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Distributed Fast Histogram Algorithm (#4011)

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* add back train method but mark as deprecated

* add back train method but mark as deprecated

* add back train method but mark as deprecated

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* fix scalastyle error

* init

* allow hist algo

* more changes

* temp

* update

* remove hist sync

* udpate rabit

* change hist size

* change the histogram

* update kfactor

* sync per node stats

* temp

* update

* final

* code clean

* update rabit

* more cleanup

* fix errors

* fix failed tests

* enforce c++11

* fix lint issue

* broadcast subsampled feature correctly

* revert some changes

* fix lint issue

* enable monotone and interaction constraints

* don't specify default for monotone and interactions

* update docs
This commit is contained in:
Nan Zhu 2019-02-05 05:12:53 -08:00 committed by GitHub
parent 8905df4a18
commit ae3bb9c2d5
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GPG Key ID: 4AEE18F83AFDEB23
16 changed files with 169 additions and 88 deletions
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4
doc/parameter.rst
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@ -110,7 +110,7 @@ Parameters for Tree Booster
* ``tree_method`` string [default= ``auto``]
- The tree construction algorithm used in XGBoost. See description in the `reference paper <http://arxiv.org/abs/1603.02754>`_.
- Distributed and external memory version only support ``tree_method=approx``.
- XGBoost supports ``hist`` and ``approx`` for distributed training and only support ``approx`` for external memory version.
- Choices: ``auto``, ``exact``, ``approx``, ``hist``, ``gpu_exact``, ``gpu_hist``
- ``auto``: Use heuristic to choose the fastest method.
@ -152,7 +152,7 @@ Parameters for Tree Booster
- ``refresh``: refreshes tree's statistics and/or leaf values based on the current data. Note that no random subsampling of data rows is performed.
- ``prune``: prunes the splits where loss < min_split_loss (or gamma).
- In a distributed setting, the implicit updater sequence value would be adjusted to ``grow_histmaker,prune``.
- In a distributed setting, the implicit updater sequence value would be adjusted to ``grow_histmaker,prune`` by default, and you can set ``tree_method`` as ``hist`` to use ``grow_histmaker``.
* ``refresh_leaf`` [default=1]

10
include/xgboost/build_config.h
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@ -1,20 +1,18 @@
/*!
* Copyright (c) 2018 by Contributors
* Copyright 2019 by Contributors
* \file build_config.h
* \brief Fall-back logic for platform-specific feature detection.
* \author Hyunsu Philip Cho
*/
#ifndef XGBOOST_BUILD_CONFIG_H_
#define XGBOOST_BUILD_CONFIG_H_
/* default logic for software pre-fetching */
#if (defined(_MSC_VER) && (defined(_M_IX86) || defined(_M_AMD64))) || defined(__INTEL_COMPILER)
// Enable _mm_prefetch for Intel compiler and MSVC+x86
// Enable _mm_prefetch for Intel compiler and MSVC+x86
#define XGBOOST_MM_PREFETCH_PRESENT
#define XGBOOST_BUILTIN_PREFETCH_PRESENT
#elif defined(__GNUC__)
// Enable __builtin_prefetch for GCC
#define XGBOOST_BUILTIN_PREFETCH_PRESENT
// Enable __builtin_prefetch for GCC
#define XGBOOST_BUILTIN_PREFETCH_PRESENT
#endif
#endif // XGBOOST_BUILD_CONFIG_H_

1
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala
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@ -31,7 +31,6 @@ object SparkTraining {
println("Usage: program input_path")
sys.exit(1)
}
val spark = SparkSession.builder().getOrCreate()
val inputPath = args(0)
val schema = new StructType(Array(

6
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
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@ -263,8 +263,10 @@ object XGBoost extends Serializable {
validateSparkSslConf(sparkContext)
if (params.contains("tree_method")) {
require(params("tree_method") != "hist", "xgboost4j-spark does not support fast histogram" +
" for now")
require(params("tree_method") == "hist" ||
params("tree_method") == "approx" ||
params("tree_method") == "auto", "xgboost4j-spark only supports tree_method as 'hist'," +
" 'approx' and 'auto'")
}
if (params.contains("train_test_ratio")) {
logger.warn("train_test_ratio is deprecated since XGBoost 0.82, we recommend to explicitly" +

