add config_helper.

doc/api/v2/config/layer.rst

@@ -372,6 +372,11 @@ cos_sim
 ..  autoclass:: paddle.v2.layer.cos_sim
     :noindex:
 
+l2_distance
+-----------
+..  autoclass:: paddle.v2.layer.l2_distance
+    :noindex:
+
 trans
 -----
 ..  autoclass:: paddle.v2.layer.trans

paddle/gserver/layers/L2DistanceLayer.cpp

@@ -25,9 +25,9 @@ bool L2DistanceLayer::init(const LayerMap& layerMap,
   /* Initialize the basic parent class */
   Layer::init(layerMap, parameterMap);
 
-  CHECK_EQ(inputLayers_.size(), 2UL) << "The L2 distance layer accepts two and "
+  CHECK_EQ(inputLayers_.size(), 2UL) << "The L2DistanceLayer accepts two and "
                                      << "only two inputs.";
-  CHECK_EQ(getSize(), 1UL) << "The output dimensionality of L2 distance"
+  CHECK_EQ(getSize(), 1UL) << "The output dimensionality of L2DistanceLayer "
                            << "is fixed to be 1.";
 
   return true;
@@ -41,9 +41,9 @@ void L2DistanceLayer::forward(PassType passType) {
 
   CHECK(inV1 && inV2);
   CHECK_EQ(inV1->getHeight(), inV2->getHeight())
-      << "The height of two inputs to this layer must be the same.";
+      << "The height of two inputs of this layer must be the same.";
   CHECK_EQ(inV1->getWidth(), inV2->getWidth())
-      << "The width of two inputs to this layer must be the same.";
+      << "The width of two inputs of this layer must be the same.";
 
   int batchSize = inV1->getHeight();
   int output_dim = getSize();
@@ -66,22 +66,21 @@ void L2DistanceLayer::forward(PassType passType) {
 void L2DistanceLayer::backward(const UpdateCallback& callback) {
   const auto outG = getOutputGrad();
   const auto outV = getOutputValue();
-  const auto inV1 = getInputValue(0);
-  const auto inV2 = getInputValue(1);
+  CHECK(outG && outV);
+
   auto inGrad1 = getInputGrad(0);
   auto inGrad2 = getInputGrad(1);
-  CHECK(outG && outV && inV1 && inV2 && inGrad1 && inGrad2);
 
   {
     REGISTER_TIMER_INFO("L2DistanceBpAtvTimer", getName().c_str());
 
+    if (inGrad1 || inGrad2) {
       outV->scalarDiv(*outV, 1.);
       outV->dotMul(*outG, *outV);
-
-    if (inGrad1) {
-      inGrad1->addRowScale(0, *inputSub_, *outV);
     }
 
+    if (inGrad1) inGrad1->addRowScale(0, *inputSub_, *outV);
+
     if (inGrad2) {
       inputSub_->mulScalar(-1.);
       inGrad2->addRowScale(0, *inputSub_, *outV);

paddle/gserver/layers/L2DistanceLayer.h

@@ -16,12 +16,11 @@ limitations under the License. */
 
 #include "Layer.h"
 #include "paddle/math/Matrix.h"
-#include "paddle/utils/ThreadLocal.h"
 
 namespace paddle {
 
 /**
- * @brief A layer for calculating l2 distance between the two input vectors.
+ * @brief The layer calculates the l2 distance between two input vectors.
  * \f[
  * f(\bf{x}, \bf{y}) = \sqrt{\sum_{i=1}^D(x_i - y_i)}
  * \f]
@@ -30,13 +29,12 @@ namespace paddle {
  * - Input2: A vector (batchSize * dataDim)
  * - Output: A vector (batchSize * 1)
  *
- * The config file api is l2_distance.
+ * The configuration api is: l2_distance_layer.
  */
 
 class L2DistanceLayer : public Layer {
 public:
   explicit L2DistanceLayer(const LayerConfig& config) : Layer(config) {}
-
   ~L2DistanceLayer() {}
 
   bool init(const LayerMap& layerMap,
@@ -46,7 +44,8 @@ public:
   void backward(const UpdateCallback& callback = nullptr) override;
 
 private:
-  // Store result of subtracting Input2 from Input1.
+  // Store the result of subtracting Input2 from Input1 in forward computation,
+  // which will be reused in backward computation.
   MatrixPtr inputSub_;
 };
 

python/paddle/trainer/config_parser.py

@@ -3330,6 +3330,18 @@ class RowL2NormLayer(LayerBase):
         self.set_layer_size(input_layer.size)
 
 
+@config_layer('cos')
+class CosSimLayer(LayerBase):
+    def __init__(self, name, inputs, cos_scale=1, device=None):
+        super(CosSimLayer, self).__init__(
+            name, 'cos', 1, inputs=inputs, device=device)
+        config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs')
+        config_assert(
+            self.get_input_layer(0).size == self.get_input_layer(1).size,
+            'inputs of CosSimLayer must have same dim')
+        self.config.cos_scale = cos_scale
+
+
 @config_layer('cos_vm')
 class CosSimVecMatLayer(LayerBase):
     def __init__(self, name, size, inputs, cos_scale=1.0, device=None):
@@ -3343,6 +3355,20 @@ class CosSimVecMatLayer(LayerBase):
             'Wrong input size for CosSimVecMatLayer')
 
