Fix LlmConfig enum usage

ghstack-source-id: 96329fe
ghstack-comment-id: 2992966686
Pull-Request: #11833

Author: jackzhxng

examples/models/llama/config/llm_config.py

@@ -26,19 +26,19 @@
 
 
 class ModelType(str, Enum):
-    STORIES110M = "stories110m"
-    LLAMA2 = "llama2"
-    LLAMA3 = "llama3"
-    LLAMA3_1 = "llama3_1"
-    LLAMA3_2 = "llama3_2"
-    LLAMA3_2_VISION = "llama3_2_vision"
-    STATIC_LLAMA = "static_llama"
-    QWEN2_5 = "qwen2_5"
-    QWEN3_0_6B = "qwen3-0_6b"
-    QWEN3_1_7B = "qwen3-1_7b"
-    QWEN3_4B = "qwen3-4b"
-    PHI_4_MINI = "phi_4_mini"
-    SMOLLM2 = "smollm2"
+    stories110m = "stories110m"
+    llama2 = "llama2"
+    llama3 = "llama3"
+    llama3_1 = "llama3_1"
+    llama3_2 = "llama3_2"
+    llama3_2_vision = "llama3_2_vision"
+    static_llama = "static_llama"
+    qwen2_5 = "qwen2_5"
+    qwen3_0_6b = "qwen3-0_6b"
+    qwen3_1_7b = "qwen3-1_7b"
+    qwen3_4b = "qwen3-4b"
+    phi_4_mini = "phi_4_mini"
+    smollm2 = "smollm2"
 
 
 class PreqMode(str, Enum):
@@ -49,8 +49,8 @@ class PreqMode(str, Enum):
     are still around to preserve backward compatibility.
     """
 
-    PREQ_8DA4W = "8da4w"
-    PREQ_8DA4W_OUT_8DA8W = "8da4w_output_8da8w"
+    preq_8da4w = "8da4w"
+    preq_8da4w_out_8da8w = "8da4w_output_8da8w"
 
 
 @dataclass
@@ -82,7 +82,7 @@ class BaseConfig:
             are loaded.
     """
 
-    model_class: ModelType = ModelType.LLAMA3
+    model_class: ModelType = ModelType.llama3
     params: Optional[str] = None
     checkpoint: Optional[str] = None
     checkpoint_dir: Optional[str] = None
@@ -107,9 +107,9 @@ class DtypeOverride(str, Enum):
     is not recommended.
     """
 
-    FP32 = "fp32"
-    FP16 = "fp16"
-    BF16 = "bf16"
+    fp32 = "fp32"
+    fp16 = "fp16"
+    bf16 = "bf16"
 
 
 @dataclass
@@ -147,7 +147,7 @@ class ModelConfig:
             [16] pattern specifies all layers have a sliding window of 16.
     """
 
-    dtype_override: DtypeOverride = DtypeOverride.FP32
+    dtype_override: DtypeOverride = DtypeOverride.fp32
     enable_dynamic_shape: bool = True
     use_shared_embedding: bool = False
     use_sdpa_with_kv_cache: bool = False
@@ -270,22 +270,22 @@ class Pt2eQuantize(str, Enum):
     and is source transform-based.
     """
 
-    XNNPACK_DYNAMIC = "xnnpack_dynamic"
-    XNNPACK_DYNAMIC_QC4 = "xnnpack_dynamic_qc4"
-    QNN_8A8W = "qnn_8a8w"
-    QNN_16A16W = "qnn_16a16w"
-    QNN_16A4W = "qnn_16a4w"
-    COREML_C4W = "coreml_c4w"
-    COREML_8A_C8W = "coreml_8a_c8w"
-    COREML_8A_C4W = "coreml_8a_c4w"
-    COREML_BASELINE_8A_C8W = "coreml_baseline_8a_c8w"
-    COREML_BASELINE_8A_C4W = "coreml_baseline_8a_c4w"
-    VULKAN_8W = "vulkan_8w"
+    xnnpack_dynamic = "xnnpack_dynamic"
+    xnnpack_dynamic_qc4 = "xnnpack_dynamic_qc4"
+    qnn_8a8w = "qnn_8a8w"
+    qnn_16a16w = "qnn_16a16w"
+    qnn_16a4w = "qnn_16a4w"
+    coreml_c4w = "coreml_c4w"
+    coreml_8a_c8w = "coreml_8a_c8w"
+    coreml_8a_c4w = "coreml_8a_c4w"
+    coreml_baseline_8a_c8w = "coreml_baseline_8a_c8w"
+    coreml_baseline_8a_c4w = "coreml_baseline_8a_c4w"
+    vulkan_8w = "vulkan_8w"
 
 
 class SpinQuant(str, Enum):
-    CUDA = "cuda"
-    NATIVE = "native"
+    cuda = "cuda"
+    native = "native"
 