33
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala
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@ -50,10 +50,21 @@ private[spark] trait BoosterParams extends Params {
* overfitting. [default=6] range: [1, Int.MaxValue]
*/
final val maxDepth = new IntParam(this, "maxDepth", "maximum depth of a tree, increase this " +
"value will make model more complex/likely to be overfitting.", (value: Int) => value >= 1)
"value will make model more complex/likely to be overfitting.", (value: Int) => value >= 0)
final def getMaxDepth: Int = $(maxDepth)
/**
* Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.
*/
final val maxLeaves = new IntParam(this, "maxLeaves",
"Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.",
(value: Int) => value >= 0)
final def getMaxLeaves: Int = $(maxDepth)
/**
* minimum sum of instance weight(hessian) needed in a child. If the tree partition step results
* in a leaf node with the sum of instance weight less than min_child_weight, then the building
@ -147,7 +158,9 @@ private[spark] trait BoosterParams extends Params {
* growth policy for fast histogram algorithm
*/
final val growPolicy = new Param[String](this, "growPolicy",
"growth policy for fast histogram algorithm",
"Controls a way new nodes are added to the tree. Currently supported only if" +
" tree_method is set to hist. Choices: depthwise, lossguide. depthwise: split at nodes" +
" closest to the root. lossguide: split at nodes with highest loss change.",
(value: String) => BoosterParams.supportedGrowthPolicies.contains(value))
final def getGrowPolicy: String = $(growPolicy)
@ -242,6 +255,22 @@ private[spark] trait BoosterParams extends Params {
final def getTreeLimit: Int = $(treeLimit)
final val monotoneConstraints = new Param[String](this, name = "monotoneConstraints",
doc = "a list in length of number of features, 1 indicate monotonic increasing, - 1 means " +
"decreasing, 0 means no constraint. If it is shorter than number of features, 0 will be " +
"padded ")
final def getMonotoneConstraints: String = $(monotoneConstraints)
final val interactionConstraints = new Param[String](this,
name = "interactionConstraints",
doc = "Constraints for interaction representing permitted interactions. The constraints" +
" must be specified in the form of a nest list, e.g. [[0, 1], [2, 3, 4]]," +
" where each inner list is a group of indices of features that are allowed to interact" +
" with each other. See tutorial for more information")
final def getInteractionConstraints: String = $(interactionConstraints)
setDefault(eta -> 0.3, gamma -> 0, maxDepth -> 6,
minChildWeight -> 1, maxDeltaStep -> 0,
growPolicy -> "depthwise", maxBins -> 16,

5
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala
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@ -231,10 +231,11 @@ private[spark] trait ParamMapFuncs extends Params {
def XGBoostToMLlibParams(xgboostParams: Map[String, Any]): Unit = {
for ((paramName, paramValue) <- xgboostParams) {
if ((paramName == "booster" && paramValue != "gbtree") ||
(paramName == "updater" && paramValue != "grow_histmaker,prune")) {
(paramName == "updater" && (paramValue != "grow_histmaker,prune" ||
paramValue != "hist"))) {
throw new IllegalArgumentException(s"you specified $paramName as $paramValue," +
s" XGBoost-Spark only supports gbtree as booster type" +
" and grow_histmaker,prune as the updater type")
" and grow_histmaker,prune or hist as the updater type")
}
val name = CaseFormat.LOWER_UNDERSCORE.to(CaseFormat.LOWER_CAMEL, paramName)
params.find(_.name == name) match {

