Use llm_config instead of args in export_llama functions

backends/arm/test/models/test_llama.py

@@ -22,6 +22,9 @@
     TosaPipelineMI,
 )
 
+from executorch.examples.models.llama.config.llm_config_utils import (
+    convert_args_to_llm_config,
+)
 from executorch.examples.models.llama.export_llama_lib import (
     build_args_parser,
     get_llama_model,
@@ -89,8 +92,9 @@ def prepare_model(self):
         ]
         parser = build_args_parser()
         args = parser.parse_args(args)
+        llm_config = convert_args_to_llm_config(args)
 
-        llama_model, llama_inputs, llama_meta = get_llama_model(args)
+        llama_model, llama_inputs, llama_meta = get_llama_model(llm_config)
 
         return llama_model, llama_inputs, llama_meta
 

examples/apple/mps/scripts/mps_example.py

@@ -20,6 +20,7 @@
     serialize_from_bundled_program_to_flatbuffer,
 )
 
+from executorch.examples.models.llama.config.llm_config import LlmConfig
 from executorch.exir import (
     EdgeCompileConfig,
     EdgeProgramManager,
@@ -131,28 +132,24 @@ def parse_args():
     return args
 
 
-def get_model_config(args):
-    model_config = {}
-    model_config["module_name"] = MODEL_NAME_TO_MODEL[args.model_name][0]
-    model_config["model_class_name"] = MODEL_NAME_TO_MODEL[args.model_name][1]
-
-    if args.model_name == "llama2":
-        if args.checkpoint:
-            model_config["checkpoint"] = args.checkpoint
-        if args.params:
-            model_config["params"] = args.params
-        model_config["use_kv_cache"] = True
-    return model_config
-
-
 if __name__ == "__main__":
     args = parse_args()
 
     if args.model_name not in MODEL_NAME_TO_MODEL:
         raise RuntimeError(f"Available models are {list(MODEL_NAME_TO_MODEL.keys())}.")
 
-    model_config = get_model_config(args)
-    model, example_inputs, _, _ = EagerModelFactory.create_model(**model_config)
+    llm_config = LlmConfig()
+    if args.model_name == "llama2":
+        if args.checkpoint:
+            llm_config.base.checkpoint = args.checkpoint
+        if args.params:
+            llm_config.base.params = args.params
+        llm_config.model.use_kv_cache = True
+    model, example_inputs, _, _ = EagerModelFactory.create_model(
+        module_name=MODEL_NAME_TO_MODEL[args.model_name][0],
+        model_class_name=MODEL_NAME_TO_MODEL[args.model_name][1],
+        llm_config=llm_config,
+    )
 
     model = model.eval()
 

examples/models/llama/TARGETS

@@ -67,6 +67,7 @@ runtime.python_library(
         "//caffe2:torch",
         "//executorch/examples/models:model_base",
         "//executorch/examples/models/llama:llama_transformer",
+        "//executorch/examples/models/llama/config:llm_config",
         "//executorch/examples/models:checkpoint",
     ],
 )
@@ -266,6 +267,7 @@ runtime.python_library(
         ":export_library",
         "//executorch/examples/models/llama/config:llm_config",
         "fbsource//third-party/pypi/hydra-core:hydra-core",
+        "fbsource//third-party/pypi/omegaconf:omegaconf",
     ],
 )
 

examples/models/llama/config/llm_config_utils.py

@@ -26,92 +26,143 @@ def convert_args_to_llm_config(args: argparse.Namespace) -> LlmConfig:
     llm_config = LlmConfig()
 
     # BaseConfig
-    llm_config.base.model_class = ModelType(args.model)
-    llm_config.base.params = args.params
-    llm_config.base.checkpoint = args.checkpoint
-    llm_config.base.checkpoint_dir = args.checkpoint_dir
-    llm_config.base.tokenizer_path = args.tokenizer_path
-    llm_config.base.metadata = args.metadata
-    llm_config.base.use_lora = bool(args.use_lora)
-    llm_config.base.fairseq2 = args.fairseq2
+    if hasattr(args, "model"):
+        llm_config.base.model_class = ModelType(args.model)
+    if hasattr(args, "params"):
+        llm_config.base.params = args.params
+    if hasattr(args, "checkpoint"):
+        llm_config.base.checkpoint = args.checkpoint
+    if hasattr(args, "checkpoint_dir"):
+        llm_config.base.checkpoint_dir = args.checkpoint_dir
+    if hasattr(args, "tokenizer_path"):
+        llm_config.base.tokenizer_path = args.tokenizer_path
+    if hasattr(args, "metadata"):
+        llm_config.base.metadata = args.metadata
+    if hasattr(args, "use_lora"):
+        llm_config.base.use_lora = args.use_lora
+    if hasattr(args, "fairseq2"):
+        llm_config.base.fairseq2 = args.fairseq2
 
