs;
}
delete varSpace;
return varSet;
}
2019-07-22 13:34:08 +02:00
VariablesSet* executeStoredGraph(Nd4jPointer *extraPointers, Nd4jLong graphId, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int* inputIndices, int numInputs) {
2019-08-26 18:57:51 +02:00
try {
return executeStoredGraphT(extraPointers, graphId, inputBuffers, inputShapes, inputIndices, numInputs);
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
Nd4jLong getVariablesSetSize(sd::graph::VariablesSet* set) {
2019-07-24 14:14:54 +02:00
return set->size();
}
2020-03-02 10:49:41 +01:00
Nd4jStatus getVariablesSetStatus(sd::graph::VariablesSet* set) {
2019-07-24 14:14:54 +02:00
return set->status();
}
2020-03-02 10:49:41 +01:00
sd::graph::Variable* getVariable(sd::graph::VariablesSet* set, Nd4jLong i) {
2019-07-24 14:14:54 +02:00
return set->at(i);
}
2020-03-02 10:49:41 +01:00
int getVariableId(sd::graph::Variable* variable) {
2019-07-24 14:14:54 +02:00
return variable->id();
}
2020-03-02 10:49:41 +01:00
int getVariableIndex(sd::graph::Variable* variable) {
2019-07-24 14:14:54 +02:00
return variable->index();
}
2020-03-02 10:49:41 +01:00
const char* getVariableName(sd::graph::Variable* variable) {
2019-07-24 14:14:54 +02:00
return variable->getName()->c_str();
}
2020-05-09 07:06:14 +02:00
Nd4jLong const* getVariableShape(sd::graph::Variable* variable) {
2019-07-24 14:14:54 +02:00
return variable->getNDArray()->shapeInfo();
}
2020-03-02 10:49:41 +01:00
void* getVariableBuffer(sd::graph::Variable* variable) {
2019-07-24 14:14:54 +02:00
return variable->getNDArray()->buffer();
}
2019-07-22 13:34:08 +02:00
int unregisterGraph(Nd4jPointer *extraPointers, Nd4jLong graphId) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
sd::graph::GraphHolder::getInstance()->dropGraphAny(graphId);
2019-08-26 18:57:51 +02:00
return ND4J_STATUS_OK;
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return 1;
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void deletePointerArray(Nd4jPointer pointer) {
2019-06-06 14:21:15 +02:00
Nd4jPointer *ptr = reinterpret_cast<Nd4jPointer *>(pointer);
delete[] ptr;
}
2019-07-22 13:34:08 +02:00
void deleteCharArray(Nd4jPointer pointer) {
2019-07-12 07:21:15 +02:00
auto ptr = reinterpret_cast<char *>(pointer);
delete[] ptr;
}
2019-07-22 13:34:08 +02:00
void deleteIntArray(Nd4jPointer pointer) {
2019-06-06 14:21:15 +02:00
auto ptr = reinterpret_cast<int *>(pointer);
delete[] ptr;
}
2019-07-22 13:34:08 +02:00
void deleteLongArray(Nd4jPointer pointer) {
2019-06-06 14:21:15 +02:00
auto ptr = reinterpret_cast<Nd4jLong *>(pointer);
delete[] ptr;
}
2020-03-02 10:49:41 +01:00
void deleteVariablesSet(sd::graph::VariablesSet* pointer) {
2019-07-26 09:22:44 +02:00
delete pointer;
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void deleteShapeList(Nd4jPointer shapeList) {
2020-03-02 10:49:41 +01:00
sd::ShapeList* list = reinterpret_cast<sd::ShapeList*>(shapeList);
2019-06-06 14:21:15 +02:00
//list->destroy();
delete list;
}
2019-07-22 13:34:08 +02:00
const char* getAllOperations() {
2020-03-02 10:49:41 +01:00
return sd::OpTracker::getInstance()->exportOperations();
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
Nd4jPointer getGraphState(Nd4jLong id) {
2020-03-02 10:49:41 +01:00
return (Nd4jPointer) new sd::graph::GraphState(id);
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void deleteGraphState(Nd4jPointer state) {
2020-03-02 10:49:41 +01:00
auto stateP = reinterpret_cast<sd::graph::GraphState*>(state);
2019-06-06 14:21:15 +02:00
delete stateP;
}
2020-03-02 10:49:41 +01:00
Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, sd::graph::GraphState *state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) {
2019-06-06 14:21:15 +02:00
/**
* That's basically exec, with VariableSpace provided in GraphState:
* depending on operation (i.e. while of if), different logic executors could be used
*/
auto graph = state->graph();
auto varSpace = state->variableSpace();
// Node is dynamically created, and has nothing beyond it: only inputs and outputs
// this node has id of 0, and inputs are
Node node(OpType_LOGIC, opHash, 0);
// mapping inputs
for (int e = 0; e < numInputs; e++) {
auto buffer = inputBuffers[e];
auto shapeInfo = reinterpret_cast<Nd4jLong *>(inputShapes[e]);
2020-03-02 10:49:41 +01:00
auto array = new sd::NDArray(buffer, shapeInfo, varSpace->launchContext());
2019-06-06 14:21:15 +02:00
// now we just put array to VarSpace
varSpace->putVariable(0, e, array);
node.pickInput(0, e);
}
// mapping scopes
for (int e = 0; e < numScopes; e++) {
// we should check scope existence in GraphState/Graph
int scopeId = (int) scopes[e];
if (!state->hasScope(scopeId)) {
// nd4j_printf("execCustomOpWithScope: referenced scope [%i] doesn't exist\n", scopeId);
return Status::THROW();
}
node.pickInput(scopeId, 0);
}
auto dZ = LogicExecutor::processNode(graph, &node);
if (dZ != Status::OK())
return dZ;
// mapping outputs
for (int e = 0; e < numOutputs; e++) {
auto buffer = outputBuffers[e];
auto shapeInfo = reinterpret_cast<Nd4jLong *>(outputShapes[e]);
NDArray array(buffer, shapeInfo, varSpace->launchContext());
// now we just put array to VarSpace to the same ID
//varSpace->putVariable(0, e, array);
auto t = varSpace->getVariable(0, e)->getNDArray();
array.assign(t);
}
// removing input variables
for (int e = 0; e < numInputs; e++) {
varSpace->dropVariable(0, e);
}
// after some bla-bla-bla we should have Graph and Node for current op
return Status::OK();
}
2019-07-22 13:34:08 +02:00
Nd4jStatus execCustomOpWithScope(Nd4jPointer *extraPointers, Nd4jPointer state, Nd4jLong opHash, Nd4jLong *scopes, int numScopes, Nd4jPointer *inputBuffers, Nd4jPointer *inputShapes, int numInputs, Nd4jPointer *outputBuffers, Nd4jPointer *outputShapes, int numOutputs) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
return execCustomOpWithScope(extraPointers, reinterpret_cast<sd::graph::GraphState *>(state), opHash, scopes,
2019-08-26 18:57:51 +02:00
numScopes, inputBuffers, inputShapes, numInputs, outputBuffers, outputShapes,
numOutputs);
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return 1;
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void deleteResultWrapper(Nd4jPointer ptr) {
2019-06-06 14:21:15 +02:00
// just 0 room for compiler s@!t
2020-03-02 10:49:41 +01:00
auto p = reinterpret_cast<sd::graph::ResultWrapper *>(ptr);
2019-06-06 14:21:15 +02:00
delete p;
}
2020-05-09 07:06:14 +02:00
int estimateThreshold(Nd4jPointer *extraPointers, Nd4jPointer dX, Nd4jLong const* dXShapeInfo, int N, float threshold) {
2019-06-06 14:21:15 +02:00
throw std::runtime_error("estimateThreshold: Not implemented yet");
}
/*
* TypeDef:
* void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer dX, long N, int dstType, Nd4jPointer dZ);
*/
2019-07-22 13:34:08 +02:00
void convertTypes(Nd4jPointer *extras, int srcType, Nd4jPointer dX, Nd4jLong N, int dstType, Nd4jPointer dZ) {
2019-08-26 18:57:51 +02:00
try {
auto dx = reinterpret_cast<void *>(dX);
auto dz = reinterpret_cast<void *>(dZ);
if (srcType == ND4J_FLOAT8) {
if (dstType == ND4J_FLOAT8) {
2020-03-02 10:49:41 +01:00
// convertKernel<double, sd::float8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::float8, sd::int8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::float8, sd::uint8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::float8, float16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT16) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::float8, sd::int16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT16) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::float8, sd::uint16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT24) {
} else if (dstType == ND4J_FLOAT32) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::float8, float>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_DOUBLE) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::float8, double>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_INT8) {
if (dstType == ND4J_FLOAT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<sd::int8, sd::float8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT8) {
2020-03-02 10:49:41 +01:00
//convertKernel<sd::int8, sd::int8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int8_t, uint8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int8_t, float16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int8_t, int16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int8_t, uint16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT24) {
// TODO: eventually we might want to add it
} else if (dstType == ND4J_FLOAT32) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int8_t, float>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_DOUBLE) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int8_t, double>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_UINT8) {
if (dstType == ND4J_FLOAT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<uint8_t, sd::float8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<uint8_t, int8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<uint8_t, uint8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<uint8_t, float16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<uint8_t, int16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<uint8_t, uint16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT24) {
// TODO: still might want to add
} else if (dstType == ND4J_FLOAT32) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<uint8_t, float>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_DOUBLE) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<uint8_t, double>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_FLOAT16) {
if (dstType == ND4J_FLOAT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<float16, sd::float8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float16, int8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float16, uint8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float16, float16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float16, int16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float16, uint16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT24) {
// TODO: .... ^^^
} else if (dstType == ND4J_FLOAT32) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float16, float>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_DOUBLE) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float16, double>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_THRESHOLD) {
2020-03-02 10:49:41 +01:00
//sd::convertToThreshold<float16>(nullptr, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_INT16) {
if (dstType == ND4J_FLOAT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<int16_t, sd::float8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int16_t, int8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int16_t, uint8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int16_t, float16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int16_t, int16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int16_t, uint16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT24) {
// TODO...
