wgan-gp训练导致segfault

[yinghdb]

Describe the bug

wgan-gp训练导致segfault. Caught segfault at address 0x60, thread_name: 'C2', flush log... Segfault, exit [e 1013 21:41:41.304692 96 parallel_compiler.cc:318] Segfault happen, main thread exit

训练代码来自wgan_gp

Full Log

Caught segfault at address 0x60, thread_name: 'C2', flush log... Segfault, exit [e 1013 21:41:41.304692 96 parallel_compiler.cc:318] Segfault happen, main thread exit *** Error in `python': corrupted double-linked list: 0x0000564dab9b8380 *** ======= Backtrace: ========= /lib/x86_64-linux-gnu/libc.so.6(+0x777f5)[0x7f03299997f5] /lib/x86_64-linux-gnu/libc.so.6(+0x80c81)[0x7f03299a2c81] /lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)[0x7f03299a658c] /usr/lib/x86_64-linux-gnu/libcuda.so.1(+0x1f4e17)[0x7f032452de17] /usr/lib/x86_64-linux-gnu/libcuda.so.1(+0x10a53a)[0x7f032444353a] /usr/lib/x86_64-linux-gnu/libcuda.so.1(cuModuleUnload+0x4f)[0x7f032459358f] /usr/local/cuda/lib64/libcudart.so(+0x2ebdf)[0x7f0328047bdf] /usr/local/cuda/lib64/libcudart.so(+0x302d3)[0x7f03280492d3] /usr/local/cuda/lib64/libcudart.so(+0x322c1)[0x7f032804b2c1] /usr/local/cuda/lib64/libcudart.so(+0x2543e)[0x7f032803e43e] /usr/local/cuda/lib64/libcudart.so(+0x1067a)[0x7f032802967a] /usr/local/cuda/lib64/libcudart.so(cudaDeviceSynchronize+0x59)[0x7f0328050bf9] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(+0x22b823)[0x7f0325efc823] /lib/x86_64-linux-gnu/libc.so.6(+0x3a008)[0x7f032995c008] /lib/x86_64-linux-gnu/libc.so.6(+0x3a055)[0x7f032995c055] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/jit_utils_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor18segfault_sigactionEiP9siginfo_tPv+0x247)[0x7f03284240f7] /lib/x86_64-linux-gnu/libc.so.6(+0x354c0)[0x7f03299574c0] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor18LoopVarAnalyzePass3runEv+0x30aa)[0x7f0325fa21ca] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor11PassManager8run_passINS_18LoopVarAnalyzePassEEEvv+0x33f)[0x7f0325f42e8f] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor11PassManager10run_passesEv+0xcb)[0x7f0325f3872b] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor12TunerManager4tuneB5cxx11Ev+0x6c)[0x7f0325f60fec] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor10OpCompiler10do_compileEPNS_2OpE+0xad)[0x7f0325eb7d3d] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(+0x1c4dd9)[0x7f0325e95dd9] /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jittor_core.cpython-38-x86_64-linux-gnu.so(_ZN6jittor12SimpleThread3runEv+0x147)[0x7f0325e9a367] /home/yinghui/miniconda3/envs/jittor/bin/../lib/libstdc++.so.6(+0xc92bd)[0x7f032835c2bd] /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba)[0x7f0329cf36ba] /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d)[0x7f0329a2951d] ======= Memory map: ======== 200000000-200200000 rw-s 00000000 00:06 443 /dev/nvidiactl 200200000-200400000 ---p 00000000 00:00 0 200400000-200600000 rw-s 00000000 00:06 443 /dev/nvidiactl 200600000-202600000 rw-s 00000000 00:06 443 /dev/nvidiactl 202600000-205600000 rw-s 00000000 00:06 443 /dev/nvidiactl 205600000-206200000 ---p 00000000 00:00 0 206200000-206400000 rw-s 00000000 00:06 443 /dev/nvidiactl 206400000-206600000 rw-s 00000000 00:06 443 /dev/nvidiactl 206600000-206800000 rw-s 206600000 00:06 490 /dev/nvidia-uvm 206800000-206a00000 ---p 00000000 00:00 0 206a00000-206c00000 rw-s 00000000 00:06 443 /dev/nvidiactl 206c00000-500200000 ---p 00000000 00:00 0 10000000000-10404000000 ---p 00000000 00:00 0 564d7bd9e000-564d7bdfd000 r--p 00000000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7bdfd000-564d7bff4000 r-xp 0005f000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7bff4000-564d7c0db000 r--p 00256000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7c0dc000-564d7c0e1000 r--p 0033d000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7c0e1000-564d7c119000 rw-p 00342000 fd:02 34000027 /home/yinghui/miniconda3/envs/jittor/bin/python3.8 564d7c119000-564d7c139000 rw-p 00000000 00:00 0 564d7ca0d000-564daae38000 rw-p 00000000 00:00 0 [heap] 564daae38000-564daae39000 ---p 00000000 00:00 0 [heap] 564daae39000-564daba22000 rw-p 00000000 00:00 0 [heap] 7f0230000000-7f0230021000 rw-p 00000000 00:00 0 7f0230021000-7f0234000000 ---p 00000000 00:00 0 7f0236000000-7f0236200000 rw-s 7f0236000000 00:06 490 /dev/nvidia-uvm 7f0236200000-7f0237600000 rw-s 7f0236200000 00:06 490 /dev/nvidia-uvm 7f0237600000-7f0237800000 rw-s 7f0237600000 00:06 490 /dev/nvidia-uvm 7f0237800000-7f0237a00000 rw-s 7f0237800000 00:06 490 /dev/nvidia-uvm 7f0237a00000-7f0238000000 ---p 00000000 00:00 0 7f0238000000-7f02381ff000 rw-p 00000000 00:00 0 7f02381ff000-7f0238200000 ---p 00000000 00:00 0 7f0238200000-7f023825c000 rw-p 00000000 00:00 0 7f023825c000-7f023c000000 ---p 00000000 00:00 0 7f023cd7f000-7f023cd82000 r-xp 00000000 fd:02 34003611 /home/yinghui/.cache/jittor/jt1.3.1/g++5.4.0/py3.8.11/Linux-4.15.0-1x27/IntelRCoreTMi7x6f/default/cu10.0.130_sm_61/jit/_opkey0_broadcast_to_Tx_float32__DIM_3__BCAST_1__JIT_1__JIT_cuda_1__index_t_int32___opkey1___hash_de1e18f0012636b2_op.so 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/usr/local/cuda-10.0/lib64/libcublas.so.10.0.130 7f02b3929000-7f02b396d000 rw-p 04340000 fd:02 92545858 /usr/local/cuda-10.0/lib64/libcublas.so.10.0.130 7f02b396d000-7f02b397f000 rw-p 00000000 00:00 0 7f02b397f000-7f02b5599000 r-xp 00000000 fd:02 92019958 /usr/lib/x86_64-linux-gnu/libcublasLt.so.10.2.2.89 7f02b5599000-7f02b5798000 ---p 01c1a000 fd:02 92019958 /usr/lib/x86_64-linux-gnu/libcublasLt.so.10.2.2.89 7f02b5798000-7f02b5809000 rw-p 01c19000 fd:02 92019958 /usr/lib/x86_64-linux-gnu/libcublasLt.so.10.2.2.89Aborted (core dumped)

Minimal Reproduce

训练代码来自wgan_gp 应该是计算gradient penalty导致的问题。

[Jittor]

感谢您的反馈，该问题已经在最新版本1.3.1.1修复