Add basic unittests for gpu-hist method. (#3785)
* Split building histogram into separated class. * Extract `InitCompressedRow` definition. * Basic tests for gpu-hist. * Document the code more verbosely. * Removed `HistCutUnit`. * Removed some duplicated copies in `GPUHistMaker`. * Implement LCG and use it in tests.
This commit is contained in:
trivialfis
Rory Mitchell
parent
184efff9f9
commit
516457fadc
@ -151,10 +151,11 @@ struct Entry {
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};
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/*!
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* \brief in-memory storage unit of sparse batch
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* \brief In-memory storage unit of sparse batch, stored in CSR format.
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*/
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class SparsePage {
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public:
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// Offset for each row.
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HostDeviceVector<size_t> offset;
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/*! \brief the data of the segments */
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HostDeviceVector<Entry> data;
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@ -204,7 +204,7 @@ inline void LaunchN(int device_idx, size_t n, L lambda) {
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const int GRID_SIZE =
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static_cast<int>(DivRoundUp(n, ITEMS_PER_THREAD * BLOCK_THREADS));
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LaunchNKernel<<<GRID_SIZE, BLOCK_THREADS>>>(static_cast<size_t>(0), n,
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lambda);
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lambda);
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}
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/*
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@ -365,6 +365,7 @@ class DVec2 {
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T *other() { return buff_.Alternate(); }
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};
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/*! \brief Helper for allocating large block of memory. */
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template <MemoryType MemoryT>
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class BulkAllocator {
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std::vector<char *> d_ptr_;
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@ -53,34 +53,15 @@ struct GHistEntry {
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}
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};
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/*! \brief Cut configuration for one feature */
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struct HistCutUnit {
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/*! \brief the index pointer of each histunit */
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const bst_float* cut;
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/*! \brief number of cutting point, containing the maximum point */
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uint32_t size;
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// default constructor
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HistCutUnit() = default;
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// constructor
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HistCutUnit(const bst_float* cut, uint32_t size)
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: cut(cut), size(size) {}
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};
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/*! \brief cut configuration for all the features. */
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/*! \brief Cut configuration for all the features. */
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struct HistCutMatrix {
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/*! \brief unit pointer to rows by element position */
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/*! \brief Unit pointer to rows by element position */
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std::vector<uint32_t> row_ptr;
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/*! \brief minimum value of each feature */
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std::vector<bst_float> min_val;
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/*! \brief the cut field */
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std::vector<bst_float> cut;
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uint32_t GetBinIdx(const Entry &e);
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/*! \brief Get histogram bound for fid */
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inline HistCutUnit operator[](bst_uint fid) const {
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return {dmlc::BeginPtr(cut) + row_ptr[fid],
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row_ptr[fid + 1] - row_ptr[fid]};
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}
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using WXQSketch = common::WXQuantileSketch<bst_float, bst_float>;
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@ -189,7 +170,7 @@ class GHistIndexBlockMatrix {
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/*!
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* \brief histogram of graident statistics for a single node.
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* Consists of multiple GHistEntry's, each entry showing total graident statistics
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* Consists of multiple GHistEntry's, each entry showing total graident statistics
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* for that particular bin
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* Uses global bin id so as to represent all features simultaneously
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*/
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@ -75,7 +75,8 @@ inline void CheckGradientMax(const std::vector<GradientPair>& gpair) {
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auto* ptr = reinterpret_cast<const float*>(gpair.data());
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float abs_max =
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std::accumulate(ptr, ptr + (gpair.size() * 2), 0.f,
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[=](float a, float b) { return max(abs(a), abs(b)); });
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[=](float a, float b) {
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return std::max(abs(a), abs(b)); });
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CHECK_LT(abs_max, std::pow(2.0f, 16.0f))
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<< "Labels are too large for this algorithm. Rescale to less than 2^16.";
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@ -254,6 +255,7 @@ XGBOOST_DEVICE float inline LossChangeMissing(const GradientPairT& scan,
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const float& parent_gain,
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const GPUTrainingParam& param,
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bool& missing_left_out) { // NOLINT
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// Put gradients of missing values to left
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float missing_left_loss =
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DeviceCalcLossChange(param, scan + missing, parent_sum, parent_gain);
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float missing_right_loss =
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File diff suppressed because it is too large
Load Diff
@ -1,3 +1,6 @@
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/*!
