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GPU Plugin: Add subsample, colsample_bytree, colsample_bylevel (#1895)

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This commit is contained in:
Rory Mitchell
2016-12-23 04:30:36 +13:00
committed by Tianqi Chen
parent cee4aafb93
commit b49b339183
10 changed files with 331 additions and 324 deletions
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125
plugin/updater_gpu/src/device_helpers.cuh
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@@ -7,6 +7,7 @@
#include <thrust/device_vector.h>
#include <thrust/system/cuda/error.h>
#include <thrust/system_error.h>
#include <thrust/random.h>
#include <algorithm>
#include <ctime>
#include <sstream>
@@ -147,6 +148,8 @@ struct Timer {
LARGE_INTEGER now;
QueryPerformanceCounter(&now);
return static_cast<double>(now.QuadPart) / s_frequency.QuadPart;
#else
return 0;
#endif
}
@@ -160,12 +163,14 @@ struct Timer {
#ifdef _WIN32
_ReadWriteBarrier();
return seconds_now() - start;
#else
return 0;
#endif
}
void printElapsed(char *label) {
void printElapsed(std::string label) {
#ifdef TIMERS
safe_cuda(cudaDeviceSynchronize());
printf("%s:\t %1.4fs\n", label, elapsed());
printf("%s:\t %1.4fs\n", label.c_str(), elapsed());
#endif
}
};
@@ -233,46 +238,6 @@ template <typename T> __device__ range block_stride_range(T begin, T end) {
return r;
}
/*
* Utility functions
*/
template <typename T>
void print(const thrust::device_vector<T> &v, size_t max_items = 10) {
thrust::host_vector<T> h = v;
for (int i = 0; i < std::min(max_items, h.size()); i++) {
std::cout << " " << h[i];
}
std::cout << "\n";
}
template <typename T>
void print(char *label, const thrust::device_vector<T> &v,
const char *format = "%d ", int max = 10) {
thrust::host_vector<T> h_v = v;
std::cout << label << ":\n";
for (int i = 0; i < std::min(static_cast<int>(h_v.size()), max); i++) {
printf(format, h_v[i]);
}
std::cout << "\n";
}
template <typename T1, typename T2> T1 div_round_up(const T1 a, const T2 b) {
return static_cast<T1>(ceil(static_cast<double>(a) / b));
}
template <typename T> thrust::device_ptr<T> dptr(T *d_ptr) {
return thrust::device_pointer_cast(d_ptr);
}
template <typename T> T *raw(thrust::device_vector<T> &v) { // NOLINT
return raw_pointer_cast(v.data());
}
template <typename T> size_t size_bytes(const thrust::device_vector<T> &v) {
return sizeof(T) * v.size();
}
// Threadblock iterates over range, filling with value
template <typename IterT, typename ValueT>
@@ -306,11 +271,11 @@ template <typename T> class dvec {
public:
dvec() : _ptr(NULL), _size(0) {}
size_t size() { return _size; }
bool empty() { return _ptr == NULL || _size == 0; }
size_t size() const { return _size; }
bool empty() const { return _ptr == NULL || _size == 0; }
T *data() { return _ptr; }
std::vector<T> as_vector() {
std::vector<T> as_vector() const {
std::vector<T> h_vector(size());
safe_cuda(cudaMemcpy(h_vector.data(), _ptr, size() * sizeof(T),
cudaMemcpyDeviceToHost));
@@ -454,6 +419,55 @@ inline std::string device_name() {
return std::string(prop.name);
}
/*
* Utility functions
*/
template <typename T>
void print(const thrust::device_vector<T> &v, size_t max_items = 10) {
thrust::host_vector<T> h = v;
for (int i = 0; i < std::min(max_items, h.size()); i++) {
std::cout << " " << h[i];
}
std::cout << "\n";
}
template <typename T>
void print(const dvec<T> &v, size_t max_items = 10) {
