Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into topology

demo/sentiment/dataprovider.py

@@ -32,4 +32,6 @@ def process(settings, file_name):
             word_slot = [
                 settings.word_dict[w] for w in words if w in settings.word_dict
             ]
+            if not word_slot:
+                continue
             yield word_slot, label

demo/sentiment/predict.py

@@ -138,7 +138,11 @@ def main():
 
     batch = []
     for line in sys.stdin:
-        batch.append([predict.get_index(line)])
+        words = predict.get_index(line)
+        if words:
+            batch.append([words])
+        else:
+            print('All the words in [%s] are not in the dictionary.' % line)
         if len(batch) == batch_size:
             predict.batch_predict(batch)
             batch = []

doc/api/trainer_config_helpers/layers.rst

@@ -279,6 +279,12 @@ concat_layer
     :members: concat_layer
     :noindex:
 
+seq_concat_layer
+----------------
+..  automodule:: paddle.trainer_config_helpers.layers
+    :members: seq_concat_layer
+    :noindex:
+
 Reshaping Layers
 ================
 

doc/design/reader/README.md

@@ -107,7 +107,7 @@ We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["imag
 ### How to create custom data reader
 
 ```python
-def image_reader(image_path, label_path):
+def image_reader(image_path, label_path, n):
 	f = open(image_path)
 	l = open(label_path)
 	images = numpy.fromfile(
@@ -117,9 +117,10 @@ def image_reader(image_path, label_path):
 	for i in xrange(n):
 		yield images[i, :], labels[i] # a single entry of data is created each time
 	f.close()
+	l.close()
 
 # use python lambda to change image_reader into a function with no parameters.
-reader = lambda : image_reader("/path/to/image_file", "/path/to/label_file")
+reader = lambda : image_reader("/path/to/image_file", "/path/to/label_file", 1024)
 paddle.train(reader, {"image":0, "label":1}, ...)
 ```
 

paddle/gserver/evaluators/Evaluator.cpp

@@ -866,21 +866,20 @@ void PnpairEvaluator::calc(std::vector<PredictionResult>& predictArray) {
 
 ClassRegistrar<Evaluator> Evaluator::registrar_;
 Evaluator* Evaluator::create(const EvaluatorConfig& config) {
-  Evaluator* evaluator = nullptr;
-  if (config.type() == "classification_error") {
-    evaluator = new ClassificationErrorEvaluator();
-  } else if (config.type() == "sum") {
-    evaluator = new SumEvaluator();
-  } else if (config.type() == "last-column-sum") {
-    evaluator = new ColumnSumEvaluator(-1);
-  } else if (config.type() == "last-column-auc") {
-    evaluator = new AucEvaluator(-1);
-  } else {
-    evaluator = registrar_.createByType(config.type());
-  }
+  Evaluator* evaluator = registrar_.createByType(config.type());
   evaluator->init(config);
   return evaluator;
 }
+
+REGISTER_EVALUATOR(classification_error, ClassificationErrorEvaluator);
+REGISTER_EVALUATOR(sum, SumEvaluator);
+static InitFunction __reg_type_auc_sum__([]() {
+  Evaluator::registrar_.registerClass(
+      "last-column-sum", [] { return new ColumnSumEvaluator(-1); });
+  Evaluator::registrar_.registerClass("last-column-auc",
+                                      [] { return new AucEvaluator(-1); });
+});
+
 /**
  * @brief print value of each layer.
  *
@@ -888,32 +887,10 @@ Evaluator* Evaluator::create(const EvaluatorConfig& config) {
  */
 class ValuePrinter : public Evaluator {
 public:
-  ValuePrinter() {}
-
   virtual void eval(const NeuralNetwork& nn) {
     for (const std::string& name : config_.input_layers()) {
-      const Argument& argu = nn.getLayer(name)->getOutput();
-      if (argu.value) {
-        std::ostringstream os;
-        argu.value->print(os);
-        LOG(INFO) << "layer=" << name << " value matrix:\n" << os.str();
-      }
-      if (argu.ids) {
-        std::ostringstream os;
-        argu.ids->print(os, argu.ids->getSize());
-        LOG(INFO) << "layer=" << name << " ids vector:\n" << os.str();
-      }
-      if (auto startPos = argu.sequenceStartPositions) {
-        std::ostringstream os;
-        startPos->getVector(false)->print(os, startPos->getSize());
-        LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str();
-      }
-      if (auto subStartPos = argu.subSequenceStartPositions) {
-        std::ostringstream os;
-        subStartPos->getVector(false)->print(os, subStartPos->getSize());
-        LOG(INFO) << "layer=" << name << " sub-sequence pos vector:\n"
-                  << os.str();
-      }
+      nn.getLayer(name)->getOutput().printValueString(LOG(INFO),
+                                                      "layer=" + name + " ");
     }
   }
 
