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Shyrma mmul (#58) 

* - get rid of some copy procedures in mmulHelper ops

Signed-off-by: Yurii <iuriish@yahoo.com>

* - further work on embedding cuda api for batched gemm (cublasGemmBatchedEx) in our mmulHelper class

Signed-off-by: Yurii <iuriish@yahoo.com>

* - further work on cuda batched gamm api

Signed-off-by: Yurii <iuriish@yahoo.com>

* - write own cuda kernel performing batched gemm

Signed-off-by: Yurii <iuriish@yahoo.com>

* missing include in MmulHelper

Signed-off-by: raver119 <raver119@gmail.com>

* - forgot to keep in code previous correct kernels for mmulNxN, since it may happen that new onw will fail for some reason in future

Signed-off-by: Yurii <iuriish@yahoo.com>

* disable old tensordot

Signed-off-by: raver119 <raver119@gmail.com>

* - rewrite cuda kernels for usualGemm and usualGemv

Signed-off-by: Yurii <iuriish@yahoo.com>

* - profiling mmul helpers

Signed-off-by: Yurii <iuriish@yahoo.com>

* - prints to check shapes were added

Signed-off-by: Yurii <iuriish@yahoo.com>

* - correct type of output array Cin mmulNxN

Signed-off-by: Yurii <iuriish@yahoo.com>

* - take into account possible nans in C array

Signed-off-by: Yurii <iuriish@yahoo.com>

* slightly change numThreads message

Signed-off-by: raver119 <raver119@gmail.com>

* - make corrections in accordance to given notes in pr review

Signed-off-by: Yurii <iuriish@yahoo.com>
master
Yurii Shyrma 2019-11-19 15:39:36 +02:00 committed by GitHub
parent da1944e8e1
commit 66b84b38cf
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17 changed files with 1540 additions and 517 deletions
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5
libnd4j/blas/NDArray.h
View File

@ -1286,6 +1286,11 @@ namespace nd4j {
*/
Nd4jLong sizeAt(const int dim) const;
/**
* returns stride of "dim" dimension
*/
Nd4jLong strideAt(const int dim) const;
/**
* returns order of array
*/

16
libnd4j/blas/NDArray.hpp
View File

@ -1439,9 +1439,21 @@ Nd4jLong NDArray::sizeAt(const int dim) const {
throw std::runtime_error("Bad size index requested");
if (dim >= 0)
return this->_shapeInfo[1+dim];
return shape::shapeOf(_shapeInfo)[dim];
else
return this->_shapeInfo[1+(this->rankOf() + dim)];
return shape::shapeOf(_shapeInfo)[this->rankOf() + dim];
}
//////////////////////////////////////////////////////////////////////////
Nd4jLong NDArray::strideAt(const int dim) const {
if (dim >= this->rankOf() || dim < -this->rankOf())
throw std::runtime_error("NDArray::strideAt: Bad size index requested");
if (dim >= 0)
return shape::stride(_shapeInfo)[dim];
else
return shape::stride(_shapeInfo)[this->rankOf() + dim];
}
//////////////////////////////////////////////////////////////////////////

1
libnd4j/include/helpers/MmulHelper.h
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at

1
libnd4j/include/helpers/cpu/ConstantHelper.cpp
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at

664
libnd4j/include/helpers/cpu/MmulHelper.cpp
View File

@ -21,6 +21,7 @@
#include "../MmulHelper.h"
#include <NDArrayFactory.h>
#include <helpers/BlasHelper.h>
#include <helpers/ShapeUtils.h>
#include <exceptions/datatype_exception.h>
#include <execution/Threads.h>
@ -28,110 +29,124 @@
namespace nd4j {
//////////////////////////////////////////////////////////////////////////////
// MXK x KxN = MxN
// MXK x KxN = MxN -> actual sequence of axes doesn't matter
template <typename T1, typename T2, typename T3>
static void usualGemm(const char cOrder, const bool transA, const bool transB, const int M, const int N, const int K, const double alpha, const void* vA, const int lda, const void* vB, const int ldb, const double beta, void* vC, const int ldc) {
static void usualGemm(const NDArray* vA, const NDArray* vB, NDArray* vC,
const int aMaxis, const int aKaxis, const int bKaxis, const int bNaxis, const int cMaxis, const int cNaxis,
const double alpha, const double beta) {
T1* A = reinterpret_cast<T1*>(const_cast<void*>(vA));
T2* B = reinterpret_cast<T2*>(const_cast<void*>(vB));
T3* C = reinterpret_cast<T3*>(vC);
T3 alphaZ(alpha), betaZ(beta);
const T1* A = vA->bufferAsT<T1>();
const T2* B = vB->bufferAsT<T2>();
T3* C = vC->bufferAsT<T3>();
const bool flagC = cOrder == 'f';
const bool flagA = (flagC && transA) || (!flagC && !transA);
const bool flagB = (flagC && transB) || (!flagC && !transB);
const T3 alphaZ = alpha;
const T3 betaZ = beta;
// PRAGMA_OMP_PARALLEL_FOR_ARGS(OMP_IF(M*N > Environment::getInstance()->elementwiseThreshold()) schedule(guided))
// for(uint row = 0; row < M; ++row) {
const bool betaPersent = beta;
// T3* c = flagC ? (C + row) : (C + row * ldc);
const Nd4jLong* aShapeInfo = vA->getShapeInfo();
const Nd4jLong* bShapeInfo = vB->getShapeInfo();
const Nd4jLong* cShapeInfo = vC->getShapeInfo();
// for(uint col = 0; col < N; ++col)
// c[flagC ? col * ldc : col] = 0;
const int aRank = vA->rankOf();
const int bRank = vB->rankOf();
const int cRank = vC->rankOf();
// for(uint i = 0; i < K; ++i) {
const Nd4jLong cLen = vC->lengthOf();
// T3* b = flagB ? (B + i * ldb) : (B + i);
// T3* a = flagA ? (A + row * lda + i) : (A + row + i * lda);
// if(flagC) {
// PRAGMA_OMP_SIMD
// for(uint col = 0; col < N; ++col) {
// if(betaZ)
// c[col * ldc] += a * b[flagB ? col : col * ldb] + betaZ * c[col * ldc];
// else
// c[col * ldc] += a * b[flagB ? col : col * ldb];
// }
// }
// else {
// PRAGMA_OMP_SIMD
// for(uint col = 0; col < N; ++col) {
// if(betaZ)
// c[col] += a * b[flagB ? col : col * ldb] + betaZ * c[col];
// else
// c[col] += a * b[flagB ? col : col * ldb];
// }
// }
// }
// }
auto func = PRAGMA_THREADS_FOR_2D { ;
for (auto row = start_x; row < stop_x; row += inc_x) {
for (auto col = start_y; col < stop_y; col += inc_y) {
T3 *c = flagC ? (C + row + col * ldc) : (C + row * ldc + col);
T3 val = 0;
PRAGMA_OMP_SIMD
for (uint i = 0; i < K; ++i) {
T3 a = flagA ? *(A + row * lda + i) : *(A + row + i * lda);
T3 b = flagB ? *(B + col + i * ldb) : *(B + col * ldb + i);
val += alphaZ * a * b;
}
if (betaZ)
*c = val + betaZ * *c;
else
*c = val;
}
}
};
samediff::Threads::parallel_for(func, 0, M, 1, 0, N, 1);
}
//////////////////////////////////////////////////////////////////////////////
// MXN x N = M
template <typename T1, typename T2, typename T3>
static void usualGemv(const char aOrder, const int M, const int N, const double alpha, const void* vA, const int lda, const void* vX, const int incx, const double beta, void* vY, const int incy) {
T1* A = reinterpret_cast<T1*>(const_cast<void*>(vA));
T2* X = reinterpret_cast<T2*>(const_cast<void*>(vX));
T3* Y = reinterpret_cast<T3*>(vY);
T3 alphaZ(alpha), betaZ(beta);
const bool flagA = aOrder == 'f';
const int K = vA->sizeAt(aKaxis);
auto func = PRAGMA_THREADS_FOR {
for (auto row = start; row < stop; row += increment) {
T3 *y = Y + row * incy;
T3 val = 0;
std::vector<Nd4jLong> aCoords(2), bCoords(2), cCoords(2);
PRAGMA_OMP_SIMD
for (int i = 0; i < N; ++i) {
T3 a = flagA ? *(A + row + i * lda) : *(A + row * lda + i);
T3 x = *(X + i * incx);
val += alphaZ * a * x;
for (auto i = start; i < stop; ++i) {
// evaluate C coordinates
shape::index2coords(i, cShapeInfo, cCoords.data());
// evaluate A coordinates
aCoords[aMaxis] = cCoords[cMaxis];
aCoords[aKaxis] = 0;
// evaluate B coordinates
bCoords[bKaxis] = 0;
bCoords[bNaxis] = cCoords[cNaxis];
auto aOffset = shape::getOffset(aShapeInfo, aCoords.data());
auto bOffset = shape::getOffset(bShapeInfo, bCoords.data());
T3 val = A[aOffset] * B[bOffset]; // first iteration
for (uint j = 1; j < K; ++j) { // rest iterations
aOffset += shape::stride(aShapeInfo)[aKaxis];
bOffset += shape::stride(bShapeInfo)[bKaxis];
val = val + A[aOffset] * B[bOffset];
}
if (betaZ)
*y = val + betaZ * *y;
auto cOffset = shape::getOffset(cShapeInfo, cCoords.data());
if(betaPersent)
C[cOffset] = alphaZ * val + betaZ * C[cOffset];
else
*y = val;
C[cOffset] = alphaZ * val;
}
};
samediff::Threads::parallel_for(func, 0, M);
samediff::Threads::parallel_tad(func, 0, cLen);
}
//////////////////////////////////////////////////////////////////////////////
// MXN x N = M -> actual sequence of {M,N} axes doesn't matter
template <typename T1, typename T2, typename T3>
static void usualGemv(const NDArray* vA, const NDArray* vX, NDArray* vY, const int incx, const int incy, const int aMaxis, const double alpha, const double beta) {
const T1* A = vA->bufferAsT<T1>();
const T2* X = vX->bufferAsT<T2>();
T3* Y = vY->bufferAsT<T3>();
const T3 alphaZ = alpha;
const T3 betaZ = beta;
const bool betaPersent = beta;
const Nd4jLong* aShapeInfo = vA->getShapeInfo();
const Nd4jLong* xShapeInfo = vX->getShapeInfo();
const Nd4jLong* yShapeInfo = vY->getShapeInfo();
const int N = vX->lengthOf();
const int M = vY->lengthOf();
const auto aMstride = vA->strideAt(aMaxis);
const auto aNstride = vA->strideAt(aMaxis == 0 ? 1 : 0);
auto func = PRAGMA_THREADS_FOR {
for (auto i = start; i < stop; ++i) {
// evaluate offsets
auto aOffset = i * aMstride;
auto xOffset = 0;
T3 val = A[aOffset] * X[xOffset]; // first iteration
for (uint j = 1; j < N; ++j) { // rest iterations
aOffset += aNstride;
xOffset += incx;
val = val + A[aOffset] * X[xOffset];
}
auto yOffset = i * incy;
if(betaPersent)
Y[yOffset] = alphaZ * val + betaZ * Y[yOffset];
else
Y[yOffset] = alphaZ * val;
}
};
samediff::Threads::parallel_tad(func, 0, M);
}
//////////////////////////////////////////////////////////////////////////////
@ -144,12 +159,17 @@ static void usualDot(const Nd4jLong length, const double alpha, const void* vX,
T3* Z = reinterpret_cast<T3*>(vZ);
T3 alphaZ(alpha), betaZ(beta);
const bool betaPersent = beta;
T3 sum = 0;
PRAGMA_OMP_PARALLEL_FOR_ARGS(OMP_IF(length > Environment::getInstance()->elementwiseThreshold()) schedule(guided) reduction(OMP_SUMT:sum))
