Multi-GPU HostDeviceVector. (#3287)
* Multi-GPU HostDeviceVector. - HostDeviceVector instances can now span multiple devices, defined by GPUSet struct - the interface of HostDeviceVector has been modified accordingly - GPU objective functions are now multi-GPU - GPU predicting from cache is now multi-GPU - avoiding omp_set_num_threads() calls - other minor changes
This commit is contained in:
Andrew V. Adinetz
Rory Mitchell
parent
90a5c4db9d
commit
b8a0d66fe6
@ -1004,14 +1004,29 @@ class AllReducer {
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template <typename T, typename FunctionT>
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void ExecuteShards(std::vector<T> *shards, FunctionT f) {
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auto previous_num_threads = omp_get_max_threads();
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omp_set_num_threads(shards->size());
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#pragma omp parallel
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{
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auto cpu_thread_id = omp_get_thread_num();
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f(shards->at(cpu_thread_id));
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#pragma omp parallel for schedule(static, 1)
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for (int shard = 0; shard < shards->size(); ++shard) {
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f(shards->at(shard));
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}
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}
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/**
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* \brief Executes some operation on each element of the input vector, using a
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* single controlling thread for each element. In addition, passes the shard index
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* into the function.
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*
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* \tparam T Generic type parameter.
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* \tparam FunctionT Type of the function t.
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* \param shards The shards.
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* \param f The func_t to process.
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*/
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template <typename T, typename FunctionT>
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void ExecuteIndexShards(std::vector<T> *shards, FunctionT f) {
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#pragma omp parallel for schedule(static, 1)
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for (int shard = 0; shard < shards->size(); ++shard) {
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f(shard, shards->at(shard));
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}
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omp_set_num_threads(previous_num_threads);
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}
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/**
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@ -1029,15 +1044,11 @@ void ExecuteShards(std::vector<T> *shards, FunctionT f) {
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template <typename ReduceT,typename T, typename FunctionT>
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ReduceT ReduceShards(std::vector<T> *shards, FunctionT f) {
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auto previous_num_threads = omp_get_max_threads();
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omp_set_num_threads(shards->size());
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std::vector<ReduceT> sums(shards->size());
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#pragma omp parallel
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{
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auto cpu_thread_id = omp_get_thread_num();
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sums[cpu_thread_id] = f(shards->at(cpu_thread_id));
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#pragma omp parallel for schedule(static, 1)
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for (int shard = 0; shard < shards->size(); ++shard) {
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sums[shard] = f(shards->at(shard));
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}
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omp_set_num_threads(previous_num_threads);
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return std::accumulate(sums.begin(), sums.end(), ReduceT());
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}
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} // namespace dh
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@ -21,18 +21,18 @@ struct HostDeviceVectorImpl {
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};
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template <typename T>
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HostDeviceVector<T>::HostDeviceVector(size_t size, T v, int device) : impl_(nullptr) {
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HostDeviceVector<T>::HostDeviceVector(size_t size, T v, GPUSet devices) : impl_(nullptr) {
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impl_ = new HostDeviceVectorImpl<T>(size, v);
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}
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template <typename T>
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HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, int device)
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HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, GPUSet devices)
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: impl_(nullptr) {
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impl_ = new HostDeviceVectorImpl<T>(init);
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}
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template <typename T>
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HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, int device)
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HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, GPUSet devices)
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: impl_(nullptr) {
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impl_ = new HostDeviceVectorImpl<T>(init);
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}
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@ -48,7 +48,7 @@ template <typename T>
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size_t HostDeviceVector<T>::Size() const { return impl_->data_h_.size(); }
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template <typename T>
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int HostDeviceVector<T>::DeviceIdx() const { return -1; }
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GPUSet HostDeviceVector<T>::Devices() const { return GPUSet::Empty(); }
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template <typename T>
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T* HostDeviceVector<T>::DevicePointer(int device) { return nullptr; }
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@ -57,13 +57,46 @@ template <typename T>
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std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->data_h_; }
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template <typename T>
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void HostDeviceVector<T>::Resize(size_t new_size, T v, int new_device) {
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void HostDeviceVector<T>::Resize(size_t new_size, T v) {
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impl_->data_h_.resize(new_size, v);
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}
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template <typename T>
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size_t HostDeviceVector<T>::DeviceStart(int device) { return 0; }
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template <typename T>
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size_t HostDeviceVector<T>::DeviceSize(int device) { return 0; }
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template <typename T>
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void HostDeviceVector<T>::Fill(T v) {
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std::fill(HostVector().begin(), HostVector().end(), v);
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}
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template <typename T>
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void HostDeviceVector<T>::Copy(HostDeviceVector<T>* other) {
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CHECK_EQ(Size(), other->Size());
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std::copy(other->HostVector().begin(), other->HostVector().end(), HostVector().begin());
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}
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template <typename T>
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void HostDeviceVector<T>::Copy(const std::vector<T>& other) {
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CHECK_EQ(Size(), other.size());
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std::copy(other.begin(), other.end(), HostVector().begin());
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}
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template <typename T>
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void HostDeviceVector<T>::Copy(std::initializer_list<T> other) {
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CHECK_EQ(Size(), other.size());
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std::copy(other.begin(), other.end(), HostVector().begin());
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}
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template <typename T>
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void HostDeviceVector<T>::Reshard(GPUSet devices) { }
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// explicit instantiations are required, as HostDeviceVector isn't header-only
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template class HostDeviceVector<bst_float>;
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template class HostDeviceVector<GradientPair>;
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template class HostDeviceVector<unsigned int>;
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} // namespace xgboost
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@ -2,122 +2,309 @@
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* Copyright 2017 XGBoost contributors
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*/
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#include <thrust/fill.h>
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#include "./host_device_vector.h"
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#include "./device_helpers.cuh"
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namespace xgboost {
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template <typename T>
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struct HostDeviceVectorImpl {
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HostDeviceVectorImpl(size_t size, T v, int device)
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: device_(device), on_d_(device >= 0) {
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if (on_d_) {
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struct DeviceShard {
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DeviceShard() : index_(-1), device_(-1), start_(0), on_d_(false), vec_(nullptr) {}
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static size_t ShardStart(size_t size, int ndevices, int index) {
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size_t portion = dh::DivRoundUp(size, ndevices);
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size_t begin = index * portion;
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begin = begin > size ? size : begin;
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return begin;
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}
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static size_t ShardSize(size_t size, int ndevices, int index) {
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size_t portion = dh::DivRoundUp(size, ndevices);
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size_t begin = index * portion, end = (index + 1) * portion;
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begin = begin > size ? size : begin;
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end = end > size ? size : end;
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return end - begin;
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}
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void Init(HostDeviceVectorImpl<T>* vec, int device) {
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if (vec_ == nullptr) { vec_ = vec; }
