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Support distributed CPU env for categorical data. (#7575)

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* Add support for cat data in sketch allreduce.
* Share tests between CPU and GPU.
This commit is contained in:
Jiaming Yuan 2022-01-18 21:56:07 +08:00 committed by GitHub
parent deab0e32ba
commit cc06fab9a7
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5 changed files with 299 additions and 150 deletions
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205
src/common/quantile.cc
View File

@ -1,13 +1,14 @@
/*!
* Copyright 2020-2022 by XGBoost Contributors
*/
#include "quantile.h"
#include <limits>
#include <utility>
#include "rabit/rabit.h"
#include "quantile.h"
#include "hist_util.h"
#include "categorical.h"
#include "hist_util.h"
#include "rabit/rabit.h"
namespace xgboost {
namespace common {
@ -15,7 +16,7 @@ namespace common {
template <typename WQSketch>
SketchContainerImpl<WQSketch>::SketchContainerImpl(std::vector<bst_row_t> columns_size,
int32_t max_bins,
common::Span<FeatureType const> feature_types,
Span<FeatureType const> feature_types,
bool use_group, int32_t n_threads)
: feature_types_(feature_types.cbegin(), feature_types.cend()),
columns_size_{std::move(columns_size)},
@ -67,7 +68,7 @@ std::vector<bst_feature_t> SketchContainerImpl<WQSketch>::LoadBalance(SparsePage
*/
auto page = batch.GetView();
size_t const total_entries = page.data.size();
size_t const entries_per_thread = common::DivRoundUp(total_entries, nthreads);
size_t const entries_per_thread = DivRoundUp(total_entries, nthreads);
std::vector<std::vector<bst_row_t>> column_sizes(nthreads);
for (auto& column : column_sizes) {
@ -213,53 +214,162 @@ void SketchContainerImpl<WQSketch>::PushRowPage(SparsePage const &page, MetaInfo
monitor_.Stop(__func__);
}
namespace {
/**
* \brief A view over gathered sketch values.
*/
template <typename T>
struct QuantileAllreduce {
common::Span<T> global_values;
common::Span<size_t> worker_indptr;
common::Span<size_t> feature_indptr;
size_t n_features{0};
/**
* \brief Get sketch values of the a feature from a worker.
*
* \param rank rank of target worker
* \param fidx feature idx
*/
auto Values(int32_t rank, bst_feature_t fidx) const {
// get span for worker
auto wsize = worker_indptr[rank + 1] - worker_indptr[rank];
auto worker_values = global_values.subspan(worker_indptr[rank], wsize);
auto psize = n_features + 1;
auto worker_feat_indptr = feature_indptr.subspan(psize * rank, psize);
// get span for feature
auto feat_beg = worker_feat_indptr[fidx];
auto feat_size = worker_feat_indptr[fidx + 1] - feat_beg;
return worker_values.subspan(feat_beg, feat_size);
}
};
/**
* \brief Merge all categories from other workers.
*/
void AllreduceCategories(Span<FeatureType const> feature_types, int32_t n_threads,
std::vector<std::set<bst_cat_t>> *p_categories) {
auto &categories = *p_categories;
auto world_size = rabit::GetWorldSize();
auto rank = rabit::GetRank();
if (world_size == 1) {
return;
}
// CSC indptr to each feature
std::vector<size_t> feature_ptr(categories.size() + 1, 0);
for (size_t i = 0; i < categories.size(); ++i) {
auto const &feat = categories[i];
feature_ptr[i + 1] = feat.size();
}
std::partial_sum(feature_ptr.begin(), feature_ptr.end(), feature_ptr.begin());
CHECK_EQ(feature_ptr.front(), 0);
// gather all feature ptrs from workers
std::vector<size_t> global_feat_ptrs(feature_ptr.size() * world_size, 0);
size_t feat_begin = rank * feature_ptr.size(); // pointer to current worker
std::copy(feature_ptr.begin(), feature_ptr.end(), global_feat_ptrs.begin() + feat_begin);
rabit::Allreduce<rabit::op::Sum>(global_feat_ptrs.data(), global_feat_ptrs.size());
// move all categories into a flatten vector to prepare for allreduce
size_t total = feature_ptr.back();
std::vector<bst_cat_t> flatten(total, 0);
auto cursor{flatten.begin()};
for (auto const &feat : categories) {
cursor = std::copy(feat.cbegin(), feat.cend(), cursor);
}
// indptr for indexing workers
std::vector<size_t> global_worker_ptr(world_size + 1, 0);
global_worker_ptr[rank + 1] = total; // shift 1 to right for constructing the indptr
rabit::Allreduce<rabit::op::Sum>(global_worker_ptr.data(), global_worker_ptr.size());
std::partial_sum(global_worker_ptr.cbegin(), global_worker_ptr.cend(), global_worker_ptr.begin());
// total number of categories in all workers with all features
auto gtotal = global_worker_ptr.back();
// categories in all workers with all features.
