Merge pull request #4859 from will-am/factorization_machine_layer

Add Factorization Machine Layer

doc/api/v2/config/layer.rst

@@ -415,6 +415,13 @@ multiplex
 ..  autoclass:: paddle.v2.layer.multiplex
     :noindex:
 
+Factorization Machine Layer
+============================
+
+factorization_machine
+---------------------
+..  autoclass:: paddle.v2.layer.factorization_machine
+    :noindex:
 
 Slicing and Joining Layers
 ==========================

paddle/gserver/layers/FactorizationMachineLayer.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "FactorizationMachineLayer.h"
+#include <algorithm>
+#include <vector>
+#include "paddle/math/SparseMatrix.h"
+#include "paddle/utils/Logging.h"
+#include "paddle/utils/Stat.h"
+
+namespace paddle {
+
+REGISTER_LAYER(factorization_machine, FactorizationMachineLayer);
+
+bool FactorizationMachineLayer::init(const LayerMap& layerMap,
+                                     const ParameterMap& parameterMap) {
+  /* Initialize the basic parent class */
+  Layer::init(layerMap, parameterMap);
+
+  factorSize_ = config_.factor_size();
+
+  /* initialize the latentVectors_ */
+  CHECK_EQ(inputLayers_.size(), 1UL);
+  size_t inputSize = inputLayers_[0]->getSize();
+  CHECK_EQ(parameters_[0]->getSize(), inputSize * factorSize_);
+  latentVectors_ = std::unique_ptr<Weight>(
+      new Weight(inputSize, factorSize_, parameters_[0]));
+
+  return true;
+}
+
+void FactorizationMachineLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  const MatrixPtr& inputV = getInputValue(0);
+
+  size_t batchSize = inputV->getHeight();
+  size_t outputSize = getSize();
+  size_t inputSize = inputLayers_[0]->getSize();
+  reserveOutput(batchSize, outputSize);
+
+  MatrixPtr outV = getOutputValue();
+
+  Matrix::resizeOrCreate(
+      latentVectorsSquare_, inputSize, factorSize_, false, useGpu_);
+  Matrix::resizeOrCreate(
+      inputMulFactor_, batchSize, factorSize_, false, useGpu_);
+  Matrix::resizeOrCreate(tmpOut_, batchSize, factorSize_, false, useGpu_);
+
+  REGISTER_TIMER_INFO("FmInputMulFactorTimer", getName().c_str());
+  inputMulFactor_->mul(*inputV, *latentVectors_->getW());
+  inputMulFactor_->square2(*tmpOut_);
+  outV->sumRows(*tmpOut_, 0.5, 0);
+
+  if (dynamic_cast<CpuSparseMatrix*>(inputV.get())) {
+    Matrix::resizeOrCreateSparseMatrix(inputSquare_,
+                                       inputV->getHeight(),
+                                       inputV->getWidth(),
+                                       inputV->getElementCnt(),
+                                       inputV->getValueType());
+    inputSquare_->copyFrom(*inputV);
+    (dynamic_cast<CpuSparseMatrix*>(inputSquare_.get()))->square2();
+  } else {
+    Matrix::resizeOrCreate(
+        inputSquare_, inputV->getHeight(), inputV->getWidth(), false, useGpu_);
+    inputV->square2(*inputSquare_);
+  }
+  latentVectors_->getW()->square2(*latentVectorsSquare_);
+  tmpOut_->mul(*inputSquare_, *latentVectorsSquare_);
+  outV->sumRows(*tmpOut_, -0.5, 1.0);
+
+  /* activation */ {
+    REGISTER_TIMER_INFO("FmFwAtvTimer", getName().c_str());
+    forwardActivation();
+  }
+}
+
+void FactorizationMachineLayer::backward(const UpdateCallback& callback) {
+  /* Do derivation */ { backwardActivation(); }
+
+  const MatrixPtr& inputV = getInputValue(0);
+  const MatrixPtr& oGrad = getOutputGrad();
+
+  Matrix::resizeOrCreate(
+      tmpSum_, 1, latentVectors_->getW()->getHeight(), false, useGpu_);
+  MatrixPtr tmpSumTrans = Matrix::create(tmpSum_->getRowBuf(0),
+                                         latentVectors_->getW()->getHeight(),
+                                         1,
+                                         false,
+                                         useGpu_);
+
+  /* Calculate the gradients of the latentVectors_ matrix */
+  if (latentVectors_->getWGrad()) {
+    if (dynamic_cast<CpuSparseMatrix*>(inputV.get())) {
+      Matrix::resizeOrCreateSparseMatrix(tmpInput_,
+                                         inputV->getHeight(),
+                                         inputV->getWidth(),
+                                         inputV->getElementCnt());
+
+      CpuSparseMatrix* sparseInputV =
+          dynamic_cast<CpuSparseMatrix*>(inputV.get());
+      CpuSparseMatrix* sparseInputSquare =
+          dynamic_cast<CpuSparseMatrix*>(inputSquare_.get());
+      CpuSparseMatrix* sparseTmpInput =
+          dynamic_cast<CpuSparseMatrix*>(tmpInput_.get());
+      sparseTmpInput->copyFrom(*sparseInputV);
+
+      sparseTmpInput->rowScale(0, *sparseInputV, *oGrad);
+      latentVectors_->getWGrad()->mul(
+          *sparseTmpInput->getTranspose(), *inputMulFactor_, 1, 1);
+      sparseTmpInput->rowScale(0, *sparseInputSquare, *oGrad);
+
+      Matrix::resizeOrCreate(negOnes_, 1, inputV->getHeight(), false, useGpu_);
+      negOnes_->zeroMem();
+      negOnes_->add(-1);
+      tmpSum_->mul(*negOnes_, *sparseTmpInput, 1, 0);
+    } else {
+      Matrix::resizeOrCreate(
+          tmpInput_, inputV->getHeight(), inputV->getWidth(), false, useGpu_);
+
+      tmpInput_->rowScale(0, *inputV, *oGrad);
+      latentVectors_->getWGrad()->mul(
+          *tmpInput_->getTranspose(), *inputMulFactor_, 1, 1);
+      tmpInput_->rowScale(0, *inputSquare_, *oGrad);
+
+      tmpSum_->sumCols(*tmpInput_, -1, 0);
+    }
+
+    latentVectors_->getWGrad()->addRowScale(
+        0, *latentVectors_->getW(), *tmpSumTrans);
+
+    /* Increasing the number of gradient */
+    latentVectors_->getParameterPtr()->incUpdate(callback);
+  }
+
+  /* Calculate the input layers gradient */
+  MatrixPtr inGrad = getInputGrad(0);
+  if (inGrad != NULL) {
+    inGrad->mul(
+        *inputMulFactor_, *latentVectors_->getW()->getTranspose(), 1, 1);
+    tmpSumTrans->sumRows(*latentVectorsSquare_, -1, 0);
+    inGrad->addColScale(0, *inputV, *tmpSum_);
+    inGrad->rowScale(0, *inGrad, *oGrad);
+  }
+}
+
+}  // namespace paddle

