| import torch |
| from torch import Tensor |
| from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _dispatch_sqrt, |
| _stack_if_compiling, _get_scalar_dtype, _default_to_fused_or_foreach, |
| _view_as_real, _capturable_doc, _differentiable_doc, _foreach_doc,) |
| from typing import List, Optional |
| |
| __all__ = ['NAdam', 'nadam'] |
| |
| class NAdam(Optimizer): |
| def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8, |
| weight_decay=0, momentum_decay=4e-3, decoupled_weight_decay: bool = False, |
| *, foreach: Optional[bool] = None, capturable: bool = False, |
| differentiable: bool = False): |
| 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}") |
| if not 0.0 <= momentum_decay: |
| raise ValueError(f"Invalid momentum_decay value: {momentum_decay}") |
| defaults = dict(lr=lr, betas=betas, eps=eps, |
| weight_decay=weight_decay, momentum_decay=momentum_decay, |
| decoupled_weight_decay=decoupled_weight_decay, |
| foreach=foreach, capturable=capturable, 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('capturable', False) |
| group.setdefault('differentiable', False) |
| group.setdefault('decoupled_weight_decay', False) |
| for p in group["params"]: |
| p_state = self.state.get(p, []) |
| if len(p_state) != 0: |
| if 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())) |
| if not torch.is_tensor(p_state['mu_product']): |
| mu_prod_val = p_state["mu_product"] |
| p_state["mu_product"] = (torch.tensor(mu_prod_val, dtype=_get_scalar_dtype(), device=p.device) |
| if group['capturable'] else torch.tensor(mu_prod_val, dtype=_get_scalar_dtype())) |
| |
| |
| def _init_group(self, group, params_with_grad, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps): |
| has_complex = False |
| for p in group['params']: |
| if p.grad is not None: |
| has_complex |= torch.is_complex(p) |
| params_with_grad.append(p) |
| if p.grad.is_sparse: |
| raise RuntimeError('NAdam does not support sparse gradients') |
| grads.append(p.grad) |
| |
| state = self.state[p] |
| # Lazy state initialization |
| if len(state) == 0: |
| # note(crcrpar): [special device hosting for step] |
| # Deliberately host `step` and `mu_product` on CPU if capturable is False. |
| # This is because kernel launches are costly on CUDA and XLA. |
| state['step'] = ( |
| torch.zeros((), dtype=_get_scalar_dtype(), device=p.device) |
| if group['capturable'] else torch.tensor(0.0, dtype=_get_scalar_dtype()) |
| ) |
| state['mu_product'] = ( |
| torch.ones((), dtype=_get_scalar_dtype(), device=p.device) |
| if group['capturable'] else torch.tensor(1.0, dtype=_get_scalar_dtype()) |
| ) |
| # Exponential moving average of gradient values |
| state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| # Exponential moving average of squared gradient values |
| state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) |
| |
| exp_avgs.append(state['exp_avg']) |
| exp_avg_sqs.append(state['exp_avg_sq']) |
| mu_products.append(state['mu_product']) |
| 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 = [] |
| grads = [] |
| exp_avgs = [] |
| exp_avg_sqs = [] |
| mu_products = [] |
| state_steps = [] |
| beta1, beta2 = group['betas'] |
| |
| has_complex = self._init_group(group, params_with_grad, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps) |
| |
| nadam(params_with_grad, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| mu_products, |
| state_steps, |
| beta1=beta1, |
| beta2=beta2, |
| lr=group['lr'], |
| weight_decay=group['weight_decay'], |
| momentum_decay=group['momentum_decay'], |
| eps=group['eps'], |
| decoupled_weight_decay=group['decoupled_weight_decay'], |
| foreach=group['foreach'], |
| capturable=group['capturable'], |
| differentiable=group['differentiable'], |
| has_complex=has_complex) |
| |
| return loss |
| |
| NAdam.__doc__ = r"""Implements NAdam algorithm. |
