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TTS/utils/radam.py

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+# modified 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 lr < 0.0:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if eps < 0.0:
+            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().__init__(params, defaults)
+
+    def __setstate__(self, state):  # pylint: disable=useless-super-delegation
+        super().__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_(grad, grad, value=1 - beta2)
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+
+                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_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"])
+                    denom = exp_avg_sq.sqrt().add_(group["eps"])
+                    p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group["lr"])
+                    p.data.copy_(p_data_fp32)
+                elif step_size > 0:
+                    if group["weight_decay"] != 0:
+                        p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"])
+                    p_data_fp32.add_(exp_avg, alpha=-step_size * group["lr"])
+                    p.data.copy_(p_data_fp32)
+
+        return loss