paddle/gserver/layers/FactorizationMachineLayer.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#pragma once
+
+#include "Layer.h"
+#include "paddle/math/Matrix.h"
+#include "paddle/utils/ThreadLocal.h"
+
+namespace paddle {
+/**
+ * @brief The Factorization Machine models pairwise (order-2) 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. While in principle FM can model higher
+ * order feature interaction, in practice usually only order-2 feature
+ * interactions are considered. The Factorization Machine Layer here only
+ * computes the order-2 interations with the formula:
+ *
+ * \f[
+ *     y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \rangle x_i x_j
+ * \f]
+ *
+ * The detailed calculation for forward and backward can be found at this paper:
+ *
+ *     Factorization machines.
+ *
+ * The config file api is factorization_machine.
+ */
+
+class FactorizationMachineLayer : public Layer {
+protected:
+  // The latent vectors, shape: (size, factorSize_)
+  // Each row of the latentVectors_ matrix is the latent vector
+  // corresponding to one input feature dimension
+  std::unique_ptr<Weight> latentVectors_;
+  // The hyperparameter that defines the dimensionality of the factorization
+  size_t factorSize_;
+
+private:
+  // Store the square values of the letent vectors matrix
+  MatrixPtr latentVectorsSquare_;
+  // Store the square values of input matrix
+  MatrixPtr inputSquare_;
+  // The result of input matrix * latent vector matrix that will be used in
+  // both forward and backward step
+  MatrixPtr inputMulFactor_;
+  // Store temporary calculation result
+  MatrixPtr tmpOut_;
+  MatrixPtr tmpSum_;
+  MatrixPtr tmpInput_;
+  // Negative identity matrix
+  MatrixPtr negOnes_;
+
+public:
+  explicit FactorizationMachineLayer(const LayerConfig& config)
+      : Layer(config) {}
+  ~FactorizationMachineLayer() {}
+
+  bool init(const LayerMap& layerMap,
+            const ParameterMap& parameterMap) override;
+
+  void forward(PassType passType) override;
+  void backward(const UpdateCallback& callback = nullptr) override;
+};
+
+}  // namespace paddle

