GPU plug-in improvements + basic Windows continuous integration (#1752)
* GPU Plugin: Reduce memory, improve performance, fix gcc compiler bug, add out of memory exceptions * Add basic Windows continuous integration for cmake VS2013, VS2015
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RAMitchell
Tianqi Chen
parent
91b75f9b41
commit
e3a7f85f15
@ -99,7 +99,7 @@ if(PLUGIN_UPDATER_GPU)
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include_directories(${CUB_DIRECTORY})
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set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS};--expt-extended-lambda;-arch=compute_35")
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if(NOT MSVC)
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set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS};-std=c++11; -Xcompiler -fPIC")
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set(CUDA_NVCC_FLAGS "${CUDA_NVCC_FLAGS};-Xcompiler -fPIC")
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endif()
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set(SOURCES ${SOURCES}
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plugin/updater_gpu/src/updater_gpu.cc
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23
appveyor.yml
Normal file
23
appveyor.yml
Normal file
@ -0,0 +1,23 @@
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environment:
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matrix:
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- solution_name: C:/projects/xgboost/build2013/xgboost.sln
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- solution_name: C:/projects/xgboost/build2015/xgboost.sln
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platform:
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- x64
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configuration:
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- Debug
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- Release
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install:
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- git submodule update --init --recursive
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before_build:
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- mkdir build2013
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- mkdir build2015
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- cd build2013
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- cmake .. -G"Visual Studio 12 2013 Win64"
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- cd ../build2015
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- cmake .. -G"Visual Studio 14 2015 Win64"
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build_script:
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- msbuild %solution_name%
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@ -274,3 +274,7 @@ template <typename T> __device__ range block_stride_range(T begin, T end) {
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template <typename T> T *raw(thrust::device_vector<T> &v) { // NOLINT
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return raw_pointer_cast(v.data());
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}
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template <typename T> size_t size_bytes(const thrust::device_vector<T> &v) {
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return sizeof(T) * v.size();
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}
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@ -57,44 +57,66 @@ struct GPUData {
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const std::vector<bst_gpair> &in_gpair, bst_uint n_instances_in,
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bst_uint n_features_in, int max_depth, const TrainParam ¶m_in) {
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Timer t;
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// Track allocated device memory
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size_t n_bytes = 0;
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n_features = n_features_in;
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n_instances = n_instances_in;
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fvalues = in_fvalues;
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n_bytes += size_bytes(fvalues);
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foffsets = in_foffsets;
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n_bytes += size_bytes(foffsets);
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instance_id = in_instance_id;
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n_bytes += size_bytes(instance_id);
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feature_id = in_feature_id;
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n_bytes += size_bytes(feature_id);
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param = GPUTrainingParam(param_in.min_child_weight, param_in.reg_lambda,
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param_in.reg_alpha, param_in.max_delta_step);
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gpair = thrust::device_vector<gpu_gpair>(in_gpair.begin(), in_gpair.end());
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n_bytes += size_bytes(gpair);
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uint32_t max_nodes_level = 1 << max_depth;
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node_sums = thrust::device_vector<gpu_gpair>(max_nodes_level * n_features);
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n_bytes += size_bytes(node_sums);
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node_offsets = thrust::device_vector<int>(max_nodes_level * n_features);
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n_bytes += size_bytes(node_offsets);
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node_id_instance = thrust::device_vector<NodeIdT>(n_instances, 0);
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n_bytes += size_bytes(node_id_instance);
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node_id = thrust::device_vector<NodeIdT>(fvalues.size(), 0);
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n_bytes += size_bytes(node_id);
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node_id_temp = thrust::device_vector<NodeIdT>(fvalues.size());
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n_bytes += size_bytes(node_id_temp);
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uint32_t max_nodes = (1 << (max_depth + 1)) - 1;
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nodes = thrust::device_vector<Node>(max_nodes);
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n_bytes += size_bytes(nodes);
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split_candidates =
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thrust::device_vector<Split>(max_nodes_level * n_features);
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allocated = true;
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n_bytes += size_bytes(split_candidates);
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// Init items
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items = thrust::device_vector<Item>(fvalues.size());
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n_bytes += size_bytes(items);
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items_temp = thrust::device_vector<Item>(fvalues.size());
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n_bytes += size_bytes(items_temp);
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sort_index_in = thrust::device_vector<int>(fvalues.size());
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n_bytes += size_bytes(sort_index_in);
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sort_index_out = thrust::device_vector<int>(fvalues.size());
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n_bytes += size_bytes(sort_index_out);
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// std::cout << "Device memory allocated: " << n_bytes << "\n";
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this->CreateItems();
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allocated = true;
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}
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~GPUData() {}
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@ -150,7 +172,10 @@ struct GPUData {
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GPUBuilder::GPUBuilder() { gpu_data = new GPUData(); }
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void GPUBuilder::Init(const TrainParam ¶m_in) { param = param_in; }
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void GPUBuilder::Init(const TrainParam ¶m_in) {
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param = param_in;
