mirror of https://github.com/hpcaitech/ColossalAI
101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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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 .initializer_tensor import Initializer_Tensor
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from .process_group_initializer import ProcessGroupInitializer
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from ..parallel_mode import ParallelMode
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_Sequence_DP(ProcessGroupInitializer):
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"""A ProcessGroupInitializer for sequence parallelism all-reduce.
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In Sequence Parallelism, each GPU holds the full copy of model weights,
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thus, gradient all-reduce occurs across all processes in the same pipeline stage
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Args:
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rank (int): The rank of current process
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world_size (int): Size of whole communication world
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config (Config): Running configuration
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data_parallel_size (int): Size of data parallel
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pipeline_parallel_size (int): Size of pipeline parallel
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tensor_parallel_size (int): Size of tensor parallel
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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.dp_size = self.world_size // self.pipeline_parallel_size
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self.num_group = self.pipeline_parallel_size
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def init_dist_group(self):
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"""Initialize Sequence Parallel process groups used for gradient all-reduce.
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Returns:
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Tuple: A 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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cpu_group = None
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group_world_size = None
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mode = ParallelMode.SEQUENCE_DP
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for i in range(self.num_group):
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ranks = [i * self.dp_size + j for j in range(self.dp_size)]
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group = dist.new_group(ranks)
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group_cpu = dist.new_group(ranks, backend='gloo') if dist.get_backend() != 'gloo' else group
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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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cpu_group = group_cpu
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ranks_in_group = ranks
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return local_rank, group_world_size, process_group, cpu_group, ranks_in_group, mode
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@DIST_GROUP_INITIALIZER.register_module
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class Initializer_Sequence(ProcessGroupInitializer):
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"""A ProcessGroupInitializer for sequence parallelism.
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Args:
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rank (int): The rank of current process.
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world_size (int): Size of whole communication world.
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config (Config): Running configuration.
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data_parallel_size (int): Size of data parallel.
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pipeline_parallel_size (int): Size of pipeline parallel.
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tensor_parallel_size (int): Size of tensor parallel.
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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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# reuse tensor parallel initializer code
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self._sequence_initializer = Initializer_Tensor(*args, **kwargs)
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self._sequence_dp_initializer = Initializer_Sequence_DP(*args, **kwargs)
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def init_dist_group(self):
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"""Initialize Sequence parallel process groups and assign local_ranks and groups to each gpu.
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Sequence parallelism requires 2 process groups. The first is for model forward where several processes
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exchange partial query, key and value embedding to compute self attention values. The second is for
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all-reduce to synchronize the model parameters.
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Returns:
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List[Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode)]:
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A Sequence parallelism's information in list of tuples.
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"""
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parallel_setting = []
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local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode = \
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self._sequence_initializer.init_dist_group()
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# change mode to sequence
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mode = ParallelMode.SEQUENCE
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parallel_setting.append((local_rank, group_world_size, process_group, cpu_grop, ranks_in_group, mode))
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parallel_setting.append(self._sequence_dp_initializer.init_dist_group())
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return parallel_setting
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