mirror of https://github.com/hpcaitech/ColossalAI
135 lines
5.0 KiB
Python
135 lines
5.0 KiB
Python
import math
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import torch.distributed as dist
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from colossalai.registry import DIST_GROUP_INITIALIZER
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from .process_group_initializer import ProcessGroupInitializer
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from ..parallel_mode import ParallelMode
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from colossalai.global_variables import tensor_parallel_env as env
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def _check_summa_env_var(summa_dim):
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# check environment variable for SUMMA
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env_summa_dim = env.summa_dim
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if env_summa_dim:
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assert int(env_summa_dim) == summa_dim, \
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'SUMMA_DIM has been set in the current environment and ' \
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'does not match with the value passed to this initialized'
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else:
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env.summa_dim = summa_dim
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class Initializer_2D_Row(ProcessGroupInitializer):
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"""2d tensor parallel initialization among rows.
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:param num_group: The number of all tensor groups
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:param summa_dim: The dimension of SUMMA
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:param args: Args used to initialize base class
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:param kwargs: Kwargs used to initialize base class
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:type num_group: int
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:type summa_dim: int
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"""
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def __init__(self, num_group, summa_dim, *args, **kwargs):
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super(Initializer_2D_Row, self).__init__(*args, **kwargs)
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self.num_group = num_group
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self.summa_dim = summa_dim
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def init_dist_group(self):
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"""Initialize 2D tensor row parallel groups, and assign local_ranks and groups to each gpu.
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:return: 2D tensor row parallelism's information
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:rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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"""
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local_rank = None
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ranks_in_group = None
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process_group = None
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group_world_size = None
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mode = ParallelMode.PARALLEL_2D_ROW
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for i in range(self.num_group):
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for j in range(self.summa_dim):
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ranks = [i * self.tensor_parallel_size + j * self.summa_dim + k for k in range(self.summa_dim)]
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group = dist.new_group(ranks)
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if self.rank in ranks:
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local_rank = ranks.index(self.rank)
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group_world_size = len(ranks)
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process_group = group
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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class Initializer_2D_Col(ProcessGroupInitializer):
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"""2d tensor parallel initialization among cols.
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:param num_group: The number of all tensor groups
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:param summa_dim: The dimension of SUMMA
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:param args: Args used to initialize base class
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:param kwargs: Kwargs used to initialize base class
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:type num_group: int
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:type summa_dim: int
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"""
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def __init__(self, num_group, summa_dim, *args, **kwargs):
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super(Initializer_2D_Col, self).__init__(*args, **kwargs)
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self.num_group = num_group
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self.summa_dim = summa_dim
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def init_dist_group(self):
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"""Initialize 2D tensor row parallel groups, and assign local_ranks and groups to each gpu.
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:return: 2D tensor col parallelism's information
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:rtype: Tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
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"""
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local_rank = None
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ranks_in_group = None
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process_group = None
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group_world_size = None
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mode = ParallelMode.PARALLEL_2D_COL
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for i in range(self.num_group):
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for j in range(self.summa_dim):
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ranks = [i * self.tensor_parallel_size + j + k * self.summa_dim for k in range(self.summa_dim)]
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group = dist.new_group(ranks)
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if self.rank in ranks:
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local_rank = ranks.index(self.rank)
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group_world_size = len(ranks)
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process_group = group
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, ranks_in_group, mode
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_2D(ProcessGroupInitializer):
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"""
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Serve as the single entry point to 2D parallel initialization.
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:param args: Args used to initialize ProcessGroupInitializer
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:param kwargs: Kwargs used to initialize ProcessGroupInitializer
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.num_group = self.world_size // self.tensor_parallel_size
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self.summa_dim = int(math.sqrt(self.tensor_parallel_size))
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assert self.tensor_parallel_size == self.summa_dim ** 2, \
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"2D summa dim should equal to tensor parallel size ^ 0.5"
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_check_summa_env_var(self.summa_dim)
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self.col_initializer = Initializer_2D_Col(self.num_group, self.summa_dim, *args, **kwargs)
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self.row_initializer = Initializer_2D_Row(self.num_group, self.summa_dim, *args, **kwargs)
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def init_dist_group(self):
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"""Initialize 2D tensor row and col parallel groups, and assign local_ranks and groups to each gpu.
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:return: 2D tensor parallelism's information
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:rtype: list of Tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
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"""
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parallel_setting = [self.row_initializer.init_dist_group(), self.col_initializer.init_dist_group()]
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return parallel_setting
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