Missing Out Variants When Running Llama3.2 Example Without XNNPack

I am follwing the instructions in the Llama2 README to test llama model with Executorch.
I want to compare the performance of the model with and without XNNPack. From the code, it seems that DQLinear operations are delegated to XNNPack by default. However, I would like to understand how to use the quantized ops defined in Executorch, as listed in quantized.yaml. Could you provide guidance on configuring the model to use Executorch's quantized ops instead of XNNPack?

I encounter the following error when the -X(--xnnpack) flag is removed from the python export:
raise RuntimeError(f"Missing out variants: {missing_out_vars}") RuntimeError: Missing out variants: {'quantized_decomposed::choose_qparams_per_token_asymmetric', 'quantized_decomposed::dequantize_per_channel', 'quantized_decomposed::dequantize_per_channel_group', 'quantized_decomposed::dequantize_per_token', 'quantized_decomposed::quantize_per_token'}

LLAMA_QUANTIZED_CHECKPOINT=/content/SpinQuant_workspace/consolidated.00.pth
LLAMA_PARAMS= /src/gitrepo/llama/Llama3.2-1B/params.json
python -m examples.models.llama2.export_llama \
   --checkpoint "${LLAMA_QUANTIZED_CHECKPOINT:?}" \
   --params "${LLAMA_PARAMS:?}" \
   --use_sdpa_with_kv_cache \
   --preq_mode 8da4w_output_8da8w \
   --preq_group_size 32 \
   --max_seq_length 2048 \
   --output_name "llama3_2_noxnn.pte" \
   -kv \
   -d fp32 \
   --preq_embedding_quantize 8,0 \
   --use_spin_quant native \
   --metadata '{"append_eos_to_prompt": 0, "get_bos_id":128000, "get_eos_ids":[128009, 128001], "get_n_bos": 0, "get_n_eos": 0}'

What adjustments are required to resolve the "missing out variants" error when the -X flag is omitted?
Thank you for your assistance!

Versions

Collecting environment information...
PyTorch version: 2.6.0.dev20240927+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
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 3.31.0
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-5.15.167.1-1.cm2-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.6.77

Versions of relevant libraries:
[pip3] executorch==0.5.0a0+20a157f
[pip3] numpy==1.26.4
[pip3] torch==2.6.0.dev20240927+cpu
[pip3] torchao==0.5.0+git0916b5b2
[pip3] torchaudio==2.5.0.dev20240927+cpu
[pip3] torchsr==1.0.4
[pip3] torchvision==0.20.0.dev20240927+cpu
[conda] executorch 0.5.0a0+20a157f pypi_0 pypi
[conda] numpy 1.26.4 pypi_0 pypi
[conda] torch 2.6.0.dev20240927+cpu pypi_0 pypi
[conda] torchaudio 2.5.0.dev20240927+cpu pypi_0 pypi
[conda] torchsr 1.0.4 pypi_0 pypi
[conda] torchvision 0.20.0.dev20240927+cpu pypi_0 pypi

cc @digantdesai @mcr229 @JacobSzwejbka @dbort

Start with examples/models/llama2/README.md and the examples/models/llama2/export_llama entry point, then compare the export command with and without the -X/--xnnpack flag. Review kernels/quantized/quantized.yaml alongside the reported missing out variants. Done means the Llama3.2 export completes without XNNPack and uses the intended Executorch quantized operators.