 
+@config_layer('l2_distance')
+class L2DistanceLayer(LayerBase):
+    def __init__(self, name, inputs, device=None):
+        super(L2DistanceLayer, self).__init__(
+            name, 'l2_distance', 1, inputs=inputs, device=device)
+        config_assert(
+            len(self.inputs) == 2, ('The L2DistanceLayer must have '
+                                    'and only have 2 inputs.'))
+        config_assert(
+            self.get_input_layer(0).size == self.get_input_layer(1).size,
+            ('Two inputs of the L2DistanceLayer must have '
+             'the same dimensionality.'))
+
+
 @config_layer('sampling_id')
 class SamplingIdLayer(LayerBase):
     def __init__(self, name, inputs, device=None):
@@ -3384,18 +3410,6 @@ class AverageLayer(LayerBase):
         self.create_bias_parameter(bias, self.config.size)
 
 
-@config_layer('cos')
-class CosSimLayer(LayerBase):
-    def __init__(self, name, inputs, cos_scale=1, device=None):
-        super(CosSimLayer, self).__init__(
-            name, 'cos', 1, inputs=inputs, device=device)
-        config_assert(len(self.inputs) == 2, 'CosSimLayer must have 2 inputs')
-        config_assert(
-            self.get_input_layer(0).size == self.get_input_layer(1).size,
-            'inputs of CosSimLayer must have same dim')
-        self.config.cos_scale = cos_scale
-
-
 @config_layer('tensor')
 class TensorLayer(LayerBase):
     def __init__(self, name, size, inputs, bias=True, **xargs):

python/paddle/trainer_config_helpers/layers.py

@@ -51,6 +51,7 @@ __all__ = [
     'last_seq',
     'first_seq',
     'cos_sim',
+    'l2_distance_layer',
     'hsigmoid',
     'conv_projection',
     'square_error_cost',
@@ -167,6 +168,7 @@ class LayerType(object):
     COST = 'cost'
     COSINE_SIM_VEC = 'cos_vm'
     COSINE_SIM = 'cos'
+    L2_DISTANCE = 'l2_distance'
     HSIGMOID = 'hsigmoid'
     CONV_LAYER = 'conv'
     CONVTRANS_LAYER = 'convt'
@@ -2332,6 +2334,51 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
     return LayerOutput(name, LayerType.COSINE_SIM, parents=[a, b], size=size)
 
 
+@wrap_name_default()
+@layer_support()
+def l2_distance_layer(x, y, name=None, layer_attr=None):
+    """
+    This layer calculate and return the Euclidean distance between two input
+    vectors a and b. The equation is as follows:
+
+    ..  math::
+        l2_distance(\\mathbf{x}, \\mathbf{y}) = \\sqrt{\\sum_{i=1}^D(x_i - y_i)}
+
+    The output size of this layer is fixed to be 1. Note that the above
+    computation is for one sample. Multiple samples are processed in one batch.
+
+    The example usage is:
+
+    .. code-block:: python
+
+       l2_sim = l2_distance(x=layer1, y=layer2)
+
+    :param name: The name of this layer. It is optional.
+    :type name: basestring
+    :param x: The first input x for this layer, whose output is a matrix with
+              dimensionality N x D. N is the sample number in a mini-batch.
+              D is the dimensionality of x's output.
+    :type x: LayerOutput
+    :param y: The second input y for this layer, whose output is a matrix with
+              dimensionality N x D. N is the sample number in a mini-batch.
+              D is the dimensionality of y's output.
+    :type y: LayerOutput
+    :param layer_attr: The extra layer attributes, for example, drop rate.
+                       See ExtraLayerAttribute for more details.
+    :type layer_attr: ExtraLayerAttribute
+    :return: The returned LayerOutput object.
+    :rtype: LayerOutput
+    """
+
+    assert isinstance(x, LayerOutput) and isinstance(x, LayerOutput)
+    Layer(
+        name=name,
+        type=LayerType.L2_DISTANCE,
+        inputs=[x.name, x.name],
+        **ExtraLayerAttribute.to_kwargs(layer_attr))
+    return LayerOutput(name, LayerType.L2_DISTANCE, parents=[x, y], size=1)
+
+
 @wrap_name_default()
 @wrap_bias_attr_default(has_bias=True)
 @wrap_param_attr_default()

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_roi_pool_layer test_pooling3D_layer
-test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer)
+test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
+test_scale_sub_region_layer test_l2_distance_layer)
 
 export whole_configs=(test_split_datasource)

python/paddle/trainer_config_helpers/tests/configs/protostr/test_l2_distance_layer.protostr

+type: "nn"
+layers {
+  name: "x"
+  type: "data"
+  size: 128
+  active_type: ""
+}
+layers {
+  name: "y"
+  type: "data"
+  size: 128
+  active_type: ""
+}
+layers {
+  name: "__l2_distance_layer_0__"
+  type: "l2_distance"
+  size: 1
+  active_type: ""
+  inputs {
+    input_layer_name: "x"
+  }
+  inputs {
+    input_layer_name: "x"
+  }
+}
+input_layer_names: "x"
+input_layer_names: "y"
+output_layer_names: "__l2_distance_layer_0__"
+sub_models {
+  name: "root"
+  layer_names: "x"
+  layer_names: "y"
+  layer_names: "__l2_distance_layer_0__"
+  input_layer_names: "x"
+  input_layer_names: "y"
+  output_layer_names: "__l2_distance_layer_0__"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/test_l2_distance_layer.py

+from paddle.trainer_config_helpers import *
+
+outputs(
+    l2_distance_layer(
+        x=data_layer(
+            name='x', size=128), y=data_layer(
+                name='y', size=128)))