- extend plan initialize to have additional optional argument to take…

… init model path (pbuf format)

- added new function to utils.py
- rebased 21.Jan.1
- reduce cyclo-complexity of initialize function
- address review comments
- removed unused imports/vars
Signed-off-by: Shailesh Pant <[email protected]>

openfl/federated/plan/plan.py

@@ -777,3 +777,36 @@ def restore_object(self, filename):
             return None
         obj = serializer_plugin.restore_object(filename)
         return obj
+
+    def save_model_to_state_file(self, tensor_dict, round_number, output_path):
+        """Save model weights to a protobuf state file.
+
+        This method serializes the model weights into a protobuf format and saves
+        them to a file. The serialization is done using the tensor pipe to ensure
+        proper compression and formatting.
+
+        Args:
+            tensor_dict (dict): Dictionary containing model weights and their
+                corresponding tensors.
+            round_number (int): The current federation round number.
+            output_path (str): Path where the serialized model state will be
+                saved.
+
+        Raises:
+            Exception: If there is an error during model proto creation or saving
+                to file.
+        """
+        from openfl.protocols import utils  # Import here to avoid circular imports
+
+        # Get tensor pipe to properly serialize the weights
+        tensor_pipe = self.get_tensor_pipe()
+
+        # Create and save the protobuf message
+        try:
+            model_proto = utils.construct_model_proto(
+                tensor_dict=tensor_dict, round_number=round_number, tensor_pipe=tensor_pipe
+            )
+            utils.dump_proto(model_proto=model_proto, fpath=output_path)
+        except Exception as e:
+            self.logger.error(f"Failed to create or save model proto: {e}")
+            raise

openfl/interface/plan.py

@@ -95,6 +95,13 @@ def plan(context):
     help="Install packages listed under 'requirements.txt'. True/False [Default: True]",
     default=True,
 )
+@option(
+    "-i",
+    "--init_model_path",
+    required=False,
+    help="Path to initial model protobuf file",
+    type=ClickPath(exists=True),
+)
 def initialize(
     context,
     plan_config,
@@ -104,6 +111,7 @@ def initialize(
     input_shape,
     gandlf_config,
     install_reqs,
+    init_model_path,
 ):
     """Initialize Data Science plan.
 
@@ -119,6 +127,7 @@ def initialize(
         feature_shape (str): The input shape to the model.
         gandlf_config (str): GaNDLF Configuration File Path.
         install_reqs (bool): Whether to install packages listed under 'requirements.txt'.
+        init_model_path (str): Optional path to initialization model protobuf file.
     """
 
     for p in [plan_config, cols_config, data_config]:
@@ -133,29 +142,8 @@ def initialize(
         gandlf_config = Path(gandlf_config).absolute()
 
     if install_reqs:
-        requirements_filename = "requirements.txt"
-        requirements_path = Path(requirements_filename).absolute()
-
-        if isfile(f"{str(requirements_path)}"):
-            check_call(
-                [
-                    sys.executable,
-                    "-m",
-                    "pip",
-                    "install",
-                    "-r",
-                    f"{str(requirements_path)}",
-                ],
-                shell=False,
-            )
-            echo(f"Successfully installed packages from {requirements_path}.")
-
-            # Required to restart the process for newly installed packages to be recognized
-            args_restart = [arg for arg in sys.argv if not arg.startswith("--install_reqs")]
-            args_restart.append("--install_reqs=False")
-            os.execv(args_restart[0], args_restart)
-        else:
-            echo("No additional requirements for workspace defined. Skipping...")
+        requirements_path = Path("requirements.txt").absolute()
+        _handle_requirements_install(requirements_path)
 
     plan = Plan.parse(
         plan_config_path=plan_config,
@@ -165,21 +153,20 @@ def initialize(
     )
 
     init_state_path = plan.config["aggregator"]["settings"]["init_state_path"]
-
     # This is needed to bypass data being locally available
     if input_shape is not None:
         logger.info(
             f"Attempting to generate initial model weights with custom input shape {input_shape}"
         )
 
