diff --git a/paddle/gserver/evaluators/Evaluator.cpp b/paddle/gserver/evaluators/Evaluator.cpp index 2f99281911..ae7508e2bb 100644 --- a/paddle/gserver/evaluators/Evaluator.cpp +++ b/paddle/gserver/evaluators/Evaluator.cpp @@ -78,7 +78,7 @@ public: useGpu(arguments[0].deviceId)); errorMat->zeroMem(); if (label != nullptr) { - errorMat->classificationError(output, label); + errorMat->classificationError(*output, *label); } else if (dynamic_cast(multiBinaryLabel.get()) || dynamic_cast(multiBinaryLabel.get())) { errorMat->classificationErrorMulti( diff --git a/paddle/gserver/layers/ContextProjection.cpp b/paddle/gserver/layers/ContextProjection.cpp index 7ac56e3a2a..51c0ae5cc9 100644 --- a/paddle/gserver/layers/ContextProjection.cpp +++ b/paddle/gserver/layers/ContextProjection.cpp @@ -90,8 +90,8 @@ void ContextProjection::forward() { REGISTER_TIMER_INFO("ContextProjectionForward", getName().c_str()); bool isPadding = config_.trainable_padding(); out_->value->contextProjectionForward( - in_->value, - state_ ? state_ : isPadding ? weight_->getW() : nullptr, + *(in_->value), + state_ ? state_.get() : isPadding ? weight_->getW().get() : nullptr, *startPositions, config_.context_length(), config_.context_start(), @@ -128,8 +128,8 @@ void ContextProjection::backward(const UpdateCallback& callback) { bool isPadding = config_.trainable_padding(); if (!out_->grad->useGpu()) { out_->grad->contextProjectionBackward( - in_->grad, - isPadding ? weight_->getWGrad() : nullptr, + in_->grad.get(), + isPadding ? weight_->getWGrad().get() : nullptr, *startPositions, config_.context_length(), config_.context_start(), @@ -137,7 +137,7 @@ void ContextProjection::backward(const UpdateCallback& callback) { isPadding); } else { if (in_->grad) { - out_->grad->contextProjectionBackwardData(in_->grad, + out_->grad->contextProjectionBackwardData(*(in_->grad), *startPositions, config_.context_length(), config_.context_start()); @@ -145,7 +145,7 @@ void ContextProjection::backward(const UpdateCallback& callback) { if (isPadding && weight_->getWGrad()) { out_->grad->contextProjectionBackwardWeight( - weight_->getWGrad(), + *(weight_->getWGrad()), *startPositions, config_.context_length(), config_.context_start(), diff --git a/paddle/gserver/layers/ConvexCombinationLayer.cpp b/paddle/gserver/layers/ConvexCombinationLayer.cpp index 3f4d77a2fe..ed57f2af3c 100644 --- a/paddle/gserver/layers/ConvexCombinationLayer.cpp +++ b/paddle/gserver/layers/ConvexCombinationLayer.cpp @@ -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); } } } diff --git a/paddle/gserver/layers/ExpandConvBaseLayer.cpp b/paddle/gserver/layers/ExpandConvBaseLayer.cpp index 25948747fe..9ddccc2027 100644 --- a/paddle/gserver/layers/ExpandConvBaseLayer.cpp +++ b/paddle/gserver/layers/ExpandConvBaseLayer.cpp @@ -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(); diff --git a/paddle/gserver/layers/FullMatrixProjection.cpp b/paddle/gserver/layers/FullMatrixProjection.cpp index 9e72a33a3c..b8b6f403d6 100644 --- a/paddle/gserver/layers/FullMatrixProjection.cpp +++ b/paddle/gserver/layers/FullMatrixProjection.cpp @@ -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); diff --git a/paddle/gserver/layers/FullyConnectedLayer.cpp b/paddle/gserver/layers/FullyConnectedLayer.cpp index 89afe33c36..d8a667ff8d 100644 --- a/paddle/gserver/layers/FullyConnectedLayer.cpp +++ b/paddle/gserver/layers/FullyConnectedLayer.cpp @@ -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); diff --git a/paddle/gserver/layers/LinearChainCRF.cpp b/paddle/gserver/layers/LinearChainCRF.cpp index af550c7a01..b7f748f3bb 100644 --- a/paddle/gserver/layers/LinearChainCRF.cpp +++ b/paddle/gserver/layers/LinearChainCRF.cpp @@ -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(); diff --git a/paddle/gserver/layers/LstmLayer.cpp b/paddle/gserver/layers/LstmLayer.cpp index 2543d1b49a..01cc5fec8b 