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* Copyright 2016-2018 XGBoost contributors
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*/
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#include "./helpers.h"
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#include "xgboost/c_api.h"
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#include <random>
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@ -14,7 +17,7 @@ std::string TempFileName() {
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bool FileExists(const std::string name) {
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struct stat st;
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return stat(name.c_str(), &st) == 0;
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return stat(name.c_str(), &st) == 0;
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}
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long GetFileSize(const std::string filename) {
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@ -106,17 +109,42 @@ xgboost::bst_float GetMetricEval(xgboost::Metric * metric,
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return metric->Eval(preds, info, false);
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}
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namespace xgboost {
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bool IsNear(std::vector<xgboost::bst_float>::const_iterator _beg1,
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std::vector<xgboost::bst_float>::const_iterator _end1,
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std::vector<xgboost::bst_float>::const_iterator _beg2) {
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for (auto iter1 = _beg1, iter2 = _beg2; iter1 != _end1; ++iter1, ++iter2) {
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if (std::abs(*iter1 - *iter2) > xgboost::kRtEps){
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return false;
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}
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}
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return true;
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}
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SimpleLCG::StateType SimpleLCG::operator()() {
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state_ = (alpha_ * state_) % mod_;
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return state_;
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}
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SimpleLCG::StateType SimpleLCG::Min() const {
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return seed_ * alpha_;
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}
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SimpleLCG::StateType SimpleLCG::Max() const {
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return max_value_;
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}
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std::shared_ptr<xgboost::DMatrix>* CreateDMatrix(int rows, int columns,
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float sparsity, int seed) {
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const float missing_value = -1;
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std::vector<float> test_data(rows * columns);
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std::mt19937 gen(seed);
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std::uniform_real_distribution<float> dis(0.0f, 1.0f);
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xgboost::SimpleLCG gen(seed);
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SimpleRealUniformDistribution<float> dis(0.0f, 1.0f);
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for (auto &e : test_data) {
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if (dis(gen) < sparsity) {
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if (dis(&gen) < sparsity) {
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e = missing_value;
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} else {
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e = dis(gen);
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e = dis(&gen);
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}
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}
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@ -126,16 +154,4 @@ std::shared_ptr<xgboost::DMatrix>* CreateDMatrix(int rows, int columns,
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return static_cast<std::shared_ptr<xgboost::DMatrix> *>(handle);
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}
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namespace xgboost {
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bool IsNear(std::vector<xgboost::bst_float>::const_iterator _beg1,
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std::vector<xgboost::bst_float>::const_iterator _end1,
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std::vector<xgboost::bst_float>::const_iterator _beg2) {
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for (auto iter1 = _beg1, iter2 = _beg2; iter1 != _end1; ++iter1, ++iter2) {
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if (std::abs(*iter1 - *iter2) > xgboost::kRtEps){
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return false;
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}
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}
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return true;
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}
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}
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} // namespace xgboost
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@ -1,3 +1,6 @@
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/*!
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* Copyright 2016-2018 XGBoost contributors
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*/
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#ifndef XGBOOST_TESTS_CPP_HELPERS_H_
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#define XGBOOST_TESTS_CPP_HELPERS_H_
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@ -56,7 +59,86 @@ namespace xgboost {
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bool IsNear(std::vector<xgboost::bst_float>::const_iterator _beg1,
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std::vector<xgboost::bst_float>::const_iterator _end1,
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std::vector<xgboost::bst_float>::const_iterator _beg2);
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}
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/*!
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* \brief Linear congruential generator.
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*
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* The distribution defined in std is not portable. Given the same seed, it
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* migth produce different outputs on different platforms or with different
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* compilers. The SimpleLCG implemented here is to make sure all tests are
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* reproducible.
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*/
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class SimpleLCG {
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private:
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using StateType = int64_t;
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static StateType constexpr default_init_ = 3;
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static StateType constexpr default_alpha_ = 61;
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static StateType constexpr max_value_ = ((StateType)1 << 32) - 1;
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StateType state_;
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StateType const alpha_;
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StateType const mod_;
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StateType const seed_;
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public:
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SimpleLCG() : state_{default_init_},
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alpha_{default_alpha_}, mod_{max_value_}, seed_{state_}{}
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/*!
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* \brief Initialize SimpleLCG.
