void PowerLayer<Dtype>::Backward_gpu(
const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) {
if (propagate_down[0]) {
Dtype* bottom_diff = (bottom)[0]->mutable_gpu_diff();
const int count = (bottom)[0]->count();
const Dtype* top_diff = top[0]->gpu_diff();
if (diff_scale_ == Dtype(0) || power_ == Dtype(1)) {
caffe_gpu_set(count, diff_scale_, bottom_diff);
} else {
const Dtype* bottom_data = (bottom)[0]->gpu_data();
// Compute dy/dx = scale * power * (shift + scale * x)^(power - 1)
// = diff_scale * y / (shift + scale * x)
if (power_ == Dtype(2)) {
// Special case for y = (shift + scale * x)^2
// -> dy/dx = 2 * scale * (shift + scale * x)
// = diff_scale * shift + diff_scale * scale * x
caffe_gpu_axpby(
count,
diff_scale_ * scale_,
bottom_data,
Dtype(0),
bottom_diff);
if (shift_ != Dtype(0)) {
caffe_gpu_add_scalar(count, diff_scale_ * shift_, bottom_diff);
}
} else if (shift_ == Dtype(0)) {
// Special case for y = (scale * x)^power
// -> dy/dx = scale * power * (scale * x)^(power - 1)
// = scale * power * (scale * x)^power * (scale * x)^(-1)
// = power * y / x
const Dtype* top_data = top[0]->gpu_data();
caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
caffe_gpu_scal(count, power_, bottom_diff);
} else {
caffe_copy(count, bottom_data, bottom_diff);
if (scale_ != Dtype(1)) {
caffe_gpu_scal(count, scale_, bottom_diff);
}
if (shift_ != Dtype(0)) {
caffe_gpu_add_scalar(count, shift_, bottom_diff);
}
const Dtype* top_data = top[0]->gpu_data();
caffe_gpu_div<Dtype>(count, top_data, bottom_diff, bottom_diff);
if (diff_scale_ != Dtype(1)) {
caffe_gpu_scal(count, diff_scale_, bottom_diff);
}
}
}
caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
}
}
void Blob<Dtype>::scale_diff(Dtype scale_factor) {
Dtype* diff;
if (!diff_) { return; }
switch (diff_->head()) {
case SyncedMemory::SYNCED_PRV:
case SyncedMemory::HEAD_AT_PRV:
diff = mutable_prv_diff();
caffe_scal(prv_diff_count(), scale_factor, diff);
break;
case SyncedMemory::HEAD_AT_CPU:
diff = mutable_cpu_diff();
caffe_scal(count_, scale_factor, diff);
return;
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
diff = mutable_gpu_diff();
caffe_gpu_scal(count_, scale_factor, diff);
return;
#else
NO_GPU;
#endif
case SyncedMemory::UNINITIALIZED:
return;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << diff_->head();
}
}
void NCCL<Dtype>::run(int layer) {
CHECK(solver_->param().layer_wise_reduce());
vector<shared_ptr<Blob<Dtype> > >& blobs =
solver_->net()->layers()[layer]->blobs();
#ifdef DEBUG
// Assert blobs are contiguous to reduce in one step (e.g. bias often small)
for (int i = 1; i < blobs.size(); ++i) {
CHECK_EQ(blobs[i - 1]->gpu_diff() + blobs[i - 1]->count(),
blobs[i + 0]->gpu_diff());
}
#endif
if (blobs.size() > 0) {
// Make sure default stream is done computing gradients. Could be
// replaced by cudaEventRecord+cudaStreamWaitEvent to avoid
// blocking the default stream, but it's actually slower.
CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
// Reduce asynchronously
int size = 0;
for (int i = 0; i < blobs.size(); ++i) {
size += blobs[i]->count();
}
if (barrier_) { // NULL in multi process case
barrier_->wait();
}
NCCL_CHECK(ncclAllReduce(blobs[0]->mutable_gpu_diff(),
blobs[0]->mutable_gpu_diff(),
size,
nccl::dataType<Dtype>::type,
ncclSum, comm_, stream_));
caffe_gpu_scal(size, (Dtype) 1.0 / Caffe::solver_count(),
blobs[0]->mutable_gpu_diff(), stream_);
}
}
void P2PSync<Dtype>::on_gradients_ready(Timer* timer, ostringstream* timing) {
#ifndef CPU_ONLY
#ifdef DEBUG
int device;
CUDA_CHECK(cudaGetDevice(&device));
CHECK(device == solver_->param().device_id());
#endif
// Sum children gradients as they appear in the queue
for (int i = 0; i < children_.size(); ++i) {
timer->Start();
P2PSync<Dtype> *child = queue_.pop();
Dtype* src = child->parent_grads_;
Dtype* dst = diff_;
#ifdef DEBUG
bool ok = false;
for (int j = 0; j < children_.size(); ++j) {
if (child == children_[j]) {
ok = true;
}
}
CHECK(ok);
cudaPointerAttributes attributes;
CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
CHECK(attributes.device == device);
CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
CHECK(attributes.device == device);
#endif
caffe_gpu_add(size_, src, dst, dst);
*timing << " add_grad: " << timer->MilliSeconds();
}
// Send gradients to parent
if (parent_) {
timer->Start();
Dtype* src = diff_;
Dtype* dst = parent_grads_;
#ifdef DEBUG
cudaPointerAttributes attributes;
CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
CHECK(attributes.device == device);
CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
CHECK(attributes.device == parent_->solver_->param().device_id());
#endif
CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype), //
cudaMemcpyDeviceToDevice, cudaStreamDefault));
CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
parent_->queue_.push(this);
*timing << " send_grad: " << timer->MilliSeconds();
} else {
// Loss functions divide gradients by the batch size, so to compensate
// for split batch, the root solver divides by number of solvers.
