mirror of
https://github.com/zebrajr/pytorch.git
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d9acfef831
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69982
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D33767865
Pulled By: mikaylagawarecki
fbshipit-source-id: c5efd351e359825d38b71f57a2c61a2055c3c114
(cherry picked from commit 37bb80c2d7)
260 lines
9.9 KiB
Python
260 lines
9.9 KiB
Python
import torch
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from torch import Tensor
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from .optimizer import Optimizer
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from typing import List, Optional
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class Adamax(Optimizer):
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r"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
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.. math::
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\begin{aligned}
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&\rule{110mm}{0.4pt} \\
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&\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2
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\text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)},
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\: \lambda \text{ (weight decay)}, \\
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&\hspace{13mm} \epsilon \text{ (epsilon)} \\
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&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
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u_0 \leftarrow 0 \text{ ( infinity norm)} \\[-1.ex]
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&\rule{110mm}{0.4pt} \\
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&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
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&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm}if \: \lambda \neq 0 \\
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&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
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&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
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&\hspace{5mm}u_t \leftarrow \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon) \\
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&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_t} \\
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&\rule{110mm}{0.4pt} \\[-1.ex]
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&\bf{return} \: \theta_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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\end{aligned}
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For further details regarding the algorithm we refer to `Adam: A Method for Stochastic Optimization`_.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 2e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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foreach (bool, optional): whether foreach implementation of optimizer is used (default: None)
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.. _Adam\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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"""
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def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=0, foreach: Optional[bool] = None):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, foreach=foreach)
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super(Adamax, self).__init__(params, defaults)
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group.setdefault('foreach', None)
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state_values = list(self.state.values())
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step_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['step'])
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if not step_is_tensor:
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for s in state_values:
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s['step'] = torch.tensor(float(s['step']))
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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exp_avgs = []
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exp_infs = []
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state_steps = []
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beta1, beta2 = group['betas']
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eps = group['eps']
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lr = group['lr']
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weight_decay = group['weight_decay']
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foreach = group['foreach']
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for p in group['params']:
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if p.grad is None:
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continue
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('Adamax does not support sparse gradients')
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grads.append(p.grad)
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = torch.tensor(0.)
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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state['exp_inf'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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exp_avgs.append(state['exp_avg'])
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exp_infs.append(state['exp_inf'])
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state_steps.append(state['step'])
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adamax(params_with_grad,
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grads,
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exp_avgs,
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exp_infs,
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state_steps,
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eps=eps,
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beta1=beta1,
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beta2=beta2,
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lr=lr,
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weight_decay=weight_decay,
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foreach=foreach)
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return loss
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def adamax(params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_infs: List[Tensor],
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state_steps: List[Tensor],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
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foreach: bool = None,
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*,
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eps: float,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float):
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r"""Functional API that performs adamax algorithm computation.
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See :class:`~torch.optim.Adamax` for details.
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"""
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if not all([isinstance(t, torch.Tensor) for t in state_steps]):
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raise RuntimeError("API has changed, `state_steps` argument must contain a list of singleton tensors")
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if foreach is None:
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# Placeholder for more complex foreach logic to be added when value is not set
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foreach = False
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if foreach and torch.jit.is_scripting():
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raise RuntimeError('torch.jit.script not supported with foreach optimizers')
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if foreach and not torch.jit.is_scripting():
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func = _multi_tensor_adamax
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else:
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func = _single_tensor_adamax
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func(params,
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grads,
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exp_avgs,
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exp_infs,
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state_steps,
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eps=eps,
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beta1=beta1,
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beta2=beta2,
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lr=lr,
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weight_decay=weight_decay)
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def _single_tensor_adamax(params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_infs: List[Tensor],
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state_steps: List[Tensor],
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*,
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eps: float,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float):
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for i, param in enumerate(params):
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grad = grads[i]
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exp_avg = exp_avgs[i]
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exp_inf = exp_infs[i]
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step_t = state_steps[i]
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# update step
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step_t += 1
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step = step_t.item()
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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# Update biased first moment estimate.
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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# Update the exponentially weighted infinity norm.
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norm_buf = torch.cat([
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exp_inf.mul_(beta2).unsqueeze(0),
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grad.abs().add_(eps).unsqueeze_(0)
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], 0)
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torch.amax(norm_buf, 0, keepdim=False, out=exp_inf)
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bias_correction = 1 - beta1 ** step
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clr = lr / bias_correction
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param.addcdiv_(exp_avg, exp_inf, value=-clr)
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def _multi_tensor_adamax(params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_infs: List[Tensor],
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state_steps: List[Tensor],
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*,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float,
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eps: float):
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if len(params) == 0:
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return
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# Update steps
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torch._foreach_add_(state_steps, 1)
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if weight_decay != 0:
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torch._foreach_add_(grads, params, alpha=weight_decay)
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# Update biased first moment estimate.
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torch._foreach_mul_(exp_avgs, beta1)
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torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
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# Update the exponentially weighted infinity norm.
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torch._foreach_mul_(exp_infs, beta2)
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for exp_inf, grad in zip(exp_infs, grads):
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norm_buf = torch.cat([
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exp_inf.unsqueeze(0),
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grad.abs().add_(eps).unsqueeze_(0)
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], 0)
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torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
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bias_corrections = [1 - beta1 ** step.item() for step in state_steps]
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clr = [-1 * (lr / bias_correction) for bias_correction in bias_corrections]
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torch._foreach_addcdiv_(params, exp_avgs, exp_infs, clr)
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