[Feature] Implement SGDSaI optimizer

README.md

@@ -10,7 +10,7 @@
 
 ## The reasons why you use `pytorch-optimizer`.
 
-* Wide range of supported optimizers. Currently, **85 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
+* Wide range of supported optimizers. Currently, **86 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
 * Including many variants such as `Cautious`, `AdamD`, `Gradient Centrailiaztion`
 * Easy to use, clean, and tested codes
 * Active maintenance
@@ -194,6 +194,7 @@ get_supported_optimizers(['adam*', 'ranger*'])
 | LaProp        | *Separating Momentum and Adaptivity in Adam*                                                      | [github](https://github.com/Z-T-WANG/LaProp-Optimizer)                                                         | <https://arxiv.org/abs/2002.04839>                                                          | [cite](https://github.com/Z-T-WANG/LaProp-Optimizer?tab=readme-ov-file#citation)                                                    |
 | APOLLO        | *SGD-like Memory, AdamW-level Performance*                                                        | [github](https://github.com/zhuhanqing/APOLLO)                                                                 | <https://arxiv.org/abs/2412.05270>                                                          | [cite](https://github.com/zhuhanqing/APOLLO?tab=readme-ov-file#-citation)                                                           |
 | MARS          | *Unleashing the Power of Variance Reduction for Training Large Models*                            | [github](https://github.com/AGI-Arena/MARS)                                                                    | <https://arxiv.org/abs/2411.10438>                                                          | [cite](https://github.com/AGI-Arena/MARS/tree/main?tab=readme-ov-file#citation)                                                     |
+| SGDSaI        | *No More Adam: Learning Rate Scaling at Initialization is All You Need*                           | [github](https://github.com/AnonymousAlethiometer/SGD_SaI)                                                     | <https://arxiv.org/abs/2411.10438>                                                          | [cite](https://github.com/AnonymousAlethiometer/SGD_SaI?tab=readme-ov-file#citation)                                                |
 
 ## Supported LR Scheduler
 

docs/changelogs/v3.3.2.md

@@ -0,0 +1,10 @@
+### Change Log
+
+### Feature
+
+* Implement `SGDSaI` optimizer. (#315, #316)
+    * [No More Adam: Learning Rate Scaling at Initialization is All You Need](https://arxiv.org/abs/2412.11768) 
+
+### Bug
+
+* Clone `exp_avg` before calling `apply_cautious` not to mask `exp_avg`. (#316)

docs/index.md

@@ -10,7 +10,7 @@
 
 ## The reasons why you use `pytorch-optimizer`.
 
-* Wide range of supported optimizers. Currently, **85 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
+* Wide range of supported optimizers. Currently, **86 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
 * Including many variants such as `Cautious`, `AdamD`, `Gradient Centrailiaztion`
 * Easy to use, clean, and tested codes
 * Active maintenance
@@ -194,6 +194,7 @@ get_supported_optimizers(['adam*', 'ranger*'])
 | LaProp        | *Separating Momentum and Adaptivity in Adam*                                                      | [github](https://github.com/Z-T-WANG/LaProp-Optimizer)                                                         | <https://arxiv.org/abs/2002.04839>                                                          | [cite](https://github.com/Z-T-WANG/LaProp-Optimizer?tab=readme-ov-file#citation)                                                    |
 | APOLLO        | *SGD-like Memory, AdamW-level Performance*                                                        | [github](https://github.com/zhuhanqing/APOLLO)                                                                 | <https://arxiv.org/abs/2412.05270>                                                          | [cite](https://github.com/zhuhanqing/APOLLO?tab=readme-ov-file#-citation)                                                           |
 | MARS          | *Unleashing the Power of Variance Reduction for Training Large Models*                            | [github](https://github.com/AGI-Arena/MARS)                                                                    | <https://arxiv.org/abs/2411.10438>                                                          | [cite](https://github.com/AGI-Arena/MARS/tree/main?tab=readme-ov-file#citation)                                                     |
+| SGDSaI        | *No More Adam: Learning Rate Scaling at Initialization is All You Need*                           | [github](https://github.com/AnonymousAlethiometer/SGD_SaI)                                                     | <https://arxiv.org/abs/2411.10438>                                                          | [cite](https://github.com/AnonymousAlethiometer/SGD_SaI?tab=readme-ov-file#citation)                                                |
 