for(int i = 0; i < length; ++i)
sum += X[i * incx] * Y[i * incy];
*Z = alphaZ * sum + betaZ * *Z;
if(betaPersent)
*Z = alphaZ * sum + betaZ * *Z;
else
*Z = alphaZ * sum;
}
//////////////////////////////////////////////////////////////////////////////
@ -169,11 +189,10 @@ NDArray* MmulHelper::mmulMxM(const NDArray* A, const NDArray* B, NDArray* C, con
const auto M = A->sizeAt(0);
const auto K = A->sizeAt(1);
const auto N = B->sizeAt(1);
const auto bRows = B->sizeAt(0);
if(C != nullptr && C->rankOf() != 2)
throw std::runtime_error("MmulHelper::mmulMxM: rank of C array is not equal 2 !");
if(bRows != K)
if(B->sizeAt(0) != K)
throw std::runtime_error("MmulHelper::mmulMxM: B array has wrong number of rows !");
if(C != nullptr && C->sizeAt(0) != M)
throw std::runtime_error("MmulHelper::mmulMxM: C array has wrong number of rows !");
@ -183,59 +202,77 @@ NDArray* MmulHelper::mmulMxM(const NDArray* A, const NDArray* B, NDArray* C, con
if(C == nullptr)
C = new NDArray(outOrder, {M,N}, DataTypeUtils::pickPairwiseResultType(A->dataType(), B->dataType()), A->getContext());
NDArray *pA(const_cast<NDArray*>(A)), *pB(const_cast<NDArray*>(B)), *pC(const_cast<NDArray*>(C));
const auto cOrder = C->ordering();
if(A->ews() != 1)
pA = pA->dup(cOrder);
if(B->ews() != 1)
pB = pB->dup(cOrder);
if(C->ews() != 1)
pC = pC->dup(cOrder);
const auto aOrder = pA->ordering();
const auto bOrder = pB->ordering();
const bool transA = aOrder != cOrder;
const bool transB = bOrder != cOrder;
const CBLAS_ORDER blasOrder = cOrder == 'f' ? CblasColMajor : CblasRowMajor;
const CBLAS_TRANSPOSE transAblas = transA ? CblasTrans : CblasNoTrans;
const CBLAS_TRANSPOSE transBblas = transB ? CblasTrans : CblasNoTrans;
const int lda = aOrder == 'f' ? M : K;
const int ldb = bOrder == 'f' ? K : N;
const int ldc = cOrder == 'f' ? M : N;
const auto aType = pA->dataType();
const auto bType = pB->dataType();
const auto cType = pC->dataType();
const auto aType = A->dataType();
const auto bType = B->dataType();
const auto cType = C->dataType();
const bool AB(aType == bType), AC(aType == cType), ABC(AB && AC);
const bool hasGemm = BlasHelper::getInstance()->hasGEMM(aType);
// we'll use platform-specific gemm here eventually. maybe tomorrow.
// TODO: put proper _gemm here
if (ABC && hasGemm && aType == DataType::FLOAT32) {
BlasHelper::getInstance()->sgemm()(blasOrder, transAblas, transBblas, M, N, K, (float) alpha, reinterpret_cast<float *>(pA->getBuffer()), lda, reinterpret_cast<float *>(pB->getBuffer()), ldb, (float) beta, reinterpret_cast<float *>(pC->getBuffer()), ldc);
}
else if (ABC && hasGemm && aType == DataType::DOUBLE) {
BlasHelper::getInstance()->dgemm()(blasOrder, transAblas, transBblas, M, N, K, (double) alpha, reinterpret_cast<double *>(pA->getBuffer()), lda, reinterpret_cast<double *>(pB->getBuffer()), ldb, (double) beta, reinterpret_cast<double *>(pC->getBuffer()), ldc);
const bool typeDouble = hasGemm && ABC && aType == DataType::DOUBLE;
const bool typeFloat = hasGemm && ABC && aType == DataType::FLOAT32;
if(!typeFloat && !typeDouble) {
BUILD_SINGLE_SELECTOR_THRICE(aType, usualGemm, (A, B, C, 0, 1, 0, 1, 0, 1, alpha, beta), NUMERIC_TYPES);
// BUILD_TRIPLE_SELECTOR(aType, bType, cType, usualGemm, (A, B, C, 0, 1, 0, 1, 0, 1, alpha, beta), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
}
else {
BUILD_SINGLE_SELECTOR_THRICE(aType, usualGemm, (cOrder, transA, transB, M, N, K, alpha, pA->getBuffer(), lda, pB->getBuffer(), ldb, beta, pC->getBuffer(), ldc), NUMERIC_TYPES);
//BUILD_TRIPLE_SELECTOR(aType, bType, cType, usualGemm, (cOrder, transA, transB, M, N, K, alpha, pA->getBuffer(), lda, pB->getBuffer(), ldb, beta, pC->getBuffer(), ldc), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
}
if(pC != C) {
C->assign(pC);
delete pC;
std::vector<NDArray*> toDelete;
NDArray *pA(const_cast<NDArray*>(A)), *pB(const_cast<NDArray*>(B)), *pC(const_cast<NDArray*>(C));
bool aMcont = M == 1 || A->strideAt(0) == 1;
bool aKcont = K == 1 || A->strideAt(1) == 1;
bool bKcont = K == 1 || B->strideAt(0) == 1;
bool bNcont = N == 1 || B->strideAt(1) == 1;
bool cMcont = M == 1 || C->strideAt(0) == 1;
bool cNcont = N == 1 || C->strideAt(1) == 1;
if(!aMcont && !aKcont) {
pA = A->dup('f');
toDelete.push_back(pA);
aMcont = true;
}
if(!bKcont && !bNcont) {
pB = B->dup('f');
toDelete.push_back(pB);
bKcont = true;
}
if(!cMcont && !cNcont) {
pC = C->dup('f');
toDelete.push_back(pC);
cMcont = true;
}
const CBLAS_ORDER blasOrder = cMcont ? CblasColMajor : CblasRowMajor;
const bool transA = (!aMcont && cMcont) || (aMcont && !cMcont);
const bool transB = (!bKcont && cMcont) || (bKcont && !cMcont);
const CBLAS_TRANSPOSE transAblas = transA ? CblasTrans : CblasNoTrans;
const CBLAS_TRANSPOSE transBblas = transB ? CblasTrans : CblasNoTrans;
const int lda = (aMcont && aKcont) ? M : !aMcont ? pA->strideAt(0) : pA->strideAt(1);
const int ldb = (bKcont && bNcont) ? K : !bKcont ? pB->strideAt(0) : pB->strideAt(1);
const int ldc = (cMcont && cNcont) ? M : !cMcont ? pC->strideAt(0) : pC->strideAt(1);
if(typeFloat) {
BlasHelper::getInstance()->sgemm()(blasOrder, transAblas, transBblas, M, N, K, (float) alpha, reinterpret_cast<float *>(pA->getBuffer()), lda, reinterpret_cast<float *>(pB->getBuffer()), ldb, (float) beta, reinterpret_cast<float *>(pC->getBuffer()), ldc);
}
else if(typeDouble) {
BlasHelper::getInstance()->dgemm()(blasOrder, transAblas, transBblas, M, N, K, (double) alpha, reinterpret_cast<double *>(pA->getBuffer()), lda, reinterpret_cast<double *>(pB->getBuffer()), ldb, (double) beta, reinterpret_cast<double *>(pC->getBuffer()), ldc);
}
if(pC != C) {
C->assign(pC);
delete pC;
}
if(pA != A)
delete pA;
if(pB != B)
delete pB;
}
if(pA != A)
delete pA;
if(pB != B)
delete pB;
return C;
}
@ -243,6 +280,7 @@ NDArray* MmulHelper::mmulMxM(const NDArray* A, const NDArray* B, NDArray* C, con
////////////////////////////////////////////////////////////////////////////
// MXN x N = M
NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, nd4j::NDArray* Y, const double alpha, const double beta, const char outOrder) {
if (X->dataType() != A->dataType())
throw datatype_exception::build("mmulMxV expects all data types to be the same", A->dataType(), X->dataType());
@ -269,40 +307,49 @@ NDArray* MmulHelper::mmulMxV(const NDArray* A, const NDArray* X, nd4j::NDArray*
if(Y == nullptr)
Y = new NDArray(outOrder, {M}, DataTypeUtils::pickPairwiseResultType(A->dataType(), X->dataType()), A->getContext());
NDArray *pA(const_cast<NDArray*>(A));
if(A->ews() != 1)
pA = pA->dup();
CBLAS_ORDER blasOrder;
int lda;
if (pA->ordering() == 'f') {blasOrder = CblasColMajor; lda = M; }
else {blasOrder = CblasRowMajor; lda = N; }
const int incx = X->stridesOf()[xLenDim];
