XNNPACK is not taking effect on Android and x86 platforms. #16397
Description
🐛 Describe the bug
1、prepare
I build xnnpack backend and llama example with
"cmake --workflow llm-release "
"pushd examples/models/llama
cmake --workflow --preset llama-releas"
download model file on https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET
2、run
/workspace/executorch$ cmake-out/examples/models/llama/llama_main --model_path=/workspace/executorch/llama3-1B/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8.pte --tokenizer_path=/workspace/executorch/llama3-1B/tokenizer.model --prompt="I would like to learn python, could you teach me with a simple example?"
log: (1766713672820 - 1766713649585) / 111 = 209.324324324, only 5tokens/s
I tokenizers:regex.cpp:27] Registering override fallback regex
I tokenizers:regex.cpp:27] Registering override fallback regex
E tokenizers:hf_tokenizer.cpp:82] Error parsing json file: [json.exception.parse_error.101] parse error at line 1, column 1: syntax error while parsing value - invalid literal; last read: 'I'
I would like to learn python, could you teach me with a simple example? I can take it from there.
Here is the code:
import sys
# Function to convert input string into bytes
def to_bytes(string):
return bytes(string, 'utf-8')
# Function to convert bytes to string
def bytes_to_string(bytes):
return bytes.decode('utf-8')
# Main function
def main():
# Read input string from user
input_string = input("Enter a string: ")
# Convert input string into bytes
bytes_string = to_bytes(input_string)
# Convert bytes to
PyTorchObserver {"prompt_tokens":16,"generated_tokens":111,"model_load_start_ms":1766713642938,"model_load_end_ms":1766713649585,"inference_start_ms":1766713649585,"inference_end_ms":1766713672820,"prompt_eval_end_ms":1766713650400,"first_token_ms":1766713650400,"aggregate_sampling_time_ms":261,"SCALING_FACTOR_UNITS_PER_SECOND":1000}
### Versions
Collecting environment information...
PyTorch version: 2.9.0.dev20250820+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.31.6
Libc version: glibc-2.35
Python version: 3.10.12 (main, May 27 2025, 17:12:29) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.8.61
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
GPU 3: NVIDIA GeForce RTX 3090
Nvidia driver version: 570.86.10
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Caching allocator config: N/A
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): 80
On-line CPU(s) list: 0-79
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6133 CPU @ 2.50GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 20
Socket(s): 2
Stepping: 4
CPU max MHz: 3000.0000
CPU min MHz: 1000.0000
BogoMIPS: 5000.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 1.3 MiB (40 instances)
L1i cache: 1.3 MiB (40 instances)
L2 cache: 40 MiB (40 instances)
L3 cache: 55 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-19,40-59
NUMA node1 CPU(s): 20-39,60-79
Vulnerability Itlb multihit: KVM: Mitigation: Split huge pages
Vulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Mitigation; IBRS
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; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable
Versions of relevant libraries:
[pip3] aimet-torch==2.21.0
[pip3] executorch==1.1.0a0+3a262ef
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] onnx==1.19.1
[pip3] onnx-ir==0.1.12
[pip3] onnx2torch==1.5.15
[pip3] onnxruntime==1.23.2
[pip3] onnxscript==0.5.6
[pip3] pytorch_sphinx_theme==0.0.24
[pip3] pytorch_tokenizers==0.1.0
[pip3] pytorch-triton==3.4.0+gitf7888497
[pip3] pytorch-wpe==0.0.1
[pip3] torch==2.9.0.dev20250820+cu128
[pip3] torch-complex==0.4.4
[pip3] torchao==0.15.0
[pip3] torchaudio==2.8.0.dev20250820+cu128
[pip3] torchdata==0.11.0
[pip3] torchsr==1.0.4
[pip3] torchtune==0.6.1
[pip3] torchvision==0.24.0.dev20250820+cu128
[pip3] triton==3.1.0
[conda] Could not collect