Update LlmConfig preq_mode enums (#11936)

Author: jackzhxng

.github/workflows/android-perf.yml

@@ -221,7 +221,7 @@ jobs:
                       model.use_sdpa_with_kv_cache=true \
                       backend.xnnpack.enabled=true \
                       backend.xnnpack.extended_ops=true \
-                      base.preq_mode="8da4w_output_8da8w" \
+                      base.preq_mode="preq_8da4w_out_8da8w" \
                       base.preq_group_size=32 \
                       export.max_seq_length=2048 \
                       export.max_context_length=2048 \
@@ -247,7 +247,7 @@ jobs:
                       base.params="${DOWNLOADED_PATH}/params.json" \
                       quantization.use_qat=true \
                       base.use_lora=16 \
-                      base.preq_mode="8da4w_output_8da8w" \
+                      base.preq_mode="preq_8da4w_out_8da8w" \
                       base.preq_group_size=32 \
                       base.preq_embedding_quantize=\'8,0\' \
                       model.use_sdpa_with_kv_cache=true \

examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md

@@ -55,15 +55,15 @@ In this demo app, we support text-only inference with up-to-date Llama models an
 Meta has released prequantized INT4 SpinQuant Llama 3.2 models that ExecuTorch supports on the XNNPACK backend.
 * Export Llama model and generate .pte file as below:
 ```
-python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="8da4w_output_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' quantization.use_spin_quant="native" base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_spinquant.pte"
+python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="preq_8da4w_out_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' quantization.use_spin_quant="native" base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_spinquant.pte"
 ```
 For convenience, an [exported ExecuTorch SpinQuant model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_SpinQuant_INT4_EO8.ipynb).
 
 ### For Llama 3.2 1B and 3B QAT+LoRA models
 Meta has released prequantized INT4 QAT+LoRA Llama 3.2 models that ExecuTorch supports on the XNNPACK backend.
 * Export Llama model and generate .pte file as below:
 ```
-python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> quantization.use_qat=True base.use_lora=16 model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="8da4w_output_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_qat_lora.pte"
+python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> quantization.use_qat=True base.use_lora=16 model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="preq_8da4w_out_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_qat_lora.pte"
 ```
 For convenience, an [exported ExecuTorch QAT+LoRA model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-QLORA_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_QLORA_INT4_EO8.ipynb).
 

examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md

@@ -51,15 +51,15 @@ In this demo app, we support text-only inference with up-to-date Llama models an
 Meta has released prequantized INT4 SpinQuant Llama 3.2 models that ExecuTorch supports on the XNNPACK backend.
 * Export Llama model and generate .pte file as below:
 ```
-python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="8da4w_output_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' quantization.use_spin_quant="native" base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_spinquant.pte"
+python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="preq_8da4w_out_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' quantization.use_spin_quant="native" base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_spinquant.pte"
 ```
 For convenience, an [exported ExecuTorch SpinQuant model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_SpinQuant_INT4_EO8.ipynb).
 
 ### For Llama 3.2 1B and 3B QAT+LoRA models
 Meta has released prequantized INT4 QAT+LoRA Llama 3.2 models that ExecuTorch supports on the XNNPACK backend.
 * Export Llama model and generate .pte file as below:
 ```
-python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> quantization.use_qat=True base.use_lora=16 model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="8da4w_output_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_qat_lora.pte"
+python -m extension.llm.export.export_llm base.model_class="llama3_2" base.checkpoint=<path-to-your-checkpoint.pth> base.params=<path-to-your-params.json> quantization.use_qat=True base.use_lora=16 model.use_kv_cache=True model.use_sdpa_with_kv_cache=True backend.xnnpack.enabled=True model.dtype_override="fp32" backend.xnnpack.extended_ops=True base.preq_mode="preq_8da4w_out_8da8w" base.preq_group_size=32 export.max_seq_length=2048 export.max_context_length=2048 base.preq_embedding_quantize=\'8,0\' base.metadata='"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"' export.output_name="llama3_2_qat_lora.pte"
 ```
 For convenience, an [exported ExecuTorch QAT+LoRA model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-QLORA_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_QLORA_INT4_EO8.ipynb).
 

examples/models/llama/README.md

@@ -196,7 +196,7 @@ python -m extension.llm.export.export_llm \
    model.use_sdpa_with_kv_cache=True \
    backend.xnnpack.enabled=True \
    backend.xnnpack.extended_ops=True \
-   base.preq_mode="8da4w_output_8da8w" \
+   base.preq_mode="preq_8da4w_out_8da8w" \
    base.preq_group_size=32 \
    export.max_seq_length=2048 \
    export.max_context_length=2048 \
@@ -224,7 +224,7 @@ python -m extension.llm.export.export_llm \
    base.params="${LLAMA_PARAMS:?}" \
    quantization.use_qat=True \
    base.use_lora=16 \
-   base.preq_mode="8da4w_output_8da8w" \
+   base.preq_mode="preq_8da4w_out_8da8w" \
    base.preq_group_size=32 \
    base.preq_embedding_quantize=\'8,0\' \
    model.use_sdpa_with_kv_cache=True \