Repository: kozistr/pytorch_optimizer

Issue:
FAdam 
## Paper or Code
please check it!
paper: [https://arxiv.org/pdf/2405.12807](url)
code: [https://github.com/lessw2020/fadam_pytorch](url)



Discussion:

Patch:
diff --git a/README.md b/README.md
index a78dd76f..0217cbbd 100644
--- a/README.md
+++ b/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, **67 optimizers (+ `bitsandbytes`)**, **11 lr schedulers**, and **13 loss functions** are supported!  
+Currently, **68 optimizers (+ `bitsandbytes`)**, **11 lr schedulers**, and **13 loss functions** are supported!  
 
 Highly inspired by [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer).
 
@@ -164,6 +164,7 @@ supported_optimizers = get_supported_optimizers()
 | Adalite       | *Adalite optimizer*                                                                               | [github](https://github.com/VatsaDev/adalite)                                                                  | <https://github.com/VatsaDev/adalite>                                                      | [cite](https://github.com/VatsaDev/adalite)                                                                       |
 | bSAM          | *SAM as an Optimal Relaxation of Bayes*                                                           | [github](https://github.com/team-approx-bayes/bayesian-sam)                                                    | <https://arxiv.org/abs/2210.01620>                                                         | [cite](https://ui.adsabs.harvard.edu/abs/2022arXiv221001620M/exportcitation)                                      |
 | Schedule-Free | *Schedule-Free Optimizers*                                                                        | [github](https://github.com/facebookresearch/schedule_free)                                                    | <https://github.com/facebookresearch/schedule_free>                                        | [cite](https://github.com/facebookresearch/schedule_free)                                                         |
+| FAdam         | *Adam is a natural gradient optimizer using diagonal empirical Fisher information*                | [github](https://github.com/lessw2020/fadam_pytorch)                                                           | <https://arxiv.org/abs/2405.12807>                                                         | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv240512807H/exportcitation)                                      |
 
 ## Supported LR Scheduler
 
diff --git a/docs/changelogs/v3.0.1.md b/docs/changelogs/v3.0.1.md
new file mode 100644
index 00000000..41bd9e09
--- /dev/null
+++ b/docs/changelogs/v3.0.1.md
@@ -0,0 +1,15 @@
+## Change Log
+
+### Feature
+
+* Implement `FAdam` optimizer. (#241, #242)
+  * [Adam is a natural gradient optimizer using diagonal empirical Fisher information](https://arxiv.org/abs/2405.12807)
+
+### Bug
+
+* Wrong typing of reg_noise. (#239, #240)
+* Lookahead`s param_groups attribute is not loaded from checkpoint. (#237, #238)
+
+## Contributions
+
+thanks to @michaldyczko
diff --git a/docs/index.md b/docs/index.md
index a78dd76f..0217cbbd 100644
--- a/docs/index.md
+++ b/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, **67 optimizers (+ `bitsandbytes`)**, **11 lr schedulers**, and **13 loss functions** are supported!  
+Currently, **68 optimizers (+ `bitsandbytes`)**, **11 lr schedulers**, and **13 loss functions** are supported!  
 
 Highly inspired by [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer).
 
@@ -164,6 +164,7 @@ supported_optimizers = get_supported_optimizers()
 | Adalite       | *Adalite optimizer*                                                                               | [github](https://github.com/VatsaDev/adalite)                                                                  | <https://github.com/VatsaDev/adalite>                                                      | [cite](https://github.com/VatsaDev/adalite)                                                                       |
 | bSAM          | *SAM as an Optimal Relaxation of Bayes*                                                           | [github](https://github.com/team-approx-bayes/bayesian-sam)                                                    | <https://arxiv.org/abs/2210.01620>                                                         | [cite](https://ui.adsabs.harvard.edu/abs/2022arXiv221001620M/exportcitation)                                      |
 | Schedule-Free | *Schedule-Free Optimizers*                                                                        | [github](https://github.com/facebookresearch/schedule_free)                                                    | <https://github.com/facebookresearch/schedule_free>                                        | [cite](https://github.com/facebookresearch/schedule_free)                                                         |
+| FAdam         | *Adam is a natural gradient optimizer using diagonal empirical Fisher information*                | [github](https://github.com/lessw2020/fadam_pytorch)                                                           | <https://arxiv.org/abs/2405.12807>                                                         | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv240512807H/exportcitation)                                      |
 
 ## Supported LR Scheduler
 
diff --git a/docs/optimizer.md b/docs/optimizer.md
index 604f811a..523fc1a6 100644
--- a/docs/optimizer.md
+++ b/docs/optimizer.md
@@ -136,6 +136,10 @@
     :docstring:
     :members:
 
+::: pytorch_optimizer.FAdam
+    :docstring:
+    :members:
+
 ::: pytorch_optimizer.Fromage
     :docstring:
     :members:
diff --git a/poetry.lock b/poetry.lock
index b311b67a..3265a114 100644
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+++ b/poetry.lock
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+    {file = "coverage-7.5.3.tar.gz", hash = "sha256:04aefca5190d1dc7a53a4c1a5a7f8568811306d7a8ee231c42fb69215571944f"},
 ]
 
 [package.dependencies]
@@ -546,43 +546,43 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "ruff"
-version = "0.4.4"
+version = "0.4.7"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "ruff-0.4.7-py3-none-win_amd64.whl", hash = "sha256:9e3ab684ad403a9ed1226894c32c3ab9c2e0718440f6f50c7c5829932bc9e054"},
+    {file = "ruff-0.4.7-py3-none-win_arm64.whl", hash = "sha256:10f2204b9a613988e3484194c2c9e96a22079206b22b787605c255f130db5ed7"},
+    {file = "ruff-0.4.7.tar.gz", hash = "sha256:2331d2b051dc77a289a653fcc6a42cce357087c5975738157cd966590b18b5e1"},
 ]
 
 [[package]]
 name = "sympy"
-version = "1.12"
+version = "1.12.1"
 description = "Computer algebra system (CAS) in Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
-    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
+    {file = "sympy-1.12.1-py3-none-any.whl", hash = "sha256:9b2cbc7f1a640289430e13d2a56f02f867a1da0190f2f99d8968c2f74da0e515"},
+    {file = "sympy-1.12.1.tar.gz", hash = "sha256:2877b03f998cd8c08f07cd0de5b767119cd3ef40d09f41c30d722f6686b0fb88"},
 ]
 
 [package.dependencies]
-mpmath = ">=0.19"
+mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
 name = "tbb"
@@ -647,13 +647,13 @@ reference = "torch"
 
 [[package]]
 name = "typing-extensions"
-version = "4.11.0"
+version = "4.12.1"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
-    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
+    {file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
+    {file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
 ]
 
 [extras]
diff --git a/pyproject.toml b/pyproject.toml
index 2a298d6e..f324215e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pytorch_optimizer"
-version = "3.0.0"
+version = "3.0.1"
 description = "optimizer & lr scheduler & objective function collections in PyTorch"
 license = "Apache-2.0"
 authors = ["kozistr <kozistr@gmail.com>"]
@@ -13,9 +13,9 @@ 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", "Fromage", "GaLore", "Gravity", "GSAM", "LARS",
-    "Lamb", "Lion", "LOMO", "Lookahead", "MADGRAD", "MSVAG", "Nero", "NovoGrad", "PAdam", "PCGrad", "PID", "PNM",
-    "Prodigy", "QHAdam", "QHM", "RAdam", "Ranger", "Ranger21", "RotoGrad", "SAM", "ScheduleFreeSGD",
+    "DAdaptAdam", "DAdaptAdan", "DAdaptSGD", "DAdaptLion", "DiffGrad", "FAdam", "Fromage", "GaLore", "Gravity", "GSAM",
+    "LARS", "Lamb", "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", "SWATS",
     "Tiger", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal", "FocalCosine", "SoftF1", "Dice", "LDAM", "Jaccard",
     "Bi-Tempered", "Tversky", "FocalTversky", "LovaszHinge", "bitsandbytes",
diff --git a/pytorch_optimizer/__init__.py b/pytorch_optimizer/__init__.py
index 1420e5b4..03e2e63a 100644
--- a/pytorch_optimizer/__init__.py
+++ b/pytorch_optimizer/__init__.py
@@ -54,6 +54,7 @@ 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
 from pytorch_optimizer.optimizer.diffgrad import DiffGrad
+from pytorch_optimizer.optimizer.fadam import FAdam
 from pytorch_optimizer.optimizer.fp16 import DynamicLossScaler, SafeFP16Optimizer
 from pytorch_optimizer.optimizer.fromage import Fromage
 from pytorch_optimizer.optimizer.galore import GaLore, GaLoreProjector
@@ -190,6 +191,7 @@ OPTIMIZER_LIST: List[OPTIMIZER] = [
     BSAM,
     ScheduleFreeSGD,
     ScheduleFreeAdamW,
+    FAdam,
 ]
 OPTIMIZERS: Dict[str, OPTIMIZER] = {str(optimizer.__name__).lower(): optimizer for optimizer in OPTIMIZER_LIST}
 