@@ -929,8 +906,6 @@ REGISTER_EVALUATOR(value_printer, ValuePrinter);
  */
 class GradientPrinter : public Evaluator {
 public:
-  GradientPrinter() {}
-
   virtual void eval(const NeuralNetwork& nn) {
     for (const std::string& name : config_.input_layers()) {
       const Argument& argu = nn.getLayer(name)->getOutput();
@@ -939,11 +914,6 @@ public:
         argu.grad->print(os);
         LOG(INFO) << "layer=" << name << " grad matrix:\n" << os.str();
       }
-      if (auto startPos = argu.sequenceStartPositions) {
-        std::ostringstream os;
-        startPos->getVector(false)->print(os, startPos->getSize());
-        LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str();
-      }
     }
   }
 

paddle/gserver/layers/PrintLayer.cpp

@@ -19,38 +19,17 @@ namespace paddle {
 class PrintLayer : public Layer {
 public:
   explicit PrintLayer(const LayerConfig& config) : Layer(config) {}
-  void forward(PassType passType) override;
-  void backward(const UpdateCallback& callback) override {}
-};
 
-void PrintLayer::forward(PassType passType) {
-  Layer::forward(passType);
-  for (size_t i = 0; i != inputLayers_.size(); ++i) {
-    const auto& argu = getInput(i);
-    const std::string& name = inputLayers_[i]->getName();
-    if (argu.value) {
-      std::ostringstream os;
-      argu.value->print(os);
-      LOG(INFO) << "layer=" << name << " value matrix:\n" << os.str();
-    }
-    if (argu.ids) {
-      std::ostringstream os;
-      argu.ids->print(os, argu.ids->getSize());
-      LOG(INFO) << "layer=" << name << " ids vector:\n" << os.str();
-    }
-    if (auto startPos = argu.sequenceStartPositions) {
-      std::ostringstream os;
-      startPos->getVector(false)->print(os, startPos->getSize());
-      LOG(INFO) << "layer=" << name << " sequence pos vector:\n" << os.str();
-    }
-    if (auto subStartPos = argu.subSequenceStartPositions) {
-      std::ostringstream os;
-      subStartPos->getVector(false)->print(os, subStartPos->getSize());
-      LOG(INFO) << "layer=" << name << " sub-sequence pos vector:\n"
-                << os.str();
+  void forward(PassType passType) override {
+    Layer::forward(passType);
+    for (size_t i = 0; i != inputLayers_.size(); ++i) {
+      getInput(i).printValueString(LOG(INFO),
+                                   "layer=" + inputLayers_[i]->getName() + " ");
     }
   }
-}
+
+  void backward(const UpdateCallback& callback) override {}
+};
 
 REGISTER_LAYER(print, PrintLayer);
 

paddle/gserver/layers/SequenceConcatLayer.cpp

@@ -21,9 +21,11 @@ namespace paddle {
 
 /**
  * A layer for concatenating the first sequence with the second sequence
- * following the first
- * Input: two sequences each containing some instances
+ * Input: two sequences each containing the same number of instances
+ *        seq1 = [a1, a2, ..., an]
+ *        seq2 = [b1, b2, ..., bn]
  * Output: a concatenated sequence of the two input sequences
+ *        out = [a1, b1, a2, b2, ..., an, bn]
  */
 
 class SequenceConcatLayer : public Layer {

paddle/parameter/Argument.cpp

@@ -602,6 +602,44 @@ void Argument::degradeSequence(const Argument& input, bool useGpu) {
   tgtBuf[numSequences] = numSubSequences;
 }
 
+void Argument::getValueString(
+    std::unordered_map<std::string, std::string>* out) const {
+  if (value) {
+    std::ostringstream os;
+    value->print(os);
+    out->insert({"value", os.str()});
+  }
+  if (ids) {
+    std::ostringstream os;
+    ids->print(os, ids->getSize());
+    out->insert({"ids", os.str()});
+  }
+  if (sequenceStartPositions) {
+    std::ostringstream os;
+    sequenceStartPositions->getVector(false)->print(
+        os, sequenceStartPositions->getSize());
+    out->insert({"sequence pos", os.str()});
+  }
+  if (subSequenceStartPositions) {
+    std::ostringstream os;
+    subSequenceStartPositions->getVector(false)->print(
+        os, subSequenceStartPositions->getSize());
+    out->insert({"sub-sequence pos", os.str()});
+  }
+}
+
+void Argument::printValueString(std::ostream& stream,
+                                const std::string& prefix) const {
+  std::unordered_map<std::string, std::string> out;
+  getValueString(&out);
+  for (auto field : {"value", "id", "sequence pos", "sub-sequence pos"}) {
+    auto it = out.find(field);
+    if (it != out.end()) {
+      stream << prefix << field << ":\n" << it->second;
+    }
+  }
+}
+
 void Argument::subArgFrom(const Argument& input,
                           size_t offset,
                           size_t height,

paddle/parameter/Argument.h

@@ -297,6 +297,23 @@ struct Argument {
    sequence has sub-sequence degrades to a sequence.
    */
   void degradeSequence(const Argument& input, bool useGpu);
+
+  /**
+   * @brief getValueString will return the argument's output in string. There
+   * are several kinds of output. The keys of output dictionary are 'value',
+   * 'id', 'sequence pos', 'sub-sequence pos'.
+   * @param out [out]: the return values.
+   */
+  void getValueString(std::unordered_map<std::string, std::string>* out) const;
+
+  /**
+   * @brief printValueString will print the argument's output in order of
+   * 'value', 'id', 'sequence pos', 'sub-sequence pos'.
+   * @param stream: Output stream
+   * @param prefix: line prefix for printing.
+   */
+  void printValueString(std::ostream& stream,
+                        const std::string& prefix = "") const;
 };
 
 }  // namespace paddle

python/CMakeLists.txt

@@ -24,6 +24,7 @@ add_custom_target(paddle_python ALL DEPENDS
     ${OUTPUT_DIR}/.timestamp)
 
 add_subdirectory(paddle/trainer_config_helpers/tests)
+add_subdirectory(paddle/reader/tests)
 
 install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/dist/
     DESTINATION opt/paddle/share/wheels

python/paddle/reader/__init__.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# 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.
+
+# It would be too lengthy to require our users to prefix decorators with `decorator`.
+# For example, we want the following line
+#
+#     r = paddle.reader.decorator.bufferd(paddle.reader.creator.text("hello.txt"))
+#
+# to be a shorter version:
+#
+#     r = paddle.reader.buffered(paddle.reader.creator.text("hello.txt"))
+from decorator import *

python/paddle/reader/decorator.py

+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# 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.
+
+__all__ = ['buffered']
+
+from Queue import Queue
+from threading import Thread
+
+
+def buffered(reader, size):
+    """Creates a buffered data reader.
+
+    The buffered data reader will read and save data entries into a buffer.
+    Reading from the buffered data reader will proceed as long as the buffer
+    is not empty.
+    
+    Args:
+        reader: the data reader to read from.
+        size: max buffer size.
+    
+    Returns:
+        The buffered data reader.
+    """
+
+    class EndSignal():
+        pass
+
+    end = EndSignal()
+
+    def read_worker(r, q):
+        for d in r:
+            q.put(d)
+        q.put(end)
+
+    def create_reader():
+        r = reader()
+        q = Queue(maxsize=size)
+        t = Thread(
+            target=read_worker, args=(
+                r,
+                q, ))
+        t.daemon = True
+        t.start()
+        e = q.get()
+        while e != end:
+            yield e
+            e = q.get()
+
+    return create_reader

python/paddle/reader/tests/CMakeLists.txt

+add_test(NAME reader_decorator_test
+  COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
+        ${PYTHON_EXECUTABLE} ${PROJ_ROOT}/python/paddle/reader/tests/decorator_test.py
+    WORKING_DIRECTORY ${PROJ_ROOT}/python/paddle)

python/paddle/reader/tests/decorator_test.py

+# Copyright PaddlePaddle contributors. All Rights Reserved
+#
+# 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.
+import unittest
+import paddle.reader
+import time
+
+
+def reader_10(dur):
+    for i in range(10):
+        time.sleep(dur)
+        yield i
+
+
+class TestBuffered(unittest.TestCase):
+    def test_read(self):
+        for size in range(20):
+            b = paddle.reader.buffered(lambda: reader_10(0), size)
+            c = 0
+            for i in b():
+                self.assertEqual(i, c)
+                c += 1
+            self.assertEqual(c, 10)
+
+    def test_buffering(self):
+        # read have 30ms delay.
+        b = paddle.reader.buffered(lambda: reader_10(0.03), 10)
+        last_time = time.time()
+        for idx, i in enumerate(b()):
+            elapsed_time = time.time() - last_time
+            if i == 0:
+                time.sleep(0.3)
+            else:
+                # read time should be short, meaning already buffered.
+                self.assertLess(elapsed_time, 0.01)
+            last_time = time.time()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/trainer_config_helpers/layers.py