const int incy = Y->stridesOf()[yLenDim];
const auto aType = pA->dataType();
const auto aType = A->dataType();
const auto xType = X->dataType();
const auto yType = Y->dataType();
const bool AX(aType == xType), AY(aType == yType), AXY(AX && AY);
const bool hasGemv = BlasHelper::getInstance()->hasGEMV(aType);
// choose appropriate cuda gemm api depending on data types
if(AXY && hasGemv && aType == DataType::DOUBLE) {
BlasHelper::getInstance()->dgemv()(blasOrder, CblasNoTrans, M, N, alpha, (double*)pA->getBuffer(), lda, (double*)X->getBuffer(), incx, beta, (double*)Y->getBuffer(), incy);
}
else if(AXY && hasGemv && aType == DataType::FLOAT32) {
BlasHelper::getInstance()->sgemv()(blasOrder, CblasNoTrans, M, N, (float)alpha, (float*)pA->getBuffer(), lda, (float*)X->getBuffer(), incx, (float)beta, (float*)Y->getBuffer(), incy);
const bool typeDouble = hasGemv && AXY && aType == DataType::DOUBLE;
const bool typeFloat = hasGemv && AXY && aType == DataType::FLOAT32;
if(!typeDouble && !typeFloat) {
BUILD_SINGLE_SELECTOR_THRICE(aType, usualGemv, (A, X, Y, incx, incy, 0, alpha, beta), NUMERIC_TYPES);
// BUILD_TRIPLE_SELECTOR(aType, xType, yType, usualGemv, (A, X, Y, incx, incy, 0, alpha, beta), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
}
else {
BUILD_SINGLE_SELECTOR_THRICE(aType, usualGemv, (pA->ordering(), M, N, alpha, pA->getBuffer(), lda, X->getBuffer(), incx, beta, Y->getBuffer(), incy), NUMERIC_TYPES);
//BUILD_TRIPLE_SELECTOR(aType, xType, yType, usualGemv, (pA->ordering(), M, N, alpha, pA->getBuffer(), lda, X->getBuffer(), incx, beta, Y->getBuffer(), incy), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
}
if(pA != A)
delete pA;
NDArray *pA(const_cast<NDArray*>(A));
bool aMcont = M == 1 || A->strideAt(0) == 1;
bool aNcont = N == 1 || A->strideAt(1) == 1;
if(!aMcont && !aNcont) {
pA = A->dup('f');
aMcont = true;
}
const CBLAS_ORDER blasOrder = aMcont ? CblasColMajor : CblasRowMajor;
const int lda = (aMcont && aNcont) ? M : !aMcont ? pA->strideAt(0) : pA->strideAt(1);
// choose appropriate cuda gemm api depending on data types
if(typeDouble) {
BlasHelper::getInstance()->dgemv()(blasOrder, CblasNoTrans, M, N, alpha, (double*)pA->getBuffer(), lda, (double*)X->getBuffer(), incx, beta, (double*)Y->getBuffer(), incy);
}
else if(typeFloat) {
BlasHelper::getInstance()->sgemv()(blasOrder, CblasNoTrans, M, N, (float)alpha, (float*)pA->getBuffer(), lda, (float*)X->getBuffer(), incx, (float)beta, (float*)Y->getBuffer(), incy);
}
if(pA != A)
delete pA;
}
return Y;
}
@ -346,6 +393,311 @@ NDArray* MmulHelper::dot(const NDArray* X, const NDArray* Y, nd4j::NDArray* Z, c
return Z;
}
//////////////////////////////////////////////////////////////////////////////
// [bS,M,K] x [bS,K,N] = [bS,M,N]
// [bS,M,K] x [K,N] = [bS,M,N]
// [M,K] x [bS,K,N] = [bS,M,N]
// bS could stand for several axes
template <typename T1, typename T2, typename T3>
static void batchedGemm(const NDArray* vA, const NDArray* vB, NDArray* vC,
const int* aBatchDims, const int* bBatchDims, const int* cBatchDims,
const int aMaxis, const int aKaxis, const int bKaxis, const int bNaxis, const int cMaxis, const int cNaxis,
const double alpha, const double beta) {
const T1* A = vA->bufferAsT<T1>();
const T2* B = vB->bufferAsT<T2>();
T3* C = vC->bufferAsT<T3>();
const T3 alphaZ = alpha;
const T3 betaZ = beta;
const bool betaPersent = beta;
const Nd4jLong* aShapeInfo = vA->getShapeInfo();
const Nd4jLong* bShapeInfo = vB->getShapeInfo();
const Nd4jLong* cShapeInfo = vC->getShapeInfo();
const int aRank = vA->rankOf();
const int bRank = vB->rankOf();
const int cRank = vC->rankOf();
const Nd4jLong cLen = vC->lengthOf();
const int K = vA->sizeAt(aKaxis);
auto func = PRAGMA_THREADS_FOR {
std::vector<Nd4jLong> aCoords(aRank), bCoords(bRank), cCoords(cRank);
for (auto i = start; i < stop; ++i) {
// evaluate C coordinates
shape::index2coords(i, cShapeInfo, cCoords.data());
// calculate index of current batch
Nd4jLong batchInd;
if(cRank > 2)
batchInd = shape::coords2index(cShapeInfo, cCoords.data(), cRank - 2, cBatchDims);
// evaluate A coordinates
if(aRank > 2)
shape::index2coords(batchInd, aShapeInfo, aCoords.data(), aRank - 2, aBatchDims);
aCoords[aMaxis] = cCoords[cMaxis];
aCoords[aKaxis] = 0;
// evaluate B coordinates
if(bRank > 2)
shape::index2coords(batchInd, bShapeInfo, bCoords.data(), bRank - 2, bBatchDims);
bCoords[bKaxis] = 0;
bCoords[bNaxis] = cCoords[cNaxis];
auto aOffset = shape::getOffset(aShapeInfo, aCoords.data());
auto bOffset = shape::getOffset(bShapeInfo, bCoords.data());
T3 val = A[aOffset] * B[bOffset]; // first iteration
for (uint j = 1; j < K; ++j) { // rest iterations
aOffset += shape::stride(aShapeInfo)[aKaxis];
bOffset += shape::stride(bShapeInfo)[bKaxis];
val = val + A[aOffset] * B[bOffset];
}
auto cOffset = shape::getOffset(cShapeInfo, cCoords.data());
if(betaPersent)
C[cOffset] = alphaZ * val + betaZ * C[cOffset];
else
C[cOffset] = alphaZ * val;
}
};
samediff::Threads::parallel_tad(func, 0, cLen);
}
//////////////////////////////////////////////////////////////////////////
// [bS,M,K] x [bS,K,N] = [bS,M,N]
// [bS,M,K] x [K,N] = [bS,M,N]
// [M,K] x [bS,K,N] = [bS,M,N]
// bS could stand for several axes
NDArray* MmulHelper::mmulNxN(const NDArray* A, const NDArray* B, NDArray* C, const double alpha, const double beta, const char outOrder) {
const int aRank = A->rankOf();
const int bRank = B->rankOf();
// input ranks validation
if(aRank > bRank && bRank != 2)
throw std::runtime_error("MmulHelper::mmulNxN: rank of B array should be equal 2 !");
else if(bRank > aRank && aRank != 2)
throw std::runtime_error("MmulHelper::mmulNxN: rank of A array should be equal 2 !");
else if (aRank == bRank ) {
for(int i = 0; i < aRank - 2; ++i)
if(A->sizeAt(i) != B->sizeAt(i))
throw std::runtime_error("MmulHelper::mmulNxN: shapes of A and B arrays are not suitable for matrix multiplication !");
}
if(A->sizeAt(-1) != B->sizeAt(-2))
throw std::runtime_error("MmulHelper::mmulNxN: shapes of A and B arrays are not suitable for matrix multiplication !");
// validation of C array
std::vector<Nd4jLong> cExpectedShape = aRank > bRank ? A->getShapeAsVector() : B->getShapeAsVector();
cExpectedShape[cExpectedShape.size() - 2] = A->sizeAt(-2);
cExpectedShape[cExpectedShape.size() - 1] = B->sizeAt(-1);
if(C != nullptr ) {
if(!C->isSameShape(cExpectedShape))
throw std::runtime_error("MmulHelper::mmulNxN: shape of C array is not suitable for AxB matrix multiplication !");
}
else {
C = new NDArray(outOrder, cExpectedShape, B->dataType());
}
const int cRank = C->rankOf();
const int aMaxis(aRank-2), aKaxis(aRank-1), bKaxis(bRank-2), bNaxis(bRank-1), cMaxis(cRank-2), cNaxis(cRank-1);
std::vector<int> aBatchDims, bBatchDims, cBatchDims;
if(aRank > 2)
aBatchDims = ShapeUtils::evalDimsToExclude(aRank, {aMaxis, aKaxis});