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*
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* \param state Initial state, can also be considered as seed. If set to
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* zero, SimpleLCG will use internal default value.
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* \param alpha multiplier
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* \param mod modulo
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*/
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SimpleLCG(StateType state,
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StateType alpha=default_alpha_, StateType mod=max_value_)
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: state_{state == 0 ? default_init_ : state},
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alpha_{alpha}, mod_{mod} , seed_{state} {}
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StateType operator()();
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StateType Min() const;
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StateType Max() const;
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};
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template <typename ResultT>
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class SimpleRealUniformDistribution {
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private:
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ResultT const lower;
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ResultT const upper;
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/*! \brief Over-simplified version of std::generate_canonical. */
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template <size_t Bits, typename GeneratorT>
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ResultT GenerateCanonical(GeneratorT* rng) const {
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static_assert(std::is_floating_point<ResultT>::value,
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"Result type must be floating point.");
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long double const r = (static_cast<long double>(rng->Max())
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- static_cast<long double>(rng->Min())) + 1.0L;
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size_t const log2r = std::log(r) / std::log(2.0L);
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size_t m = std::max<size_t>(1UL, (Bits + log2r - 1UL) / log2r);
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ResultT sum_value = 0, r_k = 1;
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for (size_t k = m; k != 0; --k) {
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sum_value += ResultT((*rng)() - rng->Min()) * r_k;
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r_k *= r;
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}
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ResultT res = sum_value / r_k;
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return res;
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}
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public:
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SimpleRealUniformDistribution(ResultT l, ResultT u) :
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lower{l}, upper{u} {}
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template <typename GeneratorT>
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ResultT operator()(GeneratorT* rng) const {
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ResultT tmp = GenerateCanonical<std::numeric_limits<ResultT>::digits,
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GeneratorT>(rng);
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return (tmp * (upper - lower)) + lower;
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}
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};
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/**
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* \fn std::shared_ptr<xgboost::DMatrix> CreateDMatrix(int rows, int columns, float sparsity, int seed);
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@ -70,7 +152,8 @@ bool IsNear(std::vector<xgboost::bst_float>::const_iterator _beg1,
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*
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* \return The new d matrix.
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*/
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std::shared_ptr<xgboost::DMatrix> *CreateDMatrix(int rows, int columns,
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float sparsity, int seed = 0);
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} // namespace xgboost
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#endif
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@ -7,7 +7,7 @@ typedef std::pair<std::string, std::string> arg;
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TEST(Linear, shotgun) {
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typedef std::pair<std::string, std::string> arg;
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auto mat = CreateDMatrix(10, 10, 0);
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auto mat = xgboost::CreateDMatrix(10, 10, 0);
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("shotgun"));
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updater->Init({{"eta", "1."}});
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@ -26,7 +26,7 @@ TEST(Linear, shotgun) {
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TEST(Linear, coordinate) {
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typedef std::pair<std::string, std::string> arg;
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auto mat = CreateDMatrix(10, 10, 0);
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auto mat = xgboost::CreateDMatrix(10, 10, 0);
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auto updater = std::unique_ptr<xgboost::LinearUpdater>(
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xgboost::LinearUpdater::Create("coord_descent"));
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updater->Init({{"eta", "1."}});
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@ -1,86 +1,396 @@
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/*!
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* Copyright 2017 XGBoost contributors
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* Copyright 2017-2018 XGBoost contributors
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*/
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#include <thrust/device_vector.h>
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#include <xgboost/base.h>
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#include <random>
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#include "../helpers.h"
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#include "gtest/gtest.h"
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#include "../../../src/data/sparse_page_source.h"
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#include "../../../src/gbm/gbtree_model.h"
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#include "../../../src/tree/updater_gpu_hist.cu"
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#include "../../../src/tree/updater_gpu_common.cuh"
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#include "../../../src/common/common.h"
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namespace xgboost {
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namespace tree {
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TEST(gpu_hist_experimental, TestSparseShard) {
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int rows = 100;
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int columns = 80;
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int max_bins = 4;
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auto dmat = CreateDMatrix(rows, columns, 0.9f);
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common::GHistIndexMatrix gmat;
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gmat.Init((*dmat).get(),max_bins);
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TrainParam p;
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p.max_depth = 6;
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void BuildGidx(DeviceShard* shard, int n_rows, int n_cols,
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bst_float sparsity=0) {
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auto dmat = CreateDMatrix(n_rows, n_cols, sparsity, 3);
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const SparsePage& batch = *(*dmat)->GetRowBatches().begin();
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DeviceShard shard(0, 0, 0, rows, p);
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shard.InitRowPtrs(batch);
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shard.InitCompressedData(gmat.cut, batch);
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ASSERT_LT(shard.row_stride, columns);
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common::HistCutMatrix cmat;
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cmat.row_ptr = {0, 3, 6, 9, 12, 15, 18, 21, 24};
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cmat.min_val = {0.1, 0.2, 0.3, 0.1, 0.2, 0.3, 0.2, 0.2};
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// 24 cut fields, 3 cut fields for each feature (column).
