no_grad() changes output of TransformerDecoder module during evaluation 
Labels: oncall: transformer/mha

### 🐛 Describe the bug

The TransformerDecoder module gives different outputs at validation time depending on whether gradient calculations are being performed. 

Minimal example: 

  ```
import torch 
  torch.manual_seed(5)
  
  class TestModule(torch.nn.Module):
     def __init__(self):
         
        super().__init__()

        layer = torch.nn.TransformerDecoderLayer(
            d_model=8,
            nhead=2,
            batch_first=True,
            dropout=0
        )

        self.transformer_decoder = torch.nn.TransformerDecoder(layer, num_layers=1)

        self.final = torch.nn.Linear(8, 1)
    
    def forward(self, batch):
        tgt, memory = batch

        mask = (~torch.triu(torch.ones(tgt.shape[1], tgt.shape[1], dtype=torch.bool)).transpose(0, 1)).type_as(tgt)

        preds = self.transformer_decoder(
            tgt=tgt,
            memory=memory,
            tgt_mask=mask
        )

        return torch.sum(self.final(preds))

model = TestModule()
tgt = torch.normal(0, 1, size=(1, 5, 8))
mem = torch.zeros(1, 5, 8)

model.train()
print(model((tgt, mem)))

model.train()
with torch.no_grad():
    print(model((tgt, mem)))

model.eval()
print(model((tgt, mem)))

model.eval()
with torch.no_grad():
    print(model((tgt, mem)))
````

**Output:**

> tensor(1.8914, grad_fn=<SumBackward0>)
> tensor(1.8914)
> tensor(1.8914, grad_fn=<SumBackward0>)
> tensor(2.9639)

**Expected output:** 

All four outputs should be the same - whether or not the computation graph is stored shouldn't affect the output of the model. 

The difference isn't caused by unexpected train vs. test logic somewhere in the TransformerDecoderLayer (eg. a hidden dropout or batch norm), since the model behaves as expected when run in eval model but with gradients. 

### Versions

**Output of collect_env.py:** 
PyTorch version: 2.0.1+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 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 3.25.2
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.109+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 11.8.89
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.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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 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 Retbleed:          Vulnerable
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
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.22.4
[pip3] torch==2.0.1+cu118
[pip3] torchaudio==2.0.2+cu118
[pip3] torchdata==0.6.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.15.2
[pip3] torchvision==0.15.2+cu118
[pip3] triton==2.0.0

cc @jbschlosser @bhosmer @cpuhrsch @erichan1 @drisspg