# mypy: allow-untyped-decorators # mypy: allow-untyped-defs r"""Implementation for the Resilient backpropagation.""" from typing import cast, List, Optional, Tuple, Union import torch from torch import Tensor from .optimizer import ( _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, Optimizer, ParamsT, ) __all__ = ["Rprop", "rprop"] class Rprop(Optimizer): # noqa: D101 def __init__( self, params: ParamsT, lr: Union[float, Tensor] = 1e-2, etas: Tuple[float, float] = (0.5, 1.2), step_sizes: Tuple[float, float] = (1e-6, 50), *, capturable: bool = False, foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, ): # noqa: D107 if isinstance(lr, Tensor) and lr.numel() != 1: raise ValueError("Tensor lr must be 1-element") if not 0.0 <= lr: raise ValueError(f"Invalid learning rate: {lr}") if not 0.0 < etas[0] < 1.0 < etas[1]: raise ValueError(f"Invalid eta values: {etas[0]}, {etas[1]}") defaults = dict( lr=lr, etas=etas, step_sizes=step_sizes, foreach=foreach, maximize=maximize, differentiable=differentiable, capturable=capturable, ) super().__init__(params, defaults) def __setstate__(self, state): # noqa: D105 super().__setstate__(state) for group in self.param_groups: group.setdefault("foreach", None) group.setdefault("maximize", False) group.setdefault("differentiable", False) group.setdefault("capturable", False) for p in group["params"]: p_state = self.state.get(p, []) if len(p_state) != 0 and not torch.is_tensor(p_state["step"]): step_val = float(p_state["step"]) p_state["step"] = ( torch.tensor( step_val, dtype=_get_scalar_dtype(), device=p.device ) if group["capturable"] else torch.tensor(step_val, dtype=_get_scalar_dtype()) ) def _init_group(self, group, params, grads, prevs, step_sizes, state_steps): has_complex = False for p in group["params"]: if p.grad is None: continue has_complex |= torch.is_complex(p) 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"] = ( torch.zeros((), dtype=_get_scalar_dtype(), device=p.device) if group["capturable"] else torch.zeros((), dtype=_get_scalar_dtype()) ) 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"] = torch.full_like( grad, complex(group["lr"], group["lr"]) ) else: state["step_size"] = torch.full_like(grad, group["lr"]) prevs.append(state["prev"]) step_sizes.append(state["step_size"]) state_steps.append(state["step"]) return has_complex @_use_grad_for_differentiable def step(self, closure=None): """Perform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. """ self._cuda_graph_capture_health_check() loss = None if closure is not None: with torch.enable_grad(): loss = closure() for group in self.param_groups: params: List[Tensor] = [] grads: List[Tensor] = [] prevs: List[Tensor] = [] step_sizes: List[Tensor] = [] state_steps: List[Tensor] = [] etaminus, etaplus = group["etas"] step_size_min, step_size_max = group["step_sizes"] foreach = group["foreach"] maximize = group["maximize"] has_complex = self._init_group( group, params, grads, prevs, step_sizes, state_steps ) rprop( params, grads, prevs, step_sizes, state_steps, step_size_min=step_size_min, step_size_max=step_size_max, etaminus=etaminus, etaplus=etaplus, foreach=foreach, maximize=maximize, differentiable=group["differentiable"], capturable=group["capturable"], has_complex=has_complex, ) 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 `_. """ + rf""" 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_doc} {_capturable_doc} {_maximize_doc} {_differentiable_doc} """ ) def _single_tensor_rprop( params: List[Tensor], grads: List[Tensor], prevs: List[Tensor], step_sizes: List[Tensor], state_steps: List[Tensor], *, step_size_min: float, step_size_max: float, etaminus: float, etaplus: float, maximize: bool, capturable: bool, differentiable: bool, has_complex: 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] step = state_steps[i] # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable] if not torch._utils.is_compiling() and capturable: capturable_supported_devices = _get_capturable_supported_devices() assert ( param.device.type == step.device.type and param.device.type in capturable_supported_devices ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}." step += 1 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() if capturable: sign.copy_(torch.where(sign.gt(0), etaplus, sign)) sign.copy_(torch.where(sign.lt(0), etaminus, sign)) sign.copy_(torch.where(sign.eq(0), 1, sign)) else: 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) if capturable: grad.copy_(torch.where(sign.eq(etaminus), 0, grad)) else: 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], state_steps: List[Tensor], *, step_size_min: float, step_size_max: float, etaminus: float, etaplus: float, maximize: bool, capturable: bool, differentiable: bool, has_complex: bool, ): if len(params) == 0: return assert not differentiable, "_foreach ops don't support autograd" # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable] if not torch._utils.is_compiling() and capturable: capturable_supported_devices = _get_capturable_supported_devices() assert all( p.device.type == step.device.type and p.device.type in capturable_supported_devices for p, step in zip(params, state_steps) ), f"If capturable=True, params and state_steps must be on supported devices: {capturable_supported_devices}." grouped_tensors = Optimizer._group_tensors_by_device_and_dtype( [params, grads, prevs, step_sizes, state_steps] # type: ignore[list-item] ) for ( grouped_params_, grouped_grads_, grouped_prevs_, grouped_step_sizes_, grouped_state_steps_, ), _ in grouped_tensors.values(): grouped_params = cast(List[Tensor], grouped_params_) grouped_grads = cast(List[Tensor], grouped_grads_) grouped_prevs = cast(List[Tensor], grouped_prevs_) grouped_step_sizes = cast(List[Tensor], grouped_step_sizes_) grouped_state_steps = cast(List[Tensor], grouped_state_steps_) # Update steps # If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over # and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just # wrapped it once now. The alpha is required to assure we go to the right overload. if not torch._utils.is_compiling() and grouped_state_steps[0].is_cpu: torch._foreach_add_( grouped_state_steps, torch.tensor(1.0, device="cpu"), alpha=1.0 ) else: torch._foreach_add_(grouped_state_steps, 1) # Handle complex params if has_complex: _view_as_real( grouped_params, grouped_grads, grouped_prevs, grouped_step_sizes ) signs = torch._foreach_mul(grouped_grads, grouped_prevs) if maximize: torch._foreach_neg_(signs) # At the end of the step, grouped_prevs will contain the current grads, so we reuse # grouped_prevs memory instead of creating a new buffer, but, for clarity, we reassign # to keep referring to the buffer as grouped_grads. torch._foreach_copy_(grouped_prevs, grouped_grads) if maximize: torch._foreach_neg_(grouped_prevs) grouped_grads = grouped_prevs torch._foreach_sign_(signs) if capturable: for sign in signs: sign.copy_(torch.where(sign.gt(0), etaplus, sign)) sign.copy_(torch.where(sign.lt(0), etaminus, sign)) sign.copy_(torch.where(sign.eq(0), 1, sign)) else: 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].copy_( torch.where(signs[i].eq(etaminus), 0, grouped_grads[i]) ) # explicitly del signs as it's not used after here to save memory del signs # update parameters grad_signs = [grad.sign() for grad in grouped_grads] torch._foreach_addcmul_( grouped_params, grad_signs, grouped_step_sizes, value=-1 ) # Logically, you may expect grouped_prevs to get updated to grouped_grads, but that's # basically already happened since we've been using grouped_prevs' memory to store # updated grouped_grads! @_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_rprop) def rprop( params: List[Tensor], grads: List[Tensor], prevs: List[Tensor], step_sizes: List[Tensor], state_steps: 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, capturable: bool = False, maximize: bool = False, differentiable: bool = False, has_complex: 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. """ # this check is slow during compilation, so we skip it # if it's strictly needed we can add this check back in dynamo if not torch._utils.is_compiling() and not all( isinstance(t, torch.Tensor) for t in state_steps ): raise RuntimeError( "API has changed, `state_steps` argument must contain a list of singleton tensors" ) 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, state_steps, step_size_min=step_size_min, step_size_max=step_size_max, etaminus=etaminus, etaplus=etaplus, capturable=capturable, maximize=maximize, differentiable=differentiable, has_complex=has_complex, )