Make multimethod generic in llm_config

examples/models/llama/export_llama_lib.py

@@ -897,15 +897,15 @@ def _validate_args(llm_config):
                 "Shared embedding is only supported with torchao quantization."
             )
 
-    if llm_config.multimethod_lora.enabled:
+    if llm_config.multimethod.enabled:
         if llm_config.base.lora_config is not None:
             raise ValueError(
-                "Cannot use both base.lora_config and multimethod_lora.methods. "
-                "Use multimethod_lora.methods for all LoRA variants."
+                "Cannot use both base.lora_config and multimethod.methods. "
+                "Use multimethod.methods for all LoRA variants."
             )
         if llm_config.quantization.pt2e_quantize is not None:
             raise ValueError(
-                "PT2E quantization is not supported with multimethod_lora export."
+                "PT2E quantization is not supported with multimethod export."
             )
         if (
             llm_config.backend.coreml.enabled
@@ -915,7 +915,7 @@ def _validate_args(llm_config):
             or llm_config.backend.openvino.enabled
         ):
             raise ValueError(
-                "multimethod_lora export only supports XNNPACK backend or portable ops"
+                "multimethod export only supports XNNPACK backend or portable ops. "
                 "Please disable other backends (coreml, vulkan, qnn, mps, openvino)."
             )
 
@@ -1230,7 +1230,7 @@ def _to_edge_and_lower_llama(  # noqa: C901
 
 
 def _get_xnnpack_partitioners(llm_config: LlmConfig) -> Optional[List[Partitioner]]:
-    """Get XNNPACK partitioners for multimethod_lora export."""
+    """Get XNNPACK partitioners for multimethod export."""
     partitioners = []
 
     # Order matters here, dynamic quantization should be applied first when
@@ -1268,20 +1268,20 @@ def _export_llama_multimethod(llm_config: LlmConfig) -> LLMEdgeManager:
     """
     Export multiple methods (base + LoRA variants) to a single .pte file.
 
-    For each method in llm_config.multimethod_lora.methods:
+    For each method in llm_config.multimethod.methods:
     - If LoraConfig is None: use base model
     - If LoraConfig is provided: create model with LoRA weights
 
     Limitations:
-    - Only XNNPACK backend is supported for multimethod_lora export.
+    - Only XNNPACK backend is supported for multimethod export.
     - PT2E quantization is not supported.
     - Each method is exported separately; export time scales linearly
       with the number of methods.
     - The final .pte file deduplicates shared weights automatically.
     """
-    num_methods = len(llm_config.multimethod_lora.methods)
+    num_methods = len(llm_config.multimethod.methods)
     logging.info(
-        f"multimethod_lora export: exporting {num_methods} method(s). "
+        f"multimethod export: exporting {num_methods} method(s). "
         "Each method requires separate model instantiation and export."
     )
 
@@ -1293,14 +1293,14 @@ def _export_llama_multimethod(llm_config: LlmConfig) -> LLMEdgeManager:
     method_to_program: Dict[str, ExportedProgram] = {}
     first_builder = None
 
-    for method_name, lora_config in llm_config.multimethod_lora.methods.items():
-        logging.info(f"Exporting method: {method_name}")
+    for method in llm_config.multimethod.methods:
+        logging.info(f"Exporting method: {method.method_name}")
 
         # Create a copy of config with this method's LoRA setting
         method_config = copy.deepcopy(llm_config)
-        method_config.base.lora_config = lora_config
-        # Disable multimethod_lora to avoid infinite recursion
-        method_config.multimethod_lora.methods = {}
+        method_config.base.lora_config = method.lora_config
+        # Disable multimethod to avoid infinite recursion
+        method_config.multimethod.methods = []
 
         # Load and prepare model for this method
         builder = _prepare_for_llama_export(method_config)
@@ -1309,7 +1309,7 @@ def _export_llama_multimethod(llm_config: LlmConfig) -> LLMEdgeManager:
 
         # Get the exported program
         exported_program = builder._export(builder.pre_autograd_graph_module)
-        method_to_program[method_name] = exported_program
+        method_to_program[method.method_name] = exported_program
 
         if first_builder is None:
             first_builder = builder
@@ -1319,7 +1319,7 @@ def _export_llama_multimethod(llm_config: LlmConfig) -> LLMEdgeManager:
     # Get partitioners based on backend config
     partitioners = _get_xnnpack_partitioners(llm_config)
 
