Datasets:
File size: 2,017 Bytes
c2f8cf1 | 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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from __future__ import annotations
import math
from typing import Sequence
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import Tensor
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
def solution(
X_local: Tensor,
y_local: Tensor,
flat_param_shard: Tensor,
param_shapes: Sequence[tuple[int, ...]],
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
world_size = dist.get_world_size()
p = flat_param_shard.numel()
device = flat_param_shard.device
dtype = flat_param_shard.dtype
templates = [torch.zeros(shape, dtype=dtype, device=device) for shape in param_shapes]
full_flat = torch.empty(world_size * p, dtype=dtype, device=device)
dist.all_gather_into_tensor(full_flat, flat_param_shard.contiguous())
params_f = _unflatten_dense_tensors(full_flat, templates)
params = [t.detach().requires_grad_(True) for t in params_f]
h = F.relu(F.linear(X_local, params[0], params[1]))
out = F.linear(h, params[2], params[3])
loss = F.mse_loss(out, y_local)
loss.backward()
flat_g = _flatten_dense_tensors([x.grad for x in params])
g_shard = torch.empty(p, dtype=flat_g.dtype, device=flat_g.device)
dist.reduce_scatter_tensor(g_shard, flat_g.contiguous(), op=dist.ReduceOp.SUM)
g_shard.div_(world_size)
m = exp_avg_shard.clone()
v = exp_avg_sq_shard.clone()
theta = flat_param_shard.clone()
m.mul_(beta1).add_(g_shard, alpha=1.0 - beta1)
v.mul_(beta2).addcmul_(g_shard, g_shard, 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 |