add madgradw optimizer

tests/test_optim.py

@@ -490,7 +490,7 @@ def test_lamb(optimizer):
     _test_model(optimizer, dict(lr=1e-3))
 
 
-@pytest.mark.parametrize('optimizer',  ['madgrad'])
+@pytest.mark.parametrize('optimizer',  ['madgrad', 'madgradw'])
 def test_madgrad(optimizer):
     _test_basic_cases(
         lambda weight, bias: create_optimizer_v2([weight, bias], optimizer, lr=1e-3)

timm/optim/madgrad.py

@@ -53,7 +53,13 @@ class MADGRAD(torch.optim.Optimizer):
     """
 
     def __init__(
-        self, params: _params_t, lr: float = 1e-2, momentum: float = 0.9, weight_decay: float = 0, eps: float = 1e-6,
+            self,
+            params: _params_t,
+            lr: float = 1e-2,
+            momentum: float = 0.9,
+            weight_decay: float = 0,
+            eps: float = 1e-6,
+            decoupled_decay: bool = False,
     ):
         if momentum < 0 or momentum >= 1:
             raise ValueError(f"Momentum {momentum} must be in the range [0,1]")
@@ -64,7 +70,8 @@ class MADGRAD(torch.optim.Optimizer):
         if eps < 0:
             raise ValueError(f"Eps must be non-negative")
 
-        defaults = dict(lr=lr, eps=eps, momentum=momentum, weight_decay=weight_decay)
+        defaults = dict(
+            lr=lr, eps=eps, momentum=momentum, weight_decay=weight_decay, decoupled_decay=decoupled_decay)
         super().__init__(params, defaults)
 
     @property
@@ -95,7 +102,7 @@ class MADGRAD(torch.optim.Optimizer):
         for group in self.param_groups:
             eps = group["eps"]
             lr = group["lr"] + eps
-            decay = group["weight_decay"]
+            weight_decay = group["weight_decay"]
             momentum = group["momentum"]
 
             ck = 1 - momentum
@@ -120,11 +127,13 @@ class MADGRAD(torch.optim.Optimizer):
                 s = state["s"]
 
                 # Apply weight decay
-                if decay != 0:
+                if weight_decay != 0:
+                    if group['decoupled_decay']:
+                        p.data.mul_(1.0 - group['lr'] * weight_decay)
+                    else:
                         if grad.is_sparse:
                             raise RuntimeError("weight_decay option is not compatible with sparse gradients")
-
-                    grad.add_(p.data, alpha=decay)
+                        grad.add_(p.data, alpha=weight_decay)
 
                 if grad.is_sparse:
                     grad = grad.coalesce()

timm/optim/optim_factory.py

@@ -165,6 +165,8 @@ def create_optimizer_v2(
         optimizer = Lamb(parameters, **opt_args)
     elif opt_lower == 'madgrad':
         optimizer = MADGRAD(parameters, momentum=momentum, **opt_args)
+    elif opt_lower == 'madgradw':
+        optimizer = MADGRAD(parameters, momentum=momentum, decoupled_decay=True, **opt_args)
     elif opt_lower == 'novograd' or opt_lower == 'nvnovograd':
         optimizer = NvNovoGrad(parameters, **opt_args)
     elif opt_lower == 'rmsprop':