QNN backend. The fake quant model (the graph after convert_pt2e) does not generate accurate result

🐛 Describe the bug

the fake quant model (the graph after convert_pt2e) does not generate accurate result.
I have used 130 pieces of real data, but the accuracy results are as follows

Is it necessary to use more quantitative data, or what?

Versions

Collecting environment information...
PyTorch version: 2.9.0.dev20250725+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-5.4.241-1-tlinux4-0017.5-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):                          10
On-line CPU(s) list:             0-9
Vendor ID:                       GenuineIntel
BIOS Vendor ID:                  Smdbmds
Model name:                      Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz
BIOS Model name:                 3.0
CPU family:                      6
Model:                           85
Thread(s) per core:              2
Core(s) per socket:              5
Socket(s):                       1
Stepping:                        5
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 arch_perfmon 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 invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 arat avx512_vnni
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       320 KiB (10 instances)
L1i cache:                       320 KiB (10 instances)
L2 cache:                        20 MiB (5 instances)
L3 cache:                        35.8 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-9
Vulnerability Itlb multihit:     KVM: Vulnerable
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 Retbleed:          Vulnerable
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
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown

Versions of relevant libraries:
[pip3] executorch==0.8.0a0+2c84f70
[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] onnxconverter-common==1.15.0
[pip3] onnxruntime==1.22.0
[pip3] onnxsim==0.4.36
[pip3] pytorch_tokenizers==0.1.0
[pip3] torch==2.9.0.dev20250725+cpu
[pip3] torchao==0.13.0+git2eb4f9762
[pip3] torchaudio==2.8.0.dev20250725+cpu
[pip3] torchdata==0.11.0
[pip3] torchsr==1.0.4
[pip3] torchsummary==1.5.1
[pip3] torchtune==0.6.1
[pip3] torchvision==0.24.0.dev20250725+cpu
[pip3] triton==3.3.1
[conda] executorch                0.8.0a0+2c84f70          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.dev20250725+cpu          pypi_0    pypi
[conda] torchao                   0.13.0+git2eb4f9762          pypi_0    pypi
[conda] torchaudio                2.8.0.dev20250725+cpu          pypi_0    pypi
[conda] torchdata                 0.11.0                   pypi_0    pypi
[conda] torchsr                   1.0.4                    pypi_0    pypi
[conda] torchsummary              1.5.1                    pypi_0    pypi
[conda] torchtune                 0.6.1                    pypi_0    pypi
[conda] torchvision               0.24.0.dev20250725+cpu          pypi_0    pypi
[conda] triton                    3.3.1                    pypi_0    pypi

cc @cccclai @cbilgin

The report names the QNN backend and the graph after convert_pt2e, but provides no source file, test, model, or reproducible conversion commands. Start by obtaining the missing model and evaluation steps, then compare fake-quant results with a reference model. Done means the cause of the accuracy discrepancy is identified and the result is validated against an agreed accuracy criterion.