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[autoparallel] move ckpt solvers to autoparallel folder / refactor code (#1764)
* [autoparallel] first move. * [autoparallel] add solver rotor. * [autoparallel] add ckpt solvers. * [autoparallel] modify codegen. * [fx] fix annotation in test. * [fx] remove check. * [autoparallel] polish docstring. * [fx] refactor MetaTensor.
This commit is contained in:
Super Daniel
GitHub
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from .ckpt_solver_base import CheckpointSolverBase
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from .ckpt_solver_chen import CheckpointSolverChen
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from .ckpt_solver_rotor import CheckpointSolverRotor
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167
colossalai/auto_parallel/checkpoint/ckpt_solver_base.py
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167
colossalai/auto_parallel/checkpoint/ckpt_solver_base.py
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from abc import ABC, abstractmethod
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from copy import deepcopy
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from typing import Any, List
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from torch.fx import Graph, Node
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from colossalai.fx.codegen.activation_checkpoint_codegen import ActivationCheckpointCodeGen
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from colossalai.fx.profiler.memory_utils import is_inplace
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__all___ = ['CheckpointSolverBase']
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def _copy_output(src: Graph, dst: Graph):
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"""Copy the output node from src to dst"""
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for n_src, n_dst in zip(src.nodes, dst.nodes):
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if n_src.op == 'output':
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n_dst.meta = n_src.meta
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class CheckpointSolverBase(ABC):
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def __init__(
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self,
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graph: Graph,
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memory_budget: float = -1.0,
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parameter_size: float = 0,
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requires_linearize: bool = False,
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cnode: List[str] = None,
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):
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"""CheckpointSolver class will integrate information provided by the components
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and use an existing solver to find a possible optimal strategies combination for
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target computing graph.
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Existing Solvers:
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Chen's Greedy solver: https://arxiv.org/abs/1604.06174 (CheckpointSolverChen)
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Rotor solver: https://hal.inria.fr/hal-02352969 (CheckpointSolverRotor)
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Args:
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graph (Graph): The computing graph to be optimized.
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memory_budget (float): Memory constraint for the solution.
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parameter_size (float): The size of parameter of this model. Use `parameter_size(model)` to estimate.
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requires_linearize (bool): Whether the graph needs to be linearized.
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cnode (List[str], optional): Common node List, should be the subset of input. Default to None.
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Warnings:
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`MetaInfoProp` should be done before constructing the solver. Meta information of the graph is required.
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"""
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# super-dainiu: this graph is a temporary graph which can refer to
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# the owning module, but we will return another deepcopy of it after
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# the solver is executed.
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self.graph = deepcopy(graph)
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self.graph.owning_module = graph.owning_module
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_copy_output(graph, self.graph)
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self.graph.set_codegen(ActivationCheckpointCodeGen())
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# check if `MetaInfoProp` is done
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if any(len(node.meta) == 0 for node in self.graph.nodes):
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raise RuntimeError(
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"Nodes meta information hasn't been prepared! Please run MetaInfoProp before constructing the solver!")
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self.memory_budget = memory_budget
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self.parameter_size = parameter_size
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self.cnode = cnode
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self.requires_linearize = requires_linearize
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if self.requires_linearize:
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self.node_list = self._linearize_graph()
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else:
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self.node_list = self.get_node_list()
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@abstractmethod
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def solve(self):
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"""Solve the checkpointing problem and return the solution.
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"""
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pass
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def get_node_list(self):
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"""Get the node list.
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"""
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return [[node] for node in self.graph.nodes]
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def _linearize_graph(self) -> List[List[Node]]:
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"""Linearizing the graph
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Args:
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graph (Graph): The computing graph to be optimized.
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Returns:
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List[List[Node]]: List of list, each inside list of Node presents
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the actual 'node' in linearized manner.
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Remarks:
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Do merge the inplace ops into the previous node.
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"""
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# Common nodes are type of nodes that could be seen as attributes and remain
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# unchanged throughout the whole model, it will be used several times by
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# different blocks of model, so that it is hard for us to linearize the graph
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# when we encounter those kinds of nodes. We let users to annotate some of the
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# input as common node, such as attention mask, and the followings are some of
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# the ops that could actually be seen as common nodes. With our common node prop,
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# we could find some of the "real" common nodes (e.g. the real attention mask
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# used in BERT and GPT), the rule is simple, for node who's parents are all common
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# nodes or it's op belongs to the following operations, we view this node as a
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# newly born common node.
