Not implemented error for `aten.quantize_per_tensor.tensor_qparams`
Labels: triaged, oncall: pt2

### 🐛 Describe the bug

The aforementioned error exists for quantized models in torchbench like `resnet50_quantized_qat` and `mobilenet_v2_quantized_qat`. My repro:
```
python benchmarks/dynamo/torchbench.py --only resnet50_quantized_qat -dcpu --performance
```

Stacktrace:
```
Traceback (most recent call last):
  File "/home/yj/pytorch/torch/_subclasses/fake_tensor.py", line 1243, in dispatch
    r = func(*args, **kwargs)
  File "/home/yj/pytorch/torch/_ops.py", line 284, in __call__
    return self._op(*args, **kwargs or {})
  File "/home/yj/pytorch/torch/_ops.py", line 377, in _get_dispatch
    final_key = resolve_key(self, key)
  File "/home/yj/pytorch/torch/_ops.py", line 106, in resolve_key
    raise NotImplementedError(f"could not find kernel for {op} at dispatch key {k}")
NotImplementedError: could not find kernel for aten.quantize_per_tensor.tensor_qparams at dispatch key DispatchKey.Meta
```

### Versions

PyTorch version: 2.0.0a0+git64b8fae
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.31

Python version: 3.8.15 (default, Nov 24 2022, 15:19:38)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-56-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3070
Nvidia driver version: 510.108.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.6.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.6.0
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn.so.8.4.1
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.4.1
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.4.1
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.4.1
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.4.1
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.4.1
/usr/local/cuda-11.6/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.4.1
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
Byte Order:                      Little Endian
Address sizes:                   48 bits physical, 48 bits virtual
CPU(s):                          24
On-line CPU(s) list:             0-23
Thread(s) per core:              2
Core(s) per socket:              12
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       AuthenticAMD
CPU family:                      25
Model:                           33
Model name:                      AMD Ryzen 9 5900X 12-Core Processor
Stepping:                        0
Frequency boost:                 enabled
CPU MHz:                         2200.000
CPU max MHz:                     4950.1948
CPU min MHz:                     2200.0000
BogoMIPS:                        7400.08
Virtualization:                  AMD-V
L1d cache:                       384 KiB
L1i cache:                       384 KiB
L2 cache:                        6 MiB
L3 cache:                        64 MiB
NUMA node0 CPU(s):               0-23
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          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; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm

Versions of relevant libraries:
[pip3] bert-pytorch==0.0.1a4
[pip3] clip-anytorch==2.5.0
[pip3] CoCa-pytorch==0.0.7
[pip3] dalle2-pytorch==1.10.5
[pip3] ema-pytorch==0.1.4
[pip3] functorch==1.14.0a0+408bcf1
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.5
[pip3] pytorch-transformers==1.2.0
[pip3] pytorch-triton==2.0.0+0d7e753227
[pip3] pytorch-warmup==0.1.1
[pip3] rotary-embedding-torch==0.2.1
[pip3] torch==2.0.0a0+git64b8fae
[pip3] torch-fidelity==0.3.0
[pip3] torch-struct==0.5
[pip3] torchaudio==2.0.0a0+9368f33
[pip3] torchdata==0.7.0a0+f083d52
[pip3] torchmetrics==0.11.0
[pip3] torchrec-nightly==2023.1.25
[pip3] torchtext==0.15.0a0+bb0efcd
[pip3] torchvision==0.15.0a0+85983a5
[pip3] torchx==0.4.0
[pip3] vector-quantize-pytorch==0.10.15
[conda] bert-pytorch              0.0.1a4                   dev_0    <develop>
[conda] clip-anytorch             2.5.0                    pypi_0    pypi
[conda] coca-pytorch              0.0.7                    pypi_0    pypi
[conda] dalle2-pytorch            1.10.5                   pypi_0    pypi
[conda] ema-pytorch               0.1.4                    pypi_0    pypi
[conda] functorch                 1.14.0a0+408bcf1          pypi_0    pypi
[conda] numpy                     1.23.5                   pypi_0    pypi
[conda] pytorch-transformers      1.2.0                    pypi_0    pypi
[conda] pytorch-triton            2.0.0+0d7e753227          pypi_0    pypi
[conda] pytorch-warmup            0.1.1                    pypi_0    pypi
[conda] rotary-embedding-torch    0.2.1                    pypi_0    pypi
[conda] torch                     2.0.0a0+git64b8fae           dev_0    <develop>
[conda] torch-fidelity            0.3.0                    pypi_0    pypi
[conda] torch-struct              0.5                      pypi_0    pypi
[conda] torchaudio                2.0.0a0+9368f33          pypi_0    pypi
[conda] torchdata                 0.7.0a0+f083d52          pypi_0    pypi
[conda] torchmetrics              0.11.0                   pypi_0    pypi
[conda] torchrec-nightly          2023.1.25                pypi_0    pypi
[conda] torchtext                 0.15.0a0+bb0efcd          pypi_0    pypi
[conda] torchvision               0.15.0a0+85983a5          pypi_0    pypi
[conda] torchx                    0.4.0                    pypi_0    pypi
[conda] vector-quantize-pytorch   0.10.15                  pypi_0    pypi

cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel @ezyang @soumith @msaroufim @wconstab @ngimel @bdhirsh