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e1bd4963e2
Summary: In this PR we change Multi-Tensor Optimizers to Functional API. We can see that in the file : https://github.com/pytorch/pytorch/blob/master/torch/optim/_functional.py , there has been functional API defined for most of Optimizers. However we do not have similar file / functionality for multi tensors : https://github.com/pytorch/pytorch/tree/master/torch/optim/_multi_tensor Therefore we are adding it in this PR here. Pull Request resolved: https://github.com/pytorch/pytorch/pull/60735 Reviewed By: vincentqb Differential Revision: D29392253 Pulled By: iramazanli fbshipit-source-id: cebc8e7b07ab11156370f5297cfb419cd9f20b46
119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
import torch
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from . import _functional as F
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from ..optimizer import Optimizer
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from collections import defaultdict
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class Adamax(Optimizer):
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"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
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It has been proposed in `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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__ 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):
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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)
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super(Adamax, self).__init__(params, defaults)
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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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grads = []
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params_with_grad = []
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states = []
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exp_avgs = []
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exp_infs = []
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beta1, beta2 = group['betas']
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for p in group['params']:
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if p.grad is not None:
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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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params_with_grad.append(p)
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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'] = 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['step'] += 1
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states.append(state)
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F.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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states,
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beta1=beta1,
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beta2=beta2,
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lr=group['lr'],
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weight_decay=group['weight_decay'],
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eps=group['eps'])
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return loss
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# TODO: refactor to a base class once foreach ops are in a good shape.
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def zero_grad(self, set_to_none: bool = False):
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per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is not None:
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if set_to_none:
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p.grad = None
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else:
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if p.grad.grad_fn is not None:
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p.grad.detach_()
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else:
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p.grad.requires_grad_(False)
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if p.grad.is_sparse:
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p.grad.zero_()
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else:
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per_device_and_dtype_grads[p.grad.device][p.grad.dtype].append(p.grad)
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for _, per_dtype_grads in per_device_and_dtype_grads.items():
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for grads in per_dtype_grads.values():
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torch._foreach_zero_(grads)
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