} else if (dstType == ND4J_FLOAT32) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int16_t, float>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_DOUBLE) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<int16_t, double>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_FLOAT24) {
} else if (srcType == ND4J_FLOAT32) {
if (dstType == ND4J_FLOAT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<float, sd::float8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float, int8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float, uint8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float, float16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float, int16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float, uint16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT24) {
} else if (dstType == ND4J_DOUBLE) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<float, double>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_THRESHOLD) {
2020-03-02 10:49:41 +01:00
//sd::convertToThreshold<float>(nullptr, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_DOUBLE) {
if (dstType == ND4J_FLOAT8) {
2020-03-02 10:49:41 +01:00
//sd::TypeCast::convertGenericCuda<double, sd::float8>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<double, int8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT8) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<double, uint8_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<double, float16>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_INT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<double, int16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_UINT16) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<double, uint16_t>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT24) {
} else if (dstType == ND4J_FLOAT32) {
2020-03-02 10:49:41 +01:00
sd::TypeCast::convertGenericCuda<double, float>(extras, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_DOUBLE) {
//
} else if (dstType == ND4J_THRESHOLD) {
2020-03-02 10:49:41 +01:00
//sd::convertToThreshold<double>(nullptr, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
} else if (srcType == ND4J_THRESHOLD) {
if (dstType == ND4J_FLOAT16) {
2020-03-02 10:49:41 +01:00
//sd::convertFromThreshold<float16>(nullptr, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_FLOAT32) {
2020-03-02 10:49:41 +01:00
//sd::convertFromThreshold<float>(nullptr, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else if (dstType == ND4J_DOUBLE) {
2020-03-02 10:49:41 +01:00
//sd::convertFromThreshold<double>(nullptr, dx, N, dz);
2019-08-26 18:57:51 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
2019-06-06 14:21:15 +02:00
} else {
nd4j_printf("Unsupported types conversion: [%i] -> [%i]\n", srcType, dstType);
}
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-06-06 14:21:15 +02:00
}
}
2019-07-22 13:34:08 +02:00
Nd4jPointer createUtf8String(Nd4jPointer *extraPointers, const char *string, int length) {
2020-03-02 10:49:41 +01:00
auto u = new sd::utf8string(string, length);
2019-06-06 14:21:15 +02:00
return reinterpret_cast<Nd4jPointer>(u);
}
2019-07-24 14:14:54 +02:00
Nd4jLong getUtf8StringLength(Nd4jPointer *extraPointers, Nd4jPointer ptr) {
2020-03-02 10:49:41 +01:00
return reinterpret_cast<sd::utf8string*>(ptr)->_length;
2019-07-24 14:14:54 +02:00
}
char* getUtf8StringBuffer(Nd4jPointer *extraPointers, Nd4jPointer ptr) {
2020-03-02 10:49:41 +01:00
return reinterpret_cast<sd::utf8string*>(ptr)->_buffer;
2019-07-24 14:14:54 +02:00
}
2019-07-22 13:34:08 +02:00
void deleteUtf8String(Nd4jPointer *extraPointers, Nd4jPointer ptr) {
2020-03-02 10:49:41 +01:00
delete(reinterpret_cast<sd::utf8string*>(ptr));
2019-06-06 14:21:15 +02:00
}
///////////////////////////////////////////////////////////////////
2019-10-31 10:23:09 +01:00
template<typename T, typename I>
2019-06-06 14:21:15 +02:00
__global__ static void scatterUpdateCuda(const int opCode, const int numOfSubArrs,
2020-05-09 07:06:14 +02:00
void* vx, const Nd4jLong* xShapeInfo, const Nd4jLong *xOffsets,
2019-06-06 14:21:15 +02:00
void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets,
2019-10-31 10:23:09 +01:00
const void* vindexes) {
2019-06-06 14:21:15 +02:00
__shared__ T *x, *y;
__shared__ Nd4jLong arrLenX, arrLenY;
2019-10-31 10:23:09 +01:00
auto indexes = reinterpret_cast<const I*>(vindexes);
2019-06-06 14:21:15 +02:00
for (int e = 0; e < numOfSubArrs; e++ ) {
const auto xIndex = indexes[e];
const bool isOwner = xIndex < gridDim.x ? blockIdx.x == xIndex : blockIdx.x == xIndex % gridDim.x;
if (!isOwner)
continue;
if (threadIdx.x == 0) {
x = reinterpret_cast<T*>(vx) + xOffsets[xIndex];
y = reinterpret_cast<T*>(vy) + yOffsets[e];
arrLenX = shape::length(xShapeInfo);
arrLenY = shape::length(yShapeInfo);
}
__syncthreads();
if (arrLenX != arrLenY)
return;
for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {
2019-09-11 19:12:09 +02:00
const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
const auto yOffset = shape::getIndexOffset(i, yShapeInfo);
2019-06-06 14:21:15 +02:00
switch (opCode) {
case 0:
x[xOffset] += y[yOffset];
break;
case 1:
x[xOffset] -= y[yOffset];
break;
case 2:
x[xOffset] *= y[yOffset];
break;
case 3:
x[xOffset] /= y[yOffset];
break;
case 4:
x[xOffset] = y[yOffset] - x[xOffset];
break;
case 5:
x[xOffset] = y[yOffset] / x[xOffset];
break;
case 6:
x[xOffset] = y[yOffset];
break;
default:
continue;
}
}
__syncthreads();
}
}
2019-10-31 10:23:09 +01:00
template<typename T, typename I>
2020-05-09 07:06:14 +02:00
__host__ static void scatterUpdateCudaLauncher(const cudaStream_t* stream, const int opCode, const int numOfSubArrs, void* vx, const Nd4jLong const* xShapeInfo, const Nd4jLong* xOffsets, void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets, const void* indexes) {
2019-06-06 14:21:15 +02:00
2019-10-31 10:23:09 +01:00
scatterUpdateCuda<T, I><<<512, 256, MAX_NUM_THREADS, *stream>>>(opCode, numOfSubArrs, vx, xShapeInfo, xOffsets, vy, yShapeInfo, yOffsets, indexes);
2019-06-06 14:21:15 +02:00
}
//////////////////////////////////////////////////////////////////////////
2019-07-22 13:34:08 +02:00
void scatterUpdate(Nd4jPointer *extraPointers, int opCode, int numOfSubArrs,
2020-05-09 07:06:14 +02:00
void* hX, Nd4jLong const* hXShapeInfo, Nd4jLong const* hXOffsets,
void* dX, Nd4jLong const* dXShapeInfo, Nd4jLong const* dXOffsets,
void* hY, Nd4jLong const* hYShapeInfo, Nd4jLong const* hYOffsets,
void* dY, Nd4jLong const* dYShapeInfo, Nd4jLong const* dYOffsets,
void* hIindexes, Nd4jLong const* hIndicesShapeInfo, void* dIindexes, Nd4jLong const* dIndicesShapeInfo) {
2019-08-26 18:57:51 +02:00
try {
auto stream = reinterpret_cast<cudaStream_t *>(extraPointers[1]);
2019-10-31 10:23:09 +01:00
auto type = ArrayOptions::dataType(hXShapeInfo);
auto iType = ArrayOptions::dataType(hIndicesShapeInfo);
2019-08-26 18:57:51 +02:00
2019-11-01 07:13:15 +01:00
BUILD_DOUBLE_SELECTOR(type, iType, scatterUpdateCudaLauncher,
(stream, opCode, numOfSubArrs, dX, dXShapeInfo, dXOffsets, dY, dYShapeInfo, dYOffsets, dIindexes),
2019-10-31 10:23:09 +01:00
LIBND4J_TYPES, INDEXING_TYPES);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::checkErrorCode(stream, "scatterUpdate(...) failed");
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
void inspectArray(Nd4jPointer *extraPointers, Nd4jPointer buffer, Nd4jLong *shapeInfo, Nd4jPointer specialBuffer, Nd4jLong *specialShapeInfo, Nd4jPointer debugInfo) {
2019-08-26 18:57:51 +02:00
try {
LaunchContext lc(extraPointers[1], extraPointers[4], extraPointers[5], extraPointers[3]);
2020-03-02 10:49:41 +01:00
auto p = reinterpret_cast<sd::DebugInfo *>(debugInfo);
2019-08-26 18:57:51 +02:00
NDArray array(buffer, specialBuffer, shapeInfo, &lc);
2020-03-02 10:49:41 +01:00
sd::DebugHelper::retrieveDebugStatistics(p, &array);
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
void __global__ tryPointerKernel(void* p, int len) {
auto buf = reinterpret_cast<int8_t*>(p);
auto tid = threadIdx.x + blockIdx.x * blockDim.x;
__shared__ int b;
if (tid < len)
atomicAdd(&b, buf[tid]);
__syncthreads();
if (threadIdx.x ==0 && blockIdx.x == 0)
printf("Pointer check complete: %i\n", b);
}
2019-07-22 13:34:08 +02:00
void tryPointer(Nd4jPointer extra, Nd4jPointer p, int len) {
2019-08-26 18:57:51 +02:00
try {
cudaStream_t stream;
cudaStreamCreate(&stream);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
tryPointerKernel << < 256, 512, len + 64, stream >> > (p, len);
auto e = cudaStreamSynchronize(stream);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
if (e != 0)
2020-03-02 10:49:41 +01:00
throw sd::cuda_exception::build("tryPointer failed", e);
2019-06-06 14:21:15 +02:00
2019-08-26 18:57:51 +02:00
cudaStreamDestroy(stream);
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
}
2019-06-06 14:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
int dataTypeFromNpyHeader(void *header) {
2019-06-06 14:21:15 +02:00
return (int) cnpy::dataTypeFromHeader(reinterpret_cast<char *>(header));
}
2020-03-02 10:49:41 +01:00
sd::ConstantDataBuffer* shapeBuffer(int rank, Nd4jLong *shape, Nd4jLong *strides, sd::DataType dtype, char order, Nd4jLong ews, bool empty) {
2019-08-26 18:57:51 +02:00
try {
auto buffer = new ConstantDataBuffer();
2020-03-02 10:49:41 +01:00
*buffer = sd::ConstantShapeHelper::getInstance()->bufferForShapeInfo(
2019-08-26 18:57:51 +02:00
ShapeDescriptor(dtype, order, shape, strides, rank, ews, empty));
return buffer;
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
void deleteShapeBuffer(sd::ConstantDataBuffer* ptr) {
2019-07-24 14:14:54 +02:00
delete ptr;
2019-07-22 13:00:24 +02:00
}
2020-03-02 10:49:41 +01:00
void deleteTadPack(sd::TadPack* ptr) {
2019-07-24 14:14:54 +02:00
delete ptr;
2019-07-22 14:55:28 +02:00
}
2019-09-04 13:41:08 +02:00
bool isBlasVersionMatches(int major, int minor, int build) {
auto result = major == Environment::getInstance()->_blasMajorVersion && minor == Environment::getInstance()->_blasMinorVersion && build == Environment::getInstance()->_blasPatchVersion;
if (!result) {
nd4j_printf("CUDA/cuBLAS version mismatch. Expected: %i.%i.%i but got %i.%i.%i instead\n", Environment::getInstance()->_blasMajorVersion, Environment::getInstance()->_blasMinorVersion, Environment::getInstance()->_blasPatchVersion, major, minor, build);
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(152);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage("CUDA/cuBLAS version mismatch");
2019-09-04 13:41:08 +02:00
}
return result;
}
2020-05-09 07:06:14 +02:00
sd::ConstantDataBuffer* constantBufferLong(sd::DataType dtype, Nd4jLong const* data, int length) {
2020-03-02 10:49:41 +01:00
return sd::ConstantHelper::getInstance()->constantBuffer(ConstantDescriptor(data, length), dtype);
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
sd::ConstantDataBuffer* constantBufferDouble(sd::DataType dtype, double *data, int length) {
return sd::ConstantHelper::getInstance()->constantBuffer(ConstantDescriptor(data, length), dtype);
2019-06-06 14:21:15 +02:00
}
2020-03-02 10:49:41 +01:00
sd::ConstantDataBuffer* constantBuffer(sd::DataType dtype, sd::ConstantDescriptor *descriptor) {
return sd::ConstantHelper::getInstance()->constantBuffer(*descriptor, dtype);
2019-06-06 14:21:15 +02:00
}
2019-06-15 13:34:34 +02:00
2019-07-24 14:14:54 +02:00
2020-03-02 10:49:41 +01:00
Nd4jPointer getConstantDataBufferPrimary(sd::ConstantDataBuffer* dbf) {
2019-07-24 14:14:54 +02:00
return dbf->primary();
}
2020-03-02 10:49:41 +01:00
Nd4jPointer getConstantDataBufferSpecial(sd::ConstantDataBuffer* dbf) {
2019-07-24 14:14:54 +02:00
return dbf->special();
}
2020-03-02 10:49:41 +01:00
Nd4jLong getConstantDataBufferLength(sd::ConstantDataBuffer* dbf) {
2019-07-24 14:14:54 +02:00
return dbf->length();
}
2020-03-02 10:49:41 +01:00
Nd4jLong getConstantDataBufferSizeOf(sd::ConstantDataBuffer* dbf) {
2019-07-24 14:14:54 +02:00
return dbf->sizeOf();
}
2020-03-02 10:49:41 +01:00
sd::graph::Context* createGraphContext(int nodeId) {
return new sd::graph::Context(nodeId);
2019-07-24 14:14:54 +02:00
}
[WIP] multi-device support (#80)
* fix pad javadoc and @see links. (#72)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* [WIP] More fixes (#73)
* special tests for ConstantTadHelper/ConstantShapeHelper
Signed-off-by: raver119 <raver119@gmail.com>
* release methods for data buffers
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary TadPack C++/Java side (#74)
Signed-off-by: raver119 <raver119@gmail.com>
* Zoo model TF import test updates (#75)
* argLine fix, update compression_gru comment
* updated comment for xception
* undid but commented argLine change
* updated xlnet comment
* copyright headers
* - new NDArray methods like()/ulike() (#77)
- fix for depthwise_conv2d_bp + special test
Signed-off-by: raver119 <raver119@gmail.com>
* upsampling2d fix CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* DL4J trace logging (#79)
* MLN/CG trace logging for debugging
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tiny tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* strided_slice_bp shape fn leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff fixes and naming (#78)
* remove SDVariable inplace methods
* import methods
* npe fix in OpVal
* removed SameDiff inplace ops from tests
* Naming updates, moved to centralized methods in SameDiff, should use op_#:# for everything
* quick fixes
* javadoc
* SDVariable eval with placeholders
* use regex match
* better matching
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* fix javadoc. (#76)
* fix javadoc.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace most @see with @link s.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* 4 additional tests
Signed-off-by: raver119 <raver119@gmail.com>
* launch context reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* per-device LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* Various DL4J/ND4J fixes (#81)
* #7954 Force refresh of UI when switching tabs on overview page
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8017 Concurrent modification exception (synchronize) fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8033 Don't initialize updater in middle of writing memory crash dump
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8208 Fix shape checks for ND4J int[] creator methods
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6385 #7992 Keras import naming fixes + cleanup
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8016 Upsampling3D - add NDHWC format support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Refactor NativeOps.h to export C functions
* Actually export functions from NativeOps.h
* Adapt the Java wrappers in ND4J generated with JavaCPP
* Create C wrappers for some of the C++ classes currently used by ND4J
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* remove duplicate code in createBufferDetached. (#83)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Keras model import - updater lr fix (#84)
* Keras model import - updater lr fix
Signed-off-by: eraly <susan.eraly@gmail.com>
* Keras model import - updater lr fix, cleanup
Signed-off-by: eraly <susan.eraly@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Fix functions of OpaqueVariablesSet
* thread-local buffers/affinity
Signed-off-by: raver119 <raver119@gmail.com>
* thread safety for LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* more of thread safety
Signed-off-by: raver119 <raver119@gmail.com>
* one more multi threaded test
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff Convolution Config validation, better output methods (#82)
* Conv Config validation & tests
Signed-off-by: Ryan Nett <rnett@skymind.io>
* stackOutputs utility method
Signed-off-by: Ryan Nett <rnett@skymind.io>
* use constructor for validation, support negative kernel sizes (infered from weights)
Signed-off-by: Ryan Nett <rnett@skymind.io>
* better output methods
Signed-off-by: Ryan Nett <rnett@skymind.io>
* move output to be with fit and evaluate
Signed-off-by: Ryan Nett <rnett@skymind.io>
* fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* more fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* refactor duplicate code from pad methods. (#86)
* refactor duplicate code from pad methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace switch with if.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes and improvements (#87)
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6488 ElementWiseVertex broadcast support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Constructors and broadcast supported it Transforms.max/min
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8054 ElementWiseVertex now supports broadcast inputs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8057 Nd4j.create overload dtype fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7551 ND4J Shape validation fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Numpy boolean import (#91)
* numpy bool type
Signed-off-by: raver119 <raver119@gmail.com>
* numpy bool java side
Signed-off-by: raver119 <raver119@gmail.com>
* remove create method with unused parameter. (#89)
* remove create method with unused parameter.
* removed more unused methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* removing more unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* last removal of unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* remove createSparse methods. (#92)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes (#90)
* Deprecate Old*Op instances
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8063 #8054 Broadcast exceptions + cleanup inplace ops
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Remove bad test condition
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7993 Fix shape function issue in crop_and_resize op
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* DL4J SameDiff lambda layer fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8029 Fix for pnorm backprop math
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8038 Fix Op profiler NaN/Inf triggering + add tests (#93)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* createUninitializedDetached refactoring. (#94)
* wip
* update interface, add null implementations.
* Breaking one test in a weird way.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* createUninitializedDetached refactored.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* cuda build fix for issues introduced by recent refactoring
Signed-off-by: raver119 <raver119@gmail.com>
* [WIP] More of CUDA (#95)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* Implementation of hashcode cuda helper. Working edition.
* Fixed parallel test input arangements.
* Fixed tests for hashcode op.
* Fixed shape calculation for image:crop_and_resize op and test.
* NativeOps tests. Initial test suite.
* Added tests for indexReduce methods.
* Added test on execBroadcast with NDArray as dimensions.
* Added test on execBroadcastBool with NDArray as dimensions.
* Added tests on execPairwiseTransform and execPairwiseTransofrmBool.
* Added tests for execReduce with scalar results.
* Added reduce tests for non-empty dims array.
* Added tests for reduce3.
* Added tests for execScalar.
* Added tests for execSummaryStats.
* - provide cpu/cuda code for batch_to_space
- testing it
Signed-off-by: Yurii <yurii@skymind.io>
* - remove old test for batch_to_space (had wrong format and numbers were not checked)
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed complilation errors with test.
* Added test for execTransformFloat.
* Added test for execTransformSame.
* Added test for execTransformBool.
* Added test for execTransformStrict.
* Added tests for execScalar/execScalarBool with TADs.
* Added test for flatten.
* - provide cpu/cuda code for space_to_Batch operaion
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for concat.
* comment unnecessary stuff in s_t_b
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for specialConcat.
* Added tests for memcpy/set routines.
* Fixed pullRow cuda test.
* Added pullRow test.
* Added average test.
* - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...)
Signed-off-by: Yurii <yurii@skymind.io>
* - debugging and fixing cuda tests in JavaInteropTests file
Signed-off-by: Yurii <yurii@skymind.io>
* - correct some tests
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for shuffle.
* Fixed ops declarations.
* Restored omp and added shuffle test.
* Added convertTypes test.
* Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps.
* Added sort tests.
* Added tests for execCustomOp.
* - further debuging and fixing tests terminated with crash
Signed-off-by: Yurii <yurii@skymind.io>
* Added tests for calculateOutputShapes.
* Addded Benchmarks test.
* Commented benchmark tests.
* change assertion
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for apply_sgd op. Added cpu helper for that op.
* Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps.
* Added test for assign broadcastable.
* Added tests for assign_bp op.
* Added tests for axpy op.
* - assign/execScalar/execTransformAny signature change
- minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed axpy op.
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* - fix tests for nativeOps::concat
Signed-off-by: Yurii <yurii@skymind.io>
* sequential transform/scalar
Signed-off-by: raver119 <raver119@gmail.com>
* allow nested parallelism
Signed-off-by: raver119 <raver119@gmail.com>
* assign_bp leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* block setRNG fix
Signed-off-by: raver119 <raver119@gmail.com>
* enable parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* enable nested parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* Added cuda implementation for row_count helper.
* Added implementation for tnse gains op helper.
* - take into account possible situations when input arrays are empty in reduce_ cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces.
* Added kernel for tsne/symmetrized op heleper.
* Implementation of tsne/symmetrized op cuda helper. Working edition.
* Eliminated waste printfs.
* Added test for broadcastgradientargs op.
* host-only fallback for empty reduce float
Signed-off-by: raver119 <raver119@gmail.com>
* - some tests fixes
Signed-off-by: Yurii <yurii@skymind.io>
* - correct the rest of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* - further correction of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for Cbow op. Also added cuda implementation for cbow helpers.
* - improve code of stack operation for scalar case
Signed-off-by: Yurii <yurii@skymind.io>
* - provide cuda kernel for gatherND operation
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of cbow helpers with cuda kernels.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* - further correction of cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implementatation of cbow op helper with cuda kernels. Working edition.
* Skip random testing for cudablas case.
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for ELU and ELU_BP ops.
* Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops.
* Added tests for neq_scalar.
* Added test for noop.
* - further work on clipbynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* - get rid of concat op call, use instead direct concat helper call
Signed-off-by: Yurii <yurii@skymind.io>
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for lrelu and lrelu_bp.
* Added tests for selu and selu_bp.
* Fixed lrelu derivative helpers.
* - some corrections in lstm
Signed-off-by: Yurii <yurii@skymind.io>
* operator * result shape fix
Signed-off-by: raver119 <raver119@gmail.com>
* - correct typo in lstmCell
Signed-off-by: Yurii <yurii@skymind.io>
* few tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA inverse broadcast bool fix
Signed-off-by: raver119 <raver119@gmail.com>
* disable MMAP test for CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* BooleanOp syncToDevice
Signed-off-by: raver119 <raver119@gmail.com>
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* additional data types for im2col/col2im
Signed-off-by: raver119 <raver119@gmail.com>
* Added test for firas_sparse op.
* one more RandomBuffer test excluded
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for flatten op.
* Added test for Floor op.
* bunch of tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* mmulDot tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Implemented floordiv_bp op and tests.
* Fixed scalar case with cuda implementation for bds.
* - work on cuda kernel for clip_by_norm backprop op is completed
Signed-off-by: Yurii <yurii@skymind.io>
* Eliminate cbow crach.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminated abortion with batched nlp test.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed shared flag initializing.
* disabled bunch of cpu workspaces tests
Signed-off-by: raver119 <raver119@gmail.com>
* scalar operators fix: missing registerSpecialUse call
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed logdet for cuda and tests.