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# List of target name that could be seen as common node
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common_ops = ["getattr", "getitem", "size"]
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def _is_cop(target: Any) -> bool:
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"""Check if an op could be seen as common node
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Args:
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target (Any): node target
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Returns:
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bool
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"""
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if isinstance(target, str):
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return target in common_ops
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else:
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return target.__name__ in common_ops
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def _is_sink() -> bool:
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"""Check if we can free all dependencies
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Returns:
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bool
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"""
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return not sum([v for _, v in deps.items()]) and not any(map(is_inplace, n.users))
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# make sure that item in cnode is valid
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if self.cnode:
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for name in self.cnode:
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try:
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assert next(node for node in self.graph.nodes if node.name == name).op == "placeholder", \
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f"Common node {name} is not an input of the model."
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except StopIteration:
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raise ValueError(f"Common node name {name} not in graph.")
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else:
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self.cnode = []
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deps = {}
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node_list = []
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region = []
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for n in self.graph.nodes:
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if n.op != "placeholder" and n.op != "output":
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for n_par in n.all_input_nodes:
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if n_par.op != "placeholder" and n_par.name not in self.cnode:
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deps[n_par] -= 1
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region.append(n)
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# if the node could free all dependencies in graph
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# we could begin a new node
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if _is_sink():
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node_list.append(region)
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region = []
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# propagate common node attr if possible
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if len(n.all_input_nodes) == len([node for node in n.all_input_nodes if node.name in self.cnode
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]) or _is_cop(n.target):
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self.cnode.append(n.name)
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else:
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deps[n] = len([user for user in n.users if user.op != "output"])
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return node_list
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87
colossalai/auto_parallel/checkpoint/ckpt_solver_chen.py
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87
colossalai/auto_parallel/checkpoint/ckpt_solver_chen.py
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import math
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from copy import deepcopy
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from typing import List, Set, Tuple
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from torch.fx import Graph, Node
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from colossalai.fx.profiler import calculate_fwd_in, calculate_fwd_tmp
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from .ckpt_solver_base import CheckpointSolverBase
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__all__ = ['CheckpointSolverChen']
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class CheckpointSolverChen(CheckpointSolverBase):
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def __init__(self, graph: Graph, cnode: List[str] = None, num_grids: int = 6):
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"""
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This is the simple implementation of Algorithm 3 in https://arxiv.org/abs/1604.06174.
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Note that this algorithm targets at memory optimization only, using techniques in appendix A.
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Usage:
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Assume that we have a `GraphModule`, and we already applied the `MetaInfoProp`
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to the graph to retrieve all information needed, then we could use the following
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code to find a solution using `CheckpointSolverChen`:
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>>> solver = CheckpointSolverChen(gm.graph)
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>>> chen_graph = solver.solve()
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>>> gm.graph = chen_graph # set the graph to a new graph
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Args:
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graph (Graph): The computing graph to be optimized.
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cnode (List[str], optional): Common node List, should be the subset of input. Defaults to None.
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num_grids (int, optional): Number of grids to search for b. Defaults to 6.
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"""
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super().__init__(graph, 0, 0, True, cnode)
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self.num_grids = num_grids
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def solve(self) -> Graph:
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"""Solve the checkpointing problem using Algorithm 3.
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Returns:
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graph (Graph): The optimized graph, should be a copy of the original graph.
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"""
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checkpointable_op = ['call_module', 'call_method', 'call_function', 'get_attr']
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ckpt = self.grid_search()
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for i, seg in enumerate(ckpt):
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for idx in range(*seg):
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nodes = self.node_list[idx]
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for n in nodes:
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if n.op in checkpointable_op:
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n.meta['activation_checkpoint'] = i
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return deepcopy(self.graph)
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def run_chen_greedy(self, b: int = 0) -> Tuple[Set, int]:
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"""
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This is the simple implementation of Algorithm 3 in https://arxiv.org/abs/1604.06174.
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"""
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ckpt_intv = []
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temp = 0
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x = 0
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y = 0
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prev_idx = 2
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for idx, nodes in enumerate(self.node_list):
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for n in nodes:
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n: Node
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temp += calculate_fwd_in(n) + calculate_fwd_tmp(n)
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y = max(y, temp)
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if temp > b and idx > prev_idx:
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x += calculate_fwd_in(nodes[0])
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temp = 0
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ckpt_intv.append((prev_idx, idx + 1))
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prev_idx = idx + 1
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return ckpt_intv, math.floor(math.sqrt(x * y))
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def grid_search(self) -> Set:
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"""
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Search ckpt strategy with b = 0, then run the allocation algorithm again with b = √xy.
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Grid search over [√2/2 b, √2 b] for ckpt_opt over num_grids as in appendix A.