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CHECK_EQ(vec, vec_);
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device_ = device;
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index_ = vec_->devices_.Index(device);
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size_t size_h = vec_->Size();
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int ndevices = vec_->devices_.Size();
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start_ = ShardStart(size_h, ndevices, index_);
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size_t size_d = ShardSize(size_h, ndevices, index_);
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dh::safe_cuda(cudaSetDevice(device_));
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data_d_.resize(size, v);
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data_.resize(size_d);
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on_d_ = !vec_->on_h_;
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}
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void ScatterFrom(const T* begin) {
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// TODO(canonizer): avoid full copy of host data
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LazySyncDevice();
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dh::safe_cuda(cudaSetDevice(device_));
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dh::safe_cuda(cudaMemcpy(data_.data().get(), begin + start_,
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data_.size() * sizeof(T), cudaMemcpyDefault));
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}
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void GatherTo(thrust::device_ptr<T> begin) {
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LazySyncDevice();
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dh::safe_cuda(cudaSetDevice(device_));
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dh::safe_cuda(cudaMemcpy(begin.get() + start_, data_.data().get(),
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data_.size() * sizeof(T), cudaMemcpyDefault));
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}
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void Fill(T v) {
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// TODO(canonizer): avoid full copy of host data
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LazySyncDevice();
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dh::safe_cuda(cudaSetDevice(device_));
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thrust::fill(data_.begin(), data_.end(), v);
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}
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void Copy(DeviceShard* other) {
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// TODO(canonizer): avoid full copy of host data for this (but not for other)
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LazySyncDevice();
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other->LazySyncDevice();
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dh::safe_cuda(cudaSetDevice(device_));
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dh::safe_cuda(cudaMemcpy(data_.data().get(), other->data_.data().get(),
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data_.size() * sizeof(T), cudaMemcpyDefault));
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}
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void LazySyncHost() {
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dh::safe_cuda(cudaSetDevice(device_));
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thrust::copy(data_.begin(), data_.end(), vec_->data_h_.begin() + start_);
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on_d_ = false;
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}
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void LazySyncDevice() {
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if (on_d_) { return; }
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// data is on the host
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size_t size_h = vec_->data_h_.size();
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int ndevices = vec_->devices_.Size();
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start_ = ShardStart(size_h, ndevices, index_);
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size_t size_d = ShardSize(size_h, ndevices, index_);
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dh::safe_cuda(cudaSetDevice(device_));
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data_.resize(size_d);
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thrust::copy(vec_->data_h_.begin() + start_,
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vec_->data_h_.begin() + start_ + size_d, data_.begin());
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on_d_ = true;
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// this may cause a race condition if LazySyncDevice() is called
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// from multiple threads in parallel;
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// however, the race condition is benign, and will not cause problems
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vec_->on_h_ = false;
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vec_->size_d_ = vec_->data_h_.size();
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}
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int index_;
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int device_;
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thrust::device_vector<T> data_;
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size_t start_;
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// true if there is an up-to-date copy of data on device, false otherwise
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bool on_d_;
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HostDeviceVectorImpl<T>* vec_;
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};
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HostDeviceVectorImpl(size_t size, T v, GPUSet devices)
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: devices_(devices), on_h_(devices.IsEmpty()), size_d_(0) {
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if (!devices.IsEmpty()) {
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size_d_ = size;
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InitShards();
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Fill(v);
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} else {
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data_h_.resize(size, v);
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}
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}
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// Init can be std::vector<T> or std::initializer_list<T>
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template <class Init>
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HostDeviceVectorImpl(const Init& init, int device)
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: device_(device), on_d_(device >= 0) {
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if (on_d_) {
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dh::safe_cuda(cudaSetDevice(device_));
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data_d_.resize(init.size());
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thrust::copy(init.begin(), init.end(), data_d_.begin());
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HostDeviceVectorImpl(const Init& init, GPUSet devices)
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: devices_(devices), on_h_(devices.IsEmpty()), size_d_(0) {
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if (!devices.IsEmpty()) {
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size_d_ = init.size();
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InitShards();
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Copy(init);
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} else {
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data_h_ = init;
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}
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}
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void InitShards() {
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int ndevices = devices_.Size();
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shards_.resize(ndevices);
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dh::ExecuteIndexShards(&shards_, [&](int i, DeviceShard& shard) {
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shard.Init(this, devices_[i]);
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});
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}
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HostDeviceVectorImpl(const HostDeviceVectorImpl<T>&) = delete;
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HostDeviceVectorImpl(HostDeviceVectorImpl<T>&&) = delete;
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void operator=(const HostDeviceVectorImpl<T>&) = delete;
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void operator=(HostDeviceVectorImpl<T>&&) = delete;
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size_t Size() const { return on_d_ ? data_d_.size() : data_h_.size(); }
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size_t Size() const { return on_h_ ? data_h_.size() : size_d_; }
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int DeviceIdx() const { return device_; }
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GPUSet Devices() const { return devices_; }
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T* DevicePointer(int device) {
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CHECK(devices_.Contains(device));
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LazySyncDevice(device);
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return data_d_.data().get();
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return shards_[devices_.Index(device)].data_.data().get();
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}
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size_t DeviceSize(int device) {
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CHECK(devices_.Contains(device));
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LazySyncDevice(device);
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return shards_[devices_.Index(device)].data_.size();
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}
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size_t DeviceStart(int device) {
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CHECK(devices_.Contains(device));
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LazySyncDevice(device);
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return shards_[devices_.Index(device)].start_;
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}
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thrust::device_ptr<T> tbegin(int device) { // NOLINT
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return thrust::device_ptr<T>(DevicePointer(device));
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}
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thrust::device_ptr<T> tend(int device) { // NOLINT
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auto begin = tbegin(device);
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return begin + Size();
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return tbegin(device) + DeviceSize(device);
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}
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void ScatterFrom(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
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CHECK_EQ(end - begin, Size());
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if (on_h_) {
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thrust::copy(begin, end, data_h_.begin());
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} else {
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dh::ExecuteShards(&shards_, [&](DeviceShard& shard) {
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shard.ScatterFrom(begin.get());
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});
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}
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}
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void GatherTo(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
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CHECK_EQ(end - begin, Size());
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if (on_h_) {
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thrust::copy(data_h_.begin(), data_h_.end(), begin);
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} else {
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dh::ExecuteShards(&shards_, [&](DeviceShard& shard) { shard.GatherTo(begin); });
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}
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}
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void Fill(T v) {
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if (on_h_) {
