[dask] Mitigate non-deterministic test. (#8077)

tests/python/test_with_dask.py

@@ -1,39 +1,45 @@
 """Copyright 2019-2022 XGBoost contributors"""
-from pathlib import Path
+import asyncio
+import json
+import os
 import pickle
 import socket
-import testing as tm
-import pytest
-import xgboost as xgb
+import subprocess
 import sys
+import tempfile
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+from itertools import starmap
+from math import ceil
+from operator import attrgetter, getitem
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
+
+import hypothesis
 import numpy as np
+import pytest
 import scipy
-import json
-from typing import List, Tuple, Dict, Optional, Type, Any
-import asyncio
-from functools import partial
-from concurrent.futures import ThreadPoolExecutor
-import tempfile
-from sklearn.datasets import make_classification
 import sklearn
-import os
-import subprocess
-import hypothesis
-from hypothesis import given, settings, note, HealthCheck
-from test_updaters import hist_parameter_strategy, exact_parameter_strategy
-from test_with_sklearn import run_feature_weights, run_data_initialization
+import testing as tm
+from hypothesis import HealthCheck, given, note, settings
+from sklearn.datasets import make_classification, make_regression
 from test_predict import verify_leaf_output
-from sklearn.datasets import make_regression
+from test_updaters import exact_parameter_strategy, hist_parameter_strategy
+from test_with_sklearn import run_data_initialization, run_feature_weights
+from xgboost.data import _is_cudf_df
+
+import xgboost as xgb
 
 if sys.platform.startswith("win"):
     pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
 if tm.no_dask()['condition']:
     pytest.skip(msg=tm.no_dask()['reason'], allow_module_level=True)
 
-from distributed import LocalCluster, Client
 import dask
-import dask.dataframe as dd
 import dask.array as da
+import dask.dataframe as dd
+from distributed import Client, LocalCluster
+from toolz import sliding_window  # dependency of dask
 from xgboost.dask import DaskDMatrix
 
 dask.config.set({"distributed.scheduler.allowed-failures": False})
@@ -125,6 +131,63 @@ def generate_array(
     return X, y, None
 
 
+def deterministic_persist_per_worker(df, client):
+    # Got this script from https://github.com/dmlc/xgboost/issues/7927
+    # Query workers
+    n_workers = len(client.cluster.workers)
+    workers = map(attrgetter("worker_address"), client.cluster.workers.values())
+
+    # Slice data into roughly equal partitions
+    subpartition_size = ceil(df.npartitions / n_workers)
+    subpartition_divisions = range(
+        0, df.npartitions + subpartition_size, subpartition_size
+    )
+    subpartition_slices = starmap(slice, sliding_window(2, subpartition_divisions))
+    subpartitions = map(partial(getitem, df.partitions), subpartition_slices)
+
+    # Persist each subpartition on each worker
+    # Rebuild dataframe from persisted subpartitions
+    df2 = dd.concat(
+        [
+            sp.persist(workers=w, allow_other_workers=False)
+            for sp, w in zip(subpartitions, workers)
+        ]
+    )
+
+    return df2
+
+
+def deterministic_repartition(
+    client: Client,
+    X: dd.DataFrame,
+    y: dd.Series,
+    m: Optional[Union[dd.DataFrame, dd.Series]],
+) -> Tuple[dd.DataFrame, dd.Series, Optional[Union[dd.DataFrame, dd.Series]]]:
+    # force repartition the data to avoid non-deterministic result
+    if any(X.map_partitions(lambda x: _is_cudf_df(x)).compute()):
+        # dask_cudf seems to be doing fine for now
+        return X, y, m
+
+    X["_y"] = y
+    if m is not None:
+        if isinstance(m, dd.DataFrame):
+            m_columns = m.columns
+            X = dd.concat([X, m], join="outer", axis=1)
+        else:
+            m_columns = ["_m"]
+            X["_m"] = m
+
+    X = deterministic_persist_per_worker(X, client)
+
+    y = X["_y"]
+    X = X[X.columns.difference(["_y"])]
+    if m is not None:
+        m = X[m_columns]
+        X = X[X.columns.difference(m_columns)]
+
+    return X, y, m
+
+
 def test_from_dask_dataframe() -> None:
     with LocalCluster(n_workers=kWorkers, dashboard_address=":0") as cluster:
         with Client(cluster) as client:
@@ -347,22 +410,25 @@ def test_dask_predict_shape_infer(client: "Client") -> None:
 
