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cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
Apache License 2.0
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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***
     /datasets/charlesb/mambaforge/envs/dask-sql-gpuci-py38/bin/nvcc
     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_PREFIX                    : /datasets/charlesb/mambaforge/envs/dask-sql-gpuci-py38
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     ***conda packages***
     conda is /datasets/charlesb/mambaforge/condabin/conda
     /datasets/charlesb/mambaforge/condabin/conda
     # packages in environment at /datasets/charlesb/mambaforge/envs/dask-sql-gpuci-py38:
     #
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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
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     pooch                     1.7.0              pyha770c72_3    conda-forge
     pre-commit                3.2.2              pyha770c72_0    conda-forge
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     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
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     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
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     pycparser                 2.21               pyhd8ed1ab_0    conda-forge
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     pylibraft                 23.06.00a       cuda11_py38_230417_g574f8f88_49    rapidsai-nightly
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     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
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     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
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     ucx                       1.14.0               h8c404fb_1    conda-forge
     ucx-proc                  1.0.0                       gpu    rapidsai
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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!