[Feature] Implement APOLLO optimizer

README.md

@@ -10,7 +10,7 @@
 
 ## The reasons why you use `pytorch-optimizer`.
 
-* Wide range of supported optimizers. Currently, **83 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
+* Wide range of supported optimizers. Currently, **84 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
@@ -192,6 +192,7 @@ get_supported_optimizers(['adam*', 'ranger*'])
 | MicroAdam     | *Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence*                 | [github](https://github.com/IST-DASLab/MicroAdam)                                                              | <https://arxiv.org/abs/2405.15593>                                                          | [cite](https://github.com/IST-DASLab/MicroAdam?tab=readme-ov-file#citing)                                                           |
 | Muon          | *MomentUm Orthogonalized by Newton-schulz*                                                        | [github](https://github.com/KellerJordan/Muon)                                                                 | <https://x.com/kellerjordan0/status/1842300916864844014>                                    | [cite](https://github.com/KellerJordan/Muon)                                                                                        |
 | 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)                                                           |
 
 ## Supported LR Scheduler
 

docs/changelogs/v3.3.1.md

@@ -4,6 +4,9 @@
 
 * Support `Cautious` variant to `AdaShift` optimizer. (#310)
 * Save the state of the `Lookahead` optimizer too. (#310)
+* Implement `APOLLO` optimizer. (#311, #312)
+    * [SGD-like Memory, AdamW-level Performance](https://arxiv.org/abs/2412.05270) 
+* Rename the `Apollo` (`An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization`) optimizer name to `ApolloDQN` not to overlap with the new optimizer name `APOLLO`. (#312)
 
 ### Bug
 

docs/index.md

@@ -10,7 +10,7 @@
 
 ## The reasons why you use `pytorch-optimizer`.
 
-* Wide range of supported optimizers. Currently, **83 optimizers (+ `bitsandbytes`, `qgalore`, `torchao`)**, **16 lr schedulers**, and **13 loss functions** are supported!
+* Wide range of supported optimizers. Currently, **84 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
@@ -192,6 +192,7 @@ get_supported_optimizers(['adam*', 'ranger*'])
 | MicroAdam     | *Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence*                 | [github](https://github.com/IST-DASLab/MicroAdam)                                                              | <https://arxiv.org/abs/2405.15593>                                                          | [cite](https://github.com/IST-DASLab/MicroAdam?tab=readme-ov-file#citing)                                                           |
 | Muon          | *MomentUm Orthogonalized by Newton-schulz*                                                        | [github](https://github.com/KellerJordan/Muon)                                                                 | <https://x.com/kellerjordan0/status/1842300916864844014>                                    | [cite](https://github.com/KellerJordan/Muon)                                                                                        |
 | 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)                                                           |
 
 ## Supported LR Scheduler
 

docs/optimizer.md

@@ -116,7 +116,11 @@
     :docstring:
     :members:
 
-::: pytorch_optimizer.Apollo
+::: pytorch_optimizer.APOLLO
+    :docstring:
+    :members:
+
+::: pytorch_optimizer.ApolloDQN
     :docstring:
     :members:
 

pyproject.toml

@@ -13,14 +13,14 @@ keywords = [
     "pytorch", "deep-learning", "optimizer", "lr scheduler", "A2Grad", "ASGD", "AccSGD", "AdaBelief", "AdaBound",
     "AdaDelta", "AdaFactor", "AdaMax", "AdamG", "AdaMod", "AdaNorm", "AdaPNM", "AdaSmooth", "AdEMAMix", "ADOPT",
     "AdaHessian", "Adai", "Adalite", "AdaLomo", "AdamMini", "AdamP", "AdamS", "Adan", "AggMo", "Aida", "AliG", "Amos",
-    "Apollo", "AvaGrad", "bSAM", "CAME", "DAdaptAdaGrad", "DAdaptAdam", "DAdaptAdan", "DAdaptSGD", "DAdaptLion",
-    "DeMo", "DiffGrad", "FAdam", "Fromage", "FTRL", "GaLore", "Gravity", "GrokFast", "GSAM", "Kate", "Lamb", "LaProp",
-    "LARS", "Lion", "LOMO", "Lookahead", "MADGRAD", "MSVAG", "Muno", "Nero", "NovoGrad", "PAdam", "PCGrad", "PID",
-    "PNM", "Prodigy", "QHAdam", "QHM", "RAdam", "Ranger", "Ranger21", "RotoGrad", "SAM", "ScheduleFreeSGD",
-    "ScheduleFreeAdamW", "ScheduleFreeRAdam", "SGDP", "Shampoo", "ScalableShampoo", "SGDW", "SignSGD", "SM3", "SOAP",
-    "SopihaH", "SRMM", "StableAdamW", "SWATS", "Tiger", "TRAC", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal",
-    "FocalCosine", "SoftF1", "Dice", "LDAM", "Jaccard", "Bi-Tempered", "Tversky", "FocalTversky", "LovaszHinge",
-    "bitsandbytes", "WSD", "QGaLore",
+    "Apollo", "APOLLO", "AvaGrad", "bSAM", "CAME", "DAdaptAdaGrad", "DAdaptAdam", "DAdaptAdan", "DAdaptSGD",
+    "DAdaptLion", "DeMo", "DiffGrad", "FAdam", "Fromage", "FTRL", "GaLore", "Gravity", "GrokFast", "GSAM", "Kate",
+    "Lamb", "LaProp", "LARS", "Lion", "LOMO", "Lookahead", "MADGRAD", "MSVAG", "Muno", "Nero", "NovoGrad", "PAdam",
+    "PCGrad", "PID", "PNM", "Prodigy", "QHAdam", "QHM", "RAdam", "Ranger", "Ranger21", "RotoGrad", "SAM",
+    "ScheduleFreeSGD", "ScheduleFreeAdamW", "ScheduleFreeRAdam", "SGDP", "Shampoo", "ScalableShampoo", "SGDW",
+    "SignSGD", "SM3", "SOAP", "SopihaH", "SRMM", "StableAdamW", "SWATS", "Tiger", "TRAC", "WSAM", "Yogi", "BCE",
+    "BCEFocal", "Focal", "FocalCosine", "SoftF1", "Dice", "LDAM", "Jaccard", "Bi-Tempered", "Tversky", "FocalTversky",
+    "LovaszHinge", "bitsandbytes", "WSD", "QGaLore",
 ]
 classifiers = [
     "License :: OSI Approved :: Apache Software License",

