Datasets:
File size: 1,989 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 68 69 70 71 72 73 | from __future__ import annotations
import math
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,
W1: Tensor,
b1: Tensor,
W2: Tensor,
b2: Tensor,
exp_avg_part: Tensor,
exp_avg_sq_part: Tensor,
lr: float,
beta1: float,
beta2: float,
eps: float,
step: int,
) -> tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
world_size = dist.get_world_size()
rank = dist.get_rank()
templates = [W1, b1, W2, b2]
flat_p = _flatten_dense_tensors(templates)
dist.broadcast(flat_p, src=0)
param_views = _unflatten_dense_tensors(flat_p, templates)
params = [t.detach().requires_grad_(True) for t in param_views]
part = exp_avg_part.numel()
assert flat_p.numel() == part * world_size
m_part = exp_avg_part.clone()
v_part = exp_avg_sq_part.clone()
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([p.grad for p in params])
dist.all_reduce(flat_g, op=dist.ReduceOp.SUM)
flat_g.div_(world_size)
start = rank * part
g_part = flat_g[start : start + part]
w_part = flat_p[start : start + part].clone()
assert step >= 1
bc1 = 1.0 - math.pow(beta1, step)
bc2 = 1.0 - math.pow(beta2, step)
m_part.mul_(beta1).add_(g_part, alpha=1.0 - beta1)
v_part.mul_(beta2).addcmul_(g_part, g_part, value=1.0 - beta2)
m_hat = m_part / bc1
v_hat = v_part / bc2
w_part.add_(m_hat.div(v_hat.sqrt().add(eps)).mul(-lr))
gathered = torch.empty_like(flat_p)
dist.all_gather_into_tensor(gathered, w_part.contiguous())
flat_p.copy_(gathered)
out_params = _unflatten_dense_tensors(flat_p, templates)
return (*out_params, m_part, v_part)
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