std::vector<bst_cat_t> global_categories(gtotal, 0);
auto rank_begin = global_worker_ptr[rank];
auto rank_size = global_worker_ptr[rank + 1] - rank_begin;
CHECK_EQ(rank_size, total);
std::copy(flatten.cbegin(), flatten.cend(), global_categories.begin() + rank_begin);
// gather values from all workers.
rabit::Allreduce<rabit::op::Sum>(global_categories.data(), global_categories.size());
QuantileAllreduce<bst_cat_t> allreduce_result{global_categories, global_worker_ptr,
global_feat_ptrs, categories.size()};
ParallelFor(categories.size(), n_threads, [&](auto fidx) {
if (!IsCat(feature_types, fidx)) {
return;
}
for (int32_t r = 0; r < world_size; ++r) {
if (r == rank) {
// continue if it's current worker.
continue;
}
// 1 feature of 1 worker
auto worker_feature = allreduce_result.Values(r, fidx);
for (auto c : worker_feature) {
categories[fidx].emplace(c);
}
}
});
}
} // anonymous namespace
template <typename WQSketch>
void SketchContainerImpl<WQSketch>::GatherSketchInfo(
std::vector<typename WQSketch::SummaryContainer> const &reduced,
std::vector<size_t> *p_worker_segments,
std::vector<bst_row_t> *p_sketches_scan,
std::vector<size_t> *p_worker_segments, std::vector<bst_row_t> *p_sketches_scan,
std::vector<typename WQSketch::Entry> *p_global_sketches) {
auto& worker_segments = *p_worker_segments;
auto &worker_segments = *p_worker_segments;
worker_segments.resize(1, 0);
auto world = rabit::GetWorldSize();
auto rank = rabit::GetRank();
auto n_columns = sketches_.size();
// get the size of each feature.
std::vector<bst_row_t> sketch_size;
for (auto const& sketch : reduced) {
sketch_size.push_back(sketch.size);
for (size_t i = 0; i < reduced.size(); ++i) {
if (IsCat(feature_types_, i)) {
sketch_size.push_back(0);
} else {
sketch_size.push_back(reduced[i].size);
}
}
std::vector<bst_row_t>& sketches_scan = *p_sketches_scan;
// turn the size into CSC indptr
std::vector<bst_row_t> &sketches_scan = *p_sketches_scan;
sketches_scan.resize((n_columns + 1) * world, 0);
size_t beg_scan = rank * (n_columns + 1);
std::partial_sum(sketch_size.cbegin(), sketch_size.cend(),
sketches_scan.begin() + beg_scan + 1);
size_t beg_scan = rank * (n_columns + 1); // starting storage for current worker.
std::partial_sum(sketch_size.cbegin(), sketch_size.cend(), sketches_scan.begin() + beg_scan + 1);
// Gather all column pointers
rabit::Allreduce<rabit::op::Sum>(sketches_scan.data(), sketches_scan.size());
for (int32_t i = 0; i < world; ++i) {
size_t back = (i + 1) * (n_columns + 1) - 1;
auto n_entries = sketches_scan.at(back);
worker_segments.push_back(n_entries);
}
// Offset of sketch from each worker.
