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92 lines
4.0 KiB
92 lines
4.0 KiB
from copy import deepcopy
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from dataclasses import dataclass
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from abc import ABC, abstractmethod
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from enum import Enum
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import operator
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import torch
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from functools import reduce
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from colossalai.device.device_mesh import DeviceMesh
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from colossalai.tensor.sharding_spec import ShardingSpec
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from colossalai.tensor.shape_consistency import CollectiveCommPattern, CommSpec
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from typing import Dict, List, Union, Tuple, Any
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from torch.fx.node import Node
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from .constants import *
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__all__ = ['ShardingStrategy', 'StrategiesVector']
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@dataclass
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class ShardingStrategy:
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'''
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ShardingStrategy is a structure containing sharding strategies of inputs and output of this node
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and costs information using in solver.
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Argument:
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name(str): express the sharding strategies in string, such as 'S0S1 = S0R x RS1'.
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output_sharding_spec(ShardingSpec): ShardingSpec of the output node.
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compute_cost(float): Computation cost to complete this strategy.(default to 0)
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communication_cost(float): Communication cost to complete this strategy.(default to 0)
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memory_cost(float): Memory cost of the output node using this strategy.(default to 0)
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resharding_costs(Dict[int, List[float]]): resharding_cost[i][j] means the cost of i-th argument in the output node argument list
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with j-th strategy in its strategies_vector transforms to sharding spec wanted in this
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strategy.(default to None)
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input_shardings(List(ShardingSpec)): The ShardingSpecs of the input nodes.
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'''
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name: str
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# TODO: output of fx node,such as torch.var_mean, could be a tuple, so we cannot simply suppose it is a tensor.
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output_sharding_spec: Union[ShardingSpec, Tuple[ShardingSpec]]
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compute_cost: float = 0.
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communication_cost: float = 0.
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memory_cost: float = 0.
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resharding_costs: Dict[Node, List[float]] = None
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# sometimes the input node could be a tuple of nodes, but most of op won't accept tuple of node as input.
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# Therefore, we could process them at the specific op(operator.getitem)
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input_shardings: List[ShardingSpec] = None
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class StrategiesVector(list):
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'''
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Each node in fx graph will have a corresponding StrategiesVector, to store all the possible
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strategies of the node.
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Argument:
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node (Node): node for which the list of sharding strategies are generated.
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'''
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def __init__(self, node: Node):
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super().__init__()
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self.node = node
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# fetch its input and output nodes
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# TODO: placeholder input nodes
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self.predecessor_nodes = list(node._input_nodes.keys())
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if self.node.op == 'output':
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self.predecessor_nodes = list(node._input_nodes.keys())[:1]
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self.successor_nodes = list(node.users.keys())
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def check_merge(self):
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merge_label = False
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if self.node.op == 'call_module':
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target = self.node.target
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root_module = self.node.graph.owning_module
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submod = root_module.get_submodule(target)
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submod_type = type(submod)
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# merge elementwise module node into source nodes
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# we could merge element-wise op, because the output sharding spec is always same as the input sharding spec.
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if submod_type in ELEMENTWISE_MODULE_OP:
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merge_label = True
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if self.node.op == 'call_function':
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# we could merge element-wise op, because the output sharding spec is always same as the input sharding spec.
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if self.node.target in ELEMENTWISE_FUNC_OP:
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merge_label = True
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# we could merge bcast op if the rhs is a scalar, because it will fall back to the element-wise case.
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if self.node.target in BCAST_FUNC_OP and len(self.predecessor_nodes) == 1:
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merge_label = True
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# we could merge reshape op, because the output sharding spec of reshape op is always fully replicated.
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if self.node.target in RESHAPE_FUNC_OP:
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merge_label = True
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return merge_label
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