FFT/STFT and convolution precision issues with functorch

Nobody has claimed this yet.

### 🐛 Describe the bug

Relates to https://github.com/pytorch/pytorch/issues/142029

**1. FFT/STFT Crashes (59 total, blocking):**
- All 20 FFT/STFT ops segfault under functorch transforms
- Root cause: likely missing batching rules or kernel limitation in XPU FFT
- Workaround: skip with `device_type="xpu"` in test decorators (already implemented in test_ops.py)

**2. Tolerance Issues (6 total, under triage):**
- `masked.prod`, `cumprod` (precision unknown)
- `conv_transpose3d` (measured: abs diff 1.53e-05 vs 1e-05 allowed)
- `conv3d` (measured in vmap composition)
- Root cause: GPU precision variation in reductions/matmuls

**Implementation status:**
- ✅ FFT/STFT skips added to test_ops.py
- ✅ Conv_transpose3d tolerance widened from `device_type="cuda"` to `("cuda", "xpu")`
- ✅ Cumprod, masked.prod, conv3d conservatively skipped pending full triage

### Versions

PyTorch version: 2.15.0a0+git0a3b42d
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.4 LTS (x86_64)
GCC version: (Ubuntu 14.2.0-4ubuntu2~24.04.1) 14.2.0
Clang version: Could not collect
CMake version: version 3.31.6
Libc version: glibc-2.39

Python version: 3.12.3 (main, Jun 19 2026, 12:46:00) [GCC 13.3.0] (64-bit runtime)
Python platform: Linux-6.17.0-35-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: 20260100
Intel GPU driver version:
* intel-opencl-icd: 25.18.33578.77-1146~24.04
* libze1: 1.24.0.0-1146~24.04
Intel GPU models onboard:
* Intel(R) Data Center GPU Max 1550
Intel GPU models detected:
* [0] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', device_id=0xBD5, uuid=8680d50b-2f00-0000-8c00-000000000001, driver_version='1.6.33578+77', total_memory=65520MB, local_mem_size=128KB, last_level_cache_size=196608KB, max_compute_units=512, memory_clock_rate=3200MHz, memory_bus_width=64-bit, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1, is_integrated_gpu=0)
* [1] _XpuDeviceProperties(name='Intel(R) Data Center GPU Max 1550', platform_name='Intel(R) oneAPI Unified Runtime over Level-Zero', type='gpu', device_id=0xBD5, uuid=8680d50b-2f00-0000-8c00-000000000002, driver_version='1.6.33578+77', total_memory=65520MB, local_mem_size=128KB, last_level_cache_size=196608KB, max_compute_units=512, memory_clock_rate=3200MHz, memory_bus_width=64-bit, gpu_eu_count=512, gpu_subslice_count=64, max_work_group_size=1024, max_num_sub_groups=64, sub_group_sizes=[16 32], has_fp16=1, has_fp64=1, has_atomic64=1, is_integrated_gpu=0)
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: False
Caching allocator config: N/A

CPU:
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): 64
On-line CPU(s) list: 0-63
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8352Y CPU @ 2.20GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 1
Stepping: 6
CPU(s) scaling MHz: 26%
CPU max MHz: 3400.0000
CPU min MHz: 800.0000
BogoMIPS: 4400.00
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 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 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow 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 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 1.5 MiB (32 instances)
L1i cache: 1 MiB (32 instances)
L2 cache: 40 MiB (32 instances)
L3 cache: 48 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-63
Vulnerability Gather data sampling: Vulnerable
Vulnerability Ghostwrite: Not affected
Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Old microcode: 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
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 SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected

Versions of relevant libraries:
[pip3] intel-cmplr-lib-ur==2026.0.0
[pip3] intel-openmp==2026.0.0
[pip3] mkl-include==2024.2.0
[pip3] mkl-static==2024.2.0
[pip3] mypy==1.16.0
[pip3] mypy_extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] nvidia-cuda-cupti==13.3.75
[pip3] onemkl-license==2026.0.0
[pip3] onnx==1.21.0
[pip3] onnx-ir==0.1.16
[pip3] onnxscript==0.6.2
[pip3] optree==0.13.0
[pip3] tbb==2021.13.1
[pip3] tbb-devel==2023.0.0
[pip3] tcmlib==1.5.0
[pip3] torch==2.15.0a0+git0a3b42d
[pip3] triton-xpu==3.7.1+git21033c4e
[pip3] umf==1.1.0

Start by reviewing test_ops.py and the related PyTorch issue, then examine the functorch FFT/STFT and convolution cases described here on XPU. Done means the remaining crashes and tolerance cases have been individually triaged with validated test behavior, rather than only conservatively skipped.

Written by the indexing model from the issue text.