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Matrix API refactor, when passing parameters, convert shared_ptr (MatrixPtr) to

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reference or raw matrix (Matrix & or Matrix *)
contextProjectionForward
contextProjectionBackward
contextProjectionBackwardData
contextProjectionBackwardWeight
classificationError
The mul functions would be updated later.
avx_docs
xutianbing 8 years ago
parent 80b45ad146
commit 706c572424
5 changed files with 103 additions and 136 deletions
Show Stats Download Patch File Download Diff File

2
paddle/gserver/evaluators/Evaluator.cpp
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@ -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(

12
paddle/gserver/layers/ContextProjection.cpp
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@ -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(),

171
paddle/math/Matrix.cpp
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File diff suppressed because it is too large Load Diff

34
paddle/math/Matrix.h
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@ -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,
@ -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,
@ -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);

20
paddle/math/tests/test_matrixCompare.cpp
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@ -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,
@ -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);
}

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