 ## Supported LR Scheduler
 

docs/optimizer.md

@@ -332,6 +332,10 @@
     :docstring:
     :members:
 
+::: pytorch_optimizer.SGDSaI
+    :docstring:
+    :members:
+
 ::: pytorch_optimizer.SGDP
     :docstring:
     :members:

docs/visualization.md

@@ -274,6 +274,10 @@
 
 ![image](https://raw.githubusercontent.com/kozistr/pytorch_optimizer/main/docs/visualizations/rastrigin_SGDP.png)
 
+### SGDSaI
+
+![image](https://raw.githubusercontent.com/kozistr/pytorch_optimizer/main/docs/visualizations/rastrigin_SGDSaI.png)
+
 ### SGDW
 
 ![image](https://raw.githubusercontent.com/kozistr/pytorch_optimizer/main/docs/visualizations/rastrigin_SGDW.png)
@@ -592,6 +596,10 @@
 
 ![image](https://raw.githubusercontent.com/kozistr/pytorch_optimizer/main/docs/visualizations/rosenbrock_SGDP.png)
 
+### SGDSaI
+
+![image](https://raw.githubusercontent.com/kozistr/pytorch_optimizer/main/docs/visualizations/rosenbrock_SGDSaI.png)
+
 ### SGDW
 
 ![image](https://raw.githubusercontent.com/kozistr/pytorch_optimizer/main/docs/visualizations/rosenbrock_SGDW.png)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pytorch_optimizer"
-version = "3.3.1"
+version = "3.3.2"
 description = "optimizer & lr scheduler & objective function collections in PyTorch"
 license = "Apache-2.0"
 authors = ["kozistr <[email protected]>"]

pytorch_optimizer/__init__.py

@@ -128,6 +128,7 @@
     ScheduleFreeAdamW,
     ScheduleFreeRAdam,
     ScheduleFreeSGD,
+    SGDSaI,
     Shampoo,
     SignSGD,
     SophiaH,

pytorch_optimizer/optimizer/__init__.py

@@ -74,7 +74,7 @@
 from pytorch_optimizer.optimizer.rotograd import RotoGrad
 from pytorch_optimizer.optimizer.sam import BSAM, GSAM, SAM, WSAM
 from pytorch_optimizer.optimizer.schedulefree import ScheduleFreeAdamW, ScheduleFreeRAdam, ScheduleFreeSGD
-from pytorch_optimizer.optimizer.sgd import ASGD, SGDW, AccSGD, SignSGD
+from pytorch_optimizer.optimizer.sgd import ASGD, SGDW, AccSGD, SGDSaI, SignSGD
 from pytorch_optimizer.optimizer.sgdp import SGDP
 from pytorch_optimizer.optimizer.shampoo import ScalableShampoo, Shampoo
 from pytorch_optimizer.optimizer.sm3 import SM3
@@ -281,6 +281,7 @@ def load_optimizer(optimizer: str) -> OPTIMIZER:
     ScheduleFreeRAdam,
     LaProp,
     MARS,
+    SGDSaI,
 ]
 OPTIMIZERS: Dict[str, OPTIMIZER] = {str(optimizer.__name__).lower(): optimizer for optimizer in OPTIMIZER_LIST}
 

pytorch_optimizer/optimizer/adashift.py

@@ -110,10 +110,11 @@ def step(self, closure: CLOSURE = None) -> LOSS:
                 exp_avg_sq.mul_(beta2).add_(reduced_grad_sq, alpha=1.0 - beta2)
 
                 update = exp_avg.clone()
-                update.div_(exp_avg_sq.div(bias_correction).sqrt_().add_(group['eps']))
                 if self.cautious:
                     self.apply_cautious(update, grad)
 
+                update.div_(exp_avg_sq.div(bias_correction).sqrt_().add_(group['eps']))
+
                 p.add_(update, alpha=-group['lr'])
 
         return loss

pytorch_optimizer/optimizer/ademamix.py

@@ -146,10 +146,11 @@ def step(self, closure: CLOSURE = None) -> LOSS:
 
                 de_nom = exp_avg_sq.sqrt().div_(bias_correction2_sq).add_(group['eps'])
 