std::partial_sum(worker_segments.begin(), worker_segments.end(),
worker_segments.begin());
std::partial_sum(worker_segments.begin(), worker_segments.end(), worker_segments.begin());
CHECK_GE(worker_segments.size(), 1);
auto total = worker_segments.back();
auto& global_sketches = *p_global_sketches;
global_sketches.resize(total, typename WQSketch::Entry{0, 0, 0, 0});
auto &global_sketches = *p_global_sketches;
global_sketches.resize(total, typename WQSketch::Entry{0, 0, 0, 0});
auto worker_sketch = Span<typename WQSketch::Entry>{global_sketches}.subspan(
worker_segments[rank], worker_segments[rank + 1] - worker_segments[rank]);
size_t cursor = 0;
for (auto const &sketch : reduced) {
std::copy(sketch.data, sketch.data + sketch.size,
worker_sketch.begin() + cursor);
cursor += sketch.size;
auto cursor{worker_sketch.begin()};
for (size_t fidx = 0; fidx < reduced.size(); ++fidx) {
auto const &sketch = reduced[fidx];
if (IsCat(feature_types_, fidx)) {
// nothing to do if it's categorical feature, size is 0 so no need to change cursor
continue;
} else {
cursor = std::copy(sketch.data, sketch.data + sketch.size, cursor);
}
}
static_assert(sizeof(typename WQSketch::Entry) / 4 == sizeof(float), "");
static_assert(sizeof(typename WQSketch::Entry) / 4 == sizeof(float),
"Unexpected size of sketch entry.");
rabit::Allreduce<rabit::op::Sum>(
reinterpret_cast<float *>(global_sketches.data()),
global_sketches.size() * sizeof(typename WQSketch::Entry) / sizeof(float));
@ -270,6 +380,13 @@ void SketchContainerImpl<WQSketch>::AllReduce(
std::vector<typename WQSketch::SummaryContainer> *p_reduced,
std::vector<int32_t>* p_num_cuts) {
monitor_.Start(__func__);
size_t n_columns = sketches_.size();
rabit::Allreduce<rabit::op::Max>(&n_columns, 1);
CHECK_EQ(n_columns, sketches_.size()) << "Number of columns differs across workers";
AllreduceCategories(feature_types_, n_threads_, &categories_);
auto& num_cuts = *p_num_cuts;
CHECK_EQ(num_cuts.size(), 0);
num_cuts.resize(sketches_.size());
@ -277,19 +394,19 @@ void SketchContainerImpl<WQSketch>::AllReduce(
auto &reduced = *p_reduced;
reduced.resize(sketches_.size());
size_t n_columns = sketches_.size();
rabit::Allreduce<rabit::op::Max>(&n_columns, 1);
CHECK_EQ(n_columns, sketches_.size()) << "Number of columns differs across workers";
// Prune the intermediate num cuts for synchronization.
std::vector<bst_row_t> global_column_size(columns_size_);
rabit::Allreduce<rabit::op::Sum>(global_column_size.data(), global_column_size.size());
ParallelFor(sketches_.size(), n_threads_, [&](size_t i) {
int32_t intermediate_num_cuts = static_cast<int32_t>(
std::min(global_column_size[i],
static_cast<size_t>(max_bins_ * WQSketch::kFactor)));
if (global_column_size[i] != 0) {
std::min(global_column_size[i], static_cast<size_t>(max_bins_ * WQSketch::kFactor)));
if (global_column_size[i] == 0) {
return;
}
if (IsCat(feature_types_, i)) {
intermediate_num_cuts = categories_[i].size();
} else {
typename WQSketch::SummaryContainer out;
sketches_[i].GetSummary(&out);
reduced[i].Reserve(intermediate_num_cuts);
@ -309,25 +426,21 @@ void SketchContainerImpl<WQSketch>::AllReduce(
std::vector<bst_row_t> sketches_scan((n_columns + 1) * world, 0);
std::vector<typename WQSketch::Entry> global_sketches;
this->GatherSketchInfo(reduced, &worker_segments, &sketches_scan,
