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Add Factorization Machine Layer #4859

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merged 39 commits into from
Nov 27, 2017

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@will-am will-am commented Oct 17, 2017

Resolve #4628 #3664 #3971

const MatrixPtr& inputV = getInputValue(0);

size_t batchSize = inputV->getHeight();
size_t size = getSize();
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what is getSize mean? I cannot validate this snippet of code without your comment.

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getSize returns the output size of this layer.

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请按照上面 @dzhwinter 的comment,为变量起一个更有意义的名字。

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已改为outputSize

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some simple comments first.

factorSize_ = config_.factor_size();

/* initialize the latentVectors_ */
CHECK_EQ(inputLayers_.size(), 1UL);
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35 ~ 40 行不要在 init 里面做,移到 forward 里面。

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已改

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已改

latentVectors_ =
std::unique_ptr<Weight>(new Weight(height, factorSize_, parameters_[0]));

v2_ = Matrix::create(height, factorSize_, false, useGpu_);
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v2_ 这个命名不可读,请使用有意义更可读的名字。

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已改为latentVectorsSquare_

const MatrixPtr& inputV = getInputValue(0);

size_t batchSize = inputV->getHeight();
size_t size = getSize();
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请按照上面 @dzhwinter 的comment,为变量起一个更有意义的名字。

Matrix::resizeOrCreate(tmpMul_, batchSize, factorSize_, false, useGpu_);
Matrix::resizeOrCreate(tmpOut_, batchSize, factorSize_, false, useGpu_);

REGISTER_TIMER_INFO("FwMulTimer", getName().c_str());
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如果要使用 REGISTER_TIMER_INFO 第一个参数是 Timer的名字,这里是从 FC copy过来的吧,请把名字改一下。

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已改

outV->sumRows(*tmpOut_, -0.5, 1.0);

/* activation */ {
REGISTER_TIMER_INFO("FwAtvTimer", getName().c_str());
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请改一下Timer的名字。

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已改


void FactorizationMachineLayer::backward(const UpdateCallback& callback) {
/* Do derivation */ {
REGISTER_TIMER_INFO("BpAvtTimer", getName().c_str());
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请注意改一下Timer的名字。

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已改

CpuSparseMatrix* x2_s = dynamic_cast<CpuSparseMatrix*>(x2_.get());
CpuSparseMatrix* tmpIn_s = dynamic_cast<CpuSparseMatrix*>(tmpIn.get());
tmpIn_s->copyFrom(*inputV_s);
tmpIn_s->rowScale(0, *inputV_s, *oGrad);
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inputV_s x2_s tmpIn_s inputV_s 这些命名的风格不统一,请按照layers里面的风格进行统一。并且,这些变量的命名不可读,请考虑使用有意义的名字。

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已改为sparseInputV, sparseInputSquare, sparseTmpInput

latentVectors_->getWGrad()->mul(*tmpIn_s->getTranspose(), *tmpMul_, 1, 1);
tmpIn_s->rowScale(0, *x2_s, *oGrad);

MatrixPtr ones = Matrix::create(1, inputV->getHeight(), false, useGpu_);
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把临时变量ones变成员变量。

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已改

if (inGrad != NULL) {
MatrixPtr latentVectors_T = latentVectors_->getW()->getTranspose();
inGrad->mul(*tmpMul_, *latentVectors_T, 1, 1);
tmpSum_T->sumRows(*v2_, -1, 0);
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tmpSum_T请修改一下变量的命名风格。

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已改为tmpSumTrans

config.biasSize = 0;
config.inputDefs.push_back({type, "layer_0", 128, 1280});
config.layerConfig.add_inputs();
testLayerGrad(config, "factorization_machine", 16, false, useGpu, false);
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@lcy-seso lcy-seso Nov 14, 2017

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SparseMatrix 作为输时请添加单测。

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已加

}

void FactorizationMachineLayer::forward(PassType passType) {
Layer::forward(passType);
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不支持GPU上运行请加检查并提示错误。

*
* \f[
* y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \rangle x_i x_j
* \f]
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You can cite the inference paper here.

