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Refactor MUL functions, pass object reference instead of shared_ptr.

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avx_docs
xutianbing 8 years ago
parent 706c572424
commit 4fbf94993b
21 changed files with 144 additions and 154 deletions
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6
paddle/gserver/layers/ConvexCombinationLayer.cpp
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@ -113,7 +113,7 @@ void ConvexCombinationLayer::forward(PassType passType) {
tmpRow0->setData(inV0->getData() + i * weightDim);
tmpRow1->setData(outV->getData() + i * dataDim);
tmpRow1->mul(tmpRow0, tmpMtx0, 1, 0);
tmpRow1->mul(*tmpRow0, *tmpMtx0, 1, 0);
}
}
@ -136,7 +136,7 @@ void ConvexCombinationLayer::backward(const UpdateCallback& callback) {
tmpRow1->setData(outG->getData() + i * dataDim);
tmpMtx0->setData(inV1->getData() + i * weightDim * dataDim);
tmpRow0->mul(tmpRow1, tmpMtx0->getTranspose(), 1, 1);
tmpRow0->mul(*tmpRow1, *(tmpMtx0->getTranspose()), 1, 1);
}
}
@ -146,7 +146,7 @@ void ConvexCombinationLayer::backward(const UpdateCallback& callback) {
tmpRow1->setData(outG->getData() + i * dataDim);
tmpMtx0->setData(inG1->getData() + i * weightDim * dataDim);
tmpMtx0->mul(tmpRow0->getTranspose(), tmpRow1, 1, 1);
tmpMtx0->mul(*(tmpRow0->getTranspose()), *tmpRow1, 1, 1);
}
}
}

6
paddle/gserver/layers/ExpandConvBaseLayer.cpp
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@ -150,7 +150,7 @@ void ExpandConvBaseLayer::expandFwdOnce(MatrixPtr image,
Matrix::create(wgtData, subM, subK, false, useGpu_); // mark transpose
MatrixPtr B = Matrix::create(expInData, subK, subN, false, useGpu_);
MatrixPtr C = Matrix::create(outData, subM, subN, false, useGpu_);
C->mul(A, B, 1, 1);
C->mul(*A, *B, 1, 1);
A->clear();
B->clear();
@ -185,7 +185,7 @@ void ExpandConvBaseLayer::bpropActs(MatrixPtr out,
MatrixPtr C = Matrix::create(expandInData, subK, subN, false, useGpu_);
MatrixPtr B = Matrix::create(localGradData, subM, subN, false, useGpu_);
MatrixPtr A = Matrix::create(wgtData, subM, subK, true, useGpu_);
C->mul(A, B); // mul
C->mul(*A, *B); // mul
// clear the temporary matrix
A->clear();
@ -252,7 +252,7 @@ void ExpandConvBaseLayer::bpropWeights(MatrixPtr image,
MatrixPtr A = Matrix::create(expandInData, subK, subN, true, useGpu_);
MatrixPtr B = Matrix::create(gradData, subM, subN, false, useGpu_);
MatrixPtr C = Matrix::create(wGradData, subM, subK, false, useGpu_);
C->mul(B, A, 1, 1);
C->mul(*B, *A, 1, 1);
A->clear();
B->clear();

7
paddle/gserver/layers/FullMatrixProjection.cpp
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@ -28,7 +28,7 @@ FullMatrixProjection::FullMatrixProjection(const ProjectionConfig& config,
void FullMatrixProjection::forward() {
REGISTER_TIMER_INFO("FwMulTimer", getName().c_str());
out_->value->mul(in_->value, weight_->getW(), 1, 1);
out_->value->mul(*(in_->value), *(weight_->getW()), 1, 1);
}
void FullMatrixProjection::backward(const UpdateCallback& callback) {
@ -37,7 +37,8 @@ void FullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the W-gradient for the current layer */
if (weight_->getWGrad()) {
REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
weight_->getWGrad()->mul(in_->value->getTranspose(), out_->grad, 1, 1);
weight_->getWGrad()->mul(
*(in_->value->getTranspose()), *(out_->grad), 1, 1);
}
// If callback does not change value, backward propagation error
@ -47,7 +48,7 @@ void FullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the input layers error */
if (in_->grad) {
REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
in_->grad->mul(out_->grad, weight_->getW()->getTranspose(), 1, 1);
in_->grad->mul(*(out_->grad), *(weight_->getW()->getTranspose()), 1, 1);
}
hl_set_sync_flag(syncFlag);

