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rapidsai / cudf

cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
Apache License 2.0
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[BUG] RuntimeError when datetime types are given in `dtype` param to `cudf.read_json` #6586

Closed galipremsagar closed 3 years ago

galipremsagar commented 4 years ago

Describe the bug cudf.read_json provides ability to specify the dtypes of the resulting columns to be loaded into the dataframe from a json file. When a datetime type is provided it seems to be giving a RunTimeError, but duration types seem to be functioning correctly.

Steps/Code to reproduce bug

In[28]: import pandas as pd
In[29]: pdf = pd.DataFrame({'a':[1, 32, 1000]})
In[30]: buf = pdf.to_json( orient="records", lines=True)
In[31]: import io
In[32]: new_pdf = pd.read_json(io.StringIO(buf), orient="records", lines=True, dtype={"a":np.dtype("float64")})
In[33]: new_pdf
Out[33]: 
        a
0     1.0
1    32.0
2  1000.0
In[34]: new_pdf.dtypes
Out[34]: 
a    float64
dtype: object
In[35]: new_pdf = pd.read_json(io.StringIO(buf), orient="records", lines=True, dtype={"a":np.dtype("datetime64[ns]")})
In[36]: new_pdf
Out[36]: 
                              a
0 1970-01-01 00:00:00.000000001
1 1970-01-01 00:00:00.000000032
2 1970-01-01 00:00:00.000001000
In[37]: new_pdf.dtypes
Out[37]: 
a    datetime64[ns]
dtype: object
In[38]: import cudf
In[39]: gdf = cudf.read_json(io.StringIO(buf), engine="cudf", lines=True, dtype={"a":np.dtype("float64")})
In[40]: gdf
Out[40]: 
        a
0     1.0
1    32.0
2  1000.0
In[41]: gdf.dtypes
Out[41]: 
a    float64
dtype: object
In[42]: gdf = cudf.read_json(io.StringIO(buf), engine="cudf", lines=True, dtype={"a":np.dtype("datetime64[ns]")})
Traceback (most recent call last):
  File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3417, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-42-fef55a887172>", line 1, in <module>
    gdf = cudf.read_json(io.StringIO(buf), engine="cudf", lines=True, dtype={"a":np.dtype("datetime64[ns]")})
  File "/home/pgali/anaconda3/envs/cudf_dev/lib/python3.7/site-packages/cudf/io/json.py", line 39, in read_json
    path_or_buf, dtype, lines, compression, byte_range
  File "cudf/_lib/json.pyx", line 26, in cudf._lib.json.read_json
  File "cudf/_lib/json.pyx", line 101, in cudf._lib.json.read_json
RuntimeError: cuDF failure at: /home/pgali/Desktop/cudf/cpp/include/cudf/utilities/type_dispatcher.hpp:474: Unsupported type_id.
In[43]: gdf = cudf.read_json(io.StringIO(buf), engine="cudf", lines=True, dtype={"a":np.dtype("timedelta64[ns]")})
In[44]: gdf
Out[44]: 
                          a
0 0 days 00:00:00.000000001
1 0 days 00:00:00.000000032
2    0 days 00:00:00.000001

Expected behavior We should be able to load correctly when we provide a dtype mapping which contains datetime64 types.

Environment overview (please complete the following information)

Environment details

Click here to see environment details

     **git***
     commit 63facc645d397d420173bfec6ecaf2162af575ba (HEAD -> json_fuzz_tests)
     Author: galipremsagar 
     Date:   Thu Oct 22 12:54:48 2020 -0500

     Add misc fixes to parquet fuzz tests.
     **git submodules***

     ***OS Information***
     DISTRIB_ID=Ubuntu
     DISTRIB_RELEASE=20.04
     DISTRIB_CODENAME=focal
     DISTRIB_DESCRIPTION="Ubuntu 20.04.1 LTS"
     NAME="Ubuntu"
     VERSION="20.04.1 LTS (Focal Fossa)"
     ID=ubuntu
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     SUPPORT_URL="https://help.ubuntu.com/"
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     PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
     VERSION_CODENAME=focal
     UBUNTU_CODENAME=focal
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     Thu Oct 22 17:18:35 2020
     +-----------------------------------------------------------------------------+
     | NVIDIA-SMI 450.80.02    Driver Version: 450.80.02    CUDA Version: 11.0     |
     |-------------------------------+----------------------+----------------------+
     | 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  Quadro RTX 8000     Off  | 00000000:22:00.0 Off |                  Off |
     | 34%   51C    P2    61W / 260W |   1581MiB / 48601MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   1  Quadro RTX 8000     Off  | 00000000:2D:00.0  On |                  Off |
     | 33%   38C    P8    22W / 260W |    397MiB / 48592MiB |      9%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+

