Intel MKL DFTI ERROR

[vwrewsge]

🐛 Describe the bug

Code:

import torch

input_data = torch.randn(3, 3)
output = torch.fft.fft2(input_data, dim=[])

Output:

RuntimeError: MKL FFT error: Intel MKL DFTI ERROR: Invalid configuration parameters

Versions

Collecting environment information...
PyTorch version: 2.2.1+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: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.8.18 (default, Sep 11 2023, 13:40:15) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-1015-azure-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090
Nvidia driver version: 535.161.07
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
Address sizes: 43 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 64
On-line CPU(s) list: 0-63
Vendor ID: AuthenticAMD
Model name: AMD Ryzen Threadripper 3970X 32-Core Processor
CPU family: 23
Model: 49
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 1
Stepping: 0
Frequency boost: enabled
CPU max MHz: 3900.0000
CPU min MHz: 2200.0000
BogoMIPS: 7799.91
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es
Virtualization: AMD-V
L1d cache: 1 MiB (32 instances)
L1i cache: 1 MiB (32 instances)
L2 cache: 16 MiB (32 instances)
L3 cache: 128 MiB (8 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-63
Vulnerability Gather data sampling: 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 Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; safe RET
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; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.4
[pip3] torch==2.2.1
[pip3] triton==2.2.0
[conda] numpy 1.24.4 pypi_0 pypi
[conda] torch 2.2.1 pypi_0 pypi
[conda] triton 2.2.0 pypi_0 pypi

cc @mruberry @peterbell10 @frank-wei @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10

[ZailiWang]

Cannot reproduce on Intel Xeon or Core machines, but used a recent nightly PyTorch (20240220).
Could you help verify if it can still be reproduced with a latest pytorch-nightly env?

[malfet]

@ZailiWang I guess it's because AMD cpu is used rather than Intel one

[mingfeima]

@ZailiWang I guess it's because AMD cpu is used rather than Intel one

@ZailiWang can we double check amd cpus?

[vwrewsge]

Cannot reproduce on Intel Xeon or Core machines, but used a recent nightly PyTorch (20240220). Could you help verify if it can still be reproduced with a latest pytorch-nightly env?

In pytorch_nightly, it looks like it has already been fixed.

But the following code still triggers the bug.

Code:

import torch
output = torch.fft.ihfft(torch.empty(0, 0, 0, 2))
print(output)

Output:

RuntimeError: MKL FFT error: Intel MKL DFTI ERROR: Inconsistent configuration parameters

[ZailiWang]

I can reproduce this with Intel Xeon.

output = torch.fft.ihfft(torch.empty(0, 0, 0, 2))

@mingfeima shall we fix this?