feat: Added implementation of TAdam

README.md

@@ -78,7 +78,7 @@ conda install -c frgfm pylocron
 
 ##### Main features
 
-- Optimizer: [LARS](https://arxiv.org/abs/1708.03888), [Lamb](https://arxiv.org/abs/1904.00962), [RAdam](https://arxiv.org/abs/1908.03265) and customized versions (RaLars)
+- Optimizer: [LARS](https://arxiv.org/abs/1708.03888), [Lamb](https://arxiv.org/abs/1904.00962), [RAdam](https://arxiv.org/abs/1908.03265), [TAdam](https://arxiv.org/pdf/2003.00179.pdf) and customized versions (RaLars)
 - Optimizer wrapper: [Lookahead](https://arxiv.org/abs/1907.08610), Scout (experimental)
 - Scheduler: [OneCycleScheduler](https://arxiv.org/abs/1803.09820)
 

docs/source/optim.rst

@@ -21,6 +21,8 @@ Implementations of recent parameter optimizer for Pytorch modules.
 
 .. autoclass:: RaLars
 
+.. autoclass:: TAdam
+
 
 Optimizer wrappers
 ------------------

holocron/optim/__init__.py

@@ -2,6 +2,7 @@
 from .lamb import Lamb
 from .radam import RAdam
 from .ralars import RaLars
+from .tadam import TAdam
 from . import wrapper
 from . import lr_scheduler
 

holocron/optim/tadam.py

@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+
+'''
+Extended version of Adam optimizer with Student-t mean estimation
+'''
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class TAdam(Optimizer):
+    """Implements the TAdam optimizer from `"TAdam: A Robust Stochastic Gradient Optimizer"
+    <https://arxiv.org/pdf/2003.00179.pdf>`_.
+
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining parameter groups
+        lr (float, optional): learning rate
+        betas (Tuple[float, float], optional): coefficients used for running averages (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        dof (int, optional): degrees of freedom
+    """
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, dof=None):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, dof=dof)
+        super().__init__(params, defaults)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+
+            # Get group-shared variables
+            beta1, beta2 = group['betas']
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                if grad.is_sparse:
+                    raise RuntimeError('RAdam does not support sparse gradients')
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p.data)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+                    #
+                    state['W_t'] = beta1 / (1 - beta1)
+                    state['d'] = p.data.numel()
+                    state['dof'] = state['d'] if group['dof'] is None else group['dof']
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+
+                state['step'] += 1
+
+                wt = grad.sub(exp_avg).pow_(2).div_(exp_avg_sq.add(group['eps'])).sum()
+                wt.add_(state['dof']).pow_(-1).mul_(state['dof'] + state['d'])
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(state['W_t'] / (state['W_t'] + wt)).add_(grad, alpha=wt / (state['W_t'] + wt))
+                state['W_t'] *= (2 * beta1 - 1) / beta1
+                state['W_t'] += wt
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+                # Bias corrections
+                bias_correction1 = 1 - beta1 ** state['step']
+                bias_correction2 = 1 - beta2 ** state['step']
+
+                # Weight decay
+                if group['weight_decay'] != 0:
+                    p.data.add_(p.data, alpha=-group['lr'] * group['weight_decay'])
+
+                # Adaptive momentum
+                p.data.addcdiv_(exp_avg / bias_correction1,
+                                (exp_avg_sq / bias_correction2).sqrt().add_(group['eps']), value=-group['lr'])
+
+        return loss

test/test_optim.py

@@ -115,7 +115,7 @@ def _test_wrapper(self, name):
         self.assertFalse(torch.equal(_p.data, p_val - lr * _p.grad))
 
 
-for opt_name in ['Lars', 'Lamb', 'RAdam', 'RaLars']:
+for opt_name in ['Lars', 'Lamb', 'RAdam', 'RaLars', 'TAdam']:
     def opt_test(self, opt_name=opt_name):
         self._test_optimizer(opt_name)