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已加

for (size_t i = 0; i < height_; i++) {
size_t start = getRowStartIdx(i);
size_t end = getRowStartIdx(i + 1);
CHECK(start == b.getRowStartIdx(i));
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CHECK --> CHECK_EQ

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已改

The Factorization Machine can effectively capture feature interactions
especially when the input is sparse. In practice, usually order 2 feature
interactions are considered using Factorization Machine with the formula:
.. math::
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line 7166 之前空一行。
line 7167 之后空一行,否则公式无法正常显示。

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已加

The Factorization Machine models pairwise feature interactions as inner
product of the learned latent vectors corresponding to each input feature.
The Factorization Machine can effectively capture feature interactions
especially when the input is sparse. In practice, usually order 2 feature
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  • usually order 2 feature --> this implementation only consider the 2-order feature interactions.
  • 请在注释中增加一下对FM层实现所参考的原论文的引用。

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已加

is the latent vector corresponding to each input dimesion. The size of
each latent vector is k.
.. code-block:: python
factor_machine = factorization_machine(input=input_layer, factor_size=10)
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@lcy-seso lcy-seso Nov 14, 2017

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7172 行之前空一行,
7173 行之后空一行。

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已改

@@ -540,6 +540,9 @@ message LayerConfig {

// for switch order layer
optional ReshapeConfig reshape_conf = 59;

// for factorization machine layer
optional uint32 factor_size = 60;
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为什么不能复用 Layer 的size,而新定义这个字段。

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Layer的size是输出的维度,而这个是内部使用的隐变量(factor)的维度

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要是用size感觉会有歧义

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抱歉,我理解错误。忽略。

:param layer_attr: Extra Layer config.
:type layer_attr: ExtraLayerAttribute|None
:return: LayerOutput object.
:rtype: LayerOutput
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请在comment和示例代码中注明这一层本身并不是 FM,只是完成二阶特征组合部分。需要和其它层配置使用,在simple code 部分给出一个完整的示例。

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已加



@wrap_name_default()
@wrap_act_default(act=LinearActivation())
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这里只可以使用非线性激活函数吧。如果从原理上不能使用非线性激活,就把激活写死,不要让用户来设置了。

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可以用非线性的~

especially when the input is sparse.

This implementation only consider the 2-order feature interactions using
Factorization Machine with the formula:
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  1. 除了这个注释之外,在 7426 行加一个完整的配置,方便用户看到这一层的文档时,能够写出来一个完整的 FM 模型。
  2. 注释一下支持的 input 类型和不支持的类型。

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好的,我加下

:param input: The input layer.
:type input: LayerOutput
:param factor_size: The hyperparameter that defines the dimensionality of
the latent vector size
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句末加上句号。

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好的

:param factor_size: The hyperparameter that defines the dimensionality of
the latent vector size
:type context_len: int
:param act: Activation Type. Default is linear activation.
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原理上这里可以使用非线性激活吗?应该不可以吧。

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可以的~

:param act: Activation Type. Default is linear activation.
:type act: BaseActivation
:param param_attr: The Parameter Attribute. If None, the latent vectors will
be initialized smartly. It's better to set it by
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作为注释,还是解释一下 “be initialized smartly” 到底是怎样初始化的。

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好的

Matrix::resizeOrCreate(negOnes_, 1, inputV->getHeight(), false, useGpu_);
negOnes_->zeroMem();
negOnes_->add(-1);
tmpSum_->mul(*negOnes_, *sparseTmpInput, 1, 0);
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// this = scaleAB*(a*b) + scaleT*this
mul(const Matrix& a, const Matrix& b, real scaleAB, real scaleT)

125 ~ 127 行为什么不能是:

ones_->ones();
tmpSum_->mul(*ones_, *sparseTmpInput, -1, 0);

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CHECK_EQ(scaleAB, static_cast<real>(1.0));

因为b是sparse的时候mul只支持scaleAB是1,不支持其他value


/* activation */ {
REGISTER_TIMER_INFO("FmFwAtvTimer", getName().c_str());
forwardActivation();
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FM 层可以加非线性激活吗?如果原理上不可以(我记得不可以,可以再确认下),这里可以删掉。如果允许,就保留。

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可以加非线性的激活~

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@lcy-seso lcy-seso Nov 27, 2017

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这里算的只是二阶交叉项,你的意思是如果我在二阶交叉项使用非线性激活A,一阶项使用非线性激活B,这样也可以吗 ?

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虽然没有看到这样用的,但理论上应该是可以的~

@will-am will-am merged commit 95cdbfe into PaddlePaddle:develop Nov 27, 2017
@will-am will-am deleted the factorization_machine_layer branch November 27, 2017 14:09
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3 participants