optimizers

labml_nn/optimizers/ada_belief/__init__.py

@@ -0,0 +1,175 @@
+"""
+This is forked from AdaBelief official implementation
+https://github.com/juntang-zhuang/Adabelief-Optimizer
+"""
+import math
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class AdaBelief(Optimizer):
+    r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-16)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+            (default: False)
+        weight_decouple (boolean, optional): ( default: True) If set as True, then
+            the optimizer uses decoupled weight decay as in AdamW
+        fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
+            is set as True.
+            When fixed_decay == True, the weight decay is performed as
+            $W_{new} = W_{old} - W_{old} \times decay$.
+            When fixed_decay == False, the weight decay is performed as
+            $W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the
+            weight decay ratio decreases with learning rate (lr).
+        rectify (boolean, optional): (default: True) If set as True, then perform the rectified
+            update similar to RAdam
+        degenerated_to_sgd (boolean, optional) (default:True) If set as True, then perform SGD update
+            when variance of gradient is high
+    reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients, NeurIPS 2020
+    """
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
+                 weight_decay=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True,
+                 degenerated_to_sgd=True):
+        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]))
+        if not 0.0 <= weight_decay:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+
+        if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
+            for param in params:
+                if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
+                    param['buffer'] = [[None, None, None] for _ in range(10)]
+
+        defaults = dict(lr=lr, betas=betas, eps=eps,
+                        weight_decay=weight_decay, amsgrad=amsgrad,
+                        buffer=[[None, None, None] for _ in range(10)])
+        super().__init__(params, defaults)
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        self.weight_decouple = weight_decouple
+        self.rectify = rectify
+        self.fixed_decay = fixed_decay
+
+    def __setstate__(self, state):
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('amsgrad', False)
+
+    @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:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                if grad.is_sparse:
+                    raise RuntimeError('AdaBelief does not support sparse gradients,'
+                                       ' please consider SparseAdam instead')
+
+                state = self.state[p]
+                # Lazy state initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                    # Exponential moving average of squared gradient values
+                    state['exp_avg_var'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+                    if group['amsgrad']:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state['max_exp_avg_var'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+
+                beta1, beta2 = group['betas']
+
+                # get current state variable
+                exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var']
+
+                state['step'] += 1
+                bias_correction1 = 1 - beta1 ** state['step']
+                bias_correction2 = 1 - beta2 ** state['step']
+
+                # Update first and second moment running average
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                grad_residual = grad - exp_avg
+                exp_avg_var.mul_(beta2).addcmul_(grad_residual, grad_residual, value=1 - beta2)
+
+                if group['amsgrad']:
+                    max_exp_avg_var = state['max_exp_avg_var']
+                    # Maintains the maximum of all 2nd moment running avg. till now
+                    torch.max(max_exp_avg_var, exp_avg_var, out=max_exp_avg_var)
+
+                    # Use the max. for normalizing running avg. of gradient
+                    denom = ((max_exp_avg_var + group['eps']).sqrt_() / math.sqrt(bias_correction2)).add_(group['eps'])
+                else:
+                    denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
+
+                # perform weight decay, check if decoupled weight decay
+                if self.weight_decouple:
+                    if not self.fixed_decay:
+                        p.data.mul_(1.0 - group['lr'] * group['weight_decay'])
+                    else:
+                        p.data.mul_(1.0 - group['weight_decay'])
+                else:
+                    if group['weight_decay'] != 0:
+                        grad.add_(p.data, alpha=group['weight_decay'])
+
+                # update
+                if not self.rectify:
+                    # Default update
+                    step_size = group['lr'] / bias_correction1
+                    p.data.addcdiv_(exp_avg, denom, value=-step_size)
+                else:  # Rectified update, forked from RAdam
+                    buffered = group['buffer'][int(state['step'] % 10)]
+                    if state['step'] == buffered[0]:
+                        N_sma, step_size = buffered[1], buffered[2]
+                    else:
+                        buffered[0] = state['step']
+                        beta2_t = beta2 ** state['step']
+                        N_sma_max = 2 / (1 - beta2) - 1
+                        N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+                        buffered[1] = N_sma
+
+                        # more conservative since it's an approximated value
+                        if N_sma >= 5:
+                            step_size = math.sqrt(
+                                (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
+                                        N_sma_max - 2)) / (1 - beta1 ** state['step'])
+                        elif self.degenerated_to_sgd:
+                            step_size = 1.0 / (1 - beta1 ** state['step'])
+                        else:
+                            step_size = -1
+                        buffered[2] = step_size
+
+                    if N_sma >= 5:
+                        denom = exp_avg_var.sqrt().add_(group['eps'])
+                        p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
+                    elif step_size > 0:
+                        p.data.add_(exp_avg, alpha=-step_size * group['lr'])
+
+        return loss