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"""
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_, b_approx = self.run_chen_greedy(0)
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b_min, b_max = math.floor(b_approx / math.sqrt(2)), math.ceil(b_approx * math.sqrt(2))
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b_opt = math.inf
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for b in range(b_min, b_max, (b_max - b_min) // self.num_grids):
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ckpt_intv, b_approx = self.run_chen_greedy(b)
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if b_approx < b_opt:
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b_opt = b_approx
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ckpt_opt = ckpt_intv
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return ckpt_opt
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387
colossalai/auto_parallel/checkpoint/ckpt_solver_rotor.py
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387
colossalai/auto_parallel/checkpoint/ckpt_solver_rotor.py
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from copy import deepcopy
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from typing import Dict, List, Tuple
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from torch import Tensor
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from torch.fx import Graph, Node
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from colossalai.fx.codegen.activation_checkpoint_codegen import _find_nested_ckpt_regions
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from colossalai.fx.profiler import (
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activation_size,
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calculate_bwd_time,
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calculate_fwd_out,
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calculate_fwd_time,
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calculate_fwd_tmp,
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)
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from colossalai.logging import get_dist_logger
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from .ckpt_solver_base import CheckpointSolverBase
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from .operation import Backward, Chain, ForwardCheck, ForwardEnable, ForwardNograd, Function, Loss, Sequence
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__all__ = ['CheckpointSolverBase']
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class CheckpointSolverRotor(CheckpointSolverBase):
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def __init__(self,
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graph: Graph,
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memory_budget: float = -1,
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parameter_size: float = 0,
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cnode: List[str] = None,
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memory_slots: int = 500):
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"""This is the simple implementation of dynamic programming algorithm rotor
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in https://hal.inria.fr/hal-02352969. Some code are adapted from
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https://gitlab.inria.fr/hiepacs/rotor.
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Usage:
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Assume that we have a `GraphModule`, and we already applied the `MetaInfoProp`
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to the graph to retrieve all information needed, then we could use the following
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code to find a solution using `CheckpointSolverRotor`:
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>>> solver = CheckpointSolverRotor(gm.graph, memory_budget=memory_budget, parameter_size=parameter_size)
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>>> rotor_graph = solver.solve(force_python=True) # otherwise use C solver
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>>> gm.graph = rotor_graph # set the graph to a new graph
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Args:
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graph (Graph): The computing graph to be optimized.
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memory_budget (float, optional): Memory constraint for the solution, unit is byte.
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parameter_size (float, optional): The size of parameter of this model, unit is byte. Use `parameter_size(model)` to estimate.
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cnode (List[str], optional): Common node List, should be the subset of input. Defaults to None.
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memory_slots (int, optional): Number of slots for discretizing memory budget. Defaults to 500.
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"""
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super().__init__(graph, memory_budget, parameter_size, True, cnode)
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self.memory_slots = memory_slots
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# construct chain
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unit = self.memory_budget // self.memory_slots
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self.chain = self._construct_chain(self.graph, self.node_list)
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self.chain.discretize_all(unit)
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self.cost_table = None
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self.back_ptr = None
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self.sequence = None
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def solve(self, force_python: bool = False) -> Graph:
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"""Solve the checkpointing problem using rotor algorithm.
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Args:
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force_python (bool, optional): Use Python version of solver, else use C version. Defaults to False.
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Returns:
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graph (Graph): The optimized graph, should be a copy of the original graph.
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"""
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chain = self.chain
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# compute cost table
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if force_python:
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self.cost_table, self.back_ptr = self._compute_table(chain, self.memory_slots)
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else:
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self.cost_table, self.back_ptr = self._compute_table_c(chain, self.memory_slots)
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# backtrack
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try:
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self.sequence = self._backtrack(chain, 0, chain.length, self.memory_slots, self.cost_table, self.back_ptr)
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self._annotate_from_sequence(self.sequence, self.node_list)
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except RuntimeError as e:
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# using logger to annonce that the solver is failed
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logger = get_dist_logger()
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logger.warning(f'Checkpoint solver failed: {e}')
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return deepcopy(self.graph)
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def print_chain(self):
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print('[input]', self.chain.x[0], self.chain.xbar[0], self.chain.ftmp[0], self.chain.btmp[0])
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for idx in range(len(self.node_list) - 1):
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print(self.node_list[idx], self.chain.x[idx + 1], self.chain.xbar[idx + 1], self.chain.ftmp[idx],
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self.chain.btmp[idx])
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print(f'Chain = {self.chain}')
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def print_sequence(self):
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print(f'Sequence = {self.sequence}')
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@classmethod
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def _construct_chain(cls, graph: Graph, node_list: List[List[Node]]) -> Chain:
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input_tensors = cls._extract_input(graph)
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fwd_time, bwd_time, ftmp, btmp = list(), list(), list(), list()
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xbar, x = [activation_size(input_tensors)], [activation_size(input_tensors)]
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for idx, node in enumerate(node_list):
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node_info = cls._extract_node_info(node)
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fwd_time.append(node_info[0])
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bwd_time.append(node_info[1])
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x.append(node_info[2])
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xbar.append(node_info[3])
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ftmp.append(node_info[4])
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btmp.append(node_info[5])
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# currently we view loss backward temp as zero
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bwd_time.append(0)
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btmp.append(0)
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return Chain(fwd_time, bwd_time, x, xbar, ftmp, btmp)
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@classmethod
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def _extract_node_info(cls, node: List[Node]) -> Tuple[int, ...]:
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"""Extract node info from a list of nodes"""
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xbar = 0
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fwd_time = 0
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bwd_time = 0
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for n in node:
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assert isinstance(n, Node), f'{n} is not a Node'
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xbar += calculate_fwd_tmp(n) + calculate_fwd_out(n)
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# minimum flop count is required
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fwd_time += max(calculate_fwd_time(n), 1.0)
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bwd_time += max(calculate_bwd_time(n), 1.0)
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x = calculate_fwd_out(node[-1])
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xbar = max(x, xbar)
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ftmp = cls._extract_ftmp(node)
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btmp = cls._extract_btmp(node)
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return fwd_time, bwd_time, x, xbar, ftmp, btmp
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@staticmethod
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def _extract_input(graph: Graph) -> Tuple[Tensor, ...]:
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"""Extract input tensors from a Graph"""
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input_tensors = []
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for node in graph.nodes:
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if node.op == 'placeholder':
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input_tensors.append(node.meta['fwd_out'])
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return input_tensors
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@staticmethod
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def _extract_ftmp(node: List[Node]) -> int:
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"""Extract ftmp from a list of nodes"""
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n = node[-1]
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return activation_size(n.meta['fwd_out']) - calculate_fwd_out(n)
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@staticmethod
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def _extract_btmp(node: List[Node]) -> int:
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"""Extract btmp from a list of nodes"""
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def _extract_deps_size():
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deps_size = 0
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for k, v in deps.items():
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k: Node
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if v > 0:
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deps_size += k.meta['bwd_mem_out']
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if v == float('-inf'):
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deps_size -= calculate_fwd_tmp(k) + calculate_fwd_out(k)
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return deps_size
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btmp = 0
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deps = {}
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for n in reversed(node):
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deps[n] = len(n.all_input_nodes)
|
||||
btmp = max(btmp, _extract_deps_size() + n.meta['bwd_mem_tmp'])
|
||||
for child in n.users:
|
||||
if child in deps:
|
||||
deps[child] -= 1
|
||||
if deps[child] <= 0:
|
||||
deps[child] = float('-inf') # free
|
||||
return btmp
|
||||
|
||||
@staticmethod
|
||||
def _compute_table(chain: Chain, mem_slots: int) -> Tuple:
|
||||
"""Compute the table using dynamic programming. Returns the cost table and the backtracking pointer.
|
||||
|
||||
Args:
|
||||
chain (Chain): A basic linearized structure for solving the dynamic programming problem.
|
||||
mem_slots (int): Number of slots for discretizing memory budget.
|
||||
|
||||
Returns:
|
||||
cost_table (List[List[Dict[int, Tuple]]]): cost_table[m][lmin][lmax] with lmin = 0...chain.length
|
||||
and lmax = lmin...chain.length (lmax is not included) and m = 0...mmax
|
||||
back_ptr (List[List[Dict[int, Tuple]]]): back_ptr[m][lmin][lmax] is (True,) if the optimal choice
|
||||
is a chain checkpoint (False, j) if the optimal choice is a leaf checkpoint
|
||||
of length j
|
||||
"""
|
||||
|
||||
ftime = chain.ftime + [0.0]
|
||||
btime = chain.btime
|
||||
x = chain.x + [0]
|
||||
xbar = chain.xbar + [0]
|
||||
ftmp = chain.ftmp + [0]
|
||||
btmp = chain.btmp + [0]
|
||||
|
||||
# Build table
|
||||
cost_table = [[{} for _ in range(chain.length + 1)] for _ in range(mem_slots + 1)]
|
||||
back_ptr = [[{} for _ in range(chain.length + 1)] for _ in range(mem_slots + 1)]
|
||||
# Last one is a dict because its indices go from i to l. Renumbering will wait for C implementation
|
||||
|
||||
# Initialize borders of the tables for lmax-lmin = 0
|
||||
for m in range(mem_slots + 1):
|
||||
for i in range(chain.length + 1):
|
||||
limit = max(x[i + 1] + xbar[i + 1] + ftmp[i], x[i + 1] + xbar[i + 1] + btmp[i])
|
||||
if m >= limit: # Equation (1)
|
||||
cost_table[m][i][i] = ftime[i] + btime[i]
|
||||
else:
|
||||
cost_table[m][i][i] = float("inf")
|
||||
|
||||
# Compute everything
|
||||
for m in range(mem_slots + 1):
|
||||
for d in range(1, chain.length + 1):
|
||||
for i in range(chain.length + 1 - d):
|
||||
idx = i + d
|
||||
mmin = x[idx + 1] + x[i + 1] + ftmp[i]
|
||||
if idx > i + 1:
|
||||
mmin = max(mmin, x[idx + 1] + max(x[j] + x[j + 1] + ftmp[j] for j in range(i + 1, idx)))
|
||||
if m < mmin:
|
||||
cost_table[m][i][idx] = float("inf")
|
||||
else:
|
||||
leaf_checkpoints = [(j,
|
||||
sum(ftime[i:j]) + cost_table[m - x[j]][j][idx] + cost_table[m][i][j - 1])
|
||||
for j in range(i + 1, idx + 1)
|
||||
if m >= x[j]]
|
||||
if leaf_checkpoints:
|
||||
best_leaf = min(leaf_checkpoints, key=lambda t: t[1])
|
||||
else:
|
||||
best_leaf = None
|
||||
if m >= xbar[i + 1]:
|
||||
chain_checkpoint = cost_table[m][i][i] + cost_table[m - xbar[i + 1]][i + 1][idx]
|
||||
else:
|
||||
chain_checkpoint = float("inf")
|
||||
if best_leaf and best_leaf[1] <= chain_checkpoint:
|
||||
cost_table[m][i][idx] = best_leaf[1]
|
||||
back_ptr[m][i][idx] = (False, best_leaf[0])
|
||||
else:
|
||||
cost_table[m][i][idx] = chain_checkpoint
|
||||
back_ptr[m][i][idx] = (True,)
|
||||
return cost_table, back_ptr
|
||||
|
||||
@staticmethod
|
||||
def _compute_table_c(chain: Chain, mem_slots: int) -> Tuple:
|
||||
raise NotImplementedError("C implementation not available yet")
|
||||
|
||||
def _backtrack(self, chain: Chain, lmin: int, lmax: int, mem_budget: int, cost_table: List[List[Dict[int, Tuple]]],
|
||||
back_ptr: List[List[Dict[int, int]]]) -> List[int]:
|
||||
"""Backtrack the cost table and retrieve the optimal checkpointing strategy.
|
||||
|
||||
Args:
|
||||
chain (Chain): A basic linearized structure for solving the dynamic programming problem.
|
||||
lmin (int): The left index of the interval to backtrack.
|
||||
lmax (int): The right index of the interval to backtrack.
|
||||
mem_budget (int): The memory budget for processing this interval.
|
||||
cost_table (List[List[Dict[int, Tuple]]]): See _compute_table() for definitions
|
||||
back_ptr (List[List[Dict[int, Tuple]]]): See _compute_table() for definitions
|
||||
|
||||
Raises:
|
||||
ValueError: Can not process the chain.
|
||||
|
||||
Returns:
|
||||
sequence (Sequence): The sequence of executing nodes with checkpoints.