* - correct clipBynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed crop_and_resize shape datatype.
* - correct some mmul tests
Signed-off-by: Yurii <yurii@skymind.io>
* build fix
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI (#97)
Signed-off-by: raver119 <raver119@gmail.com>
* temporary stack fix
Signed-off-by: raver119 <raver119@gmail.com>
* round robin affinity test
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy CudaContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy ContextPool classes/methods
Signed-off-by: raver119 <raver119@gmail.com>
* one legacy test removed
Signed-off-by: raver119 <raver119@gmail.com>
* few more fields rearranged
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext++
Signed-off-by: raver119 <raver119@gmail.com>
* more of OpaqueLaunchContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext -> CudaContext
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handles
Signed-off-by: raver119 <raver119@gmail.com>
* typo
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver method
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handle propagated
Signed-off-by: raver119 <raver119@gmail.com>
* blas/solver handles
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* legacy concat implementations replaced with new CustomOp
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* concat now uses way more blocks
Signed-off-by: raver119 <raver119@gmail.com>
* print
Signed-off-by: raver119 <raver119@gmail.com>
* no more triple template mmul
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bitonic sort reorganized
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* type conversions moved to generic impl
Signed-off-by: raver119 <raver119@gmail.com>
* cpu data types pass
Signed-off-by: raver119 <raver119@gmail.com>
* non_max_suppression
Signed-off-by: raver119 <raver119@gmail.com>
* sortByValue fix
Signed-off-by: raver119 <raver119@gmail.com>
* ignore all mixed datatype tests for mmul
Signed-off-by: raver119 <raver119@gmail.com>
* special handling of OpProfiler exceptions
Signed-off-by: raver119 <raver119@gmail.com>
* - one failing concat test in cpp
- Nd4j.tile now uses op internally
Signed-off-by: raver119 <raver119@gmail.com>
* get back dtype exception for legacy arrays deserialization
Signed-off-by: raver119 <raver119@gmail.com>
2019-08-14 15:52:34 +02:00
2020-03-02 10:49:41 +01:00
sd::graph::RandomGenerator* getGraphContextRandomGenerator(sd::graph::Context* ptr) {
2019-07-24 14:14:54 +02:00
return &ptr->randomGenerator();
}
2020-01-04 11:27:50 +01:00
2020-03-02 10:49:41 +01:00
void markGraphContextInplace(sd::graph::Context* ptr, bool reallyInplace) {
2019-07-24 14:14:54 +02:00
ptr->markInplace(reallyInplace);
}
2020-01-04 11:27:50 +01:00
2020-03-02 10:49:41 +01:00
void setGraphContextCudaContext(sd::graph::Context* ptr, void *stream, void *reductionPointer, void *allocationPointer) {
2019-07-24 14:14:54 +02:00
ptr->setCudaContext(stream, reductionPointer, allocationPointer);
}
2020-01-04 11:27:50 +01:00
2020-03-02 10:49:41 +01:00
void setGraphContextInputArray(sd::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) {
2019-07-24 14:14:54 +02:00
ptr->setInputArray(index, buffer, shapeInfo, specialBuffer, specialShapeInfo);
}
2020-01-04 11:27:50 +01:00
2020-03-02 10:49:41 +01:00
void setGraphContextOutputArray(sd::graph::Context* ptr, int index, void *buffer, void *shapeInfo, void *specialBuffer, void *specialShapeInfo) {
2019-07-24 14:14:54 +02:00
ptr->setOutputArray(index, buffer, shapeInfo, specialBuffer, specialShapeInfo);
}
2020-01-04 11:27:50 +01:00
void setGraphContextInputBuffer(OpaqueContext* ptr, int index, OpaqueDataBuffer *buffer, void *shapeInfo, void *specialShapeInfo) {
ptr->setInputArray(index, buffer, shapeInfo, specialShapeInfo);
}
void setGraphContextOutputBuffer(OpaqueContext* ptr, int index, OpaqueDataBuffer *buffer, void *shapeInfo, void *specialShapeInfo) {
ptr->setOutputArray(index, buffer, shapeInfo, specialShapeInfo);
}
2020-03-02 10:49:41 +01:00
void setGraphContextTArguments(sd::graph::Context* ptr, double *arguments, int numberOfArguments) {
2019-07-24 14:14:54 +02:00
ptr->setTArguments(arguments, numberOfArguments);
}
2020-01-04 11:27:50 +01:00
2020-03-02 10:49:41 +01:00
void setGraphContextIArguments(sd::graph::Context* ptr, Nd4jLong *arguments, int numberOfArguments) {
2019-07-24 14:14:54 +02:00
ptr->setIArguments(arguments, numberOfArguments);
}
2020-01-04 11:27:50 +01:00
2020-03-02 10:49:41 +01:00
void setGraphContextBArguments(sd::graph::Context* ptr, bool *arguments, int numberOfArguments) {
2019-07-24 14:14:54 +02:00
ptr->setBArguments(arguments, numberOfArguments);
}
2020-01-04 11:27:50 +01:00
2020-01-30 08:07:24 +01:00
void setGraphContextDArguments(OpaqueContext* ptr, int *arguments, int numberOfArguments) {
2020-03-02 10:49:41 +01:00
std::vector<sd::DataType> dtypes(numberOfArguments);
2020-01-30 08:07:24 +01:00
for (int e = 0; e < numberOfArguments; e++)
2020-03-02 10:49:41 +01:00
dtypes[e] = (sd::DataType) arguments[e];
2020-01-30 08:07:24 +01:00
ptr->setDArguments(dtypes);
}
2020-03-02 10:49:41 +01:00
void deleteGraphContext(sd::graph::Context* ptr) {
2019-07-24 14:14:54 +02:00
delete ptr;
}
2020-03-02 10:49:41 +01:00
sd::graph::RandomGenerator* createRandomGenerator(Nd4jLong rootSeed, Nd4jLong nodeSeed) {
2020-01-23 07:51:02 +01:00
try {
2020-03-02 10:49:41 +01:00
return new sd::graph::RandomGenerator(rootSeed, nodeSeed);
2020-01-23 07:51:02 +01:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2020-01-23 07:51:02 +01:00
return nullptr;
}
2019-07-24 14:14:54 +02:00
}
2020-03-02 10:49:41 +01:00
Nd4jLong getRandomGeneratorRootState(sd::graph::RandomGenerator* ptr) {
2019-07-24 14:14:54 +02:00
return ptr->rootState();
}
2020-03-02 10:49:41 +01:00
Nd4jLong getRandomGeneratorNodeState(sd::graph::RandomGenerator* ptr) {
2019-07-24 14:14:54 +02:00
return ptr->nodeState();
}
2020-03-02 10:49:41 +01:00
void setRandomGeneratorStates(sd::graph::RandomGenerator* ptr, Nd4jLong rootSeed, Nd4jLong nodeSeed) {
2019-07-24 14:14:54 +02:00
ptr->setStates(rootSeed, nodeSeed);
}
2020-03-02 10:49:41 +01:00
int getRandomGeneratorRelativeInt(sd::graph::RandomGenerator* ptr, Nd4jLong index) {
2019-07-24 14:14:54 +02:00
return ptr->relativeInt(index);
}
2020-03-02 10:49:41 +01:00
Nd4jLong getRandomGeneratorRelativeLong(sd::graph::RandomGenerator* ptr, Nd4jLong index) {
2019-07-24 14:14:54 +02:00
return ptr->relativeLong(index);
}
2020-03-02 10:49:41 +01:00
void deleteRandomGenerator(sd::graph::RandomGenerator* ptr) {
2019-07-24 14:14:54 +02:00
delete ptr;
}
2019-07-22 13:34:08 +02:00
Nd4jPointer shapeBufferForNumpy(Nd4jPointer npyArray) {
2019-08-26 18:57:51 +02:00
try {
cnpy::NpyArray arr = cnpy::loadNpyFromPointer(reinterpret_cast<char *>(npyArray));
unsigned int shapeSize = arr.shape.size();
std::vector<Nd4jLong> shape(shapeSize);
bool _empty = false;
for (unsigned int i = 0; i < shapeSize; i++) {
shape[i] = arr.shape[i];
if (arr.shape[i] == 0)
_empty = true;
}
auto dtype = cnpy::dataTypeFromHeader(reinterpret_cast<char *>(npyArray));
Nd4jLong *shapeBuffer;
if (shape.size() == 1 && shape[0] == 0) {
// scalar case
2020-03-02 10:49:41 +01:00
shapeBuffer = sd::ShapeBuilders::createScalarShapeInfo(dtype);
2019-08-26 18:57:51 +02:00
} else if (_empty) {
if (shapeSize > 0)
2020-03-02 10:49:41 +01:00
shapeBuffer = sd::ShapeBuilders::emptyShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape);
2019-08-26 18:57:51 +02:00
else
2020-03-02 10:49:41 +01:00
shapeBuffer = sd::ShapeBuilders::emptyShapeInfo(dtype);
2019-08-26 18:57:51 +02:00
} else {
2020-03-02 10:49:41 +01:00
shapeBuffer = sd::ShapeBuilders::createShapeInfo(dtype, arr.fortranOrder ? 'f' : 'c', shape);
2019-08-26 18:57:51 +02:00
}
2020-05-09 07:06:14 +02:00
return (Nd4jPointer)(sd::ConstantShapeHelper::getInstance()->createFromExisting(shapeBuffer, true)); // TO DO: this can lead to unpleasant crash sometimes
2019-08-26 18:57:51 +02:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
2019-06-15 13:34:34 +02:00
}
}
2019-07-12 07:21:15 +02:00
2019-07-22 13:34:08 +02:00
const char* runLightBenchmarkSuit(bool printOut) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
sd::LightBenchmarkSuit suit;
2019-08-26 18:57:51 +02:00
auto result = suit.runSuit();
if (printOut)
nd4j_printf("%s\n", result.data());
auto chars = new char[result.length() + 1];
std::memcpy(chars, result.data(), result.length());
chars[result.length()] = (char) 0x0;
return chars;
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-07-12 07:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
const char* runFullBenchmarkSuit(bool printOut) {
2019-08-26 18:57:51 +02:00
try {
2020-03-02 10:49:41 +01:00
sd::FullBenchmarkSuit suit;
2019-08-26 18:57:51 +02:00
auto result = suit.runSuit();
if (printOut)
nd4j_printf("%s\n", result.data());
auto chars = new char[result.length() + 1];
std::memcpy(chars, result.data(), result.length());
chars[result.length()] = (char) 0x0;
return chars;
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2019-08-26 18:57:51 +02:00
return nullptr;
}
2019-07-12 07:21:15 +02:00
}
2019-07-22 13:34:08 +02:00
Nd4jLong getCachedMemory(int deviceId) {
2020-03-02 10:49:41 +01:00
return sd::ConstantHelper::getInstance()->getCachedAmount(deviceId);
2019-07-22 13:34:08 +02:00
}
[WIP] multi-device support (#80)
* fix pad javadoc and @see links. (#72)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* [WIP] More fixes (#73)
* special tests for ConstantTadHelper/ConstantShapeHelper
Signed-off-by: raver119 <raver119@gmail.com>
* release methods for data buffers
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary TadPack C++/Java side (#74)
Signed-off-by: raver119 <raver119@gmail.com>
* Zoo model TF import test updates (#75)
* argLine fix, update compression_gru comment
* updated comment for xception
* undid but commented argLine change
* updated xlnet comment
* copyright headers
* - new NDArray methods like()/ulike() (#77)
- fix for depthwise_conv2d_bp + special test
Signed-off-by: raver119 <raver119@gmail.com>
* upsampling2d fix CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* DL4J trace logging (#79)
* MLN/CG trace logging for debugging
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tiny tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* strided_slice_bp shape fn leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff fixes and naming (#78)
* remove SDVariable inplace methods
* import methods
* npe fix in OpVal
* removed SameDiff inplace ops from tests
* Naming updates, moved to centralized methods in SameDiff, should use op_#:# for everything
* quick fixes
* javadoc
* SDVariable eval with placeholders
* use regex match
* better matching
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* fix javadoc. (#76)
* fix javadoc.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace most @see with @link s.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* 4 additional tests
Signed-off-by: raver119 <raver119@gmail.com>
* launch context reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* per-device LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* Various DL4J/ND4J fixes (#81)
* #7954 Force refresh of UI when switching tabs on overview page