 
 def run_boost_from_prediction_multi_class(
-    X: xgb.dask._DaskCollection,
-    y: xgb.dask._DaskCollection,
+    X: dd.DataFrame,
+    y: dd.Series,
     tree_method: str,
     client: "Client",
 ) -> None:
     model_0 = xgb.dask.DaskXGBClassifier(
         learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=768
     )
+    X, y, _ = deterministic_repartition(client, X, y, None)
     model_0.fit(X=X, y=y)
     margin = xgb.dask.inplace_predict(
         client, model_0.get_booster(), X, predict_type="margin"
     )
+    margin.columns = [f"m_{i}" for i in range(margin.shape[1])]
 
     model_1 = xgb.dask.DaskXGBClassifier(
         learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=768
     )
+    X, y, margin = deterministic_repartition(client, X, y, margin)
     model_1.fit(X=X, y=y, base_margin=margin)
     predictions_1 = xgb.dask.predict(
         client,
@@ -374,6 +440,7 @@ def run_boost_from_prediction_multi_class(
     model_2 = xgb.dask.DaskXGBClassifier(
         learning_rate=0.3, n_estimators=8, tree_method=tree_method, max_bin=768
     )
+    X, y, _ = deterministic_repartition(client, X, y, None)
     model_2.fit(X=X, y=y)
     predictions_2 = xgb.dask.inplace_predict(
         client, model_2.get_booster(), X, predict_type="margin"
@@ -391,40 +458,45 @@ def run_boost_from_prediction_multi_class(
 
 
 def run_boost_from_prediction(
-    X: xgb.dask._DaskCollection,
-    y: xgb.dask._DaskCollection,
+    X: dd.DataFrame,
+    y: dd.Series,
     tree_method: str,
     client: "Client",
 ) -> None:
-    X = client.persist(X)
-    y = client.persist(y)
+    X, y = client.persist([X, y])
 
     model_0 = xgb.dask.DaskXGBClassifier(
         learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=512
     )
+    X, y, _ = deterministic_repartition(client, X, y, None)
     model_0.fit(X=X, y=y)
-    margin = model_0.predict(X, output_margin=True)
+    margin: dd.Series = model_0.predict(X, output_margin=True)
 
     model_1 = xgb.dask.DaskXGBClassifier(
         learning_rate=0.3, n_estimators=4, tree_method=tree_method, max_bin=512
     )
+    X, y, margin = deterministic_repartition(client, X, y, margin)
     model_1.fit(X=X, y=y, base_margin=margin)
-    predictions_1 = model_1.predict(X, base_margin=margin)
+    X, y, margin = deterministic_repartition(client, X, y, margin)
+    predictions_1: dd.Series = model_1.predict(X, base_margin=margin)
 
     cls_2 = xgb.dask.DaskXGBClassifier(
         learning_rate=0.3, n_estimators=8, tree_method=tree_method, max_bin=512
     )
+    X, y, _ = deterministic_repartition(client, X, y, None)
     cls_2.fit(X=X, y=y)
-    predictions_2 = cls_2.predict(X)
+    predictions_2: dd.Series = cls_2.predict(X)
 
     assert np.all(predictions_1.compute() == predictions_2.compute())
 
     margined = xgb.dask.DaskXGBClassifier(n_estimators=4)
+    X, y, margin = deterministic_repartition(client, X, y, margin)
     margined.fit(
         X=X, y=y, base_margin=margin, eval_set=[(X, y)], base_margin_eval_set=[margin]
     )
 
     unmargined = xgb.dask.DaskXGBClassifier(n_estimators=4)
+    X, y, margin = deterministic_repartition(client, X, y, margin)
     unmargined.fit(X=X, y=y, eval_set=[(X, y)], base_margin=margin)
 
     margined_res = margined.evals_result()["validation_0"]["logloss"]