8
paddle/gserver/layers/FullyConnectedLayer.cpp
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@ -84,8 +84,8 @@ void FullyConnectedLayer::forward(PassType passType) {
auto input = getInput(i);
CHECK(input.value) << "The input of 'fc' layer must be matrix";
REGISTER_TIMER_INFO("FwMulTimer", getName().c_str());
i == 0 ? outV->mul(input.value, weights_[i]->getW(), 1, 0)
: outV->mul(input.value, weights_[i]->getW(), 1, 1);
i == 0 ? outV->mul(*input.value, *weights_[i]->getW(), 1, 0)
: outV->mul(*input.value, *weights_[i]->getW(), 1, 1);
}
/* add the bias-vector */
@ -123,7 +123,7 @@ void FullyConnectedLayer::backward(const UpdateCallback& callback) {
MatrixPtr oGrad = getOutputGrad();
{
REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
weights_[i]->getWGrad()->mul(input_T, oGrad, 1, 1);
weights_[i]->getWGrad()->mul(*input_T, *oGrad, 1, 1);
}
}
@ -136,7 +136,7 @@ void FullyConnectedLayer::backward(const UpdateCallback& callback) {
if (NULL != preGrad) {
MatrixPtr weights_T = weights_[i]->getW()->getTranspose();
REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
preGrad->mul(getOutputGrad(), weights_T, 1, 1);
preGrad->mul(*getOutputGrad(), *weights_T, 1, 1);
}
hl_set_sync_flag(syncFlag);

2
paddle/gserver/layers/LinearChainCRF.cpp
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@ -59,7 +59,7 @@ real LinearChainCRF::forward(real* x, int* s, int length) {
matX->rowMax(*maxX_);
expX_->assign(*matX);
// subtract max to avoid overflow or underflow
expX_->mul(maxX_, ones_, (real)-1, (real)1);
expX_->mul(*maxX_, *ones_, (real)-1, (real)1);
expX_->exp2();
real* a = a_->getData();

26
paddle/gserver/layers/LstmLayer.cpp
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@ -316,7 +316,7 @@ void LstmLayer::forwardSequence(int batchSize,
}
if (prevOutput_) {
frameGate->setData(lstmValue.gateValue);
frameGate->mul(prevOutput_, weight_->getW(), 1, 1);
frameGate->mul(*prevOutput_, *weight_->getW(), 1, 1);
}
}
AsyncGpuBlock asyncGpuBlock;
@ -338,7 +338,7 @@ void LstmLayer::forwardSequence(int batchSize,
frameOutput->setData(lstmValue.outputValue);
nextFrame(reversed_, getSize());
frameGate->setData(lstmValue.gateValue);
frameGate->mul(frameOutput, weight_->getW(), 1, 1);
frameGate->mul(*frameOutput, *weight_->getW(), 1, 1);
}
}
if (n != numSequences - 1) {
@ -348,7 +348,7 @@ void LstmLayer::forwardSequence(int batchSize,
if (!reversed_) {
if (!prevState_) lstmValue.prevStateValue = nullptr;
if (prevOutput_) {
frameGate->mul(frameOutput, weight_->getW(), 1, 1);
frameGate->mul(*frameOutput, *weight_->getW(), 1, 1);
}
} else {
lstmValue.prevStateValue = nullptr;
@ -470,7 +470,7 @@ void LstmLayer::backwardSequence(int batchSize,
frameGate->setData(lstmGrad.gateGrad);
nextFrame(reversed_, getSize());
frameOutput->setData(lstmGrad.outputGrad);
frameOutput->mul(frameGate, weightT, 1, 1);
frameOutput->mul(*frameGate, *weightT, 1, 1);
} else {
nextFrame(reversed_, getSize());
}
@ -479,14 +479,14 @@ void LstmLayer::backwardSequence(int batchSize,
if (weight_->getWGrad()) {
if (!reversed_) {
weight_->getWGrad()->mul(
output_.value->subMatrix(start, length - 1)->getTranspose(),
gate_.grad->subMatrix(start + 1, length - 1),
*output_.value->subMatrix(start, length - 1)->getTranspose(),
*gate_.grad->subMatrix(start + 1, length - 1),
1,
1);
} else {
weight_->getWGrad()->mul(
output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
gate_.grad->subMatrix(start, length - 1),
*output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
*gate_.grad->subMatrix(start, length - 1),
1,
1);
}
@ -541,7 +541,7 @@ void LstmLayer::forwardBatch(int batchSize,
if (n != 0) {
MatrixPtr batch1 = batchValue_->getBatchValue(n - 1, batchSize);
gateValue->mul(batch1, weight_->getW(), 1, 1);
gateValue->mul(*batch1, *weight_->getW(), 1, 1);
} else if (prevOutput_) {
Matrix::resizeOrCreate(prevBatchOutput2_,
gateValue->getHeight(),
@ -549,7 +549,7 @@ void LstmLayer::forwardBatch(int batchSize,
false,
useGpu_);
batchValue_->prevOutput2Batch(*prevOutput_, *prevBatchOutput2_);
gateValue->mul(prevBatchOutput2_, weight_->getW(), 1, 1);
gateValue->mul(*prevBatchOutput2_, *weight_->getW(), 1, 1);
batchValue_->prevOutput2Batch(*prevState_,
*totalState_->subMatrix(0, numSequences));
@ -672,16 +672,16 @@ void LstmLayer::backwardBatch(int batchSize,
if (n != 0) {
MatrixPtr tmp = batchGrad_->getBatchValue(n - 1, batchSize);
tmp->mul(gateGrad, weightT, 1, 1);
tmp->mul(*gateGrad, *weightT, 1, 1);
}
if (n != 0 && weight_->getWGrad()) {
/* backward weight */
MatrixPtr outputValue = batchValue_->getBatchValue(n - 1, batchSize);
weight_->getWGrad()->mul(outputValue->getTranspose(), gateGrad, 1, 1);
weight_->getWGrad()->mul(*outputValue->getTranspose(), *gateGrad, 1, 1);
} else if (prevOutput_ && weight_->getWGrad()) {
weight_->getWGrad()->mul(
prevBatchOutput2_->getTranspose(), gateGrad, 1, 1);
*prevBatchOutput2_->getTranspose(), *gateGrad, 1, 1);
}
}
}

