recompute函数内部(recompute函数位于python/paddle/distributed/fleet/recompute/recompute.py)由use_reentrant参数控制,会采用两种方法实现重计算功能:use_reentrant == True时会使用PyLayer来实现。但PyLayer目前不支持以关键字参数的形式传入 Tensor 类型参数(因为以 dict 形式传入的 Tensor 无法正确的创建反向节点、...
用户收到此警告是因为在调用torch.utils.checkpoint时没有明确指定use_reentrant参数。从PyTorch的文档和警告信息来看,这是为了提醒用户,在未来的版本中,use_reentrant的默认值可能会改变,因此最好显式指定以避免潜在的问题。 4. 提供解决方案:如何正确使用use_reentrant参数或采取其他措施来避免此警告 为了避免此警告并...
if self.use_checkpoint and self.training: x = checkpoint(self.resblks[i], x, octree, depth) x = checkpoint(self.resblks[i], x, octree, depth, use_reentrant=False) else: x = self.resblks[i](x, octree, depth) return x 0 comments on commit 8e192c9 Please sign in to comment....
A reentrant function is one that can be used by more than one task concurrently without fear of data corruption. Conversely, a non-reentrant function is one that cannot be shared by more than one task unless mutual exclusion to the function is ensured either by using a semaphore or by disab...
when to use reentrant lock The answer is pretty simple -- use it when you actually need something it provides that synchronized doesn't, like timed lock waits, interruptible lock waits, non-block-structured locks, multiple condition variables, or lock polling....
Use reentrant functions for safer signal handlingdoi:http://dx.doi.org/PancreasPaulino-netto A.Dipak JhaSaunders,
The current drug of choice for reentrant supraventricular tachycardia (SVT) is adenosine followed by verapamil or diltiazem. Although limitations and significant adverse events have been encountered over the years, an alternative effective and safe agent has not been available. Dexmedetomidine has recently...
Skip navigation links Overview Package Class Tree Deprecated Index Help Summary: Nested | Field | Constr | Method Detail: Field | Constr | Method SEARCH: Package com.mchange.v2.lock Class ExactReentrantSharedUseExclusiveUseLock java.lang.Object com.mchange.v2.lock.ExactReentrantSharedUseExclusiveUse...
use: module: java.base, package: java.util.concurrent.locks, class: ReentrantReadWriteLock, class: ReadLock
block, x, use_reentrant=False, debug=True) else: return self.block(x) class Mod(torch.nn.Module): def __init__(self, size: int): super().__init__() self.stack = PairStack(size) self.linear = torch.nn.Linear(size, size) def forward(self, x): with torch.set_grad_enabled(...