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cmat.cut = {0.30, 0.67, 1.64,
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0.32, 0.77, 1.95,
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0.29, 0.70, 1.80,
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0.32, 0.75, 1.85,
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0.18, 0.59, 1.69,
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0.25, 0.74, 2.00,
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0.26, 0.74, 1.98,
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0.26, 0.71, 1.83};
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auto host_gidx_buffer = shard.gidx_buffer.AsVector();
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common::CompressedIterator<uint32_t> gidx(host_gidx_buffer.data(),
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gmat.cut.row_ptr.back() + 1);
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for (int i = 0; i < rows; i++) {
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int row_offset = 0;
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for (auto j = gmat.row_ptr[i]; j < gmat.row_ptr[i + 1]; j++) {
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ASSERT_EQ(gidx[i * shard.row_stride + row_offset], gmat.index[j]);
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row_offset++;
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}
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for (; row_offset < shard.row_stride; row_offset++) {
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ASSERT_EQ(gidx[i * shard.row_stride + row_offset], shard.null_gidx_value);
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}
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}
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shard->InitRowPtrs(batch);
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shard->InitCompressedData(cmat, batch);
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delete dmat;
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}
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TEST(gpu_hist_experimental, TestDenseShard) {
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int rows = 100;
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int columns = 80;
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int max_bins = 4;
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auto dmat = CreateDMatrix(rows, columns, 0);
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common::GHistIndexMatrix gmat;
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gmat.Init((*dmat).get(),max_bins);
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TrainParam p;
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p.max_depth = 6;
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TEST(GpuHist, BuildGidxDense) {
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int const n_rows = 16, n_cols = 8;
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TrainParam param;
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param.max_depth = 1;
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param.n_gpus = 1;
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param.max_leaves = 0;
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const SparsePage& batch = *(*dmat)->GetRowBatches().begin();
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DeviceShard shard(0, 0, 0, rows, p);
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shard.InitRowPtrs(batch);
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shard.InitCompressedData(gmat.cut, batch);
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DeviceShard shard(0, 0, 0, n_rows, param);
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BuildGidx(&shard, n_rows, n_cols);
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ASSERT_EQ(shard.row_stride, columns);
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std::vector<common::CompressedByteT> h_gidx_buffer;
