wehub-resource-sync
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# The file has been adapted from the file:
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# https://github.com/laekov/fastmoe/blob/master/fmoe/functions.py
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# Git commit hash: 295a615aacce7e54a37e7935274ba15e901c78e4
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# We retain the following license from the original files:
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# Copyright 2021, Jiaao He. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License").
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import paddle
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from paddle.distributed.models.moe.utils import (
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_assign_pos,
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_limit_by_capacity,
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_number_count,
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_prune_gate_by_capacity,
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)
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from paddle.framework import in_dynamic_mode
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def _alltoall(in_tensor_list, group=None, use_calc_stream=True):
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if group is not None and not group.is_member():
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return
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if in_dynamic_mode():
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group = (
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paddle.distributed.collective._get_default_group()
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if group is None
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else group
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)
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out = paddle.empty(in_tensor_list.shape, in_tensor_list.dtype)
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task = group.process_group.alltoall(out, in_tensor_list)
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task.wait()
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return out
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else:
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ring_id = 0 if group is None else group.id
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return paddle._C_ops.all_to_all(in_tensor_list, ring_id)
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def count_by_gate(gate, num_expert, world_size, require_pos=True, group=None):
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total_expert_count = num_expert * world_size
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with paddle.no_grad():
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local_expert_count = _number_count(gate, total_expert_count)
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if world_size > 1:
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global_expert_count = _alltoall(local_expert_count, group=group)
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else:
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global_expert_count = local_expert_count
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if not require_pos:
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pos = None
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else:
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lec_cum = paddle.cumsum(local_expert_count, axis=0)
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pos = _assign_pos(gate, lec_cum)
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return pos, local_expert_count, global_expert_count
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def limit_by_capacity(topk_idx, num_expert, world_size, capacity, group=None):
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with paddle.no_grad():
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capacity = (
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paddle.ones(shape=[num_expert], dtype=paddle.int64) * capacity
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)
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pos, lec, gec = count_by_gate(
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topk_idx, num_expert, world_size, require_pos=False, group=group
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)
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new_gec = _limit_by_capacity(gec, capacity, world_size)
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if world_size > 1:
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assert group.nranks == world_size
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new_lec = _alltoall(new_gec, group=group)
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else:
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new_lec = new_gec
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topk_idx = _prune_gate_by_capacity(
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topk_idx, new_lec, num_expert, world_size
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)
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return new_lec, new_gec, topk_idx
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