QNN use_8a8w vs no Quant Performance issues

🐛 Describe the bug

I refer to your [tutorial](https://github.com/pytorch/executorch/tree/main/examples/qualcomm/util_scripts
On my 8gen3 SM8650)

python -m examples.qualcomm.util_scripts.cli quantize -a cli_example/simple_model.pt2 -o cli_example/quantize_output -c use_8a8w -i cli_example/input_list --per_channel

python -m examples.qualcomm.util_scripts.cli compile -a cli_example/quantize_output/simple_model_quantized.pt2 -o cli_example/compile_output -m SM8680

python -m examples.qualcomm.util_scripts.cli execute -a cli_example/compile_output/simple_model_quantized.pte -o cli_example/execute_output -i cli_example/input_list -s 30.21.26.12:5555 -b build-android -m SM8650

The delay is as follows

But no quant

python -m examples.qualcomm.util_scripts.cli compile -a cli_example/simple_model.pt2 -o cli_example/compile_output -m SM8680

python -m examples.qualcomm.util_scripts.cli execute -a cli_example/compile_output/simple_model.pte -o cli_example/execute_output -i cli_example/input_list -s 30.21.26.12:5555 -b build-android -m SM8650

The delay is as follows

Performance wise, it didn't get any faster, and the size of pte didn't get any smaller, so what's the point of quantization?
two pte size

-rw-r--r-- 1 root root 483K Jul 22 05:13 simple_model_quantized.pt2
-rw-r--r-- 1 root root 375K Jul 22 04:43 simple_model.pt2

Versions

(executorch) root@VM-148-82-tencentos:/workspace/executorch# python collect_env.py 
Collecting environment information...
PyTorch version: 2.9.0.dev20250714+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 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 3.31.6
Libc version: glibc-2.35

Python version: 3.10.0 (default, Mar  3 2022, 09:58:08) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-6.6.47-12.tl4.x86_64-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
Is XPU available: False
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):                               16
On-line CPU(s) list:                  0-15
Vendor ID:                            GenuineIntel
BIOS Vendor ID:                       Smdbmds
Model name:                           Intel(R) Xeon(R) Gold 6133 CPU @ 2.50GHz
BIOS Model name:                      3.0
CPU family:                           6
Model:                                94
Thread(s) per core:                   1
Core(s) per socket:                   16
Socket(s):                            1
Stepping:                             3
BogoMIPS:                             4988.28
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 cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx rdseed adx smap clflushopt xsaveopt xsavec xgetbv1 arat
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            512 KiB (16 instances)
L1i cache:                            512 KiB (16 instances)
L2 cache:                             64 MiB (16 instances)
L3 cache:                             27.5 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-15
Vulnerability Gather data sampling:   Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
Vulnerability L1tf:                   Mitigation; PTE Inversion
Vulnerability Mds:                    Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown:               Mitigation; PTI
Vulnerability Mmio stale data:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
Vulnerability Spec store bypass:      Vulnerable
Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:             Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Retpoline
Vulnerability Srbds:                  Unknown: Dependent on hypervisor status
Vulnerability Tsx async abort:        Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown

Versions of relevant libraries:
[pip3] executorch==0.8.0a0+7a37676
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] onnx==1.18.0
[pip3] onnxruntime==1.22.0
[pip3] pytorch_tokenizers==0.1.0
[pip3] torch==2.9.0.dev20250714+cpu
[pip3] torchao==0.12.0+gitaee079503
[pip3] torchaudio==2.8.0.dev20250714+cpu
[pip3] torchdata==0.11.0
[pip3] torchsr==1.0.4
[pip3] torchtune==0.6.1
[pip3] torchvision==0.24.0.dev20250714+cpu
[pip3] triton==3.3.1
[conda] executorch                0.8.0a0+7a37676          pypi_0    pypi
[conda] numpy                     2.2.6                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.6.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.6.80                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.6.77                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.6.77                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.5.1.17                 pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.0.4                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.7.77                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.1.2                 pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.4.2                 pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.6.3                    pypi_0    pypi
[conda] nvidia-nccl-cu12          2.26.2                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.6.85                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.6.77                  pypi_0    pypi
[conda] pytorch-tokenizers        0.1.0                    pypi_0    pypi
[conda] torch                     2.9.0.dev20250714+cpu          pypi_0    pypi
[conda] torchao                   0.12.0+gitaee079503          pypi_0    pypi
[conda] torchaudio                2.8.0.dev20250714+cpu          pypi_0    pypi
[conda] torchdata                 0.11.0                   pypi_0    pypi
[conda] torchsr                   1.0.4                    pypi_0    pypi
[conda] torchtune                 0.6.1                    pypi_0    pypi
[conda] torchvision               0.24.0.dev20250714+cpu          pypi_0    pypi
[conda] triton                    3.3.1                    pypi_0    pypi

cc @cccclai @cbilgin

Start with the examples/qualcomm/util_scripts/cli entry point and reproduce the quantized and unquantized compile and execute commands using cli_example/simple_model.pt2 and cli_example/input_list. Compare the reported latency and .pte sizes, then trace where the quantize, compile, and execute stages handle use_8a8w. Done means the performance and size difference is explained or corrected, with the comparison reproducible.