if(bRank > 2)
bBatchDims = ShapeUtils::evalDimsToExclude(bRank, {bKaxis, bNaxis});
if(cRank > 2)
cBatchDims = ShapeUtils::evalDimsToExclude(cRank, {cMaxis, cNaxis});
// BUILD_TRIPLE_SELECTOR(A->dataType(), B->dataType(), C->dataType(), batchedGemm, (A, B, C, aBatchDims.data(), bBatchDims.data(), cBatchDims.data(), aMaxis, aKaxis, bKaxis, bNaxis, cMaxis, cNaxis, alpha, beta), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
BUILD_SINGLE_SELECTOR_THRICE(A->dataType(), batchedGemm, (A, B, C, aBatchDims.data(), bBatchDims.data(), cBatchDims.data(), aMaxis, aKaxis, bKaxis, bNaxis, cMaxis, cNaxis, alpha, beta), NUMERIC_TYPES);
return C;
}
/*
//////////////////////////////////////////////////////////////////////////
NDArray* MmulHelper::mmulNxN(const NDArray* A, const NDArray* B, NDArray* C, const double alpha, const double beta, const char outOrder) {
const int aRank = A->rankOf();
const int bRank = B->rankOf();
// input ranks validation
if(aRank > bRank && bRank != 2)
throw std::runtime_error("MmulHelper::mmulNxN: rank of B array should be equal 2 !");
else if(bRank > aRank && aRank != 2)
throw std::runtime_error("MmulHelper::mmulNxN: rank of A array should be equal 2 !");
else if (aRank == bRank ) {
for(int i = 0; i < aRank - 2; ++i)
if(A->sizeAt(i) != B->sizeAt(i))
throw std::runtime_error("MmulHelper::mmulNxN: shapes of A and B arrays are not suitable for matrix multiplication !");
}
if(A->sizeAt(-1) != B->sizeAt(-2))
throw std::runtime_error("MmulHelper::mmulNxN: shapes of A and B arrays are not suitable for matrix multiplication !");
// validation of C array
std::vector<Nd4jLong> cExpectedShape = aRank > bRank ? A->getShapeAsVector() : B->getShapeAsVector();
cExpectedShape[cExpectedShape.size() - 2] = A->sizeAt(-2);
cExpectedShape[cExpectedShape.size() - 1] = B->sizeAt(-1);
if(C != nullptr ) {
if(!C->isSameShape(cExpectedShape))
throw std::runtime_error("MmulHelper::mmulNxN: shape of C array is not suitable for AxB matrix multiplication !");
}
else {
C = new NDArray(outOrder, cExpectedShape, B->dataType());
}
// multiplication
const std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(C->rankOf(), {-2, -1});
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(C->getShapeInfo(), dimsToExclude);
std::vector<Nd4jLong> idxRanges(2 * C->rankOf());
// #pragma omp parallel for schedule(guided) firstprivate(idxRanges)
for(Nd4jLong i = 0; i < numOfSubArrs; ++i) {
ShapeUtils::evalIdxRangesForSubArr(i, C->getShapeInfo(), dimsToExclude, idxRanges.data());
NDArray cSubArr = (*C)(idxRanges);
if(aRank > bRank) {
NDArray aSubArr = (*A)(idxRanges);
mmulMxM(&aSubArr, B, &cSubArr, 1., 0., outOrder);
}
else if(bRank > aRank) {
NDArray bSubArr = (*B)(idxRanges);
mmulMxM(A, &bSubArr, &cSubArr, 1., 0, outOrder);
}
else {
NDArray aSubArr = (*A)(idxRanges);
NDArray bSubArr = (*B)(idxRanges);
mmulMxM(&aSubArr, &bSubArr, &cSubArr, 1., 0., outOrder);
}
}
return C;
}
//////////////////////////////////////////////////////////////////////////////
// MXK x KxN = MxN
template <typename T1, typename T2, typename T3>
static void usualGemm(const char cOrder, const bool transA, const bool transB, const int M, const int N, const int K, const double alpha, const void* vA, const int lda, const void* vB, const int ldb, const double beta, void* vC, const int ldc) {
T1* A = reinterpret_cast<T1*>(const_cast<void*>(vA));
T2* B = reinterpret_cast<T2*>(const_cast<void*>(vB));
T3* C = reinterpret_cast<T3*>(vC);
T3 alphaZ(alpha), betaZ(beta);
const bool flagC = cOrder == 'f';
const bool flagA = (flagC && transA) || (!flagC && !transA);
const bool flagB = (flagC && transB) || (!flagC && !transB);
// PRAGMA_OMP_PARALLEL_FOR_ARGS(OMP_IF(M*N > Environment::getInstance()->elementwiseThreshold()) schedule(guided))
// for(uint row = 0; row < M; ++row) {
// T3* c = flagC ? (C + row) : (C + row * ldc);
// for(uint col = 0; col < N; ++col)
// c[flagC ? col * ldc : col] = 0;
// for(uint i = 0; i < K; ++i) {
// T3* b = flagB ? (B + i * ldb) : (B + i);
// T3* a = flagA ? (A + row * lda + i) : (A + row + i * lda);
// if(flagC) {
// for(uint col = 0; col < N; ++col) {
// if(betaZ)
// c[col * ldc] += a * b[flagB ? col : col * ldb] + betaZ * c[col * ldc];
// else
// c[col * ldc] += a * b[flagB ? col : col * ldb];
// }
// }
// else {
// for(uint col = 0; col < N; ++col) {
// if(betaZ)
// c[col] += a * b[flagB ? col : col * ldb] + betaZ * c[col];
// else
// c[col] += a * b[flagB ? col : col * ldb];
// }
// }
// }
// }
auto func = PRAGMA_THREADS_FOR_2D { ;
for (auto row = start_x; row < stop_x; row += inc_x) {
for (auto col = start_y; col < stop_y; col += inc_y) {
T3 *c = flagC ? (C + row + col * ldc) : (C + row * ldc + col);
T3 val = 0;
for (uint i = 0; i < K; ++i) {
T3 a = flagA ? *(A + row * lda + i) : *(A + row + i * lda);
T3 b = flagB ? *(B + col + i * ldb) : *(B + col * ldb + i);
val += alphaZ * a * b;
}
if (betaZ)
*c = val + betaZ * *c;
else
*c = val;
}
}
};
samediff::Threads::parallel_tad(func, 0, M, 1, 0, N, 1);
}
//////////////////////////////////////////////////////////////////////////////
// MXN x N = M
template <typename T1, typename T2, typename T3>
static void usualGemv(const char aOrder, const int M, const int N, const double alpha, const void* vA, const int lda, const void* vX, const int incx, const double beta, void* vY, const int incy) {
T1* A = reinterpret_cast<T1*>(const_cast<void*>(vA));
T2* X = reinterpret_cast<T2*>(const_cast<void*>(vX));
T3* Y = reinterpret_cast<T3*>(vY);
T3 alphaZ(alpha), betaZ(beta);
const bool flagA = aOrder == 'f';
auto func = PRAGMA_THREADS_FOR {
for (auto row = start; row < stop; row += increment) {
T3 *y = Y + row * incy;
T3 val = 0;
for (int i = 0; i < N; ++i) {
T3 a = flagA ? *(A + row + i * lda) : *(A + row * lda + i);
T3 x = *(X + i * incx);
val += alphaZ * a * x;
}
if (betaZ)
*y = val + betaZ * *y;
else
*y = val;
}
};
samediff::Threads::parallel_tad(func, 0, M);
}
*/
//BUILD_TRIPLE_TEMPLATE(template void usualGemm, (const char cOrder, const bool transA, const bool transB, const int M, const int N, const int K, const double alpha, const void* A, const int lda, const void* B, const int ldb, const double beta, void* C, const int ldc), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
//BUILD_TRIPLE_TEMPLATE(template void usualGemv, (const char aOrder, const int M, const int N, const double alpha, const void* A, const int lda, const void* B, const int incx, const double beta, void* C, const int incy), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);
//BUILD_TRIPLE_TEMPLATE(template void usualDot, (const Nd4jLong length, const double alpha, const void* vX, const Nd4jLong incx, const void* vY, const Nd4jLong incy, const double beta, void* vZ), LIBND4J_TYPES, FLOAT_TYPES, FLOAT_TYPES);