caffe_gpu_scal(size_, Dtype(1.0 / Caffe::solver_count()), diff_);
}
#endif
}
void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
if (!propagate_down[0]) { return; }
const int count = bottom[0]->count();
const Dtype* bottom_data = bottom[0]->gpu_data();
const Dtype* top_diff = top[0]->gpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
caffe_copy(count, bottom_data, bottom_diff);
if (input_scale_ != Dtype(1)) {
caffe_gpu_scal(count, input_scale_, bottom_diff);
}
if (input_shift_ != Dtype(0)) {
caffe_gpu_add_scalar(count, input_shift_, bottom_diff);
}
caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff);
if (backward_num_scale_ != Dtype(1)) {
caffe_gpu_scal(count, backward_num_scale_, bottom_diff);
}
caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
}
void LogLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const int count = bottom[0]->count();
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
caffe_gpu_log(count, bottom_data, top_data);
} else {
caffe_copy(count, bottom_data, top_data);
if (input_scale_ != Dtype(1)) {
caffe_gpu_scal(count, input_scale_, top_data);
}
if (input_shift_ != Dtype(0)) {
caffe_gpu_add_scalar(count, input_shift_, top_data);
}
caffe_gpu_log(count, top_data, top_data);
}
if (base_scale_ != Dtype(1)) {
caffe_gpu_scal(count, base_scale_, top_data);
}
}
void NCCL<Dtype>::on_gradients_ready() {
if (solver_->param().layer_wise_reduce()) {
CHECK_EQ(solver_->net()->params().size(),
solver_->net()->learnable_params().size())
<< "Layer-wise reduce is not supported for nets with shared weights.";
// Make sure reduction is done before applying gradients
CUDA_CHECK(cudaStreamSynchronize(stream_));
} else {
if (barrier_) { // NULL in multi process case
barrier_->wait();
}
NCCL_CHECK(ncclAllReduce(diff_, diff_, static_cast<int>(size_),
nccl::dataType<Dtype>::type, ncclSum, comm_,
cudaStreamDefault));
caffe_gpu_scal(static_cast<int>(size_),
(Dtype) 1.0 / Caffe::solver_count(), diff_);
}
}
void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
const vector<Blob<Dtype>*>& bottom) {
if (propagate_down[1]) {
LOG(FATAL) << this->type()
<< " Layer cannot backpropagate to label inputs.";
}
if (propagate_down[0]) {
// First, compute the diff
const int count = bottom[0]->count();
const int num = bottom[0]->num();
const Dtype* sigmoid_output_data = sigmoid_output_->gpu_data();
const Dtype* target = bottom[1]->gpu_data();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
caffe_copy(count, sigmoid_output_data, bottom_diff);
caffe_gpu_axpy(count, Dtype(-1), target, bottom_diff);
// Scale down gradient
const Dtype loss_weight = top[0]->cpu_diff()[0];
caffe_gpu_scal(count, loss_weight / num, bottom_diff);
}
}
void PowerLayer<Dtype>::Forward_gpu(
const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
Dtype* top_data = (top)[0]->mutable_gpu_data();
const int count = bottom[0]->count();
// Special case where we can ignore the input: scale or power is 0.