100644 --- a/paddle/gserver/layers/LstmLayer.cpp +++ b/paddle/gserver/layers/LstmLayer.cpp @@ -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); } } } diff --git a/paddle/gserver/layers/MDLstmLayer.cpp b/paddle/gserver/layers/MDLstmLayer.cpp index 1243c12889..fb41af5631 100644 --- a/paddle/gserver/layers/MDLstmLayer.cpp +++ b/paddle/gserver/layers/MDLstmLayer.cpp @@ -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); } diff --git a/paddle/gserver/layers/OuterProdLayer.cpp b/paddle/gserver/layers/OuterProdLayer.cpp index cf9a008318..b606e44365 100644 --- a/paddle/gserver/layers/OuterProdLayer.cpp +++ b/paddle/gserver/layers/OuterProdLayer.cpp @@ -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); } } } diff --git a/paddle/gserver/layers/RecurrentLayer.cpp b/paddle/gserver/layers/RecurrentLayer.cpp index 85812c9d66..94b16996a8 100644 --- a/paddle/gserver/layers/RecurrentLayer.cpp +++ b/paddle/gserver/layers/RecurrentLayer.cpp @@ -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); } diff --git a/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp b/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp index 9200a01eee..5eacff6b71 100644 --- a/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp +++ b/paddle/gserver/layers/SelectiveFullyConnectedLayer.cpp @@ -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); } { diff --git a/paddle/gserver/layers/TensorLayer.cpp b/paddle/gserver/layers/TensorLayer.cpp index 642eb1bdd3..5be88d7c05 100644 --- a/paddle/gserver/layers/TensorLayer.cpp +++ b/paddle/gserver/layers/TensorLayer.cpp @@ -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); } } } diff --git a/paddle/gserver/layers/TransposedFullMatrixProjection.cpp b/paddle/gserver/layers/TransposedFullMatrixProjection.cpp index 3f7ff04882..2a12499e5b 100644 --- a/paddle/gserver/layers/TransposedFullMatrixProjection.cpp +++ b/paddle/gserver/layers/TransposedFullMatrixProjection.cpp @@ -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); diff --git a/paddle/math/CpuSparseMatrix.cpp b/paddle/math/CpuSparseMatrix.cpp index b5d5b6ef61..82a482f701 100644 --- a/paddle/math/CpuSparseMatrix.cpp +++ b/paddle/math/CpuSparseMatrix.cpp @@ -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(&a); + const auto b_ptr = dynamic_cast(&b); - if (dynamic_cast(a.get()) && dynamic_cast(b.get())) { - CpuMatrix::mul(dynamic_cast(a.get()), - dynamic_cast(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"; } diff --git a/paddle/math/CpuSparseMatrix.h b/paddle/math/CpuSparseMatrix.h index 9676f8864f..d3e8871cb5 100644 --- a/paddle/math/CpuSparseMatrix.h +++ b/paddle/math/CpuSparseMatrix.h @@ -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 diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index c69e074a76..0193f2f997 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -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(a); - GpuMatrixPtr b_ptr = std::dynamic_pointer_cast(b); - GpuSparseMatrixPtr a_ptr_s = std::dynamic_pointer_cast(a); - GpuSparseMatrixPtr b_ptr_s = std::dynamic_pointer_cast(b); + const auto a_ptr = dynamic_cast(&a); + const auto b_ptr = dynamic_cast(&b); + const auto a_ptr_s = dynamic_cast(&a); + const auto b_ptr_s = dynamic_cast(&b); if (a_ptr && b_ptr) { mul(*a_ptr, *b_ptr, scaleAB, scaleT); @@ -766,20 +764,19 @@ void GpuMatrix::maxoutBackward(Matrix& a, } /*calulate the error of classification */ -void GpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) { - GpuMatrixPtr output_ptr = std::dynamic_pointer_cast(output); - GpuIVectorPtr label_ptr = std::dynamic_pointer_cast(label); - +void GpuMatrix::classificationError(Matrix& output, IVector& label) { + auto output_ptr = dynamic_cast(&output); + auto label_ptr = dynamic_cast(&label); CHECK(output_ptr && label_ptr) << "Invalid argument pointer"; CHECK(height_ == output_ptr->height_ && width_ == 1) << "Matrix