diff --git a/pytorch_optimizer/optimizer/fadam.py b/pytorch_optimizer/optimizer/fadam.py
new file mode 100644
index 00000000..26ee108f
--- /dev/null
+++ b/pytorch_optimizer/optimizer/fadam.py
@@ -0,0 +1,124 @@
+import torch
+from torch.optim.optimizer import Optimizer
+
+from pytorch_optimizer.base.exception import NoSparseGradientError
+from pytorch_optimizer.base.optimizer import BaseOptimizer
+from pytorch_optimizer.base.types import BETAS, CLOSURE, DEFAULTS, LOSS, PARAMETERS
+
+
+class FAdam(Optimizer, BaseOptimizer):
+    r"""Adam is a natural gradient optimizer using diagonal empirical Fisher information.
+
+    :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 clip: float. maximum norm of the gradient.
+    :param p: float. momentum factor.
+    :param eps: float. term added to the denominator to improve numerical stability.
+    :param momentum_dtype: torch.dtype. type of momentum.
+    :param fim_dtype: torch.dtype. type of fim.
+    """
+
+    def __init__(
+        self,
+        params: PARAMETERS,
+        lr: float = 1e-3,
+        betas: BETAS = (0.9, 0.999),
+        weight_decay: float = 0.1,
+        clip: float = 1.0,
+        p: float = 0.5,
+        eps: float = 1e-8,
+        momentum_dtype: torch.dtype = torch.float32,
+        fim_dtype: torch.dtype = torch.float32,
+    ):
+        self.validate_learning_rate(lr)
+        self.validate_betas(betas)
+        self.validate_non_negative(weight_decay, 'weight_decay')
+        self.validate_positive(clip, 'clip')
+        self.validate_positive(p, 'p')
+        self.validate_non_negative(eps, 'eps')
+
+        self.momentum_dtype = momentum_dtype
+        self.fim_dtype = fim_dtype
+
+        defaults: DEFAULTS = {
+            'lr': lr,
+            'betas': betas,
+            'weight_decay': weight_decay,
+            'clip': clip,
+            'p': p,
+            'eps': eps,
+        }
+
+        super().__init__(params, defaults)
+
+    def __str__(self) -> str:
+        return 'FAdam'
+
+    @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['momentum'] = torch.zeros_like(p, dtype=self.momentum_dtype)
+                state['fim'] = torch.zeros_like(p, dtype=self.fim_dtype)
+
+    @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']
+
+            curr_beta2: float = beta2 * (1 - beta2 ** (group['step'] - 1)) / (1 - beta2 ** group['step'])
+
+            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['momentum'] = torch.zeros_like(p, dtype=self.momentum_dtype)
+                    state['fim'] = torch.zeros_like(p, dtype=self.fim_dtype)
+
+                momentum, fim = state['momentum'], state['fim']
+                fim.mul_(curr_beta2).addcmul_(grad, grad, value=1.0 - curr_beta2)
+
+                rms_grad = grad.pow(2).mean().sqrt_()
+                curr_eps = min(rms_grad, 1) * group['eps']
+
+                fim_base = fim.pow(group['p']).add_(curr_eps)
+                grad_nat = grad / fim_base
+
+                rms = grad_nat.pow(2).mean().sqrt_()
+                divisor = max(1, rms) / group['clip']
+                grad_nat.div_(divisor)
+
+                momentum.mul_(beta1).add_(grad_nat, alpha=1.0 - beta1)
+
+                grad_weights = p / fim_base
+
+                rms = torch.pow(grad_weights, 2).mean().sqrt_()
+                divisor = max(1, rms) / group['clip']
+                grad_weights.div_(divisor)
+
+                grad_weights.mul_(group['weight_decay']).add_(momentum)
+
+                p.add_(grad_weights, alpha=-group['lr'])
+
+        return loss
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 06c1a8fa..6a2ab780 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -3,7 +3,7 @@
 black==24.4.2 ; python_version >= "3.8" and python_full_version < "4.0.0"
 click==8.1.7 ; python_version >= "3.8" and python_full_version < "4.0.0"
 colorama==0.4.6 ; python_version >= "3.8" and python_full_version < "4.0.0" and (sys_platform == "win32" or platform_system == "Windows")
-coverage[toml]==7.5.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
+coverage[toml]==7.5.3 ; python_version >= "3.8" and python_full_version < "4.0.0"
 exceptiongroup==1.2.1 ; python_version >= "3.8" and python_version < "3.11"
 filelock==3.14.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 fsspec==2024.5.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
@@ -23,9 +23,9 @@ platformdirs==4.2.2 ; python_version >= "3.8" and python_full_version < "4.0.0"
 pluggy==1.5.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 pytest-cov==5.0.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 pytest==8.2.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
-ruff==0.4.4 ; python_version >= "3.8" and python_full_version < "4.0.0"
-sympy==1.12 ; python_version >= "3.8" and python_full_version < "4.0.0"
+ruff==0.4.7 ; python_version >= "3.8" and python_full_version < "4.0.0"
+sympy==1.12.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 tbb==2021.12.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows"
 tomli==2.0.1 ; python_version >= "3.8" and python_full_version <= "3.11.0a6"
 torch==2.3.0+cpu ; python_version >= "3.8" and python_full_version < "4.0.0"
-typing-extensions==4.11.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+typing-extensions==4.12.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
diff --git a/requirements.txt b/requirements.txt
index 1f3fad80..9392e86f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,7 +9,7 @@ mkl==2021.4.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and pl
 mpmath==1.3.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
 networkx==3.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 numpy==1.24.4 ; python_version >= "3.8" and python_full_version < "4.0.0"
-sympy==1.12 ; python_version >= "3.8" and python_full_version < "4.0.0"
+sympy==1.12.1 ; python_version >= "3.8" and python_full_version < "4.0.0"
 tbb==2021.12.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows"
 torch==2.3.0+cpu ; python_version >= "3.8" and python_full_version < "4.0.0"
-typing-extensions==4.11.0 ; python_version >= "3.8" and python_full_version < "4.0.0"
+typing-extensions==4.12.1 ; python_version >= "3.8" and python_full_version < "4.0.0"


