Merge branch 'main' into records

doc/source/contributor-tutorial-contribute-on-github.rst

@@ -357,23 +357,31 @@ Changelog entry
 
 When opening a new PR, inside its description, there should be a ``Changelog entry`` header.
 
-As per the comment above this section::
+Above this header you should see the following comment that explains how to write your changelog entry:
 
     Inside the following 'Changelog entry' section, 
     you should put the description of your changes that will be added to the changelog alongside your PR title.
 
     If the section is completely empty (without any token), 
     the changelog will just contain the title of the PR for the changelog entry, without any description. 
+
     If the 'Changelog entry' section is removed entirely, 
     it will categorize the PR as "General improvement" and add it to the changelog accordingly. 
+
     If the section contains some text other than tokens, it will use it to add a description to the change. 
+
     If the section contains one of the following tokens it will ignore any other text and put the PR under the corresponding section of the changelog:
 
     <general> is for classifying a PR as a general improvement.
+
     <skip> is to not add the PR to the changelog
+
     <baselines> is to add a general baselines change to the PR
+
     <examples> is to add a general examples change to the PR
+
     <sdk> is to add a general sdk change to the PR
+
     <simulations> is to add a general simulations change to the PR
 
     Note that only one token should be used.

src/py/flwr/common/configsrecord.py

@@ -25,7 +25,6 @@
 class ConfigsRecord:
     """Configs record."""
 
-    keep_input: bool
     data: Dict[str, ConfigsRecordValues] = field(default_factory=dict)
 
     def __init__(
@@ -47,12 +46,13 @@ def __init__(
             to True, the data is duplicated in memory. If memory is a concern, set
             it to False.
         """
-        self.keep_input = keep_input
         self.data = {}
         if configs_dict:
-            self.set_configs(configs_dict)
+            self.set_configs(configs_dict, keep_input=keep_input)
 
-    def set_configs(self, configs_dict: Dict[str, ConfigsRecordValues]) -> None:
+    def set_configs(
+        self, configs_dict: Dict[str, ConfigsRecordValues], keep_input: bool = True
+    ) -> None:
         """Add configs to the record.
 
         Parameters
@@ -61,6 +61,11 @@ def set_configs(self, configs_dict: Dict[str, ConfigsRecordValues]) -> None:
             A dictionary that stores basic types (i.e. `str`,`int`, `float`, `bytes` as
             defined in `ConfigsRecordValues`) and list of such types (see
             `ConfigsScalarList`).
+        keep_input : bool (default: True)
+            A boolean indicating whether config passed should be deleted from the input
+            dictionary immediately after adding them to the record. When set
+            to True, the data is duplicated in memory. If memory is a concern, set
+            it to False.
         """
         if any(not isinstance(k, str) for k in configs_dict.keys()):
             raise TypeError(f"Not all keys are of valid type. Expected {str}")
@@ -88,11 +93,15 @@ def is_valid(value: ConfigsScalar) -> None:
                 is_valid(value)
 
         # Add configs to record
-        if self.keep_input:
+        if keep_input:
             # Copy
             self.data = configs_dict.copy()
         else:
             # Add entries to dataclass without duplicating memory
             for key in list(configs_dict.keys()):
                 self.data[key] = configs_dict[key]
                 del configs_dict[key]
+
+    def __getitem__(self, key: str) -> ConfigsRecordValues:
+        """Retrieve an element stored in record."""
+        return self.data[key]

src/py/flwr/common/metricsrecord.py

@@ -25,7 +25,6 @@
 class MetricsRecord:
     """Metrics record."""
 
-    keep_input: bool
     data: Dict[str, MetricsRecordValues] = field(default_factory=dict)
 
     def __init__(
@@ -46,19 +45,25 @@ def __init__(
             to True, the data is duplicated in memory. If memory is a concern, set
             it to False.
         """
-        self.keep_input = keep_input
         self.data = {}
         if metrics_dict:
-            self.set_metrics(metrics_dict)
+            self.set_metrics(metrics_dict, keep_input=keep_input)
 
-    def set_metrics(self, metrics_dict: Dict[str, MetricsRecordValues]) -> None:
+    def set_metrics(
+        self, metrics_dict: Dict[str, MetricsRecordValues], keep_input: bool = True
+    ) -> None:
         """Add metrics to the record.
 