std::vector<T> h = v.as_vector();
for (int i = 0; i < std::min(max_items, h.size()); i++) {
std::cout << " " << h[i];
}
std::cout << "\n";
}
template <typename T>
void print(char *label, const thrust::device_vector<T> &v,
const char *format = "%d ", int max = 10) {
thrust::host_vector<T> h_v = v;
std::cout << label << ":\n";
for (int i = 0; i < std::min(static_cast<int>(h_v.size()), max); i++) {
printf(format, h_v[i]);
}
std::cout << "\n";
}
template <typename T1, typename T2> T1 div_round_up(const T1 a, const T2 b) {
return static_cast<T1>(ceil(static_cast<double>(a) / b));
}
template <typename T> thrust::device_ptr<T> dptr(T *d_ptr) {
return thrust::device_pointer_cast(d_ptr);
}
template <typename T> T *raw(thrust::device_vector<T> &v) { // NOLINT
return raw_pointer_cast(v.data());
}
template <typename T> size_t size_bytes(const thrust::device_vector<T> &v) {
return sizeof(T) * v.size();
}
/*
* Kernel launcher
*/
@@ -470,4 +484,25 @@ inline void launch_n(size_t n, L lambda) {
launch_n_kernel<<<GRID_SIZE, BLOCK_THREADS>>>(n, lambda);
}
/*
* Random
*/
struct BernoulliRng {
float p;
int seed;
__host__ __device__ BernoulliRng(float p, int seed):p(p), seed(seed) {}
__host__ __device__ bool operator()(const int i) const {
thrust::default_random_engine rng(seed);
thrust::uniform_real_distribution<float> dist;
rng.discard(i);
return dist(rng) <= p;
}
};
} // namespace dh

51
plugin/updater_gpu/src/find_split.cuh
View File

@@ -4,9 +4,11 @@
#pragma once
#include <cub/cub.cuh>
#include <xgboost/base.h>
#include <vector>
#include "device_helpers.cuh"
#include "find_split_multiscan.cuh"
#include "find_split_sorting.cuh"
#include "gpu_data.cuh"
#include "types_functions.cuh"
namespace xgboost {
@@ -62,24 +64,47 @@ void reduce_split_candidates(Split *d_split_candidates, Node *d_nodes,
dh::safe_cuda(cudaDeviceSynchronize());
}
void find_split(const ItemIter items_iter, Split *d_split_candidates,
Node *d_nodes, bst_uint num_items, int num_features,
const int *d_feature_offsets, gpu_gpair *d_node_sums,
int *d_node_offsets, const GPUTrainingParam param,
const int level, bool multiscan_algorithm) {
void colsample_level(GPUData *data, const TrainParam xgboost_param,
const std::vector<int> &feature_set_tree,
std::vector<int> *feature_set_level) {
unsigned n_bytree =
static_cast<unsigned>(xgboost_param.colsample_bytree * data->n_features);
unsigned n =
static_cast<unsigned>(n_bytree * xgboost_param.colsample_bylevel);
CHECK_GT(n, 0);
*feature_set_level = feature_set_tree;
std::shuffle((*feature_set_level).begin(),
(*feature_set_level).begin() + n_bytree, common::GlobalRandom());
data->feature_set = *feature_set_level;
data->feature_flags.fill(0);
auto d_feature_set = data->feature_set.data();
auto d_feature_flags = data->feature_flags.data();
dh::launch_n(
n, [=] __device__(int i) { d_feature_flags[d_feature_set[i]] = 1; });
}
void find_split(GPUData *data, const TrainParam xgboost_param, const int level,
bool multiscan_algorithm,
const std::vector<int> &feature_set_tree,
std::vector<int> *feature_set_level) {
colsample_level(data, xgboost_param, feature_set_tree, feature_set_level);
// Reset split candidates
data->split_candidates.fill(Split());
if (multiscan_algorithm) {
find_split_candidates_multiscan(items_iter, d_split_candidates, d_nodes,
num_items, num_features, d_feature_offsets,
param, level);