8
paddle/gserver/layers/MDLstmLayer.cpp
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@ -547,7 +547,7 @@ void MDLstmLayer::forwardOneSequence(int start, CoordIterator& coordIter) {
if (coordIter.getPrePos(delays_, i, prePos)) {
int preOffset = coordIter.offset(prePos);
frameGate_[start + offset].value->mul(
frameOutput_[start + preOffset].value, weight_->getW(), 1.0, 1.0);
*frameOutput_[start + preOffset].value, *weight_->getW(), 1.0, 1.0);
}
}
forwardGate2OutputSequence(start, coordIter);
@ -747,11 +747,11 @@ void MDLstmLayer::backwardOneSequence(int start, CoordIterator& coordIter) {
if (coordIter.getPrePos(delays_, i, prePos)) {
int preOffset = coordIter.offset(prePos);
frameOutput_[start + preOffset].grad->mul(
frameGate_[start + offset].grad, weightT, 1.0, 1.0);
*frameGate_[start + offset].grad, *weightT, 1.0, 1.0);
if (weight_->getWGrad()) {
weight_->getWGrad()->mul(
frameOutput_[start + preOffset].value->getTranspose(),
frameGate_[start + offset].grad,
*frameOutput_[start + preOffset].value->getTranspose(),
*frameGate_[start + offset].grad,
1.0,
1.0);
}

6
paddle/gserver/layers/OuterProdLayer.cpp
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@ -96,7 +96,7 @@ void OuterProdLayer::forward(PassType passType) {
tmpRow0->setData(inV0->getData() + i * dim0);
tmpRow1->setData(inV1->getData() + i * dim1);
tmpMtx0->mul(tmpRow0->getTranspose(), tmpRow1);
tmpMtx0->mul(*tmpRow0->getTranspose(), *tmpRow1);
}
}
}
@ -121,7 +121,7 @@ void OuterProdLayer::backward(const UpdateCallback& callback) {
tmpRow0->setData(inG0->getData() + i * dim0);
tmpRow1->setData(inV1->getData() + i * dim1);
tmpRow0->mul(tmpRow1, tmpMtx0->getTranspose(), 1, 1);
tmpRow0->mul(*tmpRow1, *tmpMtx0->getTranspose(), 1, 1);
}
}
@ -131,7 +131,7 @@ void OuterProdLayer::backward(const UpdateCallback& callback) {
tmpRow0->setData(inV0->getData() + i * dim0);
tmpRow1->setData(inG1->getData() + i * dim1);
tmpRow1->mul(tmpRow0, tmpMtx0, 1, 1);
tmpRow1->mul(*tmpRow0, *tmpMtx0, 1, 1);
}
}
}

