Static quantization for Transformer block : AttributeError 'function' object has no attribute 'is_cuda'
Labels: triaged, oncall: transformer/mha

### 🐛 Describe the bug

I'm trying to apply static quantization to a model using a `nn.TransformerEncoderLayer`.

But when running the model, I get the following error : 

```
File "/envs/transfo/lib/python3.10/site-packages/torch/nn/modules/transformer.py", line 556, in <genexpr>
    elif not all((x.is_cuda or 'cpu' in str(x.device)) for x in tensor_args):

AttributeError: 'function' object has no attribute 'is_cuda'
```

---

Here is a Colab notebook reproducing the issue : https://colab.research.google.com/drive/14jJBQk6DSn6DJxfOa8gx5zhpol9vwBMp?usp=sharing

Here is the script reproducing the issue : 

```python
import torch
from torch import nn
from torch.ao.quantization import qconfig


class Quantformer(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, dropout_rate, max_seq_len):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.embedding_dim = embedding_dim

        self.quant = torch.ao.quantization.QuantStub()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.pos_embedding = nn.Embedding(max_seq_len, embedding_dim)
        self.transformer = nn.TransformerEncoderLayer(
            d_model=embedding_dim,
            nhead=8,
            dim_feedforward=hidden_dim,
            dropout=dropout_rate,
            batch_first=True,
        )
        self.dropout = nn.Dropout(dropout_rate)
        self.fc = nn.Linear(embedding_dim, vocab_size)
        self.dequant = torch.ao.quantization.DeQuantStub()

    def forward(self, src):
        seq_len = src.size(1)
        batch_size = src.size(0)
        pos_ids = torch.arange(seq_len, dtype=src.dtype, device=src.device).unsqueeze(0).repeat(batch_size, 1)

        embeds = self.dropout(self.embedding(src)) + self.pos_embedding(pos_ids)
        embeds = self.quant(embeds)
        mask = nn.Transformer.generate_square_subsequent_mask(embeds.size(1), device=embeds.device)
        out = self.transformer(embeds, src_mask=mask)
        lm_logits = self.dropout(self.fc(out))

        lm_logits = self.dequant(lm_logits)
        return lm_logits


device = torch.device("cpu")
sq_model = Quantformer(
    vocab_size=10000,
    embedding_dim=128,
    hidden_dim=512,
    dropout_rate=0,
    max_seq_len=10,
).to(device)
sq_model.eval()

sq_model.qconfig = torch.ao.quantization.get_default_qconfig("qnnpack")
sq_model.embedding.qconfig = qconfig.float_qparams_weight_only_qconfig
sq_model.pos_embedding.qconfig = qconfig.float_qparams_weight_only_qconfig

sq_model_prepared = torch.ao.quantization.prepare(sq_model)

x = torch.randint(3, 10000, (1, 10))

sq_model_prepared(x)

squant_model = torch.ao.quantization.convert(sq_model_prepared)

yy = squant_model(x)
```

### Versions

Collecting environment information...
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 3.27.4
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-5.15.120+-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==1.23.5
[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
[conda] Could not collect

cc @jbschlosser @bhosmer @cpuhrsch @erichan1 @drisspg