mirror of https://github.com/hpcaitech/ColossalAI
440 lines
18 KiB
Python
440 lines
18 KiB
Python
from copy import deepcopy
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from typing import Any, 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.auto_parallel.passes.runtime_apply_pass import runtime_apply, runtime_comm_spec_apply
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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, Loss, Sequence
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__all__ = ['CheckpointSolverRotor']
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class CheckpointSolverRotor(CheckpointSolverBase):
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def __init__(self,
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graph: Graph,
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free_memory: float = -1,
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cnode: List[str] = None,
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memory_slots: int = 500,
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optim_multiplier: float = 1.0):
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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 have already done the extractions
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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, free_memory=torch.cuda.mem_get_info(device=0)[0])
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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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free_memory (float, optional): Memory constraint for the solution, unit is byte.
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Use ``torch.cuda.mem_get_info(device=0)[0]`` to estimate the free_memory. Defaults to -1.
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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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optim_multiplier (float, optional): The multiplier of extra weight storage for the
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``torch.optim.Optimizer``. Default to 1.0.
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"""
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super().__init__(graph, free_memory, True, cnode, optim_multiplier)
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self.memory_slots = memory_slots
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# construct chain
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unit = self.free_memory // 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, verbose: 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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verbose (bool, optional): Print verbose information. 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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if verbose:
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self.print_chain()
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# backtrack
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try:
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self.sequence = self._backtrack(chain, 0, len(chain), self.memory_slots - chain.x[0], self.cost_table,
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self.back_ptr)
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self._annotate_from_sequence(self.sequence, self.node_list)
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except ValueError 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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raise ValueError
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if verbose:
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self.print_sequence()
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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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ftime, btime, 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 node in node_list:
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node_info = cls._extract_node_info(node)
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ftime.append(node_info[0])
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btime.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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btime.append(0)
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btmp.append(0)
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return Chain(ftime, btime, 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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ftime = 0
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btime = 0
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fwd_mem_peak = 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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if n.target == runtime_apply or n.target == runtime_comm_spec_apply:
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# in this case we need to calculate memory usage directly based on the statics that hooked in node.meta
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xbar += n.meta['fwd_mem_out']
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fwd_mem_peak = max(fwd_mem_peak, xbar + n.meta['fwd_mem_tmp'])
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else:
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xbar += calculate_fwd_tmp(n) + calculate_fwd_out(n)
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fwd_mem_peak = max(fwd_mem_peak, xbar + n.meta['fwd_mem_tmp'] + cls._extract_unused_output(n))
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# minimum flop count is required
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ftime += max(calculate_fwd_time(n), 1.0)
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btime += 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 = fwd_mem_peak - xbar
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btmp = cls._extract_btmp(node)
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return ftime, btime, 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_unused_output(node: Node) -> int:
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"""Extract unused output from `torch.fx.Node`"""
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return activation_size(node.meta['fwd_out']) - calculate_fwd_out(node)
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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)
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btmp = max(btmp, _extract_deps_size() + n.meta['bwd_mem_tmp'])
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for child in n.users:
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if child in deps:
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deps[child] -= 1
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if deps[child] <= 0:
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deps[child] = float('-inf') # free
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return btmp
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@staticmethod
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def _compute_table(chain: Chain, mmax: int) -> Tuple:
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"""Compute the table using dynamic programming. Returns the cost table and the backtracking pointer.
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Args:
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chain (Chain): A basic linearized structure for solving the dynamic programming problem.
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mmax (int): Maximum number of memory slots.
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Returns:
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cost_table (List): cost_table[m][lhs][rhs] indicates the optimal cost of the subproblem from lhs to rhs
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with m memory slots.
