Export lora weights to sep file

.ci/scripts/test_llama_lora.sh

@@ -95,7 +95,9 @@ else
 fi
 
 # Export LoRA PTE, PTD file.
-MODEL_SEPARATE="${MODEL_NAME}_separate"
+MODEL_PROGRAM_ONLY="${MODEL_NAME}_program"
+MODEL_LORA_WEIGHTS="lora_weights"
+MODEL_FOUNDATION_WEIGHTS="foundation_weights"
 $PYTHON_EXECUTABLE -m extension.llm.export.export_llm \
     base.checkpoint="${DOWNLOADED_PATH}/consolidated.00.pth" \
     base.params="${DOWNLOADED_PATH}/params.json" \
@@ -107,14 +109,15 @@ $PYTHON_EXECUTABLE -m extension.llm.export.export_llm \
     model.dtype_override="fp32" \
     backend.xnnpack.enabled=true \
     backend.xnnpack.extended_ops=true \
-    export.output_name="${MODEL_SEPARATE}.pte" \
-    export.foundation_weights_file="${MODEL_SEPARATE}.ptd"
+    export.output_name="${MODEL_PROGRAM_ONLY}.pte" \
+    export.foundation_weights_file="${MODEL_FOUNDATION_WEIGHTS}.ptd" \
+    export.lora_weights_file="${MODEL_LORA_WEIGHTS}.ptd"
 
 # Run llama runner.
 NOW=$(date +"%H:%M:%S")
 echo "Starting to run llama runner at ${NOW}"
 # shellcheck source=/dev/null
-cmake-out/examples/models/llama/llama_main --model_path=${MODEL_SEPARATE}.pte --data_path=${MODEL_SEPARATE}.ptd --prompt="${PROMPT}" ${RUNTIME_ARGS} > result2.txt
+cmake-out/examples/models/llama/llama_main --model_path=${MODEL_PROGRAM_ONLY}.pte --data_paths="${MODEL_FOUNDATION_WEIGHTS}.ptd,${MODEL_LORA_WEIGHTS}.ptd" --prompt="${PROMPT}" ${RUNTIME_ARGS} > result2.txt
 NOW=$(date +"%H:%M:%S")
 echo "Finished at ${NOW}"
 

examples/models/llama/export_llama_lib.py

@@ -1088,13 +1088,15 @@ def _export_llama(llm_config: LlmConfig) -> LLMEdgeManager:  # noqa: C901
         llm_config.backend.xnnpack.enabled = True
 
     if llm_config.backend.xnnpack.enabled:
-        if llm_config.export.foundation_weights_file is not None:
+        if (
+            llm_config.export.foundation_weights_file is not None
+            or llm_config.export.lora_weights_file is not None
+        ):
             gen_tag_fn: Callable[[torch.fx.Node], Optional[str]] = lambda x: (
                 llm_config.export.foundation_weights_file
                 if "lora" not in x.name
-                else None
+                else llm_config.export.lora_weights_file
             )
-
             from executorch.exir.passes.external_constants_pass import (
                 delegate_external_constants_pass_unlifted,
                 external_constants_pass,

examples/models/llama/main.cpp

@@ -8,6 +8,8 @@
  */
 
 #include <gflags/gflags.h>
+#include <sstream>
+#include <vector>
 
 #include <executorch/examples/models/llama/runner/runner.h>
 
@@ -21,7 +23,10 @@ DEFINE_string(
     "llama2.pte",
     "Model serialized in flatbuffer format.");
 
-DEFINE_string(data_path, "", "Data file for the model.");
+DEFINE_string(
+    data_paths,
+    "",
+    "Data files for the model. If multiple files are provided, they should be comma separated.");
 
 DEFINE_string(tokenizer_path, "tokenizer.bin", "Tokenizer stuff.");
 
@@ -54,6 +59,26 @@ DEFINE_int32(
 
 DEFINE_bool(warmup, false, "Whether to run a warmup run.");
 
+// Helper function to parse comma-separated string lists
+std::vector<std::string> parseStringList(const std::string& input) {
+  std::vector<std::string> result;
+  if (input.empty()) {
+    return result;
+  }
+
+  std::stringstream ss(input);
+  std::string item;
+  while (std::getline(ss, item, ',')) {
+    // Trim whitespace
+    item.erase(0, item.find_first_not_of(" \t"));
+    item.erase(item.find_last_not_of(" \t") + 1);
+    if (!item.empty()) {
+      result.push_back(item);
+    }
+  }
+  return result;
+}
+
 int32_t main(int32_t argc, char** argv) {
   gflags::ParseCommandLineFlags(&argc, &argv, true);
 
@@ -62,10 +87,7 @@ int32_t main(int32_t argc, char** argv) {
   // and users can create their own DataLoaders to load from arbitrary sources.
   const char* model_path = FLAGS_model_path.c_str();
 
-  std::optional<std::string> data_path = std::nullopt;
-  if (!FLAGS_data_path.empty()) {
-    data_path = FLAGS_data_path.c_str();
-  }
+  std::vector<std::string> data_paths = parseStringList(FLAGS_data_paths);
 
   const char* tokenizer_path = FLAGS_tokenizer_path.c_str();
 
@@ -92,7 +114,7 @@ int32_t main(int32_t argc, char** argv) {
 #endif
   // create llama runner
   std::unique_ptr<::executorch::extension::llm::TextLLMRunner> runner =
-      example::create_llama_runner(model_path, tokenizer_path, data_path);
+      example::create_llama_runner(model_path, tokenizer_path, data_paths);
 
   if (runner == nullptr) {
     ET_LOG(Error, "Failed to create llama runner");

extension/llm/export/config/llm_config.py

@@ -215,9 +215,10 @@ class ExportConfig:
         so_library: Shared library to specify custom quantized operators.
         export_only: Whether to stop right after torch.export() and
             just save the exported .pt2 graph file.
-        foundation_weights_file: configure the foundation weights of a model
-            to be placed in a separate file, external to the PTE. Pass the
-            intended file name here.
+        foundation_weights_file: place the foundation weights of the model into
+            a separate file, external to the PTE. Pass the file name here.
+        lora_weights_file: place the lora weights of the model into a
+            separate file, external to the PTE. Pass the file name here.
     """
 
     max_seq_length: int = 128
@@ -227,6 +228,7 @@ class ExportConfig:
     so_library: Optional[str] = None
     export_only: bool = False
     foundation_weights_file: Optional[str] = None
+    lora_weights_file: Optional[str] = None
 
     def __post_init__(self):
         if self.max_context_length < self.max_seq_length:
@@ -572,6 +574,8 @@ def from_args(cls, args: argparse.Namespace) -> "LlmConfig":  # noqa: C901
             llm_config.export.export_only = args.export_only
         if hasattr(args, "foundation_weights_file"):
             llm_config.export.foundation_weights_file = args.foundation_weights_file
+        if hasattr(args, "lora_weights_file"):
+            llm_config.export.lora_weights_file = args.lora_weights_file
 
         # QuantizationConfig
         if hasattr(args, "quantization_mode"):