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std::fill(data_h_.begin(), data_h_.end(), v);
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} else {
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dh::ExecuteShards(&shards_, [&](DeviceShard& shard) { shard.Fill(v); });
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}
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}
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void Copy(HostDeviceVectorImpl<T>* other) {
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CHECK_EQ(Size(), other->Size());
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if (on_h_ && other->on_h_) {
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std::copy(other->data_h_.begin(), other->data_h_.end(), data_h_.begin());
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} else {
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CHECK(devices_ == other->devices_);
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dh::ExecuteIndexShards(&shards_, [&](int i, DeviceShard& shard) {
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shard.Copy(&other->shards_[i]);
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});
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}
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}
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void Copy(const std::vector<T>& other) {
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CHECK_EQ(Size(), other.size());
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if (on_h_) {
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std::copy(other.begin(), other.end(), data_h_.begin());
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} else {
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dh::ExecuteShards(&shards_, [&](DeviceShard& shard) {
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shard.ScatterFrom(other.data());
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});
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}
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}
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void Copy(std::initializer_list<T> other) {
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CHECK_EQ(Size(), other.size());
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if (on_h_) {
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std::copy(other.begin(), other.end(), data_h_.begin());
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} else {
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dh::ExecuteShards(&shards_, [&](DeviceShard& shard) {
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shard.ScatterFrom(other.begin());
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});
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}
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}
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std::vector<T>& HostVector() {
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LazySyncHost();
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return data_h_;
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}
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void Resize(size_t new_size, T v, int new_device) {
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if (new_size == this->Size() && new_device == device_)
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void Reshard(GPUSet new_devices) {
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if (devices_ == new_devices)
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return;
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if (new_device != -1)
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device_ = new_device;
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// if !on_d_, but the data size is 0 and the device is set,
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// resize the data on device instead
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if (!on_d_ && (data_h_.size() > 0 || device_ == -1)) {
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data_h_.resize(new_size, v);
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CHECK(devices_.IsEmpty());
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devices_ = new_devices;
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InitShards();
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}
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void Resize(size_t new_size, T v) {
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if (new_size == Size())
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return;
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if (Size() == 0 && !devices_.IsEmpty()) {
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// fast on-device resize
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on_h_ = false;
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size_d_ = new_size;
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InitShards();
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Fill(v);
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} else {
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dh::safe_cuda(cudaSetDevice(device_));
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data_d_.resize(new_size, v);
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on_d_ = true;
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// resize on host
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LazySyncHost();
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data_h_.resize(new_size, v);
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}
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}
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void LazySyncHost() {
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if (!on_d_)
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if (on_h_)
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return;
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if (data_h_.size() != this->Size())
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data_h_.resize(this->Size());
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dh::safe_cuda(cudaSetDevice(device_));
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thrust::copy(data_d_.begin(), data_d_.end(), data_h_.begin());
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on_d_ = false;
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if (data_h_.size() != size_d_)
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data_h_.resize(size_d_);
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dh::ExecuteShards(&shards_, [&](DeviceShard& shard) { shard.LazySyncHost(); });
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on_h_ = true;
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}
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void LazySyncDevice(int device) {
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if (on_d_)
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return;
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if (device != device_) {
|
||||
CHECK_EQ(device_, -1);
|
||||
device_ = device;
|
||||
}
|
||||
if (data_d_.size() != this->Size()) {
|
||||
dh::safe_cuda(cudaSetDevice(device_));
|
||||
data_d_.resize(this->Size());
|
||||
}
|
||||
dh::safe_cuda(cudaSetDevice(device_));
|
||||
thrust::copy(data_h_.begin(), data_h_.end(), data_d_.begin());
|
||||
on_d_ = true;
|
||||
CHECK(devices_.Contains(device));
|
||||
shards_[devices_.Index(device)].LazySyncDevice();
|
||||
}
|
||||
|
||||
std::vector<T> data_h_;
|
||||
thrust::device_vector<T> data_d_;
|
||||
// true if there is an up-to-date copy of data on device, false otherwise
|
||||
bool on_d_;
|
||||
int device_;
|
||||
bool on_h_;
|
||||
// the total size of the data stored on the devices
|
||||
size_t size_d_;
|
||||
GPUSet devices_;
|
||||
std::vector<DeviceShard> shards_;
|
||||
};
|
||||
|
||||
template <typename T>
|
||||
HostDeviceVector<T>::HostDeviceVector(size_t size, T v, int device) : impl_(nullptr) {
|
||||
impl_ = new HostDeviceVectorImpl<T>(size, v, device);
|
||||
HostDeviceVector<T>::HostDeviceVector(size_t size, T v, GPUSet devices)
|
||||
: impl_(nullptr) {
|
||||
impl_ = new HostDeviceVectorImpl<T>(size, v, devices);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, int device)
|
||||
HostDeviceVector<T>::HostDeviceVector(std::initializer_list<T> init, GPUSet devices)
|
||||
: impl_(nullptr) {
|
||||
impl_ = new HostDeviceVectorImpl<T>(init, device);
|
||||
impl_ = new HostDeviceVectorImpl<T>(init, devices);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, int device)
|
||||
HostDeviceVector<T>::HostDeviceVector(const std::vector<T>& init, GPUSet devices)
|
||||
: impl_(nullptr) {
|
||||
impl_ = new HostDeviceVectorImpl<T>(init, device);
|
||||
impl_ = new HostDeviceVectorImpl<T>(init, devices);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
@ -131,11 +318,17 @@ template <typename T>
|
||||
size_t HostDeviceVector<T>::Size() const { return impl_->Size(); }
|
||||
|
||||
template <typename T>
|
||||
int HostDeviceVector<T>::DeviceIdx() const { return impl_->DeviceIdx(); }
|
||||
GPUSet HostDeviceVector<T>::Devices() const { return impl_->Devices(); }
|
||||
|
||||
template <typename T>
|
||||
T* HostDeviceVector<T>::DevicePointer(int device) { return impl_->DevicePointer(device); }
|
||||
|
||||
template <typename T>
|
||||
size_t HostDeviceVector<T>::DeviceStart(int device) { return impl_->DeviceStart(device); }
|
||||
|
||||
template <typename T>
|
||||
size_t HostDeviceVector<T>::DeviceSize(int device) { return impl_->DeviceSize(device); }
|
||||
|
||||
template <typename T>
|
||||
thrust::device_ptr<T> HostDeviceVector<T>::tbegin(int device) { // NOLINT
|
||||
return impl_->tbegin(device);
|
||||
@ -146,16 +339,54 @@ thrust::device_ptr<T> HostDeviceVector<T>::tend(int device) { // NOLINT
|
||||
return impl_->tend(device);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::ScatterFrom
|
||||
(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
|
||||
impl_->ScatterFrom(begin, end);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::GatherTo
|
||||
(thrust::device_ptr<T> begin, thrust::device_ptr<T> end) {
|
||||
impl_->GatherTo(begin, end);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::Fill(T v) {
|
||||
impl_->Fill(v);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::Copy(HostDeviceVector<T>* other) {
|
||||
impl_->Copy(other->impl_);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::Copy(const std::vector<T>& other) {
|
||||
impl_->Copy(other);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::Copy(std::initializer_list<T> other) {
|
||||
impl_->Copy(other);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
std::vector<T>& HostDeviceVector<T>::HostVector() { return impl_->HostVector(); }
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::Resize(size_t new_size, T v, int new_device) {
|
||||
impl_->Resize(new_size, v, new_device);
|
||||
void HostDeviceVector<T>::Reshard(GPUSet new_devices) {
|
||||
impl_->Reshard(new_devices);
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
void HostDeviceVector<T>::Resize(size_t new_size, T v) {
|
||||
impl_->Resize(new_size, v);
|
||||
}
|
||||
|
||||
// explicit instantiations are required, as HostDeviceVector isn't header-only
|
||||
template class HostDeviceVector<bst_float>;
|
||||
template class HostDeviceVector<GradientPair>;
|
||||
template class HostDeviceVector<unsigned int>;
|
||||
|
||||
} // namespace xgboost
|
||||
|
||||
@ -4,6 +4,9 @@
|
||||
#ifndef XGBOOST_COMMON_HOST_DEVICE_VECTOR_H_
|
||||
#define XGBOOST_COMMON_HOST_DEVICE_VECTOR_H_
|
||||
|
||||
#include <dmlc/logging.h>
|
||||
|
||||
#include <algorithm>
|
||||
#include <cstdlib>
|
||||
#include <initializer_list>
|
||||
#include <vector>
|
||||
@ -18,6 +21,40 @@ namespace xgboost {
|
||||
|
||||
template <typename T> struct HostDeviceVectorImpl;
|
||||
|
||||
// set of devices across which HostDeviceVector can be distributed;
|
||||
// currently implemented as a range, but can be changed later to something else,
|
||||
// e.g. a bitset
|
||||
class GPUSet {
|
||||
public:
|
||||
explicit GPUSet(int start = 0, int ndevices = 0)
|
||||
: start_(start), ndevices_(ndevices) {}
|
||||
static GPUSet Empty() { return GPUSet(); }
|
||||
static GPUSet Range(int start, int ndevices) { return GPUSet(start, ndevices); }
|
||||
int Size() const { return ndevices_; }
|
||||
int operator[](int index) const {
|
||||
CHECK(index >= 0 && index < ndevices_);
|
||||
return start_ + index;
|
||||
}
|
||||
bool IsEmpty() const { return ndevices_ <= 0; }
|
||||
int Index(int device) const {
|
||||
CHECK(device >= start_ && device < start_ + ndevices_);
|
||||
return device - start_;
|
||||
}
|
||||
bool Contains(int device) const {
|
||||
return start_ <= device && device < start_ + ndevices_;
|
||||
}
|
||||
friend bool operator==(GPUSet a, GPUSet b) {
|
||||
return a.start_ == b.start_ && a.ndevices_ == b.ndevices_;
|
||||
}
|
||||
friend bool operator!=(GPUSet a, GPUSet b) {
|
||||
return a.start_ != b.start_ || a.ndevices_ != b.ndevices_;
|
||||
}
|
||||
|
||||
private:
|
||||
int start_, ndevices_;
|
||||
};
|
||||
|
||||
|
||||
/**
|
||||
* @file host_device_vector.h
|
||||
* @brief A device-and-host vector abstraction layer.