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back_ptr (List): back_ptr[m][lhs][rhs] indicates the best operation at this point. It is (True,) if the optimal choice
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is a chain checkpoint, it is (False, j) if the optimal choice is a leaf checkpoint of length j
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"""
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ftime = chain.ftime + [0.0]
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btime = chain.btime
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x = chain.x + [0]
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xbar = chain.xbar + [0]
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ftmp = chain.ftmp + [0]
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btmp = chain.btmp + [0]
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# Build table
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cost_table = [[{} for _ in range(len(chain) + 1)] for _ in range(mmax + 1)]
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back_ptr = [[{} for _ in range(len(chain) + 1)] for _ in range(mmax + 1)]
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# Initialize corner cases where length of sequence equals to 1, i.e. lhs == rhs
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for m in range(mmax + 1):
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for i in range(len(chain) + 1):
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limit = max(x[i + 1] + xbar[i + 1] + ftmp[i], x[i + 1] + xbar[i + 1] + btmp[i])
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if m >= limit:
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cost_table[m][i][i] = ftime[i] + btime[i]
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else:
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cost_table[m][i][i] = float("inf")
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# Compute tables
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for m in range(mmax + 1):
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for d in range(1, len(chain) + 1):
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for i in range(len(chain) + 1 - d):
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idx = i + d
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mmin = x[idx + 1] + x[i + 1] + ftmp[i]
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if idx > i + 1:
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mmin = max(mmin, x[idx + 1] + max(x[j] + x[j + 1] + ftmp[j] for j in range(i + 1, idx)))
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if m < mmin:
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cost_table[m][i][idx] = float("inf")
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else:
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leaf_checkpoints = [(j,
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sum(ftime[i:j]) + cost_table[m - x[j]][j][idx] + cost_table[m][i][j - 1])
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for j in range(i + 1, idx + 1)
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if m >= x[j]]
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if leaf_checkpoints:
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best_leaf = min(leaf_checkpoints, key=lambda t: t[1])
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else:
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best_leaf = None
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if m >= xbar[i + 1]:
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chain_checkpoint = cost_table[m][i][i] + cost_table[m - xbar[i + 1]][i + 1][idx]
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else:
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chain_checkpoint = float("inf")
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if best_leaf and best_leaf[1] <= chain_checkpoint:
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cost_table[m][i][idx] = best_leaf[1]
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back_ptr[m][i][idx] = (False, best_leaf[0])
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else:
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cost_table[m][i][idx] = chain_checkpoint
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back_ptr[m][i][idx] = (True,)
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return cost_table, back_ptr
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@staticmethod
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def _compute_table_c(chain: Chain, mmax: int) -> Tuple:
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try:
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from .rotorc import compute_table
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# build module if module not found
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except ModuleNotFoundError:
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import os
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import subprocess
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import sys
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logger = get_dist_logger()
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logger.info("rotorc hasn't been built! Building library...", ranks=[0])
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this_dir = os.path.dirname(os.path.abspath(__file__))
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result = subprocess.Popen(
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[
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f"{sys.executable}", f"{os.path.join(this_dir, 'build_c_ext.py')}", "build_ext",
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f"--build-lib={this_dir}"
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],
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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)
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if result.wait() == 0:
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logger.info("rotorc has been built!", ranks=[0])
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from .rotorc import compute_table
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else:
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logger.warning("rotorc built failed! Using python version!", ranks=[0])
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return CheckpointSolverRotor._compute_table(chain, mmax)
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return compute_table(chain, mmax)
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@staticmethod
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def _backtrack(chain: Chain, lhs: int, rhs: int, budget: int, cost_table: List[Any],
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back_ptr: List[Any]) -> "Sequence":
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"""Backtrack the cost table and retrieve the optimal checkpointing strategy.
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Args:
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chain (Chain): A basic linearized structure for solving the dynamic programming problem.
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lhs (int): The left index of the interval to backtrack.
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rhs (int): The right index of the interval to backtrack.
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budget (int): The memory budget for processing this interval.
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cost_table (List[Any]): See ``._compute_table()`` for definitions
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back_ptr (List[Any]): See ``._compute_table()`` for definitions
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Raises:
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ValueError: Can not process the chain.
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Returns:
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sequence (Sequence): The sequence of executing nodes with checkpoints.
