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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 | from __future__ import annotations
import torch
import torch.distributed as dist
from torch import Tensor
_FP8_E4M3_MAX = 448.0
@torch.no_grad()
def _update_amax_history(amax_history: Tensor, cur_abs_max: Tensor) -> Tensor:
out = torch.roll(amax_history, shifts=-1, dims=0)
out[-1] = cur_abs_max.to(dtype=out.dtype)
return out
@torch.no_grad()
def _fp8_round_trip_bf16(x: Tensor, scale: Tensor) -> Tensor:
xf = x.float()
qs = xf / scale
q = qs.to(torch.float8_e4m3fn)
return (q.float() * scale).to(dtype=x.dtype)
@torch.no_grad()
def solution(flat_param_shard: Tensor, amax_history: Tensor) -> tuple[Tensor, Tensor]:
world_size = dist.get_world_size()
p = flat_param_shard.numel()
cur_abs_max = flat_param_shard.abs().max().to(torch.float32)
updated_hist = _update_amax_history(amax_history, cur_abs_max)
scale = updated_hist.max().clamp(min=1e-12).to(torch.float32) / _FP8_E4M3_MAX
recon = _fp8_round_trip_bf16(flat_param_shard, scale)
full = torch.empty(world_size * p, dtype=flat_param_shard.dtype, device=flat_param_shard.device)
dist.all_gather_into_tensor(full, recon.contiguous())
return full, updated_hist
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