_utils.AveragedModel class implements SWA models, _utils implements Stochastic Weight Averaging (SWA). Set the learning rate of each parameter group using a cosine annealing schedule, where η m a x \eta_ T i is the number of epochs between two warm restarts in SGDR: Multiply the learning rate of each parameter group by the factor given in the specified function.ĭecays the learning rate of each parameter group by gamma every step_size epochs.ĭecays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.ĭecays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters.ĭecays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined milestone: total_iters.ĭecays the learning rate of each parameter group by gamma every epoch.ĭecays the learning rate of each parameter group using a polynomial function in the given total_iters. Sets the learning rate of each parameter group to the initial lr times a given function. If you are calling scheduler.step() at the wrong time. If you are unable to reproduce results after upgrading to PyTorch 1.1.0, please check (calling optimizer.step()), this will skip the first value of the learning rate schedule. The learning rate scheduler (calling scheduler.step()) before the optimizer’s update The optimizer’s update 1.1.0 changed this behavior in a BC-breaking way. Prior to PyTorch 1.1.0, the learning rate scheduler was expected to be called before Learning rate scheduling should be applied after optimizer’s update e.g., you _scheduler.ReduceLROnPlateauĪllows dynamic learning rate reducing based on some validation measurements. _scheduler provides several methods to adjust the learning You are welcomeīelow is a table showing the available and default implementations of each algorithm: Like to give them more bake-in time before flipping the switch everywhere. While fused should be even faster than foreach, the implementations are newer and we would (e.g., fused, foreach, differentiable), and all tensors are native and on CUDA. Implementation is available, the user has not specified any implementation-specific kwargs So when applicable, we default to foreach over for-loop. In general, the performance ordering of the 3 implementations is fused > foreach > for-loop. Horizontally and fused implementations as fusing vertically on top of that. We can think of foreach implementations as fusing Optimizers have even faster fused implementations, which fuse the big chunks ofĬomputation into one kernel. Of computation all at once, thereby saving many sequential kernel calls. Implementations, which combine parameters into a multi-tensor and run the big chunks For-looping is usually slower than our foreach The most straightforward implementations are for-loops over the parameters withīig chunks of computation. We have 3 major categories of implementations: for-loop, foreach (multi-tensor), andįused. Implementation for the current device if no particular implementation has been Readability and/or generality, so we attempt to default to the generally fastest Many of our algorithms have various implementations optimized for performance, Implements the resilient backpropagation algorithm. Implements L-BFGS algorithm, heavily inspired by minFunc. Implements Averaged Stochastic Gradient Descent. Implements Adamax algorithm (a variant of Adam based on infinity norm). Implements lazy version of Adam algorithm suitable for sparse tensors. Sets the gradients of all optimized torch.Tensor s to zero. Performs a single optimization step (parameter update). Returns the state of the optimizer as a dict. Options (used when a parameter group doesn’t specify them).Īdd a param group to the Optimizer s param_groups. Specifies what Tensors should be optimized.ĭefaults – (dict): a dict containing default values of optimization Params ( iterable) – an iterable of torch.Tensor s orĭict s. Satisfy those properties are sets and iterators over values of dictionaries. Ordering that is consistent between runs. Parameters need to be specified as collections that have a deterministic Extending torch.func with autograd.Function.CPU threading and TorchScript inference.CUDA Automatic Mixed Precision examples.
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