mirror of https://github.com/hpcaitech/ColossalAI
311 lines
12 KiB
Python
311 lines
12 KiB
Python
from enum import Enum
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from typing import Dict
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import torch
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import torch.distributed as dist
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from torch.distributed import ReduceOp
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__all__ = [
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'CollectiveCommPattern',
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'CommSpec',
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]
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class CollectiveCommPattern(Enum):
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GATHER_FWD_SPLIT_BWD = 'gather_fwd_split_bwd'
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ALL2ALL_FWD_ALL2ALL_BWD = 'all2all_fwd_all2all_bwd'
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SPLIT_FWD_GATHER_BWD = 'split_fwd_gather_bwd'
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ALLREDUCE_FWD_IDENTITY_BWD = 'all_reduce_fwd_identity_bwd'
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IDENTITY_FWD_ALLREDUCE_BWD = 'identity_fwd_all_reduce_bwd'
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MIXGATHER_FWD_SPLIT_BWD = "mixgather_fwd_split_bwd"
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class CommSpec:
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'''
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Communication spec is used to record the communication action. It converts the communication spec
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to real action which will be used in runtime. It contains comm_pattern to determine the
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communication method, process_groups_dict to determine the process groups, gather_dim and shard_dim
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to determine the buffer shape, and logical_process_axis
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Argument:
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comm_pattern(CollectiveCommPattern): decribe the communication method used in this spec.
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process_groups_dict(Dict): A dict which contains the process groups used to apply this CommSpec.
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gather_dim(int, Optional): The gather_dim of the tensor will be gathered.
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shard_dim(int, Optional): The shard_dim of the tensor will be sharded.
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logical_process_axis(Union(int, List[int]), Optional): The mesh_dim to implement the communication action.
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'''
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def __init__(self,
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comm_pattern: CollectiveCommPattern,
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process_groups_dict: Dict,
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gather_dim: int = None,
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shard_dim: int = None,
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logical_process_axis: int = None):
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self.comm_pattern = comm_pattern
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self.gather_dim = gather_dim
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self.shard_dim = shard_dim
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self.logical_process_axis = logical_process_axis
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self.process_groups_dict = process_groups_dict
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def __repr__(self):
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res_list = ["CommSpec:("]
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if self.comm_pattern == CollectiveCommPattern.GATHER_FWD_SPLIT_BWD:
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res_list.append(f"comm_pattern:GATHER_FWD_SPLIT_BWD, ")
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res_list.append(f"gather_dim:{self.gather_dim}, ")
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res_list.append(f"shard_dim:{self.gather_dim}, ")
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res_list.append(f"logical_process_axis:{self.logical_process_axis})")
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elif self.comm_pattern == CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD:
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res_list.append(f"comm_pattern:ALL2ALL_FWD_ALL2ALL_BWD, ")
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res_list.append(f"gather_dim:{self.gather_dim}, ")
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res_list.append(f"shard_dim:{self.shard_dim}, ")
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res_list.append(f"logical_process_axis: {self.logical_process_axis})")
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elif self.comm_pattern == CollectiveCommPattern.SPLIT_FWD_GATHER_BWD:
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res_list.append(f"comm_pattern:SPLIT_FWD_GATHER_BWD, ")
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res_list.append(f"gather_dim:{self.gather_dim}, ")
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res_list.append(f"shard_dim:{self.shard_dim}, ")
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res_list.append(f"logical_process_axis:{self.logical_process_axis})")
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elif self.comm_pattern == CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD:
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res_list.append(f"comm_pattern:ALLREDUCE_FWD_IDENTITY_BWD, ")
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res_list.append(f"logical_process_axis:{self.logical_process_axis})")
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elif self.comm_pattern == CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD:
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res_list.append(f"comm_pattern:IDENTITY_FWD_ALLREDUCE_BWD, ")
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res_list.append(f"logical_process_axis:{self.logical_process_axis})")
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return ''.join(res_list)
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def covert_spec_to_action(self, tensor):
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'''
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Convert CommSpec into runtime action, implement real collection communication to target tensor.
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The collection communication action is directed by the CommSpec.
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Argument:
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tensor(torch.Tensor): Tensor stored in each device, which could be different in different ranks.
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'''
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if self.comm_pattern in pattern_to_func_dict:
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tensor = pattern_to_func_dict[self.comm_pattern](tensor, self)
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else:
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tensor = tensor
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return tensor
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def _all_gather(tensor: torch.Tensor, comm_spec: CommSpec):
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'''
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Implement all gather operation on device mesh based on information provided by comm_spec.
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'''
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process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
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for rank_list, process_group in process_groups_list:
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if dist.get_rank() in rank_list:
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tensor_list = [
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torch.zeros(tensor.shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list))
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]
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# without this contiguous operation, the all gather may get some unexpected results.
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tensor = tensor.contiguous()
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dist.all_gather(tensor_list, tensor, group=process_group)
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output = torch.cat(tuple(tensor_list), comm_spec.gather_dim).contiguous()
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return output
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def _split(tensor: torch.Tensor, comm_spec: CommSpec):
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'''
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Implement shard operation on device mesh based on information provided by comm_spec.
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'''
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process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
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for rank_list, _ in process_groups_list:
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if dist.get_rank() in rank_list:
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dim = comm_spec.shard_dim
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length = tensor.shape[comm_spec.shard_dim] // len(rank_list)
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start = length * rank_list.index(dist.get_rank())
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output = torch.narrow(tensor, dim, start, length).contiguous()
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return output
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def _all_to_all(tensor: torch.Tensor, comm_spec: CommSpec):
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'''
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Implement all to all operation on device mesh based on information provided by comm_spec.
