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import torch
class MaxFactor(torch.optim.Optimizer):
def __init__(self, params, lr=0.01, beta2_decay=-0.8, eps=(1e-10, 1e-3), d=1.0,
weight_decay=0.01, gamma=0.99, max=False):
defaults = dict(lr=lr, beta2_decay=beta2_decay, eps=eps, d=d, weight_decay=weight_decay,
gamma=gamma, max=max)
super().__init__(params=params, defaults=defaults)
@staticmethod
def _rms(tensor):
return tensor.norm() / (tensor.numel() ** 0.5)
@torch.no_grad()
def step(self, closure=None):
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad, grads, row_vars, col_vars, v, state_steps = [], [], [], [], [], []
eps1, eps2 = group["eps"]
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if grad.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
state = self.state[p]
if len(state) == 0:
state["step"] = torch.tensor(0.0, dtype=torch.float32)
if p.grad.dim() > 1:
row_shape, col_shape = list(p.grad.shape), list(p.grad.shape)
row_shape[-1], col_shape[-2] = 1, 1
state["row_var"], state["col_var"] = p.grad.new_zeros(row_shape), p.grad.new_zeros(col_shape)
state["v"] = torch.zeros_like(p, memory_format=torch.preserve_format)
state["RMS"] = self._rms(p).item()
row_vars.append(state.get("row_var", None))
col_vars.append(state.get("col_var", None))
v.append(state["v"])
state_steps.append(state["step"])
params_with_grad.append(p)
grads.append(grad)
for i, param in enumerate(params_with_grad):
grad = grads[i]
if group["max"]:
grad = -grad
step_t, row_var, col_var, vi = state_steps[i], row_vars[i], col_vars[i], v[i]
if eps1 is None:
eps1 = torch.finfo(param.dtype).eps
step_t += 1
step_float = step_t.item()
one_minus_beta2_t = step_float ** group["beta2_decay"]
state["RMS"] = self._rms(param).item()
rho_t = min(group["lr"], 1 / (step_float ** 0.5))
alpha = max(eps2, param.norm(2).item() / (param.numel() ** 0.5)) * rho_t
if group["weight_decay"] != 0:
param.mul_(1 - group["lr"] * group["weight_decay"])
if grad.dim() > 1:
row_mean = torch.norm(grad, dim=-1, keepdim=True).square_().div_(grad.size(-1) + 1e-8)
row_var.lerp_(row_mean, one_minus_beta2_t)
col_mean = torch.norm(grad, dim=-2, keepdim=True).square_().div_(grad.size(-2) + 1e-8)
col_var.lerp_(col_mean, one_minus_beta2_t)
var_estimate = row_var @ col_var
max_row_var = row_var.max(dim=-2, keepdim=True)[0]
var_estimate.div_(max_row_var.clamp_(min=eps1))
else:
vi.mul_(group["gamma"]).add_(grad ** 2, alpha=1 - group["gamma"])
var_estimate = vi
update = var_estimate.clamp_(min=eps1 * eps1).rsqrt_().mul_(grad)
update = update.div_(torch.norm(update, float('inf')).clamp_(min=eps1))
denom = max(1.0, update.norm(2).item() / ((update.numel() ** 0.5) * group["d"]))
param.add_(-alpha / denom * update.sign() * update.abs().max(dim=-1, keepdim=True)[0])
