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% python3 repro.py
Traceback (most recent call last):
File "repro.py", line 7, in <module>
torchaudio.functional.rnnt_loss(
File "/home/me/.local/lib/python3.8/site-packages/torchaudio/functional/functional.py", line 1814, in rnnt_loss
return costs.mean()
RuntimeError: CUDA error: an illegal memory access was encountered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
If you change b from 33 to 32 (→ bringing down logits to 2**31 elements), then it runs successfully, and there is plenty of GPU memory left (which makes me think 33 should be fine too):
% python3 repro.py
tensor(2498., device='cuda:0', dtype=torch.float16)
Thu Jan 25 16:00:33 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.129.03 Driver Version: 535.129.03 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| 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 NVIDIA A40 On | 00000000:11:00.0 Off | 0 |
| 0% 37C P0 155W / 300W | 8520MiB / 46068MiB | 42% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 93893 C python3 8508MiB |
+---------------------------------------------------------------------------------------+
I also tried running this using NVIDIA compute-sanitizer and torch==2.1.2 & torchaudio==2.1.2 that I built myself with CMAKE_CUDA_FLAGS=-lineinfo. It reported an invalid write in the following line: https://github.com/pytorch/audio/blob/v2.1.2/torchaudio/csrc/rnnt/gpu/kernels.h#L86 This seems to make sense since it's writing to gradients[b_t_u_d] when b_t_u_d is a 32-bit signed int which would overflow for logits with >2**31 elements.
% compute-sanitizer python3 repro.py
(...omitted...)
========= COMPUTE-SANITIZER
========= Invalid __global__ write of size 2 bytes
========= at 0x8a0 in /audio/torchaudio/csrc/rnnt/gpu/kernels.h:86:void torchaudio::rnnt::ComputeGradientsElement<c10::Half, float>(int, int, int, int, int, int, int, T2, const T1 *, const int *, const int *, const int *, const T2 *, const T2 *, const T2 *, T1 *, int, bool)
========= by thread (192,0,0) in block (0,24,32)
========= Address 0x7f023a030000 is out of bounds
========= and is 4194107392 bytes before the nearest allocation at 0x7f0334000000 of size 4429185024 bytes
========= Device Frame:/audio/torchaudio/csrc/rnnt/gpu/gpu_kernels.cuh:339:void torchaudio::rnnt::ComputeGradients<c10::Half, float>(int, int, int, int, T2, const T1 *, const int *, const int *, const int *, const T2 *, const T2 *, const T2 *, T1 *, int, bool) [0x10]
(...omitted...)
Versions
% python3 collect_env.py
Collecting environment information...
PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31
Python version: 3.8.10 (default, Nov 22 2023, 10:22:35) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-166-generic-x86_64-with-glibc2.29
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA A40
Nvidia driver version: 535.129.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 57 bits virtual
CPU(s): 6
On-line CPU(s) list: 0-5
Thread(s) per core: 2
Core(s) per socket: 3
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 106
Model name: Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz
Stepping: 6
CPU MHz: 2899.998
BogoMIPS: 5799.99
Virtualization: VT-x
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 96 KiB
L1i cache: 96 KiB
L2 cache: 12 MiB
L3 cache: 16 MiB
NUMA node0 CPU(s): 0-5
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: 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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid md_clear arch_capabilities
Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] torch==2.1.2
[pip3] torchaudio==2.1.2
[pip3] triton==2.1.0
[conda] Could not collect
The text was updated successfully, but these errors were encountered:
🐛 Describe the bug
Repro:
Observed result:
If you change
b
from 33 to 32 (→ bringing downlogits
to 2**31 elements), then it runs successfully, and there is plenty of GPU memory left (which makes me think 33 should be fine too):I also tried running this using NVIDIA compute-sanitizer and
torch==2.1.2
&torchaudio==2.1.2
that I built myself withCMAKE_CUDA_FLAGS=-lineinfo
. It reported an invalid write in the following line: https://github.com/pytorch/audio/blob/v2.1.2/torchaudio/csrc/rnnt/gpu/kernels.h#L86 This seems to make sense since it's writing togradients[b_t_u_d]
whenb_t_u_d
is a 32-bit signedint
which would overflow forlogits
with >2**31 elements.Versions
The text was updated successfully, but these errors were encountered: