Warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

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Hello, I have RTX Ada 6000 48GB gpu installed. and I am planning to setup a local environment here. somehow. TensorFlow giving issues. I have installed cuda 11.8, cudnn 8.7 and also tensorrt. but tensorflow-gpu still couldn’t recognize my GPU. can you please guide me how to successfully setup a local environment ? I originally installed tensorflow 2.16 but since it ddnt worked I downgraded to 2.15. somehow it is abled to detect my GPU. but when I do some training, I get out of memory error from cuda driver

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@kavindamadhujith Please start a new post as your issue is more like a setup issue. Note this is forum support for develop tools - cuda-gdb. Please post your problem under “CUDA set up and installation” forum. You can get support there. Thanks !

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Hi, @892516165

Sorry for the late response. We built your source code internal successfully.
And we can also see “warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)” printed multi times, but it doesn’t impact the debug. For example:

warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

warning: Cuda API error detected: cuModuleLoadFatBinary returned (0xd1)

[Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread (0,0,0), device 0, sm 0, warp 0, lane 0]

CUDA thread hit application kernel entry function breakpoint, 0x00007fffb5313600 in void cusparselt::pruning_kernel<64u, 8u, (cusparselt::(anonymous namespace)::Sparsity)1, true, __half>(__half const*, __half*, long, long, long, long)
<<<(1,1,1),(64,8,1)>>> ()
(cuda-gdb)
(cuda-gdb)
(cuda-gdb)
(cuda-gdb) l
62 }
63
64 define CHECK_CUSPARSE(func)
65 {
66 cusparseStatus_t status = (func);
67 if (status != CUSPARSE_STATUS_SUCCESS) {
68 printf(“CUSPARSE API failed at line %d with error: %s (%d)\n”,
69 LINE, cusparseGetErrorString(status), status);
70 return EXIT_FAILURE;
71 }
(cuda-gdb) n
Single stepping until exit from function _ZN10cusparselt14pruning_kernelILj64ELj8ELNS_43_GLOBAL__N__47f2a60d_10_pruning_cu_35f6c0908SparsityE1ELb1E6__halfEEvPKT3_PS4_llll,
which has no line number information.
[Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread (32,0,0), device 0, sm 0, warp 1, lane 0]
0x00007fffb5313600 in void cusparselt::pruning_kernel<64u, 8u, (cusparselt::(anonymous namespace)::Sparsity)1, true, __half>(__half const*, __half*, long, long, long, long)<<<(1,1,1),(64,8,1)>>> ()
(cuda-gdb)

So in your case, I’m thinking is it possible that you get latest CUDA12.5 package to rebuild and debug ?

FYI, the compile command we use is “nvcc -lcusparseLt -lcusparse -ldl -gencode arch=compute_80,code=sm_80 -g -G -o test a.cu” (We are using Ampere so we choose 80 here)

Also we install cusparselt from here: cuSPARSELt Downloads | NVIDIA Developer

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After reinstalling cusparseLt, it still fails to run properly. I’m wondering if the issue might be related to environment variables or WSL (Windows Subsystem for Linux). Here’s my .bashrc configuration:

export TVM_LOG_DEBUG="ir/transform.cc=1,relay/ir/transform.cc=1"
export PATH="/home/whh/environ/bsc/bin:$PATH"
export LIBRARY="/home/whh/environ/bsc/lib:$LIBRARY_PATH"

export TVM_HOME=/home/whh/tvm
export PYTHONPATH=$TVM_HOME/python:${PYTHONPATH}
export PYTHONPATH=$TVM_HOME/src:${PYTHONPATH}

#export PATH=/usr/local/cuda-12.1/bin${PATH:+:${PATH}}
export PATH=/usr/local/cuda-12.5/bin${PATH:+:${PATH}}
#export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64:/usr/local/lib:/home/whh/anaconda3/envs/caffe_test/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.5/lib64:/usr/local/lib:/home/whh/anaconda3/envs/caffe_test/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
#export CUDA_HOME=/usr/local/cuda-12.1:$CUDA_HOME
export CUDA_HOME=/usr/local/cuda-12.5:$CUDA_HOME
# export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
#export LD_LIBRARY_PATH=/usr/local/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-12.5/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
# cuda gdb on wsl
#export CUDBG_USE_LEGACY_DEBUGGER=1
# export QT_DEBUG_PLUGINS=1
# 不确定是否需要配置
# export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/:${LD_LIBRARY_PATH}
#export CUDA_TOOLKIT_PATH=/usr/local/cuda-12.1:${CUDA_TOOLKIT_PATH}
export CUDA_TOOLKIT_PATH=/usr/local/cuda-12.5:${CUDA_TOOLKIT_PATH}
#export CUDA_VISIBLE_DEVICES="0"
#export CUDA_DEBUGGER_SOFTWARE_PREEMPTION=1
#export CUDA_LAUNCH_BLOCKING=1
#export CUSPARSELT_DIR=/home/whh/workspace/env/cusparseLt
export CUSPARSELT_DIR=/home/whh/workspace/env/libcusparse_lt-linux-x86_64-0.6.1.0-archive
#export LD_LIBRARY_PATH=${CUSPARSELT_DIR}/lib64:${LD_LIBRARY_PATH}
export LD_LIBRARY_PATH=${CUSPARSELT_DIR}/lib:${LD_LIBRARY_PATH}

# LAPACK
export LIBRARY_PATH="$LIBRARY_PATH:~/workspace/env/lapack-3.12.0"
export C_INCLUDE_PATH="$C_INCLUDE_PATH:~/workspace/env/lapack-3.12.0/LAPACKE/include:~/workspace/env/lapack-3.12.0/CBLAS/include"
export LAPACK_LIBRARIES="/home/whh/workspace/env/lapack-3.12.0/liblapack.a"
export BLAS_LIBRARIES="/home/whh/workspace/env/lapack-3.12.0/libcblas.a"

#export NVLOG_CONFIG_FILE=${HOME}/workspace/env/nvlog.config
#export NVLOG_CONFIG_FILE=${HOME}/nvlog.local.config

Moreover, even when I comment out the NVLOG_CONFIG_FILE line, cuda-gdb keeps outputting error messages during usage.

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Hi, @892516165

I noticed you set “export CUDBG_USE_LEGACY_DEBUGGER=1”
Please remove this and try.

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