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rapidsai / cudf

cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
Apache License 2.0
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[BUG] Out of memory with jit_unspill=True #8640

Closed vidosits closed 3 years ago

vidosits commented 3 years ago

Describe the bug I think I shouldn't be able to get OOM error with jit_unspill=True with a conservative device_memory_limit

Steps/Code to reproduce bug Input is ~8 GB CSV / 843 MB parquet.

import dask_cudf
import cudf
import rmm
from dask_cuda import LocalCUDACluster
from dask.distributed import Client, wait
from dask.utils import parse_bytes
from xgboost import dask as dxgb

cluster = LocalCUDACluster(n_workers=2, device_memory_limit="5GB", jit_unspill=True)
client = Client(cluster)
# client.run(cudf.set_allocator, allocator="managed")

df = dask_cudf.read_parquet('modeling-data-subset-40-percent.parquet', npartitions=50)
X, y = df.drop('target', axis=1), df['target']

param = {'objective': 'mae',
         'eval_metric': ['rmse'],
         'tree_method': 'gpu_hist',
         'verbosity': 2}

num_boosting_rounds = 2

dtrain = dxgb.DaskDeviceQuantileDMatrix(client, X, y)
output = dxgb.train(client,
                        param,
                        dtrain,
                        num_boost_round=num_boosting_rounds)

Output with : device_memory_limit="5GB"

XGBoostError: scan failed to synchronize: cudaErrorIllegalAddress: an illegal memory access was encountered

or with device_memory_limit="9GB"

XGBoostError: [21:43:38] /opt/conda/envs/rapids/conda-bld/xgboost_1619020864980/work/src/c_api/../data/../common/device_helpers.cuh:414: Memory allocation error on worker 0: std::bad_alloc: CUDA error at: /home/jovyan/.conda/include/rmm/mr/device/cuda_memory_resource.hpp:69: cudaErrorMemoryAllocation out of memory
- Free memory: 3557097472
- Requested memory: 11814988752

Expected behavior Data spills to RAM. If device_memory_limit is specified and moderately low.

Environment overview (please complete the following information)

Environment details

Click here to see environment details

     **git***
     Not inside a git repository

     ***OS Information***
     DISTRIB_ID=Ubuntu
     DISTRIB_RELEASE=20.04
     DISTRIB_CODENAME=focal
     DISTRIB_DESCRIPTION="Ubuntu 20.04.2 LTS"
     NAME="Ubuntu"
     VERSION="20.04.2 LTS (Focal Fossa)"
     ID=ubuntu
     ID_LIKE=debian
     PRETTY_NAME="Ubuntu 20.04.2 LTS"
     VERSION_ID="20.04"
     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=focal
     UBUNTU_CODENAME=focal
     Linux jupyter 5.4.0-77-generic #86-Ubuntu SMP Thu Jun 17 02:35:03 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux

     ***GPU Information***
     Thu Jul  1 21:46:15 2021
     +-----------------------------------------------------------------------------+
     | NVIDIA-SMI 460.73.01    Driver Version: 460.73.01    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  GeForce RTX 3080    Off  | 00000000:01:00.0 Off |                  N/A |
     |  0%   53C    P8    29W / 320W |   1046MiB / 10015MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+
     |   1  GeForce RTX 3080    Off  | 00000000:21:00.0 Off |                  N/A |
     |  0%   50C    P8    22W / 320W |   6625MiB / 10018MiB |      0%      Default |
     |                               |                      |                  N/A |
     +-------------------------------+----------------------+----------------------+

     +-----------------------------------------------------------------------------+
     | Processes:                                                                  |
     |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
     |        ID   ID                                                   Usage      |
     |=============================================================================|
     +-----------------------------------------------------------------------------+