62
jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala
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@ -18,18 +18,21 @@ package ml.dmlc.xgboost4j.scala.spark
import java.nio.file.Files
import java.util.concurrent.LinkedBlockingDeque
import ml.dmlc.xgboost4j.java.Rabit
import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
import ml.dmlc.xgboost4j.scala.DMatrix
import ml.dmlc.xgboost4j.scala.rabit.RabitTracker
import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.TaskContext
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql._
import org.scalatest.FunSuite
import scala.util.Random
import ml.dmlc.xgboost4j.java.Rabit
class XGBoostGeneralSuite extends FunSuite with PerTest {
test("test Rabit allreduce to validate Scala-implemented Rabit tracker") {
@ -108,66 +111,89 @@ class XGBoostGeneralSuite extends FunSuite with PerTest {
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
ignore("test with fast histo depthwise") {
test("test with fast histo with monotone_constraints") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "6", "silent" -> "1",
val paramMap = Map("eta" -> "1",
"max_depth" -> "6", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "depthwise",
"eval_metric" -> "error", "num_round" -> 5, "num_workers" -> math.min(numWorkers, 2))
// TODO: histogram algorithm seems to be very very sensitive to worker number
"num_round" -> 5, "num_workers" -> numWorkers, "monotone_constraints" -> "(1, 0)")
val model = new XGBoostClassifier(paramMap).fit(training)
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
ignore("test with fast histo lossguide") {
test("test with fast histo with interaction_constraints") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1",
"max_depth" -> "6", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "depthwise",
"num_round" -> 5, "num_workers" -> numWorkers, "interaction_constraints" -> "[[1,2],[2,3,4]]")
val model = new XGBoostClassifier(paramMap).fit(training)
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
test("test with fast histo depthwise") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1",
"max_depth" -> "6", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "depthwise",
"num_round" -> 5, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
}
test("test with fast histo lossguide") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "1",
"objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "lossguide",
"max_leaves" -> "8", "eval_metric" -> "error", "num_round" -> 5,
"num_workers" -> math.min(numWorkers, 2))
"max_leaves" -> "8", "num_round" -> 5,
"num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)
}
ignore("test with fast histo lossguide with max bin") {
test("test with fast histo lossguide with max bin") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "0",
"objective" -> "binary:logistic", "tree_method" -> "hist",
"grow_policy" -> "lossguide", "max_leaves" -> "8", "max_bin" -> "16",
"eval_metric" -> "error", "num_round" -> 5, "num_workers" -> math.min(numWorkers, 2))
"eval_metric" -> "error", "num_round" -> 5, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)
}
ignore("test with fast histo depthwidth with max depth") {
test("test with fast histo depthwidth with max depth") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "0",
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "6", "silent" -> "0",
"objective" -> "binary:logistic", "tree_method" -> "hist",
"grow_policy" -> "depthwise", "max_leaves" -> "8", "max_depth" -> "2",
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> math.min(numWorkers, 2))
"grow_policy" -> "depthwise", "max_depth" -> "2",
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)
}
ignore("test with fast histo depthwidth with max depth and max bin") {
test("test with fast histo depthwidth with max depth and max bin") {
val eval = new EvalError()
val training = buildDataFrame(Classification.train)
val testDM = new DMatrix(Classification.test.iterator)
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "0", "silent" -> "0",
val paramMap = Map("eta" -> "1", "gamma" -> "0.5", "max_depth" -> "6", "silent" -> "0",
"objective" -> "binary:logistic", "tree_method" -> "hist",
"grow_policy" -> "depthwise", "max_depth" -> "2", "max_bin" -> "2",
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> math.min(numWorkers, 2))
"eval_metric" -> "error", "num_round" -> 10, "num_workers" -> numWorkers)
val model = new XGBoostClassifier(paramMap).fit(training)
val x = eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM)
assert(x < 0.1)

5
jvm-packages/xgboost4j/src/test/java/ml/dmlc/xgboost4j/java/BoosterImplTest.java
View File

@ -382,11 +382,12 @@ public class BoosterImplTest {
metrics, null, null, 0);
for (int i = 0; i < metrics.length; i++)
for (int j = 1; j < metrics[i].length; j++) {
TestCase.assertTrue(metrics[i][j] >= metrics[i][j - 1]);
TestCase.assertTrue(metrics[i][j] >= metrics[i][j - 1] ||
Math.abs(metrics[i][j] - metrics[i][j - 1]) < 0.1);
}
for (int i = 0; i < metrics.length; i++)
for (int j = 0; j < metrics[i].length; j++) {
TestCase.assertTrue(metrics[i][j] >= threshold);
TestCase.assertTrue(metrics[i][j] >= threshold);
}
booster.dispose();
}

