Cudf: [BUG] CudaIllegalAccessException calling slice from map_partitions
Describe the bug
Code is the best description. This worked in 0.13, but stopped working as of 2020/03/24 build.
Steps/Code to reproduce bug
import dask_cudf
import dask
import pandas as pd
#create a dataframe and pull into dask_cudf. Original issue found in df from read_csv
pdf=pd.DataFrame([ ( i, str(d) ) for i,d in enumerate(range(20150101, 20150130))], columns=['id','date'])
ddf=dask_cudf.from_dask_dataframe( dask.dataframe.from_pandas( pdf, npartitions=2 ))
def get_date_parts( df ):
df['date'].astype('str').str.slice(4, 6).astype('int')
return df
ddf.map_partitions( get_date_parts )
Expected behavior
Returns a dask dataframe, not raises an exception.
Environment overview (please complete the following information)
- Environment location: Reproduced on both bare-metal and in Docker, occurs for docker builds with Cuda 10.,0
- Above reproduction installed with conda install on DGX as follows
channels:
- rapidsai-nightly
- rapidsai
- conda-forge
- nvidia
- defaults
dependencies:
- python=3.7
- cudf=0.14*
- dask-cuda=0.14*
- dask-cudf=0.14*
Environment details
In comments
Additional context
The last known working conda environment listing json follows as comment
kevingerman
All 12 comments
Click here to see environment details
**git***
commit d276b178da98a7a6e3411efdebc76d46b47d6a1f (HEAD -> results)
Author: Kevin German <[email protected]>
Date: Mon Mar 23 08:46:46 2020 -0700
tracking results
**git submodules***
***OS Information***
DGX_NAME="DGX Server"
DGX_PRETTY_NAME="NVIDIA DGX Server"
DGX_SWBUILD_DATE="2018-01-10"
DGX_SWBUILD_VERSION="3.1.4"
DGX_COMMIT_ID="660a5f359205297159909ff1631b15af9ecc3aef"
DGX_SERIAL_NUMBER=QTFCOU6460056-R1
DGX_OTA_VERSION="3.1.4"
DGX_OTA_DATE="Fri Feb 9 12:13:38 PST 2018"
DGX_OTA_VERSION="3.1.7"
DGX_OTA_DATE="Wed Jun 27 11:44:16 PDT 2018"
DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.6 LTS"
NAME="Ubuntu"
VERSION="16.04.6 LTS (Xenial Xerus)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 16.04.6 LTS"
VERSION_ID="16.04"
HOME_URL="http://www.ubuntu.com/"
SUPPORT_URL="http://help.ubuntu.com/"
BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial
Linux dgx01 4.4.0-142-generic #168-Ubuntu SMP Wed Jan 16 21:00:45 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux
***GPU Information***
Wed Apr 8 09:19:24 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 Tesla P100-SXM2... On | 00000000:06:00.0 Off | 0 |
| N/A 32C P0 33W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla P100-SXM2... On | 00000000:07:00.0 Off | 0 |
| N/A 33C P0 34W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla P100-SXM2... On | 00000000:0A:00.0 Off | 0 |
| N/A 30C P0 32W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla P100-SXM2... On | 00000000:0B:00.0 Off | 0 |
| N/A 31C P0 31W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 4 Tesla P100-SXM2... On | 00000000:85:00.0 Off | 0 |
| N/A 32C P0 32W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 5 Tesla P100-SXM2... On | 00000000:86:00.0 Off | 0 |
| N/A 30C P0 34W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 6 Tesla P100-SXM2... On | 00000000:89:00.0 Off | 0 |
| N/A 31C P0 34W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 7 Tesla P100-SXM2... On | 00000000:8A:00.0 Off | 0 |
| N/A 32C P0 32W / 300W | 0MiB / 16280MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
***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: 2706.945
CPU max MHz: 3600.0000
CPU min MHz: 1200.0000
BogoMIPS: 4391.50
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 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 epb invpcid_single intel_pt ssbd ibrs ibpb stibp kaiser tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdseed adx smap xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts flush_l1d
***CMake***
/usr/local/bin/cmake
cmake version 3.12.0
CMake suite maintained and supported by Kitware (kitware.com/cmake).
