Refact method of grad store (#2687)

colossalai/zero/sharded_optim/bookkeeping/gradient_store.py

@@ -6,7 +6,6 @@ from .base_store import BaseStore
 
 
 class GradientStore(BaseStore):
-
     def __init__(self, *args):
         super().__init__(*args)
         # bookkeeping data structures
@@ -15,7 +14,7 @@ class GradientStore(BaseStore):
         # for backward reduction hooks
         self._grad_acc_objs = []
 
-    def add_accumulate_grad_object(self, obj):
+    def append_accumulate_grad_object(self, obj):
         """
         Keep :class:`AccumulateGrad` objects. If these objects are not kept, reduction hooks may not
         be attached successfully.
@@ -36,10 +35,12 @@ class GradientStore(BaseStore):
         :return: Return the list of averaged gradients of a parameter group. Each element is a gradient, not a parameter.
         :rtype: List[torch.Tensor]
         """
+        if group_id not in self._averaged_gradients:
+            self._averaged_gradients[group_id] = []
 
         return self._averaged_gradients[group_id]
 
-    def add_average_gradient_by_group(self, group_id: int, tensor: Tensor) -> None:
+    def append_average_gradient_by_group(self, group_id: int, tensor: Tensor) -> None:
         """
         Append an average gradient to the list of averaged gradients of a parameter group
 
@@ -55,6 +56,22 @@ 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:
+        """
+        Add an average gradient to the list of averaged gradients of a parameter group
+
+        :param group_id: The index of a parameter group
+        :param tensor_idx: The index of a tensor in the list of averaged gradients
+        :param tensor: A :class:`torch.Tensor` object
+        :type group_id: int
+        :type tensor_idx: int
+        :type tensor: torch.Tensor
+
+        """
+        self._averaged_gradients[group_id][tensor_idx].add_(tensor)
+
     def reset_average_gradients_by_group(self, group_id: int) -> None:
         """
         Reset the bookkeeping data structure for averaged gradients to an empty list

colossalai/zero/sharded_optim/low_level_optim.py

@@ -550,20 +550,24 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
             reduction_states[tensor] = False
 
         # accumulate gradient
-        avg_gradients = self._grad_store._averaged_gradients
         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)
 
-            if group_id not in avg_gradients:
+            avg_gradients_group = self._grad_store.get_averaged_gradients_by_group(
-                avg_gradients[group_id] = []
+                group_id
+            )
 
             param_idx = 0
             for param in param_group:
                 if param.grad is not None:
-                    if len(avg_gradients[group_id]) == param_idx:
+                    if len(avg_gradients_group) == param_idx:
-                        avg_gradients[group_id].append(param.grad)
+                        self._grad_store.append_average_gradient_by_group(
+                            group_id, param.grad
+                        )
                     else:
-                        avg_gradients[group_id][param_idx].add_(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
@@ -590,4 +594,4 @@ 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)
+            self._run_reduction(reduce_rank)