dimensions are not equal"; - real* output_d = output_ptr->data_; - real* recResult_d = data_; - int* label_d = label_ptr->getData(); - hl_matrix_classification_error( - output_d, label_d, recResult_d, height_, output_ptr->width_); + hl_matrix_classification_error((real*)output_ptr->data_, + (int*)label_ptr->getData(), + data_, + height_, + output_ptr->width_); } /* copy -log(output[i * width + label]) to this->data[i] */ @@ -1370,86 +1367,62 @@ void GpuMatrix::maxSequenceBackward(Matrix& outputGrad, hl_max_sequence_backward(outGrad, maxIndex, inputGrad, numSequences, dim); } -void GpuMatrix::contextProjectionForward(MatrixPtr input, - MatrixPtr weight, +void GpuMatrix::contextProjectionForward(Matrix& input, + Matrix* weight, const IVector& sequence, int contextLength, int contextStart, size_t beginPad, bool isPadding) { - CHECK(dynamic_cast(input.get())); + CHECK(dynamic_cast(&input)); CHECK(dynamic_cast(&sequence)); - if (weight) CHECK(dynamic_cast(weight.get())); - - size_t numSequences = sequence.getSize() - 1; - int64_t inputDim = input->getWidth(); - int64_t dim = getWidth(); - CHECK_EQ(dim, inputDim * contextLength); - - real* outData = getData(); - real* inputData = input->getData(); - const int* starts = sequence.getData(); + if (weight) CHECK(dynamic_cast(weight)); + CHECK_EQ(getWidth(), input.getWidth() * contextLength); - hl_context_projection_forward(inputData, - starts, + hl_context_projection_forward(input.getData(), + sequence.getData(), isPadding ? weight->getData() : NULL, - outData, - numSequences, - inputDim, + getData(), + sequence.getSize() - 1, + input.getWidth(), contextLength, contextStart, beginPad, isPadding); } -void GpuMatrix::contextProjectionBackwardData(MatrixPtr inputGrad, +void GpuMatrix::contextProjectionBackwardData(Matrix& inputGrad, const IVector& sequence, int contextLength, int contextStart) { - CHECK(dynamic_cast(inputGrad.get())); + CHECK(dynamic_cast(&inputGrad)); CHECK(dynamic_cast(&sequence)); + CHECK_EQ(getWidth(), inputGrad.getWidth() * contextLength); - size_t numSequences = sequence.getSize() - 1; - int64_t inputDim = inputGrad->getWidth(); - int64_t dim = getWidth(); - CHECK_EQ(dim, inputDim * contextLength); - - real* outGrad = getData(); - real* inGrad = inputGrad->getData(); - const int* starts = sequence.getData(); - - hl_context_projection_backward_data(outGrad, - starts, - inGrad, - numSequences, - inputDim, + hl_context_projection_backward_data(getData(), + sequence.getData(), + inputGrad.getData(), + sequence.getSize() - 1, + inputGrad.getWidth(), contextLength, contextStart); } -void GpuMatrix::contextProjectionBackwardWeight(MatrixPtr weightGrad, +void GpuMatrix::contextProjectionBackwardWeight(Matrix& weightGrad, const IVector& sequence, int contextLength, int contextStart, int totalPad, size_t beginPad) { - CHECK(dynamic_cast(weightGrad.get())); + CHECK(dynamic_cast(&weightGrad)); CHECK(dynamic_cast(&sequence)); + CHECK_EQ(getWidth(), weightGrad.getWidth() * contextLength); - size_t numSequences = sequence.getSize() - 1; - int64_t weightDim = weightGrad->getWidth(); - int64_t dim = getWidth(); - CHECK_EQ(dim, weightDim * contextLength); - - real* outGrad = getData(); - real* wtGrad = weightGrad->getData(); - const int* starts = sequence.getData(); - - hl_context_projection_backward_weight(outGrad, - starts, - wtGrad, - numSequences, - weightDim, + hl_context_projection_backward_weight(getData(), + sequence.getData(), + weightGrad.getData(), + sequence.getSize() - 1, + weightGrad.getWidth(), totalPad, contextLength, contextStart, @@ -2371,23 +2344,21 @@ void CpuMatrix::maxSequenceBackward(Matrix& outputGrad, } } -void CpuMatrix::contextProjectionForward(MatrixPtr input, - MatrixPtr weight, +void CpuMatrix::contextProjectionForward(Matrix& input, + Matrix* weight, const IVector& sequence, int contextLength, int contextStart, size_t beginPad, bool