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h_gidx_buffer = shard.gidx_buffer.AsVector();
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common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
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auto host_gidx_buffer = shard.gidx_buffer.AsVector();
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ASSERT_EQ(shard.row_stride, n_cols);
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common::CompressedIterator<uint32_t> gidx(host_gidx_buffer.data(),
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gmat.cut.row_ptr.back() + 1);
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for (int i = 0; i < gmat.index.size(); i++) {
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ASSERT_EQ(gidx[i], gmat.index[i]);
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std::vector<uint32_t> solution = {
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0, 3, 8, 9, 14, 17, 20, 21,
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0, 4, 7, 10, 14, 16, 19, 22,
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1, 3, 7, 11, 14, 15, 19, 21,
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2, 3, 7, 9, 13, 16, 20, 22,
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2, 3, 6, 9, 12, 16, 20, 21,
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1, 5, 6, 10, 13, 16, 20, 21,
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2, 5, 8, 9, 13, 17, 19, 22,
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2, 4, 6, 10, 14, 17, 19, 21,
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2, 5, 7, 9, 13, 16, 19, 22,
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0, 3, 8, 10, 12, 16, 19, 22,
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1, 3, 7, 10, 13, 16, 19, 21,
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1, 3, 8, 10, 13, 17, 20, 22,
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2, 4, 6, 9, 14, 15, 19, 22,
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1, 4, 6, 9, 13, 16, 19, 21,
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2, 4, 8, 10, 14, 15, 19, 22,
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1, 4, 7, 10, 14, 16, 19, 21,
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};
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for (size_t i = 0; i < n_rows * n_cols; ++i) {
|
||||
ASSERT_EQ(solution[i], gidx[i]);
|
||||
}
|
||||
|
||||
delete dmat;
|
||||
}
|
||||
|
||||
TEST(gpu_hist_experimental, MGPU_mock) {
|
||||
TEST(GpuHist, BuildGidxSparse) {
|
||||
int const n_rows = 16, n_cols = 8;
|
||||
TrainParam param;
|
||||
param.max_depth = 1;
|
||||
param.n_gpus = 1;
|
||||
param.max_leaves = 0;
|
||||
|
||||
DeviceShard shard(0, 0, 0, n_rows, param);
|
||||
BuildGidx(&shard, n_rows, n_cols, 0.9f);
|
||||
|
||||
std::vector<common::CompressedByteT> h_gidx_buffer;
|
||||
h_gidx_buffer = shard.gidx_buffer.AsVector();
|
||||
common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
|
||||
|
||||
ASSERT_LE(shard.row_stride, 3);
|
||||
|
||||
// row_stride = 3, 16 rows, 48 entries for ELLPack
|
||||
std::vector<uint32_t> solution = {
|
||||
15, 24, 24, 0, 24, 24, 24, 24, 24, 24, 24, 24, 20, 24, 24, 24,
|
||||
24, 24, 24, 24, 24, 5, 24, 24, 0, 16, 24, 15, 24, 24, 24, 24,
|
||||
24, 7, 14, 16, 4, 24, 24, 24, 24, 24, 9, 24, 24, 1, 24, 24
|
||||
};
|
||||
for (size_t i = 0; i < n_rows * shard.row_stride; ++i) {
|
||||
ASSERT_EQ(solution[i], gidx[i]);
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<GradientPairPrecise> GetHostHistGpair() {
|
||||
// 24 bins, 3 bins for each feature (column).
|
||||
std::vector<GradientPairPrecise> hist_gpair = {
|
||||
{0.8314, 0.7147}, {1.7989, 3.7312}, {3.3846, 3.4598},
|
||||
{2.9277, 3.5886}, {1.8429, 2.4152}, {1.2443, 1.9019},
|
||||
{1.6380, 2.9174}, {1.5657, 2.5107}, {2.8111, 2.4776},
|
||||
{2.1322, 3.0651}, {3.2927, 3.8540}, {0.5899, 0.9866},
|
||||
