mirror of https://github.com/hpcaitech/ColossalAI
839 lines
36 KiB
Python
839 lines
36 KiB
Python
import torch
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from dataclasses import dataclass
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from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
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from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, shard_simulator
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from enum import Enum
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from copy import deepcopy
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from typing import Dict, List, Optional, Tuple, Union
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from colossalai.context.singleton_meta import SingletonMeta
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import torch.distributed as dist
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import math
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from functools import reduce
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import operator
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from torch.distributed import ReduceOp
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__all__ = [
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'CollectiveCommPattern', 'CommSpec', 'ShapeConsistencyManager', 'ShapeConsistencyOptions',
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'set_shape_consistency_options'
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]
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def _all_gather(tensor, comm_spec):
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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.device_mesh.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)
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for _ in range(comm_spec.device_mesh.mesh_shape[comm_spec.logical_process_axis])
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]
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tensor = tensor
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group = process_group
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dist.all_gather(tensor_list, tensor, group=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, comm_spec):
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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.device_mesh.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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tensor = tensor
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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)
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return output
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def _all_to_all(tensor, comm_spec):
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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.device_mesh.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, comm_spec):
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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.device_mesh.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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dist.all_reduce(tensor, op=ReduceOp.SUM, group=process_group)
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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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sharding_spec=comm_spec.sharding_spec,
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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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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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REDUCE_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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class CommSpec:
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'''
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Communication spec is used to record the communication action. It has two main functions:
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1. Compute the communication cost which will be used in auto parallel solver.
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2. Convert the communication spec to real action which will be used in runtime.
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It contains comm_pattern to determine the
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communication method, sharding_spec to determine the communication size, 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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sharding_spec(ShardingSpec): This is sharding spec of the tensor which will join the communication action.
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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,
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sharding_spec,
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gather_dim=None,
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shard_dim=None,
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logical_process_axis=None,
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forward_only=False):
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self.comm_pattern = comm_pattern
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self.sharding_spec = sharding_spec
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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.forward_only = forward_only
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if isinstance(self.logical_process_axis, list):
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self.device_mesh = self.sharding_spec.device_mesh.flatten_device_mesh
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self.logical_process_axis = 0
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else:
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self.device_mesh = self.sharding_spec.device_mesh
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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"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"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.REDUCE_FWD_IDENTITY_BWD:
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res_list.append(f"comm_pattern:REDUCE_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 get_comm_cost(self):
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'''
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For all_gather, all2all, and all_reduce operation, the formula provided in DeviceMesh with alpha-beta model is used to
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compute the communication cost.
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For shard operation, it is an on-chip operation, so the communication cost is zero.
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'''
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comm_size = reduce(operator.mul, self.sharding_spec.get_sharded_shape_per_device(), 1)
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if self.comm_pattern == CollectiveCommPattern.GATHER_FWD_SPLIT_BWD:
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forward_communication_cost = self.device_mesh.all_gather_cost(comm_size, self.logical_process_axis)
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# give a tiny cost to shard
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backward_communication_cost = 10
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if self.comm_pattern == CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD:
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forward_communication_cost = self.device_mesh.all_to_all_cost(comm_size, self.logical_process_axis)
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# grad should have same shape as input tensor
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# all to all operation has same logical process axis as forward.
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backward_communication_cost = self.device_mesh.all_to_all_cost(comm_size, self.logical_process_axis)
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if self.comm_pattern == CollectiveCommPattern.REDUCE_FWD_IDENTITY_BWD:
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forward_communication_cost = self.device_mesh.all_reduce_cost(comm_size, self.logical_process_axis)
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backward_communication_cost = 0
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if self.comm_pattern == CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD:
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forward_communication_cost = 0
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backward_communication_cost = self.device_mesh.all_reduce_cost(comm_size, self.logical_process_axis)
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if self.comm_pattern == CollectiveCommPattern.SPLIT_FWD_GATHER_BWD:
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# give a tiny cost to shard
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forward_communication_cost = 10
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backward_communication_cost = self.device_mesh.all_gather_cost(comm_size, self.logical_process_axis)
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try:
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if self.forward_only:
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total_communication_cost = forward_communication_cost
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else:
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total_communication_cost = forward_communication_cost + backward_communication_cost
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except:
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raise RuntimeError(f"Could not find a matching CollectiveCommPattern for {self.comm_pattern}.")
