Merge pull request #934 from tianbingsz/paddle_function_mat

Matrix API refactor

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<CpuSparseMatrix*>(multiBinaryLabel.get()) ||
                dynamic_cast<GpuSparseMatrix*>(multiBinaryLabel.get())) {
       errorMat->classificationErrorMulti(

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(),

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);
     }
   }
 }

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();

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);

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);

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();

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);
       }
     }
   }

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);
         }

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);
       }
     }
   }

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);
       }

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);
     }
 
     {

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);
       }
     }
   }

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);

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<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";
   }

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

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<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);
@@ -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<GpuMatrix>(output);
-  GpuIVectorPtr label_ptr = std::dynamic_pointer_cast<GpuIVector>(label);
-
+void GpuMatrix::classificationError(Matrix& output, IVector& label) {
+  auto output_ptr = dynamic_cast<const GpuMatrix*>(&output);
+  auto label_ptr = dynamic_cast<const GpuIVector*>(&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<GpuMatrix*>(input.get()));
+  CHECK(dynamic_cast<GpuMatrix*>(&input));
   CHECK(dynamic_cast<const GpuIVector*>(&sequence));
-  if (weight) CHECK(dynamic_cast<GpuMatrix*>(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<GpuMatrix*>(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<GpuMatrix*>(inputGrad.get()));
+  CHECK(dynamic_cast<GpuMatrix*>(&inputGrad));
   CHECK(dynamic_cast<const GpuIVector*>(&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<GpuMatrix*>(weightGrad.get()));
+  CHECK(dynamic_cast<GpuMatrix*>(&weightGrad));
   CHECK(dynamic_cast<const GpuIVector*>(&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<CpuMatrix*>(input.get()));
-  CHECK(dynamic_cast<const CpuIVector*>(&sequence));
-  if (weight) CHECK(dynamic_cast<CpuMatrix*>(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<CpuMatrix*>(&input);
+  auto seq_ptr = dynamic_cast<const CpuIVector*>(&sequence);
+  CHECK(input_ptr && seq_ptr);
+  if (weight) CHECK(dynamic_cast<CpuMatrix*>(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<CpuMatrix*>(inputGrad.get()));
-  if (weightGrad) CHECK(dynamic_cast<CpuMatrix*>(weightGrad.get()));
+  if (inputGrad) CHECK(dynamic_cast<CpuMatrix*>(inputGrad));
+  if (weightGrad) CHECK(dynamic_cast<CpuMatrix*>(weightGrad));
   CHECK(dynamic_cast<const CpuIVector*>(&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<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";
   }
@@ -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<CpuMatrix*>(output.get()));
-  CHECK(dynamic_cast<CpuIVector*>(label.get()));
+void CpuMatrix::classificationError(Matrix& output, IVector& label) {
+  CHECK(dynamic_cast<const CpuMatrix*>(&output));
+  CHECK(dynamic_cast<const CpuIVector*>(&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]);
   }
 }
 

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);
 

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<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";
   }

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);

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);
 }
 

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<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);
 }
@@ -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);
 }

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);