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8017 Concurrent modification exception (synchronize) fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8033 Don't initialize updater in middle of writing memory crash dump
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8208 Fix shape checks for ND4J int[] creator methods
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6385 #7992 Keras import naming fixes + cleanup
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8016 Upsampling3D - add NDHWC format support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Refactor NativeOps.h to export C functions
* Actually export functions from NativeOps.h
* Adapt the Java wrappers in ND4J generated with JavaCPP
* Create C wrappers for some of the C++ classes currently used by ND4J
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* remove duplicate code in createBufferDetached. (#83)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Keras model import - updater lr fix (#84)
* Keras model import - updater lr fix
Signed-off-by: eraly <susan.eraly@gmail.com>
* Keras model import - updater lr fix, cleanup
Signed-off-by: eraly <susan.eraly@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Fix functions of OpaqueVariablesSet
* thread-local buffers/affinity
Signed-off-by: raver119 <raver119@gmail.com>
* thread safety for LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* more of thread safety
Signed-off-by: raver119 <raver119@gmail.com>
* one more multi threaded test
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff Convolution Config validation, better output methods (#82)
* Conv Config validation & tests
Signed-off-by: Ryan Nett <rnett@skymind.io>
* stackOutputs utility method
Signed-off-by: Ryan Nett <rnett@skymind.io>
* use constructor for validation, support negative kernel sizes (infered from weights)
Signed-off-by: Ryan Nett <rnett@skymind.io>
* better output methods
Signed-off-by: Ryan Nett <rnett@skymind.io>
* move output to be with fit and evaluate
Signed-off-by: Ryan Nett <rnett@skymind.io>
* fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* more fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* refactor duplicate code from pad methods. (#86)
* refactor duplicate code from pad methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace switch with if.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes and improvements (#87)
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6488 ElementWiseVertex broadcast support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Constructors and broadcast supported it Transforms.max/min
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8054 ElementWiseVertex now supports broadcast inputs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8057 Nd4j.create overload dtype fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7551 ND4J Shape validation fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Numpy boolean import (#91)
* numpy bool type
Signed-off-by: raver119 <raver119@gmail.com>
* numpy bool java side
Signed-off-by: raver119 <raver119@gmail.com>
* remove create method with unused parameter. (#89)
* remove create method with unused parameter.
* removed more unused methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* removing more unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* last removal of unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* remove createSparse methods. (#92)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes (#90)
* Deprecate Old*Op instances
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8063 #8054 Broadcast exceptions + cleanup inplace ops
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Remove bad test condition
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7993 Fix shape function issue in crop_and_resize op
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* DL4J SameDiff lambda layer fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8029 Fix for pnorm backprop math
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8038 Fix Op profiler NaN/Inf triggering + add tests (#93)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* createUninitializedDetached refactoring. (#94)
* wip
* update interface, add null implementations.
* Breaking one test in a weird way.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* createUninitializedDetached refactored.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* cuda build fix for issues introduced by recent refactoring
Signed-off-by: raver119 <raver119@gmail.com>
* [WIP] More of CUDA (#95)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* Implementation of hashcode cuda helper. Working edition.
* Fixed parallel test input arangements.
* Fixed tests for hashcode op.
* Fixed shape calculation for image:crop_and_resize op and test.
* NativeOps tests. Initial test suite.
* Added tests for indexReduce methods.
* Added test on execBroadcast with NDArray as dimensions.
* Added test on execBroadcastBool with NDArray as dimensions.
* Added tests on execPairwiseTransform and execPairwiseTransofrmBool.
* Added tests for execReduce with scalar results.
* Added reduce tests for non-empty dims array.
* Added tests for reduce3.
* Added tests for execScalar.
* Added tests for execSummaryStats.
* - provide cpu/cuda code for batch_to_space
- testing it
Signed-off-by: Yurii <yurii@skymind.io>
* - remove old test for batch_to_space (had wrong format and numbers were not checked)
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed complilation errors with test.
* Added test for execTransformFloat.
* Added test for execTransformSame.
* Added test for execTransformBool.
* Added test for execTransformStrict.
* Added tests for execScalar/execScalarBool with TADs.
* Added test for flatten.
* - provide cpu/cuda code for space_to_Batch operaion
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for concat.
* comment unnecessary stuff in s_t_b
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for specialConcat.
* Added tests for memcpy/set routines.
* Fixed pullRow cuda test.
* Added pullRow test.
* Added average test.
* - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...)
Signed-off-by: Yurii <yurii@skymind.io>
* - debugging and fixing cuda tests in JavaInteropTests file
Signed-off-by: Yurii <yurii@skymind.io>
* - correct some tests
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for shuffle.
* Fixed ops declarations.
* Restored omp and added shuffle test.
* Added convertTypes test.
* Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps.
* Added sort tests.
* Added tests for execCustomOp.
* - further debuging and fixing tests terminated with crash
Signed-off-by: Yurii <yurii@skymind.io>
* Added tests for calculateOutputShapes.
* Addded Benchmarks test.
* Commented benchmark tests.
* change assertion
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for apply_sgd op. Added cpu helper for that op.
* Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps.
* Added test for assign broadcastable.
* Added tests for assign_bp op.
* Added tests for axpy op.
* - assign/execScalar/execTransformAny signature change
- minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed axpy op.
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* - fix tests for nativeOps::concat
Signed-off-by: Yurii <yurii@skymind.io>
* sequential transform/scalar
Signed-off-by: raver119 <raver119@gmail.com>
* allow nested parallelism
Signed-off-by: raver119 <raver119@gmail.com>
* assign_bp leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* block setRNG fix
Signed-off-by: raver119 <raver119@gmail.com>
* enable parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* enable nested parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* Added cuda implementation for row_count helper.
* Added implementation for tnse gains op helper.
* - take into account possible situations when input arrays are empty in reduce_ cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces.
* Added kernel for tsne/symmetrized op heleper.
* Implementation of tsne/symmetrized op cuda helper. Working edition.
* Eliminated waste printfs.
* Added test for broadcastgradientargs op.
* host-only fallback for empty reduce float
Signed-off-by: raver119 <raver119@gmail.com>
* - some tests fixes
Signed-off-by: Yurii <yurii@skymind.io>
* - correct the rest of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* - further correction of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for Cbow op. Also added cuda implementation for cbow helpers.
* - improve code of stack operation for scalar case
Signed-off-by: Yurii <yurii@skymind.io>
* - provide cuda kernel for gatherND operation
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of cbow helpers with cuda kernels.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* - further correction of cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implementatation of cbow op helper with cuda kernels. Working edition.
* Skip random testing for cudablas case.
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for ELU and ELU_BP ops.
* Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops.
* Added tests for neq_scalar.
* Added test for noop.
* - further work on clipbynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* - get rid of concat op call, use instead direct concat helper call
Signed-off-by: Yurii <yurii@skymind.io>
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for lrelu and lrelu_bp.
* Added tests for selu and selu_bp.
* Fixed lrelu derivative helpers.
* - some corrections in lstm
Signed-off-by: Yurii <yurii@skymind.io>
* operator * result shape fix
Signed-off-by: raver119 <raver119@gmail.com>
* - correct typo in lstmCell
Signed-off-by: Yurii <yurii@skymind.io>
* few tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA inverse broadcast bool fix
Signed-off-by: raver119 <raver119@gmail.com>
* disable MMAP test for CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* BooleanOp syncToDevice
Signed-off-by: raver119 <raver119@gmail.com>
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* additional data types for im2col/col2im
Signed-off-by: raver119 <raver119@gmail.com>
* Added test for firas_sparse op.
* one more RandomBuffer test excluded
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for flatten op.
* Added test for Floor op.
* bunch of tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* mmulDot tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Implemented floordiv_bp op and tests.
* Fixed scalar case with cuda implementation for bds.
* - work on cuda kernel for clip_by_norm backprop op is completed
Signed-off-by: Yurii <yurii@skymind.io>
* Eliminate cbow crach.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminated abortion with batched nlp test.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed shared flag initializing.
* disabled bunch of cpu workspaces tests
Signed-off-by: raver119 <raver119@gmail.com>
* scalar operators fix: missing registerSpecialUse call
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed logdet for cuda and tests.
* - correct clipBynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed crop_and_resize shape datatype.
