回传回来的对应于 checkpoint 输出的梯度被送入其对应的反向传播函数,即 checkpoint 的backward()。 梯度送入 checkpoint 中后,需要进一步将梯度回传到目标层的输入上。由于在 checkpoint 的forward中目标层本身前向传播是处于非梯度状态下,所以回传路径上缺少目标层中操作的梯度子图。于是为了获取这部分信息,需要先梯...
def demo_checkpoint(rank, world_size): # [*] print(f"Running DDP checkpoint example on rank {rank}.") setup(rank, world_size) # [*] model = ToyModel().to(rank) ddp_model = DDP(model, device_ids=[rank]) CHECKPOINT_PATH = tempfile.gettempdir() + "/model.checkpoint" # [*] if...
To save a checkpoint in PyTorch, you can use thetorch.save()function. Here’s an example of how to save a checkpoint after training a neural network: importtorchimporttorch.nnasnnimporttorch.optimasoptim# Define a simple neural networkclassNet(nn.Module):def__init__(self):super(Net,self)...
Example >>> model =nn.Sequential(...)>>> input_var = checkpoint_sequential(model, chunks, input_var) 在DenseNet中为了解决GPU内存占用大的问题,就采用了这种策略缓解显存占用大的问题。 下面是denselayer的细节: 1class_DenseLayer(nn.Sequential):#bottleneck + conv2def__init__(self, num_input_feat...
Saving a TensorFlow checkpoint Before initializing an Estimator, we have to define the checkpoint strategy. To do so, we have to create a configuration for the Estimator using the tf.estimator.RunConfigAPI. Here's an example of how we might do this: 代码语言:javascript 代码运行次数:0 运行 ...
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中间变量带来的显存...
Saving a TensorFlow checkpoint Before initializing anEstimator, we have to define the checkpoint strategy. To do so, we have to create a configuration for the Estimator using thetf.estimator.RunConfigAPI. Here's an example of how we might do this: ...
基于FP32 checkpoint的模型量化比从零开始训练INT8模型会有更好的准确性 分析模型运行时并非总是必要的,但可以帮助识别推理过程的瓶颈层。 动态量化是最简单的,常为首选方案,特别是当模型有许多线性或RNN层。换句话说,动态量化常用以NLP领域的模型。而静态量化一般用在CV领域,主要针对CNN网络。 使用对称的每通道量化...
path.join('model', 'checkpoint.pth.tar') best_model_path = os.path.join('model', 'best_checkpoint.pth.tar') torch.save(checkpoint, model_path) if is_best: shutil.copy(model_path, best_model_path) 提取ImageNet 预训练模型某层的卷积特征 代码语言:javascript 代码运行次数:0 运行 AI代码...
🐛 Bug torch.utils.checkpoint.checkpoint fails when used with torch.cuda.amp on pytorch nightly. They both allow to reduce the memory usage so seem like a natural combination to use together. To Reproduce Here is an example program derive...