Datasets:
File size: 971 Bytes
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 42 43 44 45 | from __future__ import annotations
import math
import torch
import torch.distributed as dist
from torch import Tensor
@torch.no_grad()
def solution(
grad_shard: Tensor,
master_shard: Tensor,
exp_avg: Tensor,
exp_avg_sq: Tensor,
lr: float,
beta1: float,
beta2: float,
eps: float,
step: int,
) -> Tensor:
world_size = dist.get_world_size()
assert step >= 1
p = grad_shard.numel()
assert p > 0
m = exp_avg.clone()
v = exp_avg_sq.clone()
w = master_shard.clone()
bc1 = 1.0 - math.pow(beta1, step)
bc2 = 1.0 - math.pow(beta2, step)
g = grad_shard
m.mul_(beta1).add_(g, alpha=1.0 - beta1)
v.mul_(beta2).addcmul_(g, g, value=1.0 - beta2)
m_hat = m / bc1
v_hat = v / bc2
w.add_(m_hat.div(v_hat.sqrt().add(eps)).mul(-lr))
gathered = torch.empty(world_size * p, dtype=w.dtype, device=w.device)
dist.all_gather_into_tensor(gathered, w.contiguous())
return gathered
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