* - correct some mmul tests
Signed-off-by: Yurii <yurii@skymind.io>
* build fix
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI (#97)
Signed-off-by: raver119 <raver119@gmail.com>
* temporary stack fix
Signed-off-by: raver119 <raver119@gmail.com>
* round robin affinity test
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy CudaContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy ContextPool classes/methods
Signed-off-by: raver119 <raver119@gmail.com>
* one legacy test removed
Signed-off-by: raver119 <raver119@gmail.com>
* few more fields rearranged
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext++
Signed-off-by: raver119 <raver119@gmail.com>
* more of OpaqueLaunchContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext -> CudaContext
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handles
Signed-off-by: raver119 <raver119@gmail.com>
* typo
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver method
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handle propagated
Signed-off-by: raver119 <raver119@gmail.com>
* blas/solver handles
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* legacy concat implementations replaced with new CustomOp
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* concat now uses way more blocks
Signed-off-by: raver119 <raver119@gmail.com>
* print
Signed-off-by: raver119 <raver119@gmail.com>
* no more triple template mmul
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bitonic sort reorganized
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* type conversions moved to generic impl
Signed-off-by: raver119 <raver119@gmail.com>
* cpu data types pass
Signed-off-by: raver119 <raver119@gmail.com>
* non_max_suppression
Signed-off-by: raver119 <raver119@gmail.com>
* sortByValue fix
Signed-off-by: raver119 <raver119@gmail.com>
* ignore all mixed datatype tests for mmul
Signed-off-by: raver119 <raver119@gmail.com>
* special handling of OpProfiler exceptions
Signed-off-by: raver119 <raver119@gmail.com>
* - one failing concat test in cpp
- Nd4j.tile now uses op internally
Signed-off-by: raver119 <raver119@gmail.com>
* get back dtype exception for legacy arrays deserialization
Signed-off-by: raver119 <raver119@gmail.com>
2019-08-14 15:52:34 +02:00
2020-03-02 10:49:41 +01:00
sd::LaunchContext* defaultLaunchContext() {
[WIP] multi-device support (#80)
* fix pad javadoc and @see links. (#72)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* [WIP] More fixes (#73)
* special tests for ConstantTadHelper/ConstantShapeHelper
Signed-off-by: raver119 <raver119@gmail.com>
* release methods for data buffers
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary TadPack C++/Java side (#74)
Signed-off-by: raver119 <raver119@gmail.com>
* Zoo model TF import test updates (#75)
* argLine fix, update compression_gru comment
* updated comment for xception
* undid but commented argLine change
* updated xlnet comment
* copyright headers
* - new NDArray methods like()/ulike() (#77)
- fix for depthwise_conv2d_bp + special test
Signed-off-by: raver119 <raver119@gmail.com>
* upsampling2d fix CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* DL4J trace logging (#79)
* MLN/CG trace logging for debugging
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tiny tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* strided_slice_bp shape fn leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff fixes and naming (#78)
* remove SDVariable inplace methods
* import methods
* npe fix in OpVal
* removed SameDiff inplace ops from tests
* Naming updates, moved to centralized methods in SameDiff, should use op_#:# for everything
* quick fixes
* javadoc
* SDVariable eval with placeholders
* use regex match
* better matching
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* fix javadoc. (#76)
* fix javadoc.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace most @see with @link s.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* 4 additional tests
Signed-off-by: raver119 <raver119@gmail.com>
* launch context reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* per-device LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* Various DL4J/ND4J fixes (#81)
* #7954 Force refresh of UI when switching tabs on overview page
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8017 Concurrent modification exception (synchronize) fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8033 Don't initialize updater in middle of writing memory crash dump
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8208 Fix shape checks for ND4J int[] creator methods
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6385 #7992 Keras import naming fixes + cleanup
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8016 Upsampling3D - add NDHWC format support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Refactor NativeOps.h to export C functions
* Actually export functions from NativeOps.h
* Adapt the Java wrappers in ND4J generated with JavaCPP
* Create C wrappers for some of the C++ classes currently used by ND4J
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* remove duplicate code in createBufferDetached. (#83)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Keras model import - updater lr fix (#84)
* Keras model import - updater lr fix
Signed-off-by: eraly <susan.eraly@gmail.com>
* Keras model import - updater lr fix, cleanup
Signed-off-by: eraly <susan.eraly@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Fix functions of OpaqueVariablesSet
* thread-local buffers/affinity
Signed-off-by: raver119 <raver119@gmail.com>
* thread safety for LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* more of thread safety
Signed-off-by: raver119 <raver119@gmail.com>
* one more multi threaded test
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff Convolution Config validation, better output methods (#82)
* Conv Config validation & tests
Signed-off-by: Ryan Nett <rnett@skymind.io>
* stackOutputs utility method
Signed-off-by: Ryan Nett <rnett@skymind.io>
* use constructor for validation, support negative kernel sizes (infered from weights)
Signed-off-by: Ryan Nett <rnett@skymind.io>
* better output methods
Signed-off-by: Ryan Nett <rnett@skymind.io>
* move output to be with fit and evaluate
Signed-off-by: Ryan Nett <rnett@skymind.io>
* fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* more fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* refactor duplicate code from pad methods. (#86)
* refactor duplicate code from pad methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace switch with if.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes and improvements (#87)
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6488 ElementWiseVertex broadcast support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Constructors and broadcast supported it Transforms.max/min
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8054 ElementWiseVertex now supports broadcast inputs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8057 Nd4j.create overload dtype fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7551 ND4J Shape validation fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Numpy boolean import (#91)
* numpy bool type
Signed-off-by: raver119 <raver119@gmail.com>
* numpy bool java side
Signed-off-by: raver119 <raver119@gmail.com>
* remove create method with unused parameter. (#89)
* remove create method with unused parameter.
* removed more unused methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* removing more unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* last removal of unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* remove createSparse methods. (#92)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes (#90)
* Deprecate Old*Op instances
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8063 #8054 Broadcast exceptions + cleanup inplace ops
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Remove bad test condition
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7993 Fix shape function issue in crop_and_resize op
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* DL4J SameDiff lambda layer fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8029 Fix for pnorm backprop math
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8038 Fix Op profiler NaN/Inf triggering + add tests (#93)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* createUninitializedDetached refactoring. (#94)
* wip
* update interface, add null implementations.
* Breaking one test in a weird way.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* createUninitializedDetached refactored.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* cuda build fix for issues introduced by recent refactoring
Signed-off-by: raver119 <raver119@gmail.com>
* [WIP] More of CUDA (#95)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* Implementation of hashcode cuda helper. Working edition.
* Fixed parallel test input arangements.
* Fixed tests for hashcode op.
* Fixed shape calculation for image:crop_and_resize op and test.
* NativeOps tests. Initial test suite.
* Added tests for indexReduce methods.
* Added test on execBroadcast with NDArray as dimensions.
* Added test on execBroadcastBool with NDArray as dimensions.
* Added tests on execPairwiseTransform and execPairwiseTransofrmBool.
* Added tests for execReduce with scalar results.
* Added reduce tests for non-empty dims array.
* Added tests for reduce3.
* Added tests for execScalar.
* Added tests for execSummaryStats.
* - provide cpu/cuda code for batch_to_space
- testing it
Signed-off-by: Yurii <yurii@skymind.io>
* - remove old test for batch_to_space (had wrong format and numbers were not checked)
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed complilation errors with test.
* Added test for execTransformFloat.
* Added test for execTransformSame.
* Added test for execTransformBool.
* Added test for execTransformStrict.
* Added tests for execScalar/execScalarBool with TADs.
* Added test for flatten.
* - provide cpu/cuda code for space_to_Batch operaion
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for concat.
* comment unnecessary stuff in s_t_b
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for specialConcat.
* Added tests for memcpy/set routines.
* Fixed pullRow cuda test.
* Added pullRow test.
* Added average test.
* - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...)
Signed-off-by: Yurii <yurii@skymind.io>
* - debugging and fixing cuda tests in JavaInteropTests file
Signed-off-by: Yurii <yurii@skymind.io>
* - correct some tests
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for shuffle.
* Fixed ops declarations.
* Restored omp and added shuffle test.
* Added convertTypes test.
* Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps.
* Added sort tests.
* Added tests for execCustomOp.
* - further debuging and fixing tests terminated with crash
Signed-off-by: Yurii <yurii@skymind.io>
* Added tests for calculateOutputShapes.
* Addded Benchmarks test.
* Commented benchmark tests.
* change assertion
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for apply_sgd op. Added cpu helper for that op.
* Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps.
* Added test for assign broadcastable.
* Added tests for assign_bp op.
* Added tests for axpy op.
* - assign/execScalar/execTransformAny signature change
- minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed axpy op.
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* - fix tests for nativeOps::concat
Signed-off-by: Yurii <yurii@skymind.io>
* sequential transform/scalar
Signed-off-by: raver119 <raver119@gmail.com>
* allow nested parallelism
Signed-off-by: raver119 <raver119@gmail.com>
* assign_bp leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* block setRNG fix
Signed-off-by: raver119 <raver119@gmail.com>
* enable parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* enable nested parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* Added cuda implementation for row_count helper.
* Added implementation for tnse gains op helper.
* - take into account possible situations when input arrays are empty in reduce_ cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces.
* Added kernel for tsne/symmetrized op heleper.
* Implementation of tsne/symmetrized op cuda helper. Working edition.
* Eliminated waste printfs.
* Added test for broadcastgradientargs op.
* host-only fallback for empty reduce float
Signed-off-by: raver119 <raver119@gmail.com>
* - some tests fixes
Signed-off-by: Yurii <yurii@skymind.io>
* - correct the rest of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* - further correction of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for Cbow op. Also added cuda implementation for cbow helpers.
* - improve code of stack operation for scalar case
Signed-off-by: Yurii <yurii@skymind.io>
* - provide cuda kernel for gatherND operation
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of cbow helpers with cuda kernels.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* - further correction of cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implementatation of cbow op helper with cuda kernels. Working edition.
* Skip random testing for cudablas case.
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for ELU and ELU_BP ops.
* Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops.
* Added tests for neq_scalar.
* Added test for noop.
* - further work on clipbynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* - get rid of concat op call, use instead direct concat helper call
Signed-off-by: Yurii <yurii@skymind.io>
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for lrelu and lrelu_bp.
* Added tests for selu and selu_bp.
* Fixed lrelu derivative helpers.
* - some corrections in lstm
Signed-off-by: Yurii <yurii@skymind.io>
* operator * result shape fix
Signed-off-by: raver119 <raver119@gmail.com>
* - correct typo in lstmCell
Signed-off-by: Yurii <yurii@skymind.io>
* few tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA inverse broadcast bool fix
Signed-off-by: raver119 <raver119@gmail.com>
* disable MMAP test for CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* BooleanOp syncToDevice
Signed-off-by: raver119 <raver119@gmail.com>
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* additional data types for im2col/col2im
Signed-off-by: raver119 <raver119@gmail.com>
* Added test for firas_sparse op.
* one more RandomBuffer test excluded
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for flatten op.
* Added test for Floor op.
* bunch of tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* mmulDot tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Implemented floordiv_bp op and tests.
* Fixed scalar case with cuda implementation for bds.
* - work on cuda kernel for clip_by_norm backprop op is completed
Signed-off-by: Yurii <yurii@skymind.io>
* Eliminate cbow crach.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminated abortion with batched nlp test.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed shared flag initializing.
* disabled bunch of cpu workspaces tests
Signed-off-by: raver119 <raver119@gmail.com>
* scalar operators fix: missing registerSpecialUse call
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed logdet for cuda and tests.
* - correct clipBynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed crop_and_resize shape datatype.