32
paddle/gserver/layers/RecurrentLayer.cpp
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@ -215,12 +215,12 @@ void RecurrentLayer::forwardSequence(int batchSize,
void RecurrentLayer::forwardOneSequence(int start, int length) {
if (!reversed_) {
if (prevOutput_) {
frameOutput_[start].value->mul(prevOutput_, weight_->getW(), 1, 1);
frameOutput_[start].value->mul(*prevOutput_, *weight_->getW(), 1, 1);
}
activation_->forward(frameOutput_[start]);
for (int i = 1; i < length; ++i) {
frameOutput_[start + i].value->mul(
frameOutput_[start + i - 1].value, weight_->getW(), 1, 1);
*frameOutput_[start + i - 1].value, *weight_->getW(), 1, 1);
activation_->forward(frameOutput_[start + i]);
}
if (prevOutput_) {
@ -230,7 +230,7 @@ void RecurrentLayer::forwardOneSequence(int start, int length) {
activation_->forward(frameOutput_[start + length - 1]);
for (int i = length - 2; i >= 0; --i) {
frameOutput_[start + i].value->mul(
frameOutput_[start + i + 1].value, weight_->getW(), 1, 1);
*frameOutput_[start + i + 1].value, *weight_->getW(), 1, 1);
activation_->forward(frameOutput_[start + i]);
}
}
@ -282,13 +282,13 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
for (int i = length - 1; i > 0; --i) {
activation_->backward(frameOutput_[start + i]);
frameOutput_[start + i - 1].grad->mul(
frameOutput_[start + i].grad, weightT, 1, 1);
*frameOutput_[start + i].grad, *weightT, 1, 1);
}
activation_->backward(frameOutput_[start]);
if (weight_->getWGrad()) {
weight_->getWGrad()->mul(
output_.value->subMatrix(start, length - 1)->getTranspose(),
output_.grad->subMatrix(start + 1, length - 1),
*output_.value->subMatrix(start, length - 1)->getTranspose(),
*output_.grad->subMatrix(start + 1, length - 1),
1,
1);
}
@ -296,13 +296,13 @@ void RecurrentLayer::backwardOneSequence(int start, int length) {
for (int i = 0; i < length - 1; ++i) {
activation_->backward(frameOutput_[start + i]);
frameOutput_[start + i + 1].grad->mul(
frameOutput_[start + i].grad, weightT, 1, 1);
*frameOutput_[start + i].grad, *weightT, 1, 1);
}
activation_->backward(frameOutput_[start + length - 1]);
if (weight_->getWGrad()) {
weight_->getWGrad()->mul(
output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
output_.grad->subMatrix(start, length - 1),
*output_.value->subMatrix(start + 1, length - 1)->getTranspose(),
*output_.grad->subMatrix(start, length - 1),
1,
1);
}
@ -329,7 +329,7 @@ void RecurrentLayer::forwardBatch(int batchSize,
if (n != 0) {
MatrixPtr batch1 =
batchValue_->getBatchValue(n - 1, batch2->getHeight());
batch2->mul(batch1, weight_->getW(), 1, 1);
batch2->mul(*batch1, *weight_->getW(), 1, 1);
}
Argument arg;
arg.value = batch2;
@ -367,14 +367,14 @@ void RecurrentLayer::backwardBatch(int batchSize,
if (n != 0) {
batch1 = batchGrad_->getBatchValue(n - 1, batch2->getHeight());
batch1->mul(batch2, weightT, 1, 1);
batch1->mul(*batch2, *weightT, 1, 1);
}
if (backwardByBatch && weight_->getWGrad()) {
if (n != 0) {
/* backward weight */
batch1 = batchValue_->getBatchValue(n - 1, batch2->getHeight());
weight_->getWGrad()->mul(batch1->getTranspose(), batch2, 1, 1);
weight_->getWGrad()->mul(*batch1->getTranspose(), *batch2, 1, 1);
}
}
}
@ -389,14 +389,14 @@ void RecurrentLayer::backwardBatch(int batchSize,
int len = starts[seq + 1] - starts[seq];
if (!reversed_) {
weight_->getWGrad()->mul(
output_.value->subMatrix(starts[seq], len - 1)->getTranspose(),
output_.grad->subMatrix(starts[seq] + 1, len - 1),
*output_.value->subMatrix(starts[seq], len - 1)->getTranspose(),
*output_.grad->subMatrix(starts[seq] + 1, len - 1),
1,
1);
} else {
weight_->getWGrad()->mul(
output_.value->subMatrix(starts[seq] + 1, len - 1)->getTranspose(),
output_.grad->subMatrix(starts[seq], len - 1),
*output_.value->subMatrix(starts[seq] + 1, len - 1)->getTranspose(),
*output_.grad->subMatrix(starts[seq], len - 1),
1,
1);
}

10
paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp
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@ -155,20 +155,20 @@ void SelectiveFullyConnectedLayer::forward(PassType passType) {
// manully compute the multiplication of
// the input vector and the selected rows.
REGISTER_TIMER("selective.plain");
interOutput_->mul(input, weight->getTranspose(), 1, scaleT);
interOutput_->mul(*input, *weight->getTranspose(), 1, scaleT);
} else {
// if the indecies is not sparse enough,
// use full mul instead
REGISTER_TIMER("selective.mul");
if (fullOutput_) {
interOutput_->mul(input, weight->getTranspose(), 1, scaleT);
interOutput_->mul(*input, *weight->getTranspose(), 1, scaleT);
} else {
Matrix::resizeOrCreate(mmat_,
hsize,
wsize,
/*trans=*/false,
/*useGpu=*/useGpu_);
mmat_->mul(input, weight->getTranspose());
mmat_->mul(*input, *weight->getTranspose());
interOutput_->add3(mmat_);
}
}
@ -242,14 +242,14 @@ void SelectiveFullyConnectedLayer::backward(const UpdateCallback& callback) {
MatrixPtr preGrad = getInputGrad(i);
if (preGrad) {
REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
preGrad->mul(interOutGrad_, weights_[i]->getW(), 1, 1);
preGrad->mul(*interOutGrad_, *weights_[i]->getW(), 1, 1);
}
MatrixPtr wGrad = weights_[i]->getWGrad();
if (wGrad) {
REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
MatrixPtr input = getInputValue(i);
wGrad->mul(interOutGrad_->getTranspose(), input, 1, 1);
wGrad->mul(*interOutGrad_->getTranspose(), *input, 1, 1);
}
{