-    data_loader = get_dataloader(plan, prefer_minimal=True, input_shape=input_shape)
-
-    task_runner = plan.get_task_runner(data_loader)
-    tensor_pipe = plan.get_tensor_pipe()
+    # Initialize tensor dictionary
+    init_tensor_dict, task_runner, round_number = _initialize_tensor_dict(
+        plan, input_shape, init_model_path
+    )
 
     tensor_dict, holdout_params = split_tensor_dict_for_holdouts(
         logger,
-        task_runner.get_tensor_dict(False),
+        init_tensor_dict,
         **task_runner.tensor_dict_split_fn_kwargs,
     )
 
@@ -189,13 +176,15 @@ def initialize(
         f" values: {list(holdout_params.keys())}"
     )
 
-    model_snap = utils.construct_model_proto(
-        tensor_dict=tensor_dict, round_number=0, tensor_pipe=tensor_pipe
-    )
-
-    logger.info("Creating Initial Weights File    🠆 %s", init_state_path)
-
-    utils.dump_proto(model_proto=model_snap, fpath=init_state_path)
+    # Save the model state
+    try:
+        logger.info(f"Saving model state to {init_state_path}")
+        plan.save_model_to_state_file(
+            tensor_dict=tensor_dict, round_number=round_number, output_path=init_state_path
+        )
+    except Exception as e:
+        logger.error(f"Failed to save model state: {e}")
+        raise
 
     plan_origin = Plan.parse(
         plan_config_path=plan_config,
@@ -223,6 +212,63 @@ def initialize(
     logger.info(f"{context.obj['plans']}")
 
 
+def _handle_requirements_install(requirements_path):
+    """Handle the installation of requirements and process restart if needed.
+
+    This method checks if a requirements.txt file exists at the provided path.
+    If found, it installs the packages listed in the file using pip. After
+    successful installation, it restarts the current process with the same
+    arguments, but with the --install_reqs flag set to False to avoid
+    re-installing requirements.
+
+    If no requirements.txt file is found, it prints a message indicating that
+    no additional requirements are defined for the workspace and skips the
+    installation.
+
+    Args:
+        requirements_path (str or Path): The path to the requirements.txt file.
+    """
+    if isfile(str(requirements_path)):
+        check_call(
+            [sys.executable, "-m", "pip", "install", "-r", str(requirements_path)],
+            shell=False,
+        )
+        echo(f"Successfully installed packages from {requirements_path}.")
+
+        # Required to restart the process for newly installed packages to be recognized
+        args_restart = [arg for arg in sys.argv if not arg.startswith("--install_reqs")]
+        args_restart.append("--install_reqs=False")
+        os.execv(args_restart[0], args_restart)
+    else:
+        echo("No additional requirements for workspace defined. Skipping...")
+
+
+def _initialize_tensor_dict(plan, input_shape, init_model_path):
+    """Initialize and return the tensor dictionary.
+
+    Args:
+        plan: The federation plan object
+        input_shape: The input shape to the model
+        init_model_path: Path to initial model protobuf file
+
+    Returns:
+        Tuple of (tensor_dict, task_runner, round_number)
+    """
+    data_loader = get_dataloader(plan, prefer_minimal=True, input_shape=input_shape)
+    task_runner = plan.get_task_runner(data_loader)
+    tensor_pipe = plan.get_tensor_pipe()
+    round_number = 0
+
+    if init_model_path and isfile(init_model_path):
+        logger.info(f"Loading initial model from {init_model_path}")
+        model_proto = utils.load_proto(init_model_path)
+        init_tensor_dict, round_number = utils.deconstruct_model_proto(model_proto, tensor_pipe)
+    else:
+        init_tensor_dict = task_runner.get_tensor_dict(False)
+
+    return init_tensor_dict, task_runner, round_number
+
+
 # TODO: looks like Plan.method
 def freeze_plan(plan_config):
     """Dump the plan to YAML file.

openfl/protocols/utils.py

@@ -1,7 +1,6 @@
 # Copyright 2020-2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-
 """Proto utils."""
 
 from openfl.protocols import base_pb2