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Add data split mode to DMatrix MetaInfo (#8568)

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This commit is contained in:
Rong Ou
2022-12-25 04:37:37 -08:00
committed by GitHub
parent 77b069c25d
commit 3ceeb8c61c
20 changed files with 113 additions and 103 deletions
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30
src/c_api/c_api.cc
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@@ -206,17 +206,29 @@ XGB_DLL int XGBGetGlobalConfig(const char** json_str) {
}
XGB_DLL int XGDMatrixCreateFromFile(const char *fname, int silent, DMatrixHandle *out) {
API_BEGIN();
auto data_split_mode = DataSplitMode::kNone;
if (collective::IsFederated()) {
LOG(CONSOLE) << "XGBoost federated mode detected, not splitting data among workers";
} else if (collective::IsDistributed()) {
LOG(CONSOLE) << "XGBoost distributed mode detected, will split data among workers";
data_split_mode = DataSplitMode::kRow;
}
xgboost_CHECK_C_ARG_PTR(fname);
xgboost_CHECK_C_ARG_PTR(out);
*out = new std::shared_ptr<DMatrix>(DMatrix::Load(fname, silent != 0, data_split_mode));
Json config{Object()};
config["uri"] = std::string{fname};
config["silent"] = silent;
std::string config_str;
Json::Dump(config, &config_str);
return XGDMatrixCreateFromURI(config_str.c_str(), out);
}
XGB_DLL int XGDMatrixCreateFromURI(const char *config, DMatrixHandle *out) {
API_BEGIN();
xgboost_CHECK_C_ARG_PTR(config);
xgboost_CHECK_C_ARG_PTR(out);
auto jconfig = Json::Load(StringView{config});
std::string uri = RequiredArg<String>(jconfig, "uri", __func__);
auto silent = static_cast<bool>(OptionalArg<Integer, int64_t>(jconfig, "silent", 1));
auto data_split_mode =
static_cast<DataSplitMode>(OptionalArg<Integer, int64_t>(jconfig, "data_split_mode", 0));
*out = new std::shared_ptr<DMatrix>(DMatrix::Load(uri, silent, data_split_mode));
API_END();
}

13
src/cli_main.cc
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@@ -112,10 +112,8 @@ struct CLIParam : public XGBoostParameter<CLIParam> {
DMLC_DECLARE_FIELD(name_pred).set_default("pred.txt")
.describe("Name of the prediction file.");
DMLC_DECLARE_FIELD(dsplit).set_default(0)
.add_enum("auto", 0)
.add_enum("row", 0)
.add_enum("col", 1)
.add_enum("row", 2)
.add_enum("none", 3)
.describe("Data split mode.");
DMLC_DECLARE_FIELD(ntree_limit).set_default(0).set_lower_bound(0)
.describe("(Deprecated) Use iteration_begin/iteration_end instead.");
@@ -158,15 +156,6 @@ struct CLIParam : public XGBoostParameter<CLIParam> {
if (name_pred == "stdout") {
save_period = 0;
}
if (dsplit == static_cast<int>(DataSplitMode::kAuto)) {
if (collective::IsFederated()) {
dsplit = static_cast<int>(DataSplitMode::kNone);
} else if (collective::IsDistributed()) {
dsplit = static_cast<int>(DataSplitMode::kRow);
} else {
dsplit = static_cast<int>(DataSplitMode::kNone);
}
}
}
};

19
src/data/data.cc
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@@ -783,10 +783,14 @@ DMatrix *TryLoadBinary(std::string fname, bool silent) {
DMatrix* DMatrix::Load(const std::string& uri, bool silent, DataSplitMode data_split_mode,
const std::string& file_format) {
CHECK(data_split_mode == DataSplitMode::kRow ||
data_split_mode == DataSplitMode::kCol ||
data_split_mode == DataSplitMode::kNone)
<< "Precondition violated; data split mode can only be 'row', 'col', or 'none'";
auto need_split = false;
if (collective::IsFederated()) {
LOG(CONSOLE) << "XGBoost federated mode detected, not splitting data among workers";
} else if (collective::IsDistributed()) {
LOG(CONSOLE) << "XGBoost distributed mode detected, will split data among workers";
need_split = true;
}
std::string fname, cache_file;
size_t dlm_pos = uri.find('#');
if (dlm_pos != std::string::npos) {
@@ -794,7 +798,7 @@ DMatrix* DMatrix::Load(const std::string& uri, bool silent, DataSplitMode data_s
fname = uri.substr(0, dlm_pos);
CHECK_EQ(cache_file.find('#'), std::string::npos)
<< "Only one `#` is allowed in file path for cache file specification.";
if (data_split_mode == DataSplitMode::kRow) {
if (need_split && data_split_mode == DataSplitMode::kRow) {
std::ostringstream os;
std::vector<std::string> cache_shards = common::Split(cache_file, ':');
for (size_t i = 0; i < cache_shards.size(); ++i) {
@@ -828,7 +832,7 @@ DMatrix* DMatrix::Load(const std::string& uri, bool silent, DataSplitMode data_s
}
int partid = 0, npart = 1;
if (data_split_mode == DataSplitMode::kRow) {
if (need_split && data_split_mode == DataSplitMode::kRow) {
partid = collective::GetRank();
npart = collective::GetWorldSize();
} else {
@@ -887,7 +891,7 @@ DMatrix* DMatrix::Load(const std::string& uri, bool silent, DataSplitMode data_s
* since partitioned data not knowing the real number of features. */
collective::Allreduce<collective::Operation::kMax>(&dmat->Info().num_col_, 1);
if (data_split_mode == DataSplitMode::kCol) {
if (need_split && data_split_mode == DataSplitMode::kCol) {
if (!cache_file.empty()) {
LOG(FATAL) << "Column-wise data split is not support for external memory.";
}
@@ -898,6 +902,7 @@ DMatrix* DMatrix::Load(const std::string& uri, bool silent, DataSplitMode data_s
delete dmat;
return sliced;
} else {
dmat->Info().data_split_mode = data_split_mode;
return dmat;
}
}

