Add Adafactor and Adahessian optimizers, cleanup optimizer arg passing, add gradient clipping support.
Ross Wightman
4 years ago
parent
fcb6258877
commit
80078c47bb
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from .nadam import Nadam
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from .rmsprop_tf import RMSpropTF
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from .adamp import AdamP
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from .adamw import AdamW
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from .radam import RAdam
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from .adafactor import Adafactor
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from .adahessian import Adahessian
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from .lookahead import Lookahead
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from .nadam import Nadam
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from .novograd import NovoGrad
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from .nvnovograd import NvNovoGrad
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from .lookahead import Lookahead
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from .adamp import AdamP
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from .radam import RAdam
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from .rmsprop_tf import RMSpropTF
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from .sgdp import SGDP
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from .optim_factory import create_optimizer
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from .optim_factory import create_optimizer
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""" Adafactor Optimizer
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Lifted from https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
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Original header/copyright below.
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"""
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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import math
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class Adafactor(torch.optim.Optimizer):
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"""Implements Adafactor algorithm.
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This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost`
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(see https://arxiv.org/abs/1804.04235)
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Note that this optimizer internally adjusts the learning rate depending on the
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*scale_parameter*, *relative_step* and *warmup_init* options.
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To use a manual (external) learning rate schedule you should set `scale_parameter=False` and
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`relative_step=False`.
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining parameter groups
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lr (float, optional): external learning rate (default: None)
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eps (tuple[float, float]): regularization constants for square gradient
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and parameter scale respectively (default: (1e-30, 1e-3))
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clip_threshold (float): threshold of root mean square of final gradient update (default: 1.0)
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decay_rate (float): coefficient used to compute running averages of square gradient (default: -0.8)
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beta1 (float): coefficient used for computing running averages of gradient (default: None)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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scale_parameter (bool): if True, learning rate is scaled by root mean square of parameter (default: True)
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relative_step (bool): if True, time-dependent learning rate is computed
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instead of external learning rate (default: True)
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warmup_init (bool): time-dependent learning rate computation depends on
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whether warm-up initialization is being used (default: False)
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"""
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def __init__(self, params, lr=None, eps=1e-30, eps_scale=1e-3, clip_threshold=1.0,
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decay_rate=-0.8, betas=None, weight_decay=0.0, scale_parameter=True, warmup_init=False):
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relative_step = lr is None
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if warmup_init and not relative_step:
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raise ValueError('warmup_init requires relative_step=True')
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beta1 = None if betas is None else betas[0] # make it compat with standard betas arg
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defaults = dict(lr=lr, eps=eps, eps_scale=eps_scale, clip_threshold=clip_threshold, decay_rate=decay_rate,
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beta1=beta1, weight_decay=weight_decay, scale_parameter=scale_parameter,
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relative_step=relative_step, warmup_init=warmup_init)
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super(Adafactor, self).__init__(params, defaults)
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@staticmethod
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def _get_lr(param_group, param_state):
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if param_group['relative_step']:
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min_step = 1e-6 * param_state['step'] if param_group['warmup_init'] else 1e-2
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lr_t = min(min_step, 1.0 / math.sqrt(param_state['step']))
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param_scale = 1.0
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if param_group['scale_parameter']:
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param_scale = max(param_group['eps_scale'], param_state['RMS'])
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param_group['lr'] = lr_t * param_scale
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return param_group['lr']
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@staticmethod
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def _get_options(param_group, param_shape):
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factored = len(param_shape) >= 2
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use_first_moment = param_group['beta1'] is not None
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return factored, use_first_moment
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@staticmethod
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def _rms(tensor):
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return tensor.norm(2) / (tensor.numel() ** 0.5)
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def _approx_sq_grad(self, exp_avg_sq_row, exp_avg_sq_col):
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r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1)
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c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt()
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return torch.mul(r_factor, c_factor)
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model 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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loss = closure()
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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 None:
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continue
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grad = p.grad.data
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if grad.dtype in {torch.float16, torch.bfloat16}:
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grad = grad.float()
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if grad.is_sparse:
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raise RuntimeError('Adafactor does not support sparse gradients.')
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state = self.state[p]
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grad_shape = grad.shape
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factored, use_first_moment = self._get_options(group, grad_shape)
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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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if use_first_moment:
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(grad)
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if factored:
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state['exp_avg_sq_row'] = torch.zeros(grad_shape[:-1]).to(grad)
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state['exp_avg_sq_col'] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
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else:
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state['exp_avg_sq'] = torch.zeros_like(grad)
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state['RMS'] = 0
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else:
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if use_first_moment:
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state['exp_avg'] = state['exp_avg'].to(grad)
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if factored:
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state['exp_avg_sq_row'] = state['exp_avg_sq_row'].to(grad)
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state['exp_avg_sq_col'] = state['exp_avg_sq_col'].to(grad)
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else:
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state['exp_avg_sq'] = state['exp_avg_sq'].to(grad)
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p_data_fp32 = p.data
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if p.data.dtype in {torch.float16, torch.bfloat16}:
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p_data_fp32 = p_data_fp32.float()
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state['step'] += 1
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state['RMS'] = self._rms(p_data_fp32)
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lr_t = self._get_lr(group, state)
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beta2t = 1.0 - math.pow(state['step'], group['decay_rate'])
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update = grad ** 2 + group['eps']
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if factored:
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exp_avg_sq_row = state['exp_avg_sq_row']
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exp_avg_sq_col = state['exp_avg_sq_col']
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exp_avg_sq_row.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-1))
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exp_avg_sq_col.mul_(beta2t).add_(1.0 - beta2t, update.mean(dim=-2))
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#exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=1.0 - beta2t) # pytorch 1.6+
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#exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=1.0 - beta2t)
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# Approximation of exponential moving average of square of gradient
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update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
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update.mul_(grad)
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else:
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exp_avg_sq = state['exp_avg_sq']
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exp_avg_sq.mul_(beta2t).add_(1.0 - beta2t, update)
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#exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t) # pytorch 1.6+
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update = exp_avg_sq.rsqrt().mul_(grad)
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update.div_((self._rms(update) / group['clip_threshold']).clamp_(min=1.0))
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update.mul_(lr_t)
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if use_first_moment:
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exp_avg = state['exp_avg']
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exp_avg.mul_(group["beta1"]).add_(1 - group["beta1"], update)
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#exp_avg.mul_(group['beta1']).add_(update, alpha=1 - group['beta1']) # pytorch 1.6+
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update = exp_avg
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if group['weight_decay'] != 0:
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p_data_fp32.add_(-group["weight_decay"] * lr_t, p_data_fp32)
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#p_data_fp32.add_(p_data_fp32, alpha=-group['weight_decay'] * lr_t) # pytorch 1.6+
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p_data_fp32.add_(-update)
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if p.data.dtype in {torch.float16, torch.bfloat16}:
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p.data.copy_(p_data_fp32)
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return loss
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""" AdaHessian Optimizer
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Lifted from https://github.com/davda54/ada-hessian/blob/master/ada_hessian.py
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Originally licensed MIT, Copyright 2020, David Samuel
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"""
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import torch
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class Adahessian(torch.optim.Optimizer):
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"""
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Implements the AdaHessian algorithm from "ADAHESSIAN: An Adaptive Second OrderOptimizer for Machine Learning"
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining parameter groups
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lr (float, optional): learning rate (default: 0.1)
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betas ((float, float), optional): coefficients used for computing running averages of gradient and the
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squared hessian trace (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0.0)
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hessian_power (float, optional): exponent of the hessian trace (default: 1.0)
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update_each (int, optional): compute the hessian trace approximation only after *this* number of steps
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(to save time) (default: 1)
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n_samples (int, optional): how many times to sample `z` for the approximation of the hessian trace (default: 1)
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"""
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def __init__(self, params, lr=0.1, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.0,
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hessian_power=1.0, update_each=1, n_samples=1, avg_conv_kernel=False):
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if not 0.0 <= lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
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if not 0.0 <= hessian_power <= 1.0:
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raise ValueError(f"Invalid Hessian power value: {hessian_power}")
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self.n_samples = n_samples
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self.update_each = update_each
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self.avg_conv_kernel = avg_conv_kernel
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# use a separate generator that deterministically generates the same `z`s across all GPUs in case of distributed training
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self.seed = 2147483647
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self.generator = torch.Generator().manual_seed(self.seed)
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, hessian_power=hessian_power)
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super(Adahessian, self).__init__(params, defaults)
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for p in self.get_params():
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p.hess = 0.0
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self.state[p]["hessian step"] = 0
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@property
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def is_second_order(self):
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return True
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def get_params(self):
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"""
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Gets all parameters in all param_groups with gradients
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"""
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return (p for group in self.param_groups for p in group['params'] if p.requires_grad)
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def zero_hessian(self):
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"""
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Zeros out the accumalated hessian traces.