Test patch:
diff --git a/tests/constants.py b/tests/constants.py
index 65c0afb3..34f85214 100644
--- a/tests/constants.py
+++ b/tests/constants.py
@@ -45,6 +45,7 @@ from pytorch_optimizer import (
     DAdaptLion,
     DAdaptSGD,
     DiffGrad,
+    FAdam,
     Fromage,
     GaLore,
     Gravity,
@@ -127,6 +128,7 @@ BETA_OPTIMIZER_NAMES: List[str] = [
     'adalite',
     'bsam',
     'schedulefreeadamw',
+    'fadam',
 ]
 
 VALID_LR_SCHEDULER_NAMES: List[str] = [
@@ -444,6 +446,7 @@ OPTIMIZERS: List[Tuple[Any, Dict[str, Union[float, bool, int]], int]] = [
     (Adalite, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
     (ScheduleFreeSGD, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
     (ScheduleFreeAdamW, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
+    (FAdam, {'lr': 1e0, 'weight_decay': 1e-3}, 5),
 ]
 ADANORM_SUPPORTED_OPTIMIZERS: List[Tuple[Any, Dict[str, Union[float, bool, int]], int]] = [
     (AdaBelief, {'lr': 5e-1, 'weight_decay': 1e-3, 'adanorm': True}, 10),
diff --git a/tests/test_load_modules.py b/tests/test_load_modules.py
index f4439498..d5646b44 100644
--- a/tests/test_load_modules.py
+++ b/tests/test_load_modules.py
@@ -38,7 +38,7 @@ def test_load_lr_scheduler_invalid(invalid_lr_scheduler_names):
 
 
 def test_get_supported_optimizers():
-    assert len(get_supported_optimizers()) == 66
+    assert len(get_supported_optimizers()) == 67
 
 
 def test_get_supported_lr_schedulers():