wehub-resource-sync
85 lines
2.8 KiB
Python
85 lines
2.8 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/gates/switch_gate.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 math
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import paddle
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import paddle.nn.functional as F
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from ..utils import limit_by_capacity
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from .naive_gate import NaiveGate
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class SwitchGate(NaiveGate):
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def __init__(
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self,
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d_model,
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num_expert,
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world_size,
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topk=1,
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switch_eps=0.1,
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capacity=(1.2, 2.4),
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group=None,
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):
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assert topk == 1, "topk should be 1 in switch"
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super().__init__(d_model, num_expert, world_size, topk=1)
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self.switch_eps = switch_eps
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self.capacity = capacity
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self.group = group
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def forward(self, inp):
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score = self.gate(inp)
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if self.training:
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noise = paddle.rand(shape=score.shape)
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noise = noise * 2 * self.switch_eps + 1.0 - self.switch_eps
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score += noise
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score = F.softmax(score, axis=-1)
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top1_score, top1_idx = paddle.topk(score, k=1, axis=-1, largest=True)
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cap_rate = self.capacity[0 if self.training else 1]
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capacity = math.ceil(cap_rate * inp.shape[0])
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_new_lec, _new_gec, top1_idx = limit_by_capacity(
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top1_idx,
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self.num_expert,
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self.world_size,
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capacity,
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group=self.group,
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)
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valid_idx = top1_idx[top1_idx > -1]
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valid_idx_tmp = paddle.reshape(valid_idx, shape=[len(valid_idx), 1])
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fraction_expert = (
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paddle.scatter_nd_add(
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x=paddle.zeros(shape=[self.tot_expert]),
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index=valid_idx_tmp,
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updates=paddle.ones_like(
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valid_idx, dtype=paddle.float32
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).reshape(shape=[len(valid_idx)]),
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)
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/ valid_idx.numel()
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)
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prob_expert = score.sum(axis=0) / valid_idx.numel()
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loss = (fraction_expert * prob_expert).sum() * self.tot_expert
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self.set_loss(loss)
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return top1_score, top1_idx
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