     ***CPU***
     Architecture:                    x86_64
     CPU op-mode(s):                  32-bit, 64-bit
     Byte Order:                      Little Endian
     Address sizes:                   43 bits physical, 48 bits virtual
     CPU(s):                          48
     On-line CPU(s) list:             0-47
     Thread(s) per core:              2
     Core(s) per socket:              24
     Socket(s):                       1
     NUMA node(s):                    1
     Vendor ID:                       AuthenticAMD
     CPU family:                      23
     Model:                           49
     Model name:                      AMD Ryzen Threadripper 3960X 24-Core Processor
     Stepping:                        0
     Frequency boost:                 enabled
     CPU MHz:                         2133.877
     CPU max MHz:                     3800.0000
     CPU min MHz:                     2200.0000
     BogoMIPS:                        7600.21
     Virtualization:                  AMD-V
     L1d cache:                       768 KiB
     L1i cache:                       768 KiB
     L2 cache:                        12 MiB
     L3 cache:                        128 MiB
     NUMA node0 CPU(s):               0-47
     Vulnerability Itlb multihit:     Not affected
     Vulnerability L1tf:              Not affected
     Vulnerability Mds:               Not affected
     Vulnerability Meltdown:          Not affected
     Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
     Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
     Vulnerability Spectre v2:        Mitigation; Full AMD retpoline, IBPB conditional, STIBP conditional, RSB filling
     Vulnerability Srbds:             Not affected
     Vulnerability Tsx async abort:   Not affected
     Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate sme ssbd mba sev ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip rdpid overflow_recov succor smca

     ***CMake***
     /usr/bin/cmake
     cmake version 3.16.3

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

     ***g++***
     /usr/bin/g++
     g++ (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
     Copyright (C) 2019 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***
     /home/jovyan/.conda/bin/python
     Python 3.7.9

     ***Environment Variables***
     PATH                            : /home/jovyan/.conda/bin:/home/jovyan/.conda/condabin:/home/jovyan/.conda/bin:/home/jovyan/.local/lightgbm/LightGBM:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
     LD_LIBRARY_PATH                 : /usr/local/nvidia/lib:/usr/local/nvidia/lib64
     NUMBAPRO_NVVM                   :
     NUMBAPRO_LIBDEVICE              :
     CONDA_PREFIX                    : /home/jovyan/.conda
     PYTHON_PATH                     :

     ***conda packages***
     /home/jovyan/.conda/bin/conda
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     #
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     jupyterlab_code_formatter 1.4.10             pyhd8ed1ab_1    conda-forge
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     kiwisolver                1.3.1            py37h2527ec5_1    conda-forge
     krb5                      1.19.1               hcc1bbae_0    conda-forge
     lame                      3.100             h14c3975_1001    conda-forge
     lcms2                     2.12                 hddcbb42_0    conda-forge
     ld_impl_linux-64          2.33.1               h53a641e_7
     lerc                      2.2.1                h9c3ff4c_0    conda-forge
     libaec                    1.0.4                he1b5a44_1    conda-forge
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     libcblas                  3.9.0                9_openblas    conda-forge
     libclang                  11.1.0          default_ha53f305_1    conda-forge
     libcudf                   0.19.2          cuda11.2_gab3b3f653a_0    rapidsai
     libcudf_kafka             0.19.2            gab3b3f653a_0    rapidsai
     libcugraph                0.19.0          cuda11.2_gd72b90b0_0    rapidsai
     libcuml                   0.19.0          cuda11.2_g4cb78ff1a_0    rapidsai
     libcumlprims              0.19.0          cuda11.2_ga2abf9f_0    nvidia
     libcurl                   7.77.0               h2574ce0_0    conda-forge
     libcuspatial              0.19.0          cuda11.2_gdf1d93c_0    rapidsai
     libdap4                   3.20.6               hd7c4107_2    conda-forge
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     libedit                   3.1.20191231         h14c3975_1
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     libgcc-ng                 9.3.0               h2828fa1_19    conda-forge
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     libgd                     2.3.2                h78a0170_0    conda-forge
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     libgit2                   1.1.0                h3974521_1    conda-forge
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     libgsasl                  1.8.0                         2    conda-forge
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     libllvm10                 10.0.1               he513fc3_3    conda-forge
     libllvm11                 11.1.0               hf817b99_2    conda-forge
     libnetcdf                 4.8.0           nompi_hcd642e3_103    conda-forge
     libnghttp2                1.43.0               h812cca2_0    conda-forge
     libntlm                   1.5                  h7b6447c_0
     libogg                    1.3.5                h27cfd23_1
     libopenblas               0.3.15          pthreads_h8fe5266_1    conda-forge
     libopencv                 4.5.1            py37h90094e2_0    conda-forge
     libopus                   1.3.1                h7f98852_1    conda-forge
     libpng                    1.6.37               hed695b0_2    conda-forge
     libpq                     13.3                 hd57d9b9_0    conda-forge
     libprotobuf               3.16.0               h780b84a_0    conda-forge
     librdkafka                1.5.3                hc49e61c_1    conda-forge
     librmm                    0.19.0          cuda11.2_g7065af3_0    rapidsai
     librsvg                   2.50.7               hc3c00ef_0    conda-forge
     librttopo                 1.1.0                h1185371_6    conda-forge
     libsodium                 1.0.18               h516909a_1    conda-forge
     libsolv                   0.7.18               h780b84a_0    conda-forge
     libspatialindex           1.9.3                he1b5a44_3    conda-forge
     libspatialite             5.0.1                h20cb978_4    conda-forge
     libssh2                   1.9.0                ha56f1ee_6    conda-forge
     libstdcxx-ng              9.3.0               h6de172a_19    conda-forge
     libtasn1                  4.16.0               h27cfd23_0
     libthrift                 0.14.1               he6d91bd_1    conda-forge
     libtiff                   4.2.0                hbd63e13_2    conda-forge
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     libunistring              0.9.10               h14c3975_0    conda-forge
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     libuv                     1.41.0               h7f98852_0    conda-forge
     libvorbis                 1.3.7                he1b5a44_0    conda-forge
     libwebp                   1.2.0                h3452ae3_0    conda-forge
     libwebp-base              1.2.0                h7f98852_2    conda-forge
     libxcb                    1.14                 h7b6447c_0
     libxgboost                1.4.0dev.rapidsai0.19      cuda11.2_0    rapidsai
     libxkbcommon              1.0.3                he3ba5ed_0    conda-forge
     libxml2                   2.9.12               h72842e0_0    conda-forge
     libzip                    1.7.3                he9f05b3_0    conda-forge
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     lightgbm                  3.2.1.99                  dev_0    
     llvmlite                  0.36.0           py37h9d7f4d0_0    conda-forge
     locket                    0.2.1            py37h06a4308_1
     lz4-c                     1.9.3                h9c3ff4c_0    conda-forge
     lzo                       2.10              h516909a_1000    conda-forge
     mailchecker               4.0.8              pyhd8ed1ab_0    conda-forge
     mako                      1.1.4              pyh44b312d_0    conda-forge
     mamba                     0.13.0           py37h7f483ca_0    conda-forge
     mamba_gator               5.1.1              pyhd8ed1ab_0    conda-forge
     markdown                  3.3.4              pyhd8ed1ab_0    conda-forge
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     matplotlib-base           3.4.2            py37hdd32ed1_0    conda-forge
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     missingno                 0.4.2                      py_1    conda-forge
     mistune                   0.8.4           py37h5e8e339_1003    conda-forge
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     modin                     0.10.0                   pypi_0    pypi
     msgpack-python            1.0.2            py37h2527ec5_1    conda-forge
