o ,iPO!@s.ddlmZmZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZddgZGd ddeZd d e d e d e d e_       d)deedeedeedeedeedeedeededeedededededededef ddZd d!Zdeedeedeedeedeedeededededededededefd"d#Zdeedeedeedeedeedeededededededededefd$d%Zdeedeedeedeedeedeededededededededed&dfd'd(ZdS)*)ListOptionalN)Tensor)$_get_fused_kernels_supported_devices) _default_to_fused_or_foreach_differentiable_doc _foreach_doc_get_scalar_dtype _get_value _maximize_doc_use_grad_for_differentiable _view_as_real OptimizerParamsTAdagradadagradcseZdZ      ddddddeded ed ed ed ed eedededeeffddZfddZddZ ddZ e dddZ Z S)r{Gz?r绽|=NF)maximizedifferentiablefusedparamslrlr_decay weight_decayinitial_accumulator_valueepsforeachrrrc s~d|ks td|d|kstd|d|ks!td|d|ks,td|d|ks7td|t|||||||| | d } t|| | rx| rStdd |_td tfd d |j Dsrtd d|rxtd|j D]A} | dD]:} |j | }| drt j dt | dd| jdnt jdt d|d<t | rt||n|}t j| |t jd|d<qq{dS)NgzInvalid learning rate: zInvalid lr_decay value: zInvalid weight_decay value: z)Invalid initial_accumulator_value value: zInvalid epsilon value: ) rrrrrrrrrz)`fused` does not support `differentiable`Tcudac3s4|]}|dD]}|jjvot|VqqdS)rN)devicetypetorchis_floating_point).0pgpfused_supported_devicesQ/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch/optim/adagrad.py Gs z#Adagrad.__init__..zX`fused=True` requires all the params to be floating point Tensors of supported devices: .z0`fused` and `foreach` cannot be `True` together.rrr)is_fused)dtyper r/step) memory_formatsum) ValueErrordictsuper__init__ RuntimeError_step_supports_amp_scalingrremoveall param_groupsstater"zerosr r tensor is_complexcomplex full_likepreserve_format)selfrrrrrrrrrrdefaultsgroupr&r= init_value __class__r'r*r7sx         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) NrrFrrrr1r-r0) r6 __setstate__r< setdefaultlistr=valueslenr" is_tensorr?floatr )rDr=rrF state_valuesstep_is_tensorsrHr)r*rJhs$       zAdagrad.__setstate__cCs4|jD]}|dD] }|j|}|dq qdS)Nrr3)r<r= share_memory_)rDrFr&r=r)r)r* share_memory}s   zAdagrad.share_memoryc Cs~d\}}|dD]2}|jdur:||jjO}|t|O}||||j|j|} || d|| dq||fS)N)FFrr3r1)grad is_sparser"r@appendr=) rDrFparams_with_gradgrads state_sums state_stepshas_sparse_grad has_complexr&r=r)r)r* _init_groups      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. Nrrrrrrrr grad_scale found_inf) rrrrr]rrrr^rr`ra)r" enable_gradr<r_rgetattr) rDclosurelossrFrYrZr[r\r]r^r)r)r*r1s@     z Adagrad.step)rrrrrNN)__name__ __module__ __qualname__rrPrboolr7rJrUr_r r1 __classcell__r)r)rHr*rsJ     Pa[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`_. aH Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, 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 FrrZr[r\rr`rar]rrr^rrrrrc 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. 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