Add multi-tensor (foreach) version of Lion in style of upcoming PyTorch 2.0 optimizers

timm/optim/lion.py

@@ -16,6 +16,8 @@ Original Impl: https://github.com/google/automl/tree/master/lion
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+from typing import List
+
 import torch
 from torch.optim.optimizer import Optimizer
 
@@ -23,7 +25,15 @@ from torch.optim.optimizer import Optimizer
 class Lion(Optimizer):
     r"""Implements Lion algorithm."""
 
-    def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
+    def __init__(
+            self,
+            params,
+            lr=1e-4,
+            betas=(0.9, 0.99),
+            weight_decay=0.0,
+            maximize=False,
+            foreach=None,
+    ):
         """Initialize the hyperparameters.
 
         Args:
@@ -41,9 +51,21 @@ class Lion(Optimizer):
             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, weight_decay=weight_decay)
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            weight_decay=weight_decay,
+            foreach=foreach,
+            maximize=maximize,
+        )
         super().__init__(params, defaults)
 
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('maximize', False)
+            group.setdefault('foreach', None)
+
     @torch.no_grad()
     def step(self, closure=None):
         """Performs a single optimization step.
@@ -61,27 +83,144 @@ class Lion(Optimizer):
                 loss = closure()
 
         for group in self.param_groups:
+            params_with_grad = []
+            grads = []
+            exp_avgs = []
+            beta1, beta2 = group['betas']
+
             for p in group['params']:
                 if p.grad is None:
                     continue
+                params_with_grad.append(p)
+                if p.grad.is_sparse:
+                    raise RuntimeError('Lion does not support sparse gradients')
+                grads.append(p.grad)
 
-                # Perform stepweight decay
-                p.data.mul_(1 - group['lr'] * group['weight_decay'])
-
-                grad = p.grad
                 state = self.state[p]
+
                 # State initialization
                 if len(state) == 0:
-                    # Exponential moving average of gradient values
-                    state['exp_avg'] = torch.zeros_like(p)
+                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
 
-                exp_avg = state['exp_avg']
-                beta1, beta2 = group['betas']
+                exp_avgs.append(state['exp_avg'])
+
+            lion(
+                params_with_grad,
+                grads,
+                exp_avgs,
+                beta1=beta1,
+                beta2=beta2,
+                lr=group['lr'],
+                weight_decay=group['weight_decay'],
+                maximize=group['maximize'],
+                foreach=group['foreach'],
+            )
+
+        return loss
+
+
+def lion(
+        params: List[torch.Tensor],
+        grads: List[torch.Tensor],
+        exp_avgs: List[torch.Tensor],
+        # kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
+        # setting this as kwarg for now as functional API is compiled by torch/distributed/optim
+        maximize: bool = False,
+        foreach: bool = None,
+        *,
+        beta1: float,
+        beta2: float,
+        lr: float,
+        weight_decay: float,
+):
+    r"""Functional API that performs Lion algorithm computation.
+    """
+    if foreach is None:
+        # Placeholder for more complex foreach logic to be added when value is not set
+        foreach = False
+
+    if foreach and torch.jit.is_scripting():
+        raise RuntimeError('torch.jit.script not supported with foreach optimizers')
+
+    if foreach and not torch.jit.is_scripting():
+        func = _multi_tensor_lion
+    else:
+        func = _single_tensor_lion
+
+    func(
+        params,
+        grads,
+        exp_avgs,
+        beta1=beta1,
+        beta2=beta2,
+        lr=lr,
+        weight_decay=weight_decay,
+        maximize=maximize,
+    )
+
+
+def _single_tensor_lion(
+        params: List[torch.Tensor],
+        grads: List[torch.Tensor],
+        exp_avgs: List[torch.Tensor],
+        *,
+        beta1: float,
+        beta2: float,
+        lr: float,
+        weight_decay: float,
+        maximize: bool,
+):
+    for i, param in enumerate(params):
+        grad = grads[i] if not maximize else -grads[i]
+        exp_avg = exp_avgs[i]
+
+        if torch.is_complex(param):
+            grad = torch.view_as_real(grad)
+            exp_avg = torch.view_as_real(exp_avg)
+            param = torch.view_as_real(param)
+
+        # Perform stepweight decay
+        param.mul_(1 - lr * weight_decay)
 
         # Weight update
-                update = exp_avg * beta1 + grad * (1 - beta1)
-                p.add_(torch.sign(update), alpha=-group['lr'])
+        update = exp_avg.mul(beta1).add_(grad, alpha=1 - beta1)
+        param.add_(torch.sign(update), alpha=-lr)
+
         # Decay the momentum running average coefficient
-                exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)
+        exp_avg.lerp_(grad, 1 - beta2)
 
