Cudf: [BUG] slicing rows from df shows errors
Describe the bug
A clear and concise description of what the bug is.
Steps/Code to reproduce bug
import cudf
new_df = cudf.DataFrame({"a":range(10000000)})
print(new_df[2500000:2500001])
print(new_df[2500000:5000000])
outputs
a
2500000 2500000 <== correct
a
0 -9223372036854775808 <== WOAH NELLY!!!
1 -9223372036854775808
2 -9223372036854775808
3 -9223372036854775808
4 -9223372036854775808
... ...
4999995 4999995
4999996 4999996
4999997 4999997
4999998 4999998
4999999 4999999
Expected behavior
The data from this slice operation shouldn't return corrupt rows
Environment overview (please complete the following information)
Running on a dgx2
Environment details
Click here to see environment details
**git***
Not inside a git repository
***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-2"
DGX_SERIAL_NUMBER="0574318000143"
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-dgx2-d17-u05-rapids-dgx201 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***
Thu Jul 23 22:54:22 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 V100-SXM3... On | 00000000:34:00.0 Off | 0 |
| N/A 35C P0 66W / 350W | 3390MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla V100-SXM3... On | 00000000:36:00.0 Off | 0 |
| N/A 33C P0 66W / 350W | 1831MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla V100-SXM3... On | 00000000:39:00.0 Off | 0 |
| N/A 41C P0 68W / 350W | 1855MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla V100-SXM3... On | 00000000:3B:00.0 Off | 0 |
| N/A 43C P0 68W / 350W | 1827MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 4 Tesla V100-SXM3... On | 00000000:57:00.0 Off | 0 |
| N/A 34C P0 65W / 350W | 1827MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 5 Tesla V100-SXM3... On | 00000000:59:00.0 Off | 0 |
| N/A 42C P0 78W / 350W | 1875MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 6 Tesla V100-SXM3... On | 00000000:5C:00.0 Off | 0 |
| N/A 34C P0 66W / 350W | 1883MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 7 Tesla V100-SXM3... On | 00000000:5E:00.0 Off | 0 |
| N/A 43C P0 71W / 350W | 1837MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 8 Tesla V100-SXM3... On | 00000000:B7:00.0 Off | 0 |
| N/A 33C P0 66W / 350W | 1516MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 9 Tesla V100-SXM3... On | 00000000:B9:00.0 Off | 0 |
| N/A 36C P0 67W / 350W | 1540MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 10 Tesla V100-SXM3... On | 00000000:BC:00.0 Off | 0 |
| N/A 42C P0 69W / 350W | 1516MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 11 Tesla V100-SXM3... On | 00000000:BE:00.0 Off | 0 |
| N/A 40C P0 67W / 350W | 1564MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 12 Tesla V100-SXM3... On | 00000000:E0:00.0 Off | 0 |
| N/A 35C P0 70W / 350W | 1578MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 13 Tesla V100-SXM3... On | 00000000:E2:00.0 Off | 0 |
| N/A 34C P0 66W / 350W | 1558MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 14 Tesla V100-SXM3... On | 00000000:E5:00.0 Off | 0 |
| N/A 43C P0 70W / 350W | 1562MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 15 Tesla V100-SXM3... On | 00000000:E7:00.0 Off | 0 |
| N/A 41C P0 73W / 350W | 1516MiB / 32510MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 49895 C ...lipe/miniconda3/envs/nightly/bin/python 1499MiB |
| 0 53166 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 0 96615 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 1 53167 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 1 96618 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 2 53172 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 2 96622 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 3 53176 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 3 96626 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 4 53180 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 4 96630 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 5 53183 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 5 96633 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 6 53186 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 6 96636 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 7 53189 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 7 96719 C ...e/felipe/miniconda3/envs/ucx/bin/python 305MiB |
| 8 53192 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 9 53195 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 10 53198 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 11 53201 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 12 53204 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 13 53207 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 14 53210 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
| 15 53213 C ...niconda3/envs/rapids-tpcx-bb/bin/python 1499MiB |
+-----------------------------------------------------------------------------+
***CPU***
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz
Stepping: 4
CPU MHz: 3290.480
CPU max MHz: 3700.0000
CPU min MHz: 1200.0000
BogoMIPS: 5400.00
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 1024K
L3 cache: 33792K
NUMA node0 CPU(s): 0-23,48-71
NUMA node1 CPU(s): 24-47,72-95
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 art 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 mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke md_clear flush_l1d
***CMake***
/usr/bin/cmake
cmake version 3.10.2
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***
***Python***
/home/felipe/miniconda3/envs/nightly/bin/python
Python 3.7.8
***Environment Variables***
PATH : /home/felipe/miniconda3/envs/nightly/bin:/home/felipe/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/felipe/miniconda3/envs/nightly
PYTHON_PATH :
***conda packages***
/home/felipe/miniconda3/condabin/conda
# packages in environment at /home/felipe/miniconda3/envs/nightly:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 0_gnu conda-forge
abseil-cpp 20200225.2 he1b5a44_1 conda-forge
alsa-lib 1.2.3 h516909a_0 conda-forge
arrow-cpp 0.17.1 py37h1234567_11_cuda conda-forge
arrow-cpp-proc 1.0.0 cuda conda-forge
attrs 19.3.0 py_0 conda-forge
aws-sdk-cpp 1.7.164 hc831370_1 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
blazingsql 0.15.0a cuda10.2_py37_401 blazingsql-nightly/label/cuda10.2
bleach 3.1.5 pyh9f0ad1d_0 conda-forge
bokeh 2.1.1 py37hc8dfbb8_0 conda-forge
boost-cpp 1.72.0 h7b93d67_2 conda-forge
brotli 1.0.7 he1b5a44_1004 conda-forge
brotlipy 0.7.0 py37h8f50634_1000 conda-forge
bzip2 1.0.8 h516909a_2 conda-forge
c-ares 1.16.1 h516909a_0 conda-forge
ca-certificates 2020.6.20 hecda079_0 conda-forge
cairo 1.16.0 h3fc0475_1005 conda-forge
certifi 2020.6.20 py37hc8dfbb8_0 conda-forge
cffi 1.14.0 py37hd463f26_0 conda-forge
chardet 3.0.4 py37hc8dfbb8_1006 conda-forge
click 7.1.2 pyh9f0ad1d_0 conda-forge
cloudpickle 1.5.0 py_0 conda-forge
cppzmq 4.6.0 hc9558a2_0 conda-forge
cryptography 3.0 py37hb09aad4_0 conda-forge
cudatoolkit 10.2.89 h6bb024c_0 nvidia
cudf 0.15.0a200723 py37_gd23252798_3207 rapidsai-nightly
cudnn 7.6.5 cuda10.2_0
cupy 7.6.0 py37h940342b_0 conda-forge
curl 7.71.1 he644dc0_3 conda-forge
cyrus-sasl 2.1.27 h063b49f_1 conda-forge
cytoolz 0.10.1 py37h516909a_0 conda-forge
dask 2.21.0 py_0 conda-forge
dask-core 2.21.0 py_0 conda-forge
dask-cuda 0.15.0a200723 py37_76 rapidsai-nightly
dask-cudf 0.15.0a200723 py37_gd23252798_3207 rapidsai-nightly
decorator 4.4.2 py_0 conda-forge
defusedxml 0.6.0 py_0 conda-forge
distributed 2.21.0 py37hc8dfbb8_0 conda-forge
dlpack 0.3 he1b5a44_1 conda-forge
double-conversion 3.1.5 he1b5a44_2 conda-forge
entrypoints 0.3 py37hc8dfbb8_1001 conda-forge
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fastrlock 0.5 py37h3340039_0 conda-forge
fontconfig 2.13.1 h1056068_1002 conda-forge
freetype 2.10.2 he06d7ca_0 conda-forge
fsspec 0.7.4 py_0 conda-forge
future 0.18.2 py37hc8dfbb8_1 conda-forge
gettext 0.19.8.1 hc5be6a0_1002 conda-forge
gflags 2.2.2 he1b5a44_1004 conda-forge
giflib 5.2.1 h516909a_2 conda-forge
glib 2.65.0 h6f030ca_0 conda-forge
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googleapis-cpp 0.10.0 h52dead3_2 conda-forge
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grpc-cpp 1.30.2 heedbac9_0 conda-forge
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heapdict 1.0.1 py_0 conda-forge
icu 67.1 he1b5a44_0 conda-forge
idna 2.10 pyh9f0ad1d_0 conda-forge
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ipython 7.16.1 py37h43977f1_0 conda-forge
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jinja2 2.11.2 pyh9f0ad1d_0 conda-forge
jpeg 9d h516909a_0 conda-forge
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libblas 3.8.0 17_openblas conda-forge
