outputs = run_function(*args)returnoutputs@staticmethoddefbackward(ctx, *args):ifnottorch.autograd._is_checkpoint_valid():raiseRuntimeError("Checkpointing is not compatible with .grad() or when an `inputs` parameter"" is passed to .backward(). Please use .backward() and do not pass its ...
🐛 Describe the bug When running the code below, I get the following error: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/workspaces/pytorch/grad/checkpoint.py", line 45, in main loss.backward() File "/opt/...
幸运的是,这两种设计的复杂性都包装在一个简单易用的 API 中 - 要使用的新实现是通过 use_reentrant 标志指定的,其中使用False(即新实现)将在将来的版本中成为默认值: fromtorch.utils.checkpointimportcheckpointcheckpoint(run_function,args,use_reentrant=False) 总结 本文介绍了 PyTorch 中的激活检查点技术,旨在...
def checkpoint(function, *args, use_reentrant: bool = True, **kwargs): r"""Checkpoint a model or part of the model Checkpointing works by trading compute for memory. Rather than storing all intermediate activations of the entire computation graph for computing backward, the checkpointed part ...
🐛 Describe the bug Hi! So this is quite straight-forward. import torch from torch.utils.checkpoint import checkpoint with torch.device('meta'): m = torch.nn.Linear(20, 30) x = torch.randn(1, 20) out = checkpoint(m, x, use_reentrant=False...
因为checkpoint 是在 torch.no_grad() 模式下计算的目标操作的前向函数,这并不会修改原本的叶子结点的状态,有梯度的还会保持。只是关联这些叶子结点的临时生成的中间变量会被设置为不需要梯度,因此梯度链式关系会被断开。 通过这样的方式,虽然延长了反向传播的时间,但是却也在一定程度上缓解了存储大量...
激活检查点是一种减小内存占用的技巧，以牺牲部分计算资源为代价。这种方法通过仅在需要时重新计算反向传播所需的中间张量，从而避免保存这些张量。PyTorch中包含两种激活检查点实现，即可重新进入和不可重新进入版本。不可重新进入版本在解决可重新进入检查点的限制方面更为先进，可通过use_reentrant标志指定使用...
that processes// tasks (ex: device threads). When graph_task is non-null (ex: reentrant// backwards, user thread), this function is expected to exit once that// graph_task complete.// local_ready_queue should already been initialized when we get into thread_mainwhile(graph_task==nullptr...
Reused parameters in multiple reentrant backward passes. For example, if you use multiplecheckpointfunctions to wrap the same part of your model, it would result in the same set of parameters been used by different reentrant backward passes multiple times, and hence marking a variable ready multipl...
因为现在很多框架，比如像pytorch他内部的分布式训练用到的就是这个。 所以知道他的原理的话也方便我们...