[zero] trivial zero optimizer refactoring (#2869)

* Fix mionr grad store interface

* Apply lint

colossalai/zero/sharded_optim/bookkeeping/gradient_store.py

@@ -6,6 +6,7 @@ from .base_store import BaseStore
 
 
 class GradientStore(BaseStore):
+
     def __init__(self, *args):
         super().__init__(*args)
         # bookkeeping data structures
@@ -56,9 +57,7 @@ class GradientStore(BaseStore):
         else:
             self._averaged_gradients[group_id] = [tensor]
 
-    def add_average_gradient_by_group(
-        self, group_id: int, tensor_idx: int, tensor: Tensor
-    ) -> None:
+    def add_average_gradient_by_group(self, group_id: int, tensor_idx: int, tensor: Tensor) -> None:
         """
         Add an average gradient to the list of averaged gradients of a parameter group
 
@@ -81,3 +80,9 @@ class GradientStore(BaseStore):
         """
 
         self._averaged_gradients[group_id] = []
+
+    def reset_all_average_gradients(self) -> None:
+        """
+        Reset the bookkeeping data structure for averaged gradients to an empty list
+        """
+        self._averaged_gradients = dict()

colossalai/zero/sharded_optim/low_level_optim.py

@@ -416,7 +416,7 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
         :param set_to_none: Whether set the gradient to None. Default value is True.
         :type set_to_none: bool
         """
-        for group_id, param_group in self._fp16_param_groups.items():
+        for _, param_group in self._fp16_param_groups.items():
             for param in param_group:
                 if set_to_none:
                     param.grad = None
@@ -438,7 +438,7 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
 
         # update loss scale if overflow occurs
         if found_inf:
-            self._grad_store._averaged_gradients = dict()
+            self._grad_store.reset_all_average_gradients()
             self.zero_grad()
             return
 
@@ -448,7 +448,7 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
 
         for group_id in range(self.num_param_groups):
             # compute norm
-            norm_group = compute_norm(gradients=self._grad_store._averaged_gradients[group_id],
+            norm_group = compute_norm(gradients=self._grad_store.get_averaged_gradients_by_group(group_id),
                                       params=self._param_store.get_fp16_params_by_rank_group(group_id=group_id,
                                                                                              rank=self._local_rank),
                                       dp_group=self._dp_torch_group,
@@ -469,8 +469,7 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
             single_grad_partition_groups.append(flat_fp32_avg_grads)
             device = self._fp32_flat_param_groups_of_current_rank[group_id].device
             self._fp32_flat_param_groups_of_current_rank[group_id].grad = flat_fp32_avg_grads.to(device)
-            self._grad_store._averaged_gradients[group_id] = []
-            self._grad_store._averaged_gradients[group_id] = []
+            self._grad_store.reset_average_gradients_by_group(group_id)
 
         # unscale and clip grads
         global_norm = calculate_global_norm_from_list(norm_list=norm_groups)
@@ -546,28 +545,22 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
     def _sync_grad(self):
         # update param already reduced flag
         reduction_states = self._param_store.get_param_reduction_states()
-        for tensor, state in reduction_states.items():
+        for tensor, _ in reduction_states.items():
             reduction_states[tensor] = False
 
         # accumulate gradient
         for group_id in range(self.num_param_groups):
             param_group = self._param_store.get_fp16_params_by_rank_group(self._local_rank, group_id)
 
-            avg_gradients_group = self._grad_store.get_averaged_gradients_by_group(
-                group_id
-            )
+            avg_gradients_group = self._grad_store.get_averaged_gradients_by_group(group_id)
 
             param_idx = 0
             for param in param_group:
                 if param.grad is not None:
                     if len(avg_gradients_group) == param_idx:
-                        self._grad_store.append_average_gradient_by_group(
-                            group_id, param.grad
-                        )
+                        self._grad_store.append_average_gradient_by_group(group_id, param.grad)
                     else:
-                        self._grad_store.add_average_gradient_by_group(
-                            group_id, param_idx, param.grad
-                        )
+                        self._grad_store.add_average_gradient_by_group(group_id, param_idx, param.grad)
                     param_idx += 1
 
         # the gradients needed are stored in the avg_gradients buffer
@@ -594,4 +587,4 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
         # only need to reduce the gradients
         # left in the communication bucket
         for reduce_rank in range(self._world_size):
-            self._run_reduction(reduce_rank)
\ No newline at end of file
+            self._run_reduction(reduce_rank)