 
 @dataclass
@@ -378,15 +378,15 @@ class XNNPackConfig:
 
 
 class CoreMLQuantize(str, Enum):
-    B4W = "b4w"
-    C4W = "c4w"
+    b4w = "b4w"
+    c4w = "c4w"
 
 
 class CoreMLComputeUnit(str, Enum):
-    CPU_ONLY = "cpu_only"
-    CPU_AND_GPU = "cpu_and_gpu"
-    CPU_AND_NE = "cpu_and_ne"
-    ALL = "all"
+    cpu_only = "cpu_only"
+    cpu_and_gpu = "cpu_and_gpu"
+    cpu_and_ne = "cpu_and_ne"
+    all = "all"
 
 
 @dataclass
@@ -400,7 +400,7 @@ class CoreMLConfig:
     preserve_sdpa: bool = False
     quantize: Optional[CoreMLQuantize] = None
     ios: int = 15
-    compute_units: CoreMLComputeUnit = CoreMLComputeUnit.CPU_ONLY
+    compute_units: CoreMLComputeUnit = CoreMLComputeUnit.cpu_only
 
     def __post_init__(self):
         if self.ios not in (15, 16, 17, 18):

examples/models/llama/export_llama_lib.py

@@ -590,7 +590,7 @@ def export_llama(
 
     # If a checkpoint isn't provided for an HF OSS model, download and convert the
     # weights first.
-    model_name = llm_config.base.model_class
+    model_name = llm_config.base.model_class.value
     if not llm_config.base.checkpoint and model_name in HUGGING_FACE_REPO_IDS:
         repo_id = HUGGING_FACE_REPO_IDS[model_name]
         if model_name == "qwen2_5":
@@ -668,7 +668,7 @@ def _prepare_for_llama_export(llm_config: LlmConfig) -> LLMEdgeManager:
     llm_config.export.output_dir = output_dir_path
 
     # Convert dtype override string to actual type.
-    dtype_override = DType[llm_config.model.dtype_override]
+    dtype_override = DType[llm_config.model.dtype_override.value]
 
     edge_manager = _load_llama_model(llm_config)
 
@@ -702,7 +702,11 @@ def _prepare_for_llama_export(llm_config: LlmConfig) -> LLMEdgeManager:
             checkpoint=llm_config.base.checkpoint,
             checkpoint_dtype=DType.from_torch_dtype(checkpoint_dtype),  # type: ignore
             tokenizer_path=llm_config.base.tokenizer_path,
-            use_spin_quant=llm_config.quantization.use_spin_quant,
+            use_spin_quant=(
+                llm_config.quantization.use_spin_quant.value
+                if llm_config.quantization.use_spin_quant
+                else None
+            ),
             embedding_quantize=llm_config.quantization.embedding_quantize,
             use_shared_embedding=llm_config.model.use_shared_embedding,
             quantization_mode=llm_config.quantization.qmode,
@@ -726,7 +730,9 @@ def _prepare_for_llama_export(llm_config: LlmConfig) -> LLMEdgeManager:
             vulkan=llm_config.backend.vulkan.enabled,
             use_qat=llm_config.quantization.use_qat,
             use_lora=llm_config.base.use_lora,
-            preq_mode=llm_config.base.preq_mode,
+            preq_mode=(
+                llm_config.base.preq_mode.value if llm_config.base.preq_mode else None
+            ),
             preq_group_size=llm_config.base.preq_group_size,
             preq_embedding_quantize=llm_config.base.preq_embedding_quantize,
             local_global_attention=llm_config.model.local_global_attention,
@@ -738,25 +744,34 @@ def _prepare_for_llama_export(llm_config: LlmConfig) -> LLMEdgeManager:
 
