o p i5@sddlmZddlmZmZddlZddlZddlm Z ddl m Z m Z m Z er4ddlmZddlmZ   d'd(d%d&ZdS))) annotations) TYPE_CHECKINGLiteralN) strong_wolfe)_value_and_gradient&check_initial_inverse_hessian_estimatecheck_input_type)Callable)Tensord2:0yE>r?float32objective_funcCallable[[Tensor], Tensor]initial_positionr history_sizeint max_iterstolerance_gradfloattolerance_change initial_inverse_hessian_estimate Tensor | Noneline_search_fnLiteral['strong_wolfe']max_line_search_itersinitial_step_lengthdtypeLiteral['float32', 'float64']name str | Nonereturn(tuple[bool, int, Tensor, Tensor, Tensor]c sdvr tddd} t|d| |dur#tj|jddn t|d | t||t|} t| \}}tj d gdd d }tj d gd dd }tj d gd dd }tj d gd d d }tj gd d tj d gd d d }tj d gdd d }|jd}tj d |fd}tj d |fd}tj d d fd}tj d d fdfdd} f dd}tj j j |||||||| ||||||g d||| ||fS)a Minimizes a differentiable function `func` using the L-BFGS method. The L-BFGS is a quasi-Newton method for solving an unconstrained optimization problem over a differentiable function. Closely related is the Newton method for minimization. Consider the iterate update formula: .. math:: x_{k+1} = x_{k} + H_k \nabla{f_k} If :math:`H_k` is the inverse Hessian of :math:`f` at :math:`x_k`, then it's the Newton method. If :math:`H_k` is symmetric and positive definite, used as an approximation of the inverse Hessian, then it's a quasi-Newton. In practice, the approximated Hessians are obtained by only using the gradients, over either whole or part of the search history, the former is BFGS, the latter is L-BFGS. Reference: Jorge Nocedal, Stephen J. Wright, Numerical Optimization, Second Edition, 2006. pp179: Algorithm 7.5 (L-BFGS). Args: objective_func: the objective function to minimize. ``objective_func`` accepts a 1D Tensor and returns a scalar. initial_position (Tensor): the starting point of the iterates, has the same shape with the input of ``objective_func`` . history_size (Scalar): the number of stored vector pairs {si,yi}. Default value: 100. max_iters (int, optional): the maximum number of minimization iterations. Default value: 50. tolerance_grad (float, optional): terminates if the gradient norm is smaller than this. Currently gradient norm uses inf norm. Default value: 1e-7. tolerance_change (float, optional): terminates if the change of function value/position/parameter between two iterations is smaller than this value. Default value: 1e-9. initial_inverse_hessian_estimate (Tensor, optional): the initial inverse hessian approximation at initial_position. It must be symmetric and positive definite. If not given, will use an identity matrix of order N, which is size of ``initial_position`` . Default value: None. line_search_fn (str, optional): indicate which line search method to use, only support 'strong wolfe' right now. May support 'Hager Zhang' in the future. Default value: 'strong wolfe'. max_line_search_iters (int, optional): the maximum number of line search iterations. Default value: 50. initial_step_length (float, optional): step length used in first iteration of line search. different initial_step_length may cause different optimal result. For methods like Newton and quasi-Newton the initial trial step length should always be 1.0. Default value: 1.0. dtype ('float32' | 'float64', optional): data type used in the algorithm, the data type of the input parameter must be consistent with the dtype. Default value: 'float32'. name (str, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default value: None. Returns: output(tuple): - is_converge (bool): Indicates whether found the minimum within