Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into my_unpool_max_2d

benchmark/IntelOptimizedPaddle.md

@@ -12,11 +12,11 @@ Machine:
 
 System: CentOS release 6.3 (Final), Docker 1.12.1.
 
-PaddlePaddle: paddlepaddle/paddle:latest (TODO: will rerun after 0.11.0)
-
-- MKL-DNN tag v0.10
-- MKLML 2018.0.20170720
+PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)
+- MKL-DNN tag v0.11
+- MKLML 2018.0.1.20171007
 - OpenBLAS v0.2.20
+(TODO: will rerun after 0.11.0)
 	 
 On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
 
@@ -31,15 +31,26 @@ Input image size - 3 * 224 * 224, Time: images/second
 
 | BatchSize    | 64    | 128  | 256     |
 |--------------|-------| -----| --------|
-| OpenBLAS     | 7.82  | 8.62  | 10.34  | 
-| MKLML        | 11.02 | 12.86 | 15.33  |
-| MKL-DNN      | 27.69 | 28.8 | 29.27  |
+| OpenBLAS     | 7.80  | 9.00  | 10.80  | 
+| MKLML        | 12.12 | 13.70 | 16.18  |
+| MKL-DNN      | 28.46 | 29.83 | 30.44  |
+
+
+chart on batch size 128
+TBD
+
+ - ResNet-50
+
+| BatchSize    | 64    | 128   | 256    |
+|--------------|-------| ------| -------|
+| OpenBLAS     | 25.22 | 25.68 | 27.12  | 
+| MKLML        | 32.52 | 31.89 | 33.12  |
+| MKL-DNN      | 81.69 | 82.35 | 84.08  |
 
 
 chart on batch size 128
 TBD
 
- - ResNet
  - GoogLeNet
 
 ### Laptop

paddle/gserver/layers/MKLDNNLayer.cpp

@@ -294,22 +294,8 @@ void MKLDNNLayer::resetMergeGrad(MKLDNNMatrixPtr& out) {
     srcs.push_back(*src);
   }
 
-  // TODO(TJ): remove me when mkldnn sum support different formats
-  for (size_t i = 1; i < srcPDs.size(); ++i) {
-    CHECK(srcPDs[0] == srcPDs[i]);
-  }
-  tmpOutGrad_ = out;
-  tmpCvt_ = nullptr;
-  if (out->getPrimitiveDesc() != srcPDs[0]) {
-    tmpOutGrad_ = MKLDNNMatrix::create(srcPDs[0]);
-    tmpCvt_ = MKLDNNMatrix::createReorder(tmpOutGrad_, out);
-    CHECK(tmpCvt_);
-    pipelineMergeGrad_.push_back(*tmpCvt_);
-  }
-
-  auto sumPD =
-      sum::primitive_desc(tmpOutGrad_->getMemoryDesc(), scales, srcPDs);
-  mergeGrad_.reset(new sum(sumPD, srcs, *tmpOutGrad_));
+  auto sumPD = sum::primitive_desc(out->getMemoryDesc(), scales, srcPDs);
+  mergeGrad_.reset(new sum(sumPD, srcs, *out));
   pipelineMergeGrad_.insert(pipelineMergeGrad_.begin(), *mergeGrad_);
 }
 

paddle/gserver/layers/MKLDNNLayer.h

@@ -36,7 +36,7 @@ class MKLDNNLayer : public Layer {
 protected:
   // batch size
   int bs_;
-  // they sizes are always from the first input layer
+  // their sizes are always from the first input layer
   // input image channel, height and width
   int ic_, ih_, iw_;
   // output image channel, height and width
@@ -94,11 +94,6 @@ protected:
   std::vector<mkldnn::primitive> pipelineMergeGrad_;
   // tmp input argument to save input grad, only used to merge grad
   Argument tmpInArg_;
-  // since mkldnn sum do not support different formats:
-  // can refer to https://github.com/01org/mkl-dnn/issues/134
-  // so need create reorder manually and save tmp MKLDNNMatrix
-  MKLDNNMatrixPtr tmpOutGrad_;
-  std::shared_ptr<mkldnn::primitive> tmpCvt_;
 