| |
| .. math:: |
| \begin{aligned} |
| &\rule{110mm}{0.4pt} \\ |
| &\textbf{input} : \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)}, |
| \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\ |
| &\hspace{13mm} \: \lambda \text{ (weight decay)}, \:\psi \text{ (momentum decay)} \\ |
| &\hspace{13mm} \: \textit{decoupled\_weight\_decay} \\ |
| &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, |
| v_0 \leftarrow 0 \text{ ( second moment)} \\[-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} \theta_t \leftarrow \theta_{t-1} \\ |
| &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ |
| &\hspace{10mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\ |
| &\hspace{15mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\ |
| &\hspace{10mm}\textbf{else} \\ |
| &\hspace{15mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ |
| &\hspace{5mm} \mu_t \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{t \psi} \big) \\ |
| &\hspace{5mm} \mu_{t+1} \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{(t+1)\psi}\big)\\ |
| &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ |
| &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ |
| &\hspace{5mm}\widehat{m_t} \leftarrow \mu_{t+1} m_t/(1-\prod_{i=1}^{t+1}\mu_i)\\[-1.ex] |
| & \hspace{11mm} + (1-\mu_t) g_t /(1-\prod_{i=1}^{t} \mu_{i}) \\ |
| &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ |
| &\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/ |
| \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ |
| &\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 `Incorporating Nesterov Momentum into Adam`_. |
| """ + fr""" |
| Args: |
| params (iterable): iterable of parameters to optimize or dicts defining |
| parameter groups |
| lr (float, optional): learning rate (default: 2e-3) |
| betas (Tuple[float, float], optional): coefficients used for computing |
| running averages of gradient and its square (default: (0.9, 0.999)) |
| 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) |
| momentum_decay (float, optional): momentum momentum_decay (default: 4e-3) |
| decoupled_weight_decay (bool, optional): whether to use decoupled weight |
| decay as in AdamW to obtain NAdamW (default: False) |
| {_foreach_doc} |
| {_capturable_doc} |
| {_differentiable_doc} |
| |
| .. _Incorporating Nesterov Momentum into Adam: |
| https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ |
| .. _Decoupled Weight Decay Regularization: |
| https://arxiv.org/abs/1711.05101 |
| |
| """ |
| |
| |
| def nadam(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| mu_products: 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 |
| decoupled_weight_decay: bool = False, |
| foreach: Optional[bool] = None, |
| capturable: bool = False, |
| differentiable: bool = False, |
| has_complex: bool = False, |
| *, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| momentum_decay: float, |
| eps: float): |
| r"""Functional API that performs NAdam algorithm computation. |
| |
| See :class:`~torch.optim.NAdam` for details. |
| """ |
| |
| |
| if 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 not all(isinstance(t, torch.Tensor) for t in mu_products): |
| raise RuntimeError("API has changed, `mu_products` 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_nadam |
| else: |
| func = _single_tensor_nadam |
| |
| func(params, |
| grads, |
| exp_avgs, |
| exp_avg_sqs, |
| mu_products, |
| state_steps, |
| beta1=beta1, |
| beta2=beta2, |
| lr=lr, |
| weight_decay=weight_decay, |
| momentum_decay=momentum_decay, |
| decoupled_weight_decay=decoupled_weight_decay, |
| eps=eps, |
| capturable=capturable, |
| differentiable=differentiable, |
| has_complex=has_complex) |
| |
| |
| def _single_tensor_nadam(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| mu_products: List[Tensor], |
