[ADD] Draft exponential moving average for KFAC

curvlinops/kfac.py

@@ -1233,3 +1233,75 @@ def from_state_dict(
                 kfac.to_device(old_device)
 
         return kfac
+
+    def exponential_moving_average_(
+        self, other: KFACLinearOperator, beta: float
+    ) -> None:
+        """Incorporate the Kronecker matrices of another KFAC using EMA.
+
+        Updates the Kronecker factors of the current KFAC linear operator in-place.
+
+        Args:
+            other: The other KFAC linear operator whose Kronecker factors are
+                incorporated.
+            beta: The EMA decay factor. Must be in ``[0, 1]``. ``1`` means the
+                Kronecker factors are not updated, and ``0`` means the Kronecker factors
+                are replaced by the other KFAC's Kronecker factors.
+
+        Raises:
+            ValueError: If ``beta`` is not in ``[0, 1]``.
+            ValueError: If the Kronecker factors have incompatible data formats.
+
+        """
+        if not 0 <= beta <= 1:
+            raise ValueError("Beta must be in [0, 1].")
+
+        # make sure both KFACs have computed the Kronecker matrices
+        if not self._input_covariances and not self._gradient_covariances:
+            self._compute_kfac()
+        if not other._input_covariances and not other._gradient_covariances:
+            other._compute_kfac()
+
+        # make sure the Kronecker matrices have the same keys and shapes
+        keys = set(self._input_covariances.keys())
+        other_keys = set(other._input_covariances.keys())
+        if keys != other_keys:
+            raise ValueError(
+                f"Input covariance keys do not match: {keys} != {other_keys}."
+            )
+        for key in keys:
+            aaT = self._input_covariances[key]
+            aaT_other = other._input_covariances[key]
+            if aaT.shape != aaT_other.shape:
+                raise ValueError(
+                    f"Input covariance shapes for {key!r} do not match:"
+                    f" {aaT.shape} != {aaT_other.shape}."
+                )
+
+        keys = set(self._gradient_covariances.keys())
+        other_keys = set(other._gradient_covariances.keys())
+        if keys != other_keys:
+            raise ValueError(
+                f"Gradient covariance keys do not match: {keys} != {other_keys}."
+            )
+        for key in keys:
+            ggT = self._gradient_covariances[key]
+            ggT_other = other._gradient_covariances[key]
+            if ggT.shape != ggT_other.shape:
+                raise ValueError(
+                    f"Gradient covariance shapes for {key!r} do not match:"
+                    f" {ggT.shape} != {ggT_other.shape}."
+                )
+
+        # apply exponential moving average
+        if self._input_covariances and self._gradient_covariances:
+            for key in self._input_covariances.keys():
+                self._input_covariances[key].mul_(beta).add_(
+                    other._input_covariances[key], alpha=1 - beta
+                )
+            for key in self._gradient_covariances.keys():
+                self._gradient_covariances[key].mul_(beta).add_(
+                    other._gradient_covariances[key], alpha=1 - beta
+                )
+
+        self._reset_matrix_properties()