Interleave optimizer variable creation to restore backward-compatibility

keras/src/optimizers/adadelta.py

@@ -75,11 +75,10 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._accumulated_grads = self.add_optimizer_variables(
-            var_list, "accumulated_grad"
-        )
-        self._accumulated_delta_vars = self.add_optimizer_variables(
-            var_list, "accumulated_delta_var"
+        self._accumulated_grads, self._accumulated_delta_vars = (
+            self.add_optimizer_variables(
+                var_list, ["accumulated_grad", "accumulated_delta_var"]
+            )
         )
 
     def update_step(self, grad, variable, learning_rate):

keras/src/optimizers/adafactor.py

@@ -1,3 +1,4 @@
+from keras.src import backend
 from keras.src import ops
 from keras.src.api_export import keras_export
 from keras.src.optimizers import optimizer
@@ -96,11 +97,16 @@ def build(self, var_list):
         self._c = []
         self._v = []
         for var in var_list:
-            if (
-                self._overwrite_variable_with_gradient(var)
-                or len(var.shape) < 2
-            ):
-                # Don't factor if variable is of dimension < 2.
+            if len(var.shape) < 2:
+                # Don't factor if variable is of dimension < 2, but we still
+                # need to create dummy variables as placeholder.
+                self._r.append(
+                    backend.Variable(0, name=var.name, trainable=False)
+                )
+                self._c.append(
+                    backend.Variable(0, name=var.name, trainable=False)
+                )
+            elif self._overwrite_variable_with_gradient(var):
                 self._r.append(None)
                 self._c.append(None)
             else:

keras/src/optimizers/adam.py

@@ -90,8 +90,9 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._momentums = self.add_optimizer_variables(var_list, "momentum")
-        self._velocities = self.add_optimizer_variables(var_list, "velocity")
+        self._momentums, self._velocities = self.add_optimizer_variables(
+            var_list, ["momentum", "velocity"]
+        )
 
         if self.amsgrad:
             self._velocity_hats = self.add_optimizer_variables(

keras/src/optimizers/adamax.py

@@ -98,8 +98,9 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._m = self.add_optimizer_variables(var_list, "momentum")
-        self._u = self.add_optimizer_variables(var_list, "norm")
+        self._m, self._u = self.add_optimizer_variables(
+            var_list, ["momentum", "norm"]
+        )
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/base_optimizer.py

@@ -338,29 +338,64 @@ def add_optimizer_variables(
         Args:
             trainable_variables: `keras.Variable`, the corresponding model
                 variable to the optimizer variable to be created.
-            name: The name prefix of the optimizer variable to be created. The
-                variable name will follow the pattern
+            name: The name prefix(es) of the optimizer variable(s) to be
+                created. Can be a single string or list of strings.  If a
+                list of strings, will create an optimizer variable for each
+                prefix.  The variable name will follow the pattern
                 `{variable_name}_{trainable_variable.name}`, e.g.,
-                `momemtum/dense_1`. Defaults to `None`.
-            initializer: Initializer object to use to populate the initial
-                variable value, or string name of a built-in initializer (e.g.
-                `"random_normal"`). If unspecified, defaults to `"zeros"`.
+                `momemtum/dense_1`.
+            initializer: Initializer object(s) to use to populate the initial
+                variable value(s), or string name of a built-in initializer
+                (e.g. `"random_normal"`). If unspecified, defaults to
+                `"zeros"`.
 
         Returns:
             A list of optimizer variables, in the format of `keras.Variable`s.
+            If multiple names are provide, returns a tuple of lists.
         """
-        optimizer_variables = []
+        name_list = name
+        initializer_list = initializer
+        if isinstance(name, str):
+            # Single name/initializer.
+            name_list = [name]
+            initializer_list = [initializer]
+        else:
+            # Multiple names/initializers.
+            # If there is only one initializer, use it for all names.
+            if isinstance(initializer, str) or isinstance(
+                initializer, initializers.Initializer
+            ):
+                initializer_list = [initializer] * len(name_list)
+
+        if len(name_list) != len(initializer_list):
+            raise ValueError(
+                f"The number of provided names must match the number of "
+                f"provided initializers.  Received name='{name}', "
+                f"initializer='{initializer}'"
+            )
+
+        # Build up lists of optimizer variables.
+        optimizer_variables = tuple([] for _ in name_list)
         for variable in trainable_variables:
+            # Interleaves adding variables for backward-compatibility.
             if not self._overwrite_variable_with_gradient(variable):
-                optimizer_variables.append(
-                    self.add_variable_from_reference(
-                        variable,
-                        name=name,
-                        initializer=initializer,
+                for i, (var_name, var_init) in enumerate(
+                    zip(name_list, initializer_list)
+                ):
+                    optimizer_variables[i].append(
+                        self.add_variable_from_reference(
+                            variable,
+                            name=var_name,
+                            initializer=var_init,
+                        )
                     )
-                )
             else:
-                optimizer_variables.append(None)
+                for i in range(len(name_list)):
+                    optimizer_variables[i].append(None)
+
+        # If single input name, return the single list.
+        if isinstance(name, str):
+            return optimizer_variables[0]
 
         return optimizer_variables
 

keras/src/optimizers/ftrl.py

@@ -162,10 +162,11 @@ def build(self, var_list):
         accumulator_initializer = initializers.Constant(
             self.initial_accumulator_value,
         )
-        self._accumulators = self.add_optimizer_variables(
-            var_list, "accumulator", initializer=accumulator_initializer
+        self._accumulators, self._linears = self.add_optimizer_variables(
+            var_list,
+            ["accumulator", "linear"],
+            initializer=[accumulator_initializer, "zeros"],
         )
-        self._linears = self.add_optimizer_variables(var_list, "linear")
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/lamb.py

@@ -82,8 +82,9 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._momentums = self.add_optimizer_variables(var_list, "momentum")
-        self._velocities = self.add_optimizer_variables(var_list, "velocity")
+        self._momentums, self._velocities = self.add_optimizer_variables(
+            var_list, ["momentum", "velocity"]
+        )
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/nadam.py

@@ -87,8 +87,9 @@ def build(self, var_list):
         else:
             dtype = backend.floatx()
         super().build(var_list)
-        self._momentums = self.add_optimizer_variables(var_list, "momentum")
-        self._velocities = self.add_optimizer_variables(var_list, "velocity")
+        self._momentums, self._velocities = self.add_optimizer_variables(
+            var_list, ["momentum", "velocity"]
+        )
         self._u_product = backend.Variable(1.0, dtype=dtype)
 
     def _backend_update_step(self, grads, trainable_variables, learning_rate):