-        return loss
+
+def _multi_tensor_lion(
+        params: List[torch.Tensor],
+        grads: List[torch.Tensor],
+        exp_avgs: List[torch.Tensor],
+        *,
+        beta1: float,
+        beta2: float,
+        lr: float,
+        weight_decay: float,
+        maximize: bool,
+):
+    if len(params) == 0:
+        return
+
+    if maximize:
+        grads = torch._foreach_neg(tuple(grads))  # type: ignore[assignment]
+
+    grads = [torch.view_as_real(x) if torch.is_complex(x) else x for x in grads]
+    exp_avgs = [torch.view_as_real(x) if torch.is_complex(x) else x for x in exp_avgs]
+    params = [torch.view_as_real(x) if torch.is_complex(x) else x for x in params]
+
+    # Perform stepweight decay
+    torch._foreach_mul_(params, 1 - lr * weight_decay)
+
+    # Weight update
+    updates = torch._foreach_mul(exp_avgs, beta1)
+    torch._foreach_add_(updates, grads, alpha=1 - beta1)
+
+    updates = [u.sign() for u in updates]
+    torch._foreach_add_(params, updates, alpha=-lr)
+
+    # Decay the momentum running average coefficient
+    torch._foreach_mul_(exp_avgs, beta2)
+    torch._foreach_add_(exp_avgs, grads, alpha=1 - beta2)

timm/optim/optim_factory.py

@@ -36,6 +36,12 @@ except ImportError:
 _logger = logging.getLogger(__name__)
 
 
+# optimizers to default to multi-tensor
+_DEFAULT_FOREACH = {
+    'lion',
+}
+
+
 def param_groups_weight_decay(
         model: nn.Module,
         weight_decay=1e-5,
@@ -162,7 +168,8 @@ def optimizer_kwargs(cfg):
         opt=cfg.opt,
         lr=cfg.lr,
         weight_decay=cfg.weight_decay,
-        momentum=cfg.momentum)
+        momentum=cfg.momentum,
+    )
     if getattr(cfg, 'opt_eps', None) is not None:
         kwargs['eps'] = cfg.opt_eps
     if getattr(cfg, 'opt_betas', None) is not None:
@@ -171,6 +178,8 @@ def optimizer_kwargs(cfg):
         kwargs['layer_decay'] = cfg.layer_decay
     if getattr(cfg, 'opt_args', None) is not None:
         kwargs.update(cfg.opt_args)
+    if getattr(cfg, 'opt_foreach', None) is not None:
+        kwargs['foreach'] = cfg.opt_foreach
     return kwargs
 
 
@@ -191,6 +200,7 @@ def create_optimizer_v2(
         lr: Optional[float] = None,
         weight_decay: float = 0.,
         momentum: float = 0.9,
+        foreach: Optional[bool] = None,
         filter_bias_and_bn: bool = True,
         layer_decay: Optional[float] = None,
         param_group_fn: Optional[Callable] = None,
@@ -209,6 +219,7 @@ def create_optimizer_v2(
         lr: initial learning rate
         weight_decay: weight decay to apply in optimizer
         momentum:  momentum for momentum based optimizers (others may use betas via kwargs)
+        foreach: Enable / disable foreach (multi-tensor) operation if True / False. Choose safe default if None
         filter_bias_and_bn:  filter out bias, bn and other 1d params from weight decay
         **kwargs: extra optimizer specific kwargs to pass through
 
@@ -228,7 +239,8 @@ def create_optimizer_v2(
                 model_or_params,
                 weight_decay=weight_decay,
                 layer_decay=layer_decay,
-                no_weight_decay_list=no_weight_decay)
+                no_weight_decay_list=no_weight_decay,
+            )
             weight_decay = 0.
         elif weight_decay and filter_bias_and_bn:
             parameters = param_groups_weight_decay(model_or_params, weight_decay, no_weight_decay)
@@ -246,9 +258,16 @@ def create_optimizer_v2(
         assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
 
     opt_args = dict(weight_decay=weight_decay, **kwargs)
+
     if lr is not None:
         opt_args.setdefault('lr', lr)
 
+    if foreach is None:
+        if opt in _DEFAULT_FOREACH:
+            opt_args.setdefault('foreach', True)
+    else:
+        opt_args['foreach'] = foreach
+
     # basic SGD & related
     if opt_lower == 'sgd' or opt_lower == 'nesterov':
         # NOTE 'sgd' refers to SGD + nesterov momentum for legacy / backwards compat reasons