Repository: pytorch
Issue: iSTFT gives wrong results for some batched input
Labels: module: cuda,triaged,module: correctness (silent),module: fft

### 🐛 Describe the bug
The tensor `X` in the given file `x.gz` has a shape of [18, 129, 251].
Batched iSTFT on `X[:16]` gives the same results when compared to the results of individual iSTFT, i.e. `iSTFT(X[i]) for i in range(16)`.
But, batched iSTFT on `X` with a batch size larger than `16`, i.e. `X[:17]` and `X[:18]`, gives totally different results.

The code:
```python
import torch

win = torch.hann_window(256).cuda()

X = torch.load('x.gz', map_location='cpu').cuda()  # [18, 129, 251]
# 1. batched iSTFT
Xi = torch.istft(X, n_fft=256, hop_length=128, win_length=256, window=win)  # [18, 32000]
Xi_ = torch.istft(X[:16], n_fft=256, hop_length=128, win_length=256, window=win)  # [16, 32000]
# 2. iSTFT individually, and compare results
for b in range(16):
    Xib = torch.istft(X[b], n_fft=256, hop_length=128, win_length=256, window=win)
    print((Xi[b] == Xib).all().item(), (Xi_[b] == Xib).all().item())  # False, True
# the differences between Xi and Xib are large
Xi0 = torch.istft(X[0], n_fft=256, hop_length=128, win_length=256, window=win)
print(Xi0, '\n', Xi_[0], '\n', Xi[0], '\n')
#  tensor([ 1.8308e-04,  1.4268e-05,  5.5112e-04,  ...,  1.2051e-04,
#         -1.9071e-04, -1.5009e-04], device='cuda:0') 
#  tensor([ 1.8308e-04,  1.4268e-05,  5.5112e-04,  ...,  1.2051e-04,
#         -1.9071e-04, -1.5009e-04], device='cuda:0') 
#  tensor([-0.0130,  0.0008, -0.0127,  ...,  0.0006, -0.0151,  0.0003],
#        device='cuda:0')
```

`x.gz`：
[x.gz](https://github.com/pytorch/pytorch/files/13394216/x.gz)



### Versions

Collecting environment information...
PyTorch version: 2.1.0
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: Could not collect
Libc version: glibc-2.35

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-87-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB

Nvidia driver version: 535.129.03
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:                      46 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             128
On-line CPU(s) list:                0-127
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
CPU family:                         6
Model:                              106
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          2
Stepping:                           6
CPU max MHz:                        3400.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5200.00
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 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 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi 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 avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          3 MiB (64 instances)
L1i cache:                          2 MiB (64 instances)
L2 cache:                           80 MiB (64 instances)
L3 cache:                           96 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-31,64-95
NUMA node1 CPU(s):                  32-63,96-127
Vulnerability Gather data sampling: Mitigation; Microcode
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 Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] mypy==1.6.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.0
[pip3] pytorch-lightning==2.1.0
[pip3] torch==2.1.0
[pip3] torch-tb-profiler==0.4.3
[pip3] torchaudio==2.1.0
[pip3] torchmetrics==1.2.0
[pip3] torchtnt==0.2.1
[pip3] torchvision==0.16.0
[pip3] triton==2.1.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libjpeg-turbo             2.0.0                h9bf148f_0    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46343  
[conda] mkl-service               2.4.0           py310h5eee18b_1  
[conda] mkl_fft                   1.3.8           py310h5eee18b_0  
[conda] mkl_random                1.2.4           py310hdb19cb5_0  
[conda] numpy                     1.26.0          py310h5f9d8c6_0  
[conda] numpy-base                1.26.0          py310hb5e798b_0  
[conda] pytorch                   2.1.0           py3.10_cuda12.1_cudnn8.9.2_0    pytorch
[conda] pytorch-cuda              12.1                 ha16c6d3_5    pytorch
[conda] pytorch-lightning         2.1.0                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch-tb-profiler         0.4.3                    pypi_0    pypi
[conda] torchaudio                2.1.0               py310_cu121    pytorch
[conda] torchmetrics              1.2.0                    pypi_0    pypi
[conda] torchtnt                  0.2.1                    pypi_0    pypi
[conda] torchtriton               2.1.0                     py310    pytorch
[conda] torchvision               0.16.0              py310_cu121    pytorch

cc @ptrblck @mruberry @peterbell10