1159
libnd4j/include/helpers/cuda_off/MmulHelper.cu
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File diff suppressed because it is too large Load Diff

63
libnd4j/include/helpers/impl/MmulHelper.cpp
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@ -184,69 +184,6 @@ NDArray* nd4j::MmulHelper::tensorDot(const nd4j::NDArray* a, const nd4j::NDArray
#endif
//////////////////////////////////////////////////////////////////////////
NDArray* MmulHelper::mmulNxN(const NDArray* A, const NDArray* B, NDArray* C, const double alpha, const double beta, const char outOrder) {
const int aRank = A->rankOf();
const int bRank = B->rankOf();
// input ranks validation
if(aRank > bRank && bRank != 2)
throw std::runtime_error("MmulHelper::mmulNxN: rank of B array should be equal 2 !");
else if(bRank > aRank && aRank != 2)
throw std::runtime_error("MmulHelper::mmulNxN: rank of A array should be equal 2 !");
else if (aRank == bRank ) {
for(int i = 0; i < aRank - 2; ++i)
if(A->sizeAt(i) != B->sizeAt(i))
throw std::runtime_error("MmulHelper::mmulNxN: shapes of A and B arrays are not suitable for matrix multiplication !");
}
if(A->sizeAt(-1) != B->sizeAt(-2))
throw std::runtime_error("MmulHelper::mmulNxN: shapes of A and B arrays are not suitable for matrix multiplication !");
// validation of C array
std::vector<Nd4jLong> cExpectedShape = aRank > bRank ? A->getShapeAsVector() : B->getShapeAsVector();
cExpectedShape[cExpectedShape.size() - 2] = A->sizeAt(-2);
cExpectedShape[cExpectedShape.size() - 1] = B->sizeAt(-1);
if(C != nullptr ) {
if(!C->isSameShape(cExpectedShape))
throw std::runtime_error("MmulHelper::mmulNxN: shape of C array is not suitable for AxB matrix multiplication !");
}
else {
C = new NDArray(outOrder, cExpectedShape, B->dataType());
}
// multiplication
const std::vector<int> dimsToExclude = ShapeUtils::evalDimsToExclude(C->rankOf(), {-2, -1});
const Nd4jLong numOfSubArrs = ShapeUtils::getNumOfSubArrs(C->getShapeInfo(), dimsToExclude);
std::vector<Nd4jLong> idxRanges(2 * C->rankOf());
// #pragma omp parallel for schedule(guided) firstprivate(idxRanges)
for(Nd4jLong i = 0; i < numOfSubArrs; ++i) {
ShapeUtils::evalIdxRangesForSubArr(i, C->getShapeInfo(), dimsToExclude, idxRanges.data());
NDArray cSubArr = (*C)(idxRanges);
if(aRank > bRank) {
NDArray aSubArr = (*A)(idxRanges);
mmulMxM(&aSubArr, B, &cSubArr, 1., 0., outOrder);
}
else if(bRank > aRank) {
NDArray bSubArr = (*B)(idxRanges);
mmulMxM(A, &bSubArr, &cSubArr, 1., 0, outOrder);
}
else {
NDArray aSubArr = (*A)(idxRanges);
NDArray bSubArr = (*B)(idxRanges);
mmulMxM(&aSubArr, &bSubArr, &cSubArr, 1., 0., outOrder);
}
}
return C;
}
//////////////////////////////////////////////////////////////////////////
nd4j::NDArray* MmulHelper::mmul(const nd4j::NDArray* A, const nd4j::NDArray* B, nd4j::NDArray* C , const double alpha, const double beta, const char outOrder) {

41
libnd4j/include/helpers/shape.h
View File

@ -901,6 +901,10 @@ namespace shape {
*/
ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong *coords);
ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const int rank, const Nd4jLong *shape, Nd4jLong *coords);
/**
* take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order!
*/
ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong *coords, const int dimsSize, const int* tadDims);
@ -910,6 +914,10 @@ namespace shape {
*/
ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *coords);
ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *coords);
/**
* take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order!
*/
ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *coords, const int dimsSize, const int* tadDims);
/**
* increment n-dimensional array by one iteration by changing coord appropriately
@ -1762,6 +1770,19 @@ INLINEDEF _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape,
return index;
}
INLINEDEF _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *coords, const int dimsSize, const int* tadDims) {
Nd4jLong index, shift = 1;;
index = coords[tadDims[dimsSize - 1]];
for(uint i = dimsSize - 1; i >= 1; --i) {
shift *= shapeInfo[tadDims[i]];
index += shift * coords[i - 1];
}
return index;
}
template <typename T>
INLINEDEF _CUDA_HD void fill(T* buffer, T value, Nd4jLong length) {
@ -3957,9 +3978,13 @@ INLINEDEF _CUDA_H bool reshapeC(const int oldRank, const Nd4jLong* oldShapeInfo,
oldStart = oldStop++;
}
newShapeInfo[2 * newRank + 3] = shape::order(oldShapeInfo); // order
newShapeInfo[2 * newRank + 2] = shape::elementWiseStride(oldShapeInfo); // ews
newShapeInfo[2 * newRank + 1] = shape::type(oldShapeInfo); // type
// rest of strides should be unities (if there is remainder in strides space, that is newStart < newRank)
for (int i = newStart; i < newRank; ++i)
newStrides[i] = 1;
newShapeInfo[2 * newRank + 3] = shape::order(oldShapeInfo); // order
newShapeInfo[2 * newRank + 2] = shape::elementWiseStride(oldShapeInfo); // ews
newShapeInfo[2 * newRank + 1] = shape::type(oldShapeInfo); // type
return true;
}
@ -4705,6 +4730,16 @@ INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const int rank, const Nd4jL
coords[0] = index; // last iteration
}
//////////////////////////////////////////////////////////////////////
INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong *coords, const int dimsSize, const int* tadDims) {
for(uint i = dimsSize - 1; i > 0; --i) {
coords[tadDims[i]] = index % shapeInfo[1 + tadDims[i]];
index /= shapeInfo[1 + tadDims[i]];
}
coords[tadDims[0]] = index; // last iteration
}
//////////////////////////////////////////////////////////////////////
INLINEDEF _CUDA_HD void calcOffsets(const Nd4jLong *xShapeInfo, Nd4jLong*& xOffsets, const Nd4jLong *yShapeInfo, Nd4jLong*& yOffsets, const Nd4jLong* zShapeInfo, Nd4jLong*& zOffsets, const char order) {

1
libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at

1
libnd4j/include/ops/declarable/helpers/cuda/transforms.cu
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at

2
libnd4j/tests_cpu/layers_tests/HelpersTests1.cpp
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
@ -1798,6 +1799,7 @@ TEST_F(HelpersTests1, tensordot_test_6) {
// [iC, bS*oH*oW, kW*kH] x [iC, kH*kW, mC] = [iC, bS*oH*oW, mC]
MmulHelper::tensorDot(&a, &b, &cR, {{1,0,4,5,2,3}, {iC,bS*oH*oW,kW*kH}}, {{2,0,1,3},{iC,kH*kW,mC}}, {{3,0,1,2,4},{iC, bS*oH*oW, mC}});
// c.printBuffer();
ASSERT_TRUE(c.isSameShape(expected));
ASSERT_TRUE(c.equalsTo(expected));

