beckernick
commented
3 years ago This came up while @shaneding was working on #7997 , as this presents when ~accessing~ returning list.leaves
Open shaneding opened 3 years ago
beckernick
commented
3 years ago This came up while @shaneding was working on #7997 , as this presents when ~accessing~ returning list.leaves
beckernick
commented
3 years ago Thinking more, the monotonically increasing index in the original example is due to how this is constructed here (from a cuDF object). I think this may simply be fine as is. The computed index is just the collection of the the underlying indexes.
kkraus14
commented
3 years ago I think specifically that the character_tokenize function resets the index because there isn't a clean way to maintain it, so when the function is run against each dask partition, each partition gets a reset index.
github-actions[bot]
commented
2 years ago This issue has been labeled inactive-90d due to no recent activity in the past 90 days. 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.
vyasr
commented
1 year ago @beckernick do you think there's anything to do here, or should we close this issue as expected behavior?
vyasr
commented
3 months ago Hmm the current results here seem to have gotten worse. @beckernick WDYT of this? I'm not familiar with the API or what it's meant to do, but this seems quite wrong.
In [105]: import cudf
...: import dask_cudf
...: s = cudf.Series(["ab", "cd", "ef", "gh", "ij"])
...: ds = dask_cudf.from_cudf(s, 2)
...: print(ds.compute())
...: print(ds.map_partitions(lambda x: x.str.character_tokenize(), meta=ds._meta).compute())
0 ab
1 cd
2 ef
3 gh
4 ij
dtype: object
0 a
0 b
1 c
1 d
2 e
2 f
3 j
3
4
4
dtype: object
Describe the bug When using map_partitions() on a
dask_cudfdataframe we can potentially get an unexpected index. This does not affect.compute()by default as it reconstructs a global index.Steps/Code to reproduce bug
This prints
Expected behavior Would love to discuss what the expected output would be, but below is what I would expect.
Environment details
Click here to see environment details
**git*** commit 512e485c41f0b9aa2261430ba426fa8c05c98c2a (HEAD -> list-accessor, origin/list-accessor) Merge: bf68778b78 5f9dade58a Author: Shane Ding
Date: Fri May 7 14:33:32 2021 +0000
Merge branch 'branch-0.20' of https://github.com/rapidsai/cudf into list-accessor
**git submodules***
***OS Information***
DGX_NAME="DGX Server"
DGX_PRETTY_NAME="NVIDIA DGX Server"
DGX_SWBUILD_DATE="2019-12-02"
DGX_SWBUILD_VERSION="4.3.0"
DGX_COMMIT_ID="3015363"
DGX_PLATFORM="DGX Server for DGX-1"
DGX_SERIAL_NUMBER="QTFCOU9270061"
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=18.04
DISTRIB_CODENAME=bionic
DISTRIB_DESCRIPTION="Ubuntu 18.04.3 LTS"
NAME="Ubuntu"
VERSION="18.04.3 LTS (Bionic Beaver)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 18.04.3 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 rl-dgx-r11-u30-rapids-dgx103 4.15.0-55-generic #60-Ubuntu SMP Tue Jul 2 18:22:20 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
***GPU Information***
Mon May 10 17:20:28 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| 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 35C P0 48W / 163W | 718MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 Tesla V100-SXM2... On | 00000000:07:00.0 Off | 0 |
| N/A 33C P0 42W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 2 Tesla V100-SXM2... On | 00000000:0A:00.0 Off | 0 |
| N/A 34C P0 44W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 3 Tesla V100-SXM2... On | 00000000:0B:00.0 Off | 0 |
| N/A 31C P0 43W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 4 Tesla V100-SXM2... On | 00000000:85:00.0 Off | 0 |
| N/A 36C P0 43W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 5 Tesla V100-SXM2... On | 00000000:86:00.0 Off | 0 |
| N/A 35C P0 43W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 6 Tesla V100-SXM2... On | 00000000:89:00.0 Off | 0 |
| N/A 37C P0 41W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 7 Tesla V100-SXM2... On | 00000000:8A:00.0 Off | 0 |
| N/A 34C P0 42W / 163W | 3MiB / 32510MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 44033 C .../envs/cudf_dev/bin/python 715MiB |
+-----------------------------------------------------------------------------+
***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: 1967.306
CPU max MHz: 3600.0000
CPU min MHz: 1200.0000
BogoMIPS: 4390.35
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***
/raid/sding/miniconda3/envs/cudf_dev/bin/cmake
cmake version 3.18.5
CMake suite maintained and supported by Kitware (kitware.com/cmake).
***g++***
/usr/bin/g++
g++ (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0
Copyright (C) 2017 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) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_14_21:12:58_PST_2021
Cuda compilation tools, release 11.2, V11.2.152
Build cuda_11.2.r11.2/compiler.29618528_0
***Python***
/raid/sding/miniconda3/envs/cudf_dev/bin/python
Python 3.8.8
***Environment Variables***
PATH : /home/u00u97shnnb9gHdDUD357/.vscode-server/bin/3c4e3df9e89829dce27b7b5c24508306b151f30d/bin:/raid/sding/miniconda3/envs/cudf_dev/bin:/raid/sding/miniconda3/condabin:/home/u00u97shnnb9gHdDUD357/.vscode-server/bin/3c4e3df9e89829dce27b7b5c24508306b151f30d/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/usr/local/cuda/bin:/usr/local/cuda/bin
LD_LIBRARY_PATH :
NUMBAPRO_NVVM :
NUMBAPRO_LIBDEVICE :
CONDA_PREFIX : /raid/sding/miniconda3/envs/cudf_dev
PYTHON_PATH :
***conda packages***
/raid/sding/miniconda3/condabin/conda
# packages in environment at /raid/sding/miniconda3/envs/cudf_dev:
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terminado 0.9.4 py38h578d9bd_0 conda-forge
testpath 0.4.4 py_0 conda-forge
tk 8.6.10 h21135ba_1 conda-forge
tokenizers 0.10.1 py38hb63a372_0 conda-forge
toml 0.10.2 pyhd8ed1ab_0 conda-forge
toolz 0.11.1 py_0 conda-forge
tornado 6.1 py38h497a2fe_1 conda-forge
tqdm 4.60.0 pyhd8ed1ab_0 conda-forge
traitlets 5.0.5 py_0 conda-forge
transformers 4.5.1 pyhd8ed1ab_1 conda-forge
typed-ast 1.4.3 py38h497a2fe_0 conda-forge
typing_extensions 3.7.4.3 py_0 conda-forge
unknown 0.0.0 dev_0
urllib3 1.26.4 pyhd8ed1ab_0 conda-forge
virtualenv 20.4.4 py38h578d9bd_0 conda-forge
wcwidth 0.2.5 pyh9f0ad1d_2 conda-forge
webencodings 0.5.1 py_1 conda-forge
wheel 0.36.2 pyhd3deb0d_0 conda-forge
xz 5.2.5 h516909a_1 conda-forge
yaml 0.2.5 h516909a_0 conda-forge
zeromq 4.3.4 h9c3ff4c_0 conda-forge
zict 2.0.0 py_0 conda-forge
zipp 3.4.1 pyhd8ed1ab_0 conda-forge
zlib 1.2.11 h516909a_1010 conda-forge
zstd 1.4.9 ha95c52a_0 conda-forge