diff --git a/README.md b/README.md index c06903f99..2567280d7 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, **74 optimizers (+ `bitsandbytes`, `qgalore`)**, **16 lr schedulers**, and **13 loss functions** are supported! +Currently, **75 optimizers (+ `bitsandbytes`, `qgalore`)**, **16 lr schedulers**, and **13 loss functions** are supported! Highly inspired by [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer). @@ -172,6 +172,7 @@ supported_optimizers = get_supported_optimizers() | StableAdamW | *Stable and low-precision training for large-scale vision-language models* | | | [cite](https://ui.adsabs.harvard.edu/abs/2023arXiv230413013W/exportcitation) | | AdamMini | *Use Fewer Learning Rates To Gain More* | [github](https://github.com/zyushun/Adam-mini) | | [cite](https://github.com/zyushun/Adam-mini?tab=readme-ov-file#citation) | | TRAC | *Adaptive Parameter-free Optimization* | [github](https://github.com/ComputationalRobotics/TRAC) | | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv240516642M/exportcitation) | +| AdamG | *Towards Stability of Parameter-free Optimization* | | | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv240504376P/exportcitation) | ## Supported LR Scheduler diff --git a/docs/changelogs/v3.1.1.md b/docs/changelogs/v3.1.1.md index 07a3115df..a9c630d17 100644 --- a/docs/changelogs/v3.1.1.md +++ b/docs/changelogs/v3.1.1.md @@ -5,6 +5,8 @@ * Implement `TRAC` optimizer. (#263) * [Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning](https://arxiv.org/abs/2405.16642) * Support `AdamW` optimizer via `create_optimizer()`. (#263) +* Implement `AdamG` optimizer. (#264, #265) + * [Towards Stability of Parameter-free Optimization](https://arxiv.org/abs/2405.04376) ### Bug diff --git a/docs/index.md b/docs/index.md index c06903f99..2567280d7 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, **74 optimizers (+ `bitsandbytes`, `qgalore`)**, **16 lr schedulers**, and **13 loss functions** are supported! +Currently, **75 optimizers (+ `bitsandbytes`, `qgalore`)**, **16 lr schedulers**, and **13 loss functions** are supported! Highly inspired by [pytorch-optimizer](https://github.com/jettify/pytorch-optimizer). @@ -172,6 +172,7 @@ supported_optimizers = get_supported_optimizers() | StableAdamW | *Stable and low-precision training for large-scale vision-language models* | | | [cite](https://ui.adsabs.harvard.edu/abs/2023arXiv230413013W/exportcitation) | | AdamMini | *Use Fewer Learning Rates To Gain More* | [github](https://github.com/zyushun/Adam-mini) | | [cite](https://github.com/zyushun/Adam-mini?tab=readme-ov-file#citation) | | TRAC | *Adaptive Parameter-free Optimization* | [github](https://github.com/ComputationalRobotics/TRAC) | | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv240516642M/exportcitation) | +| AdamG | *Towards Stability of Parameter-free Optimization* | | | [cite](https://ui.adsabs.harvard.edu/abs/2024arXiv240504376P/exportcitation) | ## Supported LR Scheduler diff --git a/docs/optimizer.md b/docs/optimizer.md index 0fb3628fc..e6f5d5b84 100644 --- a/docs/optimizer.md +++ b/docs/optimizer.md @@ -44,6 +44,10 @@ :docstring: :members: +::: pytorch_optimizer.AdamG + :docstring: + :members: + ::: pytorch_optimizer.AdaMod :docstring: :members: diff --git a/poetry.lock b/poetry.lock index d4ae9cb20..7bef65d2b 