Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Remove extra sync for dense data (#7120)

Browse Source 
Co-authored-by: SHVETS, KIRILL <kirill.shvets@intel.com>
This commit is contained in:
ShvetsKS 2021-07-22 14:02:31 +03:00 committed by GitHub
parent e6088366df
commit caa9e527dd
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 33 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
src/tree/updater_quantile_hist.cc
View File

@ -329,8 +329,8 @@ void QuantileHistMaker::Builder<GradientSumT>::InitRoot(
for (const size_t *it = e.begin; it < e.end; ++it) { for (const size_t *it = e.begin; it < e.end; ++it) {
grad_stat.Add(gpair_h[*it].GetGrad(), gpair_h[*it].GetHess()); grad_stat.Add(gpair_h[*it].GetGrad(), gpair_h[*it].GetHess());
} }
histred_.Allreduce(&grad_stat, 1);
} }
histred_.Allreduce(&grad_stat, 1);
auto weight = evaluator_->InitRoot(GradStats{grad_stat}); auto weight = evaluator_->InitRoot(GradStats{grad_stat});
p_tree->Stat(RegTree::kRoot).sum_hess = grad_stat.GetHess(); p_tree->Stat(RegTree::kRoot).sum_hess = grad_stat.GetHess();

32
tests/python/test_with_dask.py
View File

@ -21,6 +21,7 @@ from hypothesis import given, settings, note, HealthCheck
from test_updaters import hist_parameter_strategy, exact_parameter_strategy from test_updaters import hist_parameter_strategy, exact_parameter_strategy
from test_with_sklearn import run_feature_weights, run_data_initialization from test_with_sklearn import run_feature_weights, run_data_initialization
from test_predict import verify_leaf_output from test_predict import verify_leaf_output
from sklearn.datasets import make_regression
if sys.platform.startswith("win"): if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True) pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
@ -1493,6 +1494,37 @@ def test_parallel_submits(client: "Client") -> None:
for i, cls in enumerate(classifiers): for i, cls in enumerate(classifiers):
assert cls.get_booster().num_boosted_rounds() == i + 1 assert cls.get_booster().num_boosted_rounds() == i + 1
@pytest.mark.parametrize("tree_method", ["hist", "approx"])
def test_hist_root_stats_with_different_num_worker(tree_method: str) -> None:
"""assert that different workers count dosn't affect summ statistic's on root"""
def dask_train(n_workers, X, y, num_obs, num_features):
cluster = LocalCluster(n_workers=n_workers)
client = Client(cluster)
chunk_size = num_obs/n_workers
X = da.from_array(X, chunks=(chunk_size, num_features))
y = da.from_array(y.reshape(num_obs,1), chunks=(chunk_size, 1))
dtrain = xgb.dask.DaskDMatrix(client, X, y)
output = xgb.dask.train(
client,
{"verbosity": 0, "tree_method": tree_method, "objective": "reg:squarederror", 'max_depth': 2},
dtrain,
num_boost_round=1
)
dump_model = output['booster'].get_dump(with_stats=True)
client.shutdown()
return dump_model
num_obs = 1000
num_features = 10
X, y = make_regression(num_obs, num_features, random_state=777)
first_model = dask_train(1, X, y, num_obs, num_features)[0]
second_model = dask_train(2, X, y, num_obs, num_features)[0]
first_summ_stats = first_model[first_model.find('cover='):first_model.find('\n')]
second_summ_stats = second_model[second_model.find('cover='):second_model.find('\n')]
assert first_summ_stats == second_summ_stats
def test_parallel_submit_multi_clients() -> None: def test_parallel_submit_multi_clients() -> None:
"""Test for running multiple train simultaneously from multiple clients.""" """Test for running multiple train simultaneously from multiple clients."""