推荐系统(十六)多任务学习:腾讯PLE模型(Progressive Layered Extraction model)
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推荐系统(十六)多任务学习:腾讯PLE模型(Progressive Layered Extraction model)
推荐系统系列博客:
- 推荐系统(一)推荐系统整体概览
- 推荐系统(二)GBDT+LR模型
- 推荐系统(三)Factorization Machines(FM)
- 推荐系统(四)Field-aware Factorization Machines(FFM)
- 推荐系统(五)wide&deep
- 推荐系统(六)Deep & Cross Network(DCN)
- 推荐系统(七)xDeepFM模型
- 推荐系统(八)FNN模型(FM+MLP=FNN)
- 推荐系统(九)PNN模型(Product-based Neural Networks)
- 推荐系统(十)DeepFM模型
- 推荐系统(十一)阿里深度兴趣网络(一):DIN模型(Deep Interest Network)
- 推荐系统(十二)阿里深度兴趣网络(二):DIEN模型(Deep Interest Evolution Network)
- 推荐系统(十三)阿里深度兴趣网络(三):DSIN模型(Deep Session Interest Network)
- 推荐系统(十四)多任务学习:阿里ESMM(完整空间多任务模型)
- 推荐系统(十五)多任务学习:谷歌MMoE(Multi-gate Mixture-of-Experts )
PLE模型是腾讯发表在RecSys ’20上的文章,这篇paper获得了recsys’20的best paper award,也算为腾讯脱离技术贫民的大业添砖加瓦了。这篇文章号称极大的缓解了多任务学习中存在的两大顽疾:负迁移(negative transfer)现象和跷跷板(seesaw phenomenon),由此带来了相比较其他MTL模型比较大的性能提升。从论文呈现的实验结果也确实是这样的,但从模型结构上来看,更像是大力出奇迹,即性能的提升是由参数量变多而带来的(仅仅是个人看法~)。这篇paper能拿best paper,一方面是实验结果所呈现出来的比较大的性能提升,另一方面是数据分析做的很好,实验做的也很全,因此看起来工作做的很扎实,这是非常值得学习的地方。
本篇博客依然延续之前博客的大纲,会从动机、模型细节结构、代码实现来介绍PLE模型。
一、动机
说到动机,先来说说多任务学习领域中存在的两大问题:
- 负迁移(negative transfer):MTL提出来的目的是为了不同任务,尤其是数据量较少的任务可以借助transfer learning(通过共享embedding,当然你也可以不仅共享embedding,再往上共享基层全连接网络等等这些很常见的操作)。但经常事与愿违,当两个任务之间的相关性很弱(比如一个任务是判断一张图片是否是狗,另一个任务是判断是否是飞机)或者非常复杂时,往往发生负迁移,即共享了之后效果反而很差,还不如不共享。
- 跷跷板现象:还是当两个task之间相关性很弱或者很复杂时,往往出现的现象是:一个task性能的提升是通过损害另一个task的性能做到的。这种现象存在很久,PLE论文里给它起了个非常贴切的名字『跷跷板』,想象一下你小时候玩跷跷板的情形吧,胖子把瘦子跷起来。
PLE这篇论文,通过大量的实验发现,当前的MTL模型跷跷板现象非常严重(从数据实验中发现问题,进而提出解决办法,会让你的motivation显得非常扎实)。如下图所示,这里的VTR(View-Through Rate)是有效观看率,其定义是用户观看某个视频超过一定时间即认为是一次有效观看,所以是个二分类任务;VCR(View Completion Ratio)是视频观看完成率,是一个回归任务。下面这个图越靠近右上角说明模型在两个task上表现都比较好,左下角则是都很差,因此明显看出,目前MTL领域中主流的模型基本上都存在跷跷板问题(至少在腾讯视频的数据场景下),表现比较好的也就是上一篇博客中介绍的谷歌的MMoE,但还是存在。
二、PLE模型细节
先来从整体上看看这个模型,大家自行对比下MMoE(参见我的博客推荐系统(十五)多任务学习:谷歌MMoE(Multi-gate Mixture-of-Experts )),可能就能体会到我前面说PLE性能提升更像是复杂参数所带来的这句话了,因为粗略的看,PLE做了deep化(expert完后再来一层expert),显然要比浅层的效果来得好。
我们来自底向上的拆解下PLE:
2.1、Extraction Network
PLE这里相比较MMoE做了比较大的创新,在MMoE里,不同task通过gate(网络)共享相同的expert(网络),而PLE中则把expert分为了两种:共享的expert(即上图中的experts Shared)和每个task单独的expert(task-specific experts)。因此,这种设计即保留了transfer learning(通过共享expert)能力,又能够避免有害参数的干扰(避免negative transfer)。
同样的,在gate网络部分,也分为了task-specific和Shared,以上图左边的这个gate(即上图中红色的gate)为例,它的输入有两部分,分别为Experts A和Experts Shared。而shared gate(上图中蓝颜色的gate)的输入则为三部分:Experts A、Experts Shared和Experts B。最终三部分直接作为下一层的输入,分别对应到下一层的Experts A、Experts Shared和Experts B。有一说一,图figure5如果把中间那个extraction network去掉,会更加清晰。算了,原来想偷个懒,不想画图的,我还是上个简化版的图吧。
额,上完这个简化版的图后,我觉得都不用在写文字了…因为再多的文字也是苍白。
2.2、其他一些细节
- 腾讯视频rerank模块的公式为: s c o r e = P V T R W V T R × P V C R W V C R × P S H R W S H R × . . . × P C M R W C M R × f ( v i d e o _ l e n ) score=P_VTR^W_VTR \\times P_VCR^W_VCR \\times P_SHR^W_SHR \\times ... \\timesP_CMR^W_CMR \\times f(video\\_len) score=PVTRWVTR×PVCRWVCR×PSHRWSHR×...×PCMRWCMR×f(video_len),其中,VCR(View Completion Ratio)为视频观看完成率,VTR(View- Through Rate)为视频是否为有效观看,CMR(Comment Rate)为评论率,SHR(Share Rate)为分享率。 W W W为权重因子,用于调整每个指标的权重。
- 关于loss函数,每个task的loss加了个可学习的权重参数 w w w,用于模型自动学习每个task的loss的权重。
三、代码实现
老规矩,直接上paddle给出的实现代码,我这里把向量维度加了注释,方便理解。
组网代码:
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__()
"""
feature_size: 499
"""
self.task_num = task_num # 2
self.exp_per_task = exp_per_task # 3
self.shared_num = shared_num # 2
self.expert_size = expert_size # 16
self.tower_size = tower_size # 8
self.level_number = level_number # 2
# ple layer
"""
ple_layers:
[SinglePLELayer(
(lev_0_exp_0_0): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_0_0)
(lev_0_exp_0_1): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_0_1)
(lev_0_exp_0_2): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_0_2)
(lev_0_exp_1_0): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_1_0)
(lev_0_exp_1_1): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_1_1)
(lev_0_exp_1_2): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_1_2)
(lev_0_exp_shared_0): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_shared_0)
(lev_0_exp_shared_1): Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_shared_1)
(lev_0_gate_0): Linear(in_features=499, out_features=5, dtype=float32, name=lev_0_gate_0)
(lev_0_gate_1): Linear(in_features=499, out_features=5, dtype=float32, name=lev_0_gate_1)
(lev_0_gate_shared_): Linear(in_features=499, out_features=8, dtype=float32, name=lev_0_gate_shared_)
