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"""RAdam Optimizer. |
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Implementation lifted from: https://github.com/LiyuanLucasLiu/RAdam |
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Paper: `On the Variance of the Adaptive Learning Rate and Beyond` - https://arxiv.org/abs/1908.03265 |
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""" |
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import math |
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import torch |
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from torch.optim.optimizer import Optimizer, required |
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class RAdam(Optimizer): |
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): |
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) |
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self.buffer = [[None, None, None] for ind in range(10)] |
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super(RAdam, self).__init__(params, defaults) |
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def __setstate__(self, state): |
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super(RAdam, self).__setstate__(state) |
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def step(self, closure=None): |
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loss = None |
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if closure is not None: |
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loss = closure() |
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for group in self.param_groups: |
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for p in group["params"]: |
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if p.grad is None: |
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continue |
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grad = p.grad.data.float() |
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if grad.is_sparse: |
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raise RuntimeError("RAdam does not support sparse gradients") |
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p_data_fp32 = p.data.float() |
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state = self.state[p] |
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if len(state) == 0: |
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state["step"] = 0 |
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state["exp_avg"] = torch.zeros_like(p_data_fp32) |
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state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) |
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else: |
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state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32) |
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state["exp_avg_sq"] = state["exp_avg_sq"].type_as(p_data_fp32) |
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exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] |
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beta1, beta2 = group["betas"] |
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) |
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exp_avg.mul_(beta1).add_(1 - beta1, grad) |
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state["step"] += 1 |
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buffered = self.buffer[int(state["step"] % 10)] |
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if state["step"] == buffered[0]: |
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N_sma, step_size = buffered[1], buffered[2] |
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else: |
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buffered[0] = state["step"] |
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beta2_t = beta2 ** state["step"] |
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N_sma_max = 2 / (1 - beta2) - 1 |
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N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1 - beta2_t) |
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buffered[1] = N_sma |
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if N_sma >= 5: |
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step_size = ( |
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group["lr"] |
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* math.sqrt( |
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(1 - beta2_t) |
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* (N_sma - 4) |
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/ (N_sma_max - 4) |
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* (N_sma - 2) |
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/ N_sma |
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* N_sma_max |
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/ (N_sma_max - 2) |
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) |
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/ (1 - beta1 ** state["step"]) |
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) |
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else: |
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step_size = group["lr"] / (1 - beta1 ** state["step"]) |
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buffered[2] = step_size |
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if group["weight_decay"] != 0: |
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p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32) |
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if N_sma >= 5: |
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denom = exp_avg_sq.sqrt().add_(group["eps"]) |
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p_data_fp32.addcdiv_(-step_size, exp_avg, denom) |
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else: |
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p_data_fp32.add_(-step_size, exp_avg) |
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p.data.copy_(p_data_fp32) |
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return loss |
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class PlainRAdam(Optimizer): |
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): |
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) |
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super(PlainRAdam, self).__init__(params, defaults) |
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def __setstate__(self, state): |
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super(PlainRAdam, self).__setstate__(state) |
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def step(self, closure=None): |
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loss = None |
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if closure is not None: |
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loss = closure() |
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for group in self.param_groups: |
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for p in group["params"]: |
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if p.grad is None: |
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continue |
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grad = p.grad.data.float() |
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if grad.is_sparse: |
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raise RuntimeError("RAdam does not support sparse gradients") |
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p_data_fp32 = p.data.float() |
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state = self.state[p] |
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if len(state) == 0: |
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state["step"] = 0 |
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state["exp_avg"] = torch.zeros_like(p_data_fp32) |
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state["exp_avg_sq"] = torch.zeros_like(p_data_fp32) |
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else: |
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state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32) |
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state["exp_avg_sq"] = state["exp_avg_sq"].type_as(p_data_fp32) |
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exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] |
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beta1, beta2 = group["betas"] |
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) |
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exp_avg.mul_(beta1).add_(1 - beta1, grad) |
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state["step"] += 1 |
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beta2_t = beta2 ** state["step"] |
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N_sma_max = 2 / (1 - beta2) - 1 |
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N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1 - beta2_t) |
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if group["weight_decay"] != 0: |
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p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32) |
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if N_sma >= 5: |
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step_size = ( |
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group["lr"] |
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* math.sqrt( |
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(1 - beta2_t) |
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* (N_sma - 4) |
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/ (N_sma_max - 4) |
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* (N_sma - 2) |
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/ N_sma |
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* N_sma_max |
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/ (N_sma_max - 2) |
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) |
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/ (1 - beta1 ** state["step"]) |
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) |
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denom = exp_avg_sq.sqrt().add_(group["eps"]) |
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p_data_fp32.addcdiv_(-step_size, exp_avg, denom) |
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else: |
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step_size = group["lr"] / (1 - beta1 ** state["step"]) |
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p_data_fp32.add_(-step_size, exp_avg) |
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p.data.copy_(p_data_fp32) |
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return loss |
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