 public:
   explicit MKLDNNLayer(const LayerConfig& config)

paddle/gserver/tests/test_MKLDNN.cpp

@@ -315,7 +315,7 @@ TEST(MKLDNNLayer, AddtoLayer) {
 
 static void getMKLDNNConcatConfig(TestConfig& cfg,
                                   const std::vector<testImageDesc>& inputs) {
-  CHECK_GE(inputs.size(), 2) << "at least two inputs";
+  CHECK_GE(inputs.size(), 2UL) << "at least two inputs";
   int oc = inputs[0].ic;
   for (size_t i = 1; i < inputs.size(); ++i) {
     CHECK_EQ(inputs[i].bs, inputs[0].bs);

paddle/operators/beam_search_op.cc

@@ -139,7 +139,7 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
   items->reserve(framework::product(ids.dims()));
   for (size_t offset = abs_lod[lod_level_][sent_offset_];
        offset < abs_lod[lod_level_][sent_offset_ + 1]; offset++) {
-    for (int d = 0; d < instance_dim; d++) {
+    for (size_t d = 0; d < instance_dim; d++) {
       const size_t dim_offset = offset * instance_dim + d;
       items->emplace_back(offset, ids_data[dim_offset],
                           scores_data[dim_offset]);

paddle/trainer/Trainer.cpp

@@ -138,7 +138,7 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
   }
 
   if (FLAGS_use_mkldnn) {
-    CHECK_EQ(FLAGS_trainer_count, 1UL) << "MKLDNN only need 1 trainer";
+    CHECK_EQ(FLAGS_trainer_count, 1) << "MKLDNN only need 1 trainer";
   }
 
   if (testing) {

python/paddle/trainer/config_parser.py

@@ -2037,13 +2037,20 @@ class ParameterReluLayer(LayerBase):
     def __init__(self, name, inputs, partial_sum=1, **args):
         super(ParameterReluLayer, self).__init__(
             name, self.layer_type, 0, inputs=inputs, **args)
+
         input_layer = self.get_input_layer(0)
         config_assert(len(self.inputs) == 1, "prelu layer has only one input.")
         config_assert(input_layer.size % partial_sum == 0,
                       "a wrong setting for partial_sum")
+
+        dims = [1, input_layer.size / partial_sum]
         self.set_layer_size(input_layer.size)
         self.config.partial_sum = partial_sum
-        self.create_input_parameter(0, input_layer.size / partial_sum)
+        self.create_input_parameter(0, input_layer.size / partial_sum, dims)
+
+        self.set_layer_height_width(self.get_input_layer(0).height, \
+                                        self.get_input_layer(0).width)
+        self.set_layer_depth(self.get_input_layer(0).depth)
 
 
 @config_layer('conv')

python/paddle/trainer_config_helpers/evaluators.py

@@ -297,7 +297,7 @@ def auc_evaluator(
 def pnpair_evaluator(
         input,
         label,
-        info,
+        query_id,
         weight=None,
         name=None, ):
     """
@@ -308,16 +308,20 @@ def pnpair_evaluator(
 
     .. code-block:: python
 
-       eval = pnpair_evaluator(input, label, info)
+       eval = pnpair_evaluator(input, label, query_id)
 
     :param input: Input Layer name. The output prediction of network.
     :type input: LayerOutput
     :param label: Label layer name.
     :type label: LayerOutput
-    :param info: Info layer name. (TODO, explaination)
-    :type info: LayerOutput
+    :param query_id: Query_id layer name. Query_id indicates that which query
+     each sample belongs to. Its shape should be
+     the same as output of Label layer.
+    :type query_id: LayerOutput
     :param weight: Weight Layer name. It should be a matrix with size
-                  [sample_num, 1]. (TODO, explaination)
+                  [sample_num, 1] which indicates the weight of each sample.
+                  The default weight of sample is 1 if the weight layer is None.
+                  And the pair weight is the mean of the two samples' weight.
     :type weight: LayerOutput
     :param name: Evaluator name.
     :type name: None|basestring
@@ -326,8 +330,8 @@ def pnpair_evaluator(
         input = [input]
     if label:
         input.append(label)
-    if info:
-        input.append(info)
+    if query_id:
+        input.append(query_id)
     evaluator_base(
         input=input,
         type="pnpair",

python/paddle/trainer_config_helpers/layers.py

@@ -6604,10 +6604,11 @@ def row_conv_layer(input,
 
 @layer_support()
 @wrap_name_default()
-@wrap_param_attr_default()
 def prelu_layer(input,
                 name=None,
                 partial_sum=1,
+                channel_shared=None,
+                num_channels=None,
                 param_attr=None,
                 layer_attr=None):
     """
@@ -6638,6 +6639,14 @@ def prelu_layer(input,
         - partial_sum = number of outputs, indicates all elements share the same weight.
 
     :type partial_sum: int
+    :param channel_shared: whether or not the parameter are shared across channels.
+
+        - channel_shared = True, we set the partial_sum to the number of outputs.
+        - channel_shared = False, we set the partial_sum to the number of elements in one channel.
+
+    :type channel_shared: bool
+    :param num_channels: number of input channel.
+    :type num_channels: int
     :param param_attr: The parameter attribute. See ParameterAttribute for details.
     :type param_attr: ParameterAttribute
     :param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
@@ -6648,7 +6657,25 @@ def prelu_layer(input,
     """
 
     assert isinstance(input, LayerOutput), 'prelu_layer accepts only one input.'
-    assert isinstance(param_attr, ParameterAttribute)
+
+    if not param_attr:
+        param_attr = ParamAttr(initial_mean=0.25, initial_std=0.0)
+    else:
+        assert isinstance(param_attr, ParameterAttribute)
+
+    if num_channels is None:
+        assert input.num_filters is not None, \
+                'the input channel cannot be detected, please specify the num_channels parameter'
+        num_channels = input.num_filters
+
+    if channel_shared is not None:
+        assert isinstance(channel_shared, bool)
+        assert (input.height != 0 and input.width != 0), \
+            'input height and widht must be setted'
+        if channel_shared:
+            partial_sum = input.height * input.width * num_channels
+        else:
+            partial_sum = input.height * input.width
 
     l = Layer(
         name=name,
@@ -6660,6 +6687,7 @@ def prelu_layer(input,
         name=name,
         layer_type=LayerType.PRELU,
         parents=input,
+        num_filters=num_channels,
         size=l.config.size)
 
 

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

@@ -4,6 +4,8 @@ layers {
   type: "data"
   size: 300
   active_type: ""
+  height: 10
+  width: 10
 }
 layers {
   name: "__prelu_layer_0__"
@@ -15,6 +17,9 @@ layers {
     input_parameter_name: "___prelu_layer_0__.w0"
   }
   partial_sum: 1
+  height: 10
+  width: 10
+  depth: 1
 }
 layers {
   name: "__prelu_layer_1__"
@@ -26,6 +31,9 @@ layers {
     input_parameter_name: "___prelu_layer_1__.w0"
   }
   partial_sum: 1
+  height: 10
+  width: 10
+  depth: 1
 }
 layers {
   name: "__prelu_layer_2__"
@@ -37,41 +45,100 @@ layers {
     input_parameter_name: "___prelu_layer_2__.w0"
   }
   partial_sum: 5
+  height: 10
+  width: 10
+  depth: 1
+}
+layers {
+  name: "__prelu_layer_3__"
+  type: "prelu"
+  size: 300
+  active_type: ""
+  inputs {
+    input_layer_name: "input"
+    input_parameter_name: "___prelu_layer_3__.w0"
+  }
+  partial_sum: 300
+  height: 10
+  width: 10
+  depth: 1
+}
+layers {
+  name: "__prelu_layer_4__"
+  type: "prelu"
+  size: 300
+  active_type: ""
+  inputs {
+    input_layer_name: "input"
+    input_parameter_name: "___prelu_layer_4__.w0"
+  }
+  partial_sum: 100
+  height: 10
+  width: 10
+  depth: 1
 }
 parameters {
   name: "___prelu_layer_0__.w0"
   size: 300
-  initial_mean: 0.0
-  initial_std: 0.057735026919
+  initial_mean: 0.25
+  initial_std: 0.0
+  dims: 1
+  dims: 300
   initial_strategy: 0
-  initial_smart: true
+  initial_smart: false
 }
 parameters {
   name: "___prelu_layer_1__.w0"
   size: 300
-  initial_mean: 0.0
-  initial_std: 0.057735026919
+  initial_mean: 0.25
+  initial_std: 0.0
+  dims: 1
+  dims: 300
   initial_strategy: 0
-  initial_smart: true
+  initial_smart: false
 }
 parameters {
   name: "___prelu_layer_2__.w0"
   size: 60
-  initial_mean: 0.0
-  initial_std: 0.129099444874
+  initial_mean: 0.25
+  initial_std: 0.0
+  dims: 1
+  dims: 60
+  initial_strategy: 0
+  initial_smart: false
+}
+parameters {
+  name: "___prelu_layer_3__.w0"
+  size: 1
+  initial_mean: 0.25
+  initial_std: 0.0
+  dims: 1
+  dims: 1
+  initial_strategy: 0
+  initial_smart: false
+}
+parameters {
+  name: "___prelu_layer_4__.w0"
+  size: 3
+  initial_mean: 0.25
+  initial_std: 0.0
+  dims: 1
+  dims: 3
   initial_strategy: 0
-  initial_smart: true
+  initial_smart: false
 }
 input_layer_names: "input"
-output_layer_names: "__prelu_layer_2__"
+output_layer_names: "__prelu_layer_4__"
 sub_models {
   name: "root"
   layer_names: "input"
   layer_names: "__prelu_layer_0__"
   layer_names: "__prelu_layer_1__"
   layer_names: "__prelu_layer_2__"
+  layer_names: "__prelu_layer_3__"
+  layer_names: "__prelu_layer_4__"
   input_layer_names: "input"
-  output_layer_names: "__prelu_layer_2__"
+  output_layer_names: "__prelu_layer_4__"
   is_recurrent_layer_group: false
 }
 

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

 from paddle.trainer_config_helpers import *
 
-data = data_layer(name='input', size=300)
-prelu = prelu_layer(input=data)
-prelu = prelu_layer(input=data, partial_sum=1)
-prelu = prelu_layer(input=data, partial_sum=5)
+data = data_layer(name='input', size=300, height=10, width=10)
+prelu = prelu_layer(input=data, num_channels=3)
+prelu = prelu_layer(input=data, partial_sum=1, num_channels=3)
+prelu = prelu_layer(input=data, partial_sum=5, num_channels=3)
+prelu = prelu_layer(input=data, channel_shared=True, num_channels=3)
+prelu = prelu_layer(input=data, channel_shared=False, num_channels=3)
 
 outputs(prelu)

python/paddle/v2/__init__.py

@@ -62,21 +62,15 @@ __all__ = [
 cp.begin_parse()
 
 
-def init(**kwargs):
-    import py_paddle.swig_paddle as api
-    args = []
-    args_dict = {}
-    # NOTE: append arguments if they are in ENV
-    for ek, ev in os.environ.iteritems():
-        if ek.startswith("PADDLE_INIT_"):
-            args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
+def set_omp_mkl_env_vars(trainer_count):
+    '''Auto set CPU environment if have not set before.
+       export KMP_AFFINITY, OMP_DYNAMIC according to the Hyper Threading status.
+       export OMP_NUM_THREADS, MKL_NUM_THREADS according to trainer_count.
+    '''
+    import platform
+    if not platform.system() in ['Linux', 'Darwin']:
+        return
 
-    args_dict.update(kwargs)
-    # NOTE: overwrite arguments from ENV if it is in kwargs
-    for key in args_dict.keys():
-        args.append('--%s=%s' % (key, str(args_dict[key])))
-
-    # auto set cpu environment
     def set_env(key, value):
         '''If the key has not been set in the environment, set it with value.'''
         assert isinstance(key, str)
@@ -85,22 +79,59 @@ def init(**kwargs):
         if envset is None:
             os.environ[key] = value
 
-    ht = os.popen("lscpu |grep \"per core\"|awk -F':' '{print $2}'|xargs")
-    ht = int(ht.read())
-    if ht == 1:  # ht is off
-        set_env("OMP_DYNAMIC", "false")
-        set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
-    else:
+    def num_physical_cores():
+        '''Get the number of physical cores'''
+        if platform.system() == "Linux":
+            num_sockets = int(
+                os.popen("lscpu |grep \"Socket\" |awk -F':' '{print $2}'|xargs")
+                .read())
+            num_cores_per_socket = int(
+                os.popen(
+                    "lscpu |grep \"per socket\" |awk -F':' '{print $2}'|xargs")
+                .read())
+            return num_sockets * num_cores_per_socket
+        else:
+            cmds = {"Darwin": "sysctl hw.physicalcpu"}
+            return int(os.popen(cmds.get(platform.system(), "expr 1")).read())
+
+    def num_logical_processors():
+        '''Get the number of logical processors'''
+        cmds = {
+            "Linux": "grep \"processor\" /proc/cpuinfo|sort -u|wc -l",
+            "Darwin": "sysctl hw.logicalcpu"
+        }
+        return int(os.popen(cmds.get(platform.system(), "expr 1")).read())
+
+    num_cores = num_physical_cores()
+    num_processors = num_logical_processors()
+    if num_processors > num_cores:  # Hyper Threading is enabled
         set_env("OMP_DYNAMIC", "true")
         set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
-    processors = os.popen("grep \"processor\" /proc/cpuinfo|sort -u|wc -l")
-    processors = int(processors.read())
-    trainers = kwargs.get('trainer_count', 1)
-    threads = processors / trainers
+    else:
+        set_env("OMP_DYNAMIC", "false")
+        set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
+    threads = num_processors / trainer_count
     threads = '1' if threads < 1 else str(threads)
     set_env("OMP_NUM_THREADS", threads)
     set_env("MKL_NUM_THREADS", threads)
 
+
+def init(**kwargs):
+    import py_paddle.swig_paddle as api
+    args = []
+    args_dict = {}
+    # NOTE: append arguments if they are in ENV
+    for ek, ev in os.environ.iteritems():
+        if ek.startswith("PADDLE_INIT_"):
+            args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
+
+    args_dict.update(kwargs)
+    # NOTE: overwrite arguments from ENV if it is in kwargs
+    for key in args_dict.keys():
+        args.append('--%s=%s' % (key, str(args_dict[key])))
+
+    set_omp_mkl_env_vars(kwargs.get('trainer_count', 1))
+
     if 'use_gpu' in kwargs:
         cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
     if 'use_mkldnn' in kwargs:

python/paddle/v2/fluid/initializer.py

@@ -285,3 +285,86 @@ class XavierInitializer(Initializer):
                 })
         var.op = op
         return op
+
+
+class MSRAInitializer(Initializer):
+    """Implements the MSRA initializer a.k.a. Kaiming Initializer
+
+    This class implements the weight initialization from the paper
+    Delving Deep into Rectifiers: Surpassing Human-Level Performance on
+    ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren
+    and Jian Sun. This is a robust initialization method that particularly
+    considers the rectifier nonlinearities. In case of Uniform distribution,
+    the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal
+    distribution, the mean is 0 and the standard deviation
+    is sqrt(2/ fan_in).
+
+    References:
+        [1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
+            on ImageNet Classification
+            (https://arxiv.org/abs/1502.01852)
+    """
+
+    def __init__(self, uniform=True, fan_in=None, seed=0):
+        """Constructor for MSRAInitializer
+
+        Args:
+            uniform: whether to use uniform or normal distribution
+            fan_in: fan_in for MSRAInitializer. If None, it is
+                    inferred from the variable.
+            seed: random seed
+
+        Note: It is recommended to set fan_in to None for most cases.
+        """
+        assert uniform is not None
+        assert seed is not None
+        super(MSRAInitializer, self).__init__()
+        self._uniform = uniform
+        self._fan_in = fan_in
+        self._seed = seed
+
+    def __call__(self, var, block):
+        """Add MSRA initialization ops for a variable
+
+        Args:
+            var: Variable that needs to be initialized
+            block: The block in which initialization ops
+                   should be added
+
+        Returns:
+            the initialization op
+        """
+        assert isinstance(var, framework.Variable)
+        assert isinstance(block, framework.Block)
+        f_in, f_out = self._compute_fans(var)
+
+        # If fan_in is passed, use it
+        fan_in = f_in if self._fan_in is None else self._fan_in
+
+        if self._uniform:
+            limit = np.sqrt(6.0 / float(fan_in))
+            op = block.prepend_op(
+                type="uniform_random",
+                outputs={"Out": var},
+                attrs={
+                    "shape": var.shape,
+                    "data_type": int(var.data_type),
+                    "min": -limit,
+                    "max": limit,
+                    "seed": self._seed
+                })
+
+        else:
+            std = np.sqrt(2.0 / float(fan_in))
+            op = block.prepend_op(
+                type="gaussian_random",
+                outputs={"Out": var},
+                attrs={
+                    "shape": var.shape,
+                    "data_type": int(var.data_type),
+                    "mean": 0.0,
+                    "std": std,
+                    "seed": self._seed
+                })
+        var.op = op
+        return op

python/paddle/v2/fluid/layers.py

@@ -17,13 +17,13 @@ __all__ = [
 
 def fc(input,
        size,
+       num_flatten_dims=1,
        param_attr=None,
        param_initializer=None,
        bias_attr=None,
        bias_initializer=None,
-       name=None,
        act=None,
-       num_flatten_dims=1,
+       name=None,
        main_program=None,
        startup_program=None):
     """
@@ -32,15 +32,15 @@ def fc(input,
     Args:
        input: The input tensor to the function
        size: The size of the layer
+       num_flatten_dims: Number of columns in input
        param_attr: The parameters/weights to the FC Layer
        param_initializer: Initializer used for the weight/parameter.
        If None, XavierInitializer() is used
        bias_attr: The bias parameter for the FC layer
        bias_initializer: Initializer used for the bias.
        If None, then ConstantInitializer() is used
-       name: Name/alias of the function
        act: Activation to be applied to the output of FC layer
-       num_flatten_dims: Number of columns in input
+       name: Name/alias of the function
        main_program: Name of the main program that calls this
        startup_program: Name of the startup program
 
@@ -111,9 +111,9 @@ def fc(input,
 
 def embedding(input,
               size,
-              data_type='float32',
               is_sparse=False,
               param_attr=None,
+              data_type='float32',
               main_program=None,
               startup_program=None):
     """
@@ -122,9 +122,9 @@ def embedding(input,
     Args:
        input: The input to the function
        size: The size of the layer
-       data_type: The type of data : float32, float_16, int etc
        is_sparse: A flag that decleares whether the input is sparse
        param_attr: Parameters for this layer
+       data_type: The type of data : float32, float_16, int etc
        main_program: Name of the main program that calls this
        startup_program: Name of the startup program
 
