QNN backend model convert cost many memory.

[enduringstack]

🐛 Describe the bug

according to this link: https://github.com/pytorch/executorch/blob/main/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md

model convert: python3 -m examples.models.llama2.export_llama --checkpoint "${MODEL_DIR}/consolidated.00.pth" -p "${MODEL_DIR}/params.json" -kv --disable_dynamic_shape --qnn --pt2e_quantize qnn_16a4w -d fp32 --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name="testchenllama3dot2_1B.pte"

model is llama3.2 1B， the convert process cost 30+GB memory and it's still incresing.....

Versions

Collecting environment information... PyTorch version: N/A Is debug build: N/A CUDA used to build PyTorch: N/A ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 LTS (x86_64) GCC version: (Ubuntu 11.2.0-19ubuntu1) 11.2.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35

Python version: 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-5.15.0-25-generic-x86_64-with-glibc2.35 Is CUDA available: N/A CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: N/A GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: N/A

CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 42 bits physical, 48 bits virtual Byte Order: Little Endian CPU(s): 16 On-line CPU(s) list: 0-15 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 6278C CPU @ 2.60GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 8 Socket(s): 1 Stepping: 7 BogoMIPS: 5200.00 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 tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced 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 arat avx512_vnni md_clear flush_l1d arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 256 KiB (8 instances) L1i cache: 256 KiB (8 instances) L2 cache: 8 MiB (8 instances) L3 cache: 35.8 MiB (1 instance) NUMA node(s): 1 NUMA node0 CPU(s): 0-15 Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: 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; Enhanced IBRS, IBPB conditional, RSB filling Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown

Versions of relevant libraries: [pip3] flake8==7.0.0 [pip3] mypy==1.10.0 [pip3] mypy-extensions==1.0.0 [pip3] numpy==1.26.4 [pip3] numpydoc==1.7.0 [conda] _anaconda_depends 2024.06 py312_mkl_2 https://repo.anaconda.com/pkgs/main [conda] blas 1.0 mkl https://repo.anaconda.com/pkgs/main [conda] mkl 2023.1.0 h213fc3f_46344 https://repo.anaconda.com/pkgs/main [conda] mkl-service 2.4.0 py312h5eee18b_1 https://repo.anaconda.com/pkgs/main [conda] mkl_fft 1.3.8 py312h5eee18b_0 https://repo.anaconda.com/pkgs/main [conda] mkl_random 1.2.4 py312hdb19cb5_0 https://repo.anaconda.com/pkgs/main [conda] numpy 1.26.4 py312hc5e2394_0 https://repo.anaconda.com/pkgs/main [conda] numpy-base 1.26.4 py312h0da6c21_0 https://repo.anaconda.com/pkgs/main [conda] numpydoc 1.7.0 py312h06a4308_0 https://repo.anaconda.com/pkgs/main

[larryliu0820]

@cccclai is anyone actively looking at this?