# 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. from paddle.distributed.models.moe.utils import * 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