+                update = exp_avg.clone()
                 if self.cautious:
-                    self.apply_cautious(exp_avg, grad)
+                    self.apply_cautious(update, grad)
 
-                update = (exp_avg + alpha_t * exp_avg_slow).div_(de_nom)
+                update.add_(exp_avg_slow, alpha=alpha_t).div_(de_nom)
 
                 p.add_(update, alpha=-step_size)
 

pytorch_optimizer/optimizer/mars.py

@@ -121,26 +121,27 @@ def optimize_mixed(
 
         exp_avg.mul_(beta1).add_(c_t, alpha=1.0 - beta1)
 
+        update = exp_avg.clone()
         if cautious:
-            self.apply_cautious(exp_avg, grad)
+            self.apply_cautious(update, grad)
 
         if mars_type == 'adamw' or (mars_type == 'shampoo' and not is_grad_2d):
             exp_avg_sq.mul_(beta2).addcmul_(c_t, c_t, value=1.0 - beta2)
 
             bias_correction1: float = self.debias(beta1, step)
             bias_correction2_sq: float = math.sqrt(self.debias(beta2, step))
 
-            update = self.apply_ams_bound(ams_bound, exp_avg_sq, max_exp_avg_sq, eps)
-            update.div_(bias_correction2_sq).mul_(bias_correction1)
+            de_nom = self.apply_ams_bound(ams_bound, exp_avg_sq, max_exp_avg_sq, eps)
+            de_nom.div_(bias_correction2_sq).mul_(bias_correction1)
 
-            return exp_avg.div(update)
+            return update.div_(de_nom)
 
         if mars_type == 'lion':
-            return exp_avg.sign()
+            return update.sign_()
 
-        factor: float = max(1.0, grad.size(0) / grad.size(1)) ** 0.5
+        factor: float = math.sqrt(max(1.0, grad.size(0) / grad.size(1)))
 
-        return zero_power_via_newton_schulz_5(exp_avg.mul(1.0 / (1.0 - beta1)), eps=eps).mul_(factor)
+        return zero_power_via_newton_schulz_5(update.mul_(1.0 / (1.0 - beta1)), eps=eps).mul_(factor)
 
     def optimize_1d(
         self,
@@ -162,13 +163,15 @@ def optimize_1d(
         exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
         exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
 
-        update = self.apply_ams_bound(ams_bound, exp_avg_sq, max_exp_avg_sq, eps)
-        update.div_(bias_correction2_sq).mul_(bias_correction1)
+        update = exp_avg.clone()
 
         if cautious:
-            self.apply_cautious(exp_avg, grad)
+            self.apply_cautious(update, grad)
 
-        return exp_avg.div(update)
+        de_nom = self.apply_ams_bound(ams_bound, exp_avg_sq, max_exp_avg_sq, eps)
+        de_nom.div_(bias_correction2_sq).mul_(bias_correction1)
+
+        return update.div_(de_nom)
 
     @torch.no_grad()
     def step(self, closure: CLOSURE = None) -> LOSS:

pytorch_optimizer/optimizer/sgd.py

@@ -356,6 +356,9 @@ def __init__(
         }
         super().__init__(params, defaults)
 
+    def __str__(self) -> str:
+        return 'SignSGD'
+
     @torch.no_grad()
     def reset(self):
         for group in self.param_groups:
@@ -396,3 +399,123 @@ def step(self, closure: CLOSURE = None) -> LOSS:
                 p.add_(torch.sign(buf), alpha=-group['lr'])
 
