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Avoid default tokenization in Dask #10398

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merged 6 commits into from
Jun 14, 2024
Merged

Avoid default tokenization in Dask #10398

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74e4c83
remove deprecated code and avoid default dask tokenization
rjzamora Jun 6, 2024
6bd0645
remove comment
rjzamora Jun 6, 2024
92a440b
Remove dask pin.
trivialfis Jun 7, 2024
e96a57f
Merge remote-tracking branch 'upstream/master' into avoid-default-tok…
rjzamora Jun 7, 2024
6a1babf
Merge branch 'master' into avoid-default-tokenize
trivialfis Jun 12, 2024
1d8aa74
Typing.
trivialfis Jun 14, 2024
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7 changes: 7 additions & 0 deletions python-package/xgboost/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
import os
import re
import sys
import uuid
import warnings
import weakref
from abc import ABC, abstractmethod
Expand Down Expand Up @@ -3143,3 +3144,9 @@ def get_split_value_histogram(
UserWarning,
)
return nph_stacked

def __dask_tokenize__(self) -> uuid.UUID:
# TODO: Implement proper tokenization to avoid unnecessary re-computation in
# Dask. However, default tokenzation causes problems after
# https://github.com/dask/dask/pull/10883
return uuid.uuid4()
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Two consecutive calls to tokenize() will return two different values, and this can cause problems. I would advise caching the output: dask/dask#11179 (comment)

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Thanks @crusaderky ! The purpose of this PR was to effectively roll back behavior to "match" tokenization before 10883. However, I agree that it makes sense to cache the value.

2 changes: 1 addition & 1 deletion tests/ci_build/Dockerfile.gpu
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Expand Up @@ -25,7 +25,7 @@ RUN \
mamba create -y -n gpu_test -c rapidsai -c conda-forge -c nvidia \
python=3.10 cudf=$RAPIDS_VERSION_ARG* rmm=$RAPIDS_VERSION_ARG* cudatoolkit=$CUDA_VERSION_ARG \
"nccl>=${NCCL_SHORT_VER}" \
dask=2024.1.1 \
dask \
dask-cuda=$RAPIDS_VERSION_ARG* dask-cudf=$RAPIDS_VERSION_ARG* cupy \
numpy pytest pytest-timeout scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz hypothesis \
"pyspark>=3.4.0" cloudpickle cuda-python && \
Expand Down
2 changes: 1 addition & 1 deletion tests/ci_build/Dockerfile.gpu_dev_ver
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Expand Up @@ -28,7 +28,7 @@ RUN \
mamba create -y -n gpu_test -c rapidsai-nightly -c conda-forge -c nvidia \
python=3.10 "cudf=$RAPIDS_VERSION_ARG.*" "rmm=$RAPIDS_VERSION_ARG.*" cudatoolkit=$CUDA_VERSION_ARG \
"nccl>=${NCCL_SHORT_VER}" \
dask=2024.1.1 \
dask \
"dask-cuda=$RAPIDS_VERSION_ARG.*" "dask-cudf=$RAPIDS_VERSION_ARG.*" cupy \
numpy pytest pytest-timeout scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz hypothesis \
"pyspark>=3.4.0" cloudpickle cuda-python && \
Expand Down
4 changes: 2 additions & 2 deletions tests/ci_build/conda_env/linux_cpu_test.yml
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Expand Up @@ -17,8 +17,8 @@ dependencies:
- scikit-learn
- pandas
- matplotlib
- dask>=2022.6
- distributed>=2022.6
- dask
- distributed
- python-graphviz
- hypothesis>=6.46
- astroid
Expand Down
10 changes: 5 additions & 5 deletions tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
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All changes in this file are related to the recent deprecation of dask_cudf.from_dask_dataframe.

Original file line number Diff line number Diff line change
Expand Up @@ -248,10 +248,10 @@ def test_categorical(self, local_cuda_client: Client) -> None:
import dask_cudf

X, y = make_categorical(local_cuda_client, 10000, 30, 13)
X = dask_cudf.from_dask_dataframe(X)
X = X.to_backend("cudf")

X_onehot, _ = make_categorical(local_cuda_client, 10000, 30, 13, True)
X_onehot = dask_cudf.from_dask_dataframe(X_onehot)
X_onehot = X_onehot.to_backend("cudf")
run_categorical(local_cuda_client, "hist", "cuda", X, X_onehot, y)

@given(
Expand Down Expand Up @@ -383,9 +383,9 @@ def test_dask_classifier(self, model: str, local_cuda_client: Client) -> None:

X_, y_, w_ = generate_array(with_weights=True)
y_ = (y_ * 10).astype(np.int32)
X = dask_cudf.from_dask_dataframe(dd.from_dask_array(X_))
y = dask_cudf.from_dask_dataframe(dd.from_dask_array(y_))
w = dask_cudf.from_dask_dataframe(dd.from_dask_array(w_))
X = dd.from_dask_array(X_).to_backend("cudf")
y = dd.from_dask_array(y_).to_backend("cudf")
w = dd.from_dask_array(w_).to_backend("cudf")
run_dask_classifier(X, y, w, model, "hist", "cuda", local_cuda_client, 10)

def test_empty_dmatrix(self, local_cuda_client: Client) -> None:
Expand Down
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