Accept model type parameter in export_llama (#6507)

Summary:
Specify model to export in the CLI.


Test Plan:
Exported the stories 110M model.
```
python -m examples.models.llama.export_llama -c stories110M/stories110M.pt -p stories110M/params.json -X -kv
```

PR chain:
- [Add kwarg example inputs to eager model base](#5765)
- [Llama2 model cleanup](#5859)
- **YOU ARE HERE ~>** [Accept model type parameter in export_llama](#5910)
- [Export TorchTune llama3_2_vision in ET](#5911)
- [Runner changes for TorchTune Llama3.2 vision text decoder](#6610)
- [Add et version of TorchTune MHA for swapping with custom op](#5912)

Reviewed By: helunwencser

Differential Revision: D65612837

Pulled By: dvorjackz

Author: jackzhxng

docs/source/llm/build-run-llama3-qualcomm-ai-engine-direct-backend.md

@@ -39,7 +39,7 @@ To export Llama 3 8B instruct with the Qualcomm AI Engine Direct Backend, ensure
 
 ```bash
 # Please note that calibration_data must include the prompt template for special tokens.
-python -m examples.models.llama.export_llama  -t <path_to_tokenizer.model>
+python -m examples.models.llama.export_llama -t <path_to_tokenizer.model>
 llama3/Meta-Llama-3-8B-Instruct/tokenizer.model -p <path_to_params.json> -c <path_to_checkpoint_for_Meta-Llama-3-8B-Instruct>  --use_kv_cache  --qnn --pt2e_quantize qnn_16a4w --disable_dynamic_shape --num_sharding 8 --calibration_tasks wikitext --calibration_limit 1 --calibration_seq_length 128 --optimized_rotation_path <path_to_optimized_matrix> --calibration_data "<|start_header_id|>system<|end_header_id|>\n\nYou are a funny chatbot.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCould you tell me about Facebook?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
 ```
 

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

@@ -158,7 +158,7 @@ To export Llama 3 8B instruct with the Qualcomm AI Engine Direct Backend, ensure
 * 8B models might need 16GB RAM on the device to run.
 ```
 # Please note that calibration_data must include the prompt template for special tokens.
-python -m examples.models.llama.export_llama  -t <path_to_tokenizer.model> -p <path_to_params.json> -c <path_to_checkpoint_for_Meta-Llama-3-8B-Instruct>  --use_kv_cache  --qnn --pt2e_quantize qnn_16a4w --disable_dynamic_shape --num_sharding 8 --calibration_tasks wikitext --calibration_limit 1 --calibration_seq_length 128 --optimized_rotation_path <path_to_optimized_matrix> --calibration_data "<|start_header_id|>system<|end_header_id|>\n\nYou are a funny chatbot.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCould you tell me about Facebook?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+python -m examples.models.llama.export_llama -t <path_to_tokenizer.model> -p <path_to_params.json> -c <path_to_checkpoint_for_Meta-Llama-3-8B-Instruct>  --use_kv_cache  --qnn --pt2e_quantize qnn_16a4w --disable_dynamic_shape --num_sharding 8 --calibration_tasks wikitext --calibration_limit 1 --calibration_seq_length 128 --optimized_rotation_path <path_to_optimized_matrix> --calibration_data "<|start_header_id|>system<|end_header_id|>\n\nYou are a funny chatbot.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCould you tell me about Facebook?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
 ```
 
 ## Pushing Model and Tokenizer

examples/models/llama/README.md

@@ -239,9 +239,19 @@ You can export and run the original Llama 3 8B instruct model.
 
