8000 `torch.nn.functional.rrelu` crashes on CPU with `training=True` when `lower` or `upper` is set to `inf` · Issue #153281 · pytorch/pytorch · GitHub
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torch.nn.functional.rrelu crashes on CPU with training=True when lower or upper is set to inf #153281

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jiren-the-gray opened this issue May 9, 2025 · 2 comments
Open

torch.nn.functional.rrelu crashes on CPU with training=True when lower or upper is set to inf #153281

jiren-the-gray opened this issue May 9, 2025 · 2 comments
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module: edge cases Adversarial inputs unlikely to occur in practice module: NaNs and Infs Problems related to NaN and Inf handling in floating point module: nn Related to torch.nn triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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@jiren-the-gray
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jiren-the-gray commented May 9, 2025 •
edited by pytorch-bot bot
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🐛 Describe the bug

When torch.nn.functional.rrelu is called with either lower=float('-inf') or upper=float('inf') with training=True, it crashes on CPU but no error occurs on CUDA. It throws the following error: RuntimeError: Expected to - from <= std::numeric_limits<T>::max() to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.). The error also does not appear if training is set to False.

Colab: link

Torch version: 2.6.0+cu124

Minimal reproduction code:

import torch

input = torch.tensor([-1.0])
lower = 0
upper = float('inf')

training = False

out_gpu = torch.nn.functional.rrelu(input.cuda(), lower, upper, training=training).cpu()
print(out_gpu) # tensor([-inf])

out_cpu = torch.nn.functional.rrelu(input, lower, upper, training=training) # No error
print(out_cpu) # tensor([-inf])

training = True

out_gpu = torch.nn.functional.rrelu(input.cuda(), lower, upper, training=training).cpu()
print(out_gpu) # tensor([-inf])

out_cpu = torch.nn.functional.rrelu(input, lower, upper, training=training) 
# RuntimeError: Expected to - from <= std::numeric_limits<T>::max() to be true, but got false.  (Could this error message be improved?  If so, please report an enhancement request to PyTorch.)
print(out_cpu)

Versions

Collecting environment information...
PyTorch version: 2.6.0+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.31.6
Libc version: glibc-2.35

Python version: 3.11.12 (main, Apr 9 2025, 08:55:54) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.123+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 550.54.15
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1
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
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 3
BogoMIPS: 4000.28
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 rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 1 MiB (1 instance)
L3 cache: 38.5 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable; SMT Host state unknown
Vulnerability Meltdown: Vulnerable
Vulnerability Mmio stale data: Vulnerable
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Vulnerable
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2: Vulnerable; IBPB: disabled; STIBP: disabled; PBRSB-eIBRS: Not affected; BHI: Vulnerable
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable

Versions of relevant libraries:
[pip3] numpy==2.0.2
[pip3] nvidia-cublas-cu12==12.5.3.2
[pip3] nvidia-cuda-cupti-cu12==12.5.82
[pip3] nvidia-cuda-nvrtc-cu12==12.5.82
[pip3] nvidia-cuda-runtime-cu12==12.5.82
[pip3] nvidia-cudnn-cu12==9.3.0.75
[pip3] nvidia-cufft-cu12==11.2.3.61
[pip3] nvidia-curand-cu12==10.3.6.82
[pip3] nvidia-cusolver-cu12==11.6.3.83
[pip3] nvidia-cusparse-cu12==12.5.1.3
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.5.82
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] nvtx==0.2.11
[pip3] optree==0.15.0
[pip3] pynvjitlink-cu12==0.5.2
[pip3] torch==2.6.0+cu124
[pip3] torchaudio==2.6.0+cu124
[pip3] torchsummary==1.5.1
[pip3] torchvision==0.21.0+cu124
[pip3] triton==3.2.0
[conda] Could not collect

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki
@bigachin
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Can I take up this issue?

@mingfeima
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@bigachin sure, please go ahead! I expect we need to provide better error message here for for both cpu and cuda device.

@colesbury colesbury added module: nn Related to torch.nn triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module module: edge cases Adversarial inputs unlikely to occur in practice labels May 13, 2025
@albanD albanD added the module: NaNs and Infs Problems related to NaN and Inf handling in floating point label May 14, 2025
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module: edge cases Adversarial inputs unlikely to occur in practice module: NaNs and Infs Problems related to NaN and Inf handling in floating point module: nn Related to torch.nn triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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