labml_nn/optimizers/ada_belief/mnist.py

@@ -0,0 +1,111 @@
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.data
+
+from labml import experiment, tracker
+from labml.configs import option
+from labml_helpers.datasets.mnist import MNISTConfigs
+from labml_helpers.device import DeviceConfigs
+from labml_helpers.metrics.accuracy import Accuracy
+from labml_helpers.module import Module
+from labml_helpers.optimizer import OptimizerConfigs
+from labml_helpers.seed import SeedConfigs
+from labml_helpers.train_valid import TrainValidConfigs, BatchIndex, hook_model_outputs
+
+
+class Net(Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 20, 5, 1)
+        self.conv2 = nn.Conv2d(20, 50, 5, 1)
+        self.fc1 = nn.Linear(4 * 4 * 50, 500)
+        self.fc2 = nn.Linear(500, 10)
+
+    def __call__(self, x: torch.Tensor):
+        x = F.relu(self.conv1(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = F.relu(self.conv2(x))
+        x = F.max_pool2d(x, 2, 2)
+        x = x.view(-1, 4 * 4 * 50)
+        x = F.relu(self.fc1(x))
+        return self.fc2(x)
+
+
+class Configs(MNISTConfigs, TrainValidConfigs):
+    optimizer: torch.optim.Adam
+    model: nn.Module
+    set_seed = SeedConfigs()
+    device: torch.device = DeviceConfigs()
+    epochs: int = 10
+
+    is_save_models = True
+    model: nn.Module
+    inner_iterations = 10
+
+    accuracy_func = Accuracy()
+    loss_func = nn.CrossEntropyLoss()
+
+    def init(self):
+        tracker.set_queue("loss.*", 20, True)
+        tracker.set_scalar("accuracy.*", True)
+        hook_model_outputs(self.mode, self.model, 'model')
+        self.state_modules = [self.accuracy_func]
+
+    def step(self, batch: any, batch_idx: BatchIndex):
+        data, target = batch[0].to(self.device), batch[1].to(self.device)
+
+        if self.mode.is_train:
+            tracker.add_global_step(len(data))
+
+        with self.mode.update(is_log_activations=batch_idx.is_last):
+            output = self.model(data)
+
+        loss = self.loss_func(output, target)
+        self.accuracy_func(output, target)
+        tracker.add("loss.", loss)
+
+        if self.mode.is_train:
+            loss.backward()
+
+            self.optimizer.step()
+            if batch_idx.is_last:
+                tracker.add('model', self.model)
+                tracker.add('optimizer', (self.optimizer, {'model': self.model}))
+            self.optimizer.zero_grad()
+
+        tracker.save()
+
+
+@option(Configs.model)
+def model(c: Configs):
+    return Net().to(c.device)
+
+
+@option(OptimizerConfigs.optimizer, 'AdaBelief')
+def ada_belief(c: OptimizerConfigs):
+    from labml_nn.optimizers.ada_belief import AdaBelief
+    return AdaBelief(c.parameters, lr=c.learning_rate, betas=c.betas, eps=c.eps)
+
+
+@option(Configs.optimizer)
+def _optimizer(c: Configs):
+    opt_conf = OptimizerConfigs()
+    opt_conf.parameters = c.model.parameters()
+    return opt_conf
+
+
+def main():
+    conf = Configs()
+    conf.inner_iterations = 10
+    experiment.create(name='mnist_ada_belief')
+    experiment.configs(conf, {'inner_iterations': 10,
+                              'optimizer.optimizer': 'AdaBelief',
+                              'optimizer.learning_rate': 1.5e-4})
+    conf.set_seed.set()
+    experiment.add_pytorch_models(dict(model=conf.model))
+    with experiment.start():
+        conf.run()
+
+
+if __name__ == '__main__':
+    main()