{1.5185, 1.6263}, {2.0686, 3.1844}, {2.4278, 3.0950},
|
||||
{1.5105, 2.1403}, {2.6922, 4.2217}, {1.8122, 1.5437},
|
||||
{0.0000, 0.0000}, {4.3245, 5.7955}, {1.6903, 2.1103},
|
||||
{2.4012, 4.4754}, {3.6136, 3.4303}, {0.0000, 0.0000}
|
||||
};
|
||||
return hist_gpair;
|
||||
}
|
||||
|
||||
void TestBuildHist(GPUHistBuilderBase& builder) {
|
||||
int const n_rows = 16, n_cols = 8;
|
||||
|
||||
TrainParam param;
|
||||
param.max_depth = 6;
|
||||
param.n_gpus = 1;
|
||||
param.max_leaves = 0;
|
||||
|
||||
DeviceShard shard(0, 0, 0, n_rows, param);
|
||||
|
||||
BuildGidx(&shard, n_rows, n_cols);
|
||||
|
||||
xgboost::SimpleLCG gen;
|
||||
xgboost::SimpleRealUniformDistribution<bst_float> dist(0.0f, 1.0f);
|
||||
std::vector<GradientPair> h_gpair(n_rows);
|
||||
for (size_t i = 0; i < h_gpair.size(); ++i) {
|
||||
bst_float grad = dist(&gen);
|
||||
bst_float hess = dist(&gen);
|
||||
h_gpair[i] = GradientPair(grad, hess);
|
||||
}
|
||||
|
||||
thrust::device_vector<GradientPair> gpair (n_rows);
|
||||
gpair = h_gpair;
|
||||
|
||||
int num_symbols = shard.n_bins + 1;
|
||||
|
||||
thrust::host_vector<common::CompressedByteT> h_gidx_buffer (
|
||||
shard.gidx_buffer.Size());
|
||||
|
||||
common::CompressedByteT* d_gidx_buffer_ptr = shard.gidx_buffer.Data();
|
||||
dh::safe_cuda(cudaMemcpy(h_gidx_buffer.data(), d_gidx_buffer_ptr,
|
||||
sizeof(common::CompressedByteT) * shard.gidx_buffer.Size(),
|
||||
cudaMemcpyDeviceToHost));
|
||||
auto gidx = common::CompressedIterator<uint32_t>(h_gidx_buffer.data(),
|
||||
num_symbols);
|
||||
|
||||
shard.ridx_segments.resize(1);
|
||||
shard.ridx_segments[0] = Segment(0, n_rows);
|
||||
shard.hist.AllocateHistogram(0);
|
||||
shard.gpair.copy(gpair.begin(), gpair.end());
|
||||
thrust::sequence(shard.ridx.CurrentDVec().tbegin(),
|
||||
shard.ridx.CurrentDVec().tend());
|
||||
|
||||
builder.Build(&shard, 0);
|
||||
DeviceHistogram d_hist = shard.hist;
|
||||
|
||||
GradientPairSumT* d_histptr {d_hist.GetHistPtr(0)};
|
||||
// d_hist.data stored in float, not gradient pair
|
||||
thrust::host_vector<GradientPairSumT> h_result (d_hist.data.size()/2);
|
||||
size_t data_size = sizeof(GradientPairSumT) / (
|
||||
sizeof(GradientPairSumT) / sizeof(GradientPairSumT::ValueT));
|
||||
data_size *= d_hist.data.size();
|
||||
dh::safe_cuda(cudaMemcpy(h_result.data(), d_histptr, data_size,
|
||||
cudaMemcpyDeviceToHost));
|
||||
|
||||
std::vector<GradientPairPrecise> solution = GetHostHistGpair();
|
||||
std::cout << std::fixed;
|
||||
for (size_t i = 0; i < h_result.size(); ++i) {
|
||||
EXPECT_NEAR(h_result[i].GetGrad(), solution[i].GetGrad(), 0.01f);
|
||||
EXPECT_NEAR(h_result[i].GetHess(), solution[i].GetHess(), 0.01f);
|
||||
}
|
||||
}
|
||||
|
||||
TEST(GpuHist, BuildHistGlobalMem) {
|
||||
GlobalMemHistBuilder builder;
|
||||
TestBuildHist(builder);
|
||||
}
|
||||
|
||||
TEST(GpuHist, BuildHistSharedMem) {
|
||||
SharedMemHistBuilder builder;
|
||||
TestBuildHist(builder);
|
||||
}
|
||||
|
||||
common::HistCutMatrix GetHostCutMatrix () {
|
||||
common::HistCutMatrix cmat;
|
||||
cmat.row_ptr = {0, 3, 6, 9, 12, 15, 18, 21, 24};
|
||||
cmat.min_val = {0.1, 0.2, 0.3, 0.1, 0.2, 0.3, 0.2, 0.2};
|
||||
// 24 cut fields, 3 cut fields for each feature (column).
|
||||
// Each row of the cut represents the cuts for a data column.
|
||||
cmat.cut = {0.30, 0.67, 1.64,
|
||||
0.32, 0.77, 1.95,
|
||||
0.29, 0.70, 1.80,
|
||||
0.32, 0.75, 1.85,
|
||||
0.18, 0.59, 1.69,
|
||||
0.25, 0.74, 2.00,
|
||||
0.26, 0.74, 1.98,
|
||||
0.26, 0.71, 1.83};
|
||||
return cmat;
|
||||
}
|
||||
|
||||
// TODO(trivialfis): This test is over simplified.
|
||||
TEST(GpuHist, EvaluateSplits) {
|
||||
constexpr int n_rows = 16;
|
||||
constexpr int n_cols = 8;
|
||||
|
||||
TrainParam param;
|
||||
param.max_depth = 1;
|
||||
param.n_gpus = 1;
|
||||
param.colsample_bylevel = 1;
|
||||
param.colsample_bytree = 1;
|
||||
param.min_child_weight = 0.01;
|
||||
|
||||
// Disable all parameters.