&global_sketches);
this->GatherSketchInfo(reduced, &worker_segments, &sketches_scan, &global_sketches);
std::vector<typename WQSketch::SummaryContainer> final_sketches(n_columns);
QuantileAllreduce<typename WQSketch::Entry> allreduce_result{global_sketches, worker_segments,
sketches_scan, n_columns};
ParallelFor(n_columns, n_threads_, [&](auto fidx) {
int32_t intermediate_num_cuts = num_cuts[fidx];
auto nbytes =
WQSketch::SummaryContainer::CalcMemCost(intermediate_num_cuts);
auto nbytes = WQSketch::SummaryContainer::CalcMemCost(intermediate_num_cuts);
if (IsCat(feature_types_, fidx)) {
return;
}
for (int32_t i = 1; i < world + 1; ++i) {
auto size = worker_segments.at(i) - worker_segments[i - 1];
auto worker_sketches =
Span<typename WQSketch::Entry>{global_sketches}.subspan(worker_segments[i - 1], size);
auto worker_scan =
Span<bst_row_t>(sketches_scan)
.subspan((i - 1) * (n_columns + 1), (n_columns + 1));
auto worker_feature = worker_sketches.subspan(
worker_scan[fidx], worker_scan[fidx + 1] - worker_scan[fidx]);
for (int32_t r = 0; r < world; ++r) {
// 1 feature of 1 worker
auto worker_feature = allreduce_result.Values(r, fidx);
CHECK(worker_feature.data());
typename WQSketch::Summary summary(worker_feature.data(), worker_feature.size());
auto &out = final_sketches.at(fidx);

16
tests/cpp/common/test_quantile.cc
View File

@ -58,10 +58,17 @@ void TestDistributedQuantile(size_t rows, size_t cols) {
// Generate cuts for distributed environment.
auto sparsity = 0.5f;
auto rank = rabit::GetRank();
std::vector<FeatureType> ft(cols);
for (size_t i = 0; i < ft.size(); ++i) {
ft[i] = (i % 2 == 0) ? FeatureType::kNumerical : FeatureType::kCategorical;
}
auto m = RandomDataGenerator{rows, cols, sparsity}
.Seed(rank)
.Lower(.0f)
.Upper(1.0f)
.Type(ft)
.MaxCategory(13)
.GenerateDMatrix();
std::vector<float> hessian(rows, 1.0);
@ -95,6 +102,8 @@ void TestDistributedQuantile(size_t rows, size_t cols) {
for (auto rank = 0; rank < world; ++rank) {
auto m = RandomDataGenerator{rows, cols, sparsity}
.Seed(rank)
.Type(ft)
.MaxCategory(13)
.Lower(.0f)
.Upper(1.0f)
.GenerateDMatrix();
@ -181,8 +190,15 @@ TEST(Quantile, SameOnAllWorkers) {
kRows, [=](int32_t seed, size_t n_bins, MetaInfo const &info) {
auto rank = rabit::GetRank();
HostDeviceVector<float> storage;
std::vector<FeatureType> ft(kCols);
for (size_t i = 0; i < ft.size(); ++i) {
ft[i] = (i % 2 == 0) ? FeatureType::kNumerical : FeatureType::kCategorical;
}
auto m = RandomDataGenerator{kRows, kCols, 0}
.Device(0)
.Type(ft)
.MaxCategory(17)
.Seed(rank + seed)
.GenerateDMatrix();
auto cuts = SketchOnDMatrix(m.get(), n_bins);

2
tests/cpp/helpers.cc
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@ -183,7 +183,7 @@ bool IsNear(std::vector<xgboost::bst_float>::const_iterator _beg1,
}
SimpleLCG::StateType SimpleLCG::operator()() {
state_ = (alpha_ * state_) % mod_;
state_ = (alpha_ * state_ + (state_ == 0 ? kDefaultInit : 0)) % mod_;
return state_;
}
SimpleLCG::StateType SimpleLCG::Min() const { return min(); }

106
tests/python-gpu/test_gpu_with_dask.py
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@ -1,3 +1,4 @@
"""Copyright 2019-2022 XGBoost contributors"""
import sys
import os
from typing import Type, TypeVar, Any, Dict, List, Tuple
@ -36,7 +37,8 @@ from test_with_dask import generate_array # noqa