1
src/data/simple_dmatrix.cc
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@@ -65,6 +65,7 @@ DMatrix* SimpleDMatrix::SliceCol(std::size_t start, std::size_t size) {
out->Info() = this->Info().Copy();
out->Info().num_nonzero_ = h_offset.back();
}
out->Info().data_split_mode = DataSplitMode::kCol;
return out;
}

53
src/learner.cc
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@@ -273,8 +273,6 @@ void LearnerModelParam::Copy(LearnerModelParam const& that) {
}
struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// data split mode, can be row, col, or none.
DataSplitMode dsplit {DataSplitMode::kAuto};
// flag to disable default metric
bool disable_default_eval_metric {false};
// FIXME(trivialfis): The following parameters belong to model itself, but can be
@@ -284,13 +282,6 @@ struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// declare parameters
DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
DMLC_DECLARE_FIELD(dsplit)
.set_default(DataSplitMode::kAuto)
.add_enum("auto", DataSplitMode::kAuto)
.add_enum("col", DataSplitMode::kCol)
.add_enum("row", DataSplitMode::kRow)
.add_enum("none", DataSplitMode::kNone)
.describe("Data split mode for distributed training.");
DMLC_DECLARE_FIELD(disable_default_eval_metric)
.set_default(false)
.describe("Flag to disable default metric. Set to >0 to disable");
@@ -445,12 +436,6 @@ class LearnerConfiguration : public Learner {
ConsoleLogger::Configure(args);
// add additional parameters
// These are cosntraints that need to be satisfied.
if (tparam_.dsplit == DataSplitMode::kAuto && collective::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
// set seed only before the model is initialized
if (!initialized || ctx_.seed != old_seed) {
common::GlobalRandom().seed(ctx_.seed);
@@ -1055,11 +1040,6 @@ class LearnerIO : public LearnerConfiguration {
auto n = tparam_.__DICT__();
cfg_.insert(n.cbegin(), n.cend());
// copy dsplit from config since it will not run again during restore
if (tparam_.dsplit == DataSplitMode::kAuto && collective::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
this->need_configuration_ = true;
}
@@ -1199,16 +1179,6 @@ class LearnerImpl : public LearnerIO {
local_map->erase(this);
}
}
// Configuration before data is known.
void CheckDataSplitMode() {
if (collective::IsDistributed()) {
CHECK(tparam_.dsplit != DataSplitMode::kAuto)
<< "Precondition violated; dsplit cannot be 'auto' in distributed mode";
if (tparam_.dsplit == DataSplitMode::kCol) {
LOG(FATAL) << "Column-wise data split is currently not supported.";
}
}
}
std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats,
std::string format) override {
@@ -1266,7 +1236,6 @@ class LearnerImpl : public LearnerIO {
common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
}
this->CheckDataSplitMode();
this->ValidateDMatrix(train.get(), true);
auto local_cache = this->GetPredictionCache();
@@ -1295,7 +1264,6 @@ class LearnerImpl : public LearnerIO {
common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
}
this->CheckDataSplitMode();
this->ValidateDMatrix(train.get(), true);
auto local_cache = this->GetPredictionCache();
@@ -1444,19 +1412,14 @@ class LearnerImpl : public LearnerIO {
MetaInfo const& info = p_fmat->Info();
info.Validate(ctx_.gpu_id);
auto const row_based_split = [this]() {
return tparam_.dsplit == DataSplitMode::kRow || tparam_.dsplit == DataSplitMode::kAuto;
};
if (row_based_split()) {
if (is_training) {
CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
} else {
CHECK_GE(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
}
if (is_training) {
CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
} else {
CHECK_GE(learner_model_param_.num_feature, p_fmat->Info().num_col_)
<< "Number of columns does not match number of features in "
"booster.";
}
if (p_fmat->Info().num_row_ == 0) {