ColossalAI/colossalai/zero/sharded_optim/bookkeeping/parameter_store.py

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from typing import List, Optional
from torch import Tensor
from colossalai.tensor import ProcessGroup
from .base_store import BaseStore
class ParameterStore(BaseStore):
def __init__(self, pg: Optional[ProcessGroup] = None):
super().__init__(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()