|
||||
param.reg_alpha = 0.0;
|
||||
param.reg_lambda = 0;
|
||||
param.max_delta_step = 0.0;
|
||||
|
||||
for (size_t i = 0; i < n_cols; ++i) {
|
||||
param.monotone_constraints.emplace_back(0);
|
||||
}
|
||||
|
||||
int max_bins = 4;
|
||||
|
||||
// Initialize DeviceShard
|
||||
std::unique_ptr<DeviceShard> shard {new DeviceShard(0, 0, 0, n_rows, param)};
|
||||
// Initialize DeviceShard::node_sum_gradients
|
||||
shard->node_sum_gradients = {{6.4, 12.8}};
|
||||
|
||||
// Initialize DeviceShard::cut
|
||||
common::HistCutMatrix cmat = GetHostCutMatrix();
|
||||
|
||||
// Copy cut matrix to device.
|
||||
DeviceShard::DeviceHistCutMatrix cut;
|
||||
shard->ba.Allocate(0, true,
|
||||
&(shard->cut_.feature_segments), cmat.row_ptr.size(),
|
||||
&(shard->cut_.min_fvalue), cmat.min_val.size(),
|
||||
&(shard->cut_.gidx_fvalue_map), 24,
|
||||
&(shard->monotone_constraints), n_cols);
|
||||
shard->cut_.feature_segments.copy(cmat.row_ptr.begin(), cmat.row_ptr.end());
|
||||
shard->cut_.gidx_fvalue_map.copy(cmat.cut.begin(), cmat.cut.end());
|
||||
shard->monotone_constraints.copy(param.monotone_constraints.begin(),
|
||||
param.monotone_constraints.end());
|
||||
|
||||
// Initialize DeviceShard::hist
|
||||
shard->hist.Init(0, (max_bins - 1) * n_cols);
|
||||
shard->hist.AllocateHistogram(0);
|
||||
// Each row of hist_gpair represents gpairs for one feature.
|
||||
// Each entry represents a bin.
|
||||
std::vector<GradientPairPrecise> hist_gpair = GetHostHistGpair();
|
||||
std::vector<bst_float> hist;
|
||||
for (auto pair : hist_gpair) {
|
||||
hist.push_back(pair.GetGrad());
|
||||
hist.push_back(pair.GetHess());
|
||||
}
|
||||
|
||||
ASSERT_EQ(shard->hist.data.size(), hist.size());
|
||||
thrust::copy(hist.begin(), hist.end(),
|
||||
shard->hist.data.begin());
|
||||
|
||||
|
||||
// Initialize GPUHistMaker
|
||||
GPUHistMaker hist_maker = GPUHistMaker();
|
||||
hist_maker.param_ = param;
|
||||
hist_maker.shards_.push_back(std::move(shard));
|
||||
hist_maker.column_sampler_.Init(n_cols,
|
||||
param.colsample_bylevel,
|
||||
param.colsample_bytree,
|
||||
false);
|
||||
|
||||
RegTree tree;
|
||||
tree.InitModel();
|
||||
|
||||
MetaInfo info;
|
||||
info.num_row_ = n_rows;
|
||||
info.num_col_ = n_cols;
|
||||
|
||||
hist_maker.info_ = &info;
|
||||
hist_maker.node_value_constraints_.resize(1);
|
||||
hist_maker.node_value_constraints_[0].lower_bound = -1.0;
|
||||
hist_maker.node_value_constraints_[0].upper_bound = 1.0;
|
||||
|
||||
std::vector<DeviceSplitCandidate> res =
|
||||
hist_maker.EvaluateSplits({0}, &tree);
|
||||
|
||||
ASSERT_EQ(res.size(), 1);
|
||||
ASSERT_EQ(res[0].findex, 7);
|
||||
ASSERT_NEAR(res[0].fvalue, 0.26, xgboost::kRtEps);
|
||||
}
|
||||
|
||||
TEST(GpuHist, ApplySplit) {
|
||||
GPUHistMaker hist_maker = GPUHistMaker();
|
||||
int constexpr nid = 0;
|
||||
int constexpr n_rows = 16;
|
||||
int constexpr n_cols = 8;
|
||||
|
||||
TrainParam param;
|
||||
param.silent = true;
|
||||
|
||||
// Initialize shard
|
||||
for (size_t i = 0; i < n_cols; ++i) {
|
||||
param.monotone_constraints.emplace_back(0);
|
||||
}
|
||||
|
||||
hist_maker.shards_.resize(1);
|
||||
hist_maker.shards_[0].reset(new DeviceShard(0, 0, 0, n_rows, param));
|
||||
|
||||
auto& shard = hist_maker.shards_.at(0);
|
||||
shard->ridx_segments.resize(3); // 3 nodes.