         return loss
+
+
+class SGDSaI(BaseOptimizer):
+    r"""No More Adam: Learning Rate Scaling at Initialization is All You Need.
+
+    :param params: PARAMETERS. iterable of parameters to optimize or dicts defining parameter groups.
+    :param lr: float. learning rate.
+    :param momentum: float.  coefficients used for computing running averages of gradient.
+    :param weight_decay: float. weight decay (L2 penalty).
+    :param weight_decouple: bool. the optimizer uses decoupled weight decay as in AdamW.
+    :param eps: float. term added to the denominator to improve numerical stability.
+    """
+
+    def __init__(
+        self,
+        params: PARAMETERS,
+        lr: float = 1e-2,
+        momentum: float = 0.9,
+        weight_decay: float = 1e-2,
+        weight_decouple: bool = True,
+        eps: float = 1e-8,
+        **kwargs,
+    ):
+        self.validate_learning_rate(lr)
+        self.validate_range(momentum, 'beta', 0.0, 1.0)
+        self.validate_non_negative(weight_decay, 'weight_decay')
+        self.validate_non_negative(eps, 'eps')
+
+        self.has_warmup: bool = False
+
+        defaults: DEFAULTS = {
+            'lr': lr,
+            'momentum': momentum,
+            'weight_decay': weight_decay,
+            'weight_decouple': weight_decouple,
+            'eps': eps,
+        }
+        super().__init__(params, defaults)
+
+    def __str__(self) -> str:
+        return 'SGDSaI'
+
+    @torch.no_grad()
+    def reset(self):
+        for group in self.param_groups:
+            group['step'] = 0
+            for p in group['params']:
+                state = self.state[p]
+
+                if group['momentum'] > 0.0:
+                    state['momentum_buffer'] = torch.zeros_like(p)
+
+    @torch.no_grad()
+    def warmup_step(self, closure: CLOSURE = None) -> LOSS:
+        loss: LOSS = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+                if grad.is_sparse:
+                    raise NoSparseGradientError(str(self))
+
+                sigma = grad.std().nan_to_num_()
+                grad_norm = grad.norm()
+
+                g_snr = grad_norm.div_(sigma.add_(group['eps'])) if sigma != 0.0 else grad_norm
+
+                self.state[p]['gsnr'] = g_snr
+
+        self.has_warmup = True
+
+        return loss
+
+    @torch.no_grad()
+    def step(self, closure: CLOSURE = None) -> LOSS:
+        if not self.has_warmup:
+            self.warmup_step(closure)
+
+        loss: LOSS = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            momentum: float = group['momentum']
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+
+                state = self.state[p]
+
+                if momentum > 0.0:
+                    if 'momentum_buffer' not in state:
+                        state['momentum_buffer'] = grad.clone()
+
+                    buf = state['momentum_buffer']
+                    buf.mul_(momentum).add_(grad, alpha=1.0 - momentum)
+                else:
+                    buf = grad
+
+                self.apply_weight_decay(
+                    p,
+                    grad,
+                    group['lr'],
+                    group['weight_decay'],
+                    group['weight_decouple'],
+                    False,
+                )
+
+                p.add_(buf, alpha=-group['lr'] * state['gsnr'])
+
+        return loss

tests/constants.py

@@ -74,6 +74,7 @@
     ScheduleFreeAdamW,
     ScheduleFreeRAdam,
     ScheduleFreeSGD,
+    SGDSaI,
     Shampoo,
     SignSGD,
     SophiaH,
@@ -538,6 +539,8 @@
     (MARS, {'lr': 1e-1, 'weight_decay': 1e-3, 'mars_type': 'lion', 'optimize_1d': True}, 5),
     (MARS, {'lr': 5e-1, 'lr_1d': 5e-1, 'weight_decay': 1e-3, 'mars_type': 'shampoo'}, 5),
     (MARS, {'lr': 5e-1, 'lr_1d': 5e-1, 'weight_decay': 1e-3, 'mars_type': 'adamw', 'ams_bound': True}, 5),
+    (SGDSaI, {'lr': 1e0}, 15),
+    (SGDSaI, {'lr': 1e0, 'momentum': 0.0}, 15),
 ]
 ADANORM_SUPPORTED_OPTIMIZERS: List[Tuple[Any, Dict[str, Union[float, bool, int]], int]] = [
     (AdaBelief, {'lr': 5e-1, 'weight_decay': 1e-3, 'adanorm': True}, 10),

tests/test_load_modules.py

@@ -34,7 +34,7 @@ def test_load_lr_scheduler_invalid(invalid_lr_scheduler_names):
 
 
 def test_get_supported_optimizers():
-    assert len(get_supported_optimizers()) == 84
+    assert len(get_supported_optimizers()) == 85
     assert len(get_supported_optimizers('adam*')) == 7
     assert len(get_supported_optimizers(['adam*', 'ranger*'])) == 9