from test_with_dask import kCols as random_cols # noqa
from test_with_dask import suppress # noqa
from test_with_dask import run_tree_stats # noqa
from test_with_dask import run_categorical # noqa
from test_with_dask import make_categorical # noqa
try:
@ -51,49 +53,6 @@ except ImportError:
pass
def make_categorical(
client: Client,
n_samples: int,
n_features: int,
n_categories: int,
onehot: bool = False,
) -> Tuple[dd.DataFrame, dd.Series]:
workers = _get_client_workers(client)
n_workers = len(workers)
dfs = []
def pack(**kwargs: Any) -> dd.DataFrame:
X, y = tm.make_categorical(**kwargs)
X["label"] = y
return X
meta = pack(
n_samples=1, n_features=n_features, n_categories=n_categories, onehot=False
)
for i, worker in enumerate(workers):
l_n_samples = min(
n_samples // n_workers, n_samples - i * (n_samples // n_workers)
)
future = client.submit(
pack,
n_samples=l_n_samples,
n_features=n_features,
n_categories=n_categories,
onehot=False,
workers=[worker],
)
dfs.append(future)
df = dd.from_delayed(dfs, meta=meta)
y = df["label"]
X = df[df.columns.difference(["label"])]
if onehot:
return dd.get_dummies(X), y
return X, y
def run_with_dask_dataframe(DMatrixT: Type, client: Client) -> None:
import cupy as cp
cp.cuda.runtime.setDevice(0)
@ -184,69 +143,12 @@ def test_categorical(local_cuda_cluster: LocalCUDACluster) -> None:
with Client(local_cuda_cluster) as client:
import dask_cudf
rounds = 10
X, y = make_categorical(client, 10000, 30, 13)
X = dask_cudf.from_dask_dataframe(X)
X_onehot, _ = make_categorical(client, 10000, 30, 13, True)
X_onehot = dask_cudf.from_dask_dataframe(X_onehot)
parameters = {"tree_method": "gpu_hist"}
m = dxgb.DaskDMatrix(client, X_onehot, y, enable_categorical=True)
by_etl_results = dxgb.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)["history"]
m = dxgb.DaskDMatrix(client, X, y, enable_categorical=True)
output = dxgb.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)
by_builtin_results = output["history"]
np.testing.assert_allclose(
np.array(by_etl_results["Train"]["rmse"]),
np.array(by_builtin_results["Train"]["rmse"]),
rtol=1e-3,
)
assert tm.non_increasing(by_builtin_results["Train"]["rmse"])
def check_model_output(model: dxgb.Booster) -> None:
with tempfile.TemporaryDirectory() as tempdir:
path = os.path.join(tempdir, "model.json")
model.save_model(path)
with open(path, "r") as fd:
categorical = json.load(fd)
categories_sizes = np.array(
categorical["learner"]["gradient_booster"]["model"]["trees"][-1][
"categories_sizes"
]
)
assert categories_sizes.shape[0] != 0
np.testing.assert_allclose(categories_sizes, 1)
check_model_output(output["booster"])
reg = dxgb.DaskXGBRegressor(
enable_categorical=True, n_estimators=10, tree_method="gpu_hist"
)
reg.fit(X, y)
check_model_output(reg.get_booster())
reg = dxgb.DaskXGBRegressor(
enable_categorical=True, n_estimators=10
)
with pytest.raises(ValueError):
reg.fit(X, y)
run_categorical(client, "gpu_hist", X, X_onehot, y)
def to_cp(x: Any, DMatrixT: Type) -> Any:

120
tests/python/test_with_dask.py
View File

@ -1,3 +1,4 @@
"""Copyright 2019-2022 XGBoost contributors"""
from pathlib import Path
import pickle
import testing as tm
@ -64,6 +65,49 @@ def _get_client_workers(client: "Client") -> List[str]:
return list(workers.keys())
def make_categorical(
client: Client,
n_samples: int,
n_features: int,
n_categories: int,
