Add framework of the factorization machine layer

doc/api/v2/config/layer.rst

@@ -395,6 +395,13 @@ 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 height = inputLayers_[0]->getSize();
+  latentVectors_.reset(new Weight(height, factorSize_, parameters_[0]));
+
+  return true;
+}
+
+void FactorizationMachineLayer::forward(PassType passType) {
+  Layer::forward(passType);
+
+  auto input = getInput(0);
+
+  int batchSize = input.getBatchSize();
+  int size = getSize();
+  reserveOutput(batchSize, size);
+
+  MatrixPtr outV = getOutputValue();
+
+  /* activation */ {
+    REGISTER_TIMER_INFO("FwAtvTimer", getName().c_str());
+    forwardActivation();
+  }
+}
+
+void FactorizationMachineLayer::backward(const UpdateCallback& callback) {
+  /* Do derivation */ {
+    REGISTER_TIMER_INFO("BpAvtTimer", getName().c_str());
+    backwardActivation();
+  }
+}
+
+}  // 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 config file api is factorization_machine.
+ */
+
+class FactorizationMachineLayer : public Layer {
+protected:
+  /// The latent vectors, shape: (size, factorSize_)
+  std::unique_ptr<Weight> latentVectors_;
+  /// The hyperparameter that defines the dimensionality of the factorization
+  size_t factorSize_;
+
+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

@@ -2359,6 +2359,25 @@ TEST(Layer, ScaleShiftLayer) {
   }
 }
 
+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.biasSize = 1;
+  config.inputDefs.push_back({type, "layer_0", 8192, 0});
+  config.layerConfig.add_inputs();
+  testLayerGrad(config, "factorization_machine", 16, false, useGpu, false);
+}
+
+TEST(Layer, FactorizationMachineLayer) {
+  testFactorizationMachineLayer(INPUT_DATA, false);
+  testFactorizationMachineLayer(INPUT_SPARSE_FLOAT_VALUE_DATA, false);
+#ifdef PADDLE_WITH_CUDA
+  testFactorizationMachineLayer(INPUT_DATA, true);
+#endif
+}
+
 int main(int argc, char** argv) {
   testing::InitGoogleTest(&argc, argv);
   initMain(argc, argv);

proto/ModelConfig.proto

@@ -525,6 +525,9 @@ message LayerConfig {
 
   // for switch order layer
   optional ReshapeConfig reshape_conf = 59;
+
+  // for factorization machine layer
+  optional uint32 factor_size = 60;
 }
 
 message EvaluatorConfig {

python/paddle/trainer/config_parser.py

@@ -3780,6 +3780,21 @@ class SwitchOrderLayer(LayerBase):
         self.config.reshape_conf.width_axis.extend(reshape['width'])
 
 
+@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, 1]
+        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

@@ -143,6 +143,7 @@ __all__ = [
     'scale_shift_layer',
     'img_conv3d_layer',
     'resize_layer',
+    'factorization_machine',
 ]
 
 
@@ -253,6 +254,8 @@ class LayerType(object):
 
     RESIZE = 'resize'
 
+    FACTORIZATION_MACHINE = 'factorization_machine'
+
     @staticmethod
     def is_layer_type(type_name):
         """
@@ -6955,3 +6958,65 @@ def resize_layer(input, size, name=None):
     """
     Layer(name=name, type=LayerType.RESIZE, inputs=Input(input.name), size=size)
     return LayerOutput(name, LayerType.RESIZE, parents=[input], 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. In practice, usually order 2 feature
+    interactions are considered 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.
+
+    .. code-block:: python
+
+       factor_machine = factorization_machine(input=input_layer, factor_size=10)
+
+    :param input: The input layer.
+    :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. If None, the latent vectors will
+                       be initialized smartly. It's better to set it by
+                       yourself.
+    :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

@@ -10,6 +10,7 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la
 test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
 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_pooling3D_layer
-test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer)
+test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_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: 1
+  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 *
+
+settings(batch_size=1000, learning_rate=1e-5)
+
+data = data_layer(name='data', size=1024)
+
+fm = factorization_machine(input=data, factor_size=10)
+
+outputs(fm)