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Description
Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.4 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.7.0+cu126
Is debug build : False
CUDA used to build PyTorch : 12.6
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.11 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)
Python platform : Linux-5.15.0-117-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.4.131
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration : GPU 0: NVIDIA H20
Nvidia driver version : 550.90.07
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 224
On-line CPU(s) list: 0-223
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8480+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 8
CPU max MHz: 3800.0000
CPU min MHz: 800.0000
BogoMIPS: 4000.00
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 tsc_known_freq 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 224 MiB (112 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: 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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pyzmq==27.0.0
[pip3] torch==2.7.0
[pip3] torchaudio==2.7.0
[pip3] torchvision==0.22.0
[pip3] transformers==4.52.4
[pip3] triton==3.3.0
[conda] numpy 2.2.6 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.6.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.6.80 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.5.1.17 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.0.4 pypi_0 pypi
[conda] nvidia-cufile-cu12 1.11.1.6 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.7.77 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.1.2 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.4.2 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.6.77 pypi_0 pypi
[conda] pyzmq 27.0.0 pypi_0 pypi
[conda] torch 2.7.0 pypi_0 pypi
[conda] torchaudio 2.7.0 pypi_0 pypi
[conda] torchvision 0.22.0 pypi_0 pypi
[conda] transformers 4.52.4 pypi_0 pypi
[conda] triton 3.3.0 pypi_0 pypi
==============================
vLLM Info
==============================
ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.9.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X SYS SYS NODE NODE NODE NODE SYS 56-111,168-223 1 N/A
NIC0 SYS X NODE SYS SYS SYS SYS NODE
NIC1 SYS NODE X SYS SYS SYS SYS NODE
NIC2 NODE SYS SYS X NODE NODE NODE SYS
NIC3 NODE SYS SYS NODE X PIX NODE SYS
NIC4 NODE SYS SYS NODE PIX X NODE SYS
NIC5 NODE SYS SYS NODE NODE NODE X SYS
NIC6 SYS NODE NODE SYS SYS SYS SYS X
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
NIC Legend:
NIC0: mlx5_0
NIC1: mlx5_3
NIC2: mlx5_4
NIC3: mlx5_5
NIC4: mlx5_6
NIC5: mlx5_7
NIC6: mlx5_bond_0
==============================
Environment Variables
==============================
CUDA_VISIBLE_DEVICES=0
CUDA_VISIBLE_DEVICES=0
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
nsys version info:
NVIDIA Nsight Systems version 2023.4.4.54-234433681190v0
🐛 Describe the bug
For offline inference:
According to Official Docs, I use the script below to start profiling:
SLURM_PARTITION=debug
JOB_NAME=drh_profiling
NUM_GPUS=1
CPUS_PER_GPU=24
MEM_PER_GPU=242144
NUM_CPUS=$(($NUM_GPUS * $CPUS_PER_GPU))
NUM_MEMS=$(($NUM_GPUS * $MEM_PER_GPU))
MAX_MODEL_LEN=10240
MODEL_PATH=/data/nfs/Qwen3-32B
COMMAND="python ../benchmarks/benchmark_latency.py --model ${MODEL_PATH} --num-iters-warmup 5 --num-iters 1 --batch-size 16 --input-len 512 --output-len 8"
NSYS_COMMAND="nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node ${COMMAND}"
srun --partition=${SLURM_PARTITION} \
--cpus-per-task=${NUM_CPUS} \
--mem=${NUM_MEMS} \
--gres=gpu:${NUM_GPUS} \
--job-name=${JOB_NAME} \
--nodes=1 \
--ntasks=1 \
-N 1 \
${NSYS_COMMAND}../benchmarks/benchmark_latency.py is the official benchmark script.
The profiling fails with output:
[1/1] [================69% ] report.nsys-rep
Importer error status: Importation failed.
Import Failed with unexpected exception: /dvs/p4/build/sw/devtools/Agora/Rel/CUDA12.4/QuadD/Host/QdstrmImporter/main.cpp(34): Throw in function {anonymous}::Importer::Importer(const boost::filesystem::path&, const boost::filesystem::path&)
Dynamic exception type: boost::wrapexcept<QuadDCommon::RuntimeException>
std::exception::what: RuntimeException
[QuadDCommon::tag_message*] = Status: AnalysisFailed
Error {
Type: RuntimeError
SubError {
Type: InvalidArgument
Props {
Items {
Type: OriginalExceptionClass
Value: "N5boost10wrapexceptIN11QuadDCommon24InvalidArgumentExceptionEEE"
}
Items {
Type: OriginalFile
Value: "/dvs/p4/build/sw/devtools/Agora/Rel/CUDA12.4/QuadD/Host/Analysis/Modules/EventCollection.cpp"
}
Items {
Type: OriginalLine
Value: "1048"
}
Items {
Type: OriginalFunction
Value: "void QuadDAnalysis::EventCollection::CheckOrder(QuadDAnalysis::EventCollectionHelper::EventContainer&, const QuadDAnalysis::ConstEvent&) const"
}
Items {
Type: ErrorText
Value: "Wrong event order has been detected when adding events to the collection:\nnew event ={ StartNs=56202957602 StopNs=56202961310 GlobalId=340830186765238 Event={ TraceProcessEvent=[{ Correlation=183320 EventClass=0 TextId=3863 ReturnValue=0 },] } Type=48 }\nlast event ={ StartNs=57807014569 StopNs=57807016175 GlobalId=340830186765238 Event={ TraceProcessEvent=[{ Correlation=275241 EventClass=0 TextId=3863 ReturnValue=0 },] } Type=48 }"
}
}
}
}
If I remove the --trace-fork-before-exec=true, the profiling will end normally, except no CUDA events is collected.
For server:
The same with offline inference. The profiling ends with runtime error, or not collecting CUDA events.
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