|
||||
shard->node_sum_gradients.resize(3);
|
||||
|
||||
shard->ridx_segments[0] = Segment(0, n_rows);
|
||||
shard->ba.Allocate(0, true, &(shard->ridx), n_rows,
|
||||
&(shard->position), n_rows);
|
||||
shard->row_stride = n_cols;
|
||||
thrust::sequence(shard->ridx.CurrentDVec().tbegin(),
|
||||
shard->ridx.CurrentDVec().tend());
|
||||
dh::safe_cuda(cudaMallocHost(&(shard->tmp_pinned), sizeof(int64_t)));
|
||||
|
||||
// Initialize GPUHistMaker
|
||||
hist_maker.param_ = param;
|
||||
RegTree tree;
|
||||
tree.InitModel();
|
||||
|
||||
DeviceSplitCandidate candidate;
|
||||
candidate.Update(2, kLeftDir,
|
||||
0.59, 4, // fvalue has to be equal to one of the cut field
|
||||
GradientPair(8.2, 2.8), GradientPair(6.3, 3.6),
|
||||
GPUTrainingParam(param));
|
||||
GPUHistMaker::ExpandEntry candidate_entry {0, 0, candidate, 0};
|
||||
candidate_entry.nid = nid;
|
||||
|
||||
auto const& nodes = tree.GetNodes();
|
||||
size_t n_nodes = nodes.size();
|
||||
|
||||
// Used to get bin_id in update position.
|
||||
common::HistCutMatrix cmat = GetHostCutMatrix();
|
||||
hist_maker.hmat_ = cmat;
|
||||
|
||||
MetaInfo info;
|
||||
info.num_row_ = n_rows;
|
||||
info.num_col_ = n_cols;
|
||||
info.num_nonzero_ = n_rows * n_cols; // Dense
|
||||
|
||||
// Initialize gidx
|
||||
int n_bins = 24;
|
||||
int row_stride = n_cols;
|
||||
int num_symbols = n_bins + 1;
|
||||
size_t compressed_size_bytes =
|
||||
common::CompressedBufferWriter::CalculateBufferSize(
|
||||
row_stride * n_rows, num_symbols);
|
||||
shard->ba.Allocate(0, param.silent,
|
||||
&(shard->gidx_buffer), compressed_size_bytes);
|
||||
|
||||
common::CompressedBufferWriter wr(num_symbols);
|
||||
std::vector<int> h_gidx (n_rows * row_stride);
|
||||
std::iota(h_gidx.begin(), h_gidx.end(), 0);
|
||||
std::vector<common::CompressedByteT> h_gidx_compressed (compressed_size_bytes);
|
||||
|
||||
wr.Write(h_gidx_compressed.data(), h_gidx.begin(), h_gidx.end());
|
||||
shard->gidx_buffer.copy(h_gidx_compressed.begin(), h_gidx_compressed.end());
|
||||
|
||||
shard->gidx = common::CompressedIterator<uint32_t>(
|
||||
shard->gidx_buffer.Data(), num_symbols);
|
||||
|
||||
hist_maker.info_ = &info;
|
||||
hist_maker.ApplySplit(candidate_entry, &tree);
|
||||
|
||||
ASSERT_FALSE(tree[nid].IsLeaf());
|
||||
|
||||
int left_nidx = tree[nid].LeftChild();
|
||||
int right_nidx = tree[nid].RightChild();
|
||||
|
||||
ASSERT_EQ(shard->ridx_segments[left_nidx].begin, 0);
|
||||
ASSERT_EQ(shard->ridx_segments[left_nidx].end, 6);
|
||||
ASSERT_EQ(shard->ridx_segments[right_nidx].begin, 6);
|
||||
ASSERT_EQ(shard->ridx_segments[right_nidx].end, 16);
|
||||
}
|
||||
|
||||
TEST(GpuHist, MGPU_mock) {
|
||||
// Attempt to choose multiple GPU devices
|
||||
int ngpu;
|
||||
dh::safe_cuda(cudaGetDeviceCount(&ngpu));
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user