o ,i>@sddlmZmZmZmZddlZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZddgZGdddeZd d e d ed ed e d e_d eedeedeedeedeededededededededefddZd eedeedeedeedeededededededededefddZe ed    d"d eedeedeedeedeeded eedededededededefd!dZdS)#)AnyDictListOptionalN)Tensor) _capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported _foreach_doc!_get_capturable_supported_devices_get_scalar_dtype _maximize_doc_use_grad_for_differentiable _view_as_real OptimizerParamsTAdadeltaadadeltacseZdZ     dddddded ed ed ed ed eedededeffddZfddZde e e fde e de e de e de e de e f ddZed ddZZS)!r??ư>rNF) capturablemaximizedifferentiableparamslrrhoeps weight_decayforeachrrrc sd|ks td|d|krdksntd|d|ks(td|d|ks3td|t|||||||| d} t|| dS)NgzInvalid learning rate: rzInvalid rho value: zInvalid epsilon value: zInvalid weight_decay value: )rrrr rrr!r) ValueErrordictsuper__init__) selfrrrrr r!rrrdefaults __class__R/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch/optim/adadelta.pyr%s&  zAdadelta.__init__cst||jD]S}|dd|dd|dd|dd|dD]4}|j|g}t|dkr[t|ds[t |d}|drQtj |t |j d ntj |t d |d<q'q dS) Nr!rFrrrrstepdtypedevicer.) r$ __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorrr/)r&r4grouppp_statestep_valr(r*r+r1<s&         zAdadelta.__setstate__r;params_with_gradgrads square_avgs acc_deltas state_stepsc Csd}|dD]n}|jdurq|t|O}|||jjr"td||j|j|} t| dkr_|drAtjdt |j dntjdt d| d <tj |tj d | d <tj |tj d | d <|| d || d || d q|S) NFrz*Adadelta does not support sparse gradientsrrr*r-r0r,) memory_format square_avg acc_delta) gradr7 is_complexappend is_sparse RuntimeErrorr4r6zerosrr/ zeros_likepreserve_format) r&r;r?r@rArBrC has_complexr<r4r*r*r+ _init_groupOs2        zAdadelta._init_groupcCs|d}|dur!t |}Wdn1swY|jD]J}g}g}g}g}g}|d|d|d|d|d|d|d|d f\} } } } } }}}|||||||}t|||||| | | | | ||||d q$|S) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr r!rrr) rrrr r!rrrrO) _cuda_graph_capture_health_checkr7 enable_gradr2rPr)r&closurelossr;r?r@rArBrCrrrr r!rrrrOr*r*r+r,zsd    z Adadelta.step)rrrrNN)__name__ __module__ __qualname__rr9rboolr%r1rstrrrrrPrr, __classcell__r*r*r(r+rsZ     "  +aImplements Adadelta algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)}, \: \lambda \text{ (weight decay)} \\ &\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)}, \: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-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}if \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\ &\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} + \epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\ &\hspace{5mm} u_t \leftarrow u_{t-1} \rho + \Delta x^2_t (1 - \rho) \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_t \\ &\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 `ADADELTA: An Adaptive Learning Rate Method`_. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups rho (float, optional): coefficient used for computing a running average of squared gradients (default: 0.9). A higher value of `rho` will result in a slower average, which can be helpful for preventing oscillations in the learning process. eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-6). lr (float, optional): coefficient that scale delta before it is applied to the parameters (default: 1.0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) z zd .. _ADADELTA\: An Adaptive Learning Rate Method: https://arxiv.org/abs/1212.5701 rr@rArBrCrrrr rrrrOcsLtjs"| r"tddtfddt||Ds"Jddt|||||D]y\} }}}}|d7}| s9|n| }|dkrG|j| |d }t| r[t|}t|}t|}| |j ||d|d || }|| }| r|| }| | || |j ||d|d t| rt|}| j|| d q*dS) NF supports_xlac30|]\}}|jj|jjko|jjvVqdSrUr/type.0r<r,capturable_supported_devicesr*r+    z*_single_tensor_adadelta..IIf capturable=True, params and state_steps must be on supported devices: .rralphavalue)r7_utils is_compilingr allzipaddrH view_as_realmul_addcmul_sqrt_clonediv_view_as_complexadd_)rr@rArBrCrrrr rrrrOparamrGrErFr,stddeltar*rcr+_single_tensor_adadeltas>         r}cs| rJdtjs(| r(tddtfddt||Ds(Jddt|dkr0dSt|||||g} | D]\\}}}}}}| rPt |||||dj rctj |tj d d d d d nt |d | rpt|}|dkr| rtj |||d ntj|||d }t||tj|||d |dt||}t|t||}t|t||t||t||tj|||d |d| rt|tjrt|| t ||q>tj ||| d q>dS)Nz#_foreach ops don't support autogradFr\c3r^rUr_rarcr*r+re>rfz)_multi_tensor_adadelta..rgrhrrcpu)r/rirrk)r7rmrnr rorpr6r"_group_tensors_by_device_and_dtypevaluesris_cpu _foreach_add_r: _foreach_neg _foreach_add _foreach_mul__foreach_addcmul__foreach_sqrt_ _foreach_div_ isinstancer)rr@rArBrCrrrr rrrrOgrouped_tensors device_params device_gradsdevice_square_avgsdevice_acc_deltasdevice_state_steps_r{deltasr*rcr+_multi_tensor_adadelta'sr                  r)single_tensor_fnFr!c Cstjstdd|Dstd|durt||dd\}}|r*tjr*td|r4tjs4t}nt }||||||| | | | | |||d dS) zvFunctional API that performs Adadelta algorithm computation. See :class:`~torch.optim.Adadelta` for details. css|] }t|tjVqdSrU)rr7r)rbtr*r*r+res  zadadelta..zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers)rrrr rrrrO) r7rmrnrorKr jit is_scriptingrr})rr@rArBrCrr!rrOrrrr rrfuncr*r*r+rs<  )FNFF)typingrrrrr7r optimizerrr r r r r rrrrrr__all__r__doc__r9rYr}rrr*r*r*r+s 8$ 6      6      _