 2. Export model and generate `.pte` file
     ```
-    python -m examples.models.llama.export_llama --checkpoint <consolidated.00.pth> -p <params.json> -kv --use_sdpa_with_kv_cache -X -qmode 8da4w  --group_size 128 -d fp32 --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --embedding-quantize 4,32 --output_name="llama3_kv_sdpa_xnn_qe_4_32.pte"
+    python -m examples.models.llama.export_llama \
+	    --checkpoint <consolidated.00.pth> \
+		-p <params.json> \
+		-kv \
+		--use_sdpa_with_kv_cache \
+		-X \
+		-qmode 8da4w \
+		--group_size 128 \
+		-d fp32 \
+		--metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' \
+		--embedding-quantize 4,32 \
+		--output_name="llama3_kv_sdpa_xnn_qe_4_32.pte"
     ```
-
     Due to the larger vocabulary size of Llama 3, we recommend quantizing the embeddings with `--embedding-quantize 4,32` as shown above to further reduce the model size.
 
 

examples/models/llama/eval_llama_lib.py

@@ -190,7 +190,7 @@ def gen_eval_wrapper(
 
     pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)
     # GPTFastEvalWrapper: Create a wrapper around a pre-exported model
-    manager: LLMEdgeManager = _prepare_for_llama_export(model_name, args)
+    manager: LLMEdgeManager = _prepare_for_llama_export(args)
 
     if len(quantizers) != 0:
         manager = manager.export().pt2e_quantize(quantizers)

examples/models/llama/export_llama.py

@@ -23,10 +23,9 @@
 def main() -> None:
     seed = 42
     torch.manual_seed(seed)
-    modelname = "llama2"
     parser = build_args_parser()
     args = parser.parse_args()
-    export_llama(modelname, args)
+    export_llama(args)
 
 
 if __name__ == "__main__":

examples/models/llama/export_llama_lib.py

@@ -81,6 +81,10 @@
 verbosity_setting = None
 
 
+EXECUTORCH_DEFINED_MODELS = ["stories110m", "llama2", "llama3", "llama3_1", "llama3_2"]
+TORCHTUNE_DEFINED_MODELS = []
+
+
 class WeightType(Enum):
     LLAMA = "LLAMA"
     FAIRSEQ2 = "FAIRSEQ2"
@@ -105,7 +109,7 @@ def verbose_export():
 
 
 def build_model(
-    modelname: str = "model",
+    modelname: str = "llama3",
     extra_opts: str = "",
     *,
     par_local_output: bool = False,
@@ -116,11 +120,11 @@ def build_model(
     else:
         output_dir_path = "."
 
-    argString = f"--checkpoint par:{modelname}_ckpt.pt --params par:{modelname}_params.json {extra_opts} --output-dir {output_dir_path}"
+    argString = f"--model {modelname} --checkpoint par:model_ckpt.pt --params par:model_params.json {extra_opts} --output-dir {output_dir_path}"
     parser = build_args_parser()
     args = parser.parse_args(shlex.split(argString))
     # pkg_name = resource_pkg_name
-    return export_llama(modelname, args)
+    return export_llama(args)
 
 
 def build_args_parser() -> argparse.ArgumentParser:
@@ -130,6 +134,12 @@ def build_args_parser() -> argparse.ArgumentParser:
     # parser.add_argument(
     #     "-q", "--quantized_ckpt", default=None, help="quantized checkpoint file"
     # )
+    parser.add_argument(
+        "--model",
+        default="llama3",
+        choices=EXECUTORCH_DEFINED_MODELS + TORCHTUNE_DEFINED_MODELS,
+        help="The Lllama model architecture to use. stories110M, llama2, llama3, llama3_1, and llama3_2 use the same underlying LlamaTransformer architecture defined in ExecuTorch. All other models use TorchTune model definitions.",
+    )
     parser.add_argument(
         "-E",
         "--embedding-quantize",
@@ -473,13 +483,13 @@ def canonical_path(path: Union[str, Path], *, dir: bool = False) -> str:
         return return_val
 