(_param_gate_shared): Linear(in_features=499, out_features=8, dtype=float32, name=lev_0_gate_shared_)),
SinglePLELayer(
(lev_1_exp_0_0): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_0_0)
(lev_1_exp_0_1): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_0_1)
(lev_1_exp_0_2): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_0_2)
(lev_1_exp_1_0): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_1_0)
(lev_1_exp_1_1): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_1_1)
(lev_1_exp_1_2): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_1_2)
(lev_1_exp_shared_0): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_shared_0)
(lev_1_exp_shared_1): Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_shared_1)
(lev_1_gate_0): Linear(in_features=16, out_features=5, dtype=float32, name=lev_1_gate_0)
(lev_1_gate_1): Linear(in_features=16, out_features=5, dtype=float32, name=lev_1_gate_1))]
"""
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):
# [16, 8]
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)
# [8, 2]
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):
"""
input_data: Tensor(shape=[2, 499], 2--->batch_size
"""
inputs_ple = []
# task_num part + shared part
# task_num-->2
for i in range(0, self.task_num + 1):
inputs_ple.append(input_data)
# multiple ple layer
ple_out = []
# level_number-->2
for i in range(0, self.level_number):
print "^^^^^number: %d^^^^^^"%i
ple_out = self.ple_layers[i](inputs_ple)
inputs_ple = ple_out
# ple_out, [Tensor(shape=[2, 16], Tensor(shape=[2, 16]]
#assert len(ple_out) == self.task_num
output_layers = []
for i in range(0, self.task_num):
# Tensor(shape=[2, 8]
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__()
# input_feature_size: first layer: 499, second layer=expert_size-->16
self.task_num = task_num # 2
self.exp_per_task = exp_per_task # 3
self.shared_num = shared_num # 2
self.expert_size = expert_size # 16
self.if_last = if_last # 1
self._param_expert = []
# task-specific expert part
for i in range(0, self.task_num):
for j in range(0, self.exp_per_task):
# [499, 16]
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):
# [499, 16]
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):
# [499, 5]
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:
# 8
cur_expert_num = self.task_num * self.exp_per_task + self.shared_num
# [499, 8]
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):
print "****SinglePLELayer forward****"
expert_outputs = []
# task-specific expert part
# task_num: 2
# exp_per_task: 3
"""
_param_expert: level0:
[Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_0_0),
Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_0_1),
Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_0_2),
Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_1_0),
Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_1_1),
Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_1_2),
Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_shared_0),
Linear(in_features=499, out_features=16, dtype=float32, name=lev_0_exp_shared_1)]
level1:
[Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_0_0),
Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_0_1),
Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_0_2),
Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_1_0),
Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_1_1),
Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_1_2),
Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_shared_0),
Linear(in_features=16, out_features=16, dtype=float32, name=lev_1_exp_shared_1)]
"""
# [0, 1]
for i in range(0, self.task_num):
for j in range(0, self.exp_per_task): # [0,1,2]
# 0,1,2, 2,3,4
linear_out = self._param_expert[i * self.task_num + j]( # bug, task_num-->exp_per_task
input_data[i])
# Tensor(shape=[2, 16]
expert_output = F.relu(linear_out)
expert_outputs.append(expert_output)
# shared expert part
# shared_num: 2
# [0, 1]
for i in range(0, self.shared_num):
# self.exp_per_task * self.task_num = 3*2
linear_out = self._param_expert[self.exp_per_task * self.task_num + i](input_data[-1])
# Tensor(shape=[2, 16]
expert_output = F.relu(linear_out)
expert_outputs.append(expert_output)
# task gate part
outputs = []
"""
self._param_gate:
[Linear(in_features=499, out_features=5, dtype=float32, name=lev_0_gate_0),
Linear(in_features=499, out_features=5, dtype=float32, name=lev_0_gate_1)]
"""
# [0, 1]
for i in range(0, self.task_num)推荐系统(十六)多任务学习:腾讯PLE模型(Progressive Layered Extraction model)