8
paddle/gserver/layers/TensorLayer.cpp
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@ -77,7 +77,7 @@ void TensorLayer::forward(PassType passType) {
REGISTER_TIMER_INFO("TensorFwMulTimer", getName().c_str());
for (size_t i = 0; i < getSize(); ++i) {
MatrixPtr weights = weights_[i]->getW();
tmpMat->mul(input1, weights, 1, 0);
tmpMat->mul(*input1, *weights, 1, 0);
outV->rowDotMul(i, *tmpMat, *input2);
}
}
@ -112,7 +112,7 @@ void TensorLayer::backward(const UpdateCallback& callback) {
if (weights_[i]->getWGrad()) {
tmpMat->rowScale(i, *input1, *oGrad);
MatrixPtr input1_T = tmpMat->getTranspose();
weights_[i]->getWGrad()->mul(input1_T, input2, 1, 1);
weights_[i]->getWGrad()->mul(*input1_T, *input2, 1, 1);
}
}
}
@ -130,11 +130,11 @@ void TensorLayer::backward(const UpdateCallback& callback) {
if (NULL != preGrad1) { /* (grad * e2) * trans(W) */
tmpMat->rowScale(i, *input2, *oGrad);
MatrixPtr weights_T = weights->getTranspose();
preGrad1->mul(tmpMat, weights_T, 1, 1);
preGrad1->mul(*tmpMat, *weights_T, 1, 1);
}
if (NULL != preGrad2) { /* (grad * e1) * W */
tmpMat->rowScale(i, *input1, *oGrad);
preGrad2->mul(tmpMat, weights, 1, 1);
preGrad2->mul(*tmpMat, *weights, 1, 1);
}
}
}

7
paddle/gserver/layers/TransposedFullMatrixProjection.cpp
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@ -46,7 +46,7 @@ TransposedFullMatrixProjection::TransposedFullMatrixProjection(
void TransposedFullMatrixProjection::forward() {
REGISTER_TIMER_INFO("FwMulTimer", getName().c_str());
out_->value->mul(in_->value, weight_->getW()->getTranspose(), 1, 1);
out_->value->mul(*(in_->value), *(weight_->getW()->getTranspose()), 1, 1);
}
void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
@ -55,7 +55,8 @@ void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the W-gradient for the current layer */
if (weight_->getWGrad()) {
REGISTER_TIMER_INFO("GradMulTimer", getName().c_str());
weight_->getWGrad()->mul(out_->grad->getTranspose(), in_->value, 1, 1);
weight_->getWGrad()->mul(
*(out_->grad->getTranspose()), *(in_->value), 1, 1);
}
// If callback does not change value, backprop error asynchronously so that
@ -69,7 +70,7 @@ void TransposedFullMatrixProjection::backward(const UpdateCallback& callback) {
/* Calculate the input layers error */
if (in_->grad) {
REGISTER_TIMER_INFO("BpMulTimer", getName().c_str());
in_->grad->mul(out_->grad, weight_->getW(), 1, 1);
in_->grad->mul(*(out_->grad), *(weight_->getW()), 1, 1);
}
hl_set_sync_flag(syncFlag);

15
paddle/math/CpuSparseMatrix.cpp
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@ -163,15 +163,16 @@ MatrixPtr CpuSparseMatrix::getTranspose() {
SparseValueType CpuSparseMatrix::getValueType() { return valueType_; }
void CpuSparseMatrix::mul(MatrixPtr a, MatrixPtr b, real scaleAB, real scaleT) {
void CpuSparseMatrix::mul(const Matrix& a,
const Matrix& b,
real scaleAB,
real scaleT) {
CHECK(!isTransposed()) << "Not supported";
const auto a_ptr = dynamic_cast<const CpuMatrix*>(&a);
const auto b_ptr = dynamic_cast<const CpuMatrix*>(&b);
if (dynamic_cast<CpuMatrix*>(a.get()) && dynamic_cast<CpuMatrix*>(b.get())) {
CpuMatrix::mul(dynamic_cast<CpuMatrix*>(a.get()),
dynamic_cast<CpuMatrix*>(b.get()),
this,
scaleAB,
scaleT);
if (a_ptr && b_ptr) {
CpuMatrix::mul((CpuMatrix*)a_ptr, (CpuMatrix*)b_ptr, this, scaleAB, scaleT);
} else {
LOG(FATAL) << "not supported";
}

2
paddle/math/CpuSparseMatrix.h
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@ -203,7 +203,7 @@ public:
/// mem MUST be alloced outside (memAlloc=false)
void transpose(MatrixPtr matTrans, bool memAlloc);
void mul(MatrixPtr A, MatrixPtr B, real alpha, real beta);
void mul(const Matrix& A, const Matrix& B, real alpha, real beta);
/**
* @brief sparseMatrix += denseMatrix

49
paddle/math/Matrix.cpp
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@ -582,18 +582,16 @@ void GpuMatrix::mul(const GpuMatrix& a,
}
/* this = a*b */
void GpuMatrix::mul(const MatrixPtr a, const MatrixPtr b) {
mul(a, b, 1.0, 0.0);
}
void GpuMatrix::mul(const Matrix& a, const Matrix& b) { mul(a, b, 1.0, 0.0); }
void GpuMatrix::mul(const MatrixPtr a,
const MatrixPtr b,
void GpuMatrix::mul(const Matrix& a,
const Matrix& b,
real scaleAB,
real scaleT) {
GpuMatrixPtr a_ptr = std::dynamic_pointer_cast<GpuMatrix>(a);
GpuMatrixPtr b_ptr = std::dynamic_pointer_cast<GpuMatrix>(b);
GpuSparseMatrixPtr a_ptr_s = std::dynamic_pointer_cast<GpuSparseMatrix>(a);
GpuSparseMatrixPtr b_ptr_s = std::dynamic_pointer_cast<GpuSparseMatrix>(b);
const auto a_ptr = dynamic_cast<const GpuMatrix*>(&a);
const auto b_ptr = dynamic_cast<const GpuMatrix*>(&b);
const auto a_ptr_s = dynamic_cast<const GpuSparseMatrix*>(&a);
const auto b_ptr_s = dynamic_cast<const GpuSparseMatrix*>(&b);
if (a_ptr && b_ptr) {
mul(*a_ptr, *b_ptr, scaleAB, scaleT);
@ -2598,29 +2596,22 @@ void CpuMatrix::sequenceAvgForward(Matrix& a,
}
/* this = scaleAB*(a*b) + scaleT*this*/
void CpuMatrix::mul(const MatrixPtr a,
const MatrixPtr b,
void CpuMatrix::mul(const Matrix& a,
const Matrix& b,
real scaleAB,
real scaleT) {
CHECK(!isTransposed()) << "Not supported";
const auto a_ptr = dynamic_cast<const CpuMatrix*>(&a);
const auto b_ptr = dynamic_cast<const CpuMatrix*>(&b);
const auto a_ptr_s = dynamic_cast<const CpuSparseMatrix*>(&a);
const auto b_ptr_s = dynamic_cast<const CpuSparseMatrix*>(&b);
if (dynamic_cast<CpuMatrix*>(a.get()) && dynamic_cast<CpuMatrix*>(b.get())) {
mul(dynamic_cast<CpuMatrix*>(a.get()),
dynamic_cast<CpuMatrix*>(b.get()),
scaleAB,
scaleT);
} else if (dynamic_cast<CpuSparseMatrix*>(a.get()) &&
dynamic_cast<CpuMatrix*>(b.get())) {
mul(dynamic_cast<CpuSparseMatrix*>(a.get()),
dynamic_cast<CpuMatrix*>(b.get()),
scaleAB,
scaleT);
} else if (dynamic_cast<CpuMatrix*>(a.get()) &&
dynamic_cast<CpuSparseMatrix*>(b.get())) {
mul(dynamic_cast<CpuMatrix*>(a.get()),
dynamic_cast<CpuSparseMatrix*>(b.get()),
scaleAB,
scaleT);
if (a_ptr && b_ptr) {
mul((CpuMatrix*)a_ptr, (CpuMatrix*)b_ptr, scaleAB, scaleT);
} else if (a_ptr_s && b_ptr) {
mul((CpuSparseMatrix*)a_ptr_s, (CpuMatrix*)b_ptr, scaleAB, scaleT);
} else if (a_ptr && b_ptr_s) {
mul((CpuMatrix*)a_ptr, (CpuSparseMatrix*)b_ptr_s, scaleAB, scaleT);
} else {
LOG(FATAL) << "Not supported";
}
@ -3289,7 +3280,7 @@ void CpuMatrix::addColumnVector(const Matrix& b) {
}
/* this = a*b */
void CpuMatrix::mul(const MatrixPtr a, const MatrixPtr b) {
void CpuMatrix::mul(const Matrix& a, const Matrix& b) {
return mul(a, b, 1.0, 0.0);
}

14
paddle/math/Matrix.h
Unescape View File

@ -444,8 +444,8 @@ public:
* this = scaleAB*(a*b) + scaleT*this
* @endcode
*/
virtual void mul(const MatrixPtr a,
const MatrixPtr b,
virtual void mul(const Matrix& a,
const Matrix& b,
real scaleAB,
real scaleT) {
LOG(FATAL) << "Not implemented";
@ -643,7 +643,7 @@ public:
* this = a*b
* @endcode
*/
virtual void mul(const MatrixPtr a, const MatrixPtr b) {
virtual void mul(const Matrix& a, const Matrix& b) {
LOG(FATAL) << "Not implemented";
}
@ -1272,14 +1272,14 @@ public:
* this = scaleAB*(a*b) + scaleT*this
* @endcode
*/
void mul(const MatrixPtr a, const MatrixPtr b, real scaleAB, real scaleT);
void mul(const Matrix& a, const Matrix& b, real scaleAB, real scaleT);
/**
* @code
* this = a*b
* @endcode
*/
void mul(const MatrixPtr a, const MatrixPtr b);
void mul(const Matrix& a, const Matrix& b);
void mul(const GpuMatrix& a, const GpuMatrix& b, real scaleAB, real scaleT);
@ -1784,7 +1784,7 @@ public:
void addColumnVector(const Matrix& b);
void mul(const MatrixPtr a, const MatrixPtr b, real scaleAB, real scaleT);
void mul(const Matrix& a, const Matrix& b, real scaleAB, real scaleT);
void mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT);
void mul(CpuMatrix* a, CpuSparseMatrix* b, real scaleAB, real scaleT);
@ -1807,7 +1807,7 @@ public:
virtual void mul(CpuSparseMatrix* a, CpuMatrix* b, real scaleAB, real scaleT);
void mul(const MatrixPtr a, const MatrixPtr b);
void mul(const Matrix& a, const Matrix& b);
void rightMul(Matrix& b, real scaleAB, real scaleT);
void rightMul(Matrix& b);

55
paddle/math/SparseMatrix.cpp
Unescape View File

@ -571,49 +571,48 @@ void GpuSparseMatrix::transpose(MatrixPtr matTrans, bool memAlloc) {
hl_stream_synchronize(stream);
}
void GpuSparseMatrix::mul(const GpuMatrixPtr a,
const GpuMatrixPtr b,
void GpuSparseMatrix::mul(const GpuMatrix& a,
const GpuMatrix& b,
real scaleAB,
real scaleT) {
CHECK(a->useGpu_ && b->useGpu_) << "type not match";
CHECK(a.useGpu_ && b.useGpu_) << "type not match";
CHECK(!trans_) << "trans not supported";
real* A_d = a->getData();
real* B_d = b->getData();
real* A_d = (real*)a.getData();
real* B_d = (real*)b.getData();
hl_sparse_matrix_s C_d = sMatrix_.get();
hl_trans_op_t a_trans = a->trans_ ? HPPL_OP_T : HPPL_OP_N;
hl_trans_op_t b_trans = b->trans_ ? HPPL_OP_T : HPPL_OP_N;
if (!a->trans_ && !b->trans_) {
CHECK(height_ == a->getHeight());
CHECK(width_ == b->getWidth());
CHECK(a->getWidth() == b->getHeight());
} else if (a->trans_ && !b->trans_) {
CHECK(height_ == a->getWidth());
CHECK(width_ == b->getWidth());
CHECK(a->getHeight() == b->getHeight());
} else if (!a->trans_ && b->trans_) {
CHECK(height_ == a->getHeight());
CHECK(width_ == b->getHeight());
CHECK(a->getWidth() == b->getWidth());
hl_trans_op_t a_trans = a.trans_ ? HPPL_OP_T : HPPL_OP_N;
hl_trans_op_t b_trans = b.trans_ ? HPPL_OP_T : HPPL_OP_N;
if (!a.trans_ && !b.trans_) {
CHECK(height_ == a.getHeight());
CHECK(width_ == b.getWidth());
CHECK(a.getWidth() == b.getHeight());
} else if (a.trans_ && !b.trans_) {
CHECK(height_ == a.getWidth());
CHECK(width_ == b.getWidth());
CHECK(a.getHeight() == b.getHeight());
} else if (!a.trans_ && b.trans_) {
CHECK(height_ == a.getHeight());
CHECK(width_ == b.getHeight());
CHECK(a.getWidth() == b.getWidth());
} else {
LOG(INFO) << "Not support";
}
int dimM = height_;
int dimN = width_;
int dimK = !b->trans_ ? b->getHeight() : b->getWidth();
int dimK = !b.trans_ ? b.getHeight() : b.getWidth();
hl_sparse_matrix_mul(
A_d, a_trans, B_d, b_trans, C_d, dimM, dimN, dimK, scaleAB, scaleT);
}
void GpuSparseMatrix::mul(const MatrixPtr a,
const MatrixPtr b,
void GpuSparseMatrix::mul(const Matrix& a,
const Matrix& b,
real scaleAB,
real scaleT) {
if (std::dynamic_pointer_cast<GpuMatrix>(a) &&
std::dynamic_pointer_cast<GpuMatrix>(b)) {
GpuMatrixPtr a_ptr = std::dynamic_pointer_cast<GpuMatrix>(a);
GpuMatrixPtr b_ptr = std::dynamic_pointer_cast<GpuMatrix>(b);
mul(a_ptr, b_ptr, scaleAB, scaleT);
const auto a_ptr = dynamic_cast<const GpuMatrix*>(&a);
const auto b_ptr = dynamic_cast<const GpuMatrix*>(&b);
if (a_ptr && b_ptr) {
mul(*a_ptr, *b_ptr, scaleAB, scaleT);
} else {
LOG(FATAL) << "not supported";
}

7
paddle/math/SparseMatrix.h
Unescape View File

@ -104,10 +104,7 @@ public:
size_t newNnz,
SparseValueType valueType);
void mul(const GpuMatrixPtr a,
const GpuMatrixPtr b,
real scaleAB,
real scaleT);
void mul(const GpuMatrix& a, const GpuMatrix& b, real scaleAB, real scaleT);
/// B = A , B.trans = !A.trans
MatrixPtr getTranspose();
@ -218,7 +215,7 @@ protected:
void copyRow(int offsets, size_t colNum, const sparse_float_value_t* row);
public:
void mul(const MatrixPtr a, const MatrixPtr b, real scaleAB, real scaleT);
void mul(const Matrix& a, const Matrix& b, real scaleAB, real scaleT);
void copyFrom(CpuSparseMatrix& src, hl_stream_t stream);
void copyFrom(GpuSparseMatrix& src, hl_stream_t stream);

