Wrong functionalization of as_strided leads to wrong results
Labels: high priority, module: docs, triaged, oncall: pt2

### 🐛 Describe the bug

according to documentation (https://pytorch.org/docs/stable/generated/torch.as_strided.html), "as_strided" should override the existing strides and offset of the tensor storage (as opposed to other view operations which are composite in nature).
Thus, when functionalizating as_strided, we should not add it on top of previous  view operations.

This issue leads to incorrect results of torch.compile.

reproducer:
* note that when not using a custom compiler, then torch fails to compile all together
* using core_aten_decompositions doesn't change the issue

```
import torch
import torch._dynamo
import logging
import numpy as np
import torchvision.models as models

from torch._dynamo.backends.common import aot_autograd
from torch._decomp import core_aten_decompositions

print(torch.__version__)

def main():
    def inner_compiler(gm: torch.fx.GraphModule, example_inputs):
        gm.graph.print_tabular()
        return gm.forward

    def foo(a):
        e = a.diagonal()
        f = e.as_strided((2,), (1,), 0)
        f.add_(1.0)
        return a

    a = torch.randn(2, 4)
    a_ref = a.clone()
    torch._dynamo.reset()
    aot_backend = aot_autograd(fw_compiler=inner_compiler)#, decompositions=core_aten_decompositions())
    compiled_model = torch.compile(foo, dynamic=True, backend=aot_backend)
    out_ref = foo(a_ref)
    print(out_ref)
    out = compiled_model(a)
    print(out)
    print(out == out_ref)


main()
```



### Error logs

when not using custom compiler:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py](https://localhost:8080/#) in call_user_compiler(self, gm)
    669             else:
--> 670                 compiled_fn = compiler_fn(gm, self.fake_example_inputs())
    671             _step_logger()(logging.INFO, f"done compiler function {name}")

42 frames
AttributeError: The underlying op of 'aten.sym_storage_offset' has no overload name 'name'

While executing %sym_storage_offset : [#users=3] = call_function[target=torch.ops.aten.sym_storage_offset](args = (%arg0_1,), kwargs = {})
Original traceback:
  File "<ipython-input-3-6e27d662c8bc>", line 18, in foo
    e = a.diagonal()


The above exception was the direct cause of the following exception:

BackendCompilerFailed                     Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py](https://localhost:8080/#) in call_user_compiler(self, gm)
    673         except Exception as e:
    674             compiled_fn = gm.forward
--> 675             raise BackendCompilerFailed(self.compiler_fn, e) from e
    676         return compiled_fn
    677 

BackendCompilerFailed: debug_wrapper raised AttributeError: The underlying op of 'aten.sym_storage_offset' has no overload name 'name'

While executing %sym_storage_offset : [#users=3] = call_function[target=torch.ops.aten.sym_storage_offset](args = (%arg0_1,), kwargs = {})
Original traceback:
  File "<ipython-input-3-6e27d662c8bc>", line 18, in foo
    e = a.diagonal()


Set torch._dynamo.config.verbose=True for more information


You can suppress this exception and fall back to eager by setting:
    torch._dynamo.config.suppress_errors = True
### Minified repro

_No response_

### Versions

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 21676  100 21676    0     0   102k      0 --:--:-- --:--:-- --:--:--  102k
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 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: 10.0.0-4ubuntu1 
CMake version: version 3.25.2
Libc version: glibc-2.31

Python version: 3.10.12 (main, Jun  7 2023, 12:45:35) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.107+-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 525.85.12
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          2
On-line CPU(s) list:             0,1
Thread(s) per core:              2
Core(s) per socket:              1
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) CPU @ 2.00GHz
Stepping:                        3
CPU MHz:                         2000.144
BogoMIPS:                        4000.28
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       32 KiB
L1i cache:                       32 KiB
L2 cache:                        1 MiB
L3 cache:                        38.5 MiB
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
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 mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities

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
[conda] Could not collect

cc @ezyang @gchanan @zou3519 @svekars @carljparker @msaroufim @wconstab @bdhirsh @anijain2305