libnd4j/include/ops/declarable/generic/blas/matmul.cpp
/* ******************************************************************************
*
*
* 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.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* 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.
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* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com, created on 07.10.2017.
// @author GS <sgazeos@gmail.com>, modified
// @author Yurii Shyrma (iuriish@yahoo.com), fully rewritten
//
#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_matmul)
#include <helpers/MmulHelper.h>
#include <ops/declarable/CustomOperations.h>
namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(matmul, 2, 1, false, 0, -2) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto z = OUTPUT_VARIABLE(0);
int iSize = (int)block.getIArguments()->size();
int transX = iSize > 0 ? INT_ARG(0) : 0;
int transY = iSize > 1 ? INT_ARG(1) : 0;
const int transZ = iSize > 2 ? INT_ARG(2) : 0;
// optional use alpha nad beta
iSize = (int)block.getTArguments()->size();
double alpha = iSize > 0 ? T_ARG(0) : 1.0;
double beta = iSize > 1 ? T_ARG(1) : 0.0;
const int xRank = x->rankOf();
const int yRank = y->rankOf();
const int zRank = z->rankOf();
if (transZ) {
x = INPUT_VARIABLE(1);
y = INPUT_VARIABLE(0);
bool temp = transX;
transX = !transY;
transY = !temp;
}
const int xLastDim = transX ? -2 : -1;
const int yLastDim = transY ? -2 : -1;
const int xLastButOneDim = transX ? -1 : -2;
const int yLastButOneDim = transY ? -1 : -2;
// ******* input validation ******* //
REQUIRE_TRUE(xRank > 0 && yRank > 0, 0,
"MATMUL OP: input arrays must have rank bigger than 0 (should not be scalars), but got instead: x rank "
"= %i, y rank = %i !",
xRank, yRank);
if (xRank == 1 && yRank == 1) { // dot case, output is scalar (or vector with length = 1)
REQUIRE_TRUE(x->lengthOf() == y->lengthOf(), 0,
"MATMUL OP: since input arrays are vectors they must have the same length, but got x length = %i, y "
"length = %i !",
x->lengthOf(), y->lengthOf());
} else if (xRank == 1 && yRank == 2) { // vector x matrix, i.e. [4] x [4,5] = [5], output is vector
REQUIRE_TRUE(x->lengthOf() == y->sizeAt(yLastButOneDim), 0,
"MATMUL OP: input arrays have inconsistent shapes for vector-matrix product: x %s, y %s !",
ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str());
} else if (xRank == 2 && yRank == 1) { // matrix x vector , i.e. [4,5] x [5] = [4], output is vector
REQUIRE_TRUE(x->sizeAt(xLastDim) == y->lengthOf(), 0,
"MATMUL OP: input arrays have inconsistent shapes for matrix-vector product: x %s, y %s !",
ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str());
} else {
REQUIRE_TRUE(xRank == yRank && yRank == zRank, 0,
"MATMUL OP: input and output arrays must have the same rank, but got instead: x rank = %i, y rank = "
"%i, z rank = %i !",
xRank, yRank, zRank);
REQUIRE_TRUE(x->sizeAt(xLastDim) == y->sizeAt(yLastButOneDim) && x->sizeAt(xLastButOneDim) == z->sizeAt(-2) &&
y->sizeAt(yLastDim) == z->sizeAt(-1),
0, "MATMUL OP: input/output arrays have inconsistent shapes for matrix product: x %s, y %s, z %s !",
ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str(),
ShapeUtils::shapeAsString(z).c_str());
if (xRank > 2) // outer dims must be the same
for (int i = 0; i < xRank - 2; ++i)
REQUIRE_TRUE(x->sizeAt(i) == y->sizeAt(i) && y->sizeAt(i) == z->sizeAt(i), 0,
"MATMUL OP: input/output arrays have inconsistent shapes for matrix product: x %s, y %s, z %s !",
ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str(),
ShapeUtils::shapeAsString(z).c_str());
}
// ******* end of input validation ******* //
MmulHelper::matmul(x, y, z, transX, transY, alpha, beta);
return sd::Status::OK;
}
DECLARE_SYN(mMul, matmul);
DECLARE_SYN(mmul, matmul);
DECLARE_SYN(gemm, matmul);
DECLARE_SYN(gemv, matmul);
DECLARE_SYN(dot, matmul);
//////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(matmul) {
auto xShapeInfo = inputShape->at(0);
auto yShapeInfo = inputShape->at(1);
const int iSize = (int)block.getIArguments()->size();
int transX = iSize > 0 ? INT_ARG(0) : 0;
int transY = iSize > 1 ? INT_ARG(1) : 0;
const int transZ = iSize > 2 ? INT_ARG(2) : 0;
REQUIRE_TRUE(xShapeInfo[0] > 0 && yShapeInfo[0] > 0, 0,
"MATMUL OP: input arrays must have rank bigger than 0 (should not be scalars), but got instead: x rank "
