[torch.compile] `permute_linear_fusion` ignores the inplace operation for the tensor
Labels: high priority, triaged, module: correctness (silent), oncall: pt2, module: aotdispatch, module: inductor

### 🐛 Describe the bug

The `permute_linear_fusion` in `torch.compile` ignores the inplace operation for the tensor. For example,

```py
import torch

torch.manual_seed(420)

class Model(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(2, 2)

    def forward(self, x1):
        v1 = x1.permute(0, 2, 1)
        v1.resize_(1, 1, 2)
        v2 = torch.nn.functional.linear(v1, self.linear.weight, self.linear.bias)
        return v2

func = Model().cuda()

x = torch.randn(1, 2, 2).cuda()


with torch.no_grad():
    func.train(False)
    jit_func = torch.compile(func)

    res1 = func(x) # without jit
    print(res1)

    res2 = jit_func(x)
    print(res2)

    torch.testing.assert_close(res1, res2, rtol=1e-3, atol=1e-3)
```

If we run it with `TORCHINDUCTOR_PERMUTE_FUSION=1`, the output would be:

```
tensor([[[-0.2002, -0.4580]]], device='cuda:0')
tensor([[[0.2537, 0.1411],
         [0.7882, 0.8429]]], device='cuda:0')

AssertionError: The values for attribute 'shape' do not match: torch.Size([1, 1, 2]) != torch.Size([1, 2, 2]).
```

The optimized output have the different shape from the one without optimization.

### Versions

```
PyTorch version: 2.1.0.dev20230621+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 11.3.0-1ubuntu1~22.04.1) 11.3.0
Clang version: 14.0.0-1ubuntu1
CMake version: version 3.26.4
Libc version: glibc-2.35

Python version: 3.9.16 (main, May 15 2023, 23:46:34)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.0-40-generic-x86_64-with-glibc2.35
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 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.4.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.4.1
/usr/lib/x86_64-linux-gnu/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
Address sizes:                   48 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          64
On-line CPU(s) list:             0-63
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen Threadripper PRO 5975WX 32-Cores
CPU family:                      25
Model:                           8
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       1
Stepping:                        2
Frequency boost:                 enabled
CPU max MHz:                     7006.6401
CPU min MHz:                     1800.0000
BogoMIPS:                        7186.21
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 pcid sse4_1 sse4_2 x2apic 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 invpcid_single 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 amd_ppin brs 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
Virtualization:                  AMD-V
L1d cache:                       1 MiB (32 instances)
L1i cache:                       1 MiB (32 instances)
L2 cache:                        16 MiB (32 instances)
L3 cache:                        128 MiB (4 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-63
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
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

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] pytorch-triton==2.1.0+440fd1bf20
[pip3] torch==2.1.0.dev20230621+cu118
[pip3] torchaudio==2.1.0.dev20230621+cu118
[pip3] torchvision==0.16.0.dev20230621+cu118
[pip3] triton==2.0.0
[conda] blas                      1.0                         mkl
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-service               2.4.0            py39h7f8727e_0
[conda] mkl_fft                   1.3.1            py39hd3c417c_0
[conda] mkl_random                1.2.2            py39h51133e4_0
[conda] numpy                     1.23.5           py39h14f4228_0
[conda] numpy-base                1.23.5           py39h31eccc5_0
[conda] pytorch-cuda              11.7                 h778d358_3    pytorch-nightly
[conda] pytorch-mutex             1.0                        cuda    pytorch-nightly
[conda] pytorch-triton            2.1.0+440fd1bf20          pypi_0    pypi
[conda] torch                     2.1.0.dev20230621+cu118          pypi_0    pypi
[conda] torchaudio                2.1.0.dev20230621+cu118          pypi_0    pypi
[conda] torchtriton               2.0.0+0d7e753227            py39    pytorch-nightly
[conda] torchvision               0.16.0.dev20230621+cu118          pypi_0    pypi
```

cc @ezyang @gchanan @zou3519 @msaroufim @wconstab @bdhirsh @anijain2305 @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @Xia-Weiwen @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @ngimel @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov