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

.travis.yml

@@ -4,6 +4,7 @@ cache:
     - $HOME/third_party
     - $HOME/.ccache
     - $HOME/.cache/pip
+    - $HOME/Library/Caches/Homebrew
 sudo: required
 dist: trusty
 os:

demo/mnist/api_train_v2.py

@@ -25,8 +25,7 @@ def main():
                                 act=paddle.activation.Softmax())
     cost = paddle.layer.classification_cost(input=inference, label=label)
 
-    topology = paddle.layer.parse_network(cost)
-    parameters = paddle.parameters.create(topology)
+    parameters = paddle.parameters.create(cost)
     for param_name in parameters.keys():
         array = parameters.get(param_name)
         array[:] = numpy.random.uniform(low=-1.0, high=1.0, size=array.shape)
@@ -46,7 +45,7 @@ def main():
     trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
 
     trainer.train(train_data_reader=train_reader,
-                  topology=topology,
+                  topology=cost,
                   parameters=parameters,
                   event_handler=event_handler,
                   batch_size=32,  # batch size should be refactor in Data reader

doc/design/reader/README.md

+# Python Data Reader Design Doc
+
+Paddle reads data from data reader during training. It will be passed into `paddle.train` as a parameter.
+
+## Data Reader Interface
+
+Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
+
+```
+iterable = data_reader()
+```
+
+Element produced for the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int)
+
+An example implementation for single item data reader:
+
+```python
+def data_reader_fake_image():
+	while True:
+		yield numpy.random.uniform(-1, 1, size=20*20)
+```
+
+An example implementation for multiple item data reader:
+```python
+def data_reader_fake_image_and_label():
+	while True:
+		yield numpy.random.uniform(-1, 1, size=20*20), False
+```
+
+## Usage
+
+data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
+
+```python
+# two data layer is created:
+image_layer = paddle.layer.data("image", ...)
+label_layer = paddle.layer.data("label", ...)
+
+# ...
+
+paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...)
+```
+
+## Data Reader Decorators
+
+Data reader decorators takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax.
+
+Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples:
+
+### Prefetch Data
+
+Since reading data may take time and training can not proceed without data. It is generally a good idea to prefetch data.
+
+Use `paddle.reader.buffered` to prefetch data:
+
+```python
+buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100)
+```
+
+`buffered_reader` will try to buffer (prefetch) `100` data entries.
+
+### Compose Multiple Data Readers
+
+For example, we want to use a source of real images (reusing mnist dataset), and a source of fake images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
+
+We can do:
+
+```python
+def data_reader_fake_image():
+	while True:
+		yield numpy.random.uniform(-1, 1, size=20*20)
+
+def data_reader_bool(t):
+	while True:
+		yield t
+
+true_reader = lambda : data_reader_bool(True)
+false_reader = lambda : data_reader_bool(False)
+
+reader = paddle.reader.combine(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader)
+# Skipped 1 because paddle.dataset.mnist produces two items per data entry.
+# And we don't care second item at this time.
+paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
+```
+
+### Shuffle
+
+Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader that buffers `n` data entries and shuffle them before a data entry is read.
+
+Example:
+```python
+reader = paddle.reader.shuffle(paddle.dataset.mnist, 512)
+```
+
+## Q & A
+
+### Why return only a single entry, but not a mini batch?
+
+If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`.
+
+Practically, always return a single entry make reusing existing data reader much easier (e.g., if existing data reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
+
+### Why use a dictionary but not a list to provide mapping?
+
+We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`).
+
+### How to create custom data reader
+
+```python
+def image_reader(image_path, label_path):
+	f = open(image_path)
+	l = open(label_path)
+	images = numpy.fromfile(
+		f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
+	images = images / 255.0 * 2.0 - 1.0
+	labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
+	for i in xrange(n):
+		yield images[i, :], labels[i] # a single entry of data is created each time
+	f.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")
+paddle.train(reader, {"image":0, "label":1}, ...)
+```
+
+### How is `paddle.train` implemented
+
+An example implementation of paddle.train could be:
+
+```python
+def minibatch_decorater(reader, minibatch_size):
+	def ret():
+		r = reader()
+		buf = [r.next() for x in xrange(minibatch_size)]
+		while len(buf) > 0:
+			yield buf
+			buf = [r.next() for x in xrange(minibatch_size)]
+	return ret
+
+def train(reader, mapping, batch_size, total_pass):
+	for pass_idx in range(total_pass):
+		for mini_batch in minibatch_decorater(reader): # this loop will never end in online learning.
+			do_forward_backward(mini_batch, mapping)
+```

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.
  *

paddle/gserver/layers/SequenceConcatLayer.cpp

@@ -168,13 +168,17 @@ void SequenceConcatLayer::backward(const UpdateCallback& callback) {
     size_t rightNumIns = 0;
     for (size_t seqId = 0; seqId < numSequences1; ++seqId) {
       leftNumIns = starts1[seqId + 1] - starts1[seqId];
-      inputGrad1->subMatrix(starts1[seqId], leftNumIns)
-          ->add(*(outputGrad->subMatrix(offset, leftNumIns)));
+      if (inputGrad1) {
+        inputGrad1->subMatrix(starts1[seqId], leftNumIns)
+            ->add(*(outputGrad->subMatrix(offset, leftNumIns)));
+      }
       offset += leftNumIns;
 
       rightNumIns = starts2[seqId + 1] - starts2[seqId];
-      inputGrad2->subMatrix(starts2[seqId], rightNumIns)
-          ->add(*(outputGrad->subMatrix(offset, rightNumIns)));
+      if (inputGrad2) {
+        inputGrad2->subMatrix(starts2[seqId], rightNumIns)
+            ->add(*(outputGrad->subMatrix(offset, rightNumIns)));
+      }
       offset += rightNumIns;
     }
   }

python/paddle/v2/parameters.py

 import numpy as np
-
-from paddle.proto.ModelConfig_pb2 import ModelConfig
-from paddle.proto.ParameterConfig_pb2 import ParameterConfig
+from . import layer as v2_layer
 import py_paddle.swig_paddle as api
+from paddle.proto.ParameterConfig_pb2 import ParameterConfig
 
 __all__ = ['Parameters', 'create']
 
 
-def create(*topologies):
+def create(*layers):
     """
-    Create parameter pool by topologies.
+    Create parameter pool by layers. In paddle, layer can be represent a
+    model config.
 
-    :param topologies:
+    :param layers:
     :return:
     """
-    pool = Parameters()
-    for topo in topologies:
-        if not isinstance(topo, ModelConfig):
+    for layer in layers:
+        if not isinstance(layer, v2_layer.Layer):
             raise ValueError(
-                'create must pass a topologies which type is ModelConfig')
-
-        for param in topo.parameters:
-            pool.__append_config__(param)
+                'create must pass a topologies which type is paddle.layer.Layer')
+    model_config = v2_layer.parse_network(*layers)
+    pool = Parameters()
+    for param in model_config.parameters:
+        pool.__append_config__(param)
     return pool
 
 

python/paddle/v2/trainer.py

 import collections
 
 import py_paddle.swig_paddle as api
+from paddle.proto.ModelConfig_pb2 import ModelConfig
 from py_paddle import DataProviderConverter
 
-from paddle.proto.ModelConfig_pb2 import ModelConfig
+from . import event as v2_event
+from . import layer as v2_layer
 from . import optimizer as v2_optimizer
 from . import parameters as v2_parameters
-from . import event as v2_event
 
 __all__ = ['ITrainer', 'SGD']
 
@@ -73,7 +74,7 @@ class SGD(ITrainer):
         Training method. Will train num_passes of input data.
 
         :param train_data_reader:
-        :param topology: Network Topology, a protobuf ModelConfig message.
+        :param topology: Network Topology, use one or more Layers to represent it.
         :param parameters: The parameter pools.
         :param num_passes: The total train passes.
         :param test_data_reader:
@@ -87,6 +88,8 @@ class SGD(ITrainer):
         if event_handler is None:
             event_handler = default_event_handler
 
+        topology = v2_layer.parse_network(topology)
+
         __check_train_args__(**locals())
 
         gm = api.GradientMachine.createFromConfigProto(