12
src/common/hist_util.cc
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@ -83,9 +83,9 @@ void HistCutMatrix::Init
summary_array[i].Reserve(max_num_bins * kFactor);
summary_array[i].SetPrune(out, max_num_bins * kFactor);
}
CHECK_EQ(summary_array.size(), in_sketchs->size());
size_t nbytes = WXQSketch::SummaryContainer::CalcMemCost(max_num_bins * kFactor);
sreducer.Allreduce(dmlc::BeginPtr(summary_array), nbytes, summary_array.size());
this->min_val.resize(sketchs.size());
row_ptr.push_back(0);
for (size_t fid = 0; fid < summary_array.size(); ++fid) {
@ -479,14 +479,14 @@ void GHistBuilder::BuildHist(const std::vector<GradientPair>& gpair,
#pragma omp parallel for num_threads(std::min(nthread, n_blocks)) schedule(guided)
for (bst_omp_uint iblock = 0; iblock < n_blocks; iblock++) {
const size_t istart = iblock*block_size;
const size_t iend = (((iblock+1)*block_size > size) ? size : istart + block_size);
const size_t istart = iblock * block_size;
const size_t iend = (((iblock + 1) * block_size > size) ? size : istart + block_size);
const size_t bin = 2*thread_init_[0]*nbins_;
memcpy(hist_data + istart, (data + bin + istart), sizeof(double)*(iend - istart));
const size_t bin = 2 * thread_init_[0] * nbins_;
memcpy(hist_data + istart, (data + bin + istart), sizeof(double) * (iend - istart));
for (size_t i_bin_part = 1; i_bin_part < n_worked_bins; ++i_bin_part) {
const size_t bin = 2*thread_init_[i_bin_part]*nbins_;
const size_t bin = 2 * thread_init_[i_bin_part] * nbins_;
for (size_t i = istart; i < iend; i++) {
hist_data[i] += data[bin + i];
}

11
src/common/hist_util.h
View File

@ -13,6 +13,7 @@
#include "row_set.h"
#include "../tree/param.h"
#include "./quantile.h"
#include "../include/rabit/rabit.h"
namespace xgboost {
@ -43,6 +44,10 @@ struct GHistEntry {
sum_hess += e.sum_hess;
}
inline static void Reduce(GHistEntry& a, const GHistEntry& b) { // NOLINT(*)
a.Add(b);
}
/*! \brief set sum to be difference of two GHistEntry's */
inline void SetSubtract(const GHistEntry& a, const GHistEntry& b) {
sum_grad = a.sum_grad - b.sum_grad;
@ -166,7 +171,7 @@ class GHistIndexBlockMatrix {
};
/*!
* \brief histogram of graident statistics for a single node.
* \brief histogram of gradient statistics for a single node.
* Consists of multiple GHistEntry's, each entry showing total graident statistics
* for that particular bin
* Uses global bin id so as to represent all features simultaneously
@ -254,6 +259,10 @@ class GHistBuilder {
// construct a histogram via subtraction trick
void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent);
uint32_t GetNumBins() {
return nbins_;
}
private:
/*! \brief number of threads for parallel computation */
size_t nthread_;

2
src/common/random.h
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@ -17,6 +17,8 @@
#include <numeric>
#include <random>
#include "io.h"
namespace xgboost {
namespace common {
/*!

21
src/learner.cc
View File

@ -598,8 +598,8 @@ class LearnerImpl : public Learner {
}
const TreeMethod current_tree_method = tparam_.tree_method;
if (rabit::IsDistributed()) {
/* Choose tree_method='approx' when distributed training is activated */
CHECK(tparam_.dsplit != DataSplitMode::kAuto)
<< "Precondition violated; dsplit cannot be 'auto' in distributed mode";
if (tparam_.dsplit == DataSplitMode::kCol) {
@ -614,14 +614,13 @@ class LearnerImpl : public Learner {
"for distributed training.";
break;
case TreeMethod::kApprox:
case TreeMethod::kHist:
// things are okay, do nothing
break;
case TreeMethod::kExact:
case TreeMethod::kHist:
LOG(WARNING) << "Tree method was set to be '"
<< (current_tree_method == TreeMethod::kExact ?
"exact" : "hist")
<< "', but only 'approx' is available for distributed "
LOG(CONSOLE) << "Tree method was set to be "
<< "exact"
<< "', but only 'approx' and 'hist' is available for distributed "
"training. The `tree_method` parameter is now being "
"changed to 'approx'";
break;
@ -633,7 +632,15 @@ class LearnerImpl : public Learner {
LOG(FATAL) << "Unknown tree_method ("
<< static_cast<int>(current_tree_method) << ") detected";
}
tparam_.tree_method = TreeMethod::kApprox;
if (current_tree_method != TreeMethod::kHist) {
LOG(CONSOLE) << "Tree method is automatically selected to be 'approx'"
" for distributed training.";
tparam_.tree_method = TreeMethod::kApprox;
} else {
LOG(CONSOLE) << "Tree method is specified to be 'hist'"
" for distributed training.";
tparam_.tree_method = TreeMethod::kHist;
}
} else if (!p_train->SingleColBlock()) {
/* Some tree methods are not available for external-memory DMatrix */
switch (current_tree_method) {