isPadding) { - CHECK(dynamic_cast(input.get())); - CHECK(dynamic_cast(&sequence)); - if (weight) CHECK(dynamic_cast(weight.get())); - - size_t numSequences = sequence.getSize() - 1; - int64_t inputDim = input->getWidth(); - int64_t dim = getWidth(); - CHECK_EQ(dim, inputDim * contextLength); - const int* starts = sequence.getData(); - + auto input_ptr = dynamic_cast(&input); + auto seq_ptr = dynamic_cast(&sequence); + CHECK(input_ptr && seq_ptr); + if (weight) CHECK(dynamic_cast(weight)); + CHECK_EQ(getWidth(), input_ptr->getWidth() * contextLength); + + const int* starts = seq_ptr->getData(); + size_t numSequences = seq_ptr->getSize() - 1; for (size_t i = 0; i < numSequences; ++i) { for (int j = 0; j < contextLength; ++j) { int begin = starts[i] + contextStart + j; @@ -2400,7 +2371,7 @@ void CpuMatrix::contextProjectionForward(MatrixPtr input, MatrixPtr mat = this->subMatrix(starts[i], padSize); if (isPadding) { MatrixPtr sub = weight->subMatrix(j, padSize); - mat->addAtOffset(*sub, j * inputDim); + mat->addAtOffset(*sub, j * input_ptr->getWidth()); } dstBegin = starts[i] + padSize; begin = starts[i]; @@ -2412,41 +2383,36 @@ void CpuMatrix::contextProjectionForward(MatrixPtr input, if (isPadding) { MatrixPtr sub = weight->subMatrix(beginPad + contextStart + j - padSize, padSize); - mat->addAtOffset(*sub, j * inputDim); + mat->addAtOffset(*sub, j * input_ptr->getWidth()); } dstEnd = starts[i + 1] - padSize; end = starts[i + 1]; } if (end <= begin) continue; - MatrixPtr src = input->subMatrix(begin, end - begin); + MatrixPtr src = input_ptr->subMatrix(begin, end - begin); MatrixPtr dst = this->subMatrix(dstBegin, dstEnd - dstBegin); - dst->addAtOffset(*src, j * inputDim); + dst->addAtOffset(*src, j * input_ptr->getWidth()); } } } -void CpuMatrix::contextProjectionBackward(MatrixPtr inputGrad, - MatrixPtr weightGrad, +void CpuMatrix::contextProjectionBackward(Matrix* inputGrad, + Matrix* weightGrad, const IVector& sequence, int contextLength, int contextStart, size_t beginPad, bool isPadding) { - if (inputGrad) CHECK(dynamic_cast(inputGrad.get())); - if (weightGrad) CHECK(dynamic_cast(weightGrad.get())); + if (inputGrad) CHECK(dynamic_cast(inputGrad)); + if (weightGrad) CHECK(dynamic_cast(weightGrad)); CHECK(dynamic_cast(&sequence)); - int64_t inputDim = 0; - int64_t dim = getWidth(); - size_t numSequences = sequence.getSize() - 1; - const int* starts = sequence.getData(); - if (inputGrad) { - inputDim = inputGrad->getWidth(); - } else { - inputDim = weightGrad->getWidth(); - } - CHECK_EQ(dim, inputDim * contextLength); + int64_t inputDim = inputGrad ? inputGrad->getWidth() + : weightGrad ? weightGrad->getWidth() : 0; + CHECK_EQ(getWidth(), inputDim * contextLength); + const int* starts = sequence.getData(); + size_t numSequences = sequence.getSize() - 1; for (size_t i = 0; i < numSequences; ++i) { for (int j = 0; j < contextLength; ++j) { int begin = starts[i] + contextStart + j; @@ -2630,29 +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(&a); + const auto b_ptr = dynamic_cast(&b); + const auto a_ptr_s = dynamic_cast(&a); + const auto b_ptr_s = dynamic_cast(&b); - if (dynamic_cast(a.get()) && dynamic_cast(b.get())) { - mul(dynamic_cast(a.get()), - dynamic_cast(b.get()), - scaleAB, - scaleT); - } else if (dynamic_cast(a.get()) && - dynamic_cast(b.get())) { - mul(dynamic_cast(a.get()), - dynamic_cast(b.get()), - scaleAB, - scaleT); - } else if (dynamic_cast(a.get()) && - dynamic_cast(b.get())) { - mul(dynamic_cast(a.get()), - dynamic_cast(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"; } @@ -3321,7 +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); } @@ -3544,21 +3503,20 @@ void CpuMatrix::rowNormalizeL1(Matrix& out) { } /* calulate classification error */ -void CpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) { - CHECK(dynamic_cast(output.get())); - CHECK(dynamic_cast(label.get())); +void