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"""
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for p in self.get_params():
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if not isinstance(p.hess, float) and self.state[p]["hessian step"] % self.update_each == 0:
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p.hess.zero_()
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@torch.no_grad()
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def set_hessian(self):
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"""
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Computes the Hutchinson approximation of the hessian trace and accumulates it for each trainable parameter.
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"""
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params = []
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for p in filter(lambda p: p.grad is not None, self.get_params()):
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if self.state[p]["hessian step"] % self.update_each == 0: # compute the trace only each `update_each` step
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params.append(p)
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self.state[p]["hessian step"] += 1
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if len(params) == 0:
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return
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if self.generator.device != params[0].device: # hackish way of casting the generator to the right device
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self.generator = torch.Generator(params[0].device).manual_seed(self.seed)
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grads = [p.grad for p in params]
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for i in range(self.n_samples):
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# Rademacher distribution {-1.0, 1.0}
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zs = [torch.randint(0, 2, p.size(), generator=self.generator, device=p.device) * 2.0 - 1.0 for p in params]
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h_zs = torch.autograd.grad(
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grads, params, grad_outputs=zs, only_inputs=True, retain_graph=i < self.n_samples - 1)
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for h_z, z, p in zip(h_zs, zs, params):
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p.hess += h_z * z / self.n_samples # approximate the expected values of z*(H@z)
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@torch.no_grad()
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def step(self, closure=None):
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"""
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Performs a single optimization step.
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Arguments:
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closure (callable, optional) -- a closure that reevaluates the model and returns the loss (default: None)
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"""
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loss = None
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if closure is not None:
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loss = closure()
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self.zero_hessian()
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self.set_hessian()
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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 None or p.hess is None:
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continue
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if self.avg_conv_kernel and p.dim() == 4:
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p.hess = torch.abs(p.hess).mean(dim=[2, 3], keepdim=True).expand_as(p.hess).clone()
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# Perform correct stepweight decay as in AdamW
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p.mul_(1 - group['lr'] * group['weight_decay'])
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state = self.state[p]
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# State initialization
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if len(state) == 1:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p)
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# Exponential moving average of Hessian diagonal square values
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state['exp_hessian_diag_sq'] = torch.zeros_like(p)
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exp_avg, exp_hessian_diag_sq = state['exp_avg'], state['exp_hessian_diag_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(beta1).add_(p.grad, alpha=1 - beta1)
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exp_hessian_diag_sq.mul_(beta2).addcmul_(p.hess, p.hess, value=1 - beta2)
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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k = group['hessian_power']
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denom = (exp_hessian_diag_sq / bias_correction2).pow_(k / 2).add_(group['eps'])
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# make update
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step_size = group['lr'] / bias_correction1
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p.addcdiv_(exp_avg, denom, value=-step_size)
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return loss
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