rapidsai / cudf

cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
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[BUG] Incorrect meta when reading datetime data with `dask_cudf.read_parquet` #13167

Closed charlesbluca closed 7 months ago

charlesbluca commented 1 year ago

Describe the bug When reading in a parquet table containing datetime columns using dask_cudf.read_parquet, the datetime columns have mismatched precisions between the Dask object's meta and the computed frame.

Steps/Code to reproduce bug

import dask.dataframe as dd
import pandas as pd

import cudf
import dask_cudf

df = cudf.DataFrame({
    "d": [
    pd.Timestamp("2013-08-01 23:00:00"),
    pd.Timestamp("2014-09-01 23:00:00"),
    pd.Timestamp("2015-10-01 23:00:00"),
]
})
ddf_in = dd.from_pandas(df, npartitions=3)

# dtype('<M8[us]') == dtype('<M8[us]')
assert ddf_in._meta["d"].dtype == ddf_in.compute()["d"].dtype

ddf_in.to_parquet("test.parquet")
ddf_out = dask_cudf.read_parquet("test.parquet")

# dtype('<M8[ns]') == dtype('<M8[us]')
assert ddf_out._meta["d"].dtype == ddf_out.compute()["d"].dtype

Expected behavior I would expect the precision of the datetimes to match between the Dask meta and computed object.

Environment overview (please complete the following information)

Environment details

Click here to see environment details

     **git***
print_env.sh: 11: [: true: unexpected operator
     Not inside a git repository

     ***OS Information***
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     DGX_SWBUILD_DATE="2020-03-04"
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     DGX_OTA_DATE="Wed Jul 27 14:36:02 PDT 2022"

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     ***GPU Information***
     Tue Apr 18 15:16:11 2023       
     +-----------------------------------------------------------------------------+
     | NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |
     |-------------------------------+----------------------+----------------------+
     | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
     | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
     |                               |                      |               MIG M. |
     |===============================+======================+======================|
     |   0  Tesla V100-SXM2...  On   | 00000000:06:00.0 Off |                    0 |
     | N/A   32C    P0    42W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   1  Tesla V100-SXM2...  On   | 00000000:07:00.0 Off |                    0 |
     | N/A   30C    P0    41W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   2  Tesla V100-SXM2...  On   | 00000000:0A:00.0 Off |                    0 |
     | N/A   30C    P0    41W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   3  Tesla V100-SXM2...  On   | 00000000:0B:00.0 Off |                    0 |
     | N/A   27C    P0    41W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   4  Tesla V100-SXM2...  On   | 00000000:85:00.0 Off |                    0 |
     | N/A   31C    P0    42W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   5  Tesla V100-SXM2...  On   | 00000000:86:00.0 Off |                    0 |
     | N/A   29C    P0    41W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   6  Tesla V100-SXM2...  On   | 00000000:89:00.0 Off |                    0 |
     | N/A   33C    P0    42W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   7  Tesla V100-SXM2...  On   | 00000000:8A:00.0 Off |                    0 |
     | N/A   29C    P0    40W / 300W |      0MiB / 32768MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+

     +-----------------------------------------------------------------------------+
     | Processes:                                                                  |
     |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
     |        ID   ID                                                   Usage      |
     |=============================================================================|
     |  No running processes found                                                 |
     +-----------------------------------------------------------------------------+