|
||||
@ -29,24 +66,26 @@ template <typename T> struct HostDeviceVectorImpl;
|
||||
*
|
||||
* Initialization/Allocation:<br/>
|
||||
* One can choose to initialize the vector on CPU or GPU during constructor.
|
||||
* (use the 'device' argument) Or, can choose to use the 'resize' method to
|
||||
* allocate/resize memory explicitly.
|
||||
* (use the 'devices' argument) Or, can choose to use the 'Resize' method to
|
||||
* allocate/resize memory explicitly, and use the 'Reshard' method
|
||||
* to specify the devices.
|
||||
*
|
||||
* Accessing underling data:<br/>
|
||||
* Use 'data_h' method to explicitly query for the underlying std::vector.
|
||||
* If you need the raw device pointer, use the 'ptr_d' method. For perf
|
||||
* Accessing underlying data:<br/>
|
||||
* Use 'HostVector' method to explicitly query for the underlying std::vector.
|
||||
* If you need the raw device pointer, use the 'DevicePointer' method. For perf
|
||||
* implications of these calls, see below.
|
||||
*
|
||||
* Accessing underling data and their perf implications:<br/>
|
||||
* There are 4 scenarios to be considered here:
|
||||
* data_h and data on CPU --> no problems, std::vector returned immediately
|
||||
* data_h but data on GPU --> this causes a cudaMemcpy to be issued internally.
|
||||
* subsequent calls to data_h, will NOT incur this penalty.
|
||||
* (assuming 'ptr_d' is not called in between)
|
||||
* ptr_d but data on CPU --> this causes a cudaMemcpy to be issued internally.
|
||||
* subsequent calls to ptr_d, will NOT incur this penalty.
|
||||
* (assuming 'data_h' is not called in between)
|
||||
* ptr_d and data on GPU --> no problems, the device ptr will be returned immediately
|
||||
* HostVector and data on CPU --> no problems, std::vector returned immediately
|
||||
* HostVector but data on GPU --> this causes a cudaMemcpy to be issued internally.
|
||||
* subsequent calls to HostVector, will NOT incur this penalty.
|
||||
* (assuming 'DevicePointer' is not called in between)
|
||||
* DevicePointer but data on CPU --> this causes a cudaMemcpy to be issued internally.
|
||||
* subsequent calls to DevicePointer, will NOT incur this penalty.
|
||||
* (assuming 'HostVector' is not called in between)
|
||||
* DevicePointer and data on GPU --> no problems, the device ptr
|
||||
* will be returned immediately.
|
||||
*
|
||||
* What if xgboost is compiled without CUDA?<br/>
|
||||
* In that case, there's a special implementation which always falls-back to
|
||||
@ -57,35 +96,49 @@ template <typename T> struct HostDeviceVectorImpl;
|
||||
* compiling with and without CUDA toolkit. It was easier to have
|
||||
* 'HostDeviceVector' with a special-case implementation in host_device_vector.cc
|
||||
*
|
||||
* @note: This is not thread-safe!
|
||||
* @note: Size and Devices methods are thread-safe.
|
||||
* DevicePointer, DeviceStart, DeviceSize, tbegin and tend methods are thread-safe
|
||||
* if different threads call these methods with different values of the device argument.
|
||||
* All other methods are not thread safe.
|
||||
*/
|
||||
template <typename T>
|
||||
class HostDeviceVector {
|
||||
public:
|
||||
explicit HostDeviceVector(size_t size = 0, T v = T(), int device = -1);
|
||||
HostDeviceVector(std::initializer_list<T> init, int device = -1);
|
||||
explicit HostDeviceVector(const std::vector<T>& init, int device = -1);
|
||||
explicit HostDeviceVector(size_t size = 0, T v = T(),
|
||||
GPUSet devices = GPUSet::Empty());
|
||||
HostDeviceVector(std::initializer_list<T> init, GPUSet devices = GPUSet::Empty());
|
||||
explicit HostDeviceVector(const std::vector<T>& init,
|
||||
GPUSet devices = GPUSet::Empty());
|
||||
~HostDeviceVector();
|
||||
HostDeviceVector(const HostDeviceVector<T>&) = delete;
|
||||
HostDeviceVector(HostDeviceVector<T>&&) = delete;
|
||||
void operator=(const HostDeviceVector<T>&) = delete;
|
||||
void operator=(HostDeviceVector<T>&&) = delete;
|
||||
size_t Size() const;
|
||||
int DeviceIdx() const;
|
||||
GPUSet Devices() const;
|
||||
T* DevicePointer(int device);
|
||||
|
||||
T* HostPointer() { return HostVector().data(); }
|
||||
size_t DeviceStart(int device);
|
||||
size_t DeviceSize(int device);
|
||||
|
||||
// only define functions returning device_ptr
|
||||
// if HostDeviceVector.h is included from a .cu file
|
||||
#ifdef __CUDACC__
|
||||
thrust::device_ptr<T> tbegin(int device);
|
||||
thrust::device_ptr<T> tend(int device);
|
||||
thrust::device_ptr<T> tbegin(int device); // NOLINT
|
||||
thrust::device_ptr<T> tend(int device); // NOLINT
|
||||
void ScatterFrom(thrust::device_ptr<T> begin, thrust::device_ptr<T> end);
|
||||
void GatherTo(thrust::device_ptr<T> begin, thrust::device_ptr<T> end);
|
||||
#endif
|
||||
|
||||
std::vector<T>& HostVector();
|
||||
void Fill(T v);
|
||||
void Copy(HostDeviceVector<T>* other);
|
||||
void Copy(const std::vector<T>& other);
|
||||
void Copy(std::initializer_list<T> other);
|
||||
|
||||
// passing in new_device == -1 keeps the device as is
|
||||
void Resize(size_t new_size, T v = T(), int new_device = -1);
|
||||
std::vector<T>& HostVector();
|
||||
void Reshard(GPUSet devices);
|
||||
void Resize(size_t new_size, T v = T());
|
||||
|
||||
private:
|
||||
HostDeviceVectorImpl<T>* impl_;
|
||||
|
||||
@ -195,7 +195,7 @@ class GBTree : public GradientBooster {
|
||||
<< "must have exactly ngroup*nrow gpairs";
|
||||
// TODO(canonizer): perform this on GPU if HostDeviceVector has device set.