tolerance. - num_func_calls (int): number of objective function called. - position (Tensor): the position of the last iteration. If the search converged, this value is the argmin of the objective function regarding to the initial position. - objective_value (Tensor): objective function value at the `position`. - objective_gradient (Tensor): objective function gradient at the `position`. Examples: .. code-block:: python :name: code-example1 >>> # Example1: 1D Grid Parameters >>> import paddle >>> # Randomly simulate a batch of input data >>> inputs = paddle. normal(shape=(100, 1)) >>> labels = inputs * 2.0 >>> # define the loss function >>> def loss(w): ... y = w * inputs ... return paddle.nn.functional.square_error_cost(y, labels).mean() >>> # Initialize weight parameters >>> w = paddle.normal(shape=(1,)) >>> # Call the bfgs method to solve the weight that makes the loss the smallest, and update the parameters >>> for epoch in range(0, 10): ... # Call the bfgs method to optimize the loss, note that the third parameter returned represents the weight ... w_update = paddle.incubate.optimizer.functional.minimize_bfgs(loss, w)[2] ... # Use paddle.assign to update parameters in place ... paddle.assign(w_update, w) .. code-block:: python :name: code-example2 >>> # Example2: Multidimensional Grid Parameters >>> import paddle >>> def flatten(x): ... return x. flatten() >>> def unflatten(x): ... return x.reshape((2,2)) >>> # Assume the network parameters are more than one dimension >>> def net(x): ... assert len(x.shape) > 1 ... return x.square().mean() >>> # function to be optimized >>> def bfgs_f(flatten_x): ... return net(unflatten(flatten_x)) >>> x = paddle.rand([2,2]) >>> for i in range(0, 10): ... # Flatten x before using minimize_bfgs ... x_update = paddle.incubate.optimizer.functional.minimize_bfgs(bfgs_f, flatten(x))[2] ... # unflatten x_update, then update parameters ... paddle.assign(unflatten(x_update), x) )rZfloat64z?The dtype must be 'float32' or 'float64', but the specified is .minimize_lbfgsrNrr rrint64shapeZ fill_valuer Fboolc s|k|@SN) kdone is_convergenum_func_callsvaluexkg1sk_vecyk_vecrhok_vecheadtail)rr.q/home/app/PaddleOCR-VL/.venv_paddleocr/lib/python3.10/site-packages/paddle/incubate/optimizer/functional/lbfgs.pycondszminimize_lbfgs..condc  sXt|} tjgd dd} fdd} fdd}tjjj||| | gdt| }tjgddd} fd d} fd d}tjjj||| |gd| } d krpt|| d \}}}}nt d dt|||||}||}t ||tjj dk fddfdd}t r|<|<|<ntj |tj |tj |d ddtjj kfddd||}|}|d7}tjj|tjd}tjj|tjd}t||kB|kB|t||t||dkB||||||||g S)Nrr)r*c|kSr-r.iqai_vecr:r.r;r<z*minimize_lbfgs..body..condc svtr|t||||<ntj|||t||}||||}|d}|||fSNr)paddlein_dynamic_modedotstaticsetitemmodr>)rr8r6r7r.r;bodys z*minimize_lbfgs..body..bodyr<rKZ loop_varscr=r-r.)r?r)r9r.r;r<rCcsF|t||}||||}|d}||fSrD)rErGrJ)r?rMbeta)rArr8r6r7r.r;rKsr)fr4pkrrr zNCurrently only support line_search_fn = 'strong_wolfe', but the specified is ''gcstjdgddS)Nrg@@r*)rEfullr.r(r.r;sz.minimize_lbfgs..body..csdS)Nrr.r.)rhok_invr.r;rScSst|d|dSrD)rEassignrBr.r.r;true_fn sz-minimize_lbfgs..body..true_fncsSr-r.r.)r:rWr.r;rS$rU)p)rErVrRrJrHnn while_loopmatmulrNotImplementedErrorrGr<rFrIZlinalgZnormnpinf)r/r0r1r2r3r4r5r6r7r8r9r:r@r?r<rKrMrPalphag2Z ls_func_callsskZykZrhokZgnormZpk_norm) H0rAr rrrrrrr)r9rTr8r6r:rWr7r;rKs             zminimize_lbfgs..bodyrL) ValueErrorr rEeyer+rrVdetachrrRZzerosrHrYrZ)rrrrrrrrrrr r"Zop_namer4r3r5r/r0r1r2r9r:r+r6r7r8r<rKr.) rbrAr rrrrrrrrr;r'$sdd    r') r r rrNrr rrN)rrrr rrrrrrrrrrrrrrrrr r!r"r#r$r%) __future__rtypingrrnumpyr]rEZ line_searchrutilsrrr collections.abcr r r'r.r.r.r;s(