utils.py 2.4 KB
Newer Older
R
Roc 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# 
# 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.
R
Roc 已提交
14 15
from paddle.distributed.models.moe.utils import _number_count, _limit_by_capacity, _prune_gate_by_capacity, _assign_pos
import paddle
R
Roc 已提交
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


def _alltoall(in_tensor_list, group=None, use_calc_stream=True):
    if group is not None and not group.is_member():
        return
    ring_id = 0 if group is None else group.id
    nranks = len(in_tensor_list)
    return paddle._C_ops.alltoall(in_tensor_list, 'use_calc_stream',
                                  use_calc_stream, 'ring_id', ring_id)


def count_by_gate(gate, num_expert, world_size, require_pos=True, group=None):
    total_expert_count = num_expert * world_size
    with paddle.no_grad():
        local_expert_count = _number_count(gate, total_expert_count)

        if world_size > 1:
            global_expert_count = _alltoall(local_expert_count, group=group)
        else:
            global_expert_count = local_expert_count
        if not require_pos:
            pos = None
        else:
            lec_cum = paddle.cumsum(local_expert_count, axis=0)
            pos = _assign_pos(gate, lec_cum)
    return pos, local_expert_count, global_expert_count


def limit_by_capacity(topk_idx, num_expert, world_size, capacity, group=None):
    with paddle.no_grad():
        capacity = paddle.ones(
            shape=[num_expert], dtype=paddle.int64) * capacity
        pos, lec, gec = count_by_gate(
            topk_idx, num_expert, world_size, require_pos=False, group=group)
        new_gec = _limit_by_capacity(gec, capacity, world_size)
        if world_size > 1:
            assert group.nranks == world_size
            new_lec = _alltoall(new_gec, group=group)
        else:
            new_lec = new_gec

        topk_idx = _prune_gate_by_capacity(topk_idx, new_lec, num_expert,
                                           world_size)

    return new_lec, new_gec, topk_idx