Optimize ‘hist’ for multi-core CPU (#4529)
* Initial performance optimizations for xgboost * remove includes * revert float->double * fix for CI * fix for CI * fix for CI * fix for CI * fix for CI * fix for CI * fix for CI * fix for CI * fix for CI * fix for CI * Check existence of _mm_prefetch and __builtin_prefetch * Fix lint * optimizations for CPU * appling comments in review * add some comments, code refactoring * fixing issues in CI * adding runtime checks * remove 1 extra check * remove extra checks in BuildHist * remove checks * add debug info * added debug info * revert changes * added comments * Apply suggestions from code review Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu> * apply review comments * Remove unused function CreateNewNodes() * Add descriptive comment on node_idx variable in QuantileHistMaker::Builder::BuildHistsBatch()
This commit is contained in:
Egor Smirnov
committed by
Philip Hyunsu Cho
Philip Hyunsu Cho
parent
abffbe014e
commit
4d6590be3c
@@ -1,15 +1,15 @@
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/*!
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* Copyright 2017-2019 by Contributors
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* \file hist_util.h
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* \file hist_util.cc
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*/
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#include "./hist_util.h"
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#include <dmlc/timer.h>
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#include <rabit/rabit.h>
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#include <dmlc/omp.h>
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#include <numeric>
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#include <vector>
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#include "./random.h"
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#include "./column_matrix.h"
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#include "./hist_util.h"
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#include "./quantile.h"
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#include "./../tree/updater_quantile_hist.h"
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@@ -178,7 +178,7 @@ uint32_t HistCutMatrix::GetBinIdx(const Entry& e) {
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void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
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cut.Init(p_fmat, max_num_bins);
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const size_t nthread = omp_get_max_threads();
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const int32_t nthread = omp_get_max_threads();
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const uint32_t nbins = cut.row_ptr.back();
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hit_count.resize(nbins, 0);
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hit_count_tloc_.resize(nthread * nbins, 0);
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@@ -260,8 +260,8 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
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}
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#pragma omp parallel for num_threads(nthread) schedule(static)
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for (bst_omp_uint idx = 0; idx < bst_omp_uint(nbins); ++idx) {
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for (size_t tid = 0; tid < nthread; ++tid) {
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for (int32_t idx = 0; idx < int32_t(nbins); ++idx) {
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for (int32_t tid = 0; tid < nthread; ++tid) {
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hit_count[idx] += hit_count_tloc_[tid * nbins + idx];
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}
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}
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@@ -411,7 +411,7 @@ FastFeatureGrouping(const GHistIndexMatrix& gmat,
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for (auto fid : group) {
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nnz += feature_nnz[fid];
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}
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double nnz_rate = static_cast<double>(nnz) / nrow;
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float nnz_rate = static_cast<float>(nnz) / nrow;
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// take apart small sparse group, due it will not gain on speed
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if (nnz_rate <= param.sparse_threshold) {
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for (auto fid : group) {
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@@ -496,176 +496,144 @@ void GHistIndexBlockMatrix::Init(const GHistIndexMatrix& gmat,
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}
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}
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void GHistBuilder::BuildHist(const std::vector<GradientPair>& gpair,
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const RowSetCollection::Elem row_indices,
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const GHistIndexMatrix& gmat,
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GHistRow hist) {
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const size_t nthread = static_cast<size_t>(this->nthread_);
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data_.resize(nbins_ * nthread_);
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const size_t* rid = row_indices.begin;
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const size_t nrows = row_indices.Size();
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const uint32_t* index = gmat.index.data();
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const size_t* row_ptr = gmat.row_ptr.data();
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const float* pgh = reinterpret_cast<const float*>(gpair.data());
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double* hist_data = reinterpret_cast<double*>(hist.data());
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double* data = reinterpret_cast<double*>(data_.data());
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const size_t block_size = 512;
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size_t n_blocks = nrows/block_size;
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n_blocks += !!(nrows - n_blocks*block_size);
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const size_t nthread_to_process = std::min(nthread, n_blocks);
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memset(thread_init_.data(), '\0', nthread_to_process*sizeof(size_t));
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// used when data layout is kDenseDataZeroBased or kDenseDataOneBased
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// it means that "row_ptr" is not needed for hist computations
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void BuildHistLocalDense(size_t istart, size_t iend, size_t nrows, const size_t* rid,
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const uint32_t* index, const GradientPair::ValueT* pgh, const size_t* row_ptr,
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GradStatHist::GradType* data_local_hist, GradStatHist* grad_stat_global) {
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GradStatHist grad_stat; // make local var to prevent false sharing
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const size_t n_features = row_ptr[rid[istart]+1] - row_ptr[rid[istart]];
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const size_t cache_line_size = 64;
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const size_t prefetch_step = cache_line_size / sizeof(*index);
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const size_t prefetch_offset = 10;
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size_t no_prefetch_size = prefetch_offset + cache_line_size/sizeof(*rid);
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no_prefetch_size = no_prefetch_size > nrows ? nrows : no_prefetch_size;
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#pragma omp parallel for num_threads(nthread_to_process) schedule(guided)
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for (bst_omp_uint iblock = 0; iblock < n_blocks; iblock++) {
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dmlc::omp_uint tid = omp_get_thread_num();
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double* data_local_hist = ((nthread_to_process == 1) ? hist_data :
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reinterpret_cast<double*>(data_.data() + tid * nbins_));
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// if read each row in some block of bin-matrix - it's dense block
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// and we dont need SW prefetch in this case
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const bool denseBlock = (rid[iend-1] - rid[istart]) == (iend - istart - 1);
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if (!thread_init_[tid]) {
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memset(data_local_hist, '\0', 2*nbins_*sizeof(double));
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thread_init_[tid] = true;
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}
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const size_t istart = iblock*block_size;
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const size_t iend = (((iblock+1)*block_size > nrows) ? nrows : istart + block_size);
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if (iend < nrows - no_prefetch_size && !denseBlock) {
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for (size_t i = istart; i < iend; ++i) {
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const size_t icol_start = row_ptr[rid[i]];
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const size_t icol_end = row_ptr[rid[i]+1];
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const size_t icol_start = rid[i] * n_features;
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const size_t icol_start_prefetch = rid[i+prefetch_offset] * n_features;
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const size_t idx_gh = 2*rid[i];
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if (i < nrows - no_prefetch_size) {
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PREFETCH_READ_T0(row_ptr + rid[i + prefetch_offset]);
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PREFETCH_READ_T0(pgh + 2*rid[i + prefetch_offset]);
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PREFETCH_READ_T0(pgh + 2*rid[i + prefetch_offset]);
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for (size_t j = icol_start_prefetch; j < icol_start_prefetch + n_features;
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j += prefetch_step) {
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PREFETCH_READ_T0(index + j);
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}
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for (size_t j = icol_start; j < icol_end; ++j) {
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const uint32_t idx_bin = 2*index[j];
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const size_t idx_gh = 2*rid[i];
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grad_stat.sum_grad += pgh[idx_gh];
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grad_stat.sum_hess += pgh[idx_gh+1];
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for (size_t j = icol_start; j < icol_start + n_features; ++j) {
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const uint32_t idx_bin = 2*index[j];
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data_local_hist[idx_bin] += pgh[idx_gh];
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data_local_hist[idx_bin+1] += pgh[idx_gh+1];
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}
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}
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}
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} else {
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for (size_t i = istart; i < iend; ++i) {
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const size_t icol_start = rid[i] * n_features;
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const size_t idx_gh = 2*rid[i];
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grad_stat.sum_grad += pgh[idx_gh];
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grad_stat.sum_hess += pgh[idx_gh+1];
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if (nthread_to_process > 1) {
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const size_t size = (2*nbins_);
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const size_t block_size = 1024;
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size_t n_blocks = size/block_size;
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n_blocks += !!(size - n_blocks*block_size);
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size_t n_worked_bins = 0;
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for (size_t i = 0; i < nthread_to_process; ++i) {
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if (thread_init_[i]) {
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thread_init_[n_worked_bins++] = i;
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}
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}
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#pragma omp parallel for num_threads(std::min(nthread, n_blocks)) schedule(guided)
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for (bst_omp_uint iblock = 0; iblock < n_blocks; iblock++) {
