Describe the bug
When trying to use the mean() function on a cudf.core.groupby.groupby.DataFrameGroupBy object, a 'RuntimeError: Failed to deserialize program' is thrown
Steps/Code to reproduce bug
df = cudf.DataFrame(
{"key": [0, 1, 2, 3, 4], "val": [float(i + 10) for i in range(5)], "val2": [float(i + 15) for i in range(5)]}
)
g = df.groupby('key')
g.mean()
Stack Trace
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-53-c79523bc0c66> in <module>
----> 1 g.mean()
~/anaconda3/envs/rapids_dev_0.12/lib/python3.7/site-packages/cudf/core/groupby/groupby.py in mean(self)
38
39 def mean(self):
---> 40 return self._apply_aggregation("mean")
41
42 def count(self):
~/anaconda3/envs/rapids_dev_0.12/lib/python3.7/site-packages/cudf/core/groupby/groupby.py in _apply_aggregation(self, agg)
130 Applies the aggregation function(s) ``agg`` on all columns
131 """
--> 132 result = self._groupby.compute_result(agg)
133 libcudf.nvtx.nvtx_range_pop()
134 return result
~/anaconda3/envs/rapids_dev_0.12/lib/python3.7/site-packages/cudf/core/groupby/groupby.py in compute_result(self, agg)
368 aggs_as_list,
369 self.sort,
--> 370 self.dropna,
371 )
372
~/anaconda3/envs/rapids_dev_0.12/lib/python3.7/site-packages/cudf/core/groupby/groupby.py in _groupby_engine(key_columns, value_columns, aggs, sort, dropna)
549 """
550 out_key_columns, out_value_columns = libcudf.groupby.groupby(
--> 551 key_columns, value_columns, aggs, dropna=dropna
552 )
553
cudf/_lib/groupby.pyx in cudf._lib.groupby.groupby()
RuntimeError: Failed to deserialize program
Expected behavior
As of the nightly build 0.12.0a200105, the output was as expected ->
key val val2
0 0 10.0 15.0
1 1 11.0 16.0
2 2 12.0 17.0
3 3 13.0 18.0
4 4 14.0 19.0
Environment overview (please complete the following information)
- Environment location: Bare-metal
- Method of cuDF install: conda
Environment details
Output of the cudf/print_env.sh
Click here to see environment details
git*
Not inside a git repository
***OS Information***
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 aiinfra-rapids-ajay 4.15.0-72-generic #81-Ubuntu SMP Tue Nov 26 12:20:02 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
***GPU Information***
Tue Jan 7 13:20:25 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 TITAN RTX On | 00000000:02:00.0 On | N/A |
| 41% 42C P2 59W / 280W | 2364MiB / 24219MiB | 2% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2279 G /usr/lib/xorg/Xorg 28MiB |
| 0 2336 G /usr/bin/gnome-shell 50MiB |
| 0 3310 G /usr/lib/xorg/Xorg 336MiB |
| 0 3471 G /usr/bin/gnome-shell 315MiB |
| 0 7074 G ...uest-channel-token=-------------------- 321MiB |
| 0 9881 C python 355MiB |
| 0 12736 C ...aconda3/envs/rapids_dev_0.12/bin/python 355MiB |
| 0 29082 G ...uest-channel-token=-------------------- 514MiB |
| 0 31504 G ...uest-channel-token=-------------------- 71MiB |
+-----------------------------------------------------------------------------+
***CPU***
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 12
On-line CPU(s) list: 0-11
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz
Stepping: 1
CPU MHz: 1357.616
CPU max MHz: 3800.0000
CPU min MHz: 1200.0000
BogoMIPS: 6796.14
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 15360K
NUMA node0 CPU(s): 0-11
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 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***
/usr/local/bin/cmake
cmake version 3.16.2
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) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
***Python***
/home/$USER/anaconda3/envs/rapids_dev_0.12/bin/python
Python 3.7.3
***Environment Variables***
PATH : /home/$USER/anaconda3/envs/rapids_dev_0.12/bin:/home/$USER/anaconda3/condabin:/home/$USER/anaconda3/bin:/home/$USER/bin:/home/$USER/.local/bin:/home/$USER/.nvm/versions/node/v13.5.0/bin:/home/$USER/anaconda3/bin:/home/$USER/bin:/home/$USER/.local/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 : /home/$USER/anaconda3/envs/rapids_dev_0.12
