[Feature] Implement AdamMini optimizer

README.md

@@ -10,7 +10,7 @@
 
 **pytorch-optimizer** is optimizer & lr scheduler collections in PyTorch. 
 I just re-implemented (speed & memory tweaks, plug-ins) the algorithm while based on the original paper. Also, It includes useful and practical optimization ideas.  
-Currently, **71 optimizers (+ `bitsandbytes`)**, **16 lr schedulers**, and **13 loss functions** are supported!  
+Currently, **72 optimizers (+ `bitsandbytes`)**, **16 lr schedulers**, and **13 loss functions** are supported!  
 
 Highly inspired by [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer).
 
@@ -168,6 +168,7 @@ supported_optimizers = get_supported_optimizers()
 | Grokfast      | *Accelerated Grokking by Amplifying Slow Gradients*                                               | [github](https://github.com/ironjr/grokfast)                                                                   | <https://arxiv.org/abs/2405.20233>                                                         | [cite](https://github.com/ironjr/grokfast?tab=readme-ov-file#citation)                                                              |
 | Kate          | *Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad*                     | [github](https://github.com/nazya/KATE)                                                                        | <https://arxiv.org/abs/2403.02648>                                                         | [cite](https://github.com/nazya/KATE?tab=readme-ov-file#remove-that-square-root-a-new-efficient-scale-invariant-version-of-adagrad) |
 | StableAdamW   | *Stable and low-precision training for large-scale vision-language models*                        |                                                                                                                | <https://arxiv.org/abs/2304.13013>                                                         | [cite](https://ui.adsabs.harvard.edu/abs/2023arXiv230413013W/exportcitation)                                                        |
+| AdamMini      | *Use Fewer Learning Rates To Gain More*                                                           | [github](https://github.com/zyushun/Adam-mini)                                                                 | <https://arxiv.org/abs/2406.16793>                                                         | [cite](https://github.com/zyushun/Adam-mini?tab=readme-ov-file#citation)                                                            |
 
 ## Supported LR Scheduler
 

docs/changelogs/v3.0.2.md

@@ -9,6 +9,8 @@
   * [Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad](https://arxiv.org/abs/2403.02648) 
 * Implement `StableAdamW` optimizer. (#250, #252)
   * [Stable and low-precision training for large-scale vision-language models](https://arxiv.org/abs/2304.13013) 
+* Implement `AdamMini` optimizer. (#246, #253)
+  * [Use Fewer Learning Rates To Gain More](https://arxiv.org/abs/2406.16793) 
 
 ### Refactor
 

docs/index.md

@@ -10,7 +10,7 @@
 
 **pytorch-optimizer** is optimizer & lr scheduler collections in PyTorch. 
 I just re-implemented (speed & memory tweaks, plug-ins) the algorithm while based on the original paper. Also, It includes useful and practical optimization ideas.  
-Currently, **71 optimizers (+ `bitsandbytes`)**, **16 lr schedulers**, and **13 loss functions** are supported!  
+Currently, **72 optimizers (+ `bitsandbytes`)**, **16 lr schedulers**, and **13 loss functions** are supported!  
 
 Highly inspired by [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer).
 
@@ -168,6 +168,7 @@ supported_optimizers = get_supported_optimizers()
 | Grokfast      | *Accelerated Grokking by Amplifying Slow Gradients*                                               | [github](https://github.com/ironjr/grokfast)                                                                   | <https://arxiv.org/abs/2405.20233>                                                         | [cite](https://github.com/ironjr/grokfast?tab=readme-ov-file#citation)                                                              |
 | Kate          | *Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad*                     | [github](https://github.com/nazya/KATE)                                                                        | <https://arxiv.org/abs/2403.02648>                                                         | [cite](https://github.com/nazya/KATE?tab=readme-ov-file#remove-that-square-root-a-new-efficient-scale-invariant-version-of-adagrad) |
 | StableAdamW   | *Stable and low-precision training for large-scale vision-language models*                        |                                                                                                                | <https://arxiv.org/abs/2304.13013>                                                         | [cite](https://ui.adsabs.harvard.edu/abs/2023arXiv230413013W/exportcitation)                                                        |
+| AdamMini      | *Use Fewer Learning Rates To Gain More*                                                           | [github](https://github.com/zyushun/Adam-mini)                                                                 | <https://arxiv.org/abs/2406.16793>                                                         | [cite](https://github.com/zyushun/Adam-mini?tab=readme-ov-file#citation)                                                            |
 
 ## Supported LR Scheduler
 

docs/optimizer.md

@@ -32,6 +32,10 @@
     :docstring:
     :members:
 