     +-----------------------------------------------------------------------------+
     | Processes:                                                                  |
     |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
     |        ID   ID                                                   Usage      |
     |=============================================================================|
     |    0   N/A  N/A       963      G   /usr/lib/xorg/Xorg                  4MiB |
     |    0   N/A  N/A      1790      G   /usr/lib/xorg/Xorg                  4MiB |
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     |    0   N/A  N/A     33667      C   python                            521MiB |
     |    0   N/A  N/A     39796      C   python                            521MiB |
     |    1   N/A  N/A       963      G   /usr/lib/xorg/Xorg                 39MiB |
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     |    1   N/A  N/A      6538      G   ...AAAAAAAAA= --shared-files       39MiB |
     |    1   N/A  N/A     25484      G   /usr/lib/firefox/firefox            3MiB |
     +-----------------------------------------------------------------------------+

     ***CPU***
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     CPU op-mode(s):                  32-bit, 64-bit
     Byte Order:                      Little Endian
     Address sizes:                   46 bits physical, 48 bits virtual
     CPU(s):                          12
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     Thread(s) per core:              2
     Core(s) per socket:              6
     Socket(s):                       1
     NUMA node(s):                    1
     Vendor ID:                       GenuineIntel
     CPU family:                      6
     Model:                           85
     Model name:                      Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz
     Stepping:                        4
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     CPU min MHz:                     1200.0000
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     L1i cache:                       192 KiB
     L2 cache:                        6 MiB
     L3 cache:                        19.3 MiB
     NUMA node0 CPU(s):               0-11
     Vulnerability Itlb multihit:     KVM: Vulnerable
     Vulnerability L1tf:              Mitigation; PTE Inversion
     Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable
     Vulnerability Meltdown:          Mitigation; PTI
     Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
     Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
     Vulnerability Spectre v2:        Mitigation; Full generic retpoline, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling
     Vulnerability Srbds:             Not affected
     Vulnerability Tsx async abort:   Mitigation; Clear CPU buffers; SMT vulnerable
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     ***CMake***
     /home/pgali/anaconda3/envs/cudf_dev/bin/cmake
     cmake version 3.18.2

     CMake suite maintained and supported by Kitware (kitware.com/cmake).

     ***g++***
     /usr/bin/g++
     g++ (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
     Copyright (C) 2019 Free Software Foundation, Inc.
     This is free software; see the source for copying conditions.  There is NO
     warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

     ***nvcc***
     /usr/local/cuda/bin/nvcc
     nvcc: NVIDIA (R) Cuda compiler driver
     Copyright (c) 2005-2020 NVIDIA Corporation
     Built on Wed_Jul_22_19:09:09_PDT_2020
     Cuda compilation tools, release 11.0, V11.0.221
     Build cuda_11.0_bu.TC445_37.28845127_0

