mirror of https://github.com/hpcaitech/ColossalAI
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
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from torch.distributed import ProcessGroup
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from .base_store import BaseStore
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class BucketStore(BaseStore):
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def __init__(self, torch_pg: ProcessGroup):
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super().__init__(torch_pg)
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self._params = dict()
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self._num_elements_in_bucket = dict()
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self.reset()
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def num_elements_in_bucket(self, reduce_rank: int = None):
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return self._num_elements_in_bucket[reduce_rank]
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def add_num_elements_in_bucket(self, num_elements, reduce_rank: int = None):
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self._num_elements_in_bucket[reduce_rank] += num_elements
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def add_param(self, tensor, reduce_rank: int = None):
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self._params[reduce_rank].append(tensor)
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def reset(self):
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keys = [None] + list(range(self._world_size))
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self._params = {rank: [] for rank in keys}
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self._num_elements_in_bucket = {rank: 0 for rank in keys}
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def reset_by_rank(self, reduce_rank=None):
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self._params[reduce_rank] = []
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self._num_elements_in_bucket[reduce_rank] = 0
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def get_grad(self, reduce_rank: int = None):
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param_list = self.get_param(reduce_rank)
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for param in param_list:
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# the param must have grad for reduction
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assert param.grad is not None, f'Parameter of size ({param.size()}) has None grad, cannot be reduced'
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return [param.grad for param in param_list]
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def get_param(self, reduce_rank: int = None):
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return self._params[reduce_rank]
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