-    # Lower all methods together using multimethod_lora API
+    # Lower all methods together using multimethod API
     edge_config = first_builder._get_edge_config()
     edge_manager = to_edge_transform_and_lower(
         method_to_program,
@@ -1333,7 +1333,7 @@ def _export_llama_multimethod(llm_config: LlmConfig) -> LLMEdgeManager:
     first_builder.edge_manager = edge_manager
     first_builder = first_builder.to_executorch(
         passes=additional_passes,
-        share_mutable_buffers=llm_config.multimethod_lora.share_mutable_buffers,
+        share_mutable_buffers=llm_config.multimethod.share_mutable_buffers,
     )
 
     output_file = _get_output_filename(
@@ -1350,8 +1350,8 @@ def _export_llama_multimethod(llm_config: LlmConfig) -> LLMEdgeManager:
 def _export_llama(llm_config: LlmConfig) -> LLMEdgeManager:  # noqa: C901
     _validate_args(llm_config)
 
-    # Check for multimethod_lora export
-    if llm_config.multimethod_lora.enabled:
+    # Check for multimethod export
+    if llm_config.multimethod.enabled:
         return _export_llama_multimethod(llm_config)
 
     pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(

examples/models/qwen3/config/qwen3_multimethod.yaml

@@ -22,12 +22,13 @@ quantization:
   qmode: "8da4w"
   group_size: 32
 
-multimethod_lora:
+multimethod:
   methods:
     # LoRA method - adapter paths from environment variables
-    lora_forward:
-      adapter_checkpoint: ${oc.env:LORA_ADAPTER_CHECKPOINT}
-      adapter_config: ${oc.env:LORA_ADAPTER_CONFIG}
+    - method_name: lora_forward
+      lora_config:
+        adapter_checkpoint: ${oc.env:LORA_ADAPTER_CHECKPOINT}
+        adapter_config: ${oc.env:LORA_ADAPTER_CONFIG}
     # Base method - no LoRA
-    base_forward: null
+    - method_name: base_forward
   share_mutable_buffers: True

extension/llm/export/config/llm_config.py

@@ -22,7 +22,7 @@
 import re
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import ClassVar, Dict, List, Optional
+from typing import ClassVar, List, Optional
 
 
 ################################################################################
@@ -293,37 +293,52 @@ class DebugConfig:
 
 
 ################################################################################
-############################## MultimethodLoraConfig ###########################
+############################## MultimethodConfig ###########################
 ################################################################################
 
 
 @dataclass
-class MultimethodLoraConfig:
+class MethodConfig:
+    """Configuration for exporting a single method to a .pte file.
+    By default, all other fields fall back to the default configs in
+    the yaml file.
+
+    Attributes:
+        method_name: Name of the method to export.
+        lora_config: Optional LoRA configuration.
+    """
+
+    method_name: str
+    lora_config: Optional[LoraConfig] = None
+
+
+@dataclass
+class MultimethodConfig:
     """Configuration for exporting multiple methods to a single .pte file.
 
-    Maps method names to optional LoRA configurations. A None value means
-    the method uses base model weights.
+    Holds a list of method configs, as well as global options that apply
+    across all methods.
 
     Attributes:
-        methods: Dict mapping method names to optional LoRA configs.
-            Empty dict disables multimethod_lora export.
+        methods: List of MethodConfig objects with method name and config
+            for each method.
         share_mutable_buffers: Whether to share mutable buffers across methods.
             If True, sets all mutable buffers to mem_id=2. Mutable buffers with
             the same FQN (fully qualified name) will have the same offset.
 
     Example:
-        MultimethodLoraConfig(methods={
-            "forward": None,  # base model
-            "lora_forward": lora_config,  # LoRA variant
-        })
+        MultimethodConfig(methods=[
+            MethodConfig("forward", lora_config=None),  # base model
+            MethodConfig("lora_forward", lora_config=lora_config),  # LoRA variant
+        ])
     """
 
-    methods: Dict[str, Optional[LoraConfig]] = field(default_factory=dict)
+    methods: List[MethodConfig] = field(default_factory=list)
     share_mutable_buffers: bool = False
 
     @property
     def enabled(self) -> bool:
-        """Returns True if multimethod_lora export is configured."""
+        """Returns True if multimethod export is configured."""
         return len(self.methods) > 0
 
 
@@ -611,9 +626,7 @@ class LlmConfig:
     model: ModelConfig = field(default_factory=ModelConfig)
     export: ExportConfig = field(default_factory=ExportConfig)
     debug: DebugConfig = field(default_factory=DebugConfig)
-    multimethod_lora: MultimethodLoraConfig = field(
-        default_factory=MultimethodLoraConfig
-    )
+    multimethod: MultimethodConfig = field(default_factory=MultimethodConfig)
     quantization: QuantizationConfig = field(default_factory=QuantizationConfig)
     backend: BackendConfig = field(default_factory=BackendConfig)