***g++***
/usr/bin/g++
g++ (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609
Copyright (C) 2015 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***
***Python***
/raid/kgerman/miniconda/envs/wtf/bin/python
Python 3.7.6
***Environment Variables***
PATH : /raid/kgerman/miniconda/envs/wtf/bin:/home/nfs/kgerman/bin:/home/nfs/kgerman/.local/bin:/home/nfs/kgerman/bin:/home/nfs/kgerman/.local/bin:/raid/kgerman/miniconda/bin:/raid/kgerman/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games
LD_LIBRARY_PATH :
NUMBAPRO_NVVM :
NUMBAPRO_LIBDEVICE :
CONDA_PREFIX : /raid/kgerman/miniconda/envs/wtf
PYTHON_PATH :
***conda packages***
/raid/kgerman/miniconda/bin/conda
# packages in environment at /raid/kgerman/miniconda/envs/wtf:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 1_llvm conda-forge
arrow-cpp 0.15.0 py37h090bef1_2 conda-forge
bokeh 2.0.1 py37hc8dfbb8_0 conda-forge
boost-cpp 1.70.0 h8e57a91_2 conda-forge
brotli 1.0.7 he1b5a44_1001 conda-forge
bzip2 1.0.8 h516909a_2 conda-forge
c-ares 1.15.0 h516909a_1001 conda-forge
ca-certificates 2020.4.5.1 hecc5488_0 conda-forge
certifi 2020.4.5.1 py37hc8dfbb8_0 conda-forge
click 7.1.1 pyh8c360ce_0 conda-forge
cloudpickle 1.3.0 py_0 conda-forge
cudatoolkit 10.1.243 h6bb024c_0 nvidia
cudf 0.14.0a200408 py37_2726 rapidsai-nightly
cudnn 7.6.0 cuda10.1_0 nvidia
cupy 7.3.0 py37h0632833_0 conda-forge
cytoolz 0.10.1 py37h516909a_0 conda-forge
dask 2.14.0 py_0 conda-forge
dask-core 2.14.0 py_0 conda-forge
dask-cuda 0.14.0a200408 py37_40 rapidsai-nightly
dask-cudf 0.14.0a200408 py37_2726 rapidsai-nightly
distributed 2.14.0 py37hc8dfbb8_0 conda-forge
dlpack 0.2 he1b5a44_1 conda-forge
double-conversion 3.1.5 he1b5a44_2 conda-forge
fastavro 0.23.1 py37h8f50634_0 conda-forge
fastrlock 0.4 py37h3340039_1001 conda-forge
freetype 2.10.1 he06d7ca_0 conda-forge
fsspec 0.7.2 py_0 conda-forge
gflags 2.2.2 he1b5a44_1002 conda-forge
glog 0.4.0 he1b5a44_1 conda-forge
grpc-cpp 1.23.0 h18db393_0 conda-forge
heapdict 1.0.1 py_0 conda-forge
icu 64.2 he1b5a44_1 conda-forge
jinja2 2.11.1 py_0 conda-forge
jpeg 9c h14c3975_1001 conda-forge
ld_impl_linux-64 2.34 h53a641e_0 conda-forge
libblas 3.8.0 16_openblas conda-forge
libcblas 3.8.0 16_openblas conda-forge
libcudf 0.14.0a200408 cuda10.1_2726 rapidsai-nightly
libevent 2.1.10 h72c5cf5_0 conda-forge
libffi 3.2.1 he1b5a44_1007 conda-forge
libgcc-ng 9.2.0 h24d8f2e_2 conda-forge
libgfortran-ng 7.3.0 hdf63c60_5 conda-forge
liblapack 3.8.0 16_openblas conda-forge
libllvm8 8.0.1 hc9558a2_0 conda-forge
libnvstrings 0.14.0a200408 cuda10.1_2726 rapidsai-nightly
libopenblas 0.3.9 h5ec1e0e_0 conda-forge
libpng 1.6.37 hed695b0_1 conda-forge
libprotobuf 3.8.0 h8b12597_0 conda-forge
librmm 0.14.0a200408 cuda10.1_252 rapidsai-nightly
libstdcxx-ng 9.2.0 hdf63c60_2 conda-forge
libtiff 4.1.0 hfc65ed5_0 conda-forge
llvm-openmp 9.0.1 hc9558a2_2 conda-forge
llvmlite 0.31.0 py37h5202443_1 conda-forge
locket 0.2.0 py_2 conda-forge
lz4-c 1.8.3 he1b5a44_1001 conda-forge
markupsafe 1.1.1 py37h8f50634_1 conda-forge