|
||||
"""
|
||||
if mem_budget <= 0:
|
||||
raise ValueError(f"Can not process a chain with negative memory {mem_budget}")
|
||||
elif cost_table[mem_budget][lmin][lmax] == float("inf"):
|
||||
raise ValueError(f"Can not process this chain from index {lmin} to {lmax} with memory {mem_budget}")
|
||||
|
||||
sequence = Sequence(Function("Persistent", lmax - lmin, mem_budget))
|
||||
if lmin == lmax:
|
||||
if lmin == chain.length:
|
||||
sequence.insert(Loss())
|
||||
else:
|
||||
sequence.insert(ForwardEnable(lmin))
|
||||
sequence.insert(Backward(lmin))
|
||||
return sequence
|
||||
|
||||
if back_ptr[mem_budget][lmin][lmax][0]:
|
||||
sequence.insert(ForwardEnable(lmin))
|
||||
sequence.insert_sequence(
|
||||
self._backtrack(chain, lmin + 1, lmax, mem_budget - chain.xbar[lmin + 1], cost_table, back_ptr))
|
||||
sequence.insert(Backward(lmin))
|
||||
else:
|
||||
j = back_ptr[mem_budget][lmin][lmax][1]
|
||||
sequence.insert(ForwardCheck(lmin))
|
||||
for k in range(lmin + 1, j):
|
||||
sequence.insert(ForwardNograd(k))
|
||||
sequence.insert_sequence(self._backtrack(chain, j, lmax, mem_budget - chain.xbar[j], cost_table, back_ptr))
|
||||
sequence.insert_sequence(self._backtrack(chain, lmin, j - 1, mem_budget, cost_table, back_ptr))
|
||||
return sequence
|
||||
|
||||
@staticmethod
|
||||
def _annotate_from_sequence(sequence: Sequence, node_list: List[List[Node]]):
|
||||
op_list = sequence.list_operations()
|
||||
loss_op = next(op for op in op_list if isinstance(op, Loss))
|
||||
fwd_list = op_list[:op_list.index(loss_op)]
|
||||
bwd_list = op_list[op_list.index(loss_op) + 1:]
|
||||
ckpt_idx = 0
|
||||
in_ckpt = False
|
||||
ckpt_region = []
|
||||
|
||||
# forward annotation
|
||||
for idx, op in enumerate(fwd_list, 0):
|
||||
if in_ckpt:
|
||||
if isinstance(op, ForwardNograd):
|
||||
ckpt_region.append(idx)
|
||||
|
||||
elif isinstance(op, ForwardEnable):
|
||||
in_ckpt = False
|
||||
for node_idx in ckpt_region:
|
||||
for n in node_list[node_idx]:
|
||||
n.meta['activation_checkpoint'] = [ckpt_idx]
|
||||
|
||||
ckpt_idx += 1
|
||||
ckpt_region = []
|
||||
|
||||
elif isinstance(op, ForwardCheck):
|
||||
for node_idx in ckpt_region:
|
||||
for n in node_list[node_idx]:
|
||||
n.meta['activation_checkpoint'] = [ckpt_idx]
|
||||
|
||||
ckpt_idx += 1
|
||||
ckpt_region = [idx]
|
||||
|
||||
else:
|
||||
if isinstance(op, ForwardCheck):
|
||||
in_ckpt = True
|
||||
ckpt_region.append(idx)
|
||||
|
||||
# annotate the backward if there is any nested activation checkpoint
|
||||
in_recompute = False
|
||||
for op in bwd_list:
|
||||
if in_recompute:
|
||||
if isinstance(op, ForwardNograd):
|
||||
ckpt_region.append(op.index)
|
||||
|
||||
elif isinstance(op, ForwardEnable):
|
||||
for node_idx in ckpt_region:
|
||||
for n in node_list[node_idx]:
|
||||
n.meta['activation_checkpoint'].append(ckpt_idx)
|
||||
|
||||
ckpt_idx += 1
|
||||
ckpt_region = []
|
||||
|
||||
elif isinstance(op, ForwardCheck):
|
||||
for node_idx in ckpt_region:
|
||||
for n in node_list[node_idx]:
|
||||
n.meta['activation_checkpoint'].append(ckpt_idx)
|
||||
|
||||
ckpt_idx += 1
|
||||
ckpt_region = [op.index]
|
||||
|
||||
elif isinstance(op, Backward):
|
||||
for node_idx in ckpt_region:
|
||||
for n in node_list[node_idx]:
|
||||
n.meta['activation_checkpoint'].append(ckpt_idx)
|
||||
|
||||
in_recompute = False
|
||||
|
||||
else:
|
||||
if not isinstance(op, Backward):
|
||||
in_recompute = True
|
||||
ckpt_idx = 0
|
||||
ckpt_region = []
|
||||
if isinstance(op, ForwardCheck):
|
||||
ckpt_region.append(op.index)
|
||||
|
||||
# postprocess, make sure every activation checkpoint label in the
|
||||
# same activation checkpoint region (level = 0) has the same length
|
||||
op_list = []
|
||||
for node in node_list:
|
||||
op_list += node
|
||||
ckpt_regions = _find_nested_ckpt_regions(op_list)
|
||||
for (start_idx, end_idx) in ckpt_regions:
|
||||
nested_length = max(
|
||||
len(op_list[idx].meta['activation_checkpoint']) for idx in range(start_idx, end_idx + 1))
|
||||
for idx in range(start_idx, end_idx + 1):
|
||||
op_list[idx].meta['activation_checkpoint'] += [None] * (nested_length -
|
||||
len(op_list[idx].meta['activation_checkpoint']))
|
||||
241
colossalai/auto_parallel/checkpoint/operation.py
Normal file
241
colossalai/auto_parallel/checkpoint/operation.py
Normal file
@@ -0,0 +1,241 @@
|
||||
import math
|
||||
from abc import ABC
|
||||
from typing import List
|
||||
|
||||
from torch.utils._pytree import tree_map
|
||||
|
||||
|
||||
class Chain:
|
||||
|
||||
def __init__(self,
|
||||
ftime: List[float],
|
||||
btime: List[float],
|
||||
x: List[int],
|
||||
xbar: List[int],
|
||||
ftmp: List[int],
|
||||
btmp: List[int],
|
||||
check_consistency: bool = True):
|
||||
"""The chain is a basic linearized structure for solving the dynamic programming problem for activation checkpoint.
