fix(mge/optimizer): remove distributed optimizer

GitOrigin-RevId: 3e5d0612f0301f5d8e17eb0722ffe1fbc11a3858

imperative/python/megengine/optimizer/adadelta.py

@@ -12,10 +12,10 @@ import numpy as np
 
 from ..functional import sqrt
 from ..tensor_nn import Buffer, Parameter
-from .distributed_optimizer import DistributedOptimizer
+from .optimizer import Optimizer
 
 
-class Adadelta(DistributedOptimizer):
+class Adadelta(Optimizer):
     r"""Implements Adadelta algorithm.
 
     It has been proposed in `"ADADELTA: An Adaptive Learning Rate Method" <https://arxiv.org/abs/1212.5701>`_.
@@ -38,7 +38,6 @@ class Adadelta(DistributedOptimizer):
         rho: float = 0.9,
         eps: float = 1e-6,
         weight_decay: float = 0.0,
-        **kwargs
     ):
         assert lr >= 0.0, "Invalid learning rate: {}".format(lr)
         assert rho >= 0.0 and rho <= 1.0, "Invalid rho value: {}".format(rho)
@@ -48,7 +47,7 @@ class Adadelta(DistributedOptimizer):
         )
 
         defaults = dict(lr=lr, rho=rho, eps=eps, weight_decay=weight_decay)
-        super().__init__(params, defaults, **kwargs)
+        super().__init__(params, defaults)
 
     def _create_state(self, param_group):
         for param in param_group["params"]:

imperative/python/megengine/optimizer/adagrad.py

@@ -12,10 +12,10 @@ import numpy as np
 
 from ..functional import sqrt
 from ..tensor_nn import Buffer, Parameter
-from .distributed_optimizer import DistributedOptimizer
+from .optimizer import Optimizer
 
 
-class Adagrad(DistributedOptimizer):
+class Adagrad(Optimizer):
     r"""Implements Adagrad algorithm.
 
     It has been proposed in `"Adaptive Subgradient Methods for Online Learning
@@ -38,7 +38,6 @@ class Adagrad(DistributedOptimizer):
         lr_decay: float = 0.0,
         eps: float = 1e-10,
         weight_decay: float = 0.0,
-        **kwargs
     ):
         assert lr >= 0.0, "Invalid learning rate: {}".format(lr)
         assert lr_decay >= 0, "Invalid learning rate decay: {}".format(lr_decay)
@@ -48,7 +47,7 @@ class Adagrad(DistributedOptimizer):
         )
 
         defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay)
-        super().__init__(params, defaults, **kwargs)
+        super().__init__(params, defaults)
 
     def _create_state(self, param_group):
         for param in param_group["params"]:

imperative/python/megengine/optimizer/adam.py

@@ -9,10 +9,10 @@
 from typing import Iterable, Tuple, Union
 
 from ..tensor_nn import Buffer, Parameter
-from .distributed_optimizer import DistributedOptimizer
+from .optimizer import Optimizer
 
 
-class Adam(DistributedOptimizer):
+class Adam(Optimizer):
     r"""Implements Adam algorithm proposed in `"Adam: A Method for Stochastic Optimization" <https://arxiv.org/abs/1412.6980>`_.
 
     :param params: iterable of parameters to optimize or dicts defining
@@ -32,7 +32,6 @@ class Adam(DistributedOptimizer):
         betas: Tuple[float, float] = (0.9, 0.999),
         eps: float = 1e-8,
         weight_decay: float = 0.0,
-        **kwargs
     ):
         if lr < 0.0:
             raise ValueError("Invalid learning rate: {}".format(lr))
@@ -44,7 +43,7 @@ class Adam(DistributedOptimizer):
             raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
 
         defaults = dict(lr=lr, weight_decay=weight_decay, betas=betas, eps=eps)
-        super().__init__(params, defaults, **kwargs)
+        super().__init__(params, defaults)
 
     def _create_state(self, param_group):
         for param in param_group["params"]:

imperative/python/megengine/optimizer/lr_scheduler.py

@@ -8,7 +8,7 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 from abc import ABCMeta
 
-from .distributed_optimizer import DistributedOptimizer
+from .optimizer import Optimizer
 
 
 class LRScheduler(metaclass=ABCMeta):
@@ -19,9 +19,9 @@ class LRScheduler(metaclass=ABCMeta):
     """
 
     def __init__(  # pylint: disable=too-many-branches
-        self, optimizer: DistributedOptimizer, current_epoch: int = -1
+        self, optimizer: Optimizer, current_epoch: int = -1
     ):
-        if not isinstance(optimizer, DistributedOptimizer):
+        if not isinstance(optimizer, Optimizer):
             raise TypeError(
                 "optimizer argument given to the lr_scheduler should be Optimizer"
             )

imperative/python/megengine/optimizer/multi_step_lr.py

@@ -9,7 +9,7 @@
 from bisect import bisect_right
 from typing import Iterable as Iter
 
-from .distributed_optimizer import DistributedOptimizer
+from .optimizer import Optimizer
 from .lr_scheduler import LRScheduler
 
 
@@ -25,7 +25,7 @@ class MultiStepLR(LRScheduler):
 
     def __init__(
         self,
-        optimizer: DistributedOptimizer,
+        optimizer: Optimizer,
         milestones: Iter[int],
         gamma: float = 0.1,
         current_epoch: int = -1,

imperative/python/megengine/optimizer/sgd.py

@@ -9,10 +9,10 @@
 from typing import Iterable, Union
 
 from ..tensor_nn import Buffer, Parameter
-from .distributed_optimizer import DistributedOptimizer
+from .optimizer import Optimizer
 
 
-class SGD(DistributedOptimizer):
+class SGD(Optimizer):
     r"""Implements stochastic gradient descent.
 
     Nesterov momentum is based on the formula from
@@ -31,7 +31,6 @@ class SGD(DistributedOptimizer):
         lr: float,
         momentum: float = 0.0,
         weight_decay: float = 0.0,
-        **kwargs
     ):
         assert lr >= 0.0, "Invalid learning rate: {}".format(lr)
         assert momentum >= 0.0, "Invalid momentum value: {}".format(momentum)
@@ -40,7 +39,7 @@ class SGD(DistributedOptimizer):
         )
 
         defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay)
-        super().__init__(params, defaults, **kwargs)
+        super().__init__(params, defaults)
 
     def _create_state(self, param_group):
         if param_group["momentum"] != 0.0: