Revert "[API Compatiblity] Add Optimizer.load_state_dict and Optimizer.zero_g…" (PaddlePaddle#76550)

This reverts commit 820d38a.

Author: zhanghonggeng

python/paddle/optimizer/optimizer.py

@@ -512,8 +512,6 @@ def set_state_dict(self, state_dict: dict[str, Tensor]) -> None:
                         )
                 var.set_value(state_dict[var_tmp.name])
 
-    load_state_dict = set_state_dict
-
     def get_opti_var_name_list(self) -> list[str]:
         return self._opti_name_list
 
@@ -1893,10 +1891,6 @@ def clear_grad(self, set_to_zero: bool = True) -> None:
         for p in param_list:
             p.clear_gradient(set_to_zero)
 
-    @framework.non_static_only
-    def zero_grad(self, set_to_none: bool = True) -> None:
-        self.clear_grad(set_to_zero=not set_to_none)
-
     @imperative_base.no_grad()
     def minimize(
         self,

test/legacy_test/test_optimizer.py

@@ -28,17 +28,6 @@
 from paddle.io import Dataset
 
 
-class Model(paddle.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = paddle.nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = paddle.sum(x)
-        return x
-
-
 class TestOptimizerDtype(unittest.TestCase):
     '''
     The dtype of optimizer should be inferred by parameters, and the learning rate
@@ -175,36 +164,6 @@ def test_with_state_dict(self):
             np.testing.assert_array_equal(out_use_state_dict, out_no_state_dict)
 
 
-class TestNewOptimizerAPI(unittest.TestCase):
-    def test_zero_grad(self):
-        paddle.disable_static()
-        model = Model()
-        data_input = paddle.randn(64, 1, 28, 28)
-        data_output = model(data_input)
-        data_output.backward()
-        sgd = paddle.optimizer.SGD(
-            parameters=model.parameters(), weight_decay=0.0
-        )
-        sgd.zero_grad()
-
-        result = model.conv1.weight.grad
-        self.assertIsNone(result)
-
-    def test_load_state_dict(self):
-        paddle.disable_static()
-        theta = paddle.tensor([1.0, 1.0], requires_grad=True)
-        optim = paddle.optimizer.Optimizer(
-            parameters=[theta], **{"learning_rate": 1.0}
-        )
-        result_before = optim.state_dict()
-        optim.load_state_dict(result_before)
-        result_after = optim.state_dict()
-        self.assertEqual(
-            set(result_before.keys()),
-            set(result_after.keys()),
-        )
-
-
 if __name__ == '__main__':
     paddle.enable_static()
     unittest.main()