"= %i, y rank = %i !",
xShapeInfo[0], yShapeInfo[0]);
if (transZ) {
xShapeInfo = inputShape->at(1);
yShapeInfo = inputShape->at(0);
bool temp = transX;
transX = !transY;
transY = !temp;
}
auto zShapeOnly = ShapeUtils::evalShapeForMatmul(xShapeInfo, yShapeInfo, transX, transY);
auto dtypeX = ArrayOptions::dataType(xShapeInfo);
auto dtypeY = ArrayOptions::dataType(yShapeInfo);
auto xOrder = shape::order(xShapeInfo);
auto yOrder = shape::order(yShapeInfo);
auto zOrder = xOrder == 'c' && yOrder == 'c' ? 'c' : 'f';
// we just pick the higher data type out of X and Y
auto dtypeZ = dtypeX > dtypeY ? dtypeX : dtypeY;
auto newShape = ConstantShapeHelper::getInstance().createShapeInfo(dtypeZ, zOrder, zShapeOnly);
return SHAPELIST(newShape);
}
//////////////////////////////////////////////////////////////////////
DECLARE_TYPES(matmul) {
getOpDescriptor()
->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS})
->setAllowedInputTypes(1, {ALL_FLOATS, ALL_INTS})
->setAllowedOutputTypes(0, {ALL_FLOATS, ALL_INTS});
}
//////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(matmul_bp, 3, 2, false, 0, -2) {
auto x = INPUT_VARIABLE(0);
auto y = INPUT_VARIABLE(1);
auto eps = INPUT_VARIABLE(2);
auto dldx = OUTPUT_VARIABLE(0);
auto dldy = OUTPUT_VARIABLE(1);
int iSize = (int)block.getIArguments()->size();
int transX = iSize > 0 ? INT_ARG(0) : 0;
int transY = iSize > 1 ? INT_ARG(1) : 0;
const int transZ = iSize > 2 ? INT_ARG(2) : 0;
// optional use alpha nad beta
iSize = (int)block.getTArguments()->size();
double alpha = iSize > 0 ? T_ARG(0) : 1.0;
double beta = iSize > 1 ? T_ARG(1) : 0.0;
/*
In: x=[a,b], y=[b,c]
tX tY tZ x y z dz dLdx dLdy
F F F [a,b] [b,c] [a,c] [a,c] [a,c]*[b,c]T = [a,b] x*yT [a,b]T*[a,c] = [b,c] xT*y T F
F [b,a] [b,c] [a,c] [a,c] ([a,c]*[b,c]T)T = [b,a] (x*yT)T [b,a]*[a,c] = [b,c] x*y F T
F [a,b] [c,b] [a,c] [a,c] ([a,c]*[c,b]) = [a,b] x*y [a,b]T*[a,c] = [b,c] ->T xT*y T T
F [b,a] [c,b] [a,c] [a,c] ([a,c]*[c,b])T = [b,a] (x*y)T [b,a]*[a,c] = [b,c] ->T x*y F F
T [a,b] [b,c] [c,a] [c,a]
*/
// special case for scalar value
if (eps->isScalar()) {
if (x->isVector() && y->isVector()) {
if(x->isRowVector() && y->isRowVector()) {
dldx->assign((*eps) * y->sumNumber());
dldy->assign((*eps) * x->sumNumber());
} else if(x->isColumnVector() && y->isColumnVector()) {
dldx->assign((*eps) * y->sumNumber());
dldy->assign((*eps) * x->sumNumber());
} else {
dldx->assign((*eps) * (*y));
dldy->assign((*eps) * (*x));
}
} else {
// assign all ones to shape as baseline
auto alphaBetaBase = 1.0;
if (alpha > 0.0) {
alphaBetaBase *= alpha;
}
if (beta > 0.0) {
alphaBetaBase += beta;
}
dldx->assign(alphaBetaBase);
dldy->assign(alphaBetaBase);
// match the dimensions for reduction for matrix multiply: columns on first input, rows on second input
// the dimensions should match the matching dimensions to compute proper gradients wrt each input
// core gradient for each is sum(input) * eps as scalar
std::vector<sd::LongType> axesZero({0});
auto xSum = x->reduceAlongDimension(sd::reduce::Sum, &axesZero);
xSum *= *eps;
// ensure we have proper shape for broadcasted multiplication
auto xSumRow = xSum.reshape(xSum.ordering(), {xSum.lengthOf(), 1});
std::vector<sd::LongType> axes({1});
auto ySum = y->reduceAlongDimension(sd::reduce::Sum, &axes);
ySum *= *eps;
auto ySumRow = ySum.reshape(ySum.ordering(), {1, ySum.lengthOf()});
// execute proper multiplication: rows for first input, columns for second
dldx->mulRowVector(ySumRow, *dldx);
dldy->muliColumnVector(xSumRow);
}
return sd::Status::OK;
}
sd::ops::matmul op;
op.execute({eps, y}, {dldx}, {alpha, beta}, {transZ, !transY, transX}, {});
op.execute({x, eps}, {dldy}, {alpha, beta}, {!transX, transZ, transY}, {});
return sd::Status::OK;
}
//////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(matmul_bp) {
sd::LongType *xShapeInfo;
sd::LongType *yShapeInfo;
COPY_SHAPE(inputShape->at(0), xShapeInfo);
COPY_SHAPE(inputShape->at(1), yShapeInfo);
return SHAPELIST(CONSTANT(xShapeInfo), CONSTANT(yShapeInfo));
}
//////////////////////////////////////////////////////////////////////
DECLARE_TYPES(matmul_bp) {
getOpDescriptor()
->setAllowedInputTypes(0, {ALL_FLOATS})
->setAllowedInputTypes(1, {ALL_FLOATS})
->setAllowedInputTypes(2, {ALL_FLOATS})
->setAllowedOutputTypes(0, {ALL_FLOATS})
->setAllowedOutputTypes(1, {ALL_FLOATS});
}
} // namespace ops
} // namespace sd
#endif