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ebfc1e6 | 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 55 56 57 58 59 60 | # Copyright 2025 Bytedance Ltd. and/or its affiliates. and the LlamaFactory team.
#
# This code is inspired by the Bytedance's verl library.
# https://github.com/verl-project/verl/blob/77476af84cc074edf5a6437f8d5ea418d7a54916/verl/utils/ulysses.py
#
# 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 Any, Optional
import torch
import torch.distributed as dist
from torch import Tensor
def all_to_all_tensor(
local_input: Tensor,
scatter_dim: int,
gather_dim: int,
group: Optional[dist.ProcessGroup] = None,
):
seq_world_size = dist.get_world_size(group)
input_list = [t.contiguous() for t in torch.tensor_split(local_input, seq_world_size, scatter_dim)]
output_list = [torch.empty_like(input_list[0]) for _ in range(seq_world_size)]
dist.all_to_all(output_list, input_list, group=group)
return torch.cat(output_list, dim=gather_dim).contiguous()
class SeqAllToAll4D(torch.autograd.Function):
@staticmethod
def forward(
ctx: Any,
group: dist.ProcessGroup,
local_input: Tensor,
scatter_dim: int,
gather_dim: int,
) -> Tensor:
ctx.group = group
ctx.scatter_dim = scatter_dim
ctx.gather_dim = gather_dim
return all_to_all_tensor(local_input, scatter_dim, gather_dim, group)
@staticmethod
def backward(ctx: Any, *grad_output: Tensor) -> tuple[None, Tensor, None, None]:
return (
None,
all_to_all_tensor(grad_output[0], ctx.gather_dim, ctx.scatter_dim, ctx.group),
None,
None,
)
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