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[BUG] categoricals with .ordered is None in cudf and dask_cudf #11487

Closed
eriknw opened this issue Aug 7, 2022 · 3 comments · Fixed by #11604
Closed

[BUG] categoricals with .ordered is None in cudf and dask_cudf #11487

eriknw opened this issue Aug 7, 2022 · 3 comments · Fixed by #11604
Assignees
@galipremsagar
Labels
bug Something isn't working Python Affects Python cuDF API.
Projects
Bug Squashing v22.10 Release

Comments

@eriknw
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eriknw commented Aug 7, 2022

Describe the bug
I expect a categorical dtype to always have dtype.order be True or False. It is sometimes None after construction, or converted to None after an operation such as concat.

This can cause dask_cudf to fail when using workloads with categorical dtypes with the following error (the mismatch is b/c one is None, but there should be no mismatch):

  File "/home/nfs/erwelch/miniconda3/envs/cugraph_dev8/lib/python3.9/site-packages/cudf/core/dataframe.py", line 3759, in merge
    return merge_cls(
  File "/home/nfs/erwelch/miniconda3/envs/cugraph_dev8/lib/python3.9/site-packages/cudf/core/join/join.py", line 166, in perform_merge
    lcol_casted, rcol_casted = _match_join_keys(lcol, rcol, self.how)
  File "/home/nfs/erwelch/miniconda3/envs/cugraph_dev8/lib/python3.9/site-packages/cudf/core/join/_join_helpers.py", line 68, in _match_join_keys
    return _match_categorical_dtypes_both(
  File "/home/nfs/erwelch/miniconda3/envs/cugraph_dev8/lib/python3.9/site-packages/cudf/core/join/_join_helpers.py", line 126, in _match_categorical_dtypes_both
    raise TypeError(
TypeError: Merging on categorical variables with mismatched ordering is ambiguous

Steps/Code to reproduce bug

In [1]: import cudf
In [2]: import dask_cudf
In [3]: # Good; ordered is False
In [4]: print(cudf.CategoricalDtype(['foo'], ordered=False).ordered)
False

In [5]: s = cudf.Series(4*['foo'], dtype='category')
In [6]: print(s.dtype.ordered)
False

In [7]: s2 = dask_cudf.from_cudf(s, npartitions=2)
In [8]: print(s2.dtype.ordered)
False

In [9]: # Bad; ordered is None
In [10]: print(cudf.CategoricalDtype(['foo']).ordered)
None

In [11]: print(s2.compute().dtype.ordered)  # b/c finalize uses concat
None

In [12]: print(dask_cudf.concat([s, s]).dtype.ordered)
None

In [13]: print(dask_cudf.concat([s, s]).compute().dtype.ordered)
None

In [14]: print(dask_cudf.concat([s2, s2]).dtype.ordered)
None

In [15]: print(dask_cudf.concat([s2, s2]).compute().dtype.ordered)
None

Expected behavior
I expect a categorical dtype to always have dtype.order be True or False.

This behavior should probably be fixed here (default on line 130):

cudf/python/cudf/cudf/core/dtypes.py

Lines 130 to 132 in e1a4e03

def __init__(self, categories=None, ordered: bool = None) -> None:
self._categories = self._init_categories(categories)
self.ordered = ordered

and here (default on line 1475):

cudf/python/cudf/cudf/core/column/column.py

Lines 1468 to 1476 in e1a4e03

def build_categorical_column(
categories: ColumnBase,
codes: ColumnBase,
mask: Buffer = None,
size: int = None,
offset: int = 0,
null_count: int = None,
ordered: bool = None,
) -> "cudf.core.column.CategoricalColumn":

or fix it directly in DataFrame._concat here:

cudf/python/cudf/cudf/core/dataframe.py

Lines 1692 to 1694 in e1a4e03

_reassign_categories(
categories, out._data, indices[first_data_column_position:]
)

via:

cudf/python/cudf/cudf/core/dataframe.py

Lines 7300 to 7311 in e1a4e03

def _reassign_categories(categories, cols, col_idxs):
for name, idx in zip(cols, col_idxs):
if idx in categories:
cols[name] = build_categorical_column(
categories=categories[idx],
codes=build_column(
cols[name].base_data, dtype=cols[name].dtype
),
mask=cols[name].base_mask,
offset=cols[name].offset,
size=cols[name].size,
)

Environment overview (please complete the following information)

  • Environment location: [Bare-metal]
  • Method of cuDF install: [conda]

Environment details

Click here to see environment details
 **git***
 commit 3a3e05dccbfac5a3c33632cda29253d0411b3ed8 (HEAD -> pg_categorical_type)
 Merge: 2df3cefb 7d8f0fd6
 Author: Erik Welch <erik.n.welch@gmail.com>
 Date:   Fri Aug 5 13:32:52 2022 -0700

