Don't create unused optimizer variables.

keras/src/backend/torch/optimizers/torch_parallel_optimizer.py

@@ -15,7 +15,9 @@ def _backend_update_step(self, grads, trainable_variables, learning_rate):
 
     @torch_utils.no_grad
     def _backend_reset_gradient_accumulators(self):
-        acc_list = [v.value for v in self._accumulated_gradients]
+        acc_list = [
+            v.value for v in self._accumulated_gradients if v is not None
+        ]
         torch._foreach_mul_(acc_list, 0.0)
 
     @torch_utils.no_grad

keras/src/optimizers/adadelta.py

@@ -75,15 +75,12 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._accumulated_grads = []
-        self._accumulated_delta_vars = []
-        for var in var_list:
-            self._accumulated_grads.append(
-                self.add_variable_from_reference(var, "accumulated_grad")
-            )
-            self._accumulated_delta_vars.append(
-                self.add_variable_from_reference(var, "accumulated_delta_var")
-            )
+        self._accumulated_grads = self.add_optimizer_variables(
+            var_list, "accumulated_grad"
+        )
+        self._accumulated_delta_vars = self.add_optimizer_variables(
+            var_list, "accumulated_delta_var"
+        )
 
     def update_step(self, grad, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/adafactor.py

@@ -1,4 +1,3 @@
-from keras.src import backend
 from keras.src import ops
 from keras.src.api_export import keras_export
 from keras.src.optimizers import optimizer
@@ -97,16 +96,13 @@ def build(self, var_list):
         self._c = []
         self._v = []
         for var in var_list:
-            if len(var.shape) < 2:
-                # Don't factor if variable is of dimension < 2, but we still
-                # need to create dummy variables as placeholder.
-                with backend.name_scope(self.name, caller=self):
-                    self._r.append(
-                        backend.Variable(0, name=var.name, trainable=False)
-                    )
-                    self._c.append(
-                        backend.Variable(0, name=var.name, trainable=False)
-                    )
+            if (
+                self._overwrite_variable_with_gradient(var)
+                or len(var.shape) < 2
+            ):
+                # Don't factor if variable is of dimension < 2.
+                self._r.append(None)
+                self._c.append(None)
             else:
                 # Always factor the last 2 dimensions.
                 r_shape = var.shape[:-1]
@@ -125,11 +121,15 @@ def build(self, var_list):
                         name=var.name,
                     )
                 )
-            self._v.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="velocity"
+
+            if self._overwrite_variable_with_gradient(var):
+                self._v.append(None)
+            else:
+                self._v.append(
+                    self.add_variable_from_reference(
+                        reference_variable=var, name="velocity"
+                    )
                 )
-            )
 
     def _rms(self, x):
         return ops.sqrt(ops.mean(ops.square(x)))

keras/src/optimizers/adagrad.py

@@ -70,17 +70,10 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._accumulators = []
         initializer = initializers.Constant(self.initial_accumulator_value)
-        for var in var_list:
-            self._accumulators.append(
-                self.add_variable(
-                    shape=var.shape,
-                    initializer=initializer,
-                    dtype=var.dtype,
-                    name="accumulator",
-                )
-            )
+        self._accumulators = self.add_optimizer_variables(
+            var_list, "accumulator", initializer=initializer
+        )
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/adam.py

@@ -90,27 +90,13 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._momentums = []
-        self._velocities = []
-        for var in var_list:
-            self._momentums.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="momentum"
-                )
-            )
-            self._velocities.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="velocity"
-                )
-            )
+        self._momentums = self.add_optimizer_variables(var_list, "momentum")
+        self._velocities = self.add_optimizer_variables(var_list, "velocity")
+
         if self.amsgrad:
-            self._velocity_hats = []
-            for var in var_list:
-                self._velocity_hats.append(
-                    self.add_variable_from_reference(
-                        reference_variable=var, name="velocity_hat"
-                    )
-                )
+            self._velocity_hats = self.add_optimizer_variables(
+                var_list, "velocity_hat"
+            )
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/adamax.py

@@ -98,19 +98,8 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._m = []
-        self._u = []
-        for var in var_list:
-            self._m.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="momentum"
-                )
-            )
-            self._u.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="norm"
-                )
-            )
+        self._m = self.add_optimizer_variables(var_list, "momentum")
+        self._u = self.add_optimizer_variables(var_list, "norm")
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/base_optimizer.py

@@ -204,21 +204,19 @@ def iterations(self):
     def _track_variable(self, variable):
         self._tracker.add_to_store("variables", variable)
 
+    def _overwrite_variable_with_gradient(self, variable):
+        return getattr(variable, "overwrite_with_gradient", False)
+
     @tracking.no_automatic_dependency_tracking
     def build(self, variables):
         if self.use_ema:
-            self._model_variables_moving_average = []
+            self._model_variables_moving_average = self.add_optimizer_variables(
+                variables, "average"
+            )
         if self.gradient_accumulation_steps:
             self._accumulated_gradients = []
         for i, variable in enumerate(variables):
             self._trainable_variables_indices[self._var_key(variable)] = i
-            if self.use_ema:
-                self._model_variables_moving_average.append(
-                    self.add_variable_from_reference(
-                        variable,
-                        name="average",
-                    )
-                )
             if self.gradient_accumulation_steps:
                 self._accumulated_gradients.append(
                     self.add_variable_from_reference(
@@ -323,6 +321,49 @@ def add_variable_from_reference(
             name=name,
         )
 