|
||||
HostDeviceVector<GradientPair> tmp(in_gpair->Size() / ngroup,
|
||||
GradientPair(), in_gpair->DeviceIdx());
|
||||
GradientPair(), in_gpair->Devices());
|
||||
std::vector<GradientPair>& gpair_h = in_gpair->HostVector();
|
||||
auto nsize = static_cast<bst_omp_uint>(tmp.Size());
|
||||
for (int gid = 0; gid < ngroup; ++gid) {
|
||||
|
||||
@ -74,46 +74,35 @@ __global__ void pred_transform_k(float * __restrict__ preds, int n) {
|
||||
template<typename Loss>
|
||||
class GPURegLossObj : public ObjFunction {
|
||||
protected:
|
||||
// manages device data
|
||||
struct DeviceData {
|
||||
DVec<float> labels, weights;
|
||||
DVec<unsigned int> label_correct;
|
||||
|
||||
// allocate everything on device
|
||||
DeviceData(dh::BulkAllocator<dh::MemoryType::kDevice>* ba, int device_idx, size_t n) {
|
||||
ba->Allocate(device_idx, false,
|
||||
&labels, n,
|
||||
&weights, n,
|
||||
&label_correct, 1);
|
||||
}
|
||||
size_t Size() const { return labels.Size(); }
|
||||
};
|
||||
|
||||
|
||||
bool copied_;
|
||||
std::unique_ptr<dh::BulkAllocator<dh::MemoryType::kDevice>> ba_;
|
||||
std::unique_ptr<DeviceData> data_;
|
||||
HostDeviceVector<bst_float> preds_d_;
|
||||
HostDeviceVector<GradientPair> out_gpair_d_;
|
||||
HostDeviceVector<bst_float> labels_, weights_;
|
||||
HostDeviceVector<unsigned int> label_correct_;
|
||||
|
||||
// allocate device data for n elements, do nothing if enough memory is allocated already
|
||||
void LazyResize(int n) {
|
||||
if (data_.get() != nullptr && data_->Size() >= n)
|
||||
// allocate device data for n elements, do nothing if memory is allocated already
|
||||
void LazyResize(size_t n, size_t n_weights) {
|
||||
if (labels_.Size() == n && weights_.Size() == n_weights)
|
||||
return;
|
||||
copied_ = false;
|
||||
// free the old data and allocate the new data
|
||||
ba_.reset(new dh::BulkAllocator<dh::MemoryType::kDevice>());
|
||||
data_.reset(new DeviceData(ba_.get(), 0, n));
|
||||
preds_d_.Resize(n, 0.0f, param_.gpu_id);
|
||||
out_gpair_d_.Resize(n, GradientPair(), param_.gpu_id);
|
||||
|
||||
labels_.Reshard(devices_);
|
||||
weights_.Reshard(devices_);
|
||||
label_correct_.Reshard(devices_);
|
||||
|
||||
if (labels_.Size() != n) {
|
||||
labels_.Resize(n);
|
||||
label_correct_.Resize(devices_.Size());
|
||||
}
|
||||
if (weights_.Size() != n_weights)
|
||||
weights_.Resize(n_weights);
|
||||
}
|
||||
|
||||
public:
|
||||
GPURegLossObj() : copied_(false), preds_d_(0, -1), out_gpair_d_({}, -1) {}
|
||||
GPURegLossObj() : copied_(false) {}
|
||||
|
||||
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
|
||||
param_.InitAllowUnknown(args);
|
||||
CHECK(param_.n_gpus != 0) << "Must have at least one device";
|
||||
devices_ = GPUSet::Range(param_.gpu_id, dh::NDevicesAll(param_.n_gpus));
|
||||
}
|
||||
|
||||
void GetGradient(HostDeviceVector<float>* preds,
|
||||
@ -125,46 +114,50 @@ class GPURegLossObj : public ObjFunction {
|
||||
<< "labels are not correctly provided"
|
||||
<< "preds.size=" << preds->Size() << ", label.size=" << info.labels_.size();
|
||||
size_t ndata = preds->Size();
|
||||
out_gpair->Resize(ndata, GradientPair(), param_.gpu_id);
|
||||
LazyResize(ndata);
|
||||
GetGradientDevice(preds->DevicePointer(param_.gpu_id), info, iter,
|
||||
out_gpair->DevicePointer(param_.gpu_id), ndata);
|
||||
preds->Reshard(devices_);
|
||||
out_gpair->Reshard(devices_);
|
||||
out_gpair->Resize(ndata);
|
||||
LazyResize(ndata, info.weights_.size());
|
||||
GetGradientDevice(preds, info, iter, out_gpair);
|
||||
}
|
||||
|
||||
private:
|
||||
void GetGradientDevice(float* preds,
|
||||
void GetGradientDevice(HostDeviceVector<float>* preds,
|
||||
const MetaInfo &info,
|
||||
int iter,
|
||||
GradientPair* out_gpair, size_t n) {
|
||||
dh::safe_cuda(cudaSetDevice(param_.gpu_id));
|
||||
DeviceData& d = *data_;
|
||||
d.label_correct.Fill(1);
|
||||
HostDeviceVector<GradientPair>* out_gpair) {
|
||||
label_correct_.Fill(1);
|
||||
// only copy the labels and weights once, similar to how the data is copied
|
||||
if (!copied_) {
|
||||
thrust::copy(info.labels_.begin(), info.labels_.begin() + n,
|
||||
d.labels.tbegin());
|
||||
if (info.weights_.size() > 0) {
|
||||
thrust::copy(info.weights_.begin(), info.weights_.begin() + n,
|
||||
d.weights.tbegin());
|
||||
}
|
||||
labels_.Copy(info.labels_);
|
||||
if (info.weights_.size() > 0)
|
||||
weights_.Copy(info.weights_);
|
||||
copied_ = true;
|
||||
}
|
||||
|
||||
// run the kernel
|
||||
const int block = 256;
|
||||
get_gradient_k<Loss><<<dh::DivRoundUp(n, block), block>>>
|
||||
(out_gpair, d.label_correct.Data(), preds,
|
||||
d.labels.Data(), info.weights_.size() > 0 ? d.weights.Data() : nullptr,
|
||||
n, param_.scale_pos_weight);
|
||||
dh::safe_cuda(cudaGetLastError());
|
||||
#pragma omp parallel for schedule(static, 1)
|
||||
for (int i = 0; i < devices_.Size(); ++i) {
|
||||
int d = devices_[i];
|
||||
dh::safe_cuda(cudaSetDevice(d));
|
||||
const int block = 256;
|
||||
size_t n = preds->DeviceSize(d);
|
||||
if (n > 0) {
|
||||
get_gradient_k<Loss><<<dh::DivRoundUp(n, block), block>>>
|
||||
(out_gpair->DevicePointer(d), label_correct_.DevicePointer(d),
|
||||
preds->DevicePointer(d), labels_.DevicePointer(d),
|
||||
