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net.py
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net.py
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# Copyright (c) 2020 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
import math
class ESCMLayer(nn.Layer):
def __init__(self, sparse_feature_number, sparse_feature_dim, num_field,
ctr_layer_sizes, cvr_layer_sizes, expert_num, expert_size,
tower_size, counterfact_mode, feature_size):
super(ESCMLayer, self).__init__()
self.sparse_feature_number = sparse_feature_number
self.sparse_feature_dim = sparse_feature_dim
self.num_field = num_field
self.ctr_layer_sizes = ctr_layer_sizes
self.cvr_layer_sizes = cvr_layer_sizes
self.counterfact_mode = counterfact_mode
self.expert_num = expert_num
self.expert_size = expert_size
self.tower_size = tower_size
if counterfact_mode == "DR":
self.gate_num = 3
else:
self.gate_num = 2
self.feature_size = feature_size
use_sparse = True
if paddle.is_compiled_with_custom_device('npu'):
use_sparse = False
self.embedding = paddle.nn.Embedding(
self.sparse_feature_number,
self.sparse_feature_dim,
sparse=use_sparse,
padding_idx=0,
weight_attr=paddle.ParamAttr(
name="SparseFeatFactors",
initializer=paddle.nn.initializer.Uniform()))
self._param_expert = []
for i in range(0, self.expert_num):
linear = self.add_sublayer(
name='expert_' + str(i),
sublayer=nn.Linear(
self.feature_size,
self.expert_size,
#initialize the weight randly
weight_attr=nn.initializer.XavierUniform(),
bias_attr=nn.initializer.Constant(value=0.1),
#bias_attr=paddle.ParamAttr(learning_rate=1.0),
name='expert_' + str(i)))
self._param_expert.append(linear)
self._param_gate = []
self._param_tower = []
self._param_tower_out = []
for i in range(0, self.gate_num):
linear = self.add_sublayer(
name='gate_' + str(i),
sublayer=nn.Linear(
feature_size,
expert_num,
#initialize the weight randly
weight_attr=nn.initializer.XavierUniform(),
bias_attr=nn.initializer.Constant(value=0.1),
#bias_attr=paddle.ParamAttr(learning_rate=1.0),
name='gate_' + str(i)))
self._param_gate.append(linear)
linear = self.add_sublayer(
name='tower_' + str(i),
sublayer=nn.Linear(
expert_size,
tower_size,
#initialize the weight randly
weight_attr=nn.initializer.XavierUniform(),
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,
#initialize the weight randly
weight_attr=nn.initializer.XavierUniform(),
bias_attr=nn.initializer.Constant(value=0.1),
name='tower_out_' + str(i)))
self._param_tower_out.append(linear)
def forward(self, inputs):
emb = []
# input feature data
for data in inputs:
feat_emb = self.embedding(data)
# sum pooling
feat_emb = paddle.sum(feat_emb, axis=1)
emb.append(feat_emb)
concat_emb = paddle.concat(x=emb, axis=1)
expert_outputs = []
for i in range(0, self.expert_num):
linear_out = self._param_expert[i](concat_emb)
expert_output = F.relu(linear_out)
expert_outputs.append(expert_output)
expert_concat = paddle.concat(x=expert_outputs, axis=1)
expert_concat = paddle.reshape(
expert_concat, [-1, self.expert_num, self.expert_size])
output_layers = []
for i in range(0, self.gate_num):
cur_gate_linear = self._param_gate[i](concat_emb)
cur_gate = F.softmax(cur_gate_linear)
cur_gate = paddle.reshape(cur_gate, [-1, self.expert_num, 1])
cur_gate_expert = paddle.multiply(x=expert_concat, y=cur_gate)
cur_gate_expert = paddle.sum(x=cur_gate_expert, axis=1)
cur_tower = self._param_tower[i](cur_gate_expert)
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)
out_list = []
ctr_out = output_layers[0]
cvr_out = output_layers[1]
ctr_prop_one = paddle.slice(ctr_out, axes=[1], starts=[1], ends=[2])
cvr_prop_one = paddle.slice(cvr_out, axes=[1], starts=[1], ends=[2])
ctcvr_prop_one = paddle.multiply(x=ctr_prop_one, y=cvr_prop_one)
ctcvr_prop = paddle.concat(
x=[1 - ctcvr_prop_one, ctcvr_prop_one], axis=1)
out_list = []
out_list.append(ctr_out)
out_list.append(ctr_prop_one)
out_list.append(cvr_out)
out_list.append(cvr_prop_one)
out_list.append(ctcvr_prop)
out_list.append(ctcvr_prop_one)
if self.counterfact_mode == "DR":
imp_out = output_layers[2]
imp_prop_one = paddle.slice(
imp_out, axes=[1], starts=[1], ends=[2])
out_list.append(imp_prop_one)
return out_list