CpuMatrix::classificationError(Matrix& output, IVector& label) { + CHECK(dynamic_cast(&output)); + CHECK(dynamic_cast(&label)); - size_t numSamples = getHeight(); - size_t dim = output->getWidth(); - CHECK_EQ(label->getSize(), numSamples); - CHECK_EQ(output->getHeight(), numSamples); CHECK_EQ(getWidth(), (size_t)1); + size_t numSamples = getHeight(); + CHECK_EQ(label.getSize(), numSamples); + CHECK_EQ(output.getHeight(), numSamples); - real* out = output->getData(); - real* result = getData(); - int* lbl = label->getData(); - real maxData; - int maxIndex; + size_t dim = output.getWidth(); + real* out = output.getData(); + int* lbl = label.getData(); + real maxData = 0.0; + int maxIndex = -1; for (size_t i = 0; i < numSamples; ++i) { CHECK_GE(lbl[i], 0); CHECK_LT((size_t)lbl[i], dim); @@ -3570,7 +3528,7 @@ void CpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) { maxData = out[i * dim + j]; } } - result[i] = (maxIndex != lbl[i]); + getData()[i] = (maxIndex != lbl[i]); } } diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index 1cfb90a9db..dfcb0853df 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -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"; } @@ -835,7 +835,7 @@ public: * * output[i] = 0 if row i is correct. */ - virtual void classificationError(MatrixPtr output, IVectorPtr label) { + virtual void classificationError(Matrix& output, IVector& label) { LOG(FATAL) << "Not implemented"; } @@ -997,8 +997,8 @@ public: LOG(FATAL) << "Not implemeted"; } - virtual void contextProjectionForward(MatrixPtr input, - MatrixPtr weight, + virtual void contextProjectionForward(Matrix& input, + Matrix* weight, const IVector& sequence, int contextLength, int contextStart, @@ -1007,8 +1007,8 @@ public: LOG(FATAL) << "Not implemeted"; } - virtual void contextProjectionBackward(MatrixPtr inputGrad, - MatrixPtr weightGrad, + virtual void contextProjectionBackward(Matrix* inputGrad, + Matrix* weightGrad, const IVector& sequence, int contextLength, int contextStart, @@ -1017,14 +1017,14 @@ public: LOG(FATAL) << "Not implemeted"; } - virtual void contextProjectionBackwardData(MatrixPtr inputGrad, + virtual void contextProjectionBackwardData(Matrix& inputGrad, const IVector& sequence, int contextLength, int contextStart) { LOG(FATAL) << "Not implemeted"; } - virtual void contextProjectionBackwardWeight(MatrixPtr weightGrad, + virtual void contextProjectionBackwardWeight(Matrix& weightGrad, const IVector& sequence, int contextLength, int contextStart, @@ -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); @@ -1373,7 +1373,7 @@ public: void check(std::ostream& os, Matrix& refMat, bool printDiff = true); void randomizeUniform(); - void classificationError(MatrixPtr output, IVectorPtr label); + void classificationError(Matrix& output, IVector& label); void convExpand(Matrix& feature, int feaImgHeight, @@ -1487,20 +1487,20 @@ public: const IVector& sequence, IVector& index); - void contextProjectionForward(MatrixPtr input, - MatrixPtr weight, + void contextProjectionForward(Matrix& input, + Matrix* weight, const IVector& sequence, int contextLength, int contextStart, size_t beginPad, bool isPadding); - void contextProjectionBackwardData(MatrixPtr inputGrad, + void contextProjectionBackwardData(Matrix& inputGrad, const IVector& sequence, int contextLength, int contextStart); - void contextProjectionBackwardWeight(MatrixPtr weightGrad, + void contextProjectionBackwardWeight(Matrix& weightGrad, const IVector& sequence, int contextLength, int contextStart, @@ -1713,16 +1713,16 @@ public: const IVector& sequence, IVector& index); - void contextProjectionForward(MatrixPtr input, - MatrixPtr weight, + void contextProjectionForward(Matrix& input, + Matrix* weight, const IVector& sequence, int contextLength, int contextStart, size_t beginPad, bool isPadding); - void contextProjectionBackward(MatrixPtr