     ***Python***
     /home/pgali/anaconda3/envs/cudf_dev/bin/python
     Python 3.7.8

     ***Environment Variables***
     PATH                            : /home/pgali/anaconda3/envs/cudf_dev/bin:/home/pgali/anaconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/usr/local/cuda/bin
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     NUMBAPRO_LIBDEVICE              :
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     ***conda packages***
     /home/pgali/anaconda3/condabin/conda
     # packages in environment at /home/pgali/anaconda3/envs/cudf_dev:
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     pyopenssl                 19.1.0                     py_1    conda-forge
     pyorc                     0.3.0                    pypi_0    pypi
     pyparsing                 2.4.7              pyh9f0ad1d_0    conda-forge
     pyrsistent                0.17.3           py37h8f50634_1    conda-forge
     pysocks                   1.7.1            py37he5f6b98_2    conda-forge
     pytest                    6.1.1            py37hc8dfbb8_1    conda-forge
     pytest-benchmark          3.2.3              pyh9f0ad1d_0    conda-forge
     pytest-forked             1.3.0                    pypi_0    pypi
     pytest-xdist              2.1.0                    pypi_0    pypi
     python                    3.7.8           h6f2ec95_1_cpython    conda-forge
     python-dateutil           2.8.1                      py_0    conda-forge
     python_abi                3.7                     1_cp37m    conda-forge
     pytz                      2020.1             pyh9f0ad1d_0    conda-forge
     pyyaml                    5.3.1            py37hb5d75c8_1    conda-forge
     pyzmq                     19.0.2           py37hac76be4_2    conda-forge
     rapidjson                 1.1.0             he1b5a44_1002    conda-forge
     re2                       2020.10.01           he1b5a44_0    conda-forge
     readline                  8.0                  he28a2e2_2    conda-forge
     recommonmark              0.6.0                      py_0    conda-forge
     regex                     2020.10.15       py37h8f50634_0    conda-forge
     requests                  2.24.0             pyh9f0ad1d_0    conda-forge
     rhash                     1.3.6             h516909a_1001    conda-forge
     rmm                       0.17.0a201017   cuda_11.0_py37_g8cdd176_21    rapidsai-nightly
     send2trash                1.5.0                      py_0    conda-forge
     setuptools                49.6.0           py37he5f6b98_2    conda-forge
     six                       1.15.0             pyh9f0ad1d_0    conda-forge
     snappy                    1.1.8                he1b5a44_3    conda-forge
     snowballstemmer           2.0.0                      py_0    conda-forge
     sortedcontainers          2.2.2              pyh9f0ad1d_0    conda-forge
     spdlog                    1.7.0                hc9558a2_2    conda-forge
     sphinx                    3.2.1                      py_0    conda-forge
     sphinx-copybutton         0.3.0              pyh9f0ad1d_0    conda-forge
     sphinx-markdown-tables    0.0.14             pyh9f0ad1d_1    conda-forge
     sphinx_rtd_theme          0.5.0              pyh9f0ad1d_0    conda-forge
     sphinxcontrib-applehelp   1.0.2                      py_0    conda-forge
     sphinxcontrib-devhelp     1.0.2                      py_0    conda-forge
     sphinxcontrib-htmlhelp    1.0.3                      py_0    conda-forge
     sphinxcontrib-jsmath      1.0.1                      py_0    conda-forge
     sphinxcontrib-qthelp      1.0.3                      py_0    conda-forge
     sphinxcontrib-serializinghtml 1.1.4                      py_0    conda-forge
     sphinxcontrib-websupport  1.2.4              pyh9f0ad1d_0    conda-forge
     sqlite                    3.33.0               h4cf870e_1    conda-forge
     streamz                   0.6.0                    pypi_0    pypi
     tblib                     1.6.0                      py_0    conda-forge
     terminado                 0.9.1            py37hc8dfbb8_1    conda-forge
     testpath                  0.4.4                      py_0    conda-forge
     tk                        8.6.10               hed695b0_1    conda-forge
     toml                      0.10.1             pyh9f0ad1d_0    conda-forge
     toolz                     0.11.1                     py_0    conda-forge
     tornado                   6.0.4            py37h8f50634_2    conda-forge
     traitlets                 5.0.5                      py_0    conda-forge
     typed-ast                 1.4.1            py37h516909a_0    conda-forge
     typing_extensions         3.7.4.3                    py_0    conda-forge
     urllib3                   1.25.10                    py_0    conda-forge
     virtualenv                20.0.35          py37hc8dfbb8_0    conda-forge
     wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge
     webencodings              0.5.1                      py_1    conda-forge
     wheel                     0.35.1             pyh9f0ad1d_0    conda-forge
     xz                        5.2.5                h516909a_1    conda-forge
     yaml                      0.2.5                h516909a_0    conda-forge
     zeromq                    4.3.3                he1b5a44_2    conda-forge
     zict                      2.0.0                      py_0    conda-forge
     zipp                      3.3.1                      py_0    conda-forge
     zlib                      1.2.11            h516909a_1010    conda-forge
     zstd                      1.4.5                h6597ccf_2    conda-forge

Additional context Surfaced while running fuzz tests: #6001

github-actions[bot] commented 3 years ago

This issue has been marked rotten due to no recent activity in the past 90d. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.

github-actions[bot] commented 3 years ago

This issue has been marked stale due to no recent activity in the past 30d. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed. This issue will be marked rotten if there is no activity in the next 60d.

beckernick commented 3 years ago

This error no longer presents in 21.08. Instead we get the data corruption bug covered in #6382

...
print(
    cudf.read_json(io.StringIO(buf), engine="cudf", lines=True, dtype={"a":np.dtype("datetime64[ns]")})
)
                        a
0 1969-12-31 23:59:59.999
1 1969-12-31 23:59:59.999
2 1969-12-31 23:59:59.999

As a result, I'm going to close this issue to consolidate discussion in #6382