+::: pytorch_optimizer.AdamMini
+    :docstring:
+    :members:
+
 ::: pytorch_optimizer.AdaMax
     :docstring:
     :members:

pyproject.toml

@@ -12,13 +12,13 @@ documentation = "https://pytorch-optimizers.readthedocs.io/en/latest"
 keywords = [
     "pytorch", "deep-learning", "optimizer", "lr scheduler", "A2Grad", "ASGD", "AccSGD", "AdaBelief", "AdaBound",
     "AdaDelta", "AdaFactor", "AdaMax", "AdaMod", "AdaNorm", "AdaPNM", "AdaSmooth", "AdaHessian", "Adai", "Adalite",
-    "AdamP", "AdamS", "Adan", "AggMo", "Aida", "AliG", "Amos", "Apollo", "AvaGrad", "bSAM", "CAME", "DAdaptAdaGrad",
-    "DAdaptAdam", "DAdaptAdan", "DAdaptSGD", "DAdaptLion", "DiffGrad", "FAdam", "Fromage", "GaLore", "Gravity",
-    "GrokFast", "GSAM", "Kate", "Lamb", "LARS", "Lion", "LOMO", "Lookahead", "MADGRAD", "MSVAG", "Nero", "NovoGrad",
-    "PAdam", "PCGrad", "PID", "PNM", "Prodigy", "QHAdam", "QHM", "RAdam", "Ranger", "Ranger21", "RotoGrad", "SAM",
-    "ScheduleFreeSGD", "ScheduleFreeAdamW", "SGDP", "Shampoo", "ScalableShampoo", "SGDW", "SignSGD", "SM3", "SopihaH",
-    "SRMM", "StableAdamW", "SWATS", "Tiger", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal", "FocalCosine", "SoftF1",
-    "Dice", "LDAM", "Jaccard", "Bi-Tempered", "Tversky", "FocalTversky", "LovaszHinge", "bitsandbytes", "WSD",
+    "AdamMini", "AdamP", "AdamS", "Adan", "AggMo", "Aida", "AliG", "Amos", "Apollo", "AvaGrad", "bSAM", "CAME",
+    "DAdaptAdaGrad", "DAdaptAdam", "DAdaptAdan", "DAdaptSGD", "DAdaptLion", "DiffGrad", "FAdam", "Fromage", "GaLore",
+    "Gravity", "GrokFast", "GSAM", "Kate", "Lamb", "LARS", "Lion", "LOMO", "Lookahead", "MADGRAD", "MSVAG", "Nero",
+    "NovoGrad", "PAdam", "PCGrad", "PID", "PNM", "Prodigy", "QHAdam", "QHM", "RAdam", "Ranger", "Ranger21", "RotoGrad",
+    "SAM", "ScheduleFreeSGD", "ScheduleFreeAdamW", "SGDP", "Shampoo", "ScalableShampoo", "SGDW", "SignSGD", "SM3",
+    "SopihaH", "SRMM", "StableAdamW", "SWATS", "Tiger", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal", "FocalCosine",
+    "SoftF1", "Dice", "LDAM", "Jaccard", "Bi-Tempered", "Tversky", "FocalTversky", "LovaszHinge", "bitsandbytes", "WSD",
 ]
 classifiers = [
     "License :: OSI Approved :: Apache Software License",
@@ -122,6 +122,7 @@ testpaths = "tests"
 [tool.coverage.run]
 omit = [
     "./pytorch_optimizer/optimizer/rotograd.py",
+    "./pytorch_optimizer/optimizer/adam_mini.py",
 ]
 
 [build-system]

pytorch_optimizer/__init__.py

@@ -40,6 +40,7 @@
 from pytorch_optimizer.optimizer.adahessian import AdaHessian
 from pytorch_optimizer.optimizer.adai import Adai
 from pytorch_optimizer.optimizer.adalite import Adalite
+from pytorch_optimizer.optimizer.adam_mini import AdamMini
 from pytorch_optimizer.optimizer.adamax import AdaMax
 from pytorch_optimizer.optimizer.adamod import AdaMod
 from pytorch_optimizer.optimizer.adamp import AdamP
@@ -203,6 +204,7 @@
     GrokFastAdamW,
     Kate,
     StableAdamW,
+    AdamMini,
 ]
 OPTIMIZERS: Dict[str, OPTIMIZER] = {str(optimizer.__name__).lower(): optimizer for optimizer in OPTIMIZER_LIST}
 

pytorch_optimizer/base/optimizer.py

@@ -258,6 +258,11 @@ def validate_learning_rate(learning_rate: Optional[float]) -> None:
         if learning_rate is not None and learning_rate < 0.0:
             raise NegativeLRError(learning_rate)
 
+    @staticmethod
+    def validate_mod(x: int, y: int) -> None:
+        if x % y != 0:
+            raise ValueError(f'[-] {x} must be divisible by {y}')
+
     def validate_betas(self, betas: BETAS) -> None:
         if betas[0] is not None:
             self.validate_range(betas[0], 'beta1', 0.0, 1.0, range_type='[]')