8000 `torch.ldexp` incorrectly returns infinity if `exp` is larger than log2 of the max representable number · Issue #133265 · pytorch/pytorch · GitHub
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torch.ldexp incorrectly returns infinity if exp is larger than log2 of the max representable number #133265

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roman-openai opened this issue Aug 12, 2024 · 2 comments
Closed

torch.ldexp incorrectly returns infinity if exp is larger than log2 of the max representable number #133265

roman-openai opened this issue Aug 12, 2024 · 2 comments
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module: half Related to float16 half-precision floats module: type promotion Related to semantics of type promotion triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module

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@roman-openai
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roman-openai commented Aug 12, 2024 •
edited by pytorch-bot bot
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🐛 Describe the bug

import torch
x = torch.tensor([2**-10], dtype=torch.float16, device='cuda')
exp = torch.tensor([20], dtype=torch.float16, device='cuda')

torch.ldexp(x, exp)

Gives

tensor([inf], device='cuda:0', dtype=torch.float16)

While both input 2**-10 and output 2**10 are in the fp16 range. Same happens with other dtypes when exp is such that 2**exp is out of range.

Note that

torch.ldexp(torch.ldexp(x, exp / 2), exp / 2)

Gives the correct result of tensor([1024.], device='cuda:0', dtype=torch.float16)

Versions

Collecting environment information...
PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 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 3.30.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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:                             4000.22
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 (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] optree==0.12.1
[pip3] torch==2.3.1+cu121
[pip3] torchaudio==2.3.1+cu121
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.18.0
[pip3] torchvision==0.18.1+cu121
[pip3] triton==2.3.1
[conda] Could not collect

cc @nairbv @mruberry
@janeyx99
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also could be related to #133264

@janeyx99 janeyx99 added triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module module: type promotion Related to semantics of type promotion module: half Related to float16 half-precision floats labels Aug 14, 2024
@isuruf isuruf mentioned this issue May 7, 2025
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@ngimel
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ngimel commented May 12, 2025

closing as duplicate of #153069

@ngimel ngimel closed this as completed May 12, 2025
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