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return total_communication_cost
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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.data = pattern_to_func_dict[self.comm_pattern](tensor, self)
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else:
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tensor.data = tensor
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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.REDUCE_FWD_IDENTITY_BWD: reduce_input,
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CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD: reduce_grad,
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}
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@dataclass
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class ShapeConsistencyOptions:
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"""
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ShapeConsistencyOptions is a dataclass which specifies the preferences for shape consistency.
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"""
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# TODO: shape consistency option is not implemented yet
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pass
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def set_shape_consistency_options(options: ShapeConsistencyOptions):
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"""
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Configure the shape consistency manager via function call.
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"""
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manager = ShapeConsistencyManager()
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manager.options = options
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class ShapeConsistencyManager(metaclass=SingletonMeta):
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def __init__(self):
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self._options = None
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self._forward_only = False
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self.total_communication_cost = 0
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self.total_transform_steps = 0
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self.cached_spec_pairs_transform_path = {}
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@property
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def options(self):
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return self._options
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@options.setter
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def options(self, options_: ShapeConsistencyOptions):
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assert isinstance(options_, ShapeConsistencyOptions)
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self._options = options_
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@property
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def forward_only(self):
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return self._forward_only
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@forward_only.setter
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def forward_only(self, value):
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assert isinstance(value, bool)
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self._forward_only = value
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def get_all_all_gather_spec(self, source_spec, orig_cost):
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'''
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Get all valid sharding specs from source_spec with single all-gather operation, and
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accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
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For the all-gather operation, we just care about the S dimension.
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Argument:
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source_spec(ShardingSpec): the ShardingSpec of the source_spec.
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orig_cost(float): the original communication cost before this operation.
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Return:
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valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-gather operation.
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Example:
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dim_partition_dict = {0: [0], 1: [1]}
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# DistSpec:
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# shard_sequence: S0,S1,R
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# device_mesh_shape: (4, 4)
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sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
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shape_consistency_manager = ShapeConsistencyManager()
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rst_dict = shape_consistency_manager.get_all_all_gather_spec(sharding_spec, 0)
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print(rst_dict)
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Output:
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{DistSpec:
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shard_sequence: R,S1,R
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device_mesh_shape: (4, 4): 0, DistSpec:
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shard_sequence: S0,R,R
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device_mesh_shape: (4, 4): 0}
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'''
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valid_spec_dict = {}
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comm_pattern = CollectiveCommPattern.GATHER_FWD_SPLIT_BWD
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for target_pair in source_spec.dim_partition_dict.items():
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shard_list = all_gather_simulator(target_pair)
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index = target_pair[0]
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new_dim_partition_dict = deepcopy(source_spec.dim_partition_dict)
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# We won't add empty list into dim_partition_dict
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# The key will be popped if the related shard_list is empty
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if shard_list:
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new_dim_partition_dict[index] = shard_list
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else:
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new_dim_partition_dict.pop(index)
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# generate the CommSpec to record the action of source_sharding_spec->new_sharding_spec
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gather_dim = index
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logical_process_axis = target_pair[1][-1]
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comm_spec = CommSpec(
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comm_pattern,
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sharding_spec=source_spec,
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gather_dim=gather_dim,
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# shard_dim will be used during backward
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shard_dim=gather_dim,
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logical_process_axis=logical_process_axis,
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forward_only=self.forward_only)
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# compute the communication cost with CommSpec
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cost = comm_spec.get_comm_cost()
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# generate new sharding spec
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|
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
|
|
source_spec.entire_shape,
|
|
dim_partition_dict=new_dim_partition_dict)
|
|
valid_spec_dict[new_sharding_spec] = (comm_spec, orig_cost + cost)
|
|
return valid_spec_dict
|
|
|
|
def get_all_all_to_all_spec(self, source_spec, orig_cost):
|
|
'''
|
|
Get all valid sharding specs from source_spec with single all-to-all operation, and
|
|
accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
|
|
For the all-to-all operation, we just care about the pairs containing S dimension.
|
|
|
|
Argument:
|
|
source_spec(ShardingSpec): the ShardingSpec of the source_spec.
|
|
orig_cost(float): the original communication cost before this operation.
|
|
|
|
Return:
|
|
valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.
|
|
|
|
Example:
|
|
dim_partition_dict = {0: [0], 1: [1]}
|
|
# DistSpec:
|
|
# shard_sequence: S0,S1,R
|
|
# device_mesh_shape: (4, 4)
|
|
sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
|
|
shape_consistency_manager = ShapeConsistencyManager()
|
|
rst_dict = shape_consistency_manager.get_all_all_to_all_spec(sharding_spec, 0)
|
|
print(rst_dict)
|
|
|
|
Output:
|
|
{DistSpec:
|
|
shard_sequence: S01,R,R
|
|
device_mesh_shape: (4, 4): 0, DistSpec:
|
|
shard_sequence: R,S1,S0
|
|
device_mesh_shape: (4, 4): 0, DistSpec:
|
|
shard_sequence: S0,R,S1
|
|
device_mesh_shape: (4, 4): 0}
|
|
'''
|
|
valid_spec_dict = {}
|
|
comm_pattern = CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD
|
|
tensor_dims = len(source_spec.entire_shape)
|
|
for f_index in range(tensor_dims - 1):
|
|
for b_index in range(f_index + 1, tensor_dims):
|
|
# skip (R, R) cases
|
|
if f_index not in source_spec.dim_partition_dict and b_index not in source_spec.dim_partition_dict:
|
|
continue
|
|
else:
|
|
if f_index in source_spec.dim_partition_dict:
|
|
# skip (S01, R) -> (R, S01) is NOT allowed
|
|
if len(source_spec.dim_partition_dict[f_index]) >= 2:
|
|
continue
|
|
f_target_pair = (f_index, deepcopy(source_spec.dim_partition_dict[f_index]))
|
|
else:
|
|
f_target_pair = (f_index, [])
|
|
if b_index in source_spec.dim_partition_dict:
|
|
# skip (R, S01) -> (S01, R) is NOT allowed
|
|
if len(source_spec.dim_partition_dict[b_index]) >= 2:
|
|
continue
|
|
b_target_pair = (b_index, deepcopy(source_spec.dim_partition_dict[b_index]))
|
|
else:
|
|
b_target_pair = (b_index, [])
|
|
|
|
# skip (S1, S0) -> S10
|
|
if f_target_pair[1] and b_target_pair[1] and f_target_pair[1][0] >= b_target_pair[1][0]:
|
|
continue
|
|
f_shard_list, b_shard_list = all_to_all_simulator(f_target_pair, b_target_pair)
|
|
f_index = f_target_pair[0]
|
|
b_index = b_target_pair[0]
|
|
|
|
# generate the CommSpec to record the action of source_sharding_spec->new_sharding_spec
|
|
if len(f_shard_list) < len(f_target_pair[1]):
|
|
gather_dim = f_index
|
|
shard_dim = b_index
|
|
logical_process_axis = f_target_pair[1][-1]
|
|
else:
|
|
gather_dim = b_index
|
|
shard_dim = f_index
|
|
logical_process_axis = b_target_pair[1][-1]
|
|
comm_spec = CommSpec(comm_pattern,
|
|
sharding_spec=source_spec,
|
|
gather_dim=gather_dim,
|
|
shard_dim=shard_dim,
|
|
logical_process_axis=logical_process_axis,
|
|
forward_only=self.forward_only)
|
|
|
|
# compute the communication cost with CommSpec
|
|
cost = comm_spec.get_comm_cost()
|
|
new_dim_partition_dict = deepcopy(source_spec.dim_partition_dict)
|
|
|
|
# We won't add empty list into dim_partition_dict
|
|
# The key will be popped if the related shard_list is empty
|
|
if f_shard_list:
|
|
new_dim_partition_dict[f_index] = f_shard_list
|
|
else:
|
|
new_dim_partition_dict.pop(f_index)
|
|
if b_shard_list:
|
|
new_dim_partition_dict[b_index] = b_shard_list
|
|
else:
|
|
new_dim_partition_dict.pop(b_index)
|
|
|
|
# generate new sharding spec
|
|
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
|
|
source_spec.entire_shape,
|
|
dim_partition_dict=new_dim_partition_dict)
|
|
valid_spec_dict[new_sharding_spec] = (comm_spec, orig_cost + cost)
|
|
return valid_spec_dict
|
|
|
|
def get_all_shard_spec(self, source_spec, orig_cost):
|
|
'''
|
|
Get all valid sharding specs from source_spec with single shard operation, and
|
|
accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
|
|
For the sharding operation, we just care about legal sharding dimensions.