paddle/gserver/tests/test_LayerGrad.cpp

@@ -2464,6 +2464,25 @@ TEST(Layer, L2DistanceLayer) {
   }
 }
 
+void testFactorizationMachineLayer(InputType type, bool useGpu) {
+  const int FACTOR_SIZE = 10;
+  TestConfig config;
+  config.layerConfig.set_type("factorization_machine");
+  config.layerConfig.set_factor_size(FACTOR_SIZE);
+  config.layerConfig.set_size(1);
+  config.biasSize = 0;
+  config.inputDefs.push_back({type, "layer_0", 128, 1280});
+  config.layerConfig.add_inputs();
+  testLayerGrad(config, "factorization_machine", 16, false, useGpu, false);
+}
+
+TEST(Layer, FactorizationMachineLayer) {
+  for (auto useGpu : {false, true}) {
+    testFactorizationMachineLayer(INPUT_DATA, useGpu);
+  }
+  testFactorizationMachineLayer(INPUT_SPARSE_FLOAT_VALUE_DATA, false);
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);

paddle/math/CpuSparseMatrix.cpp

@@ -260,6 +260,35 @@ void CpuSparseMatrix::printOneRow(std::ostream& os, size_t idx) const {
   os << ";";
 }
 
+void CpuSparseMatrix::rowScale(size_t cCol, CpuSparseMatrix& b, Matrix& c) {
+  CHECK(getFormat() != SPARSE_CSC) << "Not supported";
+  CHECK_EQ(height_, b.getHeight());
+  CHECK_EQ(width_, b.getWidth());
+  real* A = getValue();
+  real* B = b.getValue();
+  if (b.getValueType() == FLOAT_VALUE) {
+    for (size_t i = 0; i < height_; i++) {
+      size_t start = getRowStartIdx(i);
+      size_t end = getRowStartIdx(i + 1);
+      CHECK_EQ(start, b.getRowStartIdx(i));
+      CHECK_EQ(end, b.getRowStartIdx(i + 1));
+      for (size_t j = start; j < end; j++) {
+        A[j] = B[j] * c.getElement(i, cCol);
+      }
+    }
+  } else if (b.getValueType() == NO_VALUE) {
+    for (size_t i = 0; i < height_; i++) {
+      size_t start = getRowStartIdx(i);
+      size_t end = getRowStartIdx(i + 1);
+      CHECK_EQ(start, b.getRowStartIdx(i));
+      CHECK_EQ(end, b.getRowStartIdx(i + 1));
+      for (size_t j = start; j < end; j++) {
+        A[j] = c.getElement(i, cCol);
+      }
+    }
+  }
+}
+
 void CpuSparseMatrix::randomizeUniform() {
   CHECK_LE(elementCnt_, height_ * width_);
   if (valueType_ == FLOAT_VALUE) {

paddle/math/CpuSparseMatrix.h

@@ -239,6 +239,15 @@ public:
               const unsigned int* cols,
               const real* values);
 
+  /**
+   * @brief this_row = b_row * c_row[cCol]
+   *
+   * @param[in]  cCol   the column of matrix c used to scale each row of b
+   * @param[in]  b      CpuSparseMatrix
+   * @param[in]  c      Matrix
+   */
+  void rowScale(size_t cCol, CpuSparseMatrix& b, Matrix& c);
+
   void randomizeUniform();
 
   void copyFrom(const GpuSparseMatrix& src, hl_stream_t stream);

proto/ModelConfig.proto

@@ -544,6 +544,9 @@ message LayerConfig {
   // for batch normalization layer
   // The small constant added to the variance to improve numeric stability.
   optional double epsilon = 60 [ default = 0.00001 ];
+
+  // for factorization machine layer
+  optional uint32 factor_size = 61;
 }
 
 message EvaluatorConfig {

python/paddle/trainer/config_parser.py

@@ -3870,6 +3870,21 @@ class ScaleSubRegionLayer(LayerBase):
                            image_conf.channels)
 
 
+@config_layer('factorization_machine')
+class FactorizationMachineLayer(LayerBase):
+    def __init__(self, name, inputs, factor_size, **xargs):
+        super(FactorizationMachineLayer, self).__init__(
+            name, 'factorization_machine', size=1, inputs=inputs, **xargs)
+        config_assert(
+            len(self.inputs) == 1,
+            'factorization machine layer must have one and only one input.')
+        self.config.factor_size = factor_size
+        input_layer = self.get_input_layer(0)
+        psize = input_layer.size * factor_size
+        dims = [input_layer.size, factor_size]
+        self.create_input_parameter(0, psize, dims)
+
+
 # Deprecated, use a new layer specific class instead
 @config_func
 def Layer(name, type, **xargs):

python/paddle/trainer_config_helpers/layers.py

@@ -148,6 +148,7 @@ __all__ = [
     'resize_layer',
     'sub_seq_layer',
     'scale_sub_region_layer',
+    'factorization_machine',
 ]
 
