mirror of https://github.com/hpcaitech/ColossalAI
133 lines
6.6 KiB
Python
133 lines
6.6 KiB
Python
from colossalai.auto_parallel.solver.sharding_strategy import ShardingStrategy, StrategiesVector
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from typing import List
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from torch.fx.node import Node
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class CostGraph:
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'''
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A graph data structure to simplify the edge cost graph. It has two main functions:
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1. To feed the quadratic resharding costs into solver, we need to linearize it. We build edge_cost in
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CostGraph, and it stored every combinations of strategies for a src-dst node pair in an 1D list.
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2. To reduce the searching space, we merge computationally-trivial operators, such as
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element-wise operators, transpose, and reduction, into their following nodes. The merging infomation will
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be given by the StrategiesVector depending on the type of target node and following nodes.
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Argument:
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leaf_strategies(List[StrategiesVector]): It stores StrategiesVector of every nodes on the graph.
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simplify(bool, optional): The generated cost graph will be simplified if it is true. (default to True)
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'''
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def __init__(self, leaf_strategies, simplify=True):
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self.leaf_strategies = leaf_strategies
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# stores number of strategies in each node
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self.node_lens = {strategies_vector.node: len(strategies_vector) for strategies_vector in self.leaf_strategies}
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# extra_node_costs will store the extra costs introduced by merging nodes
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self.extra_node_costs = {}
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self.simplify = simplify
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self._build_cost_graph()
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def _build_cost_graph(self):
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'''
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This method will generate edge_cost for adjacent node pair. Additionally, 'parents' and 'children' attribute will be
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set to node.
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'''
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self.edge_costs = {}
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if self.simplify:
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self.merge_pair = []
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for strategies_vector in self.leaf_strategies:
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# build edge_cost
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dst_node = strategies_vector.node
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for src_node in strategies_vector.predecessor_nodes:
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node_pair = (src_node, dst_node)
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# src_index = strategies_vector.predecessor_nodes.index(src_node)
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edge_cost = {}
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for i in range(len(strategies_vector)):
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for j in range(len(src_node.strategies_vector)):
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edge_cost[(j, i)] = strategies_vector[i].resharding_costs[src_node][j]
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self.edge_costs[node_pair] = edge_cost
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# add parents and children attribute to node
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setattr(dst_node, 'parents', strategies_vector.predecessor_nodes)
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setattr(dst_node, 'children', strategies_vector.successor_nodes)
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if self.simplify and strategies_vector.check_merge():
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for following_node in strategies_vector.successor_nodes:
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self.merge_pair.append((dst_node, following_node))
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def get_edge_cost(self, src_node, dst_node):
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return self.edge_costs[(src_node, dst_node)]
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def merge_node(self, src_node, dst_node):
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'''
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To merge src_node into dst_node, we need to do it in following steps:
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1. For each strategy in dst_node, we need to pick an appropriate strategy
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of src_node to merge, it is important because the logical resharding costs
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between the parents node of src_node and merged node depend on the src_node
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strategies dispatching. For example, for the graph 0->1->2, after merging node 1
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into node 2, edge_costs[(node 0, node 2)][(0, 0)] = edge_costs[(node 0, node 1)][(0, x)]
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x represents the picking strategy of node 1 merged into node 2 strategy 0.
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2. We need to accumulate the extra costs introduced by merging nodes, the extra costs
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contains two parts, one is resharding costs between src_node strategy and dst_node strategy,
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another is the origin extra costs in src_node strategy.
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3. Build connections between new node pairs, and remove the src_node after all consumer nodes
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detached from it.
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Argument:
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src_node(Node): The node will be merged into dst_node.
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dst_node(Node): The node to integrate src_node.
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'''
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src_node_index = dst_node.parents.index(src_node)
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# build merge_map
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merge_map = {}
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for dst_strate_index, strategy in enumerate(dst_node.strategies_vector):
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resharding_costs = strategy.resharding_costs
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resharding_cost_for_src = resharding_costs[src_node]
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lowest_cost_index = resharding_cost_for_src.index(min(resharding_cost_for_src))
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merge_map[dst_strate_index] = lowest_cost_index
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# extra_node_cost for dst node
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self.extra_node_costs[dst_node] = [0.0 for _ in range(self.node_lens[dst_node])]
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for dst_strate_index, strategy in enumerate(dst_node.strategies_vector):
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target_strate_index = merge_map[dst_strate_index]
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self.extra_node_costs[dst_node][dst_strate_index] += strategy.resharding_costs[src_node][
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target_strate_index]
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if src_node in self.extra_node_costs:
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self.extra_node_costs[dst_node][dst_strate_index] += self.extra_node_costs[src_node][
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target_strate_index]
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# add new node pair to cost graph
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for parent_node in src_node.parents:
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new_node_pair = (parent_node, dst_node)
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old_node_pair = (parent_node, src_node)
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if new_node_pair in self.edge_costs:
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continue
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edge_cost = {}
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for i in range(self.node_lens[dst_node]):
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for j in range(self.node_lens[parent_node]):
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src_strate_index = merge_map[i]
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edge_cost[(j, i)] = self.edge_costs[old_node_pair][(j, src_strate_index)]
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self.edge_costs[new_node_pair] = edge_cost
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# connect dst node and parents of src node
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dst_node.parents.remove(src_node)
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src_node.children.remove(dst_node)
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self.edge_costs.pop((src_node, dst_node))
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for parent_node in src_node.parents:
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if parent_node not in dst_node.parents:
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dst_node.parents.append(parent_node)
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if dst_node not in parent_node.children:
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parent_node.children.append(dst_node)
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# remove src node from cost graph when src node has no consumer.
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if len(src_node.children) == 0:
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parent_node.children.remove(src_node)
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node_pair = (parent_node, src_node)
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self.edge_costs.pop(node_pair)
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def simplify_graph(self):
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if not self.simplify:
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return
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for (src_node, dst_node) in self.merge_pair:
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self.merge_node(src_node, dst_node)
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