| state_steps: List[Tensor], |
| *, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| momentum_decay: float, |
| eps: float, |
| decoupled_weight_decay: bool, |
| capturable: bool, |
| differentiable: bool, |
| has_complex: bool): |
| |
| for i, param in enumerate(params): |
| grad = grads[i] |
| exp_avg = exp_avgs[i] |
| exp_avg_sq = exp_avg_sqs[i] |
| mu_product = mu_products[i] |
| step_t = state_steps[i] |
| |
| 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_avg_sq = torch.view_as_real(exp_avg_sq) |
| |
| # If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable] |
| if not torch._utils.is_compiling() and capturable: |
| assert ( |
| (param.is_cuda and mu_product.is_cuda and step_t.is_cuda) or (param.is_xla and mu_product.is_xla and step_t.is_xla) |
| ), "If capturable=True, params, mu_products, and state_steps must be CUDA or XLA tensors." |
| |
| # update step |
| step_t += 1 |
| |
| if capturable: |
| step = step_t |
| else: |
| step = _get_value(step_t) |
| |
| bias_correction2 = 1 - beta2 ** step |
| |
| if weight_decay != 0: |
| if decoupled_weight_decay: |
| # Perform stepweight decay |
| param.mul_(1 - lr * weight_decay) |
| else: |
| grad = grad.add(param, alpha=weight_decay) |
| |
| # calculate the momentum cache \mu^{t} and \mu^{t+1} |
| mu = beta1 * (1. - 0.5 * (0.96 ** (step * momentum_decay))) |
| mu_next = beta1 * (1. - 0.5 * (0.96 ** ((step + 1) * momentum_decay))) |
| |
| # update mu_product |
| mu_product *= mu |
| |
| # decay the first and second moment running average coefficient |
| exp_avg.lerp_(grad, 1 - beta1) |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) |
| denom = exp_avg_sq.div(bias_correction2).sqrt() |
| |
| if differentiable or capturable: |
| denom = denom.add(eps) |
| # Make autograd track the operations |
| # by updating the grad and exp_avg directly and not using the |
| # scalar "value" argument of addcdiv. |
| mu_product_next = mu_product * mu_next |
| grad = grad * (-lr * (1. - mu) / (1. - mu_product)) |
| exp_avg = exp_avg * (-lr * mu_next / (1. - mu_product_next)) |
| param.addcdiv_(grad, denom) |
| param.addcdiv_(exp_avg, denom) |
| else: |
| mu_product_next = _get_value(mu_product) * mu_next |
| denom.add_(eps) |
| param.addcdiv_(grad, denom, value=(-lr * (1. - mu) / (1. - _get_value(mu_product)))) |
| param.addcdiv_(exp_avg, denom, value=(-lr * mu_next) / (1. - mu_product_next)) |
| |
| |
| def _multi_tensor_nadam(params: List[Tensor], |
| grads: List[Tensor], |
| exp_avgs: List[Tensor], |
| exp_avg_sqs: List[Tensor], |
| mu_products: List[Tensor], |
| state_steps: List[Tensor], |
| *, |
| beta1: float, |
| beta2: float, |
| lr: float, |
| weight_decay: float, |
| momentum_decay: float, |
| eps: float, |
| decoupled_weight_decay: 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: |
| assert all(p.is_cuda and mp.is_cuda and step.is_cuda |
| for p, mp, step in zip(params, mu_products, state_steps)), \ |
| "If capturable=True, params, mu_products, and state_steps must be CUDA tensors." |
| |
| |
| grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_avg_sqs, mu_products, state_steps]) |
| for ((grouped_params, grouped_grads, grouped_exp_avgs, |
| grouped_exp_avg_sqs, grouped_mu_products, grouped_state_steps), _) in grouped_tensors.values(): |
| |
| # handle complex |
| if has_complex: |
| _view_as_real(grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_avg_sqs) |
| |
| # 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 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 decoupled_weight_decay: |
| # Perform stepweight decay |
| torch._foreach_mul_(grouped_params, 1 - lr * weight_decay) |
| else: |
| grouped_grads = torch._foreach_add(grouped_grads, grouped_params, alpha=weight_decay) |
| |
| # Decay the first and second moment running average coefficient |
