Shortcuts

Source code for torch.optim.rprop

import torch
from torch import Tensor
from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
                        _differentiable_doc, _foreach_doc, _maximize_doc)
from typing import List, Optional
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype

__all__ = ["Rprop", "rprop"]


[docs]class Rprop(Optimizer): def __init__( self, params, lr=1e-2, etas=(0.5, 1.2), step_sizes=(1e-6, 50), *, foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, ): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 < etas[0] < 1.0 < etas[1]: raise ValueError("Invalid eta values: {}, {}".format(etas[0], etas[1])) defaults = dict( lr=lr, etas=etas, step_sizes=step_sizes, foreach=foreach, maximize=maximize, differentiable=differentiable, ) super().__init__(params, defaults) def __setstate__(self, state): super().__setstate__(state) for group in self.param_groups: group.setdefault("foreach", None) group.setdefault("maximize", False) group.setdefault("differentiable", False) def _init_group(self, group, params, grads, prevs, step_sizes): for p in group["params"]: if p.grad is None: continue params.append(p) grad = p.grad if grad.is_sparse: raise RuntimeError("Rprop does not support sparse gradients") grads.append(grad) state = self.state[p] # State initialization if len(state) == 0: state["step"] = 0 state["prev"] = torch.zeros_like( p, memory_format=torch.preserve_format ) if p.dtype.is_complex: # Complex Number should be as if they are two independent real numbers. # Hence the step_size shouldn't be zero for imaginary part. state["step_size"] = ( grad.new() .resize_as_(grad) .fill_(complex(group["lr"], group["lr"])) ) else: state["step_size"] = ( grad.new().resize_as_(grad).fill_(group["lr"]) ) prevs.append(state["prev"]) step_sizes.append(state["step_size"]) state["step"] += 1 @_use_grad_for_differentiable def step(self, closure=None): """Performs a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params = [] grads = [] prevs = [] step_sizes = [] etaminus, etaplus = group["etas"] step_size_min, step_size_max = group["step_sizes"] foreach = group["foreach"] maximize = group["maximize"] self._init_group(group, params, grads, prevs, step_sizes) rprop( params, grads, prevs, step_sizes, step_size_min=step_size_min, step_size_max=step_size_max, etaminus=etaminus, etaplus=etaplus, foreach=foreach, maximize=maximize, differentiable=group["differentiable"], ) return loss
Rprop.__doc__ = r"""Implements the resilient backpropagation algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta) \text{ (objective)}, \\ &\hspace{13mm} \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min} \text{ (step sizes)} \\ &\textbf{initialize} : g^0_{prev} \leftarrow 0, \: \eta_0 \leftarrow \text{lr (learning rate)} \\ &\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} \textbf{for} \text{ } i = 0, 1, \ldots, d-1 \: \mathbf{do} \\ &\hspace{10mm} \textbf{if} \: g^i_{prev} g^i_t > 0 \\ &\hspace{15mm} \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+}, \Gamma_{max}) \\ &\hspace{10mm} \textbf{else if} \: g^i_{prev} g^i_t < 0 \\ &\hspace{15mm} \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-}, \Gamma_{min}) \\ &\hspace{15mm} g^i_t \leftarrow 0 \\ &\hspace{10mm} \textbf{else} \: \\ &\hspace{15mm} \eta^i_t \leftarrow \eta^i_{t-1} \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t) \\ &\hspace{5mm}g_{prev} \leftarrow g_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 the paper `A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>`_. """ + r""" Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) etas (Tuple[float, float], optional): pair of (etaminus, etaplus), that are multiplicative increase and decrease factors (default: (0.5, 1.2)) step_sizes (Tuple[float, float], optional): a pair of minimal and maximal allowed step sizes (default: (1e-6, 