info.weights_.size() > 0 ? weights_.DevicePointer(d) : nullptr,
|
||||
n, param_.scale_pos_weight);
|
||||
dh::safe_cuda(cudaGetLastError());
|
||||
}
|
||||
dh::safe_cuda(cudaDeviceSynchronize());
|
||||
}
|
||||
|
||||
// copy output data from the GPU
|
||||
unsigned int label_correct_h;
|
||||
thrust::copy_n(d.label_correct.tbegin(), 1, &label_correct_h);
|
||||
|
||||
bool label_correct = label_correct_h != 0;
|
||||
if (!label_correct) {
|
||||
LOG(FATAL) << Loss::LabelErrorMsg();
|
||||
// copy "label correct" flags back to host
|
||||
std::vector<unsigned int>& label_correct_h = label_correct_.HostVector();
|
||||
for (int i = 0; i < devices_.Size(); ++i) {
|
||||
if (label_correct_h[i] == 0)
|
||||
LOG(FATAL) << Loss::LabelErrorMsg();
|
||||
}
|
||||
}
|
||||
|
||||
@ -174,24 +167,33 @@ class GPURegLossObj : public ObjFunction {
|
||||
}
|
||||
|
||||
void PredTransform(HostDeviceVector<float> *io_preds) override {
|
||||
PredTransformDevice(io_preds->DevicePointer(param_.gpu_id), io_preds->Size());
|
||||
io_preds->Reshard(devices_);
|
||||
size_t ndata = io_preds->Size();
|
||||
PredTransformDevice(io_preds);
|
||||
}
|
||||
|
||||
void PredTransformDevice(float* preds, size_t n) {
|
||||
dh::safe_cuda(cudaSetDevice(param_.gpu_id));
|
||||
const int block = 256;
|
||||
pred_transform_k<Loss><<<dh::DivRoundUp(n, block), block>>>(preds, n);
|
||||
dh::safe_cuda(cudaGetLastError());
|
||||
dh::safe_cuda(cudaDeviceSynchronize());
|
||||
void PredTransformDevice(HostDeviceVector<float>* preds) {
|
||||
#pragma omp parallel for schedule(static, 1)
|
||||
for (int i = 0; i < devices_.Size(); ++i) {
|
||||
int d = devices_[i];
|
||||
dh::safe_cuda(cudaSetDevice(d));
|
||||
const int block = 256;
|
||||
size_t n = preds->DeviceSize(d);
|
||||
if (n > 0) {
|
||||
pred_transform_k<Loss><<<dh::DivRoundUp(n, block), block>>>(preds->DevicePointer(d), n);
|
||||
dh::safe_cuda(cudaGetLastError());
|
||||
}
|
||||
dh::safe_cuda(cudaDeviceSynchronize());
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
float ProbToMargin(float base_score) const override {
|
||||
return Loss::ProbToMargin(base_score);
|
||||
}
|
||||
|
||||
protected:
|
||||
GPURegLossParam param_;
|
||||
GPUSet devices_;
|
||||
};
|
||||
|
||||
// register the objective functions
|
||||
|
||||
@ -310,8 +310,11 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
tree_group.begin());
|
||||
|
||||
device_matrix->predictions.resize(out_preds->Size());
|
||||
thrust::copy(out_preds->tbegin(param.gpu_id), out_preds->tend(param.gpu_id),
|
||||
device_matrix->predictions.begin());
|
||||
auto& predictions = device_matrix->predictions;
|
||||
out_preds->GatherTo(predictions.data(),
|
||||
predictions.data() + predictions.size());
|
||||
|
||||
dh::safe_cuda(cudaSetDevice(param.gpu_id));
|
||||
|
||||
const int BLOCK_THREADS = 128;
|
||||
const int GRID_SIZE = static_cast<int>(
|
||||
@ -335,9 +338,8 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
model.param.num_output_group);
|
||||
|
||||
dh::safe_cuda(cudaDeviceSynchronize());
|
||||
thrust::copy(device_matrix->predictions.begin(),
|
||||
device_matrix->predictions.end(),
|
||||
out_preds->tbegin(param.gpu_id));
|
||||
out_preds->ScatterFrom(predictions.data(),
|
||||
predictions.data() + predictions.size());
|
||||
}
|
||||
|
||||
public:
|
||||
@ -366,14 +368,13 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
const gbm::GBTreeModel& model) const {
|
||||
size_t n = model.param.num_output_group * info.num_row_;
|
||||
const std::vector<bst_float>& base_margin = info.base_margin_;
|
||||
out_preds->Resize(n, 0.0f, param.gpu_id);
|
||||
out_preds->Reshard(devices);
|
||||
out_preds->Resize(n);
|
||||
if (base_margin.size() != 0) {
|
||||
CHECK_EQ(out_preds->Size(), n);
|
||||
thrust::copy(base_margin.begin(), base_margin.end(),
|
||||
out_preds->tbegin(param.gpu_id));
|
||||
out_preds->Copy(base_margin);
|
||||
} else {
|
||||
thrust::fill(out_preds->tbegin(param.gpu_id),
|
||||
out_preds->tend(param.gpu_id), model.base_margin);
|
||||
out_preds->Fill(model.base_margin);
|
||||
}
|
||||
}
|
||||
|
||||
@ -385,11 +386,9 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
if (it != cache_.end()) {
|
||||
HostDeviceVector<bst_float>& y = it->second.predictions;
|
||||
if (y.Size() != 0) {
|
||||
dh::safe_cuda(cudaSetDevice(param.gpu_id));
|
||||
out_preds->Resize(y.Size(), 0.0f, param.gpu_id);
|
||||
dh::safe_cuda(cudaMemcpy(
|
||||
out_preds->DevicePointer(param.gpu_id), y.DevicePointer(param.gpu_id),
|
||||
out_preds->Size() * sizeof(bst_float), cudaMemcpyDefault));
|
||||
out_preds->Reshard(devices);
|
||||
out_preds->Resize(y.Size());
|
||||
out_preds->Copy(&y);
|
||||
return true;
|
||||
}
|
||||
}
|
||||
@ -410,18 +409,15 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
HostDeviceVector<bst_float>& predictions = e.predictions;
|
||||
|
||||
if (predictions.Size() == 0) {
|
||||
// ensure that the device in predictions is correct
|
||||
predictions.Resize(0, 0.0f, param.gpu_id);
|
||||
cpu_predictor->PredictBatch(dmat, &predictions, model, 0,
|
||||
static_cast<bst_uint>(model.trees.size()));
|
||||
} else if (model.param.num_output_group == 1 && updaters->size() > 0 &&
|
||||
num_new_trees == 1 &&
|
||||
