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877c96cddf
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/30008 Test Plan: Imported from OSS Differential Revision: D18575981 Pulled By: VitalyFedyunin fbshipit-source-id: ec3418257089ad57913932be1a8608cd20ce054c
105 lines
4.6 KiB
Python
105 lines
4.6 KiB
Python
import math
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import torch
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from .optimizer import Optimizer
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class SparseAdam(Optimizer):
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r"""Implements lazy version of Adam algorithm suitable for sparse tensors.
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In this variant, only moments that show up in the gradient get updated, and
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only those portions of the gradient get applied to the parameters.
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Arguments:
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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: 1e-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 (default: (0.9, 0.999))
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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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.. _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=1e-3, betas=(0.9, 0.999), eps=1e-8):
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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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defaults = dict(lr=lr, betas=betas, eps=eps)
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super(SparseAdam, self).__init__(params, defaults)
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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
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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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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 not grad.is_sparse:
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raise RuntimeError('SparseAdam does not support dense gradients, please consider Adam instead')
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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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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p.data, memory_format=torch.preserve_format)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data, memory_format=torch.preserve_format)
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state['step'] += 1
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grad = grad.coalesce() # the update is non-linear so indices must be unique
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grad_indices = grad._indices()
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grad_values = grad._values()
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size = grad.size()
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def make_sparse(values):
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constructor = grad.new
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if grad_indices.dim() == 0 or values.dim() == 0:
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return constructor().resize_as_(grad)
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return constructor(grad_indices, values, size)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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# Decay the first and second moment running average coefficient
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# old <- b * old + (1 - b) * new
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# <==> old += (1 - b) * (new - old)
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old_exp_avg_values = exp_avg.sparse_mask(grad)._values()
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exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1)
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exp_avg.add_(make_sparse(exp_avg_update_values))
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old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values()
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exp_avg_sq_update_values = grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2)
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exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values))
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# Dense addition again is intended, avoiding another sparse_mask
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numer = exp_avg_update_values.add_(old_exp_avg_values)
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exp_avg_sq_update_values.add_(old_exp_avg_sq_values)
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denom = exp_avg_sq_update_values.sqrt_().add_(group['eps'])
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del exp_avg_update_values, exp_avg_sq_update_values
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
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p.data.add_(make_sparse(-step_size * numer.div_(denom)))
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return loss
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