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'''
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process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
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for rank_list, process_group in process_groups_list:
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if dist.get_rank() in rank_list:
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new_shape = list(tensor.shape)
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new_shape[comm_spec.shard_dim] = new_shape[comm_spec.shard_dim] // len(rank_list)
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new_shape = torch.Size(new_shape)
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output_tensor_list = [
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torch.zeros(new_shape, dtype=tensor.dtype, device=tensor.device) for _ in range(len(rank_list))
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]
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dim = comm_spec.shard_dim
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length = tensor.shape[comm_spec.shard_dim] // len(rank_list)
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input_tensor_list = [
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torch.narrow(tensor, dim, length * i, length).contiguous() for i in range(len(rank_list))
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]
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group = process_group
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dist.all_to_all(output_tensor_list, input_tensor_list, group)
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output = torch.cat(tuple(output_tensor_list), comm_spec.gather_dim).contiguous()
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return output
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def _all_reduce(tensor: torch.Tensor, comm_spec: CommSpec, async_op: bool = False):
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'''
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Implement all reduce operation on device mesh based on information provided by comm_spec.
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'''
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process_groups_list = comm_spec.process_groups_dict[comm_spec.logical_process_axis]
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for rank_list, process_group in process_groups_list:
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if dist.get_rank() in rank_list:
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if not tensor.is_contiguous():
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tensor = tensor.contiguous()
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dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group, async_op=async_op)
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return tensor
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class _ReduceGrad(torch.autograd.Function):
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"""
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A customized communication operation which forward is an identity operation,
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backward is all_reduce operation.
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Args:
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input_: input matrix.
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comm_spec: comm_spec will give information like process group, rank list, etc.
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"""
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@staticmethod
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def symbolic(graph, input_):
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return input_
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@staticmethod
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def forward(ctx, input_, comm_spec):
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ctx.comm_spec = comm_spec
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return input_
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@staticmethod
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def backward(ctx, grad_output):
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return _all_reduce(grad_output, ctx.comm_spec), None
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class _ReduceInput(torch.autograd.Function):
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"""
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A customized communication operation which forward is all_reduce operation,
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backward is an identity operation.
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Args:
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input_: input matrix.
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comm_spec: comm_spec will give information like process group, rank list, etc.
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _all_reduce(input_)
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@staticmethod
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def forward(ctx, input_, comm_spec):
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return _all_reduce(input_, comm_spec)
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@staticmethod
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def backward(ctx, grad_output):
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return grad_output, None
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class _SplitForwardGatherBackward(torch.autograd.Function):
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"""
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A customized communication operation which forward is split operation,
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backward is an all gather operation.
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Args:
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input_: input matrix.
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comm_spec: comm_spec will give information like process group, rank list, etc.
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _split(input_)
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@staticmethod
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def forward(ctx, input_, comm_spec):
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ctx.comm_spec = comm_spec
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return _split(input_, comm_spec)
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@staticmethod
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def backward(ctx, grad_output):
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return _all_gather(grad_output, ctx.comm_spec), None
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class _GatherForwardSplitBackward(torch.autograd.Function):
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"""
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A customized communication operation which forward is an all gather operation,
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backward is split operation.
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Args:
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input_: input matrix.
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comm_spec: comm_spec will give information like process group, rank list, etc.
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _all_gather(input_)
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@staticmethod
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def forward(ctx, input_, comm_spec):
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ctx.comm_spec = comm_spec
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return _all_gather(input_, comm_spec)
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@staticmethod
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def backward(ctx, grad_output):
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return _split(grad_output, ctx.comm_spec), None
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class _AllToAll(torch.autograd.Function):
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"""
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A customized communication operation which forward is an all to all operation,
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backward is an all to all operation.
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Args:
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input_: input matrix.
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comm_spec: comm_spec will give information like process group, rank list, etc.
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _all_to_all(input_)
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@staticmethod
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def forward(ctx, input_, comm_spec):
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output = _all_to_all(input_, comm_spec)
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comm_spec_for_backward = CommSpec(comm_pattern=comm_spec.comm_pattern,
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process_groups_dict=comm_spec.process_groups_dict,
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gather_dim=comm_spec.shard_dim,
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shard_dim=comm_spec.gather_dim,
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logical_process_axis=comm_spec.logical_process_axis)
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ctx.comm_spec = comm_spec_for_backward
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return output
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@staticmethod
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def backward(ctx, grad_outputs):
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return _all_to_all(grad_outputs, ctx.comm_spec), None
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def reduce_grad(input_, comm_spec):
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return _ReduceGrad.apply(input_, comm_spec)
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def reduce_input(input_, comm_spec):
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return _ReduceInput.apply(input_, comm_spec)
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def split_forward_gather_backward(input_, comm_spec):
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return _SplitForwardGatherBackward.apply(input_, comm_spec)
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def gather_forward_split_backward(input_, comm_spec):
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return _GatherForwardSplitBackward.apply(input_, comm_spec)
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def all_to_all(input_, comm_spec):
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return _AllToAll.apply(input_, comm_spec)
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pattern_to_func_dict = {
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CollectiveCommPattern.GATHER_FWD_SPLIT_BWD: gather_forward_split_backward,
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CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD: all_to_all,
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CollectiveCommPattern.SPLIT_FWD_GATHER_BWD: split_forward_gather_backward,
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CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD: reduce_input,
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CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD: reduce_grad,
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}
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