+    def add_optimizer_variables(
+        self, trainable_variables, name, initializer="zeros"
+    ):
+        """Add optimizer variables from the list of trainable model variables.
+
+        Create an optimizer variable based on the information of the supplied
+        model variables.  For example, in SGD optimizer momemtum, for each model
+        variable, a corresponding momemtum variable is created of the same shape
+        and dtype.
+
+        Note that trainable variables with `v.overwrite_with_gradient == True`
+        will insert `None`, into the output list, since the optimizer variable
+        will not be used anyways, and could be wasteful.
+
+        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
+                `{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"`.
+
+        Returns:
+            A list of optimizer variables, in the format of `keras.Variable`s.
+        """
+        optimizer_variables = []
+        for variable in trainable_variables:
+            if not self._overwrite_variable_with_gradient(variable):
+                optimizer_variables.append(
+                    self.add_variable_from_reference(
+                        variable,
+                        name=name,
+                        initializer=initializer,
+                    )
+                )
+            else:
+                optimizer_variables.append(None)
+
+        return optimizer_variables
+
     def _check_variables_are_known(self, variables):
         for v in variables:
             if self._var_key(v) not in self._trainable_variables_indices:
@@ -544,7 +585,8 @@ def _backend_update_step(self, grads, trainable_variables, learning_rate):
 
     def _backend_reset_gradient_accumulators(self):
         for g_acc in self._accumulated_gradients:
-            g_acc.assign(ops.zeros(g_acc.shape, dtype=g_acc.dtype))
+            if g_acc is not None:
+                g_acc.assign(ops.zeros(g_acc.shape, dtype=g_acc.dtype))
 
     def _backend_increment_gradient_accumulators(self, grads, acc_grads):
         new_g_accs = [(g + acc_g) for g, acc_g in zip(grads, acc_grads)]
@@ -711,8 +753,8 @@ def _overwrite_variables_directly_with_gradients(self, grads, vars):
         After the update, the processed pairs will be filtered out.
         """
         # Shortcut for `tf.Variable` because it doesn't have a
-        # `overwrite_with_gradient` attr
-        if any(not hasattr(v, "overwrite_with_gradient") for v in vars):
+        # `overwrite_with_gradient` attr.
+        if not any(self._overwrite_variable_with_gradient(v) for v in vars):
             return grads, vars
 
         # Shallow copies
@@ -722,7 +764,7 @@ def _overwrite_variables_directly_with_gradients(self, grads, vars):
         # Iterate from right to left for safe popping
         for i in range(len(filtered_grads) - 1, -1, -1):
             g, v = filtered_grads[i], filtered_vars[i]
-            if v.overwrite_with_gradient:
+            if self._overwrite_variable_with_gradient(v):
                 if self.gradient_accumulation_steps:
                     # Utilize a stateless manner for JAX compatibility
                     steps = self.gradient_accumulation_steps
@@ -886,11 +928,12 @@ def _update_model_variables_moving_average(self, trainable_variables):
             for var, average in zip(
                 trainable_variables, self._model_variables_moving_average
             ):
-                not_first_step = ops.not_equal(self.iterations, 0)
-                momentum = (
-                    ops.cast(not_first_step, var.dtype) * self.ema_momentum
-                )
-                average.assign(momentum * average + (1 - momentum) * var)
+                if average is not None:
+                    not_first_step = ops.not_equal(self.iterations, 0)
+                    momentum = (
+                        ops.cast(not_first_step, var.dtype) * self.ema_momentum
+                    )
+                    average.assign(momentum * average + (1 - momentum) * var)
 
     def _overwrite_model_variables_with_average_value(
         self, trainable_variables
@@ -909,7 +952,8 @@ def _overwrite_model_variables_with_average_value(
         for var, average_var in zip(
             trainable_variables, self._model_variables_moving_average
         ):
-            var.assign(average_var)
+            if average_var is not None:
+                var.assign(average_var)
 
     def finalize_variable_values(self, var_list):
         """Set the final value of model's trainable variables.

keras/src/optimizers/ftrl.py

@@ -159,24 +159,13 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._accumulators = []
-        self._linears = []
-        for var in var_list:
-            self._accumulators.append(
-                self.add_variable(
-                    shape=var.shape,
-                    dtype=var.dtype,
-                    name="accumulator",
-                    initializer=initializers.Constant(
-                        self.initial_accumulator_value,
-                    ),
-                )
-            )
-            self._linears.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="linear"
-                )
-            )
+        accumulator_initializer = initializers.Constant(
+            self.initial_accumulator_value,
+        )
+        self._accumulators = self.add_optimizer_variables(
+            var_list, "accumulator", initializer=accumulator_initializer
+        )
+        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

@@ -82,19 +82,8 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._momentums = []
-        self._velocities = []
-        for var in var_list:
-            self._momentums.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="momentum"
-                )
-            )
-            self._velocities.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="velocity"
-                )
-            )
+        self._momentums = self.add_optimizer_variables(var_list, "momentum")
+        self._velocities = self.add_optimizer_variables(var_list, "velocity")
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/lion.py

@@ -91,13 +91,7 @@ def build(self, var_list):
         if self.built:
             return
         super().build(var_list)
-        self._momentums = []
-        for var in var_list:
-            self._momentums.append(
-                self.add_variable_from_reference(
-                    reference_variable=var, name="momentum"
-                )
-            )
+        self._momentums = self.add_optimizer_variables(var_list, "momentum")
 
     def update_step(self, gradient, variable, learning_rate):
         """Update step given gradient and the associated model variable."""

keras/src/optimizers/loss_scale_optimizer.py

@@ -102,12 +102,6 @@ def stateless_apply(self, optimizer_variables, grads, trainable_variables):
             ),
         )
 
-    def _overwrite_variable_with_gradient(self, variable):
-        return (
-            hasattr(variable, "overwrite_with_gradient")
-            and variable.overwrite_with_gradient
-        )
-
     def _stateless_handle_finite_grads(
         self, optimizer_variables, grads, trainable_variables
     ):