Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[backport] Fix loading DMatrix binary in distributed env. (#8149) #8185

Merged
merged 2 commits into from
Aug 18, 2022
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 1 addition & 3 deletions src/c_api/c_api.cc
Original file line number Diff line number Diff line change
Expand Up @@ -193,9 +193,7 @@ XGB_DLL int XGBGetGlobalConfig(const char** json_str) {
API_END();
}

XGB_DLL int XGDMatrixCreateFromFile(const char *fname,
int silent,
DMatrixHandle *out) {
XGB_DLL int XGDMatrixCreateFromFile(const char *fname, int silent, DMatrixHandle *out) {
API_BEGIN();
bool load_row_split = false;
if (rabit::IsDistributed()) {
Expand Down
96 changes: 22 additions & 74 deletions src/data/data.cc
Original file line number Diff line number Diff line change
Expand Up @@ -378,35 +378,6 @@ MetaInfo MetaInfo::Slice(common::Span<int32_t const> ridxs) const {
return out;
}

// try to load group information from file, if exists
inline bool MetaTryLoadGroup(const std::string& fname,
std::vector<unsigned>* group) {
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
if (fi == nullptr) return false;
dmlc::istream is(fi.get());
group->clear();
group->push_back(0);
unsigned nline = 0;
while (is >> nline) {
group->push_back(group->back() + nline);
}
return true;
}

// try to load weight information from file, if exists
inline bool MetaTryLoadFloatInfo(const std::string& fname,
std::vector<bst_float>* data) {
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
if (fi == nullptr) return false;
dmlc::istream is(fi.get());
data->clear();
bst_float value;
while (is >> value) {
data->push_back(value);
}
return true;
}

namespace {
template <int32_t D, typename T>
void CopyTensorInfoImpl(Context const& ctx, Json arr_interface, linalg::Tensor<T, D>* p_out) {
Expand Down Expand Up @@ -811,9 +782,7 @@ DMatrix *TryLoadBinary(std::string fname, bool silent) {
return nullptr;
}

DMatrix* DMatrix::Load(const std::string& uri,
bool silent,
bool load_row_split,
DMatrix* DMatrix::Load(const std::string& uri, bool silent, bool load_row_split,
const std::string& file_format) {
std::string fname, cache_file;
size_t dlm_pos = uri.find('#');
Expand Down Expand Up @@ -846,50 +815,47 @@ DMatrix* DMatrix::Load(const std::string& uri,
} else {
fname = uri;
}

// legacy handling of binary data loading
if (file_format == "auto") {
DMatrix* loaded = TryLoadBinary(fname, silent);
if (loaded) {
return loaded;
}
}

int partid = 0, npart = 1;
if (load_row_split) {
partid = rabit::GetRank();
npart = rabit::GetWorldSize();
} else {
// test option to load in part
npart = dmlc::GetEnv("XGBOOST_TEST_NPART", 1);
npart = 1;
}

if (npart != 1) {
LOG(CONSOLE) << "Load part of data " << partid
<< " of " << npart << " parts";
}

// legacy handling of binary data loading
if (file_format == "auto" && npart == 1) {
DMatrix *loaded = TryLoadBinary(fname, silent);
if (loaded) {
return loaded;
}
LOG(CONSOLE) << "Load part of data " << partid << " of " << npart << " parts";
}