onehot: bool = False,
) -> Tuple[dd.DataFrame, dd.Series]:
workers = _get_client_workers(client)
n_workers = len(workers)
dfs = []
def pack(**kwargs: Any) -> dd.DataFrame:
X, y = tm.make_categorical(**kwargs)
X["label"] = y
return X
meta = pack(
n_samples=1, n_features=n_features, n_categories=n_categories, onehot=False
)
for i, worker in enumerate(workers):
l_n_samples = min(
n_samples // n_workers, n_samples - i * (n_samples // n_workers)
)
future = client.submit(
pack,
n_samples=l_n_samples,
n_features=n_features,
n_categories=n_categories,
onehot=False,
workers=[worker],
)
dfs.append(future)
df = dd.from_delayed(dfs, meta=meta)
y = df["label"]
X = df[df.columns.difference(["label"])]
if onehot:
return dd.get_dummies(X), y
return X, y
def generate_array(
with_weights: bool = False
) -> Tuple[xgb.dask._DaskCollection, xgb.dask._DaskCollection,
@ -173,6 +217,80 @@ def test_dask_sparse(client: "Client") -> None:
)
def run_categorical(client: "Client", tree_method: str, X, X_onehot, y) -> None:
parameters = {"tree_method": tree_method, "max_cat_to_onehot": 9999} # force onehot
rounds = 10
m = xgb.dask.DaskDMatrix(client, X_onehot, y, enable_categorical=True)
by_etl_results = xgb.dask.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)["history"]
m = xgb.dask.DaskDMatrix(client, X, y, enable_categorical=True)
output = xgb.dask.train(
client,
parameters,
m,
num_boost_round=rounds,
evals=[(m, "Train")],
)
by_builtin_results = output["history"]
np.testing.assert_allclose(
np.array(by_etl_results["Train"]["rmse"]),
np.array(by_builtin_results["Train"]["rmse"]),
rtol=1e-3,
)
assert tm.non_increasing(by_builtin_results["Train"]["rmse"])
def check_model_output(model: xgb.dask.Booster) -> None:
with tempfile.TemporaryDirectory() as tempdir:
path = os.path.join(tempdir, "model.json")
model.save_model(path)
with open(path, "r") as fd:
categorical = json.load(fd)
categories_sizes = np.array(
categorical["learner"]["gradient_booster"]["model"]["trees"][-1][
"categories_sizes"
]
)
assert categories_sizes.shape[0] != 0
np.testing.assert_allclose(categories_sizes, 1)
check_model_output(output["booster"])
reg = xgb.dask.DaskXGBRegressor(
enable_categorical=True,
n_estimators=10,
tree_method=tree_method,
# force onehot
max_cat_to_onehot=9999
)
reg.fit(X, y)
check_model_output(reg.get_booster())
reg = xgb.dask.DaskXGBRegressor(
enable_categorical=True, n_estimators=10
)
with pytest.raises(ValueError):
reg.fit(X, y)
# check partition based
reg = xgb.dask.DaskXGBRegressor(
enable_categorical=True, n_estimators=10, tree_method=tree_method
)
reg.fit(X, y, eval_set=[(X, y)])
assert tm.non_increasing(reg.evals_result()["validation_0"]["rmse"])
def test_categorical(client: "Client") -> None:
X, y = make_categorical(client, 10000, 30, 13)
X_onehot, _ = make_categorical(client, 10000, 30, 13, True)
run_categorical(client, "approx", X, X_onehot, y)
def test_dask_predict_shape_infer(client: "Client") -> None:
X, y = make_classification(n_samples=1000, n_informative=5, n_classes=3)
X_ = dd.from_array(X, chunksize=100)
@ -1167,7 +1285,7 @@ class TestWithDask:
exe: Optional[str] = None
for possible_path in {'./testxgboost', './build/testxgboost',
'../build/testxgboost',
'../build/cpubuild/testxgboost',
'../cpu-build/testxgboost'}:
if os.path.exists(possible_path):
exe = possible_path