PYTHON_PATH :
***conda packages***
/home/$USER/anaconda3/condabin/conda
# packages in environment at /home/$USER/anaconda3/envs/rapids_dev_0.12:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main conda-forge
aiohttp 3.6.2 pypi_0 pypi
alabaster 0.7.12 py_0 conda-forge
appdirs 1.4.3 py_1 conda-forge
arrow-cpp 0.15.0 py37h090bef1_2 conda-forge
async-timeout 3.0.1 pypi_0 pypi
attrs 19.3.0 py_0 conda-forge
babel 2.7.0 py_0 conda-forge
backcall 0.1.0 py_0 conda-forge
black 19.10b0 py37_0 conda-forge
bleach 3.1.0 py_0 conda-forge
bokeh 1.4.0 py37_0 conda-forge
boost-cpp 1.70.0 h8e57a91_2 conda-forge
brotli 1.0.7 he1b5a44_1000 conda-forge
bzip2 1.0.8 h516909a_2 conda-forge
c-ares 1.15.0 h516909a_1001 conda-forge
ca-certificates 2019.11.28 hecc5488_0 conda-forge
cairo 1.16.0 hfb77d84_1002 conda-forge
certifi 2019.11.28 py37_0 conda-forge
cffi 1.13.2 py37h8022711_0 conda-forge
cfitsio 3.470 hb60a0a2_2 conda-forge
chardet 3.0.4 py37_1003 conda-forge
click 7.0 py_0 conda-forge
click-plugins 1.1.1 py_0 conda-forge
cligj 0.5.0 py_0 conda-forge
cloudpickle 1.2.2 py_1 conda-forge
colorcet 2.0.1 py_0 conda-forge
commonmark 0.9.1 py_0 conda-forge
cryptography 2.8 py37h72c5cf5_1 conda-forge
cudatoolkit 10.1.243 h6bb024c_0 nvidia
cudf 0.12.0b200107 py37_606 rapidsai-nightly
cudnn 7.6.0 cuda10.1_0 nvidia
cupy 6.6.0 py37ha7c4746_1 conda-forge
curl 7.65.3 hf8cf82a_0 conda-forge
cuxfilter 0.12.0a0+39.g9ba5616.dirty dev_0 <develop>
cycler 0.10.0 py_2 conda-forge
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dask 2.9.0 py_0 conda-forge
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fiona 1.8.9.post2 py37hdff7cfa_0 conda-forge
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jupyter_client 5.3.3 py37_1 conda-forge
jupyter_core 4.6.1 py37_0 conda-forge
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xarray 0.14.1 py_1 conda-forge
xerces-c 3.2.2 h8412b87_1004 conda-forge
xorg-kbproto 1.0.7 h14c3975_1002 conda-forge
xorg-libice 1.0.10 h516909a_0 conda-forge
xorg-libsm 1.2.3 h84519dc_1000 conda-forge
xorg-libx11 1.6.9 h516909a_0 conda-forge
xorg-libxau 1.0.9 h14c3975_0 conda-forge
xorg-libxdmcp 1.1.3 h516909a_0 conda-forge
xorg-libxext 1.3.4 h516909a_0 conda-forge
xorg-libxrender 0.9.10 h516909a_1002 conda-forge
xorg-renderproto 0.11.1 h14c3975_1002 conda-forge
xorg-xextproto 7.3.0 h14c3975_1002 conda-forge
xorg-xproto 7.0.31 h14c3975_1007 conda-forge
xz 5.2.4 h14c3975_1001 conda-forge
yaml 0.2.2 h516909a_1 conda-forge
yarl 1.4.2 pypi_0 pypi
zeromq 4.3.2 he1b5a44_2 conda-forge
zict 1.0.0 py_0 conda-forge
zipp 0.6.0 py_0 conda-forge
zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.3 h3b9ef0a_0 conda-forge
Additional context
This was not an issue in the nightly build 0.12.0a200105, but is in the build 0.12.0b200107
AjayThorve
All 3 comments
@devavret @trxcllnt This looks like a Jitify issue when the mean aggregation calls the div binary operation. Were there any jitify changes that would have broken the old binops?
jrhemstad
on 7 Jan 2020
@AjayThorve, Try cleaning out /tmp/cudf*. This would happen if you ran the same test before and after pulling the nightly on the same machine. The cache version would've changed causing an error due to mismatched header in the cache file.
Although there is a mechanism in place to prevent this bug across releases, it wouldn't work between nightlies.
devavret
on 7 Jan 2020
🚀2
yes, cleaning /tmp/cudf* resolved it. Thanks @devavret!
AjayThorve
on 7 Jan 2020
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Most helpful comment
@AjayThorve, Try cleaning out
/tmp/cudf*. This would happen if you ran the same test before and after pulling the nightly on the same machine. The cache version would've changed causing an error due to mismatched header in the cache file.Although there is a mechanism in place to prevent this bug across releases, it wouldn't work between nightlies.