pytorch_optimizer/__init__.py

@@ -42,6 +42,7 @@
 )
 from pytorch_optimizer.optimizer import (
     ADOPT,
+    APOLLO,
     ASGD,
     BSAM,
     CAME,
@@ -90,7 +91,7 @@
     Aida,
     AliG,
     Amos,
-    Apollo,
+    ApolloDQN,
     AvaGrad,
     DAdaptAdaGrad,
     DAdaptAdam,

pytorch_optimizer/optimizer/__init__.py

@@ -34,7 +34,7 @@
 from pytorch_optimizer.optimizer.aida import Aida
 from pytorch_optimizer.optimizer.alig import AliG
 from pytorch_optimizer.optimizer.amos import Amos
-from pytorch_optimizer.optimizer.apollo import Apollo
+from pytorch_optimizer.optimizer.apollo import APOLLO, ApolloDQN
 from pytorch_optimizer.optimizer.avagrad import AvaGrad
 from pytorch_optimizer.optimizer.came import CAME
 from pytorch_optimizer.optimizer.dadapt import DAdaptAdaGrad, DAdaptAdam, DAdaptAdan, DAdaptLion, DAdaptSGD
@@ -228,7 +228,8 @@ def load_optimizer(optimizer: str) -> OPTIMIZER:
     DAdaptAdan,
     AdamS,
     AdaFactor,
-    Apollo,
+    ApolloDQN,
+    APOLLO,
     SWATS,
     NovoGrad,
     Lion,

pytorch_optimizer/optimizer/apollo.py

@@ -1,14 +1,18 @@
-from typing import Optional
+import math
+from typing import Literal, Optional
 
 import numpy as np
 import torch
 
 from pytorch_optimizer.base.exception import NoSparseGradientError
 from pytorch_optimizer.base.optimizer import BaseOptimizer
-from pytorch_optimizer.base.types import CLOSURE, DEFAULTS, LOSS, PARAMETERS
+from pytorch_optimizer.base.types import BETAS, CLOSURE, DEFAULTS, LOSS, PARAMETERS
+from pytorch_optimizer.optimizer.galore_utils import GaLoreProjector
 
+SCALE_TYPE = Literal['channel', 'tensor']
 
-class Apollo(BaseOptimizer):
+
+class ApolloDQN(BaseOptimizer):
     r"""An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization.
 
     :param params: PARAMETERS. iterable of parameters to optimize or dicts defining parameter groups.
@@ -25,8 +29,8 @@ class Apollo(BaseOptimizer):
     def __init__(
         self,
         params: PARAMETERS,
-        lr: float = 1e-3,
-        init_lr: Optional[float] = None,
+        lr: float = 1e-2,
+        init_lr: Optional[float] = 1e-5,
         beta: float = 0.9,
         rebound: str = 'constant',
         weight_decay: float = 0.0,
@@ -58,7 +62,7 @@ def __init__(
         super().__init__(params, defaults)
 
     def __str__(self) -> str:
-        return 'Apollo'
+        return 'ApolloDQN'
 