find_split_candidates_multiscan(data, level);
} else {
find_split_candidates_sorted(items_iter, d_split_candidates, d_nodes,
num_items, num_features, d_feature_offsets,
d_node_sums, d_node_offsets, param, level);
find_split_candidates_sorted(data, level);
}
// Find the best split for each node
reduce_split_candidates(d_split_candidates, d_nodes, level, num_features,
param);
reduce_split_candidates(data->split_candidates.data(), data->nodes.data(),
level, data->n_features, data->param);
}
} // namespace tree
} // namespace xgboost

65
plugin/updater_gpu/src/find_split_multiscan.cuh
View File

@@ -5,6 +5,7 @@
#include <cub/cub.cuh>
#include <xgboost/base.h>
#include "device_helpers.cuh"
#include "gpu_data.cuh"
#include "types_functions.cuh"
namespace xgboost {
@@ -609,22 +610,11 @@ struct FindSplitEnactorMultiscan {
}
}
__device__ __forceinline__ void ResetSplitCandidates() {
const int max_nodes = 1 << level;
const int begin = blockIdx.x * max_nodes;
const int end = begin + max_nodes;
for (auto i : dh::block_stride_range(begin, end)) {
d_split_candidates_out[i] = Split();
}
}
__device__ __forceinline__ void ProcessRegion(const bst_uint &segment_begin,
const bst_uint &segment_end) {
// Current position
bst_uint offset = segment_begin;
ResetSplitCandidates();
ResetTileCarry();
ResetSplits();
CacheNodes();
@@ -654,8 +644,9 @@ __launch_bounds__(1024, 2)
const ItemIter items_iter, Split *d_split_candidates_out,
const Node *d_nodes, const int node_begin, bst_uint num_items,
int num_features, const int *d_feature_offsets,
const GPUTrainingParam param, const int level) {
if (num_items <= 0) {
const GPUTrainingParam param, const int *d_feature_flags,
const int level) {
if (num_items <= 0 || d_feature_flags[blockIdx.x] != 1) {
return;
}
@@ -685,69 +676,45 @@ __launch_bounds__(1024, 2)
}
template <int N_NODES>
void find_split_candidates_multiscan_variation(
const ItemIter items_iter, Split *d_split_candidates, const Node *d_nodes,
int node_begin, int node_end, bst_uint num_items, int num_features,
const int *d_feature_offsets, const GPUTrainingParam param,
const int level) {
void find_split_candidates_multiscan_variation(GPUData *data, const int level) {
const int node_begin = (1 << level) - 1;
const int BLOCK_THREADS = 512;
CHECK((node_end - node_begin) <= N_NODES) << "Multiscan: N_NODES template "
"parameter too small for given "
"node range.";
CHECK(BLOCK_THREADS / 32 < 32)
<< "Too many active warps. See FindSplitEnactor - ReduceSplits.";
typedef FindSplitParamsMultiscan<BLOCK_THREADS, N_NODES, false>
find_split_params;
typedef ReduceParamsMultiscan<BLOCK_THREADS, N_NODES, false> reduce_params;
int grid_size = num_features;
int grid_size = data->n_features;
find_split_candidates_multiscan_kernel<
find_split_params,
reduce_params><<<grid_size, find_split_params::BLOCK_THREADS>>>(
items_iter, d_split_candidates, d_nodes, node_begin, num_items,
num_features, d_feature_offsets, param, level);
data->items_iter, data->split_candidates.data(), data->nodes.data(),
node_begin, data->fvalues.size(), data->n_features, data->foffsets.data(),
data->param, data->feature_flags.data(), level);
dh::safe_cuda(cudaDeviceSynchronize());
}
void find_split_candidates_multiscan(
const ItemIter items_iter, Split *d_split_candidates, const Node *d_nodes,
bst_uint num_items, int num_features, const int *d_feature_offsets,
const GPUTrainingParam param, const int level) {