 Merge branch 'branch-22.10' into pg_categorical_type
 **git submodules***

 ***OS Information***
 DGX_NAME="DGX Server"
 DGX_PRETTY_NAME="NVIDIA DGX Server"
 DGX_SWBUILD_DATE="2020-03-04"
 DGX_SWBUILD_VERSION="4.4.0"
 DGX_COMMIT_ID="ee09ebc"
 DGX_PLATFORM="DGX Server for DGX-1"
 DGX_SERIAL_NUMBER="QTFCOU8220028"

 DGX_R418_REPO_ENABLED=20220727-142458

 DGX_OTA_VERSION="4.13.0"
 DGX_OTA_DATE="Wed Jul 27 14:38:05 PDT 2022"
 DISTRIB_ID=Ubuntu
 DISTRIB_RELEASE=18.04
 DISTRIB_CODENAME=bionic
 DISTRIB_DESCRIPTION="Ubuntu 18.04.6 LTS"
 NAME="Ubuntu"
 VERSION="18.04.6 LTS (Bionic Beaver)"
 ID=ubuntu
 ID_LIKE=debian
 PRETTY_NAME="Ubuntu 18.04.6 LTS"
 VERSION_ID="18.04"
 HOME_URL="https://www.ubuntu.com/"
 SUPPORT_URL="https://help.ubuntu.com/"
 BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
 PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
 VERSION_CODENAME=bionic
 UBUNTU_CODENAME=bionic
 Linux dgx12 4.15.0-189-generic #200-Ubuntu SMP Wed Jun 22 19:53:37 UTC 2022 x86_64 x86_64 x86_64 GNU/Linux

 ***GPU Information***
 Sat Aug  6 22:13:57 2022
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 515.48.07    Driver Version: 515.48.07    CUDA Version: 11.7     |
 |-------------------------------+----------------------+----------------------+
 | 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   34C    P0    55W / 300W |  12425MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   1  Tesla V100-SXM2...  On   | 00000000:07:00.0 Off |                    0 |
 | N/A   33C    P0    55W / 300W |  11782MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   2  Tesla V100-SXM2...  On   | 00000000:0A:00.0 Off |                    0 |
 | N/A   31C    P0    54W / 300W |  11782MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   3  Tesla V100-SXM2...  On   | 00000000:0B:00.0 Off |                    0 |
 | N/A   31C    P0    55W / 300W |  11782MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   4  Tesla V100-SXM2...  On   | 00000000:85:00.0 Off |                    0 |
 | N/A   32C    P0    54W / 300W |  11782MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   5  Tesla V100-SXM2...  On   | 00000000:86:00.0 Off |                    0 |
 | N/A   33C    P0    54W / 300W |  11782MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   6  Tesla V100-SXM2...  On   | 00000000:89:00.0 Off |                    0 |
 | N/A   36C    P0    56W / 300W |  11782MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   7  Tesla V100-SXM2...  On   | 00000000:8A:00.0 Off |                    0 |
 | N/A   31C    P0    54W / 300W |  11782MiB / 32768MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+

 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
 |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
 |        ID   ID                                                   Usage      |
 |=============================================================================|
 |    0   N/A  N/A     27368      C   ...-groupby-apply/bin/python      643MiB |
 |    0   N/A  N/A     78085      C   ...s/cugraph_dev8/bin/python    11779MiB |
 |    1   N/A  N/A     78074      C   ...s/cugraph_dev8/bin/python    11779MiB |
 |    2   N/A  N/A     78078      C   ...s/cugraph_dev8/bin/python    11779MiB |
 |    3   N/A  N/A     78081      C   ...s/cugraph_dev8/bin/python    11779MiB |
 |    4   N/A  N/A     78089      C   ...s/cugraph_dev8/bin/python    11779MiB |
 |    5   N/A  N/A     78092      C   ...s/cugraph_dev8/bin/python    11779MiB |
 |    6   N/A  N/A     78095      C   ...s/cugraph_dev8/bin/python    11779MiB |
 |    7   N/A  N/A     78098      C   ...s/cugraph_dev8/bin/python    11779MiB |
 +-----------------------------------------------------------------------------+

