Datasets:
File size: 2,360 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 74 75 76 77 78 79 80 | 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_W1: Tensor,
exp_avg_b1: Tensor,
exp_avg_W2: Tensor,
exp_avg_b2: Tensor,
exp_avg_sq_W1: Tensor,
exp_avg_sq_b1: Tensor,
exp_avg_sq_W2: Tensor,
exp_avg_sq_b2: Tensor,
lr: float,
beta1: float,
beta2: float,
eps: float,
step: int,
) -> tuple[Tensor, ...]:
world_size = dist.get_world_size()
params = [W1, b1, W2, b2]
exp_avg = [exp_avg_W1, exp_avg_b1, exp_avg_W2, exp_avg_b2]
exp_avg_sq = [exp_avg_sq_W1, exp_avg_sq_b1, exp_avg_sq_W2, exp_avg_sq_b2]
flat_params = _flatten_dense_tensors(params)
dist.broadcast(flat_params, src=0)
broadcast_params = _unflatten_dense_tensors(flat_params, params)
params = [t.detach().requires_grad_(True) for t in broadcast_params]
flat_m = _flatten_dense_tensors(exp_avg)
dist.broadcast(flat_m, src=0)
exp_avg = list(_unflatten_dense_tensors(flat_m, exp_avg))
flat_v = _flatten_dense_tensors(exp_avg_sq)
dist.broadcast(flat_v, src=0)
exp_avg_sq = list(_unflatten_dense_tensors(flat_v, exp_avg_sq))
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()
grads = [p.grad for p in params]
flat_grad = _flatten_dense_tensors(grads)
dist.all_reduce(flat_grad, op=dist.ReduceOp.SUM)
flat_grad.div_(world_size)
avg_grads = _unflatten_dense_tensors(flat_grad, grads)
for p, g in zip(params, avg_grads):
p.grad.copy_(g)
assert step >= 1
bc1 = 1.0 - math.pow(beta1, step)
bc2 = 1.0 - math.pow(beta2, step)
for p, m_buf, v_buf in zip(params, exp_avg, exp_avg_sq):
g = p.grad
m_buf.mul_(beta1).add_(g, alpha=1.0 - beta1)
v_buf.mul_(beta2).addcmul_(g, g, value=1.0 - beta2)
m_hat = m_buf / bc1
v_hat = v_buf / bc2
denom = v_hat.sqrt().add(eps)
p.data.add_(m_hat.div(denom).mul(-lr))
out_tensors = tuple(list(params) + exp_avg + exp_avg_sq)
return out_tensors
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