void find_split_candidates_multiscan(GPUData *data, const int level) {
// Select templated variation of split finding algorithm
switch (level) {
case 0:
find_split_candidates_multiscan_variation<1>(
items_iter, d_split_candidates, d_nodes, 0, 1, num_items, num_features,
d_feature_offsets, param, level);
find_split_candidates_multiscan_variation<1>(data, level);
break;
case 1:
find_split_candidates_multiscan_variation<2>(
items_iter, d_split_candidates, d_nodes, 1, 3, num_items, num_features,
d_feature_offsets, param, level);
find_split_candidates_multiscan_variation<2>(data, level);
break;
case 2:
find_split_candidates_multiscan_variation<4>(
items_iter, d_split_candidates, d_nodes, 3, 7, num_items, num_features,
d_feature_offsets, param, level);
find_split_candidates_multiscan_variation<4>(data, level);
break;
case 3:
find_split_candidates_multiscan_variation<8>(
items_iter, d_split_candidates, d_nodes, 7, 15, num_items, num_features,
d_feature_offsets, param, level);
find_split_candidates_multiscan_variation<8>(data, level);
break;
case 4:
find_split_candidates_multiscan_variation<16>(
items_iter, d_split_candidates, d_nodes, 15, 31, num_items,
num_features, d_feature_offsets, param, level);
break;
case 5:
find_split_candidates_multiscan_variation<32>(
items_iter, d_split_candidates, d_nodes, 31, 63, num_items,
num_features, d_feature_offsets, param, level);
find_split_candidates_multiscan_variation<16>(data, level);
break;
}
}

29
plugin/updater_gpu/src/find_split_sorting.cuh
View File

@@ -337,17 +337,8 @@ struct FindSplitEnactorSorting {
WriteBestSplit(node_id_adjusted);
}
__device__ __forceinline__ void ResetSplitCandidates() {
const int max_nodes = 1 << level;
const int begin = blockIdx.x * max_nodes;
dh::block_fill(d_split_candidates_out + begin, max_nodes, Split());
}
__device__ __forceinline__ void ProcessFeature(const bst_uint &segment_begin,
const bst_uint &segment_end) {
ResetSplitCandidates();
int node_begin = segment_begin;
const int max_nodes = 1 << level;
@@ -377,9 +368,9 @@ __global__ __launch_bounds__(1024, 1) void find_split_candidates_sorted_kernel(
const ItemIter items_iter, Split *d_split_candidates_out,
const Node *d_nodes, bst_uint num_items, const int num_features,
const int *d_feature_offsets, gpu_gpair *d_node_sums, int *d_node_offsets,
const GPUTrainingParam param, const int level) {
const GPUTrainingParam param, const int *d_feature_flags, const int level) {
if (num_items <= 0) {
if (num_items <= 0 || d_feature_flags[blockIdx.x] != 1) {
return;
}
@@ -408,23 +399,19 @@ __global__ __launch_bounds__(1024, 1) void find_split_candidates_sorted_kernel(
.ProcessFeature(segment_begin, segment_end);
}
void find_split_candidates_sorted(const ItemIter items_iter,
Split *d_split_candidates, Node *d_nodes,
bst_uint num_items, int num_features,
const int *d_feature_offsets,
gpu_gpair *d_node_sums, int *d_node_offsets,
const GPUTrainingParam param,
const int level) {
void find_split_candidates_sorted(GPUData * data, const int level) {
const int BLOCK_THREADS = 512;
CHECK(BLOCK_THREADS / 32 < 32) << "Too many active warps.";
int grid_size = num_features;
int grid_size = data->n_features;
find_split_candidates_sorted_kernel<
BLOCK_THREADS><<<grid_size, BLOCK_THREADS>>>(
items_iter, d_split_candidates, d_nodes, num_items, num_features,
d_feature_offsets, d_node_sums, d_node_offsets, param, level);
data->items_iter, data->split_candidates.data(), data->nodes.data(),
data->fvalues.size(), data->n_features,
data->foffsets.data(), data->node_sums.data(), data->node_offsets.data(),
data->param, data->feature_flags.data(), level);
dh::safe_cuda(cudaGetLastError());
dh::safe_cuda(cudaDeviceSynchronize());

205
plugin/updater_gpu/src/gpu_builder.cu
View File

@@ -12,143 +12,17 @@
#include <thrust/sequence.h>
#include <algorithm>
#include <random>
#include <numeric>
#include <vector>
#include "../../../src/common/random.h"
#include "device_helpers.cuh"
#include "find_split.cuh"
#include "gpu_builder.cuh"
#include "types_functions.cuh"
#include "gpu_data.cuh"
namespace xgboost {
namespace tree {
struct GPUData {
GPUData() : allocated(false), n_features(0), n_instances(0) {}
bool allocated;
int n_features;
int n_instances;
dh::bulk_allocator ba;
GPUTrainingParam param;
dh::dvec<float> fvalues;
dh::dvec<float> fvalues_temp;
dh::dvec<float> fvalues_cached;
dh::dvec<int> foffsets;
dh::dvec<bst_uint> instance_id;
dh::dvec<bst_uint> instance_id_temp;
dh::dvec<bst_uint> instance_id_cached;
dh::dvec<int> feature_id;
dh::dvec<NodeIdT> node_id;
dh::dvec<NodeIdT> node_id_temp;
dh::dvec<NodeIdT> node_id_instance;
dh::dvec<gpu_gpair> gpair;
dh::dvec<Node> nodes;
dh::dvec<Split> split_candidates;
dh::dvec<gpu_gpair> node_sums;
dh::dvec<int> node_offsets;
dh::dvec<int> sort_index_in;
dh::dvec<int> sort_index_out;
dh::dvec<char> cub_mem;
ItemIter items_iter;
void Init(const std::vector<float> &in_fvalues,
const std::vector<int> &in_foffsets,
const std::vector<bst_uint> &in_instance_id,
const std::vector<int> &in_feature_id,
const std::vector<bst_gpair> &in_gpair, bst_uint n_instances_in,
bst_uint n_features_in, int max_depth, const TrainParam &param_in) {
n_features = n_features_in;
n_instances = n_instances_in;
uint32_t max_nodes = (1 << (max_depth + 1)) - 1;
uint32_t max_nodes_level = 1 << max_depth;
// Calculate memory for sort
size_t cub_mem_size = 0;
cub::DoubleBuffer<NodeIdT> db_key;
cub::DoubleBuffer<int> db_value;
cub::DeviceSegmentedRadixSort::SortPairs(
cub_mem.data(), cub_mem_size, db_key,
db_value, in_fvalues.size(), n_features,
foffsets.data(), foffsets.data() + 1);
// Allocate memory
size_t free_memory = dh::available_memory();
ba.allocate(&fvalues, in_fvalues.size(), &fvalues_temp, in_fvalues.size(),
&fvalues_cached, in_fvalues.size(), &foffsets,
in_foffsets.size(), &instance_id, in_instance_id.size(),
&instance_id_temp, in_instance_id.size(), &instance_id_cached,
in_instance_id.size(), &feature_id, in_feature_id.size(),
&node_id, in_fvalues.size(), &node_id_temp, in_fvalues.size(),
&node_id_instance, n_instances, &gpair, n_instances, &nodes,
max_nodes, &split_candidates, max_nodes_level * n_features,
&node_sums, max_nodes_level * n_features, &node_offsets,
max_nodes_level * n_features, &sort_index_in, in_fvalues.size(),
&sort_index_out, in_fvalues.size(), &cub_mem, cub_mem_size);
if (!param_in.silent) {
const int mb_size = 1048576;
LOG(CONSOLE) << "Allocated " << ba.size() / mb_size << "/"
<< free_memory / mb_size << " MB on " << dh::device_name();
}
node_id.fill(0);
node_id_instance.fill(0);
fvalues = in_fvalues;
fvalues_cached = fvalues;
foffsets = in_foffsets;
instance_id = in_instance_id;
instance_id_cached = instance_id;
feature_id = in_feature_id;
param = GPUTrainingParam(param_in.min_child_weight, param_in.reg_lambda,
