mirror of https://github.com/hpcaitech/ColossalAI
298 lines
12 KiB
Python
298 lines
12 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import math
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import torch.distributed as dist
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from colossalai.context import Config
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from colossalai.global_variables import tensor_parallel_env as env
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from colossalai.registry import DIST_GROUP_INITIALIZER
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from ..parallel_mode import ParallelMode
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from .process_group_initializer import ProcessGroupInitializer
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def _check_tesseract_env_var(tesseract_dim: int, tesseract_dep: int):
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# check global variable for TESSERACT
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env_tesseract_dim = env.tesseract_dim
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env_tesseract_dep = env.tesseract_dep
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if env_tesseract_dim and env_tesseract_dep:
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assert int(env_tesseract_dim) == tesseract_dim, \
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'TESSERACT_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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assert int(env_tesseract_dep) == tesseract_dep, \
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'TESSERACT_DEP 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.tesseract_dim = tesseract_dim
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env.tesseract_dep = tesseract_dep
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# i row j col k dep
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class Initializer_2p5D_ROW(ProcessGroupInitializer):
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"""2.5d tensor parallel initialization among rows.
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Args:
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tesseract_dim (int): The dimension of tesseract.
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tesseract_dep (int): The dimension of depth.
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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, tesseract_dim: int, tesseract_dep: int, *args):
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super(Initializer_2p5D_ROW, self).__init__(*args)
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
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"Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"
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def init_dist_group(self):
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"""Initialize 2.5D tensor row parallel groups, and assign local_ranks and groups to each gpu.
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Returns:
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Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
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2.5D tensor row parallelism's information in a tuple.
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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.PARALLEL_2P5D_ROW
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for h in range(self.num_group):
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for j in range(self.tesseract_dim):
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for k in range(self.tesseract_dep):
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ranks = [
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h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
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for i in range(self.tesseract_dim)
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]
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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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class Initializer_2p5D_Col(ProcessGroupInitializer):
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"""2.5d tensor parallel initialization among cols.
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Args:
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tesseract_dim (int): The dimension of tesseract.
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tesseract_dep (int): The dimension of depth.
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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, tesseract_dim: int, tesseract_dep: int, *args):
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super(Initializer_2p5D_Col, self).__init__(*args)
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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def init_dist_group(self):
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"""Initialize 2.5D tensor col parallel groups, and assign local_ranks and groups to each gpu.
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Returns:
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Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
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2.5D tensor col parallelism's information in a tuple.
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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.PARALLEL_2P5D_COL
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for h in range(self.num_group):
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for i in range(self.tesseract_dim):
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for k in range(self.tesseract_dep):
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ranks = [
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h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
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for j in range(self.tesseract_dim)
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]
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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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class Initializer_2p5D_Dep(ProcessGroupInitializer):
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"""2.5D tensor parallel initialization among depths.
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Args:
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tesseract_dim (int): The dimension of tesseract.
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tesseract_dep (int): The dimension of depth.
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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, tesseract_dim: int, tesseract_dep: int, *args):
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super(Initializer_2p5D_Dep, self).__init__(*args)
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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def init_dist_group(self):
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"""Initialize 2.5D tensor depth parallel groups, and assign local_ranks and groups to each gpu.
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Returns:
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Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
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2.5D tensor depth parallelism's information in a tuple.
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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.PARALLEL_2P5D_DEP
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for h in range(self.num_group):
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for i in range(self.tesseract_dim):
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for j in range(self.tesseract_dim):
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ranks = [
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h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
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for k in range(self.tesseract_dep)
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]
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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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# i row j col k dep
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class Initializer_2p5D_XZ(ProcessGroupInitializer):
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"""2.5d tensor parallel initialization among cols times dep.
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Args:
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tesseract_dim (int): The dimension of tesseract.
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tesseract_dep (int): The dimension of depth.
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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, tesseract_dim: int, tesseract_dep: int, *args):
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super(Initializer_2p5D_XZ, self).__init__(*args)
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dep = tesseract_dep
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self.tesseract_dim = tesseract_dim
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def init_dist_group(self):
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"""Initialize 2.5D tensor colXdepth parallel groups, and assign local_ranks and groups to each gpu.
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Returns:
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Tuple (local_rank, group_world_size, process_group, ranks_in_group, mode):
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2.5D tensor colXdepth parallelism's information in a tuple.
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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.PARALLEL_2P5D_XZ
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for h in range(self.num_group):
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for i in range(self.tesseract_dim):
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ranks = [
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h * self.tensor_parallel_size + i + self.tesseract_dim * (j + self.tesseract_dim * k)
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for k in range(self.tesseract_dep)
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for j in range(self.tesseract_dim)
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]
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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_2p5D(ProcessGroupInitializer):
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"""
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Serve as the single entry point to Tesseract parallel initialization.
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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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depth (int): The depth of 2.5d parallel.
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"""
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def __init__(self, rank: int, world_size: int, config: Config, data_parallel_size: int, pipeline_parallel_size: int,
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tensor_parallel_size: int, depth: int):
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args = (rank, world_size, config, data_parallel_size, pipeline_parallel_size, tensor_parallel_size)
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super().__init__(*args)
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self.num_group = self.world_size // self.tensor_parallel_size
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self.tesseract_dim = int(math.sqrt(self.tensor_parallel_size / depth))
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self.tesseract_dep = depth
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assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
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"2.5D tesseract dim should equal to (tensor parallel size / tesseract dep) ^ 0.5"
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_check_tesseract_env_var(self.tesseract_dim, self.tesseract_dep)
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self.col_initializer = Initializer_2p5D_Col(self.tesseract_dim, self.tesseract_dep, *args)
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self.row_initializer = Initializer_2p5D_ROW(self.tesseract_dim, self.tesseract_dep, *args)
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self.dep_initializer = Initializer_2p5D_Dep(self.tesseract_dim, self.tesseract_dep, *args)
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self.xz_initializer = Initializer_2p5D_XZ(self.tesseract_dim, self.tesseract_dep, *args)
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def init_dist_group(self):
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"""Initialize 2.5D tensor row, col, depth, and colXdepth parallel groups, and assign local_ranks and groups to each gpu.
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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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Whole 2.5D tensor parallelism's information in a list of tuples.
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"""
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parallel_setting = [
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self.col_initializer.init_dist_group(),
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self.row_initializer.init_dist_group(),
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self.dep_initializer.init_dist_group(),
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self.xz_initializer.init_dist_group()
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]
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return parallel_setting
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