inputGrad, - MatrixPtr weightGrad, + void contextProjectionBackward(Matrix* inputGrad, + Matrix* weightGrad, const IVector& sequence, int contextLength, int contextStart, @@ -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); @@ -1881,7 +1881,7 @@ public: void randomizeUniform(); - void classificationError(MatrixPtr output, IVectorPtr label); + void classificationError(Matrix& output, IVector& label); void addByBitCode(size_t numClasses, const IVector& codes, const Matrix& vec); diff --git a/paddle/math/SparseMatrix.cpp b/paddle/math/SparseMatrix.cpp index 9154503c21..720a035ecb 100644 --- a/paddle/math/SparseMatrix.cpp +++ b/paddle/math/SparseMatrix.cpp @@ -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(a) && - std::dynamic_pointer_cast(b)) { - GpuMatrixPtr a_ptr = std::dynamic_pointer_cast(a); - GpuMatrixPtr b_ptr = std::dynamic_pointer_cast(b); - mul(a_ptr, b_ptr, scaleAB, scaleT); + const auto a_ptr = dynamic_cast(&a); + const auto b_ptr = dynamic_cast(&b); + if (a_ptr && b_ptr) { + mul(*a_ptr, *b_ptr, scaleAB, scaleT); } else { LOG(FATAL) << "not supported"; } diff --git a/paddle/math/SparseMatrix.h b/paddle/math/SparseMatrix.h index bd96a3301d..1d3801548e 100644 --- a/paddle/math/SparseMatrix.h +++ b/paddle/math/SparseMatrix.h @@ -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); diff --git a/paddle/math/tests/test_SparseMatrix.cpp b/paddle/math/tests/test_SparseMatrix.cpp index 88b75b6d83..0949ab7ffb 100644 --- a/paddle/math/tests/test_SparseMatrix.cpp +++ b/paddle/math/tests/test_SparseMatrix.cpp @@ -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); } diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 62de5b25e4..c6fc849ba0 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -65,16 +65,16 @@ void testMatrixProjectionForward(int contextStart, // calculate int beginPad = std::max(0, -contextStart); - cpuOutput->contextProjectionForward(cpuInput, - cpuWeight, + cpuOutput->contextProjectionForward(*cpuInput, + cpuWeight.get(), *cpuSequence, contextLength, contextStart, beginPad, padding); - gpuOutput->contextProjectionForward(gpuInput, - gpuWeight, + gpuOutput->contextProjectionForward(*gpuInput, + gpuWeight.get(), *gpuSequence, contextLength, contextStart, @@ -120,17 +120,17 @@ void testMatrixProjectionBackward(int contextStart, // calculate int beginPad = std::max(0, -contextStart); - cpuOutputGrad->contextProjectionBackward(cpuInputGrad, - cpuWeightGrad, + cpuOutputGrad->contextProjectionBackward(cpuInputGrad.get(), + cpuWeightGrad.get(), *cpuSequence, contextLength, contextStart, beginPad, padding); gpuOutputGrad->contextProjectionBackwardData( - gpuInputGrad, *gpuSequence, contextLength, contextStart); + *gpuInputGrad, *gpuSequence, contextLength, contextStart); if (padding) { - gpuOutputGrad->contextProjectionBackwardWeight(gpuWeightGrad, + gpuOutputGrad->contextProjectionBackwardWeight(*gpuWeightGrad, *gpuSequence, contextLength, contextStart, @@ -318,7 +318,7 @@ void testMatrixInverse(int height) { cpu->randomizeUniform(); MatrixPtr cpuT = cpu->getTranspose(); MatrixPtr outputCheck = std::make_shared(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); } @@ -939,8 +939,8 @@ void testClassificationError(int numSamples, int dim) { gpuOutput->copyFrom(*cpuOutput); gpuLabel->copyFrom(*cpuLabel); - cpuError->classificationError(cpuOutput, cpuLabel); - gpuError->classificationError(gpuOutput, gpuLabel); + cpuError->classificationError(*cpuOutput, *cpuLabel); + gpuError->classificationError(*gpuOutput, *gpuLabel); TensorCheckEqual(*cpuError, *gpuError); } diff --git a/paddle/math/tests/test_sparseMatrixCompare.cpp b/paddle/math/tests/test_sparseMatrixCompare.cpp index 6f6de238ba..dcdbccffc3 100644 --- a/paddle/math/tests/test_sparseMatrixCompare.cpp +++ b/paddle/math/tests/test_sparseMatrixCompare.cpp @@ -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);