|
|
|
|
Argument:
|
|
source_spec(ShardingSpec): the ShardingSpec of the source_spec.
|
|
orig_cost(float): the original communication cost before this operation.
|
|
|
|
Return:
|
|
valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.
|
|
|
|
Example:
|
|
dim_partition_dict = {0: [0]}
|
|
# DistSpec:
|
|
# shard_sequence: S0,R,R
|
|
# device_mesh_shape: (4, 4)
|
|
sharding_spec = ShardingSpec(device_mesh, entire_shape, dim_partition_dict)
|
|
shape_consistency_manager = ShapeConsistencyManager()
|
|
rst_dict = shape_consistency_manager.get_all_shard_spec(sharding_spec, 0)
|
|
print(rst_dict)
|
|
|
|
Output:
|
|
{DistSpec:
|
|
shard_sequence: S01,R,R
|
|
device_mesh_shape: (4, 4): 0, DistSpec:
|
|
shard_sequence: S0,S1,R
|
|
device_mesh_shape: (4, 4): 0, DistSpec:
|
|
shard_sequence: S0,R,S1
|
|
device_mesh_shape: (4, 4): 0}
|
|
'''
|
|
valid_spec_dict = {}
|
|
comm_pattern = CollectiveCommPattern.SPLIT_FWD_GATHER_BWD
|
|
|
|
# legal sharding dims means the mesh_id is still available to use.
|
|
legal_sharding_dims = [i for i in range(len(source_spec.device_mesh.mesh_shape))]
|
|
for dim, shard_list in source_spec.dim_partition_dict.items():
|
|
for element in shard_list:
|
|
legal_sharding_dims.remove(element)
|
|
if len(legal_sharding_dims) == 0:
|
|
return valid_spec_dict
|
|
|
|
tensor_dims = len(source_spec.entire_shape)
|
|
for index in range(tensor_dims):
|
|
if index not in source_spec.dim_partition_dict:
|
|
shard_list_list = shard_simulator((index, []), legal_sharding_dims)
|
|
else:
|
|
shard_list_list = shard_simulator((index, source_spec.dim_partition_dict[index]), legal_sharding_dims)
|
|
if not shard_list_list:
|
|
continue
|
|
for shard_list in shard_list_list:
|
|
new_dim_partition_dict = deepcopy(source_spec.dim_partition_dict)
|
|
new_dim_partition_dict[index] = shard_list
|
|
|
|
# generate the CommSpec to record the action of source_sharding_spec->new_sharding_spec
|
|
shard_dim = index
|
|
logical_process_axis = shard_list[-1]
|
|
comm_spec = CommSpec(comm_pattern,
|
|
sharding_spec=source_spec,
|
|
gather_dim=shard_dim,
|
|
shard_dim=shard_dim,
|
|
logical_process_axis=logical_process_axis,
|
|
forward_only=self.forward_only)
|
|
|
|
# compute the communication cost with CommSpec
|
|
cost = comm_spec.get_comm_cost()
|
|
|
|
# generate new sharding spec
|
|
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
|
|
source_spec.entire_shape,
|
|
dim_partition_dict=new_dim_partition_dict)
|
|
valid_spec_dict[new_sharding_spec] = (comm_spec, orig_cost + cost)
|
|
return valid_spec_dict
|
|
|
|
def get_all_one_step_transform_spec(self, source_spec, orig_cost):
|
|
'''
|
|
Get all valid sharding specs from source_spec with one step transform, and
|
|
accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
|
|
Note:
|
|
all-gather will eliminate a sharding dimension, all-to-all will keep sharding dimension same as before,
|
|
and shard will add a sharding dimension. Therefore, the result of above operations are mutual exclusive,
|
|
we could safely put them together.
|
|
|
|
Argument:
|
|
source_spec(ShardingSpec): the ShardingSpec of the source_spec.
|
|
orig_cost(float): the original communication cost before this operation.
|
|
|
|
Return:
|
|
valid_spec_dict(Dict[ShardingSpec, float]): all valid sharding specs from source_spec with single all-to-all operation.
