-0.0 cast to NaNs with float8_e4m3fn dtype casting

[ckvermaAI]

🐛 Describe the bug

-0.0 (with fp16 dtype) cast to NaNs with float8_e4m3fn dtype casting, issue is sproadic. Issue doesn't occurs on CPU or CUDA devices.

Simple reproducer

import torch

t = torch.tensor([-0.0, -0.0, -0.0, -0.0], dtype=torch.float16, device="xpu")
t_fp8 = t.to(torch.float8_e4m3fn)
print(f"{t_fp8}  has_nan: {torch.isnan(t_fp8).any().item()}")

Versions

Collecting environment information...
PyTorch version: 2.9.0+xpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.3 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version: Could not collect
CMake version: version 3.28.3
Libc version: glibc-2.39

Python version: 3.10.19 | packaged by conda-forge | (main, Jan 26 2026, 23:45:08) [GCC 14.3.0] (64-bit runtime)
Python platform: Linux-6.17.0-14-generic-x86_64-with-glibc2.39
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: True
XPU used to build PyTorch: 20250201
Intel GPU driver version:

• intel-opencl-icd: 25.48.36300.8-0
• libze1: 1.26.2-124.04ppa1
  Intel GPU models onboard:
• Intel(R) Arc(TM) B580 Graphics
  Intel GPU models detected:
• [0] _XpuDeviceProperties(name='Intel(R) Arc(TM) B580 Graphics', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', device_id=0xE20B, uuid=86800be2-0000-0000-0300-000000000000, driver_version='1.14.36300+8', total_memory=11605MB, max_compute_units=160, gpu_eu_count=160, gpu_subslice_count=20, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)
  HIP runtime version: N/A
  MIOpen runtime version: N/A
  Is XNNPACK available: True
  Caching allocator config: N/A

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 42 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Vendor ID: GenuineIntel
Model name: 13th Gen Intel(R) Core(TM) i9-13900
CPU family: 6
Model: 183
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 1
Stepping: 1
CPU(s) scaling MHz: 18%
CPU max MHz: 5600.0000
CPU min MHz: 800.0000
BogoMIPS: 3993.60
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 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 896 KiB (24 instances)
L1i cache: 1.3 MiB (24 instances)
L2 cache: 32 MiB (12 instances)
L3 cache: 36 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Ghostwrite: Not affected
Vulnerability Indirect target selection: 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 Old microcode: Not affected
Vulnerability Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Mitigation; IBPB before exit to userspace

Versions of relevant libraries:
[pip3] dpcpp-cpp-rt==2025.2.1
[pip3] impi-rt==2021.16.1
[pip3] intel-cmplr-lib-rt==2025.2.1
[pip3] intel-cmplr-lib-ur==2025.2.1
[pip3] intel-cmplr-lic-rt==2025.2.1
[pip3] intel-opencl-rt==2025.2.1
[pip3] intel-openmp==2025.2.1
[pip3] intel-pti==0.13.1
[pip3] intel-sycl-rt==2025.2.1
[pip3] mkl==2025.2.0
[pip3] numpy==2.2.6
[pip3] oneccl==2021.16.1
[pip3] oneccl-devel==2021.16.1
[pip3] onemkl-sycl-blas==2025.2.0
[pip3] onemkl-sycl-dft==2025.2.0
[pip3] onemkl-sycl-lapack==2025.2.0
[pip3] onemkl-sycl-rng==2025.2.0
[pip3] onemkl-sycl-sparse==2025.2.0
[pip3] pytorch-triton-xpu==3.5.0
[pip3] tbb==2022.2.0
[pip3] tcmlib==1.4.0
[pip3] torch==2.9.0+xpu
[pip3] torchao==0.9.0
[pip3] torchaudio==2.9.0+xpu
[pip3] torchcodec==0.8.0
[pip3] torchvision==0.24.0+xpu
[pip3] triton==3.5.0
[pip3] umf==0.11.0
[conda] dpcpp-cpp-rt 2025.2.1 pypi_0 pypi
[conda] impi-rt 2021.16.1 pypi_0 pypi
[conda] intel-cmplr-lib-rt 2025.2.1 pypi_0 pypi
[conda] intel-cmplr-lib-ur 2025.2.1 pypi_0 pypi
[conda] intel-cmplr-lic-rt 2025.2.1 pypi_0 pypi
[conda] intel-opencl-rt 2025.2.1 pypi_0 pypi
[conda] intel-openmp 2025.2.1 pypi_0 pypi
[conda] intel-pti 0.13.1 pypi_0 pypi
[conda] intel-sycl-rt 2025.2.1 pypi_0 pypi
[conda] mkl 2025.2.0 pypi_0 pypi
[conda] numpy 2.2.6 pypi_0 pypi
[conda] oneccl 2021.16.1 pypi_0 pypi
[conda] oneccl-devel 2021.16.1 pypi_0 pypi
[conda] onemkl-sycl-blas 2025.2.0 pypi_0 pypi
[conda] onemkl-sycl-dft 2025.2.0 pypi_0 pypi
[conda] onemkl-sycl-lapack 2025.2.0 pypi_0 pypi
[conda] onemkl-sycl-rng 2025.2.0 pypi_0 pypi
[conda] onemkl-sycl-sparse 2025.2.0 pypi_0 pypi
[conda] pytorch-triton-xpu 3.5.0 pypi_0 pypi
[conda] tbb 2022.2.0 pypi_0 pypi
[conda] tcmlib 1.4.0 pypi_0 pypi
[conda] torch 2.9.0+xpu pypi_0 pypi
[conda] torchao 0.9.0 pypi_0 pypi
[conda] torchaudio 2.9.0+xpu pypi_0 pypi
[conda] torchcodec 0.8.0 pypi_0 pypi
[conda] torchvision 0.24.0+xpu pypi_0 pypi
[conda] triton 3.5.0 pypi_0 pypi
[conda] umf 0.11.0 pypi_0 pypi

[polisettyvarma]

@riverliuintel @gujinghui @EikanWang can you please check and comment on this

[riverliuintel]

@Stonepia please have a look fp8 related issue

[Stonepia]

Hi @polisettyvarma , could you try with a newer version of PyTorch? It seems to be an issue with pytorch 2.9 only. PyTorch 2.10 has already fixed.

2.9 Results:

$pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 --index-url https://download.pytorch.org/whl/xpu
$python test.py
tensor([nan, nan, nan, nan], device='xpu:0', dtype=torch.float8_e4m3fn)  has_nan: True

2.10 Results:

$pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu
$python test.py
tensor([-0., -0., -0., -0.], device='xpu:0', dtype=torch.float8_e4m3fn)  has_nan: False

System Env:

OS: Ubuntu 24.10 (x86_64)
GCC version: (Ubuntu 14.2.0-4ubuntu2) 14.2.0
Clang version: 19.1.1 (1ubuntu1)
CMake version: version 3.30.3
Libc version: glibc-2.40

Is XPU available: True
XPU used to build PyTorch: 20250301
Intel GPU driver version:
* level-zero:   1.13.035227.19698-igc
* intel-opencl-icd:     25.35.35096.9-0
Intel GPU models onboard:
N/A
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Arc(TM) B580 Graphics', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero V2', type='gpu', device_id=0xE20B, uuid=86800be2-0000-0000-0400-000000000000, driver_version='1.6.35096+9', total_memory=11605MB, max_compute_units=160, gpu_eu_count=160, gpu_subslice_count=20, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1)