Add Nvidia's NovogGrad impl from Jasper (cleaner/faster than current) and Apex Fused optimizers

timm/optim/__init__.py

@@ -3,5 +3,6 @@ from .rmsprop_tf import RMSpropTF
 from .adamw import AdamW
 from .radam import RAdam
 from .novograd import NovoGrad
+from .nvnovograd import NvNovoGrad
 from .lookahead import Lookahead
 from .optim_factory import create_optimizer

timm/optim/nvnovograd.py

@@ -0,0 +1,118 @@
+""" Nvidia NovoGrad Optimizer.
+Original impl by Nvidia from Jasper example:
+    - https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper
+Paper: `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`
+    - https://arxiv.org/abs/1905.11286
+"""
+
+import torch
+from torch.optim.optimizer import Optimizer
+import math
+
+
+class NvNovoGrad(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, 0.98))
+        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, 0.98), 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(NvNovoGrad, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(NvNovoGrad, 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_(1 - beta2, norm)
+
+                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_(group['weight_decay'], p.data)
+                if group['grad_averaging']:
+                    grad.mul_(1 - beta1)
+                exp_avg.mul_(beta1).add_(grad)
+
+                p.data.add_(-group['lr'], exp_avg)
+
+        return loss

timm/optim/optim_factory.py

@@ -1,5 +1,11 @@
+import torch
 from torch import optim as optim
-from timm.optim import Nadam, RMSpropTF, AdamW, RAdam, NovoGrad, Lookahead
+from timm.optim import Nadam, RMSpropTF, AdamW, RAdam, NovoGrad, NvNovoGrad, Lookahead
+try:
+    from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD
+    has_apex = True
+except ImportError:
+    has_apex = False
 
 
 def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
@@ -20,9 +26,10 @@ def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
 def create_optimizer(args, model, filter_bias_and_bn=True):
     opt_lower = args.opt.lower()
     weight_decay = args.weight_decay
-    if opt_lower == 'adamw' or opt_lower == 'radam':
+    if 'adamw' in opt_lower or 'radam' in opt_lower:
-        # compensate for the way current AdamW and RAdam optimizers
+        # Compensate for the way current AdamW and RAdam optimizers apply LR to the weight-decay
-        # apply the weight-decay
+        # I don't believe they follow the paper or original Torch7 impl which schedules weight
+        # decay based on the ratio of current_lr/initial_lr
         weight_decay /= args.lr
     if weight_decay and filter_bias_and_bn:
         parameters = add_weight_decay(model, weight_decay)
@@ -30,12 +37,14 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
     else:
         parameters = model.parameters()
 
+    if 'fused' in opt_lower:
+        assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
+
     opt_split = opt_lower.split('_')
     opt_lower = opt_split[-1]
     if opt_lower == 'sgd':
         optimizer = optim.SGD(
-            parameters, lr=args.lr,
+            parameters, lr=args.lr, momentum=args.momentum, weight_decay=weight_decay, nesterov=True)
-            momentum=args.momentum, weight_decay=weight_decay, nesterov=True)
     elif opt_lower == 'adam':
         optimizer = optim.Adam(
             parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
@@ -61,6 +70,22 @@ def create_optimizer(args, model, filter_bias_and_bn=True):
             momentum=args.momentum, weight_decay=weight_decay)
     elif opt_lower == 'novograd':
         optimizer = NovoGrad(parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
+    elif opt_lower == 'nvnovograd':
+        optimizer = NvNovoGrad(parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
+    elif opt_lower == 'fusedsgd':
+        optimizer = FusedSGD(
+            parameters, lr=args.lr, momentum=args.momentum, weight_decay=weight_decay, nesterov=True)
+    elif opt_lower == 'fusedadam':
+        optimizer = FusedAdam(
+            parameters, lr=args.lr, adam_w_mode=False, weight_decay=weight_decay, eps=args.opt_eps)
+    elif opt_lower == 'fusedadamw':
+        optimizer = FusedAdam(
+            parameters, lr=args.lr, adam_w_mode=True, weight_decay=weight_decay, eps=args.opt_eps)
+    elif opt_lower == 'fusedlamb':
+        optimizer = FusedLAMB(parameters, lr=args.lr, weight_decay=weight_decay, eps=args.opt_eps)
+    elif opt_lower == 'fusednovograd':
+        optimizer = FusedNovoGrad(
+            parameters, lr=args.lr, betas=(0.95, 0.98), weight_decay=weight_decay, eps=args.opt_eps)
     else:
         assert False and "Invalid optimizer"
         raise ValueError