return loss
# class MaxFactor(torch.optim.Optimizer):
# __version__ = "1.0"
# def __init__(self, params, lr=0.025, beta2_decay=-0.8, eps=(1e-10, 1e-4), d=1.0,
# weight_decay=0.025, gamma=0.99, max=False, min_lr=1e-7):
# print(f"Using MaxFactor optimizer v{self.__version__}")
# defaults = dict(lr=lr, beta2_decay=beta2_decay, eps=eps, d=d, weight_decay=weight_decay,
# gamma=gamma, max=max, min_lr=min_lr)
# super().__init__(params=params, defaults=defaults)
# def get_lr(self):
# """Return current learning rates for all parameter groups."""
# param_specific_lrs = []
# for group in self.param_groups:
# group_lrs = []
# min_lr = group.get("min_lr", 1e-7)
# eps1, eps2 = group["eps"]
# for p in group["params"]:
# if p.grad is None:
# continue
# state = self.state[p]
# if "step" not in state:
# continue
# step_float = state["step"].item()
# # Calculate base learning rate (same as in step method)
# rho_t = max(min_lr, min(group["lr"], 1.0 / (step_float ** 0.5)))
# # Calculate parameter-specific scaling
# param_norm = (p.norm() / (p.numel() ** 0.5 + 1e-12)).item()
# alpha = max(eps2, param_norm) * rho_t
# group_lrs.append(alpha)
# if group_lrs:
# param_specific_lrs.append(sum(group_lrs) / len(group_lrs))
# else:
# param_specific_lrs.append(group["lr"])
# return param_specific_lrs
# def get_last_lr(self):
# return self.get_lr()
# @torch.no_grad()
# def step(self, closure=None):
# loss = None
# if closure is not None:
# with torch.enable_grad():
# loss = closure()
# for group in self.param_groups:
# params_with_grad, grads, row_vars, col_vars, v, state_steps = [], [], [], [], [], []
# eps1, eps2 = group["eps"]
# min_lr = group.get("min_lr", 1e-7)
# for p in group["params"]:
# if p.grad is None:
# continue
# grad = p.grad
# if grad.dtype in {torch.float16, torch.bfloat16}:
# grad = grad.float()
# state = self.state[p]
# if len(state) == 0:
# state["step"] = torch.tensor(0.0, dtype=torch.float32)
# if p.dim() > 1:
# row_shape, col_shape = list(p.shape), list(p.shape)
# row_shape[-1], col_shape[-2] = 1, 1
# state["row_var"] = p.new_zeros(row_shape)
# state["col_var"] = p.new_zeros(col_shape)
# state["v"] = torch.zeros_like(p, memory_format=torch.preserve_format)
# row_vars.append(state.get("row_var", None))
# col_vars.append(state.get("col_var", None))
# v.append(state["v"])
# state_steps.append(state["step"])
# params_with_grad.append(p)
# grads.append(grad)
# for i, param in enumerate(params_with_grad):
# grad = grads[i]
# state = self.state[param]
# if group["max"]:
# grad = -grad
# step_t = state_steps[i]
# row_var, col_var, vi = row_vars[i], col_vars[i], v[i]
# if eps1 is None:
# eps1 = torch.finfo(param.dtype).eps
# step_t += 1
# step_float = step_t.item()
# one_minus_beta2_t = min(0.999, max(0.001, step_float ** group["beta2_decay"]))
# rho_t = max(min_lr, min(group["lr"], 1.0 / (step_float ** 0.5)))
# alpha = max(eps2, (param.norm() / (param.numel() ** 0.5 + 1e-12)).item()) * rho_t
# if group["weight_decay"] > 0:
# param.mul_(1 - group["lr"] * group["weight_decay"])
# if grad.dim() > 1:
# row_mean = torch.norm(grad, dim=-1, keepdim=True).square_()
# row_mean.div_(grad.size(-1) + eps1)
# row_var.lerp_(row_mean, one_minus_beta2_t)
# col_mean = torch.norm(grad, dim=-2, keepdim=True).square_()
# col_mean.div_(grad.size(-2) + eps1)
# col_var.lerp_(col_mean, one_minus_beta2_t)
# var_estimate = row_var @ col_var
# max_row_var = row_var.max(dim=-2, keepdim=True)[0]
# var_estimate.div_(max_row_var.clamp_(min=eps1))
# else:
# vi.mul_(group["gamma"]).add_(grad.square_(), alpha=1 - group["gamma"])
# var_estimate = vi
# update = var_estimate.clamp_(min=eps1 * eps1).rsqrt_().mul_(grad)
# inf_norm = torch.norm(update, float('inf'))
# if inf_norm > 0:
# update.div_(inf_norm.clamp_(min=eps1))
# denom = max(1.0, update.norm(2).item() / ((update.numel() ** 0.5) * group["d"]))
# if param.dim() > 1:
# max_vals = update.abs().max(dim=-1, keepdim=True)[0]
# param.add_(-alpha / denom * update.sign() * max_vals)
# else:
# param.add_(-alpha / denom * update)
# state["step"] = step_t
# return loss
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