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453129d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | from __future__ import annotations
import math
import torch
from torch import Tensor
@torch.no_grad()
def solution(
flat_param_shard: Tensor,
flat_grad_shard: Tensor,
exp_avg_shard: Tensor,
exp_avg_sq_shard: Tensor,
lr: float,
beta1: float,
beta2: float,
eps: float,
weight_decay: float,
step: int,
) -> tuple[Tensor, Tensor, Tensor]:
assert step >= 1
m = exp_avg_shard.clone()
v = exp_avg_sq_shard.clone()
g = flat_grad_shard
theta = flat_param_shard.clone()
m.mul_(beta1).add_(g, alpha=1.0 - beta1)
v.mul_(beta2).addcmul_(g, g, value=1.0 - beta2)
bc1 = 1.0 - math.pow(beta1, step)
bc2 = 1.0 - math.pow(beta2, step)
m_hat = m / bc1
v_hat = v / bc2
denom = v_hat.sqrt().add(eps)
theta.add_(m_hat.div(denom), alpha=-lr)
theta.add_(flat_param_shard, alpha=-lr * weight_decay)
return theta, m, v
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