20
src/tree/updater_histmaker.cc
View File

@ -126,6 +126,7 @@ class HistMaker: public BaseMaker {
virtual void Update(const std::vector<GradientPair> &gpair,
DMatrix *p_fmat,
RegTree *p_tree) {
CHECK(param_.max_depth > 0) << "max_depth must be larger than 0";
this->InitData(gpair, *p_fmat, *p_tree);
this->InitWorkSet(p_fmat, *p_tree, &fwork_set_);
// mark root node as fresh.
@ -345,10 +346,7 @@ class CQHistMaker: public HistMaker<TStats> {
this->wspace_.Init(this->param_, 1);
// if it is C++11, use lazy evaluation for Allreduce,
// to gain speedup in recovery
#if __cplusplus >= 201103L
auto lazy_get_hist = [&]()
#endif
{
auto lazy_get_hist = [&]() {
thread_hist_.resize(omp_get_max_threads());
// start accumulating statistics
for (const auto &batch : p_fmat->GetSortedColumnBatches()) {
@ -371,22 +369,18 @@ class CQHistMaker: public HistMaker<TStats> {
for (size_t i = 0; i < this->qexpand_.size(); ++i) {
const int nid = this->qexpand_[i];
const int wid = this->node2workindex_[nid];
this->wspace_.hset[0][fset.size() + wid * (fset.size()+1)]
.data[0] = node_stats_[nid];
this->wspace_.hset[0][fset.size() + wid * (fset.size() + 1)]
.data[0] = node_stats_[nid];
}
};
// sync the histogram
// if it is C++11, use lazy evaluation for Allreduce
#if __cplusplus >= 201103L
this->histred_.Allreduce(dmlc::BeginPtr(this->wspace_.hset[0].data),
this->wspace_.hset[0].data.size(), lazy_get_hist);
#else
this->histred_.Allreduce(dmlc::BeginPtr(this->wspace_.hset[0].data),
this->wspace_.hset[0].data.size());
#endif
this->wspace_.hset[0].data.size(), lazy_get_hist);
}
void ResetPositionAfterSplit(DMatrix *p_fmat,
const RegTree &tree) override {
const RegTree &tree) override {
this->GetSplitSet(this->qexpand_, tree, &fsplit_set_);
}
void ResetPosAndPropose(const std::vector<GradientPair> &gpair,

