Datasets:
File size: 1,577 Bytes
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 44 45 46 47 48 49 50 51 52 53 54 | import torch
import torch.distributed as dist
from typing import Tuple, Optional
def forward(
loss: torch.Tensor,
local_valid_tokens: torch.Tensor,
global_valid_tokens: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
if local_valid_tokens.item() == 0:
loss = torch.nan_to_num(loss)
loss_sum = loss * local_valid_tokens
dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
normalized_loss = loss_sum / global_valid_tokens
return normalized_loss, loss_sum
def backward(
local_valid_tokens: torch.Tensor,
global_valid_tokens: torch.Tensor,
grad_normalized_loss: torch.Tensor,
grad_loss_sum: Optional[torch.Tensor],
) -> torch.Tensor:
grad_from_normalized = grad_normalized_loss * local_valid_tokens / global_valid_tokens
if grad_loss_sum is not None:
grad_from_sum = grad_loss_sum * local_valid_tokens
else:
grad_from_sum = torch.zeros_like(grad_normalized_loss, device=grad_normalized_loss.device)
return grad_from_normalized + grad_from_sum
def solution(
loss: torch.Tensor,
local_valid_tokens: torch.Tensor,
global_valid_tokens: torch.Tensor,
grad_normalized_loss: torch.Tensor,
grad_loss_sum: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
normalized_loss, loss_sum = forward(loss, local_valid_tokens, global_valid_tokens)
grad_loss = backward(
local_valid_tokens,
global_valid_tokens,
grad_normalized_loss,
grad_loss_sum,
)
return normalized_loss, loss_sum, grad_loss
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