@@ -59,6 +59,7 @@ __all__ = [
     'img_cmrnorm_layer',
     'addto_layer',
     'concat_layer',
+    'seq_concat_layer',
     'lstm_step_layer',
     'recurrent_group',
     'memory',
@@ -144,6 +145,7 @@ class LayerType(object):
 
     CONCAT_LAYER = 'concat'
     CONCAT_PROJ_LAYER = 'concat2'
+    SEQUENCE_CONCAT_LAYER = 'seqconcat'
 
     LSTM_STEP_LAYER = 'lstm_step'
     GRU_STEP_LAYER = 'gru_step'
@@ -2570,6 +2572,59 @@ def concat_layer(input, act=None, name=None, layer_attr=None, bias_attr=None):
         size=sz)
 
 
+@wrap_name_default("seqconcat")
+@wrap_act_default(act=IdentityActivation())
+@wrap_bias_attr_default(has_bias=False)
+@layer_support()
+def seq_concat_layer(a, b, act=None, name=None, layer_attr=None,
+                     bias_attr=None):
+    """
+    Concat sequence a with sequence b.
+
+    Inputs: 
+      - a = [a1, a2, ..., an]
+      - b = [b1, b2, ..., bn]
+      - Note that the length of a and b should be the same.
+        
+    Output: [a1, b1, a2, b2, ..., an, bn]
+
+    The example usage is:
+
+    ..  code-block:: python
+
+        concat = seq_concat_layer(a=layer1, b=layer2)
+
+    :param name: Layer name.
+    :type name: basestring
+    :param a: input sequence layer
+    :type a: LayerOutput
+    :param b: input sequence layer
+    :type b: LayerOutput
+    :param act: Activation type.
+    :type act: BaseActivation
+    :param layer_attr: Extra Layer Attribute.
+    :type layer_attr: ExtraLayerAttribute
+    :return: LayerOutput object.
+    :rtype: LayerOutput
+    """
+    assert isinstance(a, LayerOutput) and isinstance(b, LayerOutput)
+    assert a.size == b.size
+    Layer(
+        name=name,
+        type=LayerType.SEQUENCE_CONCAT_LAYER,
+        inputs=[a.name, b.name],
+        active_type=act.name,
+        bias=ParamAttr.to_bias(bias_attr),
+        **ExtraLayerAttribute.to_kwargs(layer_attr))
+
+    return LayerOutput(
+        name,
+        layer_type=LayerType.SEQUENCE_CONCAT_LAYER,
+        parents=[a, b],
+        activation=act,
+        size=a.size)
+
+
 def memory(name,
            size,
            is_seq=False,

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

@@ -4,6 +4,7 @@ test_sequence_pooling test_lstmemory_layer test_grumemory_layer
 last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
 img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers
 test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
-test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops)
+test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
+test_seq_concat)
 
 export whole_configs=(test_split_datasource)

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

+type: "nn"
+layers {
+  name: "data1"
+  type: "data"
+  size: 30
+  active_type: ""
+}
+layers {
+  name: "data2"
+  type: "data"
+  size: 30
+  active_type: ""
+}
+layers {
+  name: "__seqconcat_0__"
+  type: "seqconcat"
+  size: 30
+  active_type: ""
+  inputs {
+    input_layer_name: "data1"
+  }
+  inputs {
+    input_layer_name: "data2"
+  }
+}
+input_layer_names: "data1"
+input_layer_names: "data2"
+output_layer_names: "__seqconcat_0__"
+sub_models {
+  name: "root"
+  layer_names: "data1"
+  layer_names: "data2"
+  layer_names: "__seqconcat_0__"
+  input_layer_names: "data1"
+  input_layer_names: "data2"
+  output_layer_names: "__seqconcat_0__"
+  is_recurrent_layer_group: false
+}
+

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

+from paddle.trainer_config_helpers import *
+
+settings(batch_size=1000, learning_rate=1e-5)
+
+din1 = data_layer(name='data1', size=30)
+
+din2 = data_layer(name='data2', size=30)
+
+outputs(seq_concat_layer(a=din1, b=din2))