o ^iQ!@s8ddlmZmZmZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZddgZGdddeZd d ed e d e d e d e_       d)deedeedeedeedeedeedeededeedededededededef ddZd d!Zdeedeedeedeedeedeededededededededefd"d#Zdeedeedeedeedeedeededededededededefd$d%Zdeedeedeedeedeedeededededededededed&dfd'd(ZdS)*)castOptionalUnionN)Tensor) _default_to_fused_or_foreach_device_dtype_check_for_fused_differentiable_doc _foreach_doc_get_scalar_dtype _get_value _maximize_doc _params_doc _to_scalar_use_grad_for_differentiable _view_as_real OptimizerParamsTAdagradadagradcseZdZ      ddddddedeeefd ed ed ed ed eedededeeffddZ fddZ ddZ ddZ e dddZZS)r{Gz?r绽|=NF)maximizedifferentiablefusedparamslrlr_decay weight_decayinitial_accumulator_valueepsforeachrrrc sbt|tr|dkrtdd|kstd|d|ks%td|d|ks0td|d|ks;td|d|ksFtd||||||||| | d } t|| | rj| ratd |rgtd d |_|jD]A} | d D]:} |j | }| drt j dt | dd| j dnt jdt d|d<t | rt||n|}t j| |t jd|d<qsqmdS)NrzTensor lr must be 1-elementgzInvalid learning rate: zInvalid lr_decay value: zInvalid weight_decay value: z)Invalid initial_accumulator_value value: zInvalid epsilon value: ) rrr rrr!rrrz)`fused` does not support `differentiable`z0`fused` and `foreach` cannot be `True` together.Trris_fused)dtypedevicer%step) memory_formatsum) isinstancernumel ValueErrorsuper__init__ RuntimeError"_need_device_dtype_check_for_fused param_groupsstatetorchzerosr r&tensor is_complexcomplex full_likepreserve_format)selfrrrrrr r!rrrdefaultsgrouppr3 init_value __class__r"N/var/www/html/RAG/RAG_venv/lib/python3.10/site-packages/torch/optim/adagrad.pyr/sf       zAdagrad.__init__cst|d}|jD]}|dd|dd|dd|dd}q t|j}t|dko;t |dd}|sS|D]}tj t |dt |dd |d<q@dSdS) Nr!rFrrrr(r#r') r. __setstate__r2 setdefaultlistr3valueslenr4 is_tensorr6floatr )r;r3rr= state_valuesstep_is_tensorsr@r"rBrCbs$       zAdagrad.__setstate__cCs4|jD]}|dD] }|j|}|dq qdS)z6Calls tensor.share_memory_() on the state sum tensors.rr*N)r2r3 share_memory_)r;r=r>r3r"r"rB share_memoryws   zAdagrad.share_memoryc Csd\}}|dD]E}|jdurM|dr"t|ddr"t|ddd|_||jjO}|t|O}||||j|j|} || d|| d q||fS) N)FFrrr1T)cuda_unsupportedFr*r() gradgetattrrr1 is_sparser4r7appendr3) r;r=params_with_gradgrads state_sums state_stepshas_sparse_grad has_complexr>r3r"r"rB _init_group~s&        zAdagrad._init_groupc Csd}|durt |}Wdn1swY|jD]A}g}g}g}g}||||||\}} t|||||d|d|d|d||d|d|d| |d t|d dt|d dd q |S) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr r!rrr grad_scale found_inf) rrrr rXr!rrrYrr[r\)r4 enable_gradr2rZrrQ) r;closurelossr=rTrUrVrWrXrYr"r"rBr(s@     z Adagrad.step)rrrrrNN)__name__ __module__ __qualname__rrrIrrboolr/rCrNrZrr( __classcell__r"r"r@rBrsJ      Fa[Implements Adagrad algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\ &\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\ &\textbf{initialize} : state\_sum_0 \leftarrow \tau \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} For further details regarding the algorithm we refer to `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization`_. z Args: a lr (float, Tensor, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) initial_accumulator_value (float, optional): initial value of the sum of squares of gradients (default: 0) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-10) z a fused (bool, optional): whether the fused implementation (CPU only) is used. Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16` are supported. (default: None). Please note that the fused implementations does not support sparse or complex gradients. .. _Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: http://jmlr.org/papers/v12/duchi11a.html FrrUrVrWrr[r\rXr!rrYrrrr rc Cstdd|Ds td|dur|durt|| dd\}}|dur$d}|dur*d}|r5tjr5td|r@tjr@td|rJtjsJt}n |rTtjsTt}nt}|||||| | | |||| | ||d dS) ztFunctional API that performs Adagrad algorithm computation. See :class:`~torch.optim.Adagrad` for details. css|] }t|tjVqdSr`)r+r4r).0tr"r"rB szadagrad..zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizersz4torch.jit.script not supported with fused optimizers rrrr rXrrrYr[r\) allr0rr4jit is_scripting_fused_adagrad_multi_tensor_adagrad_single_tensor_adagrad)rrUrVrWrr[r\rXr!rrYrrrr r_funcr"r"rBrsJ  cCs|}t|||Sr`)sizer4sparse_coo_tensor)rP grad_indicesrFrsr"r"rB _make_sparse>srvc Cs|dur|dus Jtjst|}t||||D]\}}}}|d7}t|}| s,|n| }|dkrA|jr:td|j||d}|d|d|}|jr| }| }| }| t |||d||}|  | }|j t ||||| dqt|}|rt|}t|}t|}|j||dd| r|| }n| | }|j||| d|rt|}t|}qdS)Nrrz;weight_decay option is not compatible with sparse gradientsalphavalue)r4rlrmrzipr rRr0addcoalesce_indices_valuesadd_rvpow sparse_masksqrt_r7 view_as_realaddcmul_sqrtaddcdiv_view_as_complex)rrUrVrWr[r\rrrr rXrrrYparamrP state_sumstep_tr(clrru grad_valuesstd std_valuesr7r"r"rBrpCsN        rpcs| rJd|dur|dusJt|dkrdStt||||g}|D]\\}}}}}ttt|}ttt|}ttt|}ttt|}| oWtdd|D}|rmt |||||| d| | | ||dq)| rut |||| r|t |}t j s|djrt j|t jddd dd nt |d |dkr| rt j|||d nt j|||d }fd d |D}t j|||d dt |}t || |dks| rt |||}nt ||}t |||q)dS)Nz#_foreach ops don't support autogradrcss|]}|jVqdSr`)rR)rfrPr"r"rBrhs z(_multi_tensor_adagrad..Trjg?cpu)r&rwrcs&g|]} dt|dqS)r)r )rfr(rrr"rB sz)_multi_tensor_adagrad..rz)rGrr"_group_tensors_by_device_and_dtyperFrrEranyrprr4 _foreach_negcompiler is_compilingis_cpu _foreach_add_r6 _foreach_add_foreach_addcmul_ _foreach_sqrt _foreach_mul_ _foreach_mul_foreach_addcdiv_)rrUrVrWr[r\rrrr rXrrrYgrouped_tensorlistsdevice_params_ device_grads_device_state_sums_device_state_steps_rq device_params device_gradsdevice_state_sumsdevice_state_stepsdevice_has_sparse_grad minus_clrr numeratorr"rrBros             roreturncCs~|sdS| s| r td| rtdt|}|dur|j|ind}|dur*|j|ind}t||||g}|D]\\}}\\}}}}}ttt|}ttt|}ttt|}ttt|}d\}}|dur~|dur~||vrz|j |dd||<||}|dur|dur||vr|j |dd||<||}t |dt j |||||||| | ||d |durt ||gt|q9dS)Nz5`fused` does not support sparse grad or complex paramzs* <(   8     J      A      m