* - correct some mmul tests
Signed-off-by: Yurii <yurii@skymind.io>
* build fix
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI (#97)
Signed-off-by: raver119 <raver119@gmail.com>
* temporary stack fix
Signed-off-by: raver119 <raver119@gmail.com>
* round robin affinity test
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy CudaContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy ContextPool classes/methods
Signed-off-by: raver119 <raver119@gmail.com>
* one legacy test removed
Signed-off-by: raver119 <raver119@gmail.com>
* few more fields rearranged
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext++
Signed-off-by: raver119 <raver119@gmail.com>
* more of OpaqueLaunchContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext -> CudaContext
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handles
Signed-off-by: raver119 <raver119@gmail.com>
* typo
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver method
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handle propagated
Signed-off-by: raver119 <raver119@gmail.com>
* blas/solver handles
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* legacy concat implementations replaced with new CustomOp
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* concat now uses way more blocks
Signed-off-by: raver119 <raver119@gmail.com>
* print
Signed-off-by: raver119 <raver119@gmail.com>
* no more triple template mmul
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bitonic sort reorganized
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* type conversions moved to generic impl
Signed-off-by: raver119 <raver119@gmail.com>
* cpu data types pass
Signed-off-by: raver119 <raver119@gmail.com>
* non_max_suppression
Signed-off-by: raver119 <raver119@gmail.com>
* sortByValue fix
Signed-off-by: raver119 <raver119@gmail.com>
* ignore all mixed datatype tests for mmul
Signed-off-by: raver119 <raver119@gmail.com>
* special handling of OpProfiler exceptions
Signed-off-by: raver119 <raver119@gmail.com>
* - one failing concat test in cpp
- Nd4j.tile now uses op internally
Signed-off-by: raver119 <raver119@gmail.com>
* get back dtype exception for legacy arrays deserialization
Signed-off-by: raver119 <raver119@gmail.com>
2019-08-14 15:52:34 +02:00
return LaunchContext::defaultContext();
}
Nd4jPointer lcScalarPointer(OpaqueLaunchContext* lc) {
return lc->getScalarPointer();
}
Nd4jPointer lcReductionPointer(OpaqueLaunchContext* lc) {
return lc->getReductionPointer();
}
Nd4jPointer lcAllocationPointer(OpaqueLaunchContext* lc) {
return lc->getAllocationPointer();
}
Nd4jPointer lcExecutionStream(OpaqueLaunchContext* lc) {
return lc->getCudaStream();
}
Nd4jPointer lcCopyStream(OpaqueLaunchContext* lc) {
return lc->getCudaSpecialStream();
}
Nd4jPointer lcBlasHandle(OpaqueLaunchContext* lc) {
return lc->getCublasHandle();
}
Nd4jPointer lcSolverHandle(OpaqueLaunchContext* lc) {
return lc->getCusolverHandle();
2019-08-26 18:57:51 +02:00
}
int lastErrorCode() {
2020-03-02 10:49:41 +01:00
return sd::LaunchContext::defaultContext()->errorReference()->errorCode();
2019-08-26 18:57:51 +02:00
}
const char* lastErrorMessage() {
2020-03-02 10:49:41 +01:00
return sd::LaunchContext::defaultContext()->errorReference()->errorMessage();
2019-09-11 20:50:28 +02:00
}
2020-01-04 07:06:44 +01:00
void ctxShapeFunctionOverride(OpaqueContext* ptr, bool reallyOverride) {
ptr->setShapeFunctionOverride(reallyOverride);
}
2020-02-05 05:27:24 +01:00
void ctxPurge(OpaqueContext* ptr) {
ptr->clearFastPath();
}
2019-09-11 20:50:28 +02:00
int binaryLevel() {
return 0;
}
int optimalLevel() {
return 0;
}
bool isMinimalRequirementsMet() {
return true;
}
bool isOptimalRequirementsMet() {
return true;
2019-11-14 12:35:02 +01:00
}
void ctxAllowHelpers(OpaqueContext* ptr, bool reallyAllow) {
ptr->allowHelpers(reallyAllow);
2020-01-04 11:27:50 +01:00
}
2020-01-27 08:00:07 +01:00
void ctxSetExecutionMode(OpaqueContext* ptr, int execMode) {
if (execMode < 0 || execMode > 2)
execMode = 0;
2020-03-09 06:22:49 +01:00
ptr->setExecutionMode((samediff::ExecutionMode) execMode);
2020-01-27 08:00:07 +01:00
}
2020-04-28 19:38:16 +02:00
OpaqueDataBuffer* dbCreateExternalDataBuffer(Nd4jLong elements, int dataType, Nd4jPointer primary, Nd4jPointer special) {
auto buffer = dbAllocateDataBuffer(0, dataType, false);
if (primary != nullptr)
buffer->setPrimary(primary, elements);
if (special != nullptr)
buffer->setSpecial(special, elements);
return buffer;
}
OpaqueDataBuffer* dbAllocateDataBuffer(Nd4jLong elements, int dataType, bool allocateBoth) {
return allocateDataBuffer(elements, dataType, allocateBoth);
}
2020-01-04 11:27:50 +01:00
OpaqueDataBuffer* allocateDataBuffer(Nd4jLong elements, int dataType, bool allocateBoth) {
2020-01-24 08:11:09 +01:00
try {
auto dtype = DataTypeUtils::fromInt(dataType);
2020-03-02 10:49:41 +01:00
return new sd::InteropDataBuffer(elements * DataTypeUtils::sizeOf(dtype), dtype, allocateBoth);
2020-01-24 08:11:09 +01:00
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2020-01-24 08:11:09 +01:00
return nullptr;
}
2020-01-04 11:27:50 +01:00
}
Nd4jPointer dbPrimaryBuffer(OpaqueDataBuffer *dataBuffer) {
return dataBuffer->primary();
}
Nd4jPointer dbSpecialBuffer(OpaqueDataBuffer *dataBuffer) {
return dataBuffer->special();
}
void deleteDataBuffer(OpaqueDataBuffer *dataBuffer) {
delete dataBuffer;
}
void dbSetPrimaryBuffer(OpaqueDataBuffer *dataBuffer, Nd4jPointer primaryBuffer, Nd4jLong numBytes) {
dataBuffer->setPrimary(primaryBuffer, numBytes);
}
void dbSetSpecialBuffer(OpaqueDataBuffer *dataBuffer, Nd4jPointer specialBuffer, Nd4jLong numBytes) {
dataBuffer->setSpecial(specialBuffer, numBytes);
}
void dbAllocatePrimaryBuffer(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->allocatePrimary();
}
void dbAllocateSpecialBuffer(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->allocateSpecial();
}
void dbExpandBuffer(OpaqueDataBuffer *dataBuffer, Nd4jLong elements) {
2020-01-24 08:11:09 +01:00
try {
dataBuffer->dataBuffer()->expand(elements * DataTypeUtils::sizeOf(dataBuffer->dataBuffer()->getDataType()));
} catch (std::exception &e) {
2020-03-02 10:49:41 +01:00
sd::LaunchContext::defaultContext()->errorReference()->setErrorCode(1);
sd::LaunchContext::defaultContext()->errorReference()->setErrorMessage(e.what());
2020-01-24 08:11:09 +01:00
}
2020-01-04 11:27:50 +01:00
}
OpaqueDataBuffer* dbCreateView(OpaqueDataBuffer *dataBuffer, Nd4jLong length, Nd4jLong offset) {
return new InteropDataBuffer(*dataBuffer, length, offset);
}
void dbSyncToSpecial(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->syncToSpecial();
}
void dbSyncToPrimary(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->syncToPrimary(nullptr);
}
void dbTickHostRead(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->readPrimary();
}
void dbTickHostWrite(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->writePrimary();
}
void dbTickDeviceRead(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->readSpecial();
}
void dbTickDeviceWrite(OpaqueDataBuffer *dataBuffer) {
dataBuffer->dataBuffer()->writeSpecial();
}
void dbExpand(OpaqueDataBuffer *dataBuffer, Nd4jLong elements) {
dataBuffer->expand(elements);
}
void dbClose(OpaqueDataBuffer *dataBuffer) {
dataBuffer->getDataBuffer()->close();
}
int dbDeviceId(OpaqueDataBuffer *dataBuffer) {
return dataBuffer->deviceId();
}
void dbSetDeviceId(OpaqueDataBuffer *dataBuffer, int deviceId) {
dataBuffer->setDeviceId(deviceId);
}
int dbLocality(OpaqueDataBuffer *dataBuffer) {
auto p = dataBuffer->dataBuffer()->isPrimaryActual();
auto d = dataBuffer->dataBuffer()->isSpecialActual();
if (p && d)
return 0;
else if (p)
return -1;
else
return 1;
[WIP] multi-device support (#80)
* fix pad javadoc and @see links. (#72)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* [WIP] More fixes (#73)
* special tests for ConstantTadHelper/ConstantShapeHelper
Signed-off-by: raver119 <raver119@gmail.com>
* release methods for data buffers
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary buffer Java side
Signed-off-by: raver119 <raver119@gmail.com>
* delete temporary TadPack C++/Java side (#74)
Signed-off-by: raver119 <raver119@gmail.com>
* Zoo model TF import test updates (#75)
* argLine fix, update compression_gru comment
* updated comment for xception
* undid but commented argLine change
* updated xlnet comment
* copyright headers
* - new NDArray methods like()/ulike() (#77)
- fix for depthwise_conv2d_bp + special test
Signed-off-by: raver119 <raver119@gmail.com>
* upsampling2d fix CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* DL4J trace logging (#79)
* MLN/CG trace logging for debugging
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Tiny tweak
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* strided_slice_bp shape fn leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff fixes and naming (#78)
* remove SDVariable inplace methods
* import methods
* npe fix in OpVal
* removed SameDiff inplace ops from tests
* Naming updates, moved to centralized methods in SameDiff, should use op_#:# for everything
* quick fixes
* javadoc
* SDVariable eval with placeholders
* use regex match
* better matching
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* fix javadoc. (#76)
* fix javadoc.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace most @see with @link s.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* 4 additional tests
Signed-off-by: raver119 <raver119@gmail.com>
* launch context reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext reorganization
Signed-off-by: raver119 <raver119@gmail.com>
* per-device LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* Various DL4J/ND4J fixes (#81)
* #7954 Force refresh of UI when switching tabs on overview page
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8017 Concurrent modification exception (synchronize) fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8033 Don't initialize updater in middle of writing memory crash dump
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8208 Fix shape checks for ND4J int[] creator methods
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6385 #7992 Keras import naming fixes + cleanup
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8016 Upsampling3D - add NDHWC format support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Refactor NativeOps.h to export C functions
* Actually export functions from NativeOps.h
* Adapt the Java wrappers in ND4J generated with JavaCPP
* Create C wrappers for some of the C++ classes currently used by ND4J
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* remove duplicate code in createBufferDetached. (#83)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Keras model import - updater lr fix (#84)
* Keras model import - updater lr fix
Signed-off-by: eraly <susan.eraly@gmail.com>
* Keras model import - updater lr fix, cleanup
Signed-off-by: eraly <susan.eraly@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* ContextBuffers as separate entity
Signed-off-by: raver119 <raver119@gmail.com>
* Fix functions of OpaqueVariablesSet
* thread-local buffers/affinity
Signed-off-by: raver119 <raver119@gmail.com>
* thread safety for LaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* more of thread safety
Signed-off-by: raver119 <raver119@gmail.com>
* one more multi threaded test
Signed-off-by: raver119 <raver119@gmail.com>
* SameDiff Convolution Config validation, better output methods (#82)
* Conv Config validation & tests
Signed-off-by: Ryan Nett <rnett@skymind.io>
* stackOutputs utility method
Signed-off-by: Ryan Nett <rnett@skymind.io>
* use constructor for validation, support negative kernel sizes (infered from weights)
Signed-off-by: Ryan Nett <rnett@skymind.io>
* better output methods
Signed-off-by: Ryan Nett <rnett@skymind.io>
* move output to be with fit and evaluate
Signed-off-by: Ryan Nett <rnett@skymind.io>
* fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* more fixes
Signed-off-by: Ryan Nett <rnett@skymind.io>
* refactor duplicate code from pad methods. (#86)
* refactor duplicate code from pad methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* replace switch with if.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes and improvements (#87)
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Reshape and reallocate - small fixes
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #6488 ElementWiseVertex broadcast support
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Constructors and broadcast supported it Transforms.max/min
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8054 ElementWiseVertex now supports broadcast inputs
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8057 Nd4j.create overload dtype fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7551 ND4J Shape validation fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* [WIP] Numpy boolean import (#91)
* numpy bool type
Signed-off-by: raver119 <raver119@gmail.com>
* numpy bool java side
Signed-off-by: raver119 <raver119@gmail.com>
* remove create method with unused parameter. (#89)
* remove create method with unused parameter.
