Datasets:
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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 | import torch
import torch.distributed as dist
from typing import Tuple
def rotate_half(x: torch.Tensor) -> torch.Tensor:
half_dim = x.shape[-1] // 2
x1, x2 = x[..., :half_dim], x[..., half_dim:]
return torch.cat((-x2, x1), dim=-1)
def solution(
q_local: torch.Tensor,
k_local: torch.Tensor,
cos_local: torch.Tensor,
sin_local: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# Reshape cos and sin to broadcast with q and k's head dimension (dim=2)
cos = cos_local.unsqueeze(2)
sin = sin_local.unsqueeze(2)
q_embed_local = (q_local * cos) + (rotate_half(q_local) * sin)
k_embed_local = (k_local * cos) + (rotate_half(k_local) * sin)
if not dist.is_initialized():
return q_embed_local, k_embed_local
world_size = dist.get_world_size()
q_gather_list = [torch.empty_like(q_embed_local) for _ in range(world_size)]
k_gather_list = [torch.empty_like(k_embed_local) for _ in range(world_size)]
dist.all_gather(q_gather_list, q_embed_local.contiguous())
dist.all_gather(k_gather_list, k_embed_local.contiguous())
q_embed_global = torch.cat(q_gather_list, dim=1)
k_embed_global = torch.cat(k_gather_list, dim=1)
return q_embed_global, k_embed_global
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