# mypy: allow-untyped-decorators # mypy: allow-untyped-defs 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, _get_value, _maximize_doc, _use_grad_for_differentiable, _view_as_real, Optimizer, ParamsT, ) __all__ = ["Adamax", "adamax"] class Adamax(Optimizer): def __init__( self, params: ParamsT, lr: Union[float, Tensor] = 2e-3, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, weight_decay: float = 0, foreach: Optional[bool] = None, *, maximize: bool = False, differentiable: bool = False, capturable: bool = False, ): 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 <= eps: raise ValueError(f"Invalid epsilon value: {eps}") if not 0.0 <= betas[0] < 1.0: raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}") if not 0.0 <= betas[1] < 1.0: raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}") if not 0.0 <= weight_decay: raise ValueError(f"Invalid weight_decay value: {weight_decay}") defaults = dict( lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, foreach=foreach, maximize=maximize, differentiable=differentiable, capturable=capturable, ) 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) 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_with_grad, grads, exp_avgs, exp_infs, state_steps ): has_complex = False for p in group["params"]: if p.grad is None: continue has_complex |= torch.is_complex(p) params_with_grad.append(p) if p.grad.is_sparse: raise RuntimeError("Adamax does not support sparse gradients") grads.append(p.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.tensor(0.0, dtype=_get_scalar_dtype()) ) state["exp_avg"] = torch.zeros_like( p, memory_format=torch.preserve_format ) state["exp_inf"] = torch.zeros_like( p, memory_format=torch.preserve_format ) exp_avgs.append(state["exp_avg"]) exp_infs.append(state["exp_inf"]) state_steps.append(state["step"]) return has_complex @_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. """ 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_with_grad: List[Tensor] = [] grads: List[Tensor] = [] exp_avgs: List[Tensor] = [] exp_infs: List[Tensor] = [] state_steps: List[Tensor] = [] beta1, beta2 = group["betas"] eps = group["eps"] lr = group["lr"] weight_decay = group["weight_decay"] foreach = group["foreach"] maximize = group["maximize"] differentiable = group["differentiable"] capturable = group["capturable"] has_complex = self._init_group( group, params_with_grad, grads, exp_avgs, exp_infs, state_steps ) adamax( params_with_grad, grads, exp_avgs, exp_infs, state_steps, eps=eps, beta1=beta1, beta2=beta2, lr=lr, weight_decay=weight_decay, foreach=foreach, maximize=maximize, differentiable=differentiable, capturable=capturable, has_complex=has_complex, ) return loss Adamax.__doc__ = ( r"""Implements Adamax algorithm (a variant of Adam based on infinity norm). .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2 \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\ &\hspace{13mm} \epsilon \text{ (epsilon)} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-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}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_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 `Adam: A Method for Stochastic Optimization`_. """ + rf""" Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, Tensor, optional): learning rate (default: 2e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) {_foreach_doc} {_maximize_doc} {_differentiable_doc} {_capturable_doc} .. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 """ ) def _single_tensor_adamax( params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_infs: List[Tensor], state_steps: List[Tensor], *, eps: float, beta1: float, beta2: float, lr: float, weight_decay: float, maximize: bool, differentiable: bool, capturable: bool, has_complex: bool, ): for i, param in enumerate(params): grad = grads[i] grad = grad if not maximize else -grad exp_avg = exp_avgs[i] exp_inf = exp_infs[i] step_t = 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_t.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}." # update step step_t += 1 if weight_decay != 0: grad = grad.add(param, alpha=weight_decay) if torch.is_complex(param): param = torch.view_as_real(param) grad = torch.view_as_real(grad) exp_avg = torch.view_as_real(exp_avg) exp_inf = torch.view_as_real(exp_inf) # Update biased first moment estimate. exp_avg.lerp_(grad, 1 - beta1) # Update the exponentially weighted infinity norm. if not differentiable: torch.maximum( exp_inf.mul_(beta2), grad.abs().add_(eps), out=exp_inf, ) else: norm_buf = torch.cat( [exp_inf.mul_(beta2).unsqueeze(0), grad.abs().add_(eps).unsqueeze_(0)], 0, ) exp_inf.copy_(torch.amax(norm_buf, 0, keepdim=False)) if capturable: # why jump through extra hoops and negate bias_correction? check out #121238 # once fixed, we should use bias_correction with addcdiv value=-1 for readability neg_bias_correction = beta1**step_t - 1 neg_bias_correction.div_(lr) denom = exp_inf * neg_bias_correction param.addcdiv_(exp_avg, denom) else: bias_correction = 1 - beta1 ** _get_value(step_t) clr = lr / bias_correction param.addcdiv_(exp_avg, exp_inf, value=-clr) def _multi_tensor_adamax( params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_infs: List[Tensor], state_steps: List[Tensor], *, eps: float, beta1: float, beta2: float, lr: float, weight_decay: float, maximize: bool, differentiable: bool, capturable: bool, has_complex: bool, ): assert not differentiable, "_foreach ops don't support autograd" if len(params) == 0: return # 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( supports_xla=False ) 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, exp_avgs, exp_infs, state_steps] # type: ignore[list-item] ) for ( grouped_params_, grouped_grads_, grouped_exp_avgs_, grouped_exp_infs_, grouped_state_steps_, ), _ in grouped_tensors.values(): grouped_params = cast(List[Tensor], grouped_params_) grouped_grads = cast(List[Tensor], grouped_grads_) grouped_exp_avgs = cast(List[Tensor], grouped_exp_avgs_) grouped_exp_infs = cast(List[Tensor], grouped_exp_infs_) grouped_state_steps = cast(List[Tensor], grouped_state_steps_) if has_complex: _view_as_real( grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_infs ) if maximize: grouped_grads = torch._foreach_neg(grouped_grads) # type: ignore[assignment] # 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) if weight_decay != 0: if maximize: # Re-use the intermediate memory (grouped_grads) already allocated for maximize torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay) else: grouped_grads = torch._foreach_add( # type: ignore[assignment] grouped_grads, grouped_params, alpha=weight_decay ) # Update biased first moment estimate. torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1) # Update the exponentially weighted infinity norm. torch._foreach_mul_(grouped_exp_infs, beta2) # in this case, we need to introduce a copy of the grads # since one has not been introduced previously if not maximize and weight_decay == 0: grouped_grads = torch._foreach_abs(grouped_grads) # type: ignore[assignment] else: torch._foreach_abs_(grouped_grads) torch._foreach_add_(grouped_grads, eps) torch._foreach_maximum_(grouped_exp_infs, grouped_grads) bias_corrections: Union[Tuple[Tensor, ...], List[Tensor]] if capturable: bias_corrections = torch._foreach_pow(beta1, grouped_state_steps) # foreach_sub doesn't allow a scalar as the first arg torch._foreach_sub_(bias_corrections, 1) torch._foreach_div_(bias_corrections, lr) denom = torch._foreach_mul(grouped_exp_infs, bias_corrections) torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, denom) else: bias_corrections = [ 1 - beta1 ** _get_value(step) for step in grouped_state_steps ] step_size = [(_get_value(lr) / bc) * -1 for bc in bias_corrections] torch._foreach_addcdiv_( grouped_params, grouped_exp_avgs, grouped_exp_infs, step_size ) @_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_adamax) def adamax( params: List[Tensor], grads: List[Tensor], exp_avgs: List[Tensor], exp_infs: 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, maximize: bool = False, differentiable: bool = False, capturable: bool = False, has_complex: bool = False, *, eps: float, beta1: float, beta2: float, lr: float, weight_decay: float, ): r"""Functional API that performs adamax algorithm computation. See :class:`~torch.optim.Adamax` for details. """ 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_adamax else: func = _single_tensor_adamax func( params, grads, exp_avgs, exp_infs, state_steps, eps=eps, beta1=beta1, beta2=beta2, lr=lr, weight_decay=weight_decay, maximize=maximize, differentiable=differentiable, has_complex=has_complex, capturable=capturable, )