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const size_t istart = iblock * block_size;
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const size_t iend = (((iblock + 1) * block_size > size) ? size : istart + block_size);
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const size_t bin = 2 * thread_init_[0] * nbins_;
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memcpy(hist_data + istart, (data + bin + istart), sizeof(double) * (iend - istart));
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for (size_t i_bin_part = 1; i_bin_part < n_worked_bins; ++i_bin_part) {
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const size_t bin = 2 * thread_init_[i_bin_part] * nbins_;
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for (size_t i = istart; i < iend; i++) {
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hist_data[i] += data[bin + i];
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}
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for (size_t j = icol_start; j < icol_start + n_features; ++j) {
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const uint32_t idx_bin = 2*index[j];
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data_local_hist[idx_bin] += pgh[idx_gh];
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data_local_hist[idx_bin+1] += pgh[idx_gh+1];
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}
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}
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}
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grad_stat_global->Add(grad_stat);
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}
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void GHistBuilder::BuildBlockHist(const std::vector<GradientPair>& gpair,
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const RowSetCollection::Elem row_indices,
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const GHistIndexBlockMatrix& gmatb,
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GHistRow hist) {
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constexpr int kUnroll = 8; // loop unrolling factor
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const size_t nblock = gmatb.GetNumBlock();
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const size_t nrows = row_indices.end - row_indices.begin;
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const size_t rest = nrows % kUnroll;
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// used when data layout is kSparseData
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// it means that "row_ptr" is needed for hist computations
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void BuildHistLocalSparse(size_t istart, size_t iend, size_t nrows, const size_t* rid,
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const uint32_t* index, const GradientPair::ValueT* pgh, const size_t* row_ptr,
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GradStatHist::GradType* data_local_hist, GradStatHist* grad_stat_global) {
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GradStatHist grad_stat; // make local var to prevent false sharing
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#if defined(_OPENMP)
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const auto nthread = static_cast<bst_omp_uint>(this->nthread_); // NOLINT
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#endif // defined(_OPENMP)
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tree::GradStats* p_hist = hist.data();
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const size_t cache_line_size = 64;
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const size_t prefetch_step = cache_line_size / sizeof(index[0]);
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const size_t prefetch_offset = 10;
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#pragma omp parallel for num_threads(nthread) schedule(guided)
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for (bst_omp_uint bid = 0; bid < nblock; ++bid) {
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auto gmat = gmatb[bid];
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size_t no_prefetch_size = prefetch_offset + cache_line_size/sizeof(*rid);
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no_prefetch_size = no_prefetch_size > nrows ? nrows : no_prefetch_size;
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for (size_t i = 0; i < nrows - rest; i += kUnroll) {
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size_t rid[kUnroll];
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size_t ibegin[kUnroll];
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size_t iend[kUnroll];
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GradientPair stat[kUnroll];
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// if read each row in some block of bin-matrix - it's dense block
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// and we dont need SW prefetch in this case
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const bool denseBlock = (rid[iend-1] - rid[istart]) == (iend - istart);
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for (int k = 0; k < kUnroll; ++k) {
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rid[k] = row_indices.begin[i + k];
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ibegin[k] = gmat.row_ptr[rid[k]];
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iend[k] = gmat.row_ptr[rid[k] + 1];
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stat[k] = gpair[rid[k]];
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if (iend < nrows - no_prefetch_size && !denseBlock) {
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for (size_t i = istart; i < iend; ++i) {
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const size_t icol_start = row_ptr[rid[i]];
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const size_t icol_end = row_ptr[rid[i]+1];
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const size_t idx_gh = 2*rid[i];
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const size_t icol_start10 = row_ptr[rid[i+prefetch_offset]];
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const size_t icol_end10 = row_ptr[rid[i+prefetch_offset]+1];
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PREFETCH_READ_T0(pgh + 2*rid[i + prefetch_offset]);
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for (size_t j = icol_start10; j < icol_end10; j+=prefetch_step) {
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PREFETCH_READ_T0(index + j);
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}
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for (int k = 0; k < kUnroll; ++k) {
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for (size_t j = ibegin[k]; j < iend[k]; ++j) {
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const uint32_t bin = gmat.index[j];
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p_hist[bin].Add(stat[k]);
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}
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grad_stat.sum_grad += pgh[idx_gh];
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grad_stat.sum_hess += pgh[idx_gh+1];
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for (size_t j = icol_start; j < icol_end; ++j) {
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const uint32_t idx_bin = 2*index[j];
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data_local_hist[idx_bin] += pgh[idx_gh];
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data_local_hist[idx_bin+1] += pgh[idx_gh+1];
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}
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}
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for (size_t i = nrows - rest; i < nrows; ++i) {
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const size_t rid = row_indices.begin[i];
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const size_t ibegin = gmat.row_ptr[rid];
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const size_t iend = gmat.row_ptr[rid + 1];
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const GradientPair stat = gpair[rid];
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for (size_t j = ibegin; j < iend; ++j) {
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const uint32_t bin = gmat.index[j];
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p_hist[bin].Add(stat);
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} else {
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for (size_t i = istart; i < iend; ++i) {
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const size_t icol_start = row_ptr[rid[i]];
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const size_t icol_end = row_ptr[rid[i]+1];
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const size_t idx_gh = 2*rid[i];
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grad_stat.sum_grad += pgh[idx_gh];
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grad_stat.sum_hess += pgh[idx_gh+1];
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for (size_t j = icol_start; j < icol_end; ++j) {
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const uint32_t idx_bin = 2*index[j];
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data_local_hist[idx_bin] += pgh[idx_gh];
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data_local_hist[idx_bin+1] += pgh[idx_gh+1];
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}
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}
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}
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grad_stat_global->Add(grad_stat);
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}
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void GHistBuilder::SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
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const uint32_t nbins = static_cast<bst_omp_uint>(nbins_);
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constexpr int kUnroll = 8; // loop unrolling factor
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const uint32_t rest = nbins % kUnroll;
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void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
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GradStatHist* p_self = self.data();
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GradStatHist* p_sibling = sibling.data();
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GradStatHist* p_parent = parent.data();
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#if defined(_OPENMP)
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const auto nthread = static_cast<bst_omp_uint>(this->nthread_); // NOLINT
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#endif // defined(_OPENMP)
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tree::GradStats* p_self = self.data();
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tree::GradStats* p_sibling = sibling.data();
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tree::GradStats* p_parent = parent.data();
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const size_t size = self.size();
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CHECK_EQ(sibling.size(), size);
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CHECK_EQ(parent.size(), size);
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#pragma omp parallel for num_threads(nthread) schedule(static)
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for (bst_omp_uint bin_id = 0;
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bin_id < static_cast<bst_omp_uint>(nbins - rest); bin_id += kUnroll) {
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tree::GradStats pb[kUnroll];
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tree::GradStats sb[kUnroll];
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for (int k = 0; k < kUnroll; ++k) {
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pb[k] = p_parent[bin_id + k];
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const size_t block_size = 1024; // aproximatly 1024 values per block
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size_t n_blocks = size/block_size + !!(size%block_size);
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#pragma omp parallel for
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for (int iblock = 0; iblock < n_blocks; ++iblock) {
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const size_t ibegin = iblock*block_size;
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const size_t iend = (((iblock+1)*block_size > size) ? size : ibegin + block_size);
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for (bst_omp_uint bin_id = ibegin; bin_id < iend; bin_id++) {
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p_self[bin_id].SetSubstract(p_parent[bin_id], p_sibling[bin_id]);
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}
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for (int k = 0; k < kUnroll; ++k) {
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sb[k] = p_sibling[bin_id + k];
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}
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for (int k = 0; k < kUnroll; ++k) {
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p_self[bin_id + k].SetSubstract(pb[k], sb[k]);
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}
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}
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for (uint32_t bin_id = nbins - rest; bin_id < nbins; ++bin_id) {
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p_self[bin_id].SetSubstract(p_parent[bin_id], p_sibling[bin_id]);
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}
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}
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