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"""
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if budget <= 0:
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raise ValueError(f"Can not process a chain with negative memory {budget}")
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elif cost_table[budget][lhs][rhs] == float("inf"):
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raise ValueError(f"Can not process this chain from index {lhs} to {rhs} with memory {budget}")
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sequence = Sequence()
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if rhs == lhs:
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if lhs == len(chain):
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sequence += [Loss()]
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else:
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sequence += [ForwardEnable(lhs), Backward(lhs)]
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return sequence
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if back_ptr[budget][lhs][rhs][0]:
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sequence += [
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ForwardEnable(lhs),
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CheckpointSolverRotor._backtrack(chain, lhs + 1, rhs, budget - chain.xbar[lhs + 1], cost_table,
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back_ptr),
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Backward(lhs),
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]
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else:
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best_leaf = back_ptr[budget][lhs][rhs][1]
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sequence += [ForwardCheck(lhs)]
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sequence += [ForwardNograd(k) for k in range(lhs + 1, best_leaf)]
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sequence += [
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CheckpointSolverRotor._backtrack(chain, best_leaf, rhs, budget - chain.x[best_leaf], cost_table,
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back_ptr),
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CheckpointSolverRotor._backtrack(chain, lhs, best_leaf - 1, budget, cost_table, back_ptr),
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]
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return sequence
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@staticmethod
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def _annotate_from_sequence(sequence: Sequence, node_list: List[List[Node]]):
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"""Annotate the nodes in the ``node_list`` with activation checkpoint from the sequence.
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Args:
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sequence (Sequence): The sequence of executing nodes with activation checkpoint annotations.
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node_list (List[List[Node]]): The list of nodes to annotate.
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"""
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op_list = sequence.list_operations()
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loss_op = next(op for op in op_list if isinstance(op, Loss))
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fwd_list = op_list[:op_list.index(loss_op)]
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bwd_list = op_list[op_list.index(loss_op) + 1:]
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ckpt_idx = 0
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in_ckpt = False
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ckpt_region = []
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# forward annotation
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for idx, op in enumerate(fwd_list, 0):
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if in_ckpt:
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if isinstance(op, ForwardNograd):
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ckpt_region.append(idx)
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elif isinstance(op, ForwardEnable):
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in_ckpt = False
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for node_idx in ckpt_region:
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for n in node_list[node_idx]:
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n.meta['activation_checkpoint'] = [ckpt_idx]
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ckpt_idx += 1
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ckpt_region = []
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elif isinstance(op, ForwardCheck):
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for node_idx in ckpt_region:
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for n in node_list[node_idx]:
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n.meta['activation_checkpoint'] = [ckpt_idx]
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ckpt_idx += 1
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ckpt_region = [idx]
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else:
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if isinstance(op, ForwardCheck):
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in_ckpt = True
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ckpt_region.append(idx)
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# annotate the backward if there is any nested activation checkpoint
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in_recompute = False
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for op in bwd_list:
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if in_recompute:
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if isinstance(op, ForwardNograd):
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ckpt_region.append(op.index)
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elif isinstance(op, ForwardEnable):
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for node_idx in ckpt_region:
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for n in node_list[node_idx]:
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n.meta['activation_checkpoint'].append(ckpt_idx)
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ckpt_idx += 1
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ckpt_region = []
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elif isinstance(op, ForwardCheck):
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for node_idx in ckpt_region:
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for n in node_list[node_idx]:
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n.meta['activation_checkpoint'].append(ckpt_idx)
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ckpt_idx += 1
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ckpt_region = [op.index]
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elif isinstance(op, Backward):
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for node_idx in ckpt_region:
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for n in node_list[node_idx]:
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n.meta['activation_checkpoint'].append(ckpt_idx)
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in_recompute = False
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else:
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if not isinstance(op, Backward):
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in_recompute = True
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ckpt_idx = 0
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ckpt_region = []
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if isinstance(op, ForwardCheck):
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ckpt_region.append(op.index)
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# postprocess, make sure every activation checkpoint label in the
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# same activation checkpoint region (level = 0) has the same length
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op_list = []
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for node in node_list:
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op_list += node
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ckpt_regions = _find_nested_ckpt_regions(op_list)
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for (start_idx, end_idx) in ckpt_regions:
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nested_length = max(
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len(op_list[idx].meta['activation_checkpoint']) for idx in range(start_idx, end_idx + 1))
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for idx in range(start_idx, end_idx + 1):
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op_list[idx].meta['activation_checkpoint'] += [None] * (nested_length -
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len(op_list[idx].meta['activation_checkpoint']))
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