Completely different output between .pt and .ptl
Labels: oncall: mobile

### 🐛 Describe the bug

I traced a model using `torch.jit.trace()`. After tracing, I can confirm that the result compared with my original PyTorch model is the same. However, when I convert it to `.ptl` the results become very (drastically) different.

How I traced the model:
```
model = torch.load("model_kld_full.pt")
model.eval()
inp = torch.rand(1, 3, 112, 112)
model_traced = torch.jit.trace(model, inp)
model_traced.save('model_kld_tr.pt')
```

running:
```
emb = model(inp)
model_traced = torch.jit.load("model_kld_tr.pt")
emb2 = model_traced(inp)

assert emb == emb2
```
Works.

Then I convert from torchscript `.pt` to `.ptl`
```
model_traced = torch.jit.load("model_kld_tr.pt")
traced_script_module_optimized = optimize_for_mobile(model_traced)
traced_script_module_optimized._save_for_lite_interpreter("model_kld_tr.ptl")
```

running
```
emb = model(inp)
model_traced = torch.jit.load("model_kld_tr.ptl") #ptl version
emb2 = model_traced(inp)

assert emb == emb2
```
produced completely different output.

The Android team has also complained about this from their end.

### Versions

Collecting environment information...
PyTorch version: 1.13.1
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 LTS (x86_64)
GCC version: (conda-forge gcc 12.2.0-19) 12.2.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.8.0 (default, Nov  6 2019, 21:49:08)  [GCC 7.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-40-generic-x86_64-with-glibc2.10
Is CUDA available: True
CUDA runtime version: 11.6.124
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Quadro RTX 6000
GPU 1: Quadro RTX 6000
GPU 2: Quadro RTX 6000
GPU 3: Quadro RTX 6000

Nvidia driver version: 510.73.05
cuDNN version: Could not collect
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:                   43 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          48
On-line CPU(s) list:             0-47
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 7402P 24-Core Processor
CPU family:                      23
Model:                           49
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       1
Stepping:                        0
Frequency boost:                 enabled
CPU max MHz:                     2800.0000
CPU min MHz:                     1500.0000
BogoMIPS:                        5599.82
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 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 rdpid overflow_recov succor smca sme sev sev_es
Virtualization:                  AMD-V
L1d cache:                       768 KiB (24 instances)
L1i cache:                       768 KiB (24 instances)
L2 cache:                        12 MiB (24 instances)
L3 cache:                        128 MiB (8 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-47
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   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 conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==1.13.1
[pip3] torchaudio==0.13.1
[pip3] torchvision==0.14.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libblas                   3.9.0            12_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            12_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            12_linux64_mkl    conda-forge
[conda] magma                     2.7.0                h8db6258_0  
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py38h7f8727e_0  
[conda] mkl_fft                   1.3.1            py38hd3c417c_0  
[conda] mkl_random                1.2.2            py38h51133e4_0  
[conda] numpy                     1.23.5           py38h14f4228_0  
[conda] numpy-base                1.23.5           py38h31eccc5_0  
[conda] pytorch                   1.13.1          py3.8_cuda11.6_cudnn8.3.2_0    pytorch
[conda] pytorch-cuda              11.6                 h867d48c_1    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                0.13.1               py38_cu116    pytorch
[conda] torchvision               0.14.1               py38_cu116    pytorch