msgpack-python 1.0.0 py37h99015e2_1 conda-forge
nccl 2.6.4.1 h51cf6c1_0 conda-forge
ncurses 6.1 hf484d3e_1002 conda-forge
numba 0.48.0 py37hb3f55d8_0 conda-forge
numpy 1.18.1 py37h8960a57_1 conda-forge
nvstrings 0.14.0a200408 py37_2726 rapidsai-nightly
olefile 0.46 py_0 conda-forge
openssl 1.1.1f h516909a_0 conda-forge
packaging 20.1 py_0 conda-forge
pandas 0.25.3 py37hb3f55d8_0 conda-forge
parquet-cpp 1.5.1 2 conda-forge
partd 1.1.0 py_0 conda-forge
pillow 7.1.1 py37h718be6c_0 conda-forge
pip 20.0.2 py_2 conda-forge
psutil 5.7.0 py37h8f50634_1 conda-forge
pyarrow 0.15.0 py37h8b68381_1 conda-forge
pynvml 8.0.4 py_0 conda-forge
pyparsing 2.4.7 pyh9f0ad1d_0 conda-forge
python 3.7.6 h8356626_5_cpython conda-forge
python-dateutil 2.8.1 py_0 conda-forge
python_abi 3.7 1_cp37m conda-forge
pytz 2019.3 py_0 conda-forge
pyyaml 5.3.1 py37h8f50634_0 conda-forge
re2 2020.04.01 he1b5a44_0 conda-forge
readline 8.0 hf8c457e_0 conda-forge
rmm 0.14.0a200408 py37_252 rapidsai-nightly
setuptools 46.1.3 py37hc8dfbb8_0 conda-forge
six 1.14.0 py_1 conda-forge
snappy 1.1.8 he1b5a44_1 conda-forge
sortedcontainers 2.1.0 py_0 conda-forge
sqlite 3.30.1 hcee41ef_0 conda-forge
tblib 1.6.0 py_0 conda-forge
thrift-cpp 0.12.0 hf3afdfd_1004 conda-forge
tk 8.6.10 hed695b0_0 conda-forge
toolz 0.10.0 py_0 conda-forge
tornado 6.0.4 py37h8f50634_1 conda-forge
typing_extensions 3.7.4.1 py37hc8dfbb8_3 conda-forge
uriparser 0.9.3 he1b5a44_1 conda-forge
wheel 0.34.2 py_1 conda-forge
xz 5.2.5 h516909a_0 conda-forge
yaml 0.2.2 h516909a_1 conda-forge
zict 2.0.0 py_0 conda-forge
zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.3 h3b9ef0a_0 conda-forge
Click here to see conda list json for last successful execution
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kevingerman
on 8 Apr 2020
Simple repro
>>> import cudf
>>> sr = cudf.Series(["cat", "dog"])
>>> sr.astype('str').str.slice(4, 6).astype('int')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/rgsl888/Projects/cython/cudf/python/cudf/cudf/core/series.py", line 1368, in astype
raise e
File "/home/rgsl888/Projects/cython/cudf/python/cudf/cudf/core/series.py", line 1364, in astype
data=self._column.astype(dtype, **kwargs)
File "/home/rgsl888/Projects/cython/cudf/python/cudf/cudf/core/column/column.py", line 851, in astype
return self.as_numerical_column(dtype, **kwargs)
File "/home/rgsl888/Projects/cython/cudf/python/cudf/cudf/core/column/string.py", line 2035, in as_numerical_column
return _str_to_numeric_typecast_functions[str_dtype](self, **kwargs)
File "cudf/_lib/string_casting.pyx", line 288, in cudf._lib.string_casting.stol
return string_to_integer(input_col, np.dtype("int64"))
File "cudf/_lib/string_casting.pyx", line 156, in cudf._lib.string_casting.string_to_integer
cpp_to_integers(
RuntimeError: transform: failed to synchronize: cudaErrorIllegalAddress: an illegal memory access was encountered
rgsl888prabhu
on 8 Apr 2020
@kevingerman All of this boils down to same issue of map_partitions, you need to provide meta to overcome this issue.