labml_nn/optimizers/radam/__init__.py

@@ -0,0 +1,254 @@
+"""
+Forked from https://github.com/LiyuanLucasLiu/RAdam
+"""
+
+import math
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class RAdam(Optimizer):
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
+        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]))
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
+            for param in params:
+                if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
+                    param['buffer'] = [[None, None, None] for _ in range(10)]
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
+                        buffer=[[None, None, None] for _ in range(10)])
+        super(RAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(RAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError('RAdam does not support sparse gradients')
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                buffered = group['buffer'][int(state['step'] % 10)]
+                if state['step'] == buffered[0]:
+                    N_sma, step_size = buffered[1], buffered[2]
+                else:
+                    buffered[0] = state['step']
+                    beta2_t = beta2 ** state['step']
+                    N_sma_max = 2 / (1 - beta2) - 1
+                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+                    buffered[1] = N_sma
+
+                    # more conservative since it's an approximated value
+                    if N_sma >= 5:
+                        step_size = math.sqrt(
+                            (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
+                                    N_sma_max - 2)) / (1 - beta1 ** state['step'])
+                    elif self.degenerated_to_sgd:
+                        step_size = 1.0 / (1 - beta1 ** state['step'])
+                    else:
+                        step_size = -1
+                    buffered[2] = step_size
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
+                    p.data.copy_(p_data_fp32)
+                elif step_size > 0:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)
+                    p.data.copy_(p_data_fp32)
+
+        return loss
+
+
+class PlainRAdam(Optimizer):
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
+        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]))
+
+        self.degenerated_to_sgd = degenerated_to_sgd
+        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+
+        super(PlainRAdam, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(PlainRAdam, self).__setstate__(state)
+
+    def step(self, closure=None):
+
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError('RAdam does not support sparse gradients')
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                state['step'] += 1
+                beta2_t = beta2 ** state['step']
+                N_sma_max = 2 / (1 - beta2) - 1
+                N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
+
+                # more conservative since it's an approximated value
+                if N_sma >= 5:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+                    step_size = group['lr'] * math.sqrt(
+                        (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
+                                N_sma_max - 2)) / (1 - beta1 ** state['step'])
+                    denom = exp_avg_sq.sqrt().add_(group['eps'])
+                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+                    p.data.copy_(p_data_fp32)
+                elif self.degenerated_to_sgd:
+                    if group['weight_decay'] != 0:
+                        p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
+                    step_size = group['lr'] / (1 - beta1 ** state['step'])
+                    p_data_fp32.add_(-step_size, exp_avg)
+                    p.data.copy_(p_data_fp32)
+
+        return loss
+
+
+class AdamW(Optimizer):
+
+    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, warmup=0):
+        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, warmup=warmup)
+        super(AdamW, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(AdamW, self).__setstate__(state)
+
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data.float()
+                if grad.is_sparse:
+                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
+
+                p_data_fp32 = p.data.float()
+
+                state = self.state[p]
+
+                if len(state) == 0:
+                    state['step'] = 0
+                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
+                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
+                else:
+                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
+                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
+
+                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+                beta1, beta2 = group['betas']
+
+                state['step'] += 1
+
+                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
+                exp_avg.mul_(beta1).add_(1 - beta1, grad)
+
+                denom = exp_avg_sq.sqrt().add_(group['eps'])
+                bias_correction1 = 1 - beta1 ** state['step']
+                bias_correction2 = 1 - beta2 ** state['step']
+
+                if group['warmup'] > state['step']:
+                    scheduled_lr = 1e-8 + state['step'] * group['lr'] / group['warmup']
+                else:
+                    scheduled_lr = group['lr']
+
+                step_size = scheduled_lr * math.sqrt(bias_correction2) / bias_correction1
+
+                if group['weight_decay'] != 0:
+                    p_data_fp32.add_(-group['weight_decay'] * scheduled_lr, p_data_fp32)
+
+                p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+
+                p.data.copy_(p_data_fp32)
+
+        return loss