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