void SparseRowCpuMatrix::addTo(BaseMatrix& dest,
std::vector<uint32_t>& ids,
size_t tid,
size_t numThreads) {
CHECK(!dest.useGpu_);
CHECK_EQ(dest.height_ * dest.width_, this->height_ * this->width_);
std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
for (size_t i = 0; i < localIndices.size(); ++i) {
uint32_t id = localIndices[i];
if (id % numThreads == tid) {
simd::addTo(dest.rowBuf(id), getLocalRow(i), this->width_);
ids.push_back(id);
}
}
}
void SparseRowCpuMatrix::sgdUpdate(BaseMatrix& value,
IVector& t0,
real learningRate,
int currentTime,
real decayRate,
bool useL1,
bool fini) {
std::vector<unsigned int>& localIndices = indexDictHandle_->localIndices;
// t0 and value are vectors
CHECK_EQ(t0.getSize(), this->height_);
CHECK_EQ(value.width_, this->height_ * this->width_);
if (decayRate == 0.0f) {
if (fini) {
return;
}
for (size_t i = 0; i < localIndices.size(); ++i) {
real* g = getLocalRow(i);
real* v = value.rowBuf(localIndices[i]);
for (size_t j = 0; j < this->width_; ++j) {
v[j] -= learningRate * g[j];
}
}
return;
} // else
if (useL1) { // L1 decay
if (fini) {
for (size_t i = 0; i < this->height_; ++i) {
real* v = value.rowBuf(i);
int* t = t0.getData() + i;
if (t[0] < currentTime) {
// W(t0) -> W(t+1)
int tDiff = currentTime - t[0];
real delta = tDiff * learningRate * decayRate;
simd::decayL1(v, v, delta, this->width_);
}
}
return;
} // else
for (size_t i = 0; i < localIndices.size(); ++i) {
real* g = getLocalRow(i);
real* v = value.rowBuf(localIndices[i]);
int* t = t0.getData() + localIndices[i];
if (t[0] < currentTime) {
// W(t0) -> W(t)
int tDiff = currentTime - t[0];
real delta = tDiff * learningRate * decayRate;
simd::decayL1(v, v, delta, this->width_);
}
// W(t) -> W(t+1)
for (size_t j = 0; j < this->width_; ++j) {
v[j] -= learningRate * g[j];
}
simd::decayL1(v, v, learningRate * decayRate, this->width_);
// state update to t+1
t[0] = currentTime + 1;
}
} else { // L2 decay
if (fini) {
for (size_t i = 0; i < this->height_; ++i) {
real* v = value.rowBuf(i);
int* t = t0.getData() + i;
if (t[0] < currentTime) {
// W(t0) -> W(t+1)
int tDiff = currentTime - t[0];
real recip = 1.0f / (1.0f + tDiff * learningRate * decayRate);
for (size_t j = 0; j < this->width_; ++j) {
v[j] *= recip;
}
}
}
return;
} // else
real recipDecay = 1.0f / (1.0f + learningRate * decayRate);
for (size_t i = 0; i < localIndices.size(); ++i) {
real* g = getLocalRow(i);
real* v = value.rowBuf(localIndices[i]);
int* t = t0.getData() + localIndices[i];
if (t[0] < currentTime) {
// W(t0) -> W(t)
int tDiff = currentTime - t[0];
real recip = 1.0f / (1.0f + tDiff * learningRate * decayRate);
for (size_t j = 0; j < this->width_; ++j) {
v[j] *= recip;
}
}
// W(t) -> W(t+1)
for (size_t j = 0; j < this->width_; ++j) {
v[j] = recipDecay * (v[j] - learningRate * g[j]);
}
// state update to t+1
t[0] = currentTime + 1;
}
}
}