param_in.reg_alpha, param_in.max_delta_step);
gpair = in_gpair;
nodes.fill(Node());
items_iter = thrust::make_zip_iterator(thrust::make_tuple(
thrust::make_permutation_iterator(gpair.tbegin(), instance_id.tbegin()),
fvalues.tbegin(), node_id.tbegin()));
allocated = true;
dh::safe_cuda(cudaGetLastError());
}
~GPUData() {}
// Reset memory for new boosting iteration
void Reset(const std::vector<bst_gpair> &in_gpair) {
CHECK(allocated);
gpair = in_gpair;
instance_id = instance_id_cached;
fvalues = fvalues_cached;
nodes.fill(Node());
node_id_instance.fill(0);
node_id.fill(0);
}
bool IsAllocated() { return allocated; }
// Gather from node_id_instance into node_id according to instance_id
void GatherNodeId() {
// Update node_id for each item
auto d_node_id = node_id.data();
auto d_node_id_instance = node_id_instance.data();
auto d_instance_id = instance_id.data();
dh::launch_n(fvalues.size(), [=] __device__(bst_uint i) {
// Item item = d_items[i];
d_node_id[i] = d_node_id_instance[d_instance_id[i]];
});
}
};
GPUBuilder::GPUBuilder() { gpu_data = new GPUData(); }
@@ -253,15 +127,26 @@ void GPUBuilder::Sort(int level) {
}
}
void GPUBuilder::ColsampleTree() {
unsigned n = static_cast<unsigned>(
param.colsample_bytree * gpu_data->n_features);
CHECK_GT(n, 0);
feature_set_tree.resize(gpu_data->n_features);
std::iota(feature_set_tree.begin(), feature_set_tree.end(), 0);
std::shuffle(feature_set_tree.begin(), feature_set_tree.end(),
common::GlobalRandom());
}
void GPUBuilder::Update(const std::vector<bst_gpair> &gpair, DMatrix *p_fmat,
RegTree *p_tree) {
cudaProfilerStart();
try {
dh::Timer update;
dh::Timer t;
this->InitData(gpair, *p_fmat, *p_tree);
t.printElapsed("init data");
this->InitFirstNode();
this->ColsampleTree();
for (int level = 0; level < param.max_depth; level++) {
bool use_multiscan_algorithm = level < multiscan_levels;
@@ -280,11 +165,8 @@ void GPUBuilder::Update(const std::vector<bst_gpair> &gpair, DMatrix *p_fmat,
}
dh::Timer split;
find_split(gpu_data->items_iter, gpu_data->split_candidates.data(),
gpu_data->nodes.data(), (bst_uint)gpu_data->fvalues.size(),
gpu_data->n_features, gpu_data->foffsets.data(),
gpu_data->node_sums.data(), gpu_data->node_offsets.data(),
gpu_data->param, level, use_multiscan_algorithm);
find_split(gpu_data, param, level, use_multiscan_algorithm,
feature_set_tree, &feature_set_level);
split.printElapsed("split");
@@ -302,22 +184,6 @@ void GPUBuilder::Update(const std::vector<bst_gpair> &gpair, DMatrix *p_fmat,
std::cerr << "Unknown exception." << std::endl;
exit(-1);
}
cudaProfilerStop();
}
float GPUBuilder::GetSubsamplingRate(MetaInfo info) {
float subsample = 1.0;
uint32_t max_nodes = (1 << (param.max_depth + 1)) - 1;
uint32_t max_nodes_level = 1 << param.max_depth;
size_t required = 10 * info.num_row + 40 * info.num_nonzero
+ 64 * max_nodes + 76 * max_nodes_level * info.num_col;
size_t available = dh::available_memory();
while (available < required) {
subsample -= 0.05;
required = 10 * info.num_row + subsample * (44 * info.num_nonzero);
}
return subsample;
}
void GPUBuilder::InitData(const std::vector<bst_gpair> &gpair, DMatrix &fmat,
@@ -325,7 +191,7 @@ void GPUBuilder::InitData(const std::vector<bst_gpair> &gpair, DMatrix &fmat,
CHECK(fmat.SingleColBlock()) << "GPUMaker: must have single column block";
if (gpu_data->IsAllocated()) {
gpu_data->Reset(gpair);
gpu_data->Reset(gpair, param.subsample);
return;
}
@@ -333,35 +199,6 @@ void GPUBuilder::InitData(const std::vector<bst_gpair> &gpair, DMatrix &fmat,
MetaInfo info = fmat.info();
// Work out if dataset will fit on GPU
float subsample = this->GetSubsamplingRate(info);
CHECK(subsample > 0.0);
if (!param.silent && subsample < param.subsample) {
LOG(CONSOLE) << "Not enough device memory for entire dataset.";
}
// Override subsample parameter if user-specified parameter is lower
subsample = std::min(param.subsample, subsample);
std::vector<bool> row_flags;
if (subsample < 1.0) {
if (!param.silent && subsample < 1.0) {
LOG(CONSOLE) << "Subsampling " << subsample * 100 << "% of rows.";
}
const RowSet &rowset = fmat.buffered_rowset();
row_flags.resize(info.num_row);
std::bernoulli_distribution coin_flip(subsample);
auto &rnd = common::GlobalRandom();
for (size_t i = 0; i < rowset.size(); ++i) {
const bst_uint ridx = rowset[i];
if (gpair[ridx].hess < 0.0f)
continue;
row_flags[ridx] = coin_flip(rnd);
}
}
std::vector<int> foffsets;
foffsets.push_back(0);
std::vector<int> feature_id;
@@ -382,17 +219,9 @@ void GPUBuilder::InitData(const std::vector<bst_gpair> &gpair, DMatrix &fmat,
for (const ColBatch::Entry *it = col.data; it != col.data + col.length;
it++) {
bst_uint inst_id = it->index;
if (subsample < 1.0) {
if (row_flags[inst_id]) {
fvalues.push_back(it->fvalue);
instance_id.push_back(inst_id);
feature_id.push_back(i);
}
} else {
fvalues.push_back(it->fvalue);
instance_id.push_back(inst_id);
feature_id.push_back(i);
}
}
foffsets.push_back(fvalues.size());
}

6
plugin/updater_gpu/src/gpu_builder.cuh
View File

@@ -23,6 +23,7 @@ class GPUBuilder {
RegTree *p_tree);
void UpdateNodeId(int level);
private:
void InitData(const std::vector<bst_gpair> &gpair, DMatrix &fmat, // NOLINT
const RegTree &tree);
@@ -31,12 +32,15 @@ class GPUBuilder {
void Sort(int level);
void InitFirstNode();
void CopyTree(RegTree &tree); // NOLINT
void ColsampleTree();
TrainParam param;
GPUData *gpu_data;
std::vector<int> feature_set_tree;
std::vector<int> feature_set_level;
int multiscan_levels =
5; // Number of levels before switching to sorting algorithm
5; // Number of levels before switching to sorting algorithm
};
} // namespace tree
} // namespace xgboost

162
plugin/updater_gpu/src/gpu_data.cuh Normal file
View File

@@ -0,0 +1,162 @@
/*!
* Copyright 2016 Rory mitchell
*/
#pragma once
#include <cub/cub.cuh>
#include <xgboost/logging.h>
#include <thrust/sequence.h>
#include <vector>
#include "device_helpers.cuh"
#include "../../src/tree/param.h"
#include "types_functions.cuh"
namespace xgboost {
namespace tree {
struct GPUData {
GPUData() : allocated(false), n_features(0), n_instances(0) {}
bool allocated;
int n_features;
int n_instances;
dh::bulk_allocator ba;
GPUTrainingParam param;
dh::dvec<float> fvalues;
dh::dvec<float> fvalues_temp;
dh::dvec<float> fvalues_cached;
dh::dvec<int> foffsets;
dh::dvec<bst_uint> instance_id;
dh::dvec<bst_uint> instance_id_temp;
dh::dvec<bst_uint> instance_id_cached;
dh::dvec<int> feature_id;
dh::dvec<NodeIdT> node_id;
dh::dvec<NodeIdT> node_id_temp;
dh::dvec<NodeIdT> node_id_instance;
dh::dvec<gpu_gpair> gpair;
dh::dvec<Node> nodes;
dh::dvec<Split> split_candidates;
dh::dvec<gpu_gpair> node_sums;
dh::dvec<int> node_offsets;
dh::dvec<int> sort_index_in;
dh::dvec<int> sort_index_out;
dh::dvec<char> cub_mem;