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     multimethod               1.4                        py_0    conda-forge
     multipledispatch          0.6.0                      py_0    conda-forge
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     mysql-common              8.0.25               ha770c72_0    conda-forge
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     nbclient                  0.5.3              pyhd8ed1ab_0    conda-forge
     nbconvert                 6.0.7            py37h89c1867_3    conda-forge
     nbdime                    3.1.0                    pypi_0    pypi
     nbformat                  5.1.3              pyhd8ed1ab_0    conda-forge
     nccl                      2.9.9.1              hdc17891_0    conda-forge
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     nest-asyncio              1.5.1              pyhd8ed1ab_0    conda-forge
     nettle                    3.7.3                hbbd107a_1
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     notebook                  6.4.0              pyha770c72_0    conda-forge
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     nss                       3.67                 hb5efdd6_0    conda-forge
     numba                     0.53.1           py37h134767a_0    conda-forge
     numpy                     1.20.3           py37h038b26d_1    conda-forge
     nvtabular                 0.5.3                         0    nvidia
     nvtx                      0.2.3            py37h5e8e339_0    conda-forge
     oauthenticator            14.0.0             pyhd8ed1ab_0    conda-forge
     oauthlib                  3.1.1              pyhd8ed1ab_0    conda-forge
     olefile                   0.46               pyh9f0ad1d_1    conda-forge
     opencv                    4.5.1            py37h89c1867_0    conda-forge
     opencv-python             4.5.2.54                 pypi_0    pypi
     openh264                  2.1.1                h780b84a_0    conda-forge
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     orc                       1.6.7                h89a63ab_2    conda-forge
     packaging                 20.9               pyh44b312d_0    conda-forge
     pacmap                    0.4                      pypi_0    pypi
     pamela                    1.0.0                      py_0    conda-forge
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     panel                     0.10.3             pyhd8ed1ab_0    conda-forge
     pango                     1.48.5               hb8ff022_0    conda-forge
     param                     1.10.1             pyhd3deb0d_0    conda-forge
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     parso                     0.8.2              pyhd8ed1ab_0    conda-forge
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     pcre                      8.44                 he1b5a44_0    conda-forge
     pcre2                     10.36                h032f7d1_1    conda-forge
     pexpect                   4.8.0            py37hc8dfbb8_1    conda-forge
     phik                      0.11.2             pyhd8ed1ab_0    conda-forge
     phonenumbers              8.12.24            pyhd8ed1ab_1    conda-forge
     pickle5                   0.0.11           py37h8f50634_0    conda-forge
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     pip                       20.2.4           py37h06a4308_0
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     ply                       3.11                       py_1    conda-forge
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     poppler-data              0.4.10                        0    conda-forge
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     python                    3.7.9                h7579374_0
     python-benedict           0.24.0             pyhd8ed1ab_0    conda-forge
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     python_abi                3.7                     1_cp37m    conda-forge
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     qt                        5.12.9               hda022c4_4    conda-forge
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     rapids                    0.19.0          cuda11.2_py37_ga03647d_299    rapidsai
     rapids-xgboost            0.19.0          cuda11.2_py37_ga03647d_299    rapidsai
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     rmm                       0.19.0          cuda_11.2_py37_g7065af3_0    rapidsai
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     tk                        8.6.10               hbc83047_0
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     ucx                       1.9.0+gcd9efd3       cuda11.2_0    rapidsai
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     xz                        5.2.5                h7b6447c_0
     yaml                      0.2.5                h7b6447c_0
     yarl                      1.6.3            py37h5e8e339_1    conda-forge
     zc.lockfile               2.0                        py_0    conda-forge
     zeromq                    4.3.4                h9c3ff4c_0    conda-forge
     zfp                       0.5.5                h9c3ff4c_5    conda-forge
     zict                      2.0.0                      py_0    conda-forge
     zipp                      3.4.1              pyhd8ed1ab_0    conda-forge
     zlib                      1.2.11               h7b6447c_3
     zstd                      1.4.9                ha95c52a_0    conda-forge

Additional context Add any other context about the problem here.

beckernick commented 3 years ago

If the algorithms have fundamental in-memory requirements for certain operations, you may not be able to succeed with spilling alone. It looks like you have a total of 20 GB of memory across two GPUs. Perhaps @trivialfis might have some insight into whether loading an 8GB+ sized file might push the limit with Dask XGBoost on this setup.

vidosits commented 3 years ago

Wouldn't jit_unspill=True solve that though? Wouldn't the excess just spill to RAM? Or do you mean that it can't because for some fundamental (for XGBoost) operation I'd need more than 20 GBs of GPU RAM?

vidosits commented 3 years ago

The real problem was #5012. Since my data only had 1 partition there was no way to distribute it across devices. Converting to csv first and setting npartitions solved the problem.

trivialfis commented 3 years ago

Happy to see it resolved!