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CHECK(param.max_depth < 16) << "Max depth > 15 not supported.";
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}
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GPUBuilder::~GPUBuilder() { delete gpu_data; }
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@ -186,8 +211,9 @@ template <int ITEMS_PER_THREAD>
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__global__ void
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update_nodeid_fvalue_kernel(NodeIdT *d_node_id, NodeIdT *d_node_id_instance,
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Item *d_items, Node *d_nodes, const int n_nodes,
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const int *d_feature_id, const size_t n,
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const int n_features, bool cache_nodes) {
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const int *d_foffsets, const int *d_feature_id,
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const size_t n, const int n_features,
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bool cache_nodes) {
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// Load nodes into shared memory
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extern __shared__ Node s_nodes[];
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@ -254,8 +280,8 @@ void GPUBuilder::UpdateNodeId(int level) {
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ITEMS_PER_THREAD><<<GRID_SIZE, BLOCK_THREADS, smem_size>>>(
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raw(gpu_data->node_id), raw(gpu_data->node_id_instance),
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raw(gpu_data->items), raw(gpu_data->nodes), n_nodes,
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raw(gpu_data->feature_id), gpu_data->fvalues.size(),
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gpu_data->n_features, cache_nodes);
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raw(gpu_data->foffsets), raw(gpu_data->feature_id),
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gpu_data->fvalues.size(), gpu_data->n_features, cache_nodes);
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safe_cuda(cudaGetLastError());
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safe_cuda(cudaDeviceSynchronize());
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@ -267,35 +293,42 @@ void GPUBuilder::UpdateNodeId(int level) {
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void GPUBuilder::Sort(int level) {
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thrust::sequence(gpu_data->sort_index_in.begin(),
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gpu_data->sort_index_in.end());
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cub::DoubleBuffer<NodeIdT> d_keys(raw(gpu_data->node_id),
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raw(gpu_data->node_id_temp));
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cub::DoubleBuffer<int> d_values(raw(gpu_data->sort_index_in),
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raw(gpu_data->sort_index_out));
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if (!gpu_data->cub_mem.IsAllocated()) {
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cub::DeviceSegmentedRadixSort::SortPairs(
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gpu_data->cub_mem.d_temp_storage, gpu_data->cub_mem.temp_storage_bytes,
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raw(gpu_data->node_id), raw(gpu_data->node_id_temp),
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raw(gpu_data->sort_index_in), raw(gpu_data->sort_index_out),
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gpu_data->fvalues.size(), gpu_data->n_features, raw(gpu_data->foffsets),
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raw(gpu_data->foffsets) + 1);
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d_keys, d_values, gpu_data->fvalues.size(), gpu_data->n_features,
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raw(gpu_data->foffsets), raw(gpu_data->foffsets) + 1);
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gpu_data->cub_mem.Allocate();
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}
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cub::DeviceSegmentedRadixSort::SortPairs(
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gpu_data->cub_mem.d_temp_storage, gpu_data->cub_mem.temp_storage_bytes,
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raw(gpu_data->node_id), raw(gpu_data->node_id_temp),
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raw(gpu_data->sort_index_in), raw(gpu_data->sort_index_out),
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gpu_data->fvalues.size(), gpu_data->n_features, raw(gpu_data->foffsets),
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raw(gpu_data->foffsets) + 1);
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d_keys, d_values, gpu_data->fvalues.size(), gpu_data->n_features,
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raw(gpu_data->foffsets), raw(gpu_data->foffsets) + 1);
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thrust::gather(gpu_data->sort_index_out.begin(),
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gpu_data->sort_index_out.end(), gpu_data->items.begin(),
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gpu_data->items_temp.begin());
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thrust::gather(thrust::device_pointer_cast(d_values.Current()),
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thrust::device_pointer_cast(d_values.Current()) +
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gpu_data->sort_index_out.size(),
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gpu_data->items.begin(), gpu_data->items_temp.begin());
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thrust::copy(gpu_data->items_temp.begin(), gpu_data->items_temp.end(),
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gpu_data->items.begin());
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thrust::copy(gpu_data->node_id_temp.begin(), gpu_data->node_id_temp.end(),
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gpu_data->node_id.begin());
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if (d_keys.Current() == raw(gpu_data->node_id_temp)) {
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thrust::copy(gpu_data->node_id_temp.begin(), gpu_data->node_id_temp.end(),
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gpu_data->node_id.begin());
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}
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}
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void GPUBuilder::Update(const std::vector<bst_gpair> &gpair, DMatrix *p_fmat,
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RegTree *p_tree) {
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cudaProfilerStart();
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try {
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Timer update;
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Timer t;
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@ -336,7 +369,14 @@ void GPUBuilder::Update(const std::vector<bst_gpair> &gpair, DMatrix *p_fmat,
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} catch (thrust::system_error &e) {
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std::cerr << "CUDA error: " << e.what() << std::endl;
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exit(-1);
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} catch (const std::exception &e) {
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std::cerr << "Error: " << e.what() << std::endl;
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exit(-1);
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} catch (...) {
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std::cerr << "Unknown exception." << std::endl;
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exit(-1);
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}
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cudaProfilerStop();
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}
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void GPUBuilder::InitData(const std::vector<bst_gpair> &gpair, DMatrix &fmat,
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@ -7,7 +7,7 @@
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namespace xgboost {
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namespace tree {
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typedef int32_t NodeIdT;
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typedef int16_t NodeIdT;
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// gpair type defined with device accessible functions
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struct gpu_gpair {
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