50)) {foreach} {maximize} {differentiable} """.format(foreach=_foreach_doc, maximize=_maximize_doc, differentiable=_differentiable_doc) def rprop( params: List[Tensor], grads: List[Tensor], prevs: List[Tensor], step_sizes: List[Tensor], # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627 # setting this as kwarg for now as functional API is compiled by torch/distributed/optim foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, *, step_size_min: float, step_size_max: float, etaminus: float, etaplus: float, ): r"""Functional API that performs rprop algorithm computation. See :class:`~torch.optim.Rprop` for details. """ if foreach is None: _, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False) if foreach and torch.jit.is_scripting(): raise RuntimeError("torch.jit.script not supported with foreach optimizers") if foreach and not torch.jit.is_scripting(): func = _multi_tensor_rprop else: func = _single_tensor_rprop func( params, grads, prevs, step_sizes, step_size_min=step_size_min, step_size_max=step_size_max, etaminus=etaminus, etaplus=etaplus, maximize=maximize, differentiable=differentiable, ) def _single_tensor_rprop( params: List[Tensor], grads: List[Tensor], prevs: List[Tensor], step_sizes: List[Tensor], *, step_size_min: float, step_size_max: float, etaminus: float, etaplus: float, maximize: bool, differentiable: bool, ): for i, param in enumerate(params): grad = grads[i] grad = grad if not maximize else -grad prev = prevs[i] step_size = step_sizes[i] if torch.is_complex(param): grad = torch.view_as_real(grad) prev = torch.view_as_real(prev) param = torch.view_as_real(param) step_size = torch.view_as_real(step_size) if differentiable: sign = grad.mul(prev.clone()).sign() else: sign = grad.mul(prev).sign() sign[sign.gt(0)] = etaplus sign[sign.lt(0)] = etaminus sign[sign.eq(0)] = 1 # update stepsizes with step size updates step_size.mul_(sign).clamp_(step_size_min, step_size_max) # for dir<0, dfdx=0 # for dir>=0 dfdx=dfdx grad = grad.clone(memory_format=torch.preserve_format) grad[sign.eq(etaminus)] = 0 # update parameters param.addcmul_(grad.sign(), step_size, value=-1) prev.copy_(grad) def _multi_tensor_rprop( params: List[Tensor], grads: List[Tensor], prevs: List[Tensor], step_sizes: List[Tensor], *, step_size_min: float, step_size_max: float, etaminus: float, etaplus: float, maximize: bool, differentiable: bool, ): if len(params) == 0: return assert not differentiable, "_foreach ops don't support autograd" grouped_tensors = _group_tensors_by_device_and_dtype([params, grads, prevs, step_sizes]) for grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes in grouped_tensors.values(): # Handle complex params def _view_complex_as_real(tensor_list): return [ torch.view_as_real(t) if torch.is_complex(t) else t for t in tensor_list ] grouped_grads = _view_complex_as_real(grouped_grads) grouped_prevs = _view_complex_as_real(grouped_prevs) grouped_params = _view_complex_as_real(grouped_params) grouped_step_sizes = _view_complex_as_real(grouped_step_sizes) if maximize: grouped_grads = torch._foreach_neg(grouped_grads) signs = torch._foreach_mul(grouped_grads, grouped_prevs) signs = [s.sign() for s in signs] for sign in signs: sign[sign.gt(0)] = etaplus sign[sign.lt(0)] = etaminus sign[sign.eq(0)] = 1 # update stepsizes with step size updates torch._foreach_mul_(grouped_step_sizes, signs) for step_size in grouped_step_sizes: step_size.clamp_(step_size_min, step_size_max) # for dir<0, dfdx=0 # for dir>=0 dfdx=dfdx grouped_grads = list(grouped_grads) for i in range(len(grouped_grads)): grouped_grads[i] = grouped_grads[i].clone(memory_format=torch.preserve_format) grouped_grads[i][signs[i].eq(etaminus)] = 0 # update parameters grad_signs = [grad.sign() for grad in grouped_grads] torch._foreach_addcmul_(grouped_params, grad_signs, grouped_step_sizes, value=-1) for i in range(len(grouped_prevs)): grouped_prevs[i].copy_(grouped_grads[i])

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources