Repository: pytorch
Issue: TransformerEncoderLayer and TransformerDecoderLayer do not work
Labels: module: nn,triaged

### 🐛 Describe the bug

Hi,

I'm working with a next-frame prediction with Transformer model. The data I'm working with is a Secure Water Treatment (SWaT) Dataset, which is a scaled down version of a real-world industrial water treatment plant producing filtered water. The data is obtained from https://github.com/JulienAu/Anomaly_Detection_Tuto.

I normalized and standardized the data and then load the 10 consecutive data points in order to predict the next data point. I tried the Transformer model, in particular, a `TransformerEncoder` followed by a `TransformerDecoder`. In these layers, I can either use PyTorch's `TransformerEncoderLayer` and `TransformerDecoderLayer` or another implementation that I grabbed from the internet.

When I try PyTorch's implementation, I notice that the loss doesn't go down, but with the other implementation, it works. I'm not sure if this is a problem with PyTorch's implementation or there is something wrong with my parameters.

Here is the link to the Colab where I produced the issue: https://colab.research.google.com/drive/1oZWMTECuLJHmo4lZ039VdCyOF44y4WYt?usp=sharing

And here is the implementation I grabbed from the internet:

```python
class CustomTransformerEncoderLayer(nn.Module):
    def __init__(
        self,
        d_model,
        nhead,
        dim_feedforward=16,
        dropout=0,
        batch_first=False,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)

        self.self_attn = nn.MultiheadAttention(
            d_model,
            nhead,
            dropout=dropout,
            batch_first=batch_first,
        )
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

        self.activation = nn.LeakyReLU(True)

    def forward(self, src, *args, **kwargs):
        src2 = self.self_attn(src, src, src)[0]
        src = src + self.dropout1(src2)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = src + self.dropout2(src2)
        return src


class CustomTransformerDecoderLayer(nn.Module):
    def __init__(
        self,
        d_model,
        nhead,
        dim_feedforward=16,
        dropout=0,
        batch_first=False,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)

        self.self_attn = nn.MultiheadAttention(
            d_model,
            nhead,
            dropout=dropout,
            batch_first=batch_first,
        )
        self.multihead_attn = nn.MultiheadAttention(
            d_model,
            nhead,
            dropout=dropout,
            batch_first=batch_first,
        )
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.activation = nn.LeakyReLU(True)

    def forward(
        self,
        tgt,
        memory,
        *args,
        **kwargs,
    ):
        tgt2 = self.self_attn(tgt, tgt, tgt)[0]
        tgt = tgt + self.dropout1(tgt2)
        tgt2 = self.multihead_attn(tgt, memory, memory)[0]
        tgt = tgt + self.dropout2(tgt2)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
        tgt = tgt + self.dropout3(tgt2)
        return tgt
```

### Versions

```
Collecting environment information...
PyTorch version: 2.1.0+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 3.27.9
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.58+-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 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
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.1.0+cu121
[pip3] torchaudio==2.1.0+cu121
[pip3] torchdata==0.7.0
[pip3] torcheval==0.0.7
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.16.0
[pip3] torchvision==0.16.0+cu121
[pip3] triton==2.1.0
[conda] Could not collect
```

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki @bhosmer @cpuhrsch @erichan1 @drisspg