* removed more unused methods.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* removing more unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* last removal of unused code.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* remove createSparse methods. (#92)
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* Various ND4J/DL4J fixes (#90)
* Deprecate Old*Op instances
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8063 #8054 Broadcast exceptions + cleanup inplace ops
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Small fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* Remove bad test condition
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #7993 Fix shape function issue in crop_and_resize op
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* DL4J SameDiff lambda layer fix
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8029 Fix for pnorm backprop math
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* #8038 Fix Op profiler NaN/Inf triggering + add tests (#93)
Signed-off-by: AlexDBlack <blacka101@gmail.com>
* createUninitializedDetached refactoring. (#94)
* wip
* update interface, add null implementations.
* Breaking one test in a weird way.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* createUninitializedDetached refactored.
Signed-off-by: Robert Altena <Rob@Ra-ai.com>
* cuda build fix for issues introduced by recent refactoring
Signed-off-by: raver119 <raver119@gmail.com>
* [WIP] More of CUDA (#95)
* initial commit
Signed-off-by: raver119 <raver119@gmail.com>
* Implementation of hashcode cuda helper. Working edition.
* Fixed parallel test input arangements.
* Fixed tests for hashcode op.
* Fixed shape calculation for image:crop_and_resize op and test.
* NativeOps tests. Initial test suite.
* Added tests for indexReduce methods.
* Added test on execBroadcast with NDArray as dimensions.
* Added test on execBroadcastBool with NDArray as dimensions.
* Added tests on execPairwiseTransform and execPairwiseTransofrmBool.
* Added tests for execReduce with scalar results.
* Added reduce tests for non-empty dims array.
* Added tests for reduce3.
* Added tests for execScalar.
* Added tests for execSummaryStats.
* - provide cpu/cuda code for batch_to_space
- testing it
Signed-off-by: Yurii <yurii@skymind.io>
* - remove old test for batch_to_space (had wrong format and numbers were not checked)
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed complilation errors with test.
* Added test for execTransformFloat.
* Added test for execTransformSame.
* Added test for execTransformBool.
* Added test for execTransformStrict.
* Added tests for execScalar/execScalarBool with TADs.
* Added test for flatten.
* - provide cpu/cuda code for space_to_Batch operaion
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for concat.
* comment unnecessary stuff in s_t_b
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for specialConcat.
* Added tests for memcpy/set routines.
* Fixed pullRow cuda test.
* Added pullRow test.
* Added average test.
* - correct typo in NDArray::applyPairwiseTransform(nd4j::pairwise::BoolOps op...)
Signed-off-by: Yurii <yurii@skymind.io>
* - debugging and fixing cuda tests in JavaInteropTests file
Signed-off-by: Yurii <yurii@skymind.io>
* - correct some tests
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for shuffle.
* Fixed ops declarations.
* Restored omp and added shuffle test.
* Added convertTypes test.
* Added tests for execRandom. Eliminated usage of RandomBuffer with NativeOps.
* Added sort tests.
* Added tests for execCustomOp.
* - further debuging and fixing tests terminated with crash
Signed-off-by: Yurii <yurii@skymind.io>
* Added tests for calculateOutputShapes.
* Addded Benchmarks test.
* Commented benchmark tests.
* change assertion
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for apply_sgd op. Added cpu helper for that op.
* Implement cuda helper for aplly_sgd op. Fixed tests for NativeOps.
* Added test for assign broadcastable.
* Added tests for assign_bp op.
* Added tests for axpy op.
* - assign/execScalar/execTransformAny signature change
- minor test fix
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed axpy op.
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* - fix tests for nativeOps::concat
Signed-off-by: Yurii <yurii@skymind.io>
* sequential transform/scalar
Signed-off-by: raver119 <raver119@gmail.com>
* allow nested parallelism
Signed-off-by: raver119 <raver119@gmail.com>
* assign_bp leak fix
Signed-off-by: raver119 <raver119@gmail.com>
* block setRNG fix
Signed-off-by: raver119 <raver119@gmail.com>
* enable parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* enable nested parallelism by default
Signed-off-by: raver119 <raver119@gmail.com>
* Added cuda implementation for row_count helper.
* Added implementation for tnse gains op helper.
* - take into account possible situations when input arrays are empty in reduce_ cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implemented tsne/edge_forces op cuda-based helper. Parallelized cpu-based helper for edge_forces.
* Added kernel for tsne/symmetrized op heleper.
* Implementation of tsne/symmetrized op cuda helper. Working edition.
* Eliminated waste printfs.
* Added test for broadcastgradientargs op.
* host-only fallback for empty reduce float
Signed-off-by: raver119 <raver119@gmail.com>
* - some tests fixes
Signed-off-by: Yurii <yurii@skymind.io>
* - correct the rest of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* - further correction of reduce_ stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Added test for Cbow op. Also added cuda implementation for cbow helpers.
* - improve code of stack operation for scalar case
Signed-off-by: Yurii <yurii@skymind.io>
* - provide cuda kernel for gatherND operation
Signed-off-by: Yurii <yurii@skymind.io>
* Implementation of cbow helpers with cuda kernels.
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* minor tests tweaks
Signed-off-by: raver119 <raver119@gmail.com>
* - further correction of cuda stuff
Signed-off-by: Yurii <yurii@skymind.io>
* Implementatation of cbow op helper with cuda kernels. Working edition.
* Skip random testing for cudablas case.
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for ELU and ELU_BP ops.
* Added tests for eq_scalar, gt_scalar, gte_scalar and lte_scalar ops.
* Added tests for neq_scalar.
* Added test for noop.
* - further work on clipbynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* - get rid of concat op call, use instead direct concat helper call
Signed-off-by: Yurii <yurii@skymind.io>
* lstmBlockCell context fix
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for lrelu and lrelu_bp.
* Added tests for selu and selu_bp.
* Fixed lrelu derivative helpers.
* - some corrections in lstm
Signed-off-by: Yurii <yurii@skymind.io>
* operator * result shape fix
Signed-off-by: raver119 <raver119@gmail.com>
* - correct typo in lstmCell
Signed-off-by: Yurii <yurii@skymind.io>
* few tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* CUDA inverse broadcast bool fix
Signed-off-by: raver119 <raver119@gmail.com>
* disable MMAP test for CUDA
Signed-off-by: raver119 <raver119@gmail.com>
* BooleanOp syncToDevice
Signed-off-by: raver119 <raver119@gmail.com>
* meh
Signed-off-by: raver119 <raver119@gmail.com>
* additional data types for im2col/col2im
Signed-off-by: raver119 <raver119@gmail.com>
* Added test for firas_sparse op.
* one more RandomBuffer test excluded
Signed-off-by: raver119 <raver119@gmail.com>
* Added tests for flatten op.
* Added test for Floor op.
* bunch of tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* mmulDot tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Implemented floordiv_bp op and tests.
* Fixed scalar case with cuda implementation for bds.
* - work on cuda kernel for clip_by_norm backprop op is completed
Signed-off-by: Yurii <yurii@skymind.io>
* Eliminate cbow crach.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Eliminated abortion with batched nlp test.
* more tests fixed
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed shared flag initializing.
* disabled bunch of cpu workspaces tests
Signed-off-by: raver119 <raver119@gmail.com>
* scalar operators fix: missing registerSpecialUse call
Signed-off-by: raver119 <raver119@gmail.com>
* Fixed logdet for cuda and tests.
* - correct clipBynorm_bp
Signed-off-by: Yurii <yurii@skymind.io>
* Fixed crop_and_resize shape datatype.
* - correct some mmul tests
Signed-off-by: Yurii <yurii@skymind.io>
* build fix
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI
Signed-off-by: raver119 <raver119@gmail.com>
* exclude two methods for JNI (#97)
Signed-off-by: raver119 <raver119@gmail.com>
* temporary stack fix
Signed-off-by: raver119 <raver119@gmail.com>
* round robin affinity test
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy CudaContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* get rid of legacy ContextPool classes/methods
Signed-off-by: raver119 <raver119@gmail.com>
* one legacy test removed
Signed-off-by: raver119 <raver119@gmail.com>
* few more fields rearranged
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext
Signed-off-by: raver119 <raver119@gmail.com>
* OpaqueLaunchContext++
Signed-off-by: raver119 <raver119@gmail.com>
* more of OpaqueLaunchContext methods
Signed-off-by: raver119 <raver119@gmail.com>
* LaunchContext -> CudaContext
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* AffinityManger changes
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handles
Signed-off-by: raver119 <raver119@gmail.com>
* typo
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver method
Signed-off-by: raver119 <raver119@gmail.com>
* cusolver handle propagated
Signed-off-by: raver119 <raver119@gmail.com>
* blas/solver handles
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* legacy concat implementations replaced with new CustomOp
Signed-off-by: raver119 <raver119@gmail.com>
* one more test
Signed-off-by: raver119 <raver119@gmail.com>
* concat now uses way more blocks
Signed-off-by: raver119 <raver119@gmail.com>
* print
Signed-off-by: raver119 <raver119@gmail.com>
* no more triple template mmul
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of kernels have dtypes reconsidered
Signed-off-by: raver119 <raver119@gmail.com>
* bitonic sort reorganized
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* bunch of cpu stuff removed from cuda scope
Signed-off-by: raver119 <raver119@gmail.com>
* type conversions moved to generic impl
Signed-off-by: raver119 <raver119@gmail.com>
* cpu data types pass
Signed-off-by: raver119 <raver119@gmail.com>
* non_max_suppression
Signed-off-by: raver119 <raver119@gmail.com>
* sortByValue fix
Signed-off-by: raver119 <raver119@gmail.com>
* ignore all mixed datatype tests for mmul
Signed-off-by: raver119 <raver119@gmail.com>
* special handling of OpProfiler exceptions
Signed-off-by: raver119 <raver119@gmail.com>
* - one failing concat test in cpp
- Nd4j.tile now uses op internally
Signed-off-by: raver119 <raver119@gmail.com>
* get back dtype exception for legacy arrays deserialization
Signed-off-by: raver119 <raver119@gmail.com>
2019-08-14 15:52:34 +02:00
}