|
|
'''
|
|
valid_spec_dict = {}
|
|
valid_spec_dict.update(self.get_all_all_gather_spec(source_spec, orig_cost))
|
|
valid_spec_dict.update(self.get_all_all_to_all_spec(source_spec, orig_cost))
|
|
valid_spec_dict.update(self.get_all_shard_spec(source_spec, orig_cost))
|
|
return valid_spec_dict
|
|
|
|
def shape_consistency(self, source_spec, target_spec):
|
|
'''
|
|
This method will find a path to transform source_spec to target_spec with
|
|
a greedy algorithm.
|
|
The basic idea is:
|
|
Step1:
|
|
Generate all one-step transform sequences from source_spec.
|
|
Step2:
|
|
Pick the 'best' sharding spec following the heuristic function.
|
|
Step3:
|
|
Repeat above steps until the source spec transform to target spec.
|
|
|
|
During finding the transform path, commucation cost will be accumulated, and it
|
|
will be finally used in auto parallel solver.
|
|
|
|
Additionally, to avoid repeating the path search in runtime, we cached all solved path
|
|
in auto parallel strategy building time, which could handle most of cases in runtime.
|
|
|
|
Argument:
|
|
source_spec(ShardingSpec): ShardingSpec of the source activation.
|
|
target_spec(ShardingSpec): ShardingSpec of the target activation.
|
|
|
|
Return:
|
|
transform_path(List[ShardingSpec]): The transform path from source_spec to target_spec,
|
|
it contains the source_spec and target_spec.
|
|
comm_action_sequence(List[CommSpec]): Keep the communication operations to complete the shape consistency in order.
|
|
total_cost(float): total cost to complete shape consistency transform.
|
|
|
|
Example:
|
|
dim_partition_source = {1: [0, 1]}
|
|
dim_partition_target = {0: [0, 1]}
|
|
# DistSpec:
|
|
# shard_sequence: R,S01,R
|
|
# device_mesh_shape: (4, 4)
|
|
sharding_spec_source = ShardingSpec(device_mesh, entire_shape, dim_partition_source)
|
|
# DistSpec:
|
|
# shard_sequence: S01,R,R
|
|
# device_mesh_shape: (4, 4)
|
|
sharding_spec_target = ShardingSpec(device_mesh, entire_shape, dim_partition_target)
|
|
transform_path, comm_action_sequence, total_cost = shape_consistency_manager.shape_consistency(sharding_spec_source, sharding_spec_target)
|
|
print(f'transform_path: {transform_path}')
|
|
print(f'comm_action_sequence: {comm_action_sequence}')
|
|
print(f'total_cost: {total_cost}')
|
|
|
|
output:
|
|
transform_path: [DistSpec:
|
|
shard_sequence: R,S01,R
|
|
device_mesh_shape: (4, 4), DistSpec:
|
|
shard_sequence: R,S0,R
|
|
device_mesh_shape: (4, 4), DistSpec:
|
|
shard_sequence: S0,R,R
|
|
device_mesh_shape: (4, 4), DistSpec:
|
|
shard_sequence: S01,R,R
|
|
device_mesh_shape: (4, 4)]
|
|
comm_action_sequence: [CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1),
|
|
CommSpec:(comm_pattern:all2all, gather_dim:1, shard_dim:0, logical_process_axis: 0),
|
|
CommSpec:(comm_pattern:shard, shard_dim:0, logical_process_axis:1)]
|
|
total_cost: 12294.402000000002
|
|
'''
|
|
MAX_TRANSFORM_STEPS = 20
|
|
total_cost = 0
|
|
total_steps = 0
|
|
transform_path = []
|
|
comm_action_sequence = []
|
|
spec_pairs = (str(source_spec.sharding_sequence), str(target_spec.sharding_sequence))
|
|
self.cached_spec_pairs_transform_path[spec_pairs] = (None, None)
