[zero] reorganize zero/gemini folder structure (#3424)

* [zero] refactor low-level zero folder structure

* [zero] fix legacy zero import path

* [zero] fix legacy zero import path

* [zero] remove useless import

* [zero] refactor gemini folder structure

* [zero] refactor gemini folder structure

* [zero] refactor legacy zero import path

* [zero] refactor gemini folder structure

* [zero] refactor gemini folder structure

* [zero] refactor gemini folder structure

* [zero] refactor legacy zero import path

* [zero] fix test import path

* [zero] fix test

* [zero] fix circular import

* [zero] update import

colossalai/zero/low_level/bookkeeping/parameter_store.py

@@ -0,0 +1,97 @@
+from typing import List
+
+from torch import Tensor
+from torch.distributed import ProcessGroup
+
+from .base_store import BaseStore
+
+
+class ParameterStore(BaseStore):
+
+    def __init__(self, torch_pg: ProcessGroup):
+        super().__init__(torch_pg)
+        # param partitioning data structures
+        self._fp16_param_to_rank = dict()
+        self._rank_groupid_to_fp16_param_list = dict()
+        self._rank_group_id_to_flat_fp16_param = dict()
+
+        # param reduction data structures
+        self._is_param_reduced = dict()
+        self._reduced_param = []
+
+    def set_param_to_rank(self, tensor: Tensor, rank: int) -> None:
+        """
+        Set the mapping between parameter to rank, each parameter should be owned by a rank.
+
+        :param tensor: A :class:`torch.Tensor` object
+        :type tensor: torch.Tensor
+        :param rank: The rank of which the process is responsible for updating the parameter
+        :type rank: int
+        """
+
+        self._fp16_param_to_rank[tensor] = rank
+
+    def get_param_rank(self, tensor: Tensor) -> int:
+        """
+        Gives the rank which the parameter belongs to
+
+        :param tensor: A :class:`torch.Tensor` object
+        :type tensor: torch.Tensor
+        """
+        return self._fp16_param_to_rank[tensor]
+
+    def belongs_to_current_rank(self, tensor) -> bool:
+        """
+        Check whether a parameter is supposed to be updated by the process of the current rank
+
+        :param tensor: A :class:`torch.Tensor` object
+        :type tensor: torch.Tensor
+
+        :return: True if the parameter should be updated by the current rank. Otherwise false.
+        :rtype: bool
+        """
+
+        tensor_rank = self._fp16_param_to_rank[tensor]
+        return tensor_rank == self._local_rank
+
+    def add_fp16_param_list_by_rank_group(self, rank, group_id, tensor_list) -> None:
+        if rank not in self._rank_groupid_to_fp16_param_list:
+            self._rank_groupid_to_fp16_param_list[rank] = dict()
+
+        if group_id not in self._rank_groupid_to_fp16_param_list[rank]:
+            self._rank_groupid_to_fp16_param_list[rank][group_id] = []
+
+        self._rank_groupid_to_fp16_param_list[rank][group_id].extend(tensor_list)
+
+    def get_fp16_params_by_rank_group(self, rank, group_id) -> List[Tensor]:
+        return self._rank_groupid_to_fp16_param_list[rank][group_id]
+
+    def add_flat_fp16_param_by_rank_group(self, rank, group_id, tensor) -> None:
+        if rank not in self._rank_group_id_to_flat_fp16_param:
+            self._rank_group_id_to_flat_fp16_param[rank] = dict()
+
+        self._rank_group_id_to_flat_fp16_param[rank][group_id] = tensor
+
+    def get_flat_fp16_param_by_rank_group(self, rank, group_id) -> Tensor:
+        return self._rank_group_id_to_flat_fp16_param[rank][group_id]
+
+    def is_param_reduced(self, tensor):
+        return self._is_param_reduced[tensor]
+
+    def set_param_reduction_state(self, tensor, state):
+        self._is_param_reduced[tensor] = state
+
+    def get_param_reduction_states(self):
+        return self._is_param_reduced
+
+    def reset_previous_reduced_params(self):
+        self._reduced_param = []
+
+    def add_previous_reduced_param(self, tensor):
+        self._reduced_param.append(tensor)
+
+    def clear_grads_of_previous_reduced_params(self):
+        if len(self._reduced_param) > 0:
+            for param in self._reduced_param:
+                param.grad = None
+            self.reset_previous_reduced_params()