 def get_quantizer_and_quant_params(llm_config):
     pt2e_quant_params = get_pt2e_quantization_params(
-        llm_config.quantization.pt2e_quantize, llm_config.quantization.qmode
+        (
+            llm_config.quantization.pt2e_quantize.value
+            if llm_config.quantization.pt2e_quantize
+            else None
+        ),
+        llm_config.quantization.qmode,
     )
     quantizers = get_pt2e_quantizers(pt2e_quant_params, llm_config.export.so_library)
     quant_dtype = None
     if llm_config.backend.qnn.enabled and llm_config.quantization.pt2e_quantize:
         assert len(quantizers) == 0, "Should not enable both xnnpack and qnn"
         qnn_quantizer, quant_dtype = get_qnn_quantizer(
-            llm_config.quantization.pt2e_quantize, llm_config.quantization.qmode
+            llm_config.quantization.pt2e_quantize.value, llm_config.quantization.qmode
         )
         quantizers.append(qnn_quantizer)
     if llm_config.backend.coreml.enabled and llm_config.quantization.pt2e_quantize:
         assert len(quantizers) == 0, "Should not enable both xnnpack / qnn and coreml"
-        coreml_quantizer = get_coreml_quantizer(llm_config.quantization.pt2e_quantize)
+        coreml_quantizer = get_coreml_quantizer(
+            llm_config.quantization.pt2e_quantize.value
+        )
         quantizers.append(coreml_quantizer)
     if llm_config.backend.vulkan.enabled and llm_config.quantization.pt2e_quantize:
         assert (
             len(quantizers) == 0
         ), "Should not enable both vulkan and other quantizers"
-        vulkan_quantizer = get_vulkan_quantizer(llm_config.quantization.pt2e_quantize)
+        vulkan_quantizer = get_vulkan_quantizer(
+            llm_config.quantization.pt2e_quantize.value
+        )
         quantizers.append(vulkan_quantizer)
     logging.info(f"Applying quantizers: {quantizers}")
     return pt2e_quant_params, quantizers, quant_dtype
@@ -1033,7 +1048,7 @@ def _export_llama(llm_config: LlmConfig) -> LLMEdgeManager:  # noqa: C901
     )
 
     additional_passes = []
-    if llm_config.base.model_class in TORCHTUNE_DEFINED_MODELS:
+    if llm_config.base.model_class.value in TORCHTUNE_DEFINED_MODELS:
         additional_passes = [InitializedMutableBufferPass(["kv_cache_pos"])]
 
     # export_to_edge
@@ -1072,14 +1087,22 @@ def _export_llama(llm_config: LlmConfig) -> LLMEdgeManager:  # noqa: C901
             mps=llm_config.backend.mps.enabled,
             coreml=llm_config.backend.coreml.enabled,
             qnn=llm_config.backend.qnn.enabled,
-            dtype_override=llm_config.model.dtype_override,
+            dtype_override=llm_config.model.dtype_override.value,
             enable_dynamic_shape=llm_config.model.enable_dynamic_shape,
             use_kv_cache=llm_config.model.use_kv_cache,
             embedding_quantize=llm_config.quantization.embedding_quantize,
-            pt2e_quantize=llm_config.quantization.pt2e_quantize,
+            pt2e_quantize=(
+                llm_config.quantization.pt2e_quantize.value
+                if llm_config.quantization.pt2e_quantize
+                else None
+            ),
             coreml_ios=llm_config.backend.coreml.ios,
-            coreml_quantize=llm_config.backend.coreml.quantize,
-            coreml_compute_units=llm_config.backend.coreml.compute_units,
+            coreml_quantize=(
+                llm_config.backend.coreml.quantize.value
+                if llm_config.backend.coreml.quantize
+                else None
+            ),
+            coreml_compute_units=llm_config.backend.coreml.compute_units.value,
             use_qnn_sha=llm_config.backend.qnn.use_sha,
             num_sharding=llm_config.backend.qnn.num_sharding,
             soc_model=llm_config.backend.qnn.soc_model,
@@ -1152,7 +1175,7 @@ def _load_llama_model(llm_config: LlmConfig) -> "LLMEdgeManager":
         An instance of LLMEdgeManager which contains the eager mode model.
     """
 
-    modelname = llm_config.base.model_class
+    modelname = llm_config.base.model_class.value
     if modelname in EXECUTORCH_DEFINED_MODELS:
         module_name = "llama"
         model_class_name = "Llama2Model"  # TODO: Change to "LlamaModel" in examples/models/llama/model.py.
@@ -1173,7 +1196,7 @@ def _load_llama_model(llm_config: LlmConfig) -> "LLMEdgeManager":
         )
     )
     # Convert dtype override string to actual type.
-    dtype_override = DType[llm_config.model.dtype_override]
+    dtype_override = DType[llm_config.model.dtype_override.value]
 
     return LLMEdgeManager(
         model=model,

examples/models/llama/model.py

@@ -157,7 +157,7 @@ def __init__(self, llm_config: Optional[LlmConfig] = None):
 
         if model_args.use_scaled_rope:
             # Older models don't have use_scaled_rope configuration
-            model_name = str(self.llm_config.base.model_class)
+            model_name = self.llm_config.base.model_class.value
             assert model_name not in ["llama2", "stories110m"]
 