 ***CPU***
 Architecture:        x86_64
 CPU op-mode(s):      32-bit, 64-bit
 Byte Order:          Little Endian
 CPU(s):              80
 On-line CPU(s) list: 0-79
 Thread(s) per core:  2
 Core(s) per socket:  20
 Socket(s):           2
 NUMA node(s):        2
 Vendor ID:           GenuineIntel
 CPU family:          6
 Model:               79
 Model name:          Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
 Stepping:            1
 CPU MHz:             1745.439
 CPU max MHz:         3600.0000
 CPU min MHz:         1200.0000
 BogoMIPS:            4390.09
 Virtualization:      VT-x
 L1d cache:           32K
 L1i cache:           32K
 L2 cache:            256K
 L3 cache:            51200K
 NUMA node0 CPU(s):   0-19,40-59
 NUMA node1 CPU(s):   20-39,60-79
 Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d

 ***CMake***
 /home/nfs/erwelch/miniconda3/envs/cugraph_dev8/bin/cmake
 cmake version 3.23.3

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

 ***g++***
 /home/nfs/erwelch/miniconda3/envs/cugraph_dev8/bin/g++
 g++ (conda-forge gcc 10.4.0-16) 10.4.0
 Copyright (C) 2020 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***
 /home/nfs/erwelch/miniconda3/envs/cugraph_dev8/bin/nvcc



/home/nfs/erwelch/miniconda3/envs/cugraph_dev8/bin/nvcc: line 8: /bin/nvcc: No such file or directory


 ***Python***
 /home/nfs/erwelch/miniconda3/envs/cugraph_dev8/bin/python
 Python 3.9.13

 ***Environment Variables***
 PATH                            : /home/nfs/erwelch/miniconda3/envs/cugraph_dev8/bin:/home/nfs/erwelch/miniconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
 LD_LIBRARY_PATH                 :
 NUMBAPRO_NVVM                   :
 NUMBAPRO_LIBDEVICE              :
 CONDA_PREFIX                    : /home/nfs/erwelch/miniconda3/envs/cugraph_dev8
 PYTHON_PATH                     :

 ***conda packages***
 /home/nfs/erwelch/miniconda3/condabin/conda
 # packages in environment at /home/nfs/erwelch/miniconda3/envs/cugraph_dev8:
 #
 # Name                    Version                   Build  Channel
 _libgcc_mutex             0.1                 conda_forge    conda-forge
 _openmp_mutex             4.5                       2_gnu    conda-forge
 abseil-cpp                20210324.2           h9c3ff4c_0    conda-forge
 alabaster                 0.7.12                     py_0    conda-forge
 argon2-cffi               21.3.0             pyhd8ed1ab_0    conda-forge
 argon2-cffi-bindings      21.2.0           py39hb9d737c_2    conda-forge
 arrow-cpp                 8.0.1           py39h811ffd7_0_cpu    conda-forge
 asttokens                 2.0.5              pyhd8ed1ab_0    conda-forge
 asvdb                     0.4.2               g90e8f2c_40    rapidsai
 atk-1.0                   2.36.0               h3371d22_4    conda-forge
 attrs                     22.1.0             pyh71513ae_1    conda-forge
 aws-c-cal                 0.5.11               h95a6274_0    conda-forge
 aws-c-common              0.6.2                h7f98852_0    conda-forge
 aws-c-event-stream        0.2.7               h3541f99_13    conda-forge
 aws-c-io                  0.10.5               hfb6a706_0    conda-forge
 aws-checksums             0.1.11               ha31a3da_7    conda-forge
 aws-sdk-cpp               1.8.186              hb4091e7_3    conda-forge
 babel                     2.10.3             pyhd8ed1ab_0    conda-forge
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 backports                 1.0                        py_2    conda-forge
 backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
 beautifulsoup4            4.11.1             pyha770c72_0    conda-forge
 binutils                  2.36.1               hdd6e379_2    conda-forge
 binutils_impl_linux-64    2.36.1               h193b22a_2    conda-forge
 binutils_linux-64         2.36                hf3e587d_10    conda-forge
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 bokeh                     2.4.3            py39hf3d152e_0    conda-forge
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 boto3                     1.24.45            pyhd8ed1ab_0    conda-forge
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 cachetools                5.0.0              pyhd8ed1ab_0    conda-forge
 cairo                     1.16.0            ha61ee94_1011    conda-forge
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 clangxx                   11.1.0          default_ha53f305_1    conda-forge
 click                     8.1.3            py39hf3d152e_0    conda-forge
 cloudpickle               2.1.0              pyhd8ed1ab_0    conda-forge
 cmake                     3.23.3               h5432695_0    conda-forge
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 cryptography              37.0.4           py39hd97740a_0    conda-forge
 cuda-python               11.7.0           py39h3fd9d12_0    nvidia
 cudatoolkit               11.5.1               hcf5317a_9    nvidia
 cudf                      22.08.00a220804 cuda_11_py39_gb2ae9a9f9e_301    rapidsai-nightly
 cugraph                   22.8.0a0+127.gcdc563fc          pypi_0    pypi
 cupy                      10.6.0           py39hc3c280e_0    conda-forge
 cxx-compiler              1.4.2                h924138e_0    conda-forge
 cython                    0.29.32          py39h5a03fae_0    conda-forge
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 dask                      2022.7.1           pyhd8ed1ab_0    conda-forge
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 distributed               2022.7.1           pyhd8ed1ab_0    conda-forge
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 gxx_linux-64              10.4.0              h6e491c6_10    conda-forge
 harfbuzz                  5.1.0                hf9f4e7c_0    conda-forge
 heapdict                  1.0.1                      py_0    conda-forge
 icu                       70.1                 h27087fc_0    conda-forge
 idna                      3.3                pyhd8ed1ab_0    conda-forge
 imagesize                 1.4.1              pyhd8ed1ab_0    conda-forge
 importlib-metadata        4.11.4           py39hf3d152e_0    conda-forge
 importlib_resources       5.9.0              pyhd8ed1ab_0    conda-forge
 iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge
 ipycytoscape              1.3.3              pyh1d7be83_0    conda-forge
 ipykernel                 6.15.1             pyh210e3f2_0    conda-forge
 ipython                   8.4.0            py39hf3d152e_0    conda-forge
 ipython_genutils          0.2.0                      py_1    conda-forge
 ipywidgets                7.7.1              pyhd8ed1ab_0    conda-forge
 isort                     5.10.1             pyhd8ed1ab_0    conda-forge
 jedi                      0.18.1           py39hf3d152e_1    conda-forge
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Additional context
This is necessary to use categorical dtypes in cugraph's PropertyGraph: rapidsai/cugraph#2510
@eriknw eriknw added Needs Triage Need team to review and classify bug Something isn't working labels Aug 7, 2022
@github-actions github-actions bot added this to Needs prioritizing in Bug Squashing Aug 7, 2022
@galipremsagar galipremsagar self-assigned this Aug 8, 2022
@galipremsagar galipremsagar added Python Affects Python cuDF API. and removed Needs Triage Need team to review and classify labels Aug 8, 2022
@galipremsagar galipremsagar added this to Issue-Needs prioritizing in v22.10 Release via automation Aug 8, 2022
@shwina
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shwina commented Aug 9, 2022

