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fb11af9 | 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 | # Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple
import torch
import torch.distributed as dist
from .comm import (
get_unified_sequence_parallel_group,
get_unified_sequence_parallel_world_size,
)
class ReduceLoss(torch.autograd.Function):
@staticmethod
def forward(ctx: torch.autograd.Function, loss: torch.Tensor, num_valid_tokens: torch.Tensor) -> torch.Tensor:
if num_valid_tokens == 0:
loss = torch.nan_to_num(loss)
local_num_tokens = num_valid_tokens.detach().clone()
loss *= num_valid_tokens
group = get_unified_sequence_parallel_group()
dist.all_reduce(loss, group=group)
dist.all_reduce(num_valid_tokens, group=group)
ctx.save_for_backward(local_num_tokens, num_valid_tokens)
return loss / num_valid_tokens
@staticmethod
def backward(
ctx: torch.autograd.Function, grad_output: torch.Tensor
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
local_num_tokens, global_num_tokens = ctx.saved_tensors
grad_output = get_unified_sequence_parallel_world_size() * local_num_tokens * grad_output / global_num_tokens
return grad_output, None
def reduce_sequence_parallel_loss(loss: torch.Tensor, num_valid_tokens: torch.Tensor) -> torch.Tensor:
return ReduceLoss.apply(loss, num_valid_tokens)
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