libcblas 3.8.0 17_openblas conda-forge
libcrc32c 1.1.1 he1b5a44_2 conda-forge
libcudf 0.15.0a200723 cuda10.2_gd23252798_3207 rapidsai-nightly
libcurl 7.71.1 hcdd3856_3 conda-forge
libedit 3.1.20191231 h46ee950_1 conda-forge
libevent 2.1.10 hcdb4288_1 conda-forge
libffi 3.2.1 he1b5a44_1007 conda-forge
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numba 0.50.1 py37h0da4684_1 conda-forge
numpy 1.19.1 py37h8960a57_0 conda-forge
olefile 0.46 py_0 conda-forge
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openssl 1.1.1g h516909a_0 conda-forge
packaging 20.4 pyh9f0ad1d_0 conda-forge
pandas 1.0.5 py37h0da4684_0 conda-forge
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parquet-cpp 1.5.1 2 conda-forge
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partd 1.1.0 py_0 conda-forge
pcre 8.44 he1b5a44_0 conda-forge
pexpect 4.8.0 py37hc8dfbb8_1 conda-forge
pickleshare 0.7.5 py37hc8dfbb8_1001 conda-forge
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pip 20.1.1 py_1 conda-forge
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pthread-stubs 0.4 h14c3975_1001 conda-forge
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pyarrow 0.17.1 py37h1234567_11_cuda conda-forge
pycparser 2.20 pyh9f0ad1d_2 conda-forge
pygments 2.6.1 py_0 conda-forge
pyhive 0.6.2 pyh9f0ad1d_0 conda-forge
pynvml 8.0.4 py_1 conda-forge
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pyrsistent 0.16.0 py37h8f50634_0 conda-forge
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python 3.7.8 h6f2ec95_0_cpython conda-forge
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python_abi 3.7 1_cp37m conda-forge
pytz 2020.1 pyh9f0ad1d_0 conda-forge
pyyaml 5.3.1 py37h8f50634_0 conda-forge
pyzmq 19.0.1 py37hac76be4_0 conda-forge
re2 2020.07.06 he1b5a44_1 conda-forge
readline 8.0 he28a2e2_2 conda-forge
requests 2.24.0 pyh9f0ad1d_0 conda-forge
rmm 0.15.0a200723 py37_gba4436b_418 rapidsai-nightly
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six 1.15.0 pyh9f0ad1d_0 conda-forge
snappy 1.1.8 he1b5a44_3 conda-forge
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spdlog 1.7.0 hc9558a2_0 conda-forge
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tblib 1.6.0 py_0 conda-forge
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zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.5 h6597ccf_1 conda-forge
felipeblazing
All 5 comments
@galipremsagar can you take a look at this?
kkraus14
on 24 Jul 2020
👍1
Looking into it..
galipremsagar
on 24 Jul 2020
cudf 0.15.0a200723 py37_gd23252798_3207 rapidsai-nightly
dask-cudf 0.15.0a200723 py37_gd23252798_3207 rapidsai-nightly
libcudf 0.15.0a200723 cuda10.2_gd23252798_3207 rapidsai-nightly
👍1
new_df[2499840:2499901] <<== has issue
new_df[2499840:2499900] <<== works just fine
cutoff seems to be 60 i think
felipeblazing
on 24 Jul 2020
So after looking into the code, The values remain correct. It's just that __repr__ function is broken due to .iloc not functioning correctly:
https://github.com/rapidsai/cudf/blob/branch-0.15/python/cudf/cudf/core/dataframe.py#L1188
See a minimal reproducer below:
(Pdb) df = self.head(upper_rows)
(Pdb) df.to_pandas()
a
2500000 2500000
2500001 2500001
2500002 2500002
2500003 2500003
2500004 2500004
2500005 2500005
2500006 2500006
2500007 2500007
2500008 2500008
2500009 2500009
2500010 2500010
2500011 2500011
2500012 2500012
2500013 2500013
2500014 2500014
2500015 2500015
2500016 2500016
2500017 2500017
2500018 2500018
2500019 2500019
2500020 2500020
2500021 2500021
2500022 2500022
2500023 2500023
2500024 2500024
2500025 2500025
2500026 2500026
2500027 2500027
2500028 2500028
2500029 2500029
2500030 2500030
(Pdb) df.to_pandas().iloc[:, 1:]
Empty DataFrame
Columns: []
Index: [2500000, 2500001, 2500002, 2500003, 2500004, 2500005, 2500006, 2500007, 2500008, 2500009, 2500010, 2500011, 2500012, 2500013, 2500014, 2500015, 2500016, 2500017, 2500018, 2500019, 2500020, 2500021, 2500022, 2500023, 2500024, 2500025, 2500026, 2500027, 2500028, 2500029, 2500030]
(Pdb) df.iloc[:, 1:].to_pandas()
Empty DataFrame
Columns: []
Index: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]
(Pdb)
Since this incorrect half dataframe is being passed into concat we are having a corrupted dataframe. Will issue a fix 馃憤
galipremsagar
on 24 Jul 2020
👍1
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