o zi@s\ddlmZddlmZmZddlZddlmZddlm Z m Z m Z dZ Gdd d eZ dS) ) defaultdict)AnyDictN) Optimizer)OptFloatOptLossClosureState) Lookaheadc seZdZdZ ddedededdfd d Zd ee e fddfd d Z dde de fddZdeffdd Zdeddffdd ZdddZde fddZZS)r aImplements Lookahead optimization algorithm. It has been proposed in `Lookahead Optimizer: k steps forward, 1 step back`__ Arguments: optimizer: base inner optimizer optimize, like Yogi, DiffGrad or Adam. k: number of lookahead steps (default: 5) alpha: linear interpolation factor. 1.0 recovers the inner optimizer. (default: 5) Example: >>> import torch_optimizer as optim >>> yogi = optim.Yogi(model.parameters(), lr=0.1) >>> optimizer = optim.Lookahead(yogi, k=5, alpha=0.5) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/1907.08610 Note: Reference code: https://github.com/alphadl/lookahead.pytorch ? optimizerkalphareturnNcCst|dkr td||dkrtd|||_||_||_|jj|_tt|_|jj|_ |jD]}d|d<q1dS)Ngz%Invalid number of lookahead steps: {}rz'Invalid linear interpolation factor: {}counter) ValueErrorformatr rr param_groupsrdictstate fast_state)selfr rrgrouprM/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/lookahead.py__init__&s     zLookahead.__init__rcCsj|dD].}|j|}d|vrt|j|d<|d}|j|jj|d|jd|j|qdS)Nparams slow_paramg?)r) rtorchclonedatadetachmul_radd_copy_)rrfast param_stateslowrrr_update9s  zLookahead._updateclosurecCsJ|jj|d}|jD]}|ddkr|||dd|j|d<q |S)zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. )r*rrr)r steprr)r)rr*lossrrrrr+Cs    zLookahead.stepcs8tt|}|j}|d}|d}||d|dS)aReturns the state of the optimizer as a :class:`dict`. It contains two entries: * state - a dict holding current optimization state. Its content differs between optimizer classes. * param_groups - a dict containing all parameter groups rr)r slow_stater)superr state_dictr )rslow_state_dictfast_state_dictrr __class__rrr/Ps zLookahead.state_dictr/csN|d|dd}|d|dd}tt|||j||jj|_dS)zLoads the optimizer state. Arguments: state_dict: optimizer state. Should be an object returned from a call to :meth:`state_dict`. r-r)rrrN)r.r load_state_dictr rr)rr/r0r1r2rrr4bs zLookahead.load_state_dictcCs|jdS)z>Clears the gradients of all optimized :class:`torch.Tensor` s.N)r zero_grad)rrrrr5uszLookahead.zero_gradcCsZ|j}|jjd}|d7}|d|j7}|d|j7}||7}|d7}|d7}|S)Nz ( zk: {} z alpha: {} ))r __repr__r3__name__rrr)rbase_str format_stringrrrr8ys  zLookahead.__repr__)r r )N)rN)r9 __module__ __qualname____doc__rintfloatrrstrrr)rrr+r r/r4r5r8 __classcell__rrr2rr s$    r ) collectionsrtypingrrrtorch.optim.optimizerrtypesrrr __all__r rrrrs