wehub-resource-sync
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
# 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.
|
|
#
|
|
# The file has been adapted from the file:
|
|
# https://github.com/laekov/fastmoe/blob/master/fmoe/gates/gshard_gate.py
|
|
# Git commit hash: 295a615aacce7e54a37e7935274ba15e901c78e4
|
|
# We retain the following license from the original files:
|
|
# Copyright 2021, Jiaao He. All rights reserved.
|
|
# Licensed under the Apache License, Version 2.0 (the "License").
|
|
|
|
import math
|
|
|
|
import paddle
|
|
import paddle.nn.functional as F
|
|
|
|
from ..utils import limit_by_capacity
|
|
from .naive_gate import NaiveGate
|
|
|
|
|
|
class GShardGate(NaiveGate):
|
|
def __init__(
|
|
self,
|
|
d_model,
|
|
num_expert,
|
|
world_size,
|
|
topk=2,
|
|
capacity=(1.2, 2.4),
|
|
random_routing=True,
|
|
group=None,
|
|
):
|
|
assert topk == 2, "topk should be 2 in gshard"
|
|
super().__init__(d_model, num_expert, world_size)
|
|
self.capacity = capacity
|
|
self.random_routing = random_routing
|
|
self.group = group
|
|
|
|
def forward(self, x):
|
|
topk_val, topk_idx, gate_score = super().forward(
|
|
x, return_all_scores=True
|
|
)
|
|
s = gate_score.shape[0]
|
|
top1_idx = topk_idx.flatten()
|
|
c_e = (
|
|
paddle.scatter(
|
|
paddle.zeros(shape=[self.tot_expert]),
|
|
top1_idx,
|
|
paddle.ones_like(top1_idx, dtype="float32"),
|
|
overwrite=False,
|
|
)
|
|
/ s
|
|
)
|
|
m_e = paddle.mean(F.softmax(gate_score, axis=1), axis=0)
|
|
loss = paddle.mean(c_e * m_e) * (self.num_expert**2)
|
|
self.set_loss(loss)
|
|
|
|
cap_rate = self.capacity[0 if self.training else 1]
|
|
capacity = math.ceil(cap_rate * x.shape[0])
|
|
_new_lec, _new_gec, topk_idx = limit_by_capacity(
|
|
topk_idx,
|
|
self.num_expert,
|
|
self.world_size,
|
|
capacity,
|
|
group=self.group,
|
|
)
|
|
|
|
if self.random_routing:
|
|
rand_routing_prob = paddle.rand(
|
|
shape=[gate_score.shape[0]], dtype="float32"
|
|
)
|
|
topk_idx = paddle.distributed.models.moe.utils._random_routing(
|
|
topk_idx, topk_val, rand_routing_prob
|
|
)
|
|
return topk_val, topk_idx
|