updaters->back()->UpdatePredictionCache(e.data.get(),
|
||||
&predictions)) {
|
||||
this->InitOutPredictions(dmat->Info(), &predictions, model);
|
||||
}
|
||||
|
||||
if (model.param.num_output_group == 1 && updaters->size() > 0 &&
|
||||
num_new_trees == 1 &&
|
||||
updaters->back()->UpdatePredictionCache(e.data.get(), &predictions)) {
|
||||
// do nothing
|
||||
} else {
|
||||
DevicePredictInternal(dmat, &predictions, model, old_ntree,
|
||||
model.trees.size());
|
||||
DevicePredictInternal(dmat, &predictions, model, old_ntree, model.trees.size());
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -462,6 +458,7 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
Predictor::Init(cfg, cache);
|
||||
cpu_predictor->Init(cfg, cache);
|
||||
param.InitAllowUnknown(cfg);
|
||||
devices = GPUSet::Range(param.gpu_id, dh::NDevicesAll(param.n_gpus));
|
||||
max_shared_memory_bytes = dh::MaxSharedMemory(param.gpu_id);
|
||||
}
|
||||
|
||||
@ -473,6 +470,8 @@ class GPUPredictor : public xgboost::Predictor {
|
||||
thrust::device_vector<DevicePredictionNode> nodes;
|
||||
thrust::device_vector<size_t> tree_segments;
|
||||
thrust::device_vector<int> tree_group;
|
||||
thrust::device_vector<bst_float> preds;
|
||||
GPUSet devices;
|
||||
size_t max_shared_memory_bytes;
|
||||
};
|
||||
XGBOOST_REGISTER_PREDICTOR(GPUPredictor, "gpu_predictor")
|
||||
|
||||
@ -495,6 +495,11 @@ class GPUMaker : public TreeUpdater {
|
||||
int nCols;
|
||||
int maxNodes;
|
||||
int maxLeaves;
|
||||
|
||||
// devices are only used for resharding the HostDeviceVector passed as a parameter;
|
||||
// the algorithm works with a single GPU only
|
||||
GPUSet devices;
|
||||
|
||||
dh::CubMemory tmp_mem;
|
||||
dh::DVec<GradientPair> tmpScanGradBuff;
|
||||
dh::DVec<int> tmpScanKeyBuff;
|
||||
@ -510,6 +515,8 @@ class GPUMaker : public TreeUpdater {
|
||||
param.InitAllowUnknown(args);
|
||||
maxNodes = (1 << (param.max_depth + 1)) - 1;
|
||||
maxLeaves = 1 << param.max_depth;
|
||||
|
||||
devices = GPUSet::Range(param.gpu_id, dh::NDevicesAll(param.n_gpus));
|
||||
}
|
||||
|
||||
void Update(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
|
||||
@ -519,6 +526,8 @@ class GPUMaker : public TreeUpdater {
|
||||
float lr = param.learning_rate;
|
||||
param.learning_rate = lr / trees.size();
|
||||
|
||||
gpair->Reshard(devices);
|
||||
|
||||
try {
|
||||
// build tree
|
||||
for (size_t i = 0; i < trees.size(); ++i) {
|
||||
@ -688,10 +697,7 @@ class GPUMaker : public TreeUpdater {
|
||||
}
|
||||
|
||||
void transferGrads(HostDeviceVector<GradientPair>* gpair) {
|
||||
// HACK
|
||||
dh::safe_cuda(cudaMemcpy(gradsInst.Data(), gpair->DevicePointer(param.gpu_id),
|
||||
sizeof(GradientPair) * nRows,
|
||||
cudaMemcpyDefault));
|
||||
gpair->GatherTo(gradsInst.tbegin(), gradsInst.tend());
|
||||
// evaluate the full-grad reduction for the root node
|
||||
dh::SumReduction<GradientPair>(tmp_mem, gradsInst, gradSums, nRows);
|
||||
}
|
||||
|
||||
@ -369,8 +369,7 @@ struct DeviceShard {
|
||||
}
|
||||
|
||||
// Reset values for each update iteration
|
||||
void Reset(HostDeviceVector<GradientPair>* dh_gpair, int device) {
|
||||
auto begin = dh_gpair->tbegin(device);
|
||||
void Reset(HostDeviceVector<GradientPair>* dh_gpair) {
|
||||
dh::safe_cuda(cudaSetDevice(device_idx));
|
||||
position.CurrentDVec().Fill(0);
|
||||
std::fill(node_sum_gradients.begin(), node_sum_gradients.end(),
|
||||
@ -380,7 +379,7 @@ struct DeviceShard {
|
||||
|
||||
std::fill(ridx_segments.begin(), ridx_segments.end(), Segment(0, 0));
|
||||
ridx_segments.front() = Segment(0, ridx.Size());
|
||||
this->gpair.copy(begin + row_begin_idx, begin + row_end_idx);
|
||||
this->gpair.copy(dh_gpair->tbegin(device_idx), dh_gpair->tend(device_idx));
|
||||
SubsampleGradientPair(&gpair, param.subsample, row_begin_idx);
|
||||
hist.Reset();
|
||||
}
|
||||
@ -505,7 +504,7 @@ struct DeviceShard {
|
||||
dh::safe_cuda(cudaSetDevice(device_idx));
|
||||
if (!prediction_cache_initialised) {
|
||||
dh::safe_cuda(cudaMemcpy(
|
||||
prediction_cache.Data(), &out_preds_d[row_begin_idx],
|
||||
prediction_cache.Data(), out_preds_d,
|
||||
prediction_cache.Size() * sizeof(bst_float), cudaMemcpyDefault));
|
||||
}
|
||||
prediction_cache_initialised = true;
|
||||
@ -528,7 +527,7 @@ struct DeviceShard {
|
||||
});
|
||||
|
||||
dh::safe_cuda(cudaMemcpy(
|
||||
&out_preds_d[row_begin_idx], prediction_cache.Data(),
|
||||
out_preds_d, prediction_cache.Data(),
|
||||
prediction_cache.Size() * sizeof(bst_float), cudaMemcpyDefault));
|
||||
}
|
||||
};
|
||||
@ -543,6 +542,7 @@ class GPUHistMaker : public TreeUpdater {
|
||||
param_.InitAllowUnknown(args);
|
||||
CHECK(param_.n_gpus != 0) << "Must have at least one device";
|
||||
n_devices_ = param_.n_gpus;
|
||||
devices_ = GPUSet::Range(param_.gpu_id, dh::NDevicesAll(param_.n_gpus));
|
||||
|
||||
dh::CheckComputeCapability();
|
||||
|
||||
@ -610,15 +610,11 @@ class GPUHistMaker : public TreeUpdater {
|
||||
}
|
||||
|
||||
// Create device shards
|
||||
omp_set_num_threads(shards_.size());
|
||||
#pragma omp parallel
|
||||
{
|
||||
auto cpu_thread_id = omp_get_thread_num();
|
||||
shards_[cpu_thread_id] = std::unique_ptr<DeviceShard>(
|
||||
