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net.py
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# Copyright (c) 2021 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.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class PLELayer(nn.Layer):
def __init__(self, feature_size, task_num, exp_per_task, shared_num,
expert_size, tower_size, level_number):
super(PLELayer, self).__init__()
self.task_num = task_num
self.exp_per_task = exp_per_task
self.shared_num = shared_num
self.expert_size = expert_size
self.tower_size = tower_size
self.level_number = level_number
# ple layer
self.ple_layers = []
for i in range(0, self.level_number):
if i == self.level_number - 1:
ple_layer = self.add_sublayer(
name='lev_' + str(i),
sublayer=SinglePLELayer(
feature_size, task_num, exp_per_task, shared_num,
expert_size, 'lev_' + str(i), True))
self.ple_layers.append(ple_layer)
break
else:
ple_layer = self.add_sublayer(
name='lev_' + str(i),
sublayer=SinglePLELayer(
feature_size, task_num, exp_per_task, shared_num,
expert_size, 'lev_' + str(i), False))
self.ple_layers.append(ple_layer)
feature_size = expert_size
# task tower
self._param_tower = []
self._param_tower_out = []
for i in range(0, self.task_num):
linear = self.add_sublayer(
name='tower_' + str(i),
sublayer=nn.Linear(
expert_size,
tower_size,
weight_attr=nn.initializer.Constant(value=0.1),
bias_attr=nn.initializer.Constant(value=0.1),
#bias_attr=paddle.ParamAttr(learning_rate=1.0),
name='tower_' + str(i)))
self._param_tower.append(linear)
linear = self.add_sublayer(
name='tower_out_' + str(i),
sublayer=nn.Linear(
tower_size,
2,
weight_attr=nn.initializer.Constant(value=0.1),
bias_attr=nn.initializer.Constant(value=0.1),
name='tower_out_' + str(i)))
self._param_tower_out.append(linear)
def forward(self, input_data):
inputs_ple = []
# task_num part + shared part
for i in range(0, self.task_num + 1):
inputs_ple.append(input_data)
# multiple ple layer
ple_out = []
for i in range(0, self.level_number):
ple_out = self.ple_layers[i](inputs_ple)
inputs_ple = ple_out
#assert len(ple_out) == self.task_num
output_layers = []
for i in range(0, self.task_num):
cur_tower = self._param_tower[i](ple_out[i])
cur_tower = F.relu(cur_tower)
out = self._param_tower_out[i](cur_tower)
out = F.softmax(out)
out = paddle.clip(out, min=1e-15, max=1.0 - 1e-15)
output_layers.append(out)
return output_layers
class SinglePLELayer(nn.Layer):
def __init__(self, input_feature_size, task_num, exp_per_task, shared_num,
expert_size, level_name, if_last):
super(SinglePLELayer, self).__init__()
self.task_num = task_num
self.exp_per_task = exp_per_task
self.shared_num = shared_num
self.expert_size = expert_size
self.if_last = if_last
self._param_expert = []
# task-specific expert part
for i in range(0, self.task_num):
for j in range(0, self.exp_per_task):
linear = self.add_sublayer(
name=level_name + "_exp_" + str(i) + "_" + str(j),
sublayer=nn.Linear(
input_feature_size,
expert_size,
weight_attr=nn.initializer.Constant(value=0.1),
bias_attr=nn.initializer.Constant(value=0.1),
name=level_name + "_exp_" + str(i) + "_" + str(j)))
self._param_expert.append(linear)
# shared expert part
for i in range(0, self.shared_num):
linear = self.add_sublayer(
name=level_name + "_exp_shared_" + str(i),
sublayer=nn.Linear(
input_feature_size,
expert_size,
weight_attr=nn.initializer.Constant(value=0.1),
bias_attr=nn.initializer.Constant(value=0.1),
name=level_name + "_exp_shared_" + str(i)))
self._param_expert.append(linear)
# task gate part
self._param_gate = []
cur_expert_num = self.exp_per_task + self.shared_num
for i in range(0, self.task_num):
linear = self.add_sublayer(
name=level_name + "_gate_" + str(i),
sublayer=nn.Linear(
input_feature_size,
cur_expert_num,
weight_attr=nn.initializer.Constant(value=0.1),
bias_attr=nn.initializer.Constant(value=0.1),
name=level_name + "_gate_" + str(i)))
self._param_gate.append(linear)
# shared gate
if not if_last:
cur_expert_num = self.task_num * self.exp_per_task + self.shared_num
linear = self.add_sublayer(
name=level_name + "_gate_shared_",
sublayer=nn.Linear(
input_feature_size,
cur_expert_num,
weight_attr=nn.initializer.Constant(value=0.1),
bias_attr=nn.initializer.Constant(value=0.1),
name=level_name + "_gate_shared_"))
self._param_gate_shared = linear
def forward(self, input_data):
expert_outputs = []
# task-specific expert part
for i in range(0, self.task_num):
for j in range(0, self.exp_per_task):
linear_out = self._param_expert[i * self.task_num + j](
input_data[i])
expert_output = F.relu(linear_out)
expert_outputs.append(expert_output)
# shared expert part
for i in range(0, self.shared_num):
linear_out = self._param_expert[self.exp_per_task * self.task_num +
i](input_data[-1])
expert_output = F.relu(linear_out)
expert_outputs.append(expert_output)
# task gate part
outputs = []
for i in range(0, self.task_num):
cur_expert_num = self.exp_per_task + self.shared_num
linear_out = self._param_gate[i](input_data[i])
cur_gate = F.softmax(linear_out)
cur_gate = paddle.reshape(cur_gate, [-1, cur_expert_num, 1])
# f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))
cur_experts = expert_outputs[i * self.exp_per_task:(
i + 1) * self.exp_per_task] + expert_outputs[-int(
self.shared_num):]
expert_concat = paddle.concat(x=cur_experts, axis=1)
expert_concat = paddle.reshape(
expert_concat, [-1, cur_expert_num, self.expert_size])
cur_gate_expert = paddle.multiply(x=expert_concat, y=cur_gate)
cur_gate_expert = paddle.sum(x=cur_gate_expert, axis=1)
outputs.append(cur_gate_expert)
# shared gate
if not self.if_last:
cur_expert_num = self.task_num * self.exp_per_task + self.shared_num
linear_out = self._param_gate_shared(input_data[-1])
cur_gate = F.softmax(linear_out)
cur_gate = paddle.reshape(cur_gate, [-1, cur_expert_num, 1])
cur_experts = expert_outputs
expert_concat = paddle.concat(x=cur_experts, axis=1)
expert_concat = paddle.reshape(
expert_concat, [-1, cur_expert_num, self.expert_size])
cur_gate_expert = paddle.multiply(x=expert_concat, y=cur_gate)
cur_gate_expert = paddle.sum(x=cur_gate_expert, axis=1)
outputs.append(cur_gate_expert)
return outputs