|
||||
See paper https://hal.inria.fr/hal-02352969 for details.
|
||||
|
||||
Args:
|
||||
ftime (List[float]): The forward time of each node.
|
||||
btime (List[float]): The backward time of each node.
|
||||
x (List[int]): The forward memory of each node (if save_output). Same as `a` in the paper.
|
||||
xbar (List[int]): The forward memory of each node (if save_all). Same as `a_bar` in the paper.
|
||||
ftmp (List[int]): The temporary forward memory of each node.
|
||||
btmp (List[int]): The temporary backward memory of each node, can be used to control memory budget.
|
||||
check_consistency (bool, optional): Check the lengths consistency for the `Chain`. Defaults to True.
|
||||
"""
|
||||
self.ftime = ftime
|
||||
self.btime = btime
|
||||
self.x = x
|
||||
self.xbar = xbar
|
||||
self.ftmp = ftmp
|
||||
self.btmp = btmp
|
||||
self.length = len(ftime)
|
||||
if check_consistency and not self.check_lengths():
|
||||
raise AttributeError("In Chain, input lists do not have consistent lengths")
|
||||
|
||||
def check_lengths(self):
|
||||
return ((len(self.ftime) == self.length) and (len(self.btime) == self.length + 1)
|
||||
and (len(self.x) == self.length + 1) and (len(self.ftmp) == self.length)
|
||||
and (len(self.btmp) == self.length + 1) and (len(self.xbar) == self.length + 1))
|
||||
|
||||
def __repr__(self):
|
||||
chain_list = []
|
||||
for i in range(self.length):
|
||||
chain_list.append((self.ftime[i], self.btime[i], self.x[i], self.xbar[i], self.ftmp[i], self.btmp[i]))
|
||||
i = self.length
|
||||
chain_list.append((None, self.btime[i], self.x[i], self.xbar[i], None, self.btmp[i]))
|
||||
return chain_list.__repr__()
|
||||
|
||||
def discretize_all(self, unit: int):
|
||||
"""Discretize the chain into a list of chains according to unit size."""
|
||||
discretizer = lambda val: math.ceil(val / unit)
|
||||
self.x = tree_map(discretizer, self.x)
|
||||
self.xbar = tree_map(discretizer, self.xbar)
|
||||
self.ftmp = tree_map(discretizer, self.ftmp)
|
||||
self.btmp = tree_map(discretizer, self.btmp)
|
||||
|
||||
|
||||
class Operation(ABC):
|
||||
name = "Op"
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"{self.name}_{self.index}"
|
||||
|
||||
def shift(self, value):
|
||||
if type(self.index) is tuple:
|
||||
self.index = tuple(x + value for x in self.index)
|
||||
else:
|
||||
self.index += value
|
||||
|
||||
|
||||
class Forward(Operation):
|
||||
name = "F"
|
||||
|
||||
def __init__(self, index):
|
||||
self.index = index
|
||||
|
||||
def cost(self, chain: Chain):
|
||||
if chain is not None:
|
||||
return chain.ftime[self.index]
|
||||
else:
|
||||
return 1
|
||||
|
||||
|
||||
class ForwardEnable(Forward):
|
||||
name = "Fe"
|
||||
|
||||
|
||||
class ForwardNograd(Forward):
|
||||
name = "Fn"
|
||||
|
||||
|
||||
class ForwardCheck(Forward):
|
||||
name = "CF"
|
||||
|
||||
|
||||
class Forwards(Operation):
|
||||
|
||||
def __init__(self, start, end):
|
||||
self.index = (start, end)
|
||||
|
||||
def __repr__(self):
|
||||
return "F_{i}->{j}".format(i=self.index[0], j=self.index[1])
|
||||
|
||||
def cost(self, chain: Chain):
|
||||
if chain is not None:
|
||||
return sum(chain.ftime[self.index[0]:self.index[1] + 1])
|
||||
else:
|
||||
return (self.index[1] - self.index[0] + 1)
|
||||
|
||||
|
||||
def isForward(op):
|
||||
return type(op) is Forward or type(op) is Forwards
|
||||
|
||||
|
||||
class Backward(Operation):
|
||||
name = "B"
|
||||
|
||||
def __init__(self, index):
|
||||
self.index = index
|
||||
|