When you don't provide meta, map_partition in search to meta information, passes random data such as "foo" to your function, which is actually not a number and at the end this fails. You would find same issue with pandas as well. For reference https://github.com/dask/dask/issues/6078 and #4836
>>> import pandas as pd
>>> sr = pd.Series(["cat", "dog"])
>>> sr.astype('str').str.slice(4, 6).astype('int')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/rgsl888/anaconda3/envs/cudf_dev2/lib/python3.7/site-packages/pandas/core/generic.py", line 5882, in astype
dtype=dtype, copy=copy, errors=errors, **kwargs
File "/home/rgsl888/anaconda3/envs/cudf_dev2/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 581, in astype
return self.apply("astype", dtype=dtype, **kwargs)
File "/home/rgsl888/anaconda3/envs/cudf_dev2/lib/python3.7/site-packages/pandas/core/internals/managers.py", line 438, in apply
applied = getattr(b, f)(**kwargs)
File "/home/rgsl888/anaconda3/envs/cudf_dev2/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 559, in astype
return self._astype(dtype, copy=copy, errors=errors, values=values, **kwargs)
File "/home/rgsl888/anaconda3/envs/cudf_dev2/lib/python3.7/site-packages/pandas/core/internals/blocks.py", line 643, in _astype
values = astype_nansafe(vals1d, dtype, copy=True, **kwargs)
File "/home/rgsl888/anaconda3/envs/cudf_dev2/lib/python3.7/site-packages/pandas/core/dtypes/cast.py", line 707, in astype_nansafe
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
File "pandas/_libs/lib.pyx", line 547, in pandas._libs.lib.astype_intsafe
ValueError: invalid literal for int() with base 10: ''
That behavior changed between 0.13 and 0.14. Is it is an intentional change of behavior?
kevingerman
on 8 Apr 2020
@kevingerman Earlier for any non-numeric input string cudf would return 0, but due to some recent changes for empty string as in our case which is formed due to slice would produce cudaErrorIllegalAddress. In any case, as per #2707 we would be throwing error in future for such cases.
rgsl888prabhu
on 9 Apr 2020
That does raise a good point for my original workflow and the importance of using meta fields to guard against bad data.
However, in the original repro script every value was an 8 char string, and the slice(4,6) call should have always produced a series of all '01'.
kevingerman
on 9 Apr 2020
As it was mentioned in the dask issue, when map_partitions is called without meta, it tries to get meta information by sending a sample string to your function, and this sample string happens to be something like cat, dog, foo which are not what you provided, slice of this will result in an empty string and it fails.
rgsl888prabhu
on 9 Apr 2020
I was able to verify that the error is caused by an all-empty strings column:
import cudf
ds = cudf.Series(['','','','',''])
ds.astype('int')
...
RuntimeError: transform: failed to synchronize: cudaErrorIllegalAddress: an illegal memory access was encountered
If any of the those strings was not empty then the error would not occur.
This is an existing bug that was unmasked by the changes in https://github.com/rapidsai/cudf/pull/4548
Note that the astype('float') and astype('bool') for example do not throw a CUDA exception.
This is definitely a bug in libcudf. The code should be returning 0s for these and not throwing a CUDA exception. I will create a PR to fix the cudf::strings::to_integers() logic.
davidwendt
on 9 Apr 2020
@davidwendt but we were discussing about adding is_integer rather than returning 0s, isn't it ?
rgsl888prabhu
on 9 Apr 2020
And better than that would it be better to check for numerical rather than just integer or other types.
rgsl888prabhu
on 9 Apr 2020
And better than that would it be better to check for numerical rather than just integer or other types.
That is still necessary for invalid characters. But I think an empty string (or bad characters) should not be causing a CUDA exception. You should get 0 like the other converters do.
davidwendt
on 9 Apr 2020
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