checkpoint_blocks 函數(shù)實(shí)現(xiàn)了一種分塊梯度檢查點(diǎn)機(jī)制 (checkpoint_blocks)，目的是通過分塊（chunking）執(zhí)行神經(jīng)網(wǎng)絡(luò)模塊，減少內(nèi)存使用。在深度學(xué)習(xí)訓(xùn)練中，梯度檢查點(diǎn)（activation checkpointing）是一種顯存優(yōu)化技術(shù)。該代碼可以：

1. 對(duì)神經(jīng)網(wǎng)絡(luò)的塊（blocks）按需分塊，并對(duì)每塊應(yīng)用梯度檢查點(diǎn)。
2. 動(dòng)態(tài)調(diào)整計(jì)算開銷與顯存占用的權(quán)衡。

1. 源代碼：

from typing import Any, Tuple, List, Callable, Optional
import torch
import torch.utils.checkpoint
import functoolstry:import deepspeeddeepspeed_is_installed = True
except ImportError:deepspeed_is_installed = FalseBLOCK_ARG = Any
BLOCK_ARGS = Tuple[BLOCK_ARG, ...]  # List[BLOCK_ARGS]def get_checkpoint_fn():return torch.utils.checkpoint.checkpoint  # deepspeed.checkpointing.checkpointdef checkpoint_blocks(blocks: List[Callable],args: BLOCK_ARGS,blocks_per_ckpt: Optional[int],
) -> BLOCK_ARGS:"""Chunk a list of b