||||
def cost(self, chain: Chain):
|
||||
if chain is not None:
|
||||
return chain.btime[self.index]
|
||||
else:
|
||||
return 1
|
||||
|
||||
|
||||
class Loss(Operation):
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __repr__(self):
|
||||
return "L"
|
||||
|
||||
def cost(self, chain):
|
||||
return 0
|
||||
|
||||
|
||||
class MemoryAccess(Operation):
|
||||
name = "MA"
|
||||
|
||||
def __init__(self, index):
|
||||
self.index = index
|
||||
|
||||
def cost(self, chain: Chain):
|
||||
return 0
|
||||
|
||||
|
||||
class WriteMemory(MemoryAccess):
|
||||
name = "WM"
|
||||
|
||||
|
||||
class ReadMemory(MemoryAccess):
|
||||
name = "RM"
|
||||
|
||||
|
||||
class DiscardMemory(MemoryAccess):
|
||||
name = "DM"
|
||||
|
||||
|
||||
class Function:
|
||||
|
||||
def __init__(self, name, *args):
|
||||
self.name = name
|
||||
self.args = args
|
||||
self.str_args = ','.join(str(v) for v in self.args)
|
||||
|
||||
def __repr__(self):
|
||||
return "{n}({args})".format(n=self.name, args=self.str_args)
|
||||
|
||||
|
||||
class Sequence:
|
||||
|
||||
def __init__(self, function):
|
||||
self.sequence = [] #List of Operation and Sequence
|
||||
self.function = function #Description the function (name and parameters)
|
||||
|
||||
def __repr__(self):
|
||||
return repr(self.list_operations())
|
||||
|
||||
def list_operations(self):
|
||||
op_list = []
|
||||
for x in self.sequence:
|
||||
if isinstance(x, Operation):
|
||||
op_list.append(x)
|
||||
else:
|
||||
assert isinstance(x, Sequence)
|
||||
op_list += x.list_operations()
|
||||
return op_list
|
||||
|
||||
def insert(self, operation):
|
||||
self.sequence.append(operation)
|
||||
|
||||
def remove(self, operation_index):
|
||||
del self.sequence[operation_index]
|
||||
|
||||
def insert_sequence(self, sequence):
|
||||
self.sequence.append(sequence)
|
||||
|
||||
def shift(self, value):
|
||||
for x in self.sequence:
|
||||
x.shift(value)
|
||||
return self
|
||||
|
||||
def remove_useless_write(self):
|
||||
if self.sequence:
|
||||
if isinstance(self.sequence[0], WriteMemory):
|
||||
self.remove(0)
|
||||
return self
|
||||
|
||||
def get_makespan(self, chain):
|
||||
return sum(op.cost(chain) for op in self.list_operations())
|
||||
|
||||
def without_suffix(self):
|
||||
ops = self.list_operations()
|
||||
end_of_first_phase = [i for i in range(len(ops)) if type(ops[i]) is Loss][0]
|
||||
try:
|
||||
last_idx = max(i for i in range(end_of_first_phase) if not type(ops[i]) is ForwardEnable)
|
||||
except ValueError:
|
||||
last_idx = -1
|
||||
if last_idx == end_of_first_phase - 1:
|
||||
return (self, None)
|
||||
chain_length = ops[end_of_first_phase -
|
||||
1].index ## Some assumption here about the sequence (finishes with Forward_L
|
||||
start_of_fwd_enable_chain = ops[last_idx + 1].index ## And starts with B_L), but should be fine in practice
|
||||
result = Sequence(Function("Strip", self.function.name, *self.function.args, start_of_fwd_enable_chain))
|
||||
for i in range(last_idx + 1):
|
||||
result.insert(ops[i])
|
||||
result.insert(Loss())
|
||||
for i in range(chain_length, start_of_fwd_enable_chain - 1, -1):
|
||||
position = end_of_first_phase + 1 + (chain_length - i)
|
||||
assert type(ops[position]) is Backward
|
||||
assert ops[position].index == i
|
||||
for i in range(end_of_first_phase + 1 + 1 + chain_length - start_of_fwd_enable_chain, len(ops)):
|
||||
result.insert(ops[i])
|
||||
return (result, start_of_fwd_enable_chain)
|
||||
Reference in New Issue
Block a user