if (diff_scale_ == Dtype(0)) {
Dtype value = (power_ == 0) ? Dtype(1) : pow(shift_, power_);
caffe_gpu_set(count, value, top_data);
return;
}
const Dtype* bottom_data = bottom[0]->gpu_data();
caffe_copy(count, bottom_data, top_data);
if (scale_ != Dtype(1)) {
caffe_gpu_scal(count, scale_, top_data);
}
if (shift_ != Dtype(0)) {
caffe_gpu_add_scalar(count, shift_, top_data);
}
if (power_ != Dtype(1)) {
caffe_gpu_powx(count, top_data, power_, top_data);
}
}
void Blob<Dtype>::scale_data(Dtype scale_factor) {
Dtype* data;
if (!data_) { return; }
switch (data_->head()) {
case SyncedMemory::HEAD_AT_CPU:
data = mutable_cpu_data();
caffe_scal(count_, scale_factor, data);
return;
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED:
#ifndef CPU_ONLY
data = mutable_gpu_data();
caffe_gpu_scal(count_, scale_factor, data);
return;
#else
NO_GPU;
#endif
case SyncedMemory::UNINITIALIZED:
return;
default:
LOG(FATAL) << "Unknown SyncedMemory head state: " << data_->head();
}
}
void InnerProductLayer<Dtype>::normalize_weights(Dtype mnorm) {
Dtype *weight = 0;
int M = this->blobs_[0]->height();
int N = this->blobs_[0]->width();
int off = this->blobs_[0]->offset(0, 0, 0, 1);
switch (Caffe::mode()) {
case Caffe::CPU:
// apply the constraint to each column
weight = this->blobs_[0]->mutable_cpu_data();
for (int i = 0; i < N; ++i) {
// compute l2 norm
Dtype nrm = caffe_cpu_norm2(M, weight, N);
if (nrm > mnorm) {
// and scale
caffe_scal(M, mnorm / (nrm + Dtype(1e-7)), weight, N);
}
weight += off;
}
break;
case Caffe::GPU:
// apply the constraint to each column
weight = this->blobs_[0]->mutable_gpu_data();
for (int i = 0; i < N; ++i) {
// compute l2 norm
Dtype nrm = caffe_gpu_norm2(M, weight, N);
if (nrm > mnorm) {
// and scale
caffe_gpu_scal(M, mnorm / (nrm + Dtype(1e-7)), weight, N);
}
weight += off;
}
break;
default:
LOG(FATAL) << "Unknown caffe mode.";
break;
}
}
void SGDSolver<Dtype>::Normalize(int param_id) {
if (this->param_.iter_size() == 1) {
return;
}
// Scale gradient to counterbalance accumulation.
const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
switch (Caffe::mode()) {
case Caffe::CPU: {
caffe_scal(net_params[param_id]->count(), accum_normalization,
net_params[param_id]->mutable_cpu_diff());
break;
}
case Caffe::GPU: {
#ifndef CPU_ONLY
if (this->device_->backend() == BACKEND_CUDA) {
#ifdef USE_CUDA
caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
net_params[param_id]->mutable_gpu_diff());
#endif // USE_CUDA
} else {
#ifdef USE_GREENTEA
greentea_gpu_scal(this->device_->id(),
net_params[param_id]->count(), accum_normalization,
(cl_mem) (net_params[param_id]->mutable_gpu_diff()),
0);
#endif // USE_GREENTEA
}
#else
NO_GPU;
#endif
break;
}
default:
LOG(FATAL)<< "Unknown caffe mode: " << Caffe::mode();
}
}
void SGDSolver<Dtype>::Normalize(int param_id) {
if (this->param_.iter_size() == 1) { return; }
// Scale gradient to counterbalance accumulation.
const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
switch (Caffe::mode()) {
case Caffe::CPU: {
caffe_scal(net_params[param_id]->count(), accum_normalization,
net_params[param_id]->mutable_cpu_diff());
break;
}
case Caffe::GPU: {
#ifndef CPU_ONLY
caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
net_params[param_id]->mutable_gpu_diff());
#else
NO_GPU;
#endif
break;
}
default:
LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
}
}
void Blob<Dtype>::scale_diff(Dtype scale_factor) {
Dtype* diff;
if (!diff_) {
return;
}
switch (diff_->head()) {
case SyncedMemory::HEAD_AT_CPU: {
diff = mutable_cpu_diff();
caffe_scal(count_, scale_factor, diff);
return;
}
case SyncedMemory::HEAD_AT_GPU:
case SyncedMemory::SYNCED: {
#ifndef CPU_ONLY
diff = mutable_gpu_diff();
if (device_->backend() == Backend::BACKEND_CUDA) {
#ifdef USE_CUDA
caffe_gpu_scal(count_, scale_factor, diff);
#endif
} else {
#ifdef USE_GREENTEA
greentea_gpu_scal(device_->id(), count_, scale_factor,
(cl_mem) diff, 0);
#endif
}
return;
#else
NO_GPU;
#endif
}
case SyncedMemory::UNINITIALIZED:
return;
default:
LOG(FATAL)<< "Unknown SyncedMemory head state: " << diff_->head();
}
}