dh::dvec<int> feature_flags;
dh::dvec<int> feature_set;
ItemIter items_iter;
void Init(const std::vector<float> &in_fvalues,
const std::vector<int> &in_foffsets,
const std::vector<bst_uint> &in_instance_id,
const std::vector<int> &in_feature_id,
const std::vector<bst_gpair> &in_gpair, bst_uint n_instances_in,
bst_uint n_features_in, int max_depth, const TrainParam &param_in) {
n_features = n_features_in;
n_instances = n_instances_in;
uint32_t max_nodes = (1 << (max_depth + 1)) - 1;
uint32_t max_nodes_level = 1 << max_depth;
// Calculate memory for sort
size_t cub_mem_size = 0;
cub::DoubleBuffer<NodeIdT> db_key;
cub::DoubleBuffer<int> db_value;
cub::DeviceSegmentedRadixSort::SortPairs(
cub_mem.data(), cub_mem_size, db_key,
db_value, in_fvalues.size(), n_features,
foffsets.data(), foffsets.data() + 1);
// Allocate memory
size_t free_memory = dh::available_memory();
ba.allocate(&fvalues, in_fvalues.size(), &fvalues_temp, in_fvalues.size(),
&fvalues_cached, in_fvalues.size(), &foffsets,
in_foffsets.size(), &instance_id, in_instance_id.size(),
&instance_id_temp, in_instance_id.size(), &instance_id_cached,
in_instance_id.size(), &feature_id, in_feature_id.size(),
&node_id, in_fvalues.size(), &node_id_temp, in_fvalues.size(),
&node_id_instance, n_instances, &gpair, n_instances, &nodes,
max_nodes, &split_candidates, max_nodes_level * n_features,
&node_sums, max_nodes_level * n_features, &node_offsets,
max_nodes_level * n_features, &sort_index_in, in_fvalues.size(),
&sort_index_out, in_fvalues.size(), &cub_mem, cub_mem_size,
&feature_flags, n_features, &feature_set, n_features);
if (!param_in.silent) {
const int mb_size = 1048576;
LOG(CONSOLE) << "Allocated " << ba.size() / mb_size << "/"
<< free_memory / mb_size << " MB on " << dh::device_name();
}
fvalues_cached = in_fvalues;
foffsets = in_foffsets;
instance_id_cached = in_instance_id;
feature_id = in_feature_id;
param = GPUTrainingParam(param_in.min_child_weight, param_in.reg_lambda,
param_in.reg_alpha, param_in.max_delta_step);
allocated = true;
this->Reset(in_gpair, param_in.subsample);
items_iter = thrust::make_zip_iterator(thrust::make_tuple(
thrust::make_permutation_iterator(gpair.tbegin(), instance_id.tbegin()),
fvalues.tbegin(), node_id.tbegin()));
dh::safe_cuda(cudaGetLastError());
}
~GPUData() {}
// Set gradient pair to 0 with p = 1 - subsample
void MarkSubsample(float subsample) {
if (subsample == 1.0) {
return;
}
auto d_gpair = gpair.data();
dh::BernoulliRng rng(subsample, common::GlobalRandom()());
dh::launch_n(n_instances, [=] __device__(int i) {
if (!rng(i)) {
d_gpair[i] = gpu_gpair();
}
});
}
// Reset memory for new boosting iteration
void Reset(const std::vector<bst_gpair> &in_gpair, float subsample) {
CHECK(allocated);
gpair = in_gpair;
this->MarkSubsample(subsample);
instance_id = instance_id_cached;
fvalues = fvalues_cached;
nodes.fill(Node());
node_id_instance.fill(0);
node_id.fill(0);
}
bool IsAllocated() { return allocated; }
// Gather from node_id_instance into node_id according to instance_id
void GatherNodeId() {
// Update node_id for each item
auto d_node_id = node_id.data();
auto d_node_id_instance = node_id_instance.data();
auto d_instance_id = instance_id.data();
dh::launch_n(fvalues.size(), [=] __device__(bst_uint i) {
// Item item = d_items[i];
d_node_id[i] = d_node_id_instance[d_instance_id[i]];
});
}
};
} // namespace tree
} // namespace xgboost