     # PreqMode settings
-    if args.preq_mode:
+    if hasattr(args, "preq_mode") and args.preq_mode:
         llm_config.base.preq_mode = PreqMode(args.preq_mode)
-        llm_config.base.preq_group_size = args.preq_group_size
-        llm_config.base.preq_embedding_quantize = args.preq_embedding_quantize
+        if hasattr(args, "preq_group_size"):
+            llm_config.base.preq_group_size = args.preq_group_size
+        if hasattr(args, "preq_embedding_quantize"):
+            llm_config.base.preq_embedding_quantize = args.preq_embedding_quantize
 
     # ModelConfig
-    llm_config.model.dtype_override = DtypeOverride(args.dtype_override)
-    llm_config.model.enable_dynamic_shape = args.enable_dynamic_shape
-    llm_config.model.use_shared_embedding = args.use_shared_embedding
-    llm_config.model.use_sdpa_with_kv_cache = args.use_sdpa_with_kv_cache
-    llm_config.model.expand_rope_table = args.expand_rope_table
-    llm_config.model.use_attention_sink = args.use_attention_sink
-    llm_config.model.output_prune_map = args.output_prune_map
-    llm_config.model.input_prune_map = args.input_prune_map
-    llm_config.model.use_kv_cache = args.use_kv_cache
-    llm_config.model.quantize_kv_cache = args.quantize_kv_cache
-    llm_config.model.local_global_attention = args.local_global_attention
+    if hasattr(args, "dtype_override"):
+        llm_config.model.dtype_override = DtypeOverride(args.dtype_override)
+    if hasattr(args, "enable_dynamic_shape"):
+        llm_config.model.enable_dynamic_shape = args.enable_dynamic_shape
+    if hasattr(args, "use_shared_embedding"):
+        llm_config.model.use_shared_embedding = args.use_shared_embedding
+    if hasattr(args, "use_sdpa_with_kv_cache"):
+        llm_config.model.use_sdpa_with_kv_cache = args.use_sdpa_with_kv_cache
+    if hasattr(args, "expand_rope_table"):
+        llm_config.model.expand_rope_table = args.expand_rope_table
+    if hasattr(args, "use_attention_sink"):
+        llm_config.model.use_attention_sink = args.use_attention_sink
+    if hasattr(args, "output_prune_map"):
+        llm_config.model.output_prune_map = args.output_prune_map
+    if hasattr(args, "input_prune_map"):
+        llm_config.model.input_prune_map = args.input_prune_map
+    if hasattr(args, "use_kv_cache"):
+        llm_config.model.use_kv_cache = args.use_kv_cache
+    if hasattr(args, "quantize_kv_cache"):
+        llm_config.model.quantize_kv_cache = args.quantize_kv_cache
+    if hasattr(args, "local_global_attention"):
+        llm_config.model.local_global_attention = args.local_global_attention
 
     # ExportConfig
-    llm_config.export.max_seq_length = args.max_seq_length
-    llm_config.export.max_context_length = args.max_context_length
-    llm_config.export.output_dir = args.output_dir
-    llm_config.export.output_name = args.output_name
-    llm_config.export.so_library = args.so_library
-    llm_config.export.export_only = args.export_only
+    if hasattr(args, "max_seq_length"):
+        llm_config.export.max_seq_length = args.max_seq_length
+    if hasattr(args, "max_context_length"):
+        llm_config.export.max_context_length = args.max_context_length
+    if hasattr(args, "output_dir"):
+        llm_config.export.output_dir = args.output_dir
+    if hasattr(args, "output_name"):
+        llm_config.export.output_name = args.output_name
+    if hasattr(args, "so_library"):
+        llm_config.export.so_library = args.so_library
+    if hasattr(args, "export_only"):
+        llm_config.export.export_only = args.export_only
 
     # QuantizationConfig
-    llm_config.quantization.qmode = args.quantization_mode
-    llm_config.quantization.embedding_quantize = args.embedding_quantize
-    if args.pt2e_quantize:
+    if hasattr(args, "quantization_mode"):
+        llm_config.quantization.qmode = args.quantization_mode
+    if hasattr(args, "embedding_quantize"):
+        llm_config.quantization.embedding_quantize = args.embedding_quantize
+    if hasattr(args, "pt2e_quantize") and args.pt2e_quantize:
         llm_config.quantization.pt2e_quantize = Pt2eQuantize(args.pt2e_quantize)
-    llm_config.quantization.group_size = args.group_size
-    if args.use_spin_quant:
+    if hasattr(args, "group_size"):
+        llm_config.quantization.group_size = args.group_size
+    if hasattr(args, "use_spin_quant") and args.use_spin_quant:
         llm_config.quantization.use_spin_quant = SpinQuant(args.use_spin_quant)
-    llm_config.quantization.use_qat = args.use_qat
-    llm_config.quantization.calibration_tasks = args.calibration_tasks
-    llm_config.quantization.calibration_limit = args.calibration_limit
-    llm_config.quantization.calibration_seq_length = args.calibration_seq_length
-    llm_config.quantization.calibration_data = args.calibration_data
-
-    # BackendConfig
-    # XNNPack
-    llm_config.backend.xnnpack.enabled = args.xnnpack
-    llm_config.backend.xnnpack.extended_ops = args.xnnpack_extended_ops
+    if hasattr(args, "use_qat"):
+        llm_config.quantization.use_qat = args.use_qat
+    if hasattr(args, "calibration_tasks"):
+        llm_config.quantization.calibration_tasks = args.calibration_tasks
+    if hasattr(args, "calibration_limit"):
+        llm_config.quantization.calibration_limit = args.calibration_limit
+    if hasattr(args, "calibration_seq_length"):
+        llm_config.quantization.calibration_seq_length = args.calibration_seq_length
+    if hasattr(args, "calibration_data"):
+        llm_config.quantization.calibration_data = args.calibration_data
+
+    # BackendConfig - XNNPack
+    if hasattr(args, "xnnpack"):
+        llm_config.backend.xnnpack.enabled = args.xnnpack
+    if hasattr(args, "xnnpack_extended_ops"):
+        llm_config.backend.xnnpack.extended_ops = args.xnnpack_extended_ops
 
     # CoreML
-    llm_config.backend.coreml.enabled = args.coreml
+    if hasattr(args, "coreml"):
+        llm_config.backend.coreml.enabled = args.coreml
     llm_config.backend.coreml.enable_state = getattr(args, "coreml_enable_state", False)
     llm_config.backend.coreml.preserve_sdpa = getattr(
         args, "coreml_preserve_sdpa", False
     )
-    if args.coreml_quantize:
+    if hasattr(args, "coreml_quantize") and args.coreml_quantize:
         llm_config.backend.coreml.quantize = CoreMLQuantize(args.coreml_quantize)
-    llm_config.backend.coreml.ios = args.coreml_ios
-    llm_config.backend.coreml.compute_units = CoreMLComputeUnit(
-        args.coreml_compute_units
-    )
+    if hasattr(args, "coreml_ios"):
+        llm_config.backend.coreml.ios = args.coreml_ios
+    if hasattr(args, "coreml_compute_units"):
+        llm_config.backend.coreml.compute_units = CoreMLComputeUnit(
+            args.coreml_compute_units
+        )
 
     # Vulkan
-    llm_config.backend.vulkan.enabled = args.vulkan
+    if hasattr(args, "vulkan"):
+        llm_config.backend.vulkan.enabled = args.vulkan
 
     # QNN
-    llm_config.backend.qnn.enabled = args.qnn
-    llm_config.backend.qnn.use_sha = args.use_qnn_sha
-    llm_config.backend.qnn.soc_model = args.soc_model
-    llm_config.backend.qnn.optimized_rotation_path = args.optimized_rotation_path
-    llm_config.backend.qnn.num_sharding = args.num_sharding
+    if hasattr(args, "qnn"):
+        llm_config.backend.qnn.enabled = args.qnn
+    if hasattr(args, "use_qnn_sha"):
+        llm_config.backend.qnn.use_sha = args.use_qnn_sha
+    if hasattr(args, "soc_model"):
+        llm_config.backend.qnn.soc_model = args.soc_model
+    if hasattr(args, "optimized_rotation_path"):
+        llm_config.backend.qnn.optimized_rotation_path = args.optimized_rotation_path
+    if hasattr(args, "num_sharding"):
+        llm_config.backend.qnn.num_sharding = args.num_sharding
 
     # MPS
-    llm_config.backend.mps.enabled = args.mps
+    if hasattr(args, "mps"):
+        llm_config.backend.mps.enabled = args.mps
 
     # DebugConfig
-    llm_config.debug.profile_memory = args.profile_memory
-    llm_config.debug.profile_path = args.profile_path
-    llm_config.debug.generate_etrecord = args.generate_etrecord
-    llm_config.debug.generate_full_logits = args.generate_full_logits
-    llm_config.debug.verbose = args.verbose
+    if hasattr(args, "profile_memory"):
+        llm_config.debug.profile_memory = args.profile_memory
+    if hasattr(args, "profile_path"):
+        llm_config.debug.profile_path = args.profile_path
+    if hasattr(args, "generate_etrecord"):
+        llm_config.debug.generate_etrecord = args.generate_etrecord
+    if hasattr(args, "generate_full_logits"):
+        llm_config.debug.generate_full_logits = args.generate_full_logits
+    if hasattr(args, "verbose"):
+        llm_config.debug.verbose = args.verbose
 
     return llm_config