|
|
|
|
# We do nothing if the sharding spec is all the same.
|
|
if source_spec.sharding_sequence_difference(target_spec) == 0:
|
|
self.cached_spec_pairs_transform_path[spec_pairs] = (transform_path, comm_action_sequence)
|
|
return (transform_path, comm_action_sequence, total_cost)
|
|
|
|
temp_sharding_spec = source_spec
|
|
transform_path.append(temp_sharding_spec)
|
|
# To avoid dead loop, the loop will break after MAX_TRANSFORM_STEPS transforms
|
|
while total_steps <= MAX_TRANSFORM_STEPS:
|
|
valid_transform_spec_dict = self.get_all_one_step_transform_spec(temp_sharding_spec, total_cost)
|
|
best_difference_score = math.inf
|
|
|
|
for sharding_spec, info_pairs in valid_transform_spec_dict.items():
|
|
comm_spec, cost = info_pairs
|
|
spec_difference = sharding_spec.sharding_sequence_difference(target_spec)
|
|
|
|
if spec_difference == 0:
|
|
total_cost += cost
|
|
transform_path.append(sharding_spec)
|
|
comm_action_sequence.append(comm_spec)
|
|
self.cached_spec_pairs_transform_path[spec_pairs] = (transform_path, comm_action_sequence)
|
|
return (transform_path, comm_action_sequence, total_cost)
|
|
|
|
if spec_difference < best_difference_score:
|
|
temp_sharding_spec = sharding_spec
|
|
temp_cost = cost
|
|
temp_comm_spec = comm_spec
|
|
best_difference_score = spec_difference
|
|
|
|
transform_path.append(temp_sharding_spec)
|
|
comm_action_sequence.append(temp_comm_spec)
|
|
total_cost += temp_cost
|
|
total_steps += 1
|
|
|
|
raise RuntimeError(f"Could not find a valid transform path with in {MAX_TRANSFORM_STEPS} steps.")
|
|
|
|
def apply(self, tensor_with_sharding_spec, target_spec):
|
|
'''
|
|
Apply target_spec to tensor with source sharding spec, the transform path is generated by the
|
|
shape_consistency method.
|
|
|
|
Argument:
|
|
tensor_with_sharding_spec (torch.Tensor): a tensor with source sharding spec to be transformed to the target spec.
|
|
target_spec (ShardingSpec): The tensor transform processes will be directed by the target_spec.
|
|
|
|
Example:
|
|
physical_mesh_id = torch.arange(0, 4)
|
|
mesh_shape = (2, 2)
|
|
# [[0, 1,
|
|
# [2, 3]]
|
|
device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
|
|
entire_shape = torch.Size((4, 2))
|
|
shape_consistency_manager = ShapeConsistencyManager()
|
|
dim_partition_source = {0: [0]}
|
|
dim_partition_target = {1: [0]}
|
|
|
|
# DistSpec:
|
|
# shard_sequence: S0,R
|
|
# device_mesh_shape: (2, 2)
|
|
sharding_spec_source = ShardingSpec(device_mesh, entire_shape, dim_partition_source)
|
|
|
|
# DistSpec:
|
|
# shard_sequence: R,S0
|
|
# device_mesh_shape: (2, 2)
|
|
sharding_spec_target = ShardingSpec(device_mesh, entire_shape, dim_partition_target)
|
|
|
|
if rank in (0, 1):
|
|
sharded_tensor_0 = torch.zeros(2, 1)
|
|
sharded_tensor_1 = torch.ones(2, 1)
|
|
# tensor([[0., 1.],
|
|
# [0., 1.]])
|
|
tensor_to_comm = torch.cat((sharded_tensor_0, sharded_tensor_1), 1).cuda()
|
|
if rank in (2, 3):
|
|
sharded_tensor_0 = torch.ones(2, 1) * 2
|
|
sharded_tensor_1 = torch.ones(2, 1) * 3
|
|
# tensor([[2., 3.],
|
|
# [2., 3.]])
|
|
tensor_to_comm = torch.cat((sharded_tensor_0, sharded_tensor_1), 1).cuda()
|
|
|
|
tensor_to_comm.sharding_spec = sharding_spec_source
|
|
shape_consistency_manager.apply(tensor_to_comm, sharding_spec_target)
|
|
print(tensor_to_comm)
|
|
|
|
Output in rank0 and rank2:
|
|
tensor([[0.],
|
|
[0.],
|
|
[2.],
|
|
[2.]])
|
|
|
|
Output in rank1 and rank3:
|
|
tensor([[1.],
|
|
[1.],
|
|
[3.],
|
|
[3.]])
|
|
'''
|
|
_, comm_action_sequence, _ = self.shape_consistency(tensor_with_sharding_spec.sharding_spec, target_spec)
|
|
for comm_spec in comm_action_sequence:
|
|
comm_spec.covert_spec_to_action(tensor_with_sharding_spec)
|
|
tensor_with_sharding_spec.sharding_spec = target_spec
|