2
libnd4j/tests_cpu/layers_tests/NDArrayTests.cpp
View File

@ -1891,7 +1891,7 @@ TEST_F(NDArrayTest, TestMMulMultiDim) {
ASSERT_TRUE(result->isSameShape(&expected));
//result->printShapeInfo("result shape");
//result->printBuffer("result buffer");
// result->printBuffer("result buffer");
ASSERT_TRUE(result->equalsTo(&expected));
delete result;
}

6
libnd4j/tests_cpu/layers_tests/PerformanceTests.cpp
View File

@ -61,8 +61,10 @@ public:
TEST_F(PerformanceTests, test_maxpooling2d_1) {
std::vector<Nd4jLong> valuesX;
auto x = NDArrayFactory::create<float>('c', {32, 3, 224, 224});
auto z = NDArrayFactory::create<float>('c', {32, 3, 224, 224});
// auto x = NDArrayFactory::create<float>('c', {32, 3, 224, 224});
// auto z = NDArrayFactory::create<float>('c', {32, 3, 224, 224});
auto x = NDArrayFactory::create<float>('c', {8, 3, 64, 64});
auto z = NDArrayFactory::create<float>('c', {8, 3, 64, 64});
x.linspace(1.0f);
Nd4jLong k = 5;

25
libnd4j/tests_cpu/layers_tests/PlaygroundTests.cpp
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
@ -274,4 +275,28 @@ TEST_F(PlaygroundTests, test_relubp_1) {
nd4j_printf("Time: %lld; BW: %f GB/s\n", time, bw);
}
//////////////////////////////////////////////////////////////////////
TEST_F(PlaygroundTests, my) {
int bS=1, iH=56,iW=56, iC=144,mC=1, kH=3,kW=3, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oC=iC*mC;
int oH=56,oW=56;
int paddingMode = 1; // 1-SAME, 0-VALID;
int dataFormat = 1; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<float>('c', {bS, iH, iW, iC});
auto weights = NDArrayFactory::create<float>('c', {kH, kW, iC, mC});
input = 2.;
weights.linspace(0.1, 0.1);
nd4j::ops::depthwise_conv2d op;
auto results = op.execute({&input, &weights}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
delete results;
}
*/

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-native-api/src/main/java/org/nd4j/nativeblas/NativeOpsHolder.java
View File

@ -106,7 +106,7 @@ public class NativeOpsHolder {
boolean logInit = Boolean.parseBoolean(logInitProperty);
if(logInit) {
log.info("Number of threads used for OpenMP: {}", deviceNativeOps.ompGetMaxThreads());
log.info("Number of threads used for linear algebra: {}", deviceNativeOps.ompGetMaxThreads());
}
} catch (Exception | Error e) {
throw new RuntimeException(

19
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCuda.java
View File

@ -4600,6 +4600,11 @@ public native @Cast("bool") boolean isOptimalRequirementsMet();
*/
public native @Cast("Nd4jLong") long sizeAt(int dim);
/**
* returns stride of "dim" dimension
*/
public native @Cast("Nd4jLong") long strideAt(int dim);
/**
* returns order of array
*/
@ -8019,6 +8024,12 @@ public static final int PREALLOC_SIZE = 33554432;
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer coords);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer coords);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] coords);
/**
* take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order!
*/
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer coords, int dimsSize, @Const IntPointer tadDims);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer coords, int dimsSize, @Const IntBuffer tadDims);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] coords, int dimsSize, @Const int[] tadDims);
@ -8032,6 +8043,12 @@ public static final int PREALLOC_SIZE = 33554432;
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer coords);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer coords);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] coords);
/**
* take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order!
*/
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer coords, int dimsSize, @Const IntPointer tadDims);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer coords, int dimsSize, @Const IntBuffer tadDims);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] coords, int dimsSize, @Const int[] tadDims);
/**
* increment n-dimensional array by one iteration by changing coord appropriately
@ -9088,6 +9105,8 @@ public static final int PREALLOC_SIZE = 33554432;
//////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////

19
nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/nativeblas/Nd4jCpu.java
View File

@ -4600,6 +4600,11 @@ public native @Cast("bool") boolean isOptimalRequirementsMet();
*/
public native @Cast("Nd4jLong") long sizeAt(int dim);
/**
* returns stride of "dim" dimension
*/
public native @Cast("Nd4jLong") long strideAt(int dim);
/**
* returns order of array
*/
@ -8019,6 +8024,12 @@ public static final int PREALLOC_SIZE = 33554432;
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("Nd4jLong*") LongPointer coords);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("Nd4jLong*") LongBuffer coords);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("Nd4jLong*") long[] coords);
/**
* take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order!
*/
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("Nd4jLong*") LongPointer coords, int dimsSize, @Const IntPointer tadDims);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("Nd4jLong*") LongBuffer coords, int dimsSize, @Const IntBuffer tadDims);
@Namespace("shape") public static native void index2coords(@Cast("Nd4jLong") long index, @Cast("const Nd4jLong*") long[] shapeInfo, @Cast("Nd4jLong*") long[] coords, int dimsSize, @Const int[] tadDims);
@ -8032,6 +8043,12 @@ public static final int PREALLOC_SIZE = 33554432;
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") LongPointer shape, @Cast("const Nd4jLong*") LongPointer coords);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") LongBuffer shape, @Cast("const Nd4jLong*") LongBuffer coords);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(int rank, @Cast("const Nd4jLong*") long[] shape, @Cast("const Nd4jLong*") long[] coords);
/**
* take into account only dimensions stored in tadDims, tadDims must be sorted in increasing order!
*/
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongPointer shapeInfo, @Cast("const Nd4jLong*") LongPointer coords, int dimsSize, @Const IntPointer tadDims);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") LongBuffer shapeInfo, @Cast("const Nd4jLong*") LongBuffer coords, int dimsSize, @Const IntBuffer tadDims);
@Namespace("shape") public static native @Cast("Nd4jLong") long coords2index(@Cast("const Nd4jLong*") long[] shapeInfo, @Cast("const Nd4jLong*") long[] coords, int dimsSize, @Const int[] tadDims);
/**
* increment n-dimensional array by one iteration by changing coord appropriately
@ -9088,6 +9105,8 @@ public static final int PREALLOC_SIZE = 33554432;
//////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////