WildfireDetection/Swin-Transformer/apex/tests/L0/run_optimizers/test_fused_novograd.py

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)

    @unittest.skipIf(torch.cuda.device_count()<2, "more than 1 GPU required")
    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()