         Parameters
         ----------
         metrics_dict : Dict[str, MetricsRecordValues]
             A dictionary that stores basic types (i.e. `int`, `float` as defined
             in `MetricsScalar`) and list of such types (see `MetricsScalarList`).
+        keep_input : bool (default: True)
+            A boolean indicating whether metrics should be deleted from the input
+            dictionary immediately after adding them to the record. When set
+            to True, the data is duplicated in memory. If memory is a concern, set
+            it to False.
         """
         if any(not isinstance(k, str) for k in metrics_dict.keys()):
             raise TypeError(f"Not all keys are of valid type. Expected {str}.")
@@ -86,11 +91,15 @@ def is_valid(value: MetricsScalar) -> None:
                 is_valid(value)
 
         # Add metrics to record
-        if self.keep_input:
+        if keep_input:
             # Copy
             self.data = metrics_dict.copy()
         else:
             # Add entries to dataclass without duplicating memory
             for key in list(metrics_dict.keys()):
                 self.data[key] = metrics_dict[key]
                 del metrics_dict[key]
+
+    def __getitem__(self, key: str) -> MetricsRecordValues:
+        """Retrieve an element stored in record."""
+        return self.data[key]

src/py/flwr/common/parametersrecord.py

@@ -59,7 +59,6 @@ class ParametersRecord:
     PyTorch's state_dict, but holding serialised tensors instead.
     """
 
-    keep_input: bool
     data: OrderedDict[str, Array] = field(default_factory=OrderedDict[str, Array])
 
     def __init__(
@@ -82,29 +81,37 @@ def __init__(
             parameters after adding it to the record, set this flag to True. When set
             to True, the data is duplicated in memory.
         """
-        self.keep_input = keep_input
         self.data = OrderedDict()
         if array_dict:
-            self.set_parameters(array_dict)
+            self.set_parameters(array_dict, keep_input=keep_input)
 
-    def set_parameters(self, array_dict: OrderedDict[str, Array]) -> None:
+    def set_parameters(
+        self, array_dict: OrderedDict[str, Array], keep_input: bool = False
+    ) -> None:
         """Add parameters to record.
 
         Parameters
         ----------
         array_dict : OrderedDict[str, Array]
             A dictionary that stores serialized array-like or tensor-like objects.
+        keep_input : bool (default: False)
+            A boolean indicating whether parameters should be deleted from the input
+            dictionary immediately after adding them to the record.
         """
         if any(not isinstance(k, str) for k in array_dict.keys()):
             raise TypeError(f"Not all keys are of valid type. Expected {str}")
         if any(not isinstance(v, Array) for v in array_dict.values()):
             raise TypeError(f"Not all values are of valid type. Expected {Array}")
 
-        if self.keep_input:
+        if keep_input:
             # Copy
             self.data = OrderedDict(array_dict)
         else:
             # Add entries to dataclass without duplicating memory
             for key in list(array_dict.keys()):
                 self.data[key] = array_dict[key]
                 del array_dict[key]
+
+    def __getitem__(self, key: str) -> Array:
+        """Retrieve an element stored in record."""
+        return self.data[key]

src/py/flwr/common/recordset_test.py

@@ -111,7 +111,7 @@ def test_set_parameters_while_keeping_intputs() -> None:
     array_dict = OrderedDict(
         {str(i): ndarray_to_array(ndarray) for i, ndarray in enumerate(get_ndarrays())}
     )
-    p_record.set_parameters(array_dict)
+    p_record.set_parameters(array_dict, keep_input=True)
 
     # Creating a second parametersrecord passing the same `array_dict` (not erased)
     p_record_2 = ParametersRecord(array_dict)
@@ -253,7 +253,7 @@ def test_set_metrics_to_metricsrecord_with_and_without_keeping_input(
     my_metrics_copy = my_metrics.copy()
 
     # Add metric
-    m_record.set_metrics(my_metrics)
+    m_record.set_metrics(my_metrics, keep_input=keep_input)
 
     # Check metrics are actually added
     # Check that input dict has been emptied when enabled such behaviour