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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"AdaMax", "AdaMod", "AdaNorm", "AdaPNM", "AdaSmooth", "AdaHessian", "Adai", "Adalite", - "AdaLomo", "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", "TRAC", "WSAM", "Yogi", "BCE", "BCEFocal", "Focal", - "FocalCosine", "SoftF1", "Dice", "LDAM", "Jaccard", "Bi-Tempered", "Tversky", "FocalTversky", "LovaszHinge", - "bitsandbytes", "WSD", "QGaLore", + "AdaDelta", "AdaFactor", "AdaMax", "AdamG", "AdaMod", "AdaNorm", "AdaPNM", "AdaSmooth", "AdaHessian", "Adai", + "Adalite", "AdaLomo", "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", "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", diff --git a/pytorch_optimizer/__init__.py b/pytorch_optimizer/__init__.py index 25241f9f4..9b0ba60f3 100644 --- a/pytorch_optimizer/__init__.py +++ b/pytorch_optimizer/__init__.py @@ -44,6 +44,7 @@ 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.adamg import AdamG from pytorch_optimizer.optimizer.adamod import AdaMod from pytorch_optimizer.optimizer.adamp import AdamP from pytorch_optimizer.optimizer.adams import AdamS @@ -206,6 +207,7 @@ StableAdamW, AdamMini, AdaLOMO, + AdamG, ] OPTIMIZERS: Dict[str, OPTIMIZER] = {str(optimizer.__name__).lower(): optimizer for optimizer in OPTIMIZER_LIST} diff --git a/pytorch_optimizer/optimizer/adamg.py b/pytorch_optimizer/optimizer/adamg.py new file mode 100644 index 000000000..168914985 --- /dev/null +++ b/pytorch_optimizer/optimizer/adamg.py @@ -0,0 +1,127 @@ +import math + +import torch + +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 AdamG(BaseOptimizer): + r"""Towards Stability of Parameter-free Optimization. + + :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 p: float. p for a numerator function `s(x) = p * x^q`. + :param q: float. q for a numerator function `s(x) = p * x^q`. + :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 eps: float. term added to the denominator to improve numerical stability. + """ + + def __init__( + self, + params: PARAMETERS, + lr: float = 1e-3, + betas: BETAS = (0.95, 0.999, 0.95), + p: float = 0.5, + q: float = 0.25, + weight_decay: float = 0.0, + weight_decouple: bool = False, + fixed_decay: bool = False, + eps: float = 1e-8, + ): + self.validate_learning_rate(lr) + self.validate_betas(betas) + self.validate_positive(p, 'p') + self.validate_positive(q, 'q') + self.validate_non_negative(weight_decay, 'weight_decay') + self.validate_non_negative(eps, 'eps') + + self.p = p + self.q = q + + defaults: DEFAULTS = { + 'lr': lr, + 'betas': betas, + 'weight_decay': weight_decay, + 'weight_decouple': weight_decouple, + 'fixed_decay': fixed_decay, + 'eps': eps, + } + + super().__init__(params, defaults) + + def __str__(self) -> str: + return 'AdamG' + + @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['m'] = torch.zeros_like(p) + state['v'] = torch.zeros_like(p) + state['r'] = torch.zeros_like(p) + + def s(self, p: torch.Tensor) -> torch.Tensor: + r"""Numerator function f(x) = p * x^q.""" + return p.pow(self.q).mul_(self.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, beta3 = group['betas'] + + bias_correction1: float = 1.0 - self.debias(beta1, group['step']) + bias_correction2: float = 1.0 - self.debias(beta2, group['step']) + step_size: float = min(group['lr'], 1.0 / math.sqrt(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['m'] = torch.zeros_like(p) + state['v'] = torch.zeros_like(p) + state['r'] = torch.zeros_like(p) + + self.apply_weight_decay( + p=p, + grad=grad, + lr=group['lr'], + weight_decay=group['weight_decay'], + weight_decouple=group['weight_decouple'], + fixed_decay=group['fixed_decay'], + ) + + m, v, r = state['m'], state['v'], state['r'] + v.