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| import torch | |
| from torch.optim import Optimizer | |
| import math | |
| import apex | |
| import unittest | |
| from test_fused_optimizer import TestFusedOptimizer | |
| from itertools import product | |
| class Novograd(Optimizer): | |
| """ | |
| Implements Novograd algorithm. | |
| Args: | |
| params (iterable): iterable of parameters to optimize or dicts defining | |
| parameter groups | |
| lr (float, optional): learning rate (default: 1e-3) | |
| betas (Tuple[float, float], optional): coefficients used for computing | |
| running averages of gradient and its square (default: (0.95, 0)) | |
| 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) | |
| grad_averaging: gradient averaging | |
| amsgrad (boolean, optional): whether to use the AMSGrad variant of this | |
| algorithm from the paper `On the Convergence of Adam and Beyond`_ | |
| (default: False) | |
| """ | |
| def __init__(self, params, lr=1e-3, betas=(0.95, 0), eps=1e-8, | |
| weight_decay=0, grad_averaging=False, amsgrad=False): | |
| if not 0.0 <= lr: | |
| raise ValueError("Invalid learning rate: {}".format(lr)) | |
| if not 0.0 <= eps: | |
| raise ValueError("Invalid epsilon value: {}".format(eps)) | |
| if not 0.0 <= betas[0] < 1.0: | |
| raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) | |
| if not 0.0 <= betas[1] < 1.0: | |
| raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) | |
| defaults = dict(lr=lr, betas=betas, eps=eps, | |
| weight_decay=weight_decay, | |
| grad_averaging=grad_averaging, | |
| amsgrad=amsgrad) | |
| super(Novograd, self).__init__(params, defaults) | |
| def __setstate__(self, state): | |
| super(Novograd, self).__setstate__(state) | |
| for group in self.param_groups: | |
| group.setdefault('amsgrad', False) | |
| def step(self, closure=None): | |
| """Performs a single optimization step. | |
| Arguments: | |
| closure (callable, optional): A closure that reevaluates the model | |
| and returns the loss. | |
| """ | |
| loss = None | |
| if closure is not None: | |
| loss = closure() | |
| for group in self.param_groups: | |
| for p in group['params']: | |
| if p.grad is None: | |
| continue | |
| grad = p.grad.data | |
| if grad.is_sparse: | |
| raise RuntimeError('Sparse gradients are not supported.') | |
| amsgrad = group['amsgrad'] | |
| state = self.state[p] | |
| # State initialization | |
| if len(state) == 0: | |
| state['step'] = 0 | |
| # Exponential moving average of gradient values | |
| state['exp_avg'] = torch.zeros_like(p.data) | |
| # Exponential moving average of squared gradient values | |
| state['exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device) | |
| if amsgrad: | |
| # Maintains max of all exp. moving avg. of sq. grad. values | |
| state['max_exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device) | |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] | |
| if amsgrad: | |
| max_exp_avg_sq = state['max_exp_avg_sq'] | |
| beta1, beta2 = group['betas'] | |
| state['step'] += 1 | |
| norm = torch.sum(torch.pow(grad, 2)) | |
| if exp_avg_sq == 0: | |
| exp_avg_sq.copy_(norm) | |
| else: | |
| exp_avg_sq.mul_(beta2).add_(norm, alpha=1 - beta2) | |
| if amsgrad: | |
| # Maintains the maximum of all 2nd moment running avg. till now | |
| torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) | |
| # Use the max. for normalizing running avg. of gradient | |
| denom = max_exp_avg_sq.sqrt().add_(group['eps']) | |
| else: | |
| denom = exp_avg_sq.sqrt().add_(group['eps']) | |
| grad.div_(denom) | |
| if group['weight_decay'] != 0: | |
| grad.add_(p.data, alpha=group['weight_decay']) | |
| if group['grad_averaging']: | |
| grad.mul_(1 - beta1) | |
| exp_avg.mul_(beta1).add_(grad) | |
| p.data.add_(exp_avg, alpha=-group['lr']) | |
| return loss | |
| class TestFusedNovoGrad(TestFusedOptimizer): | |
| def __init__(self, *args, **kwargs): | |
| super(TestFusedNovoGrad, self).__init__(*args, **kwargs) | |
| # The options for NovoGrad and FusedNovoGrad are very specific if they | |
| # are expected to behave the same. | |
| self.options = {'lr':1e-3, 'betas':(0.95, 0), 'eps':1e-8, | |
| 'weight_decay':0, 'grad_averaging':False, 'amsgrad':False} | |
| self.tst_options = {'lr':1e-3, 'betas':(0.95, 0), 'eps':1e-8, | |
| 'weight_decay':0, 'grad_averaging':False, 'amsgrad':False, | |
| 'bias_correction':False, 'reg_inside_moment':True, | |
| 'norm_type':2, 'init_zero':False, 'set_grad_none':True} | |
| self.ref_optim = Novograd | |
| self.fused_optim = apex.optimizers.FusedNovoGrad | |
| def test_float(self): | |
| self.gen_single_type_test(param_type=torch.float) | |
| def test_half(self): | |
| self.gen_single_type_test(param_type=torch.float16) | |
| def test_multi_device(self): | |
| devices = ("cuda:1", "cuda:0") | |
| for current_dev, tensor_dev in product(devices, devices): | |
| with torch.cuda.device(current_dev): | |
| torch.cuda.synchronize() | |
| self.gen_single_type_test(param_type=torch.float, device=tensor_dev) | |
| def test_multi_params(self): | |
| sizes = [[4096, 1024], [4096], [4096, 2048], [32320, 1024], [1]] | |
| tensors = [] | |
| for size in sizes: | |
| tensors.append(torch.rand(size, dtype=torch.float, device="cuda")) | |
| ref_param, tst_param, ref_optim, tst_optim = self.gen_param_optim( | |
| tensors, self.options, self.tst_options | |
| ) | |
| for _ in range(self.iters): | |
| self.gen_grad(ref_param, tst_param) | |
| ref_optim.step() | |
| tst_optim.step() | |
| max_abs_diff, max_rel_diff = self.get_max_diff(ref_param, tst_param) | |
| self.assertLessEqual(max_abs_diff, self.max_abs_diff) | |
| self.assertLessEqual(max_rel_diff, self.max_rel_diff) | |
| if __name__ == '__main__': | |
| unittest.main() | |