 
-def export_llama(modelname, args) -> str:
+def export_llama(args) -> str:
     if args.profile_path is not None:
         try:
             from executorch.util.python_profiler import CProfilerFlameGraph
 
             with CProfilerFlameGraph(args.profile_path):
-                builder = _export_llama(modelname, args)
+                builder = _export_llama(args)
                 assert (
                     filename := builder.get_saved_pte_filename()
                 ) is not None, "Fail to get file name from builder"
@@ -490,14 +500,14 @@ def export_llama(modelname, args) -> str:
             )
             return ""
     else:
-        builder = _export_llama(modelname, args)
+        builder = _export_llama(args)
         assert (
             filename := builder.get_saved_pte_filename()
         ) is not None, "Fail to get file name from builder"
         return filename
 
 
-def _prepare_for_llama_export(modelname: str, args) -> LLMEdgeManager:
+def _prepare_for_llama_export(args) -> LLMEdgeManager:
     """
     Helper function for export_llama. Loads the model from checkpoint and params,
     and sets up a LLMEdgeManager with initial transforms and dtype conversion.
@@ -523,7 +533,7 @@ def _prepare_for_llama_export(modelname: str, args) -> LLMEdgeManager:
 
     return (
         _load_llama_model(
-            modelname=modelname,
+            args.model,
             checkpoint=checkpoint_path,
             checkpoint_dir=checkpoint_dir,
             params_path=params_path,
@@ -546,7 +556,7 @@ def _prepare_for_llama_export(modelname: str, args) -> LLMEdgeManager:
             args=args,
         )
         .set_output_dir(output_dir_path)
-        .source_transform(_get_source_transforms(modelname, dtype_override, args))
+        .source_transform(_get_source_transforms(args.model, dtype_override, args))
     )
 
 
@@ -620,13 +630,13 @@ def _validate_args(args):
             )
 
 
-def _export_llama(modelname, args) -> LLMEdgeManager:  # noqa: C901
+def _export_llama(args) -> LLMEdgeManager:  # noqa: C901
     _validate_args(args)
     pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)
 
     # export_to_edge
     builder_exported_to_edge = (
-        _prepare_for_llama_export(modelname, args)
+        _prepare_for_llama_export(args)
         .export()
         .pt2e_quantize(quantizers)
         .export_to_edge()
@@ -821,8 +831,8 @@ def _load_llama_model_metadata(
 
 
 def _load_llama_model(
+    modelname: str = "llama3",
     *,
-    modelname: str = "llama2",
     checkpoint: Optional[str] = None,
     checkpoint_dir: Optional[str] = None,
     params_path: str,
@@ -850,15 +860,27 @@ def _load_llama_model(
     Returns:
         An instance of LLMEdgeManager which contains the eager mode model.
     """
+
     assert (
         checkpoint or checkpoint_dir
     ) and params_path, "Both checkpoint/checkpoint_dir and params can't be empty"
     logging.info(
         f"Loading model with checkpoint={checkpoint}, params={params_path}, use_kv_cache={use_kv_cache}, weight_type={weight_type}"
     )
+
+    if modelname in EXECUTORCH_DEFINED_MODELS:
+        module_name = "llama"
+        model_class_name = "Llama2Model"  # TODO: Change to "LlamaModel" in examples/models/llama/model.py.
+    elif modelname in TORCHTUNE_DEFINED_MODELS:
+        raise NotImplementedError(
+            "Torchtune Llama models are not yet supported in ExecuTorch export."
+        )
+    else:
+        raise ValueError(f"{modelname} is not a valid Llama model.")
+
     model, example_inputs, example_kwarg_inputs, _ = EagerModelFactory.create_model(
-        module_name="llama",
-        model_class_name="Llama2Model",
+        module_name,
+        model_class_name,
         checkpoint=checkpoint,
         checkpoint_dir=checkpoint_dir,
         params=params_path,

examples/models/llama/runner/eager.py

@@ -37,7 +37,7 @@ def __init__(self, args):
             model_args=model_args,
             device="cuda" if torch.cuda.is_available() else "cpu",
         )
-        manager: LLMEdgeManager = _prepare_for_llama_export("llama", args)
+        manager: LLMEdgeManager = _prepare_for_llama_export(args)
         self.model = manager.model.eval().to(device=self.device)
 
     def forward(