Feature/SK-852 | Add numpy raw binary helper

fedn/network/combiner/aggregators/fedavg.py

@@ -1,9 +1,11 @@
+import traceback
+
 from fedn.common.log_config import logger
 from fedn.network.combiner.aggregators.aggregatorbase import AggregatorBase
 
 
 class Aggregator(AggregatorBase):
-    """ Local SGD / Federated Averaging (FedAvg) aggregator. Computes a weighted mean
+    """Local SGD / Federated Averaging (FedAvg) aggregator. Computes a weighted mean
         of parameter updates.
 
     :param id: A reference to id of :class: `fedn.network.combiner.Combiner`
@@ -48,8 +50,7 @@ def combine_models(self, helper=None, delete_models=True, parameters=None):
         nr_aggregated_models = 0
         total_examples = 0
 
-        logger.info(
-            "AGGREGATOR({}): Aggregating model updates... ".format(self.name))
+        logger.info("AGGREGATOR({}): Aggregating model updates... ".format(self.name))
 
         while not self.model_updates.empty():
             try:
@@ -61,28 +62,26 @@ def combine_models(self, helper=None, delete_models=True, parameters=None):
                 logger.info("AGGREGATOR({}): Loading model metadata {}.".format(self.name, model_update.model_update_id))
                 model_next, metadata = self.load_model_update(model_update, helper)
 
-                logger.info(
-                    "AGGREGATOR({}): Processing model update {}, metadata: {}  ".format(self.name, model_update.model_update_id, metadata))
+                logger.info("AGGREGATOR({}): Processing model update {}, metadata: {}  ".format(self.name, model_update.model_update_id, metadata))
 
                 # Increment total number of examples
                 total_examples += metadata["num_examples"]
 
                 if nr_aggregated_models == 0:
                     model = model_next
                 else:
-                    model = helper.increment_average(
-                        model, model_next, metadata["num_examples"], total_examples)
+                    model = helper.increment_average(model, model_next, metadata["num_examples"], total_examples)
 
                 nr_aggregated_models += 1
                 # Delete model from storage
                 if delete_models:
                     self.modelservice.temp_model_storage.delete(model_update.model_update_id)
-                    logger.info(
-                        "AGGREGATOR({}): Deleted model update {} from storage.".format(self.name, model_update.model_update_id))
+                    logger.info("AGGREGATOR({}): Deleted model update {} from storage.".format(self.name, model_update.model_update_id))
                 self.model_updates.task_done()
             except Exception as e:
-                logger.error(
-                    "AGGREGATOR({}): Error encoutered while processing model update {}, skipping this update.".format(self.name, e))
+                tb = traceback.format_exc()
+                logger.error(f"AGGREGATOR({self.name}): Error encoutered while processing model update: {e}")
+                logger.error(tb)
                 self.model_updates.task_done()
 
         data["nr_aggregated_models"] = nr_aggregated_models

fedn/utils/helpers/helperbase.py

@@ -1,5 +1,3 @@
-import os
-import tempfile
 from abc import ABC, abstractmethod
 
 
@@ -40,12 +38,3 @@ def load(self, fh):
         :return: Weights in array-like format.
         """
         pass
-
-    def get_tmp_path(self):
-        """Return a temporary output path compatible with save_model, load_model.
-
-        :return: Path to file.
-        """
-        fd, path = tempfile.mkstemp(suffix=".npz")
-        os.close(fd)
-        return path

fedn/utils/helpers/plugins/binaryhelper.py

@@ -0,0 +1,16 @@
+from fedn.utils.helpers.plugins.numpyhelper import Helper
+
+
+class Helper(Helper):
+    """FEDn helper class for models weights/parameters that can be transformed to numpy ndarrays."""
+
+    def __init__(self):
+        """Initialize helper."""
+        super().__init__()
+        self.name = "binaryhelper"
+
+    def load(self, path, file_type="raw_binary"):
+        return super().load(path, file_type)
+
+    def save(self, model, path=None, file_type="raw_binary"):
+        return super().save(model, path, file_type)

fedn/utils/helpers/plugins/numpyhelper.py

@@ -1,3 +1,7 @@
+import os
+import tempfile
+from io import BytesIO
+
 import numpy as np
 
 from fedn.utils.helpers.helperbase import HelperBase
@@ -137,33 +141,72 @@ def ones(self, m1, a):
             res.append(np.ones(np.shape(x)) * a)
         return res
 
-    def save(self, weights, path=None):
+    def save(self, weights, path=None, file_type="npz"):
         """Serialize weights to file. The serialized model must be a single binary object.
 
         :param weights: List of weights in numpy format.
         :param path: Path to file.
+        :param file_type: File type to save to. Can be 'npz' or 'raw_binary'. Default is 'npz'.
         :return: Path to file.
         """
-        if not path:
-            path = self.get_tmp_path()
+        self.check_supported_file_type(file_type)
+
+        if file_type == "npz":
+            if not path:
+                path = self.get_tmp_path()
+
+            weights_dict = {}
+            for i, w in enumerate(weights):
+                weights_dict[str(i)] = w
+
+            np.savez_compressed(path, **weights_dict)
+            return path
+        else:
+            if not path:
+                path = self.get_tmp_path(suffix=".bin")
+            weights = np.concatenate(weights)
+            weights.tofile(path)
+            return path
+
+    def load(self, path, file_type="npz"):
+        """Load weights from file or filelike.
 
-        weights_dict = {}
-        for i, w in enumerate(weights):
-            weights_dict[str(i)] = w
+        :param path: file path, filehandle, filelike.
+        :return: List of weights in numpy format.
+        """
+        self.check_supported_file_type(file_type)
+        weights = []
+        if file_type == "npz":
+            a = np.load(path)
+            for i in range(len(a.files)):
+                weights.append(a[str(i)])
+        else:
+            if isinstance(path, BytesIO):
+                a = np.frombuffer(path.read(), dtype=np.float64)
+            else:
+                a = np.fromfile(path, dtype=np.float64)
+            weights.append(a)
+        return weights
 
-        np.savez_compressed(path, **weights_dict)
+    def get_tmp_path(self, suffix=".npz"):
+        """Return a temporary output path compatible with save_model, load_model.
 
+        :param suffix: File suffix.
+        :return: Path to file.
+        """
+        fd, path = tempfile.mkstemp(suffix=suffix)
+        os.close(fd)
         return path
 
-    def load(self, fh):
-        """Load weights from file or filelike.
+    def check_supported_file_type(self, file_type):
+        """Check if the file type is supported.
 
-        :param fh: file path, filehandle, filelike.
-        :return: List of weights in numpy format.
+        :param file_type: File type to check.
+        :type file_type: str
+        :return: True if supported, False otherwise.
+        :rtype: bool
         """
-        a = np.load(fh)
-
-        weights = []
-        for i in range(len(a.files)):
-            weights.append(a[str(i)])
-        return weights
+        supported_file_types = ["npz", "raw_binary"]
+        if file_type not in supported_file_types:
+            raise ValueError("File type not supported. Supported types are: {}".format(supported_file_types))
+        return True