 
@@ -264,6 +265,8 @@ class LayerType(object):
 
     SCALE_SUB_REGION_LAYER = 'scale_sub_region'
 
+    FACTORIZATION_MACHINE = 'factorization_machine'
+
     @staticmethod
     def is_layer_type(type_name):
         """
@@ -7403,3 +7406,73 @@ def scale_sub_region_layer(input, indices, value, name=None):
         parents=[input, indices],
         num_filters=input.num_filters,
         size=input.size)
+
+
+@wrap_name_default()
+@wrap_act_default(act=LinearActivation())
+@wrap_param_attr_default()
+@layer_support()
+def factorization_machine(input,
+                          factor_size,
+                          act=None,
+                          name=None,
+                          param_attr=None,
+                          layer_attr=None):
+    """
+    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.
+
+    This implementation only consider the 2-order feature interactions using
+    Factorization Machine with the formula:
+
+    .. math::
+        y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \rangle x_i x_j
+
+    Note:
+        X is the input vector with size n. V is the factor matrix. Each row of V
+        is the latent vector corresponding to each input dimesion. The size of
+        each latent vector is k.
+
+    For details of Factorization Machine, please refer to the paper:
+    Factorization machines.
+
+    .. code-block:: python
+        first_order = paddle.layer.fc(input=input,
+                                      size=1,
+                                      act=paddle.activation.Linear())
+        second_order = paddle.layer.factorization_machine(input=input,
+                                                          factor_size=10)
+        fm = paddle.layer.addto(input=[first_order, second_order],
+                                act=paddle.activation.Linear(),
+                                bias_attr=False)
+
+    :param input: The input layer. Supported input types: all input data types
+                  on CPU, and only dense input types on GPU.
+    :type input: LayerOutput
+    :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.
+    :type act: BaseActivation
+    :param param_attr: The parameter attribute. See ParameterAttribute for
+                       details.
+    :type param_attr: ParameterAttribute
+    :param layer_attr: Extra Layer config.
+    :type layer_attr: ExtraLayerAttribute|None
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    assert isinstance(input, LayerOutput)
+    assert factor_size > 0, "the factor_size must be greater than 0."
+
+    Layer(
+        inputs=[Input(input.name, **param_attr.attr)],
+        name=name,
+        factor_size=factor_size,
+        type=LayerType.FACTORIZATION_MACHINE,
+        active_type=act.name,
+        **ExtraLayerAttribute.to_kwargs(layer_attr))
+    return LayerOutput(
+        name, LayerType.FACTORIZATION_MACHINE, input, activation=act, size=1)

python/paddle/trainer_config_helpers/tests/configs/file_list.sh

@@ -11,6 +11,7 @@ test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_l
 test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
 test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
 test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
-test_scale_sub_region_layer test_dot_prod_layer test_l2_distance_layer)
+test_scale_sub_region_layer test_dot_prod_layer test_l2_distance_layer
+test_factorization_machine)
 
 export whole_configs=(test_split_datasource)

python/paddle/trainer_config_helpers/tests/configs/protostr/test_factorization_machine.protostr

+type: "nn"
+layers {
+  name: "data"
+  type: "data"
+  size: 1024
+  active_type: ""
+}
+layers {
+  name: "__factorization_machine_0__"
+  type: "factorization_machine"
+  size: 1
+  active_type: ""
+  inputs {
+    input_layer_name: "data"
+    input_parameter_name: "___factorization_machine_0__.w0"
+  }
+  factor_size: 10
+}
+parameters {
+  name: "___factorization_machine_0__.w0"
+  size: 10240
+  initial_mean: 0.0
+  initial_std: 0.03125
+  dims: 1024
+  dims: 10
+  initial_strategy: 0
+  initial_smart: true
+}
+input_layer_names: "data"
+output_layer_names: "__factorization_machine_0__"
+sub_models {
+  name: "root"
+  layer_names: "data"
+  layer_names: "__factorization_machine_0__"
+  input_layer_names: "data"
+  output_layer_names: "__factorization_machine_0__"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/test_factorization_machine.py

+from paddle.trainer_config_helpers import *
+
+data = data_layer(name='data', size=1024)
+
+fm = factorization_machine(input=data, factor_size=10)
+
+outputs(fm)