Remove step function

runtime/onert/api/python/package/experimental/train/optimizer/adam.py

@@ -18,28 +18,3 @@ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-7):
         self.beta1 = beta1
         self.beta2 = beta2
         self.epsilon = epsilon
-        self.m = None
-        self.v = None
-        self.t = 0
-
-    def step(self, gradients, parameters):
-        """
-        Update parameters using Adam optimization.
-        Args:
-            gradients (list): List of gradients for each parameter.
-            parameters (list): List of parameters to be updated.
-        """
-        if self.m is None:
-            self.m = [0] * len(parameters)
-        if self.v is None:
-            self.v = [0] * len(parameters)
-
-        self.t += 1
-        for i, (grad, param) in enumerate(zip(gradients, parameters)):
-            self.m[i] = self.beta1 * self.m[i] + (1 - self.beta1) * grad
-            self.v[i] = self.beta2 * self.v[i] + (1 - self.beta2) * (grad**2)
-
-            m_hat = self.m[i] / (1 - self.beta1**self.t)
-            v_hat = self.v[i] / (1 - self.beta2**self.t)
-
-            param -= self.learning_rate * m_hat / (v_hat**0.5 + self.epsilon)

runtime/onert/api/python/package/experimental/train/optimizer/optimizer.py

@@ -13,12 +13,3 @@ def __init__(self, learning_rate=0.001, nums_trainable_ops=trainable_ops.ALL):
         """
         self.learning_rate = learning_rate
         self.nums_trainable_ops = nums_trainable_ops
-
-    def step(self, gradients, parameters):
-        """
-        Update parameters based on gradients. Should be implemented by subclasses.
-        Args:
-            gradients (list): List of gradients for each parameter.
-            parameters (list): List of parameters to be updated.
-        """
-        raise NotImplementedError("Subclasses must implement the `step` method.")

runtime/onert/api/python/package/experimental/train/optimizer/sgd.py

@@ -19,18 +19,3 @@ def __init__(self, learning_rate=0.001, momentum=0.0):
                 "Momentum is not supported in the current version of SGD.")
         self.momentum = momentum
         self.velocity = None
-
-    def step(self, gradients, parameters):
-        """
-        Update parameters using SGD with optional momentum.
-        Args:
-            gradients (list): List of gradients for each parameter.
-            parameters (list): List of parameters to be updated.
-        """
-        if self.velocity is None:
-            self.velocity = [0] * len(parameters)
-
-        for i, (grad, param) in enumerate(zip(gradients, parameters)):
-            self.velocity[
-                i] = self.momentum * self.velocity[i] - self.learning_rate * grad
-            parameters[i] += self.velocity[i]