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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 42 43 | from __future__ import annotations
import torch
import torch.distributed as dist
import torch.nn.functional as F
from torch import Tensor
@torch.no_grad()
def _block_int8_quant_dequant(x_flat: Tensor, block_size: int) -> Tensor:
n = x_flat.numel()
if n == 0:
return x_flat.clone()
flat = x_flat.contiguous().reshape(-1)
pad = (-n) % block_size
if pad:
flat = F.pad(flat, (0, pad))
nb = flat.numel() // block_size
xv = flat.view(nb, block_size)
scales = xv.abs().amax(dim=1).float().clamp(min=1e-8) / 127.0
q = (xv.float() / scales.unsqueeze(1)).round().clamp(-127, 127).to(torch.int8)
out = (q.float() * scales.unsqueeze(1)).reshape(-1)
return out[:n]
@torch.no_grad()
def solution(
flat_grad: Tensor,
block_size: int,
) -> Tensor:
assert block_size >= 1
world_size = dist.get_world_size()
orig_shape = flat_grad.shape
x = flat_grad.reshape(-1)
rec = _block_int8_quant_dequant(x, block_size)
acc = rec.float()
dist.all_reduce(acc, op=dist.ReduceOp.SUM)
acc.div_(world_size)
return acc.to(dtype=flat_grad.dtype).reshape(orig_shape)
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