| torch._foreach_lerp_(grouped_exp_avgs, grouped_grads, 1 - beta1) |
| |
| torch._foreach_mul_(grouped_exp_avg_sqs, beta2) |
| torch._foreach_addcmul_(grouped_exp_avg_sqs, grouped_grads, grouped_grads, 1 - beta2) |
| |
| exp_avg_sq_sqrt = torch._foreach_sqrt(grouped_exp_avg_sqs) |
| |
| if capturable: |
| # mus will be beta1 * (1 - 0.5 * 0.96 ** (step * momentum_decay)) |
| exponent = torch._foreach_mul(grouped_state_steps, momentum_decay) |
| mus = torch._foreach_pow(0.96, exponent) |
| torch._foreach_mul_(mus, -0.5) |
| torch._foreach_add_(mus, 1.0) |
| torch._foreach_mul_(mus, beta1) |
| |
| # mu_nexts will be beta1 * (1 - 0.5 * 0.96 ** ((step + 1) * momentum_decay)) |
| torch._foreach_add_(exponent, momentum_decay) |
| mu_nexts = torch._foreach_pow(0.96, exponent) |
| torch._foreach_mul_(mu_nexts, -0.5) |
| torch._foreach_add_(mu_nexts, 1.0) |
| torch._foreach_mul_(mu_nexts, beta1) |
| |
| # save peak memory as we don't need exponent anymore |
| del exponent |
| |
| bias_correction_sqrt = torch._foreach_pow(beta2, grouped_state_steps) |
| # foreach_sub doesn't allow a scalar as the first arg |
| torch._foreach_sub_(bias_correction_sqrt, 1.0) |
| torch._foreach_neg_(bias_correction_sqrt) |
| torch._foreach_sqrt_(bias_correction_sqrt) |
| else: |
| bias_correction_sqrt = [_dispatch_sqrt(1 - beta2 ** _get_value(step)) for step in grouped_state_steps] |
| mus = [beta1 * (1. - 0.5 * (0.96 ** (_get_value(step) * momentum_decay))) for step in grouped_state_steps] |
| mu_nexts = [beta1 * (1. - 0.5 * (0.96 ** ((_get_value(step) + 1) * momentum_decay))) |
| for step in grouped_state_steps] |
| |
| # update mu_products |
| torch._foreach_mul_(grouped_mu_products, mus) |
| |
| torch._foreach_div_(exp_avg_sq_sqrt, bias_correction_sqrt) |
| torch._foreach_add_(exp_avg_sq_sqrt, eps) |
| |
| # explicitly delete bias_correction refs to save memory |
| del bias_correction_sqrt |
| |
| if capturable: |
| # Build up the step_size multiplier for grad, reusing mus' memory |
| torch._foreach_sub_(mus, 1.0) |
| torch._foreach_mul_(mus, lr) |
| # foreach_sub doesn't allow a scalar as the first arg |
| denom = torch._foreach_sub(grouped_mu_products, 1.0) |
| torch._foreach_neg_(denom) |
| torch._foreach_div_(mus, denom) |
| # - lr * (1 - mu) / (1 - mu_product) |
| step_size_grads = mus |
| # explicitly delete denom to save memory |
| del denom |
| |
| # Build up the step_size multiplier for exp_avg, reusing mu_nexts' memory |
| denom = torch._foreach_mul(grouped_mu_products, mu_nexts) |
| torch._foreach_mul_(mu_nexts, lr) |
| # foreach_sub doesn't allow a scalar as the first arg, but it's okay because |
| # we need a negative here anyway |
| torch._foreach_sub_(denom, 1.0) |
| torch._foreach_div_(mu_nexts, denom) |
| # - lr * mu_next / (1 - mu_product * mu_next) |
| step_size_expavg = mu_nexts |
| # explicitly delete denom to save memory |
| del denom |
| |
| # we cannot inplace into step_size_grads cuz it is a list of ScalarTensors |
| # and mul'ing with grouped_grads will result in a list of bigger Tensors |
| numerator = torch._foreach_mul(step_size_grads, grouped_grads) |
| torch._foreach_addcmul_(numerator, step_size_expavg, grouped_exp_avgs) |
| |
| # finally, update params |
| torch._foreach_addcdiv_(grouped_params, numerator, exp_avg_sq_sqrt) |
| else: |
| step_size_grads = _stack_if_compiling([(lr * (1. - mu) / (1. - _get_value(mu_product))) * -1 |
| for mu_product, mu in zip(grouped_mu_products, mus)]) |
| step_size_expavg = _stack_if_compiling([(lr * mu_next / (1. - _get_value(mu_product) * mu_next)) * -1 |
| for mu_product, mu_next in zip(grouped_mu_products, mu_nexts)]) |
| |
| torch._foreach_addcdiv_(grouped_params, grouped_grads, exp_avg_sq_sqrt, step_size_grads) |
| torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, exp_avg_sq_sqrt, step_size_expavg) |