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2) + r.mul_(beta3).add_(self.s(v), alpha=1.0 - beta3) + m.mul_(beta1).addcmul_(r, grad, value=1.0 - beta1) + + update = (m / bias_correction1) / (v / bias_correction2).sqrt_().add_(group['eps']) + + p.add_(update, alpha=-step_size) + + return loss diff --git a/pytorch_optimizer/optimizer/trac.py b/pytorch_optimizer/optimizer/trac.py index 7dbc98e5a..7c13d70ac 100644 --- a/pytorch_optimizer/optimizer/trac.py +++ b/pytorch_optimizer/optimizer/trac.py @@ -140,7 +140,7 @@ def reset(self): self.state['trac'] = { 'betas': torch.tensor(self.betas, device=device), - 's': torch.zeros(len(self.betas), device=device), + 's': torch.zeros(1, device=device), 'variance': torch.zeros(len(self.betas), device=device), 'sigma': torch.full((len(self.betas),), 1e-8, device=device), 'step': 0, @@ -182,6 +182,7 @@ def trac_step(self, updates: Dict, grads: Dict) -> None: device = self.param_groups[0]['params'][0].device + s = self.state['trac']['s'] h = torch.zeros((1,), device=device) for group in self.param_groups: for p in group['params']: @@ -191,7 +192,7 @@ def trac_step(self, updates: Dict, grads: Dict) -> None: theta_ref = self.state['trac'][p] update = updates[p] - deltas[p] = (update - theta_ref) / torch.sum(self.state['trac']['s']).add_(self.eps) + deltas[p] = (update - theta_ref) / s.add(self.eps) update.neg_().add_(p) grad, delta = grads[p], deltas[p] @@ -201,7 +202,8 @@ def trac_step(self, updates: Dict, grads: Dict) -> None: delta.add_(update) - s = self.state['trac']['s'] + p.copy_(theta_ref) + betas = self.state['trac']['betas'] variance = self.state['trac']['variance'] sigma = self.state['trac']['sigma'] @@ -209,21 +211,17 @@ def trac_step(self, updates: Dict, grads: Dict) -> None: variance.mul_(betas.pow(2)).add_(h.pow(2)) sigma.mul_(betas).sub_(h) - s_term = self.erf_imag(sigma / (2.0 * variance).sqrt_().add_(self.eps)) - s_term.mul_(self.f_term) - s.copy_(s_term) + term = self.erf_imag(sigma / (2.0 * variance).sqrt_().add_(self.eps)).mul_(self.f_term) + s.copy_(torch.sum(term)) - scale = max(torch.sum(s), 0.0) + scale = max(s, 0.0) for group in self.param_groups: for p in group['params']: if grads[p] is None: continue - delta = deltas[p] - delta.mul_(scale).add_(self.state['trac'][p]) - - p.copy_(delta) + p.add_(deltas[p] * scale) @torch.no_grad() def step(self, closure: CLOSURE = None) -> LOSS: @@ -238,7 +236,7 @@ def step(self, closure: CLOSURE = None) -> LOSS: self.state['trac'] = { 'betas': torch.tensor(self.betas, device=device), - 's': torch.zeros(len(self.betas), device=device), + 's': torch.zeros(1, device=device), 'variance': torch.zeros(len(self.betas), device=device), 'sigma': torch.full((len(self.betas),), 1e-8, device=device), 'step': 0, diff --git a/requirements-dev.txt b/requirements-dev.txt index e4f7097fc..9d2c05616 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,18 +1,16 @@ --extra-index-url https://download.pytorch.org/whl/cpu -black==24.4.2 ; python_version >= "3.8" and python_full_version < "4.0.0" +black==24.8.0 ; 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]==7.6.0 ; python_version >= "3.8" and python_full_version < "4.0.0" +coverage[toml]==7.6.1 ; python_version >= "3.8" and python_full_version < "4.0.0" exceptiongroup==1.2.2 ; python_version >= "3.8" and python_version < "3.11" filelock==3.15.4 ; python_version >= "3.8" and python_full_version < "4.0.0" fsspec==2024.6.1 ; python_version >= "3.8" and python_full_version < "4.0.0" iniconfig==2.0.0 ; python_version >= "3.8" and python_full_version < "4.0.0" -intel-openmp==2021.4.