new DeviceShard(device_list_[cpu_thread_id], cpu_thread_id, gmat_,
|
||||
row_segments[cpu_thread_id],
|
||||
row_segments[cpu_thread_id + 1], n_bins_, param_));
|
||||
}
|
||||
dh::ExecuteIndexShards(&shards_, [&](int i, std::unique_ptr<DeviceShard>& shard) {
|
||||
shard = std::unique_ptr<DeviceShard>(
|
||||
new DeviceShard(device_list_[i], i, gmat_,
|
||||
row_segments[i], row_segments[i + 1], n_bins_, param_));
|
||||
});
|
||||
|
||||
p_last_fmat_ = dmat;
|
||||
initialised_ = true;
|
||||
@ -636,12 +632,9 @@ class GPUHistMaker : public TreeUpdater {
|
||||
|
||||
// Copy gpair & reset memory
|
||||
monitor_.Start("InitDataReset", device_list_);
|
||||
omp_set_num_threads(shards_.size());
|
||||
|
||||
// TODO(canonizer): make it parallel again once HostDeviceVector is
|
||||
// thread-safe
|
||||
for (int shard = 0; shard < shards_.size(); ++shard)
|
||||
shards_[shard]->Reset(gpair, param_.gpu_id);
|
||||
gpair->Reshard(devices_);
|
||||
dh::ExecuteShards(&shards_, [&](std::unique_ptr<DeviceShard>& shard) {shard->Reset(gpair); });
|
||||
monitor_.Stop("InitDataReset", device_list_);
|
||||
}
|
||||
|
||||
@ -676,16 +669,16 @@ class GPUHistMaker : public TreeUpdater {
|
||||
subtraction_trick_nidx = nidx_left;
|
||||
}
|
||||
|
||||
for (auto& shard : shards_) {
|
||||
shard->BuildHist(build_hist_nidx);
|
||||
}
|
||||
dh::ExecuteShards(&shards_, [&](std::unique_ptr<DeviceShard>& shard) {
|
||||
shard->BuildHist(build_hist_nidx);
|
||||
});
|
||||
|
||||
this->AllReduceHist(build_hist_nidx);
|
||||
|
||||
for (auto& shard : shards_) {
|
||||
shard->SubtractionTrick(nidx_parent, build_hist_nidx,
|
||||
subtraction_trick_nidx);
|
||||
}
|
||||
dh::ExecuteShards(&shards_, [&](std::unique_ptr<DeviceShard>& shard) {
|
||||
shard->SubtractionTrick(nidx_parent, build_hist_nidx,
|
||||
subtraction_trick_nidx);
|
||||
});
|
||||
}
|
||||
|
||||
// Returns best loss
|
||||
@ -743,22 +736,20 @@ class GPUHistMaker : public TreeUpdater {
|
||||
auto root_nidx = 0;
|
||||
// Sum gradients
|
||||
std::vector<GradientPair> tmp_sums(shards_.size());
|
||||
omp_set_num_threads(shards_.size());
|
||||
#pragma omp parallel
|
||||
{
|
||||
auto cpu_thread_id = omp_get_thread_num();
|
||||
auto& shard = shards_[cpu_thread_id];
|
||||
dh::safe_cuda(cudaSetDevice(shard->device_idx));
|
||||
tmp_sums[cpu_thread_id] = dh::SumReduction(
|
||||
shard->temp_memory, shard->gpair.Data(), shard->gpair.Size());
|
||||
}
|
||||
|
||||
dh::ExecuteIndexShards(&shards_, [&](int i, std::unique_ptr<DeviceShard>& shard) {
|
||||
dh::safe_cuda(cudaSetDevice(shard->device_idx));
|
||||
tmp_sums[i] =
|
||||
dh::SumReduction(shard->temp_memory, shard->gpair.Data(),
|
||||
shard->gpair.Size());
|
||||
});
|
||||
auto sum_gradient =
|
||||
std::accumulate(tmp_sums.begin(), tmp_sums.end(), GradientPair());
|
||||
|
||||
// Generate root histogram
|
||||
for (auto& shard : shards_) {
|
||||
shard->BuildHist(root_nidx);
|
||||
}
|
||||
dh::ExecuteShards(&shards_, [&](std::unique_ptr<DeviceShard>& shard) {
|
||||
shard->BuildHist(root_nidx);
|
||||
});
|
||||
|
||||
this->AllReduceHist(root_nidx);
|
||||
|
||||
@ -802,14 +793,11 @@ class GPUHistMaker : public TreeUpdater {
|
||||
|
||||
auto is_dense = info_->num_nonzero_ == info_->num_row_ * info_->num_col_;
|
||||
|
||||
omp_set_num_threads(shards_.size());
|
||||
#pragma omp parallel
|
||||
{
|
||||
auto cpu_thread_id = omp_get_thread_num();
|
||||
shards_[cpu_thread_id]->UpdatePosition(nidx, left_nidx, right_nidx, fidx,
|
||||
split_gidx, default_dir_left,
|
||||
is_dense, fidx_begin, fidx_end);
|
||||
}
|
||||
dh::ExecuteShards(&shards_, [&](std::unique_ptr<DeviceShard>& shard) {
|
||||
shard->UpdatePosition(nidx, left_nidx, right_nidx, fidx,
|
||||
split_gidx, default_dir_left,
|
||||
is_dense, fidx_begin, fidx_end);
|
||||
});
|
||||
}
|
||||
|
||||
void ApplySplit(const ExpandEntry& candidate, RegTree* p_tree) {
|
||||
@ -903,8 +891,6 @@ class GPUHistMaker : public TreeUpdater {
|
||||
monitor_.Stop("EvaluateSplits", device_list_);
|
||||
}
|
||||
}
|
||||
// Reset omp num threads
|
||||
omp_set_num_threads(nthread);
|
||||
}
|
||||
|
||||
bool UpdatePredictionCache(
|
||||
@ -912,13 +898,10 @@ class GPUHistMaker : public TreeUpdater {
|
||||
monitor_.Start("UpdatePredictionCache", device_list_);
|
||||
if (shards_.empty() || p_last_fmat_ == nullptr || p_last_fmat_ != data)
|
||||
return false;
|
||||
|
||||
bst_float* out_preds_d = p_out_preds->DevicePointer(param_.gpu_id);
|
||||
|
||||
#pragma omp parallel for schedule(static, 1)
|
||||
for (int shard = 0; shard < shards_.size(); ++shard) {
|
||||
shards_[shard]->UpdatePredictionCache(out_preds_d);
|
||||
}
|
||||
p_out_preds->Reshard(devices_);
|
||||
dh::ExecuteShards(&shards_, [&](std::unique_ptr<DeviceShard>& shard) {
|
||||
shard->UpdatePredictionCache(p_out_preds->DevicePointer(shard->device_idx));
|
||||
});
|
||||
monitor_.Stop("UpdatePredictionCache", device_list_);
|
||||
return true;
|
||||
}
|
||||
@ -992,6 +975,7 @@ class GPUHistMaker : public TreeUpdater {
|
||||
std::vector<int> device_list_;
|
||||
|
||||
DMatrix* p_last_fmat_;
|
||||
GPUSet devices_;
|
||||
};
|
||||
|
||||
XGBOOST_REGISTER_TREE_UPDATER(GPUHistMaker, "grow_gpu_hist")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user