     ***CPU***
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     ***nvcc***
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     nvcc: NVIDIA (R) Cuda compiler driver
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     /datasets/charlesb/mambaforge/envs/dask-sql-gpuci-py38/bin/python
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     LD_LIBRARY_PATH                 : 
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     ***conda packages***
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     /datasets/charlesb/mambaforge/condabin/conda
     # packages in environment at /datasets/charlesb/mambaforge/envs/dask-sql-gpuci-py38:
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     libnuma                   2.0.16               h0b41bf4_1    conda-forge
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     libprotobuf               3.21.12              h3eb15da_0    conda-forge
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     libraft-headers           23.06.00a       cuda11_230410_ga98295b5_42    rapidsai-nightly
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     markdown                  3.4.3              pyhd8ed1ab_0    conda-forge
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     numba                     0.56.4           py38hefa3cd7_1    conda-forge
     numpy                     1.23.5           py38h7042d01_0    conda-forge
     nvtx                      0.2.3            py38h0a891b7_2    conda-forge
     oauthlib                  3.2.2              pyhd8ed1ab_0    conda-forge
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     orc                       1.8.3                hfdbbad2_0    conda-forge
     packaging                 23.1               pyhd8ed1ab_0    conda-forge
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     pickleshare               0.7.5                   py_1003    conda-forge
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     pip                       23.1               pyhd8ed1ab_0    conda-forge
     pkgutil-resolve-name      1.3.10             pyhd8ed1ab_0    conda-forge
     platformdirs              3.2.0              pyhd8ed1ab_0    conda-forge
     pluggy                    1.0.0              pyhd8ed1ab_5    conda-forge
     pooch                     1.7.0              pyha770c72_3    conda-forge
     pre-commit                3.2.2              pyha770c72_0    conda-forge
     prometheus_client         0.16.0             pyhd8ed1ab_0    conda-forge
     prometheus_flask_exporter 0.22.3             pyhd8ed1ab_0    conda-forge
     prompt-toolkit            3.0.38             pyha770c72_0    conda-forge
     prompt_toolkit            3.0.38               hd8ed1ab_0    conda-forge
     protobuf                  4.21.12          py38h8dc9893_0    conda-forge
     psutil                    5.9.5            py38h1de0b5d_0    conda-forge
     psycopg2                  2.9.3            py38h0ca591d_2    conda-forge
     pthread-stubs             0.4               h36c2ea0_1001    conda-forge
     ptxcompiler               0.7.0            py38h241159d_3    conda-forge
     ptyprocess                0.7.0              pyhd3deb0d_0    conda-forge
     pure-sasl                 0.6.2              pyhd8ed1ab_0    conda-forge
     pure_eval                 0.2.2              pyhd8ed1ab_0    conda-forge
     py                        1.11.0             pyh6c4a22f_0    conda-forge
     py-xgboost                1.7.1dev.rapidsai23.06   cuda11_py38_3    rapidsai-nightly
     pyarrow                   11.0.0          py38hf05218d_13_cpu    conda-forge
     pycparser                 2.21               pyhd8ed1ab_0    conda-forge
     pydantic                  1.10.7           py38h1de0b5d_0    conda-forge
     pygments                  2.7.1                      py_0    conda-forge
     pyhive                    0.6.5              pyhd8ed1ab_0    conda-forge
     pyjwt                     2.6.0              pyhd8ed1ab_0    conda-forge
     pylibraft                 23.06.00a       cuda11_py38_230417_g574f8f88_49    rapidsai-nightly
     pynacl                    1.5.0            py38h0a891b7_2    conda-forge
     pynvml                    11.4.1             pyhd8ed1ab_0    conda-forge
     pyopenssl                 23.1.1             pyhd8ed1ab_0    conda-forge
     pyparsing                 3.0.9              pyhd8ed1ab_0    conda-forge
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     pytest-rerunfailures      11.1.2             pyhd8ed1ab_0    conda-forge
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     python                    3.8.16          he550d4f_1_cpython    conda-forge
     python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
     python_abi                3.8                      3_cp38    conda-forge
     pytz                      2022.7.1           pyhd8ed1ab_0    conda-forge
     pywin32-on-windows        0.1.0              pyh1179c8e_3    conda-forge
     pyyaml                    6.0              py38h0a891b7_5    conda-forge
     qpd                       0.4.1              pyhd8ed1ab_0    conda-forge
     querystring_parser        1.2.4                      py_0    conda-forge
     raft-dask                 23.06.00a       cuda11_py38_230417_g574f8f88_49    rapidsai-nightly
     re2                       2023.02.02           hcb278e6_0    conda-forge
     readline                  8.2                  h8228510_1    conda-forge
     requests                  2.28.2             pyhd8ed1ab_1    conda-forge
     rmm                       23.06.00a       cuda11_py38_230418_g0e7b2d7c_5    rapidsai-nightly
     s2n                       1.3.41               h3358134_0    conda-forge
     scikit-learn              1.0              py38hacb3eff_1    conda-forge
     scipy                     1.10.1           py38h59f1b5f_0    conda-forge
     seaborn                   0.12.2               hd8ed1ab_0    conda-forge
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     sphinxcontrib-devhelp     1.0.2                      py_0    conda-forge
     sphinxcontrib-htmlhelp    2.0.1              pyhd8ed1ab_0    conda-forge
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     tk                        8.6.12               h27826a3_0    conda-forge
     toml                      0.10.2             pyhd8ed1ab_0    conda-forge
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     toolz                     0.12.0             pyhd8ed1ab_0    conda-forge
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     treelite-runtime          3.2.0                    pypi_0    pypi
     triad                     0.8.6              pyhd8ed1ab_0    conda-forge
     typing-extensions         4.5.0                hd8ed1ab_0    conda-forge
     typing_extensions         4.5.0              pyha770c72_0    conda-forge
     tzlocal                   2.1                pyh9f0ad1d_0    conda-forge
     ucx                       1.14.0               h8c404fb_1    conda-forge
     ucx-proc                  1.0.0                       gpu    rapidsai
     ucx-py                    0.32.00a        py38_230410_g3faf968_3    rapidsai-nightly
     ukkonen                   1.0.1            py38h43d8883_3    conda-forge
     unicodedata2              15.0.0           py38h0a891b7_0    conda-forge
     update_checker            0.18.0             pyh9f0ad1d_0    conda-forge
     urllib3                   1.26.15            pyhd8ed1ab_0    conda-forge
     uvicorn                   0.21.1           py38h578d9bd_0    conda-forge
     virtualenv                20.21.0            pyhd8ed1ab_0    conda-forge
     wcwidth                   0.2.6              pyhd8ed1ab_0    conda-forge
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     wheel                     0.40.0             pyhd8ed1ab_0    conda-forge
     xgboost                   1.7.1dev.rapidsai23.06   cuda11_py38_3    rapidsai-nightly
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     xorg-libxdmcp             1.1.3                h7f98852_0    conda-forge
     xz                        5.2.6                h166bdaf_0    conda-forge
     yaml                      0.2.5                h7f98852_2    conda-forge
     zict                      3.0.0              pyhd8ed1ab_0    conda-forge
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     zlib                      1.2.13               h166bdaf_4    conda-forge
     zstd                      1.5.2                h3eb15da_6    conda-forge

mhaseeb123 commented 7 months ago

Update: This is no longer an issue in the latest branch-24.06 checkout and the dtypes are unchanged after a parquet round trip. Using the same python script by @charlesbluca and adding extra prints we get:

Input: datetime64[us] datetime64[us]
Output: datetime64[us] datetime64[us]

@charlesbluca could you also please verify at your end that this is fixed now. Thank you!