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows" isort==5.13.2 ; python_version >= "3.8" and python_full_version < "4.0.0" jinja2==3.1.4 ; python_version >= "3.8" and python_full_version < "4.0.0" markupsafe==2.1.5 ; python_version >= "3.8" and python_full_version < "4.0.0" -mkl==2021.4.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows" mpmath==1.3.0 ; python_version >= "3.8" and python_full_version < "4.0.0" mypy-extensions==1.0.0 ; 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" @@ -22,10 +20,9 @@ pathspec==0.12.1 ; python_version >= "3.8" and python_full_version < "4.0.0" 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==8.3.1 ; python_version >= "3.8" and python_full_version < "4.0.0" -ruff==0.5.4 ; python_version >= "3.8" and python_full_version < "4.0.0" -sympy==1.13.1 ; python_version >= "3.8" and python_full_version < "4.0.0" -tbb==2021.13.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows" +pytest==8.3.2 ; python_version >= "3.8" and python_full_version < "4.0.0" +ruff==0.5.7 ; python_version >= "3.8" and python_full_version < "4.0.0" +sympy==1.13.2 ; python_version >= "3.8" and python_full_version < "4.0.0" tomli==2.0.1 ; python_version >= "3.8" and python_full_version <= "3.11.0a6" -torch==2.3.1+cpu ; python_version >= "3.8" and python_full_version < "4.0.0" +torch==2.4.0+cpu ; python_version >= "3.8" and python_full_version < "4.0.0" typing-extensions==4.12.2 ; python_version >= "3.8" and python_full_version < "4.0.0" diff --git a/requirements.txt b/requirements.txt index ac61ed57e..dc544ecd7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,14 +2,11 @@ filelock==3.15.4 ; python_version >= "3.8" and python_full_version < "4.0.0" fsspec==2024.6.1 ; python_version >= "3.8" and python_full_version < "4.0.0" -intel-openmp==2021.4.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows" jinja2==3.1.4 ; python_version >= "3.8" and python_full_version < "4.0.0" markupsafe==2.1.5 ; python_version >= "3.8" and python_full_version < "4.0.0" -mkl==2021.4.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows" 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==1.13.1 ; python_version >= "3.8" and python_full_version < "4.0.0" -tbb==2021.13.0 ; python_version >= "3.8" and python_full_version < "4.0.0" and platform_system == "Windows" -torch==2.3.1+cpu ; python_version >= "3.8" and python_full_version < "4.0.0" +sympy==1.13.2 ; python_version >= "3.8" and python_full_version < "4.0.0" +torch==2.4.0+cpu ; python_version >= "3.8" and python_full_version < "4.0.0" typing-extensions==4.12.2 ; python_version >= "3.8" and python_full_version < "4.0.0" diff --git a/tests/constants.py b/tests/constants.py index 072645cee..947667a9f 100644 --- a/tests/constants.py +++ b/tests/constants.py @@ -25,6 +25,7 @@ Adai, Adalite, AdaMax, + AdamG, AdaMod, AdamP, AdamS, @@ -136,6 +137,7 @@ 'grokfastadamw', 'stableadamw', 'adammini', + 'adamg', ] VALID_LR_SCHEDULER_NAMES: List[str] = [ @@ -468,6 +470,7 @@ (GrokFastAdamW, {'lr': 1e0, 'weight_decay': 1e-3}, 10), (Kate, {'lr': 5e-2}, 10), (StableAdamW, {'lr': 1e0}, 5), + (AdamG, {'lr': 1e0}, 20), ] 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 9a23043ca..f1b1d3cd5 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()) == 73 + assert len(get_supported_optimizers()) == 74 def test_get_supported_lr_schedulers():