53
src/tree/updater_quantile_hist.cc
View File

@ -156,12 +156,18 @@ void QuantileHistMaker::Builder::Update(const GHistIndexMatrix& gmat,
const int cright = (*p_tree)[nid].RightChild();
hist_.AddHistRow(cleft);
hist_.AddHistRow(cright);
if (row_set_collection_[cleft].Size() < row_set_collection_[cright].Size()) {
if (rabit::IsDistributed()) {
// in distributed mode, we need to keep consistent across workers
BuildHist(gpair_h, row_set_collection_[cleft], gmat, gmatb, hist_[cleft]);
SubtractionTrick(hist_[cright], hist_[cleft], hist_[nid]);
} else {
BuildHist(gpair_h, row_set_collection_[cright], gmat, gmatb, hist_[cright]);
SubtractionTrick(hist_[cleft], hist_[cright], hist_[nid]);
if (row_set_collection_[cleft].Size() < row_set_collection_[cright].Size()) {
BuildHist(gpair_h, row_set_collection_[cleft], gmat, gmatb, hist_[cleft]);
SubtractionTrick(hist_[cright], hist_[cleft], hist_[nid]);
} else {
BuildHist(gpair_h, row_set_collection_[cright], gmat, gmatb, hist_[cright]);
SubtractionTrick(hist_[cleft], hist_[cright], hist_[nid]);
}
}
time_build_hist += dmlc::GetTime() - tstart;
@ -617,23 +623,34 @@ void QuantileHistMaker::Builder::InitNewNode(int nid,
{
auto& stats = snode_[nid].stats;
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased) {
/* specialized code for dense data
For dense data (with no missing value),
the sum of gradient histogram is equal to snode[nid] */
GHistRow hist = hist_[nid];
const std::vector<uint32_t>& row_ptr = gmat.cut.row_ptr;
const uint32_t ibegin = row_ptr[fid_least_bins_];
const uint32_t iend = row_ptr[fid_least_bins_ + 1];
for (uint32_t i = ibegin; i < iend; ++i) {
const GHistEntry et = hist.begin[i];
stats.Add(et.sum_grad, et.sum_hess);
GHistRow hist = hist_[nid];
if (rabit::IsDistributed()) {
// in distributed mode, the node's stats should be calculated from histogram, otherwise,
// we will have wrong results in EnumerateSplit()
// here we take the last feature in cut
for (size_t i = gmat.cut.row_ptr[0]; i < gmat.cut.row_ptr[1]; i++) {
stats.Add(hist.begin[i].sum_grad, hist.begin[i].sum_hess);
}
} else {
const RowSetCollection::Elem e = row_set_collection_[nid];
for (const size_t* it = e.begin; it < e.end; ++it) {
stats.Add(gpair[*it]);
if (data_layout_ == kDenseDataZeroBased || data_layout_ == kDenseDataOneBased ||
rabit::IsDistributed()) {
/* specialized code for dense data
For dense data (with no missing value),
the sum of gradient histogram is equal to snode[nid]
GHistRow hist = hist_[nid];*/
const std::vector<uint32_t>& row_ptr = gmat.cut.row_ptr;
const uint32_t ibegin = row_ptr[fid_least_bins_];
const uint32_t iend = row_ptr[fid_least_bins_ + 1];
for (uint32_t i = ibegin; i < iend; ++i) {
const GHistEntry et = hist.begin[i];
stats.Add(et.sum_grad, et.sum_hess);
}
} else {
const RowSetCollection::Elem e = row_set_collection_[nid];
for (const size_t* it = e.begin; it < e.end; ++it) {
stats.Add(gpair[*it]);
}
}
}
}

3
src/tree/updater_quantile_hist.h
View File

@ -105,6 +105,7 @@ class QuantileHistMaker: public TreeUpdater {
} else {
hist_builder_.BuildHist(gpair, row_indices, gmat, hist);
}
this->histred_.Allreduce(hist.begin, hist_builder_.GetNumBins());
}
inline void SubtractionTrick(GHistRow self, GHistRow sibling, GHistRow parent) {
@ -225,6 +226,8 @@ class QuantileHistMaker: public TreeUpdater {
enum DataLayout { kDenseDataZeroBased, kDenseDataOneBased, kSparseData };
DataLayout data_layout_;
rabit::Reducer<GHistEntry, GHistEntry::Reduce> histred_;
};
std::unique_ptr<Builder> builder_;

9
src/tree/updater_refresh.cc
View File

@ -52,10 +52,7 @@ class TreeRefresher: public TreeUpdater {
}
// if it is C++11, use lazy evaluation for Allreduce,
// to gain speedup in recovery
#if __cplusplus >= 201103L
auto lazy_get_stats = [&]()
#endif
{
auto lazy_get_stats = [&]() {
const MetaInfo &info = p_fmat->Info();
// start accumulating statistics
for (const auto &batch : p_fmat->GetRowBatches()) {
@ -86,11 +83,7 @@ class TreeRefresher: public TreeUpdater {
}
}
};
#if __cplusplus >= 201103L
reducer_.Allreduce(dmlc::BeginPtr(stemp[0]), stemp[0].size(), lazy_get_stats);
#else
reducer_.Allreduce(dmlc::BeginPtr(stemp[0]), stemp[0].size());
#endif
// rescale learning rate according to size of trees
float lr = param_.learning_rate;
param_.learning_rate = lr / trees.size();