Doesn't Pandas support None in addition to True and False?

https://pandas.pydata.org/docs/reference/api/pandas.CategoricalDtype.html#pandas.CategoricalDtype

@eriknw
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eriknw commented Aug 9, 2022

Yes, as a "please defer to other categorical ordered property during astype, but this is weird so users probably shouldn't use ordered=None explicitly and we wanted to deprecate this" kind of way. See details here:

pandas-dev/pandas#26336
pandas-dev/pandas#26403
pandas-dev/pandas#29955

But, pandas ordered defaults to False and ordered doesn't get ignored and set to None as is done in the code snippets a shared above. It's the latter behavior (ordered property getting changed when it shouldn't) that is affecting my code. The default should be changed to False as a matter of matching pandas.

@eriknw
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eriknw commented Aug 9, 2022

It's the latter behavior (ordered property getting changed when it shouldn't) that is affecting my code.

Let me highlight this from my examples above:

In [1]: import cudf
In [2]: import dask_cudf
In [5]: s = cudf.Series(4*['foo'], dtype='category')
In [6]: print(s.dtype.ordered)
False

In [7]: s2 = dask_cudf.from_cudf(s, npartitions=2)
In [8]: print(s2.dtype.ordered)
False

In [11]: print(s2.compute().dtype.ordered)  # b/c finalize uses concat
None

@galipremsagar galipremsagar moved this from Issue-Needs prioritizing to Issue-P1 in v22.10 Release Aug 24, 2022
@galipremsagar galipremsagar mentioned this issue Aug 25, 2022
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rapids-bot bot pushed a commit that referenced this issue Aug 26, 2022
@galipremsagar
Change default value of ordered to False in CategoricalDtype (#…
05a553b 
…11604)

Fixes: #11487 

This PR switches default value of `ordered` parameter in `CategoricalDtype` to `False`. This fixes some issues around concat and building categorical columns.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Bradley Dice (https://github.com/bdice)
  - Ashwin Srinath (https://github.com/shwina)

URL: #11604
Bug Squashing automation moved this from Needs prioritizing to Closed Aug 26, 2022
v22.10 Release automation moved this from Issue-P1 to Done Aug 26, 2022
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[REVIEW] Change default value of ordered to False in CategoricalDtype galipremsagar/cudf
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@eriknw @shwina @galipremsagar