Support exporting > 2Gb transformer models (#1514)

* initial commit

* initial commit

* Delete helpers.py

* cleanup

* fix an error in the logic

* focus on opt when it comes to tasks

* initial commit

* Delete model.py

* cleanup

* Apply suggestions from code review

src/sparseml/transformers/export.py

@@ -91,12 +91,13 @@
 __all__ = ["export_transformer_to_onnx", "load_task_model"]
 
 MODEL_ONNX_NAME = "model.onnx"
-DEPLOYMENT_FILES: List[str] = [
+EXTERNAL_ONNX_DATA_NAME = "model.data"
+MANDATORY_DEPLOYMENT_FILES: List[str] = [
     MODEL_ONNX_NAME,
-    "tokenizer.json",
     "tokenizer_config.json",
     "config.json",
 ]
+OPTIONAL_DEPLOYMENT_FILES: List[str] = [EXTERNAL_ONNX_DATA_NAME, "tokenizer.json"]
 
 _LOGGER = logging.getLogger(__name__)
 
@@ -403,7 +404,9 @@ def create_deployment_folder(
 
     if deployment_files is None:
         # set deployment files to default values
-        deployment_files = copy.deepcopy(DEPLOYMENT_FILES)
+        deployment_files = copy.deepcopy(
+            MANDATORY_DEPLOYMENT_FILES + OPTIONAL_DEPLOYMENT_FILES
+        )
         if onnx_file_name != MODEL_ONNX_NAME:
             # replace the default onnx model name with the custom one
             deployment_files[deployment_files.index(MODEL_ONNX_NAME)] = onnx_file_name
@@ -418,6 +421,12 @@ def create_deployment_folder(
         expected_file_path = os.path.join(training_directory, file_name)
         deployment_file_path = os.path.join(deployment_folder_dir, file_name)
         if not os.path.exists(expected_file_path):
+            if file_name in OPTIONAL_DEPLOYMENT_FILES:
+                _LOGGER.warning(
+                    f"Optional file {file_name} not found in {training_directory}. "
+                    f"Skipping copying to deployment folder."
+                )
+                continue
             raise ValueError(
                 f"Attempting to copy {file_name} file from {expected_file_path},"
                 f"but the file does not exits. Make sure that {training_directory} "
@@ -426,6 +435,9 @@ def create_deployment_folder(
         if file_name == MODEL_ONNX_NAME:
             # moving onnx file from training to deployment directory
             shutil.move(expected_file_path, deployment_file_path)
+        elif file_name == EXTERNAL_ONNX_DATA_NAME:
+            # moving external onnx tensors from training to deployment directory
+            shutil.move(expected_file_path, deployment_file_path)
         else:
             # copying remaining `deployment_files` from training to deployment directory
             shutil.copyfile(expected_file_path, deployment_file_path)