     @torch.no_grad()
     def reset(self):
@@ -146,3 +150,155 @@ def step(self, closure: CLOSURE = None) -> LOSS:
                 p.add_(d_p, alpha=-current_lr)
 
         return loss
+
+
+class APOLLO(BaseOptimizer):
+    r"""SGD-like Memory, AdamW-level Performance.
+
+    :param params: PARAMETERS. iterable of parameters to optimize or dicts defining parameter groups.
+    :param lr: float. learning rate.
+    :param betas: BETAS. coefficients used for computing running averages of gradient and the squared hessian trace.
+    :param weight_decay: float. weight decay (L2 penalty).
+    :param weight_decouple: bool. the optimizer uses decoupled weight decay as in AdamW.
+    :param fixed_decay: bool. fix weight decay.
+    :param correct_bias: bool. Whether to correct bias in Adam.
+    :param eps: float. term added to the denominator to improve numerical stability.
+    """
+
+    def __init__(
+        self,
+        params: PARAMETERS,
+        lr: float = 1e-2,
+        betas: BETAS = (0.9, 0.999),
+        scale_type: SCALE_TYPE = 'tensor',
+        weight_decay: float = 0.0,
+        weight_decouple: bool = True,
+        fixed_decay: bool = False,
+        correct_bias: bool = True,
+        eps: float = 1e-6,
+        **kwargs,
+    ):
+        self.validate_learning_rate(lr)
+        self.validate_betas(betas)
+        self.validate_non_negative(weight_decay, 'weight_decay')
+        self.validate_non_negative(eps, 'eps')
+
+        defaults: DEFAULTS = {
+            'lr': lr,
+            'betas': betas,
+            'scale_type': scale_type,
+            'weight_decay': weight_decay,
+            'weight_decouple': weight_decouple,
+            'fixed_decay': fixed_decay,
+            'correct_bias': correct_bias,
+            'eps': eps,
+            **kwargs,
+        }
+        super().__init__(params, defaults)
+
+    def __str__(self) -> str:
+        return 'APOLLO'
+
+    @torch.no_grad()
+    def reset(self):
+        for group in self.param_groups:
+            group['step'] = 0
+            for p in group['params']:
+                state = self.state[p]
+
+                state['exp_avg'] = torch.zeros_like(p)
+                state['exp_avg_sq'] = torch.zeros_like(p)
+
+    @torch.no_grad()
+    def 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:
+            if 'step' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            beta1, beta2 = group['betas']
+
+            step_size: float = group['lr']
+            if group['correct_bias']:
+                bias_correction1: float = self.debias(beta1, group['step'])
+                bias_correction2_sq: float = math.sqrt(self.debias(beta2, group['step']))
+                step_size *= bias_correction2_sq / bias_correction1
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+
+                grad = p.grad
+                if grad.is_sparse:
+                    raise NoSparseGradientError(str(self))
+
+                state = self.state[p]
+                if len(state) == 0:
+                    state['exp_avg'] = torch.zeros_like(p)
+                    state['exp_avg_sq'] = torch.zeros_like(p)
+
+                if 'rank' in group and p.dim() > 1:
+                    if 'projector' not in state:
+                        state['projector'] = GaLoreProjector(
+                            rank=group['rank'],
+                            update_proj_gap=group['update_proj_gap'],
+                            scale=group['scale'],
+                            projection_type=group['projection_type'],
+                        )
+
+                    grad = state['projector'].project(grad, group['step'], from_random_matrix=True)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                exp_avg.mul_(beta1).add_(grad, alpha=1.0 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
+
+                de_nom = exp_avg_sq.sqrt().add_(group['eps'])
+
+                norm_grad = exp_avg / de_nom
+                if 'rank' in group and p.dim() > 1:
+                    if group['scale_type'] == 'channel':
+                        norm_dim: int = 0 if norm_grad.shape[0] < norm_grad.shape[1] else 1
+                        scaling_factor = torch.norm(norm_grad, dim=norm_dim) / (torch.norm(grad, dim=norm_dim) + 1e-8)
+                        if norm_dim == 1:
+                            scaling_factor = scaling_factor.unsqueeze(1)
+                    else:
+                        scaling_factor = torch.norm(norm_grad) / (torch.norm(grad) + 1e-8)
+
+                    scaling_grad = grad * scaling_factor
+
+                    scaling_grad_norm = torch.norm(scaling_grad)
+                    if 'scaling_grad' in state:
+                        limiter = (
+                            max(
+                                scaling_grad_norm / (state['scaling_grad'] + 1e-8),
+                                1.01,
+                            )
+                            / 1.01
+                        )
+
+                        scaling_grad.div_(limiter)
+                        scaling_grad_norm.div_(limiter)
+
+                    state['scaling_grad'] = scaling_grad_norm
+
+                    norm_grad = scaling_grad * np.sqrt(group['scale'])
+                    norm_grad = state['projector'].project_back(norm_grad)
+
+                p.add_(norm_grad, alpha=-step_size)
+
+                self.apply_weight_decay(
+                    p,
+                    grad,
+                    lr=step_size,
+                    weight_decay=group['weight_decay'],
+                    weight_decouple=group['weight_decouple'],
+                    fixed_decay=group['fixed_decay'],
+                )
+
+        return loss