Export a lora model

Pull Request resolved: #11045

^

Program+data combined currently, using the lora linear definition.
ghstack-source-id: 298178476

Differential Revision: [D75153377](https://our.internmc.facebook.com/intern/diff/D75153377/)

Author: lucylq

backends/xnnpack/operators/node_visitor.py

@@ -622,9 +622,10 @@ def get_serialized_buffer_index(
         )
 
         external_tag = tensor.meta.get("delegate_constant_tag", None)
-        logging.info(
-            f"Adding constant data with name {tensor.name}, key {named_key} and external_tag {external_tag} to named_data_store"
-        )
+        if external_tag is not None:
+            logging.info(
+                f"Adding constant data with name {tensor.name}, key {named_key} and external_tag {external_tag} to named_data_store"
+            )
         self._named_data_store.add_named_data(
             named_key,
             bytes(array),

examples/models/llama/export_llama_lib.py

@@ -239,6 +239,18 @@ def build_args_parser() -> argparse.ArgumentParser:
         help="checkpoint directory. Use with a sharded checkpoint, not for the standard llama2 model. Note, checkpoint_dir takes precedence over checkpoint if both are set.",
     )
 
+    parser.add_argument(
+        "--adapter_checkpoint",
+        required=False,
+        help="Path to the adapter.pt file from torchtune. Used if the model has trained LoRA adapters. Must provide adapter_config.json",
+    )
+
+    parser.add_argument(
+        "--adapter_config",
+        required=False,
+        help="Path to the adapter_config.json file. Used if the model has trained LoRA adapters. Must provide adapter_checkpoint.",
+    )
+
     parser.add_argument(
         "--use_qnn_sha",
         action="store_true",

examples/models/llama/model.py

@@ -46,6 +46,13 @@ def __init__(self, llm_config: Optional[LlmConfig] = None):
         checkpoint_dir = self.llm_config.base.checkpoint_dir
         params_path = self.llm_config.base.params
 
+        # Adapter checkpoint and config.
+        adapter_checkpoint_path = self.llm_config.base.adapter_checkpoint
+        adapter_config_path = self.llm_config.base.adapter_config
+        assert (adapter_checkpoint_path is None and adapter_config_path is None) or (
+            adapter_checkpoint_path is not None and adapter_config_path is not None
+        ), "Both adapter_checkpoint_path and adapter_config_path must be specified or neither must be specified."
+
         self.use_kv_cache = self.llm_config.model.use_kv_cache
         self.use_sdpa_with_kv_cache_op = self.llm_config.model.use_sdpa_with_kv_cache
         self.generate_full_logits = self.llm_config.debug.generate_full_logits
@@ -129,6 +136,20 @@ def __init__(self, llm_config: Optional[LlmConfig] = None):
             with open(params_path, "r") as f:
                 params = json.loads(f.read())
 
+        # Get adapter checkpoint and config.
+        adapter_checkpoint = {}
+        adapter_config = {}
+        if adapter_checkpoint_path:
+            adapter_checkpoint = torch.load(
+                adapter_checkpoint_path, map_location=device, mmap=True
+            )
+            from torchtune.models import convert_weights
+
+            adapter_checkpoint = convert_weights.tune_to_meta(adapter_checkpoint)
+            with open(adapter_config_path, "r") as f:
+                adapter_config = json.loads(f.read())
+            checkpoint.update(adapter_checkpoint)
+
         output_prune_map = None
         if self.output_prune_map_path is not None:
             with open(self.output_prune_map_path, "r") as f:
@@ -153,6 +174,7 @@ def __init__(self, llm_config: Optional[LlmConfig] = None):
             output_prune_map=output_prune_map,
             enable_dynamic_shape=self.enable_dynamic_shape,
             **params,
+            **adapter_config,
         )
 
         if model_args.use_scaled_rope:

examples/models/llama/model_args.py

@@ -59,7 +59,7 @@ class ModelArgs:
     lora_args: Optional[dict] = None
 
     # LoRA arguments to set up a LoRA inference model.
-    # These arguments come directly from a torchtune LoRA config.
+    # These arguments come directly from a torchtune adapter_config.json file.
     r: Optional[int] = None  # Rank.
     lora_alpha: Optional[int] = None  # Alpha.
     # Eg. q_proj, k_proj, v_proj, output_proj

extension/llm/export/config/llm_config.py

@@ -73,10 +73,16 @@ class BaseConfig:
             if it is a Llama model or the weights will be downloaded from HuggingFace
             if it is a non-Llama model.
         checkpoint_dir: Path to directory containing sharded checkpoint files.
+        adapter_checkpoint: Path to the adapter.pt file from torchtune. Used if
+            the model has trained LoRA adapters. Must provide
+            adapter_config.json.
+        adapter_config: Path to the adapter_config.json file from torchtune.
+            Used if the model has trained LoRA adapters. Must provide adapter.pt.
         tokenizer_path: Path to the tokenizer file.
         metadata: Json string containing metadata information.
             e.g. '"{\"get_bos_id\":128000, \"get_eos_ids\":[128009, 128001]}"'
-        use_lora: Rank of the LoRA, if set to 0 then this means no LoRA. For use with QAT.
+        use_lora: Only for use with QAT. Rank of the LoRA adapter, disabled
+            if set to 0.
         fairseq2: For legacy internal use cases, this is safe to ignore.
         preq_mode: Legacy option to specify how prequantized weights are loaded.
             Going forward, ExecuTorch supports loading weights prequantized through
@@ -90,6 +96,8 @@ class BaseConfig:
     params: Optional[str] = None
     checkpoint: Optional[str] = None
     checkpoint_dir: Optional[str] = None
+    adapter_checkpoint: Optional[str] = None
+    adapter_config: Optional[str] = None
     tokenizer_path: Optional[str] = None
     metadata: Optional[str] = None
     use_lora: int = 0