DMatrix* dmat {nullptr};
try {
if (cache_file.empty()) {
std::unique_ptr<dmlc::Parser<uint32_t>> parser(
dmlc::Parser<uint32_t>::Create(fname.c_str(), partid, npart,
file_format.c_str()));
dmlc::Parser<uint32_t>::Create(fname.c_str(), partid, npart, file_format.c_str()));
data::FileAdapter adapter(parser.get());
dmat = DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(),
1, cache_file);
dmat = DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 1, cache_file);
} else {
data::FileIterator iter{fname, static_cast<uint32_t>(partid), static_cast<uint32_t>(npart),
file_format};
dmat = new data::SparsePageDMatrix{
&iter,
iter.Proxy(),
data::fileiter::Reset,
data::fileiter::Next,
std::numeric_limits<float>::quiet_NaN(),
1,
cache_file};
dmat = new data::SparsePageDMatrix{&iter,
iter.Proxy(),
data::fileiter::Reset,
data::fileiter::Next,
std::numeric_limits<float>::quiet_NaN(),
1,
cache_file};
}
} catch (dmlc::Error &e) {
} catch (dmlc::Error& e) {
std::vector<std::string> splited = common::Split(fname, '#');
std::vector<std::string> args = common::Split(splited.front(), '?');
std::string format {file_format};
Expand Down Expand Up @@ -917,24 +883,6 @@ DMatrix* DMatrix::Load(const std::string& uri,
* partitioned data will fail the train/val validation check
* since partitioned data not knowing the real number of features. */
rabit::Allreduce<rabit::op::Max>(&dmat->Info().num_col_, 1);
// backward compatiblity code.
if (!load_row_split) {
MetaInfo& info = dmat->Info();
if (MetaTryLoadGroup(fname + ".group", &info.group_ptr_) && !silent) {
LOG(CONSOLE) << info.group_ptr_.size() - 1
<< " groups are loaded from " << fname << ".group";
}
if (MetaTryLoadFloatInfo(fname + ".base_margin", &info.base_margin_.Data()->HostVector()) &&
!silent) {
LOG(CONSOLE) << info.base_margin_.Size() << " base_margin are loaded from " << fname
<< ".base_margin";
}
if (MetaTryLoadFloatInfo
(fname + ".weight", &info.weights_.HostVector()) && !silent) {
LOG(CONSOLE) << info.weights_.Size()
<< " weights are loaded from " << fname << ".weight";
}
}
return dmat;
}
template <typename DataIterHandle, typename DMatrixHandle,
Expand Down
51 changes: 48 additions & 3 deletions tests/python/test_with_dask.py
Original file line number Diff line number Diff line change
Expand Up @@ -111,9 +111,10 @@ def pack(**kwargs: Any) -> dd.DataFrame:


def generate_array(
with_weights: bool = False
) -> Tuple[xgb.dask._DaskCollection, xgb.dask._DaskCollection,
Optional[xgb.dask._DaskCollection]]:
with_weights: bool = False,
) -> Tuple[
xgb.dask._DaskCollection, xgb.dask._DaskCollection, Optional[xgb.dask._DaskCollection]
]:
chunk_size = 20
rng = da.random.RandomState(1994)
X = rng.random_sample((kRows, kCols), chunks=(chunk_size, -1))
Expand Down Expand Up @@ -1190,6 +1191,50 @@ def test_dask_iteration_range(client: "Client"):


class TestWithDask:
def test_dmatrix_binary(self, client: "Client") -> None:
def save_dmatrix(rabit_args: List[bytes], tmpdir: str) -> None:
with xgb.dask.RabitContext(rabit_args):
rank = xgb.rabit.get_rank()
X, y = tm.make_categorical(100, 4, 4, False)
Xy = xgb.DMatrix(X, y, enable_categorical=True)
path = os.path.join(tmpdir, f"{rank}.bin")
Xy.save_binary(path)

def load_dmatrix(rabit_args: List[bytes], tmpdir: str) -> None:
with xgb.dask.RabitContext(rabit_args):
rank = xgb.rabit.get_rank()
path = os.path.join(tmpdir, f"{rank}.bin")
Xy = xgb.DMatrix(path)
assert Xy.num_row() == 100
assert Xy.num_col() == 4

with tempfile.TemporaryDirectory() as tmpdir:
workers = _get_client_workers(client)
rabit_args = client.sync(
xgb.dask._get_rabit_args, len(workers), None, client
)
futures = []
for w in workers:
# same argument for each worker, must set pure to False otherwise dask
# will try to reuse the result from the first worker and hang waiting
# for it.
f = client.submit(
save_dmatrix, rabit_args, tmpdir, workers=[w], pure=False
)
futures.append(f)
client.gather(futures)

rabit_args = client.sync(
xgb.dask._get_rabit_args, len(workers), None, client
)
futures = []
for w in workers:
f = client.submit(
load_dmatrix, rabit_args, tmpdir, workers=[w], pure=False
)
futures.append(f)
client.gather(futures)

@pytest.mark.parametrize('config_key,config_value', [('verbosity', 0), ('use_rmm', True)])
def test_global_config(
self,
Expand Down