Repository: pytorch
Issue: `torch.nn.transformer.forward` returns incorrect value inside `torch.no_grad()` blocks.
Labels: module: nn,triaged

### 🐛 Describe the bug

`torch.nn.transformer` returns incorrect value inside `torch.no_grad()` blocks.

A minimal example is available. You could also find the same code in [Colab](https://colab.research.google.com/drive/1nEbCjthD3PwzR9o2LWgjCntQWSCYb6_N?usp=sharing).

```python
import torch

transformer = torch.nn.Transformer(batch_first=True, d_model=8, dim_feedforward=8, dropout=0.0)

embed_src = torch.randn(1, 2, 8)
embed_tgt = torch.randn(1, 3, 8)

padding_mask = torch.tensor([[0., float('-inf')]])

transformer.eval()

out1 = transformer(embed_src, embed_tgt, src_key_padding_mask=padding_mask)

out2 = transformer(embed_src, embed_tgt, src_key_padding_mask=padding_mask)

with torch.no_grad():
    out3 = transformer(embed_src, embed_tgt, src_key_padding_mask=padding_mask)

with torch.no_grad():
    out4 = transformer(embed_src, embed_tgt, src_key_padding_mask=padding_mask)

# expected behavior: all four statements print the same content
# what happened: #1 == #2 != #3 == #4
print(out1[0, 0].detach()) # tensor([-1.4190,  0.0401,  0.7612,  0.5342, -0.1760,  1.9109, -0.6330, -1.0183])
print(out2[0, 0].detach()) # tensor([-1.4190,  0.0401,  0.7612,  0.5342, -0.1760,  1.9109, -0.6330, -1.0183])
print(out3[0, 0])          # tensor([-1.7533, -0.4876,  1.1448,  0.2122,  0.2096,  1.6410, -0.2300, -0.7367])
print(out4[0, 0])          # tensor([-1.7533, -0.4876,  1.1448,  0.2122,  0.2096,  1.6410, -0.2300, -0.7367])
```

### 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 3.30.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Mar 22 2024, 16:50:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
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 @ 2.20GHz
CPU family:                           6
Model:                                79
Thread(s) per core:                   2
Core(s) per socket:                   1
Socket(s):                            1
Stepping:                             0
BogoMIPS:                             4399.99
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 rdseed adx smap xsaveopt 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:                             256 KiB (1 instance)
L3 cache:                             55 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] 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 @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki @bhosmer @cpuhrsch @erichan1 @drisspg