14
paddle/math/tests/test_SparseMatrix.cpp
Unescape View File

@ -33,8 +33,8 @@ TEST(Matrix, CopyCpuMatrixToSparseMatrix) {
ret2(new CpuMatrix(HEIGHT, WIDTH_TEST));
ret1->zeroMem();
ret2->zeroMem();
ret1->mul(testMatrix, mulCpuMatrix, 1.0, 1.0);
ret2->mul(testCpuMatrix, mulCpuMatrix, 1.0, 1.0);
ret1->mul(*testMatrix, *mulCpuMatrix, 1.0, 1.0);
ret2->mul(*testCpuMatrix, *mulCpuMatrix, 1.0, 1.0);
checkMatrixEqual(ret1, ret2);
}
@ -147,9 +147,9 @@ void test_sparse_matrix_mul(MatrixPara paraA,
hl_stream_synchronize(stream);
/*matrix mul*/
cpuMatrixC->mul(cpuMatrixA, cpuMatrixB, 1.0, 1.0);
gpuMatrixC->mul(gpuMatrixA, gpuMatrixB, 1.0, 1.0);
cpuDenseC->mul(cpuDenseA, cpuDenseB, 1.0, 1.0);
cpuMatrixC->mul(*cpuMatrixA, *cpuMatrixB, 1.0, 1.0);
gpuMatrixC->mul(*gpuMatrixA, *gpuMatrixB, 1.0, 1.0);
cpuDenseC->mul(*cpuDenseA, *cpuDenseB, 1.0, 1.0);
gpuMatrixC_d2h->copyFrom(*gpuMatrixC, stream);
hl_stream_synchronize(stream);
@ -224,8 +224,8 @@ TEST(Matrix, CopySparseMatrixToGpuSparseMatrix) {
MatrixPtr ret2(new GpuMatrix(HEIGHT, WIDTH_TEST));
ret1->zeroMem();
ret2->zeroMem();
ret1->mul(testMatrix, mulCpuMatrix, 1.0, 1.0);
ret2->mul(testGpuMatrix, mulGpuMatrix, 1.0, 1.0);
ret1->mul(*testMatrix, *mulCpuMatrix, 1.0, 1.0);
ret2->mul(*testGpuMatrix, *mulGpuMatrix, 1.0, 1.0);
checkMatrixEqual(ret1, ret2);
}

12
paddle/math/tests/test_matrixCompare.cpp
Unescape View File

@ -318,7 +318,7 @@ void testMatrixInverse(int height) {
cpu->randomizeUniform();
MatrixPtr cpuT = cpu->getTranspose();
MatrixPtr outputCheck = std::make_shared<CpuMatrix>(height, height);
outputCheck->mul(cpu, cpuT);
outputCheck->mul(*cpu, *cpuT);
cpu->setDiag(1.0);
cpu->add(*outputCheck);
@ -328,7 +328,7 @@ void testMatrixInverse(int height) {
TensorCheckErr(*cpuI, *gpuI);
outputCheck->mul(cpu, cpuI);
outputCheck->mul(*cpu, *cpuI);
cpu->setDiag(1.0);
TensorCheckErr(*cpu, *outputCheck);
}
@ -509,8 +509,8 @@ void testMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) {
gpuB->copyFrom(*cpuB);
gpuC->copyFrom(*cpuC);
cpuC->mul(cpuA, cpuB, alpha, beta);
gpuC->mul(gpuA, gpuB, alpha, beta);
cpuC->mul(*cpuA, *cpuB, alpha, beta);
gpuC->mul(*gpuA, *gpuB, alpha, beta);
TensorCheckErr(*cpuC, *gpuC);
}
@ -581,8 +581,8 @@ void testSubMatrixMul(bool transa, bool transb, int dimM, int dimN, int dimK) {
MatrixPtr subCpuC = cpuC->subMatrix(startM, endM, startN, endN);
MatrixPtr subGpuC = gpuC->subMatrix(startM, endM, startN, endN);
subCpuC->mul(subCpuA, subCpuB, alpha, beta);
subGpuC->mul(subGpuA, subGpuB, alpha, beta);
subCpuC->mul(*subCpuA, *subCpuB, alpha, beta);
subGpuC->mul(*subGpuA, *subGpuB, alpha, beta);
TensorCheckErr(*cpuC, *gpuC);
}

4
paddle/math/tests/test_sparseMatrixCompare.cpp
Unescape View File

@ -102,8 +102,8 @@ void testSpMatrixMul(int M, int N, int K, real rate) {
gpuC->copyFrom(*cpuC, stream);
hl_stream_synchronize(stream);
cpuC->mul(cpuA, cpuB->getTranspose(), 1, 1);
gpuC->mul(gpuA, gpuB->getTranspose(), 1, 1);
cpuC->mul(*cpuA, *cpuB->getTranspose(), 1, 1);
gpuC->mul(*gpuA, *gpuB->getTranspose(), 1, 1);
MatrixPtr outputCheck(new CpuSparseMatrix(M, N, nnz));
outputCheck->copyFrom(*gpuC, stream);

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