             # Llama3_2 and newer models in ExecuTorch repo should set larger scale factor
@@ -328,10 +328,10 @@ def get_example_inputs_kvcache_sdpa(self):
 
     def _transform_for_pre_quantization(self, checkpoint, model_args):
         assert self.llm_config.base.preq_mode, "preq_mode must be specified"
-        assert self.llm_config.base.preq_mode in [
+        assert self.llm_config.base.preq_mode.value in [
             "8da4w",
             "8da4w_output_8da8w",
-        ], f"Quantization mode {self.llm_config.base.preq_mode} is not compatible with SpinQuant."
+        ], f"Quantization mode {self.llm_config.base.preq_mode.value} is not compatible with SpinQuant."
         assert self.llm_config.base.preq_group_size, "preq_group_size must be specified"
         assert self.llm_config.model.dtype_override, "dtype_override must be specified"
 
@@ -351,22 +351,22 @@ def _transform_for_pre_quantization(self, checkpoint, model_args):
         }
 
         # Transform the output layer first if needed.
-        if self.llm_config.base.preq_mode == "8da4w_output_8da8w":
+        if self.llm_config.base.preq_mode.value == "8da4w_output_8da8w":
             from .source_transformation.pre_quantization import (
                 transform_output_linear_for_pre_quantization,
             )
 
             self.model_ = transform_output_linear_for_pre_quantization(
                 module=self.model_,
                 checkpoint=checkpoint,
-                dtype=mapping[self.llm_config.model.dtype_override],
+                dtype=mapping[self.llm_config.model.dtype_override.value],
             )
 
         self.model_ = transform_linear_for_pre_quantization(
             self.model_,
             checkpoint,
             self.llm_config.base.preq_group_size,
-            mapping[self.llm_config.model.dtype_override],
+            mapping[self.llm_config.model.dtype_override.value],
         )
 
         embedding_bit_width, embedding_group_size = None, None
@@ -390,7 +390,7 @@ def _transform_for_pre_quantization(self, checkpoint, model_args):
             self.model_ = transform_embedding_for_pre_quantization(
                 self.model_,
                 checkpoint,
-                mapping[self.llm_config.model.dtype_override],
+                mapping[self.llm_config.model.dtype_override.value],
                 int(embedding_bit_width),
                 embedding_group_size,
             )

extension/llm/export/test/test_export_llm.py

@@ -51,9 +51,20 @@ def test_with_config(self, mock_export_llama: MagicMock) -> None:
             f.write(
                 """
 base:
+  model_class: llama2
   tokenizer_path: /path/to/tokenizer.json
+  preq_mode: preq_8da4w
+model:
+  dtype_override: fp16
 export:
   max_seq_length: 256
+quantization:
+  pt2e_quantize: xnnpack_dynamic
+  use_spin_quant: cuda
+backend:
+  coreml:
+    quantize: c4w
+    compute_units: cpu_and_gpu
 """
             )
             config_file = f.name
@@ -69,7 +80,22 @@ def test_with_config(self, mock_export_llama: MagicMock) -> None:
             self.assertEqual(
                 called_config["base"]["tokenizer_path"], "/path/to/tokenizer.json"
             )
+            self.assertEqual(called_config["base"]["model_class"], "llama2")
+            self.assertEqual(called_config["base"]["preq_mode"].value, "8da4w")
+            self.assertEqual(called_config["model"]["dtype_override"].value, "fp16")
             self.assertEqual(called_config["export"]["max_seq_length"], 256)
+            self.assertEqual(
+                called_config["quantization"]["pt2e_quantize"].value, "xnnpack_dynamic"
+            )
+            self.assertEqual(
+                called_config["quantization"]["use_spin_quant"].value, "cuda"
+            )
+            self.assertEqual(
+                called_config["backend"]["coreml"]["quantize"].value, "c4w"
+            )
+            self.assertEqual(
+                called_config["backend"]["coreml"]["compute_units"].value, "cpu_and_gpu"
+            )
         finally:
             os.unlink(config_file)