@@ -152,7 +152,6 @@ def embedding(input,
 # TODO(qijun): expose H0 and C0
 def dynamic_lstm(input,
                  size,
-                 data_type='float32',
                  param_attr=None,
                  bias_attr=None,
                  use_peepholes=True,
@@ -160,6 +159,7 @@ def dynamic_lstm(input,
                  gate_activation='sigmoid',
                  cell_activation='tanh',
                  candidate_activation='tanh',
+                 data_type='float32',
                  main_program=None,
                  startup_program=None):
     helper = LayerHelper('lstm', **locals())
@@ -200,9 +200,9 @@ def dynamic_lstm(input,
 
 def data(name,
          shape,
+         append_batch_size=True,
          data_type='float32',
          type=core.VarDesc.VarType.LOD_TENSOR,
-         append_batch_size=True,
          main_program=None,
          startup_program=None,
          stop_gradient=True):
@@ -212,9 +212,9 @@ def data(name,
     Args:
        name: The name/alias of the function
        shape: Tuple declaring the shape.
+       append_batch_size: Whether or not to append the data as a batch.
        data_type: The type of data : float32, float_16, int etc
        type: The output type. By default it is LOD_TENSOR.
-       append_batch_size: Whether or not to append the data as a batch.
        main_program: Name of the main program that calls this
        startup_program: Name of the startup program
        stop_gradient: A boolean that mentions whether gradient should flow.
@@ -600,12 +600,12 @@ def sequence_conv(input,
                   num_filters,
                   filter_size=3,
                   filter_stride=1,
-                  act=None,
                   padding=None,
                   bias_attr=None,
                   bias_initializer=None,
                   param_attr=None,
                   param_initializer=None,
+                  act=None,
                   main_program=None,
                   startup_program=None):
     """
@@ -658,16 +658,16 @@ def sequence_conv(input,
 
 def conv2d(input,
            num_filters,
-           name=None,
-           filter_size=[1, 1],
-           act=None,
-           groups=None,
+           filter_size,
            stride=[1, 1],
            padding=None,
-           bias_attr=None,
-           bias_initializer=None,
+           groups=None,
            param_attr=None,
            param_initializer=None,
+           bias_attr=None,
+           bias_initializer=None,
+           act=None,
+           name=None,
            main_program=None,
            startup_program=None):
     """

python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py

@@ -54,17 +54,17 @@ def to_lodtensor(data, place):
     return res
 
 
-def chop_data(data, chop_len=80, batch_len=50):
+def chop_data(data, chop_len=80, batch_size=50):
     data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
 
-    return data[:batch_len]
+    return data[:batch_size]
 
 
 def prepare_feed_data(data, place):
     tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
 
     label = np.array(map(lambda x: x[1], data)).astype("int64")
-    label = label.reshape([50, 1])
+    label = label.reshape([len(label), 1])
     tensor_label = core.LoDTensor()
     tensor_label.set(label, place)
 
@@ -72,33 +72,41 @@ def prepare_feed_data(data, place):
 
 
 def main():
-    word_dict = paddle.dataset.imdb.word_dict()
-    cost, acc = lstm_net(dict_dim=len(word_dict), class_dim=2)
+    BATCH_SIZE = 100
+    PASS_NUM = 5
 
-    batch_size = 100
-    train_data = paddle.batch(
-        paddle.reader.buffered(
-            paddle.dataset.imdb.train(word_dict), size=batch_size * 10),
-        batch_size=batch_size)
+    word_dict = paddle.dataset.imdb.word_dict()
+    print "load word dict successfully"
+    dict_dim = len(word_dict)
+    class_dim = 2
 
-    data = chop_data(next(train_data()))
+    cost, acc = lstm_net(dict_dim=dict_dim, class_dim=class_dim)
 
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.imdb.train(word_dict), buf_size=BATCH_SIZE * 10),
+        batch_size=BATCH_SIZE)
     place = core.CPUPlace()
-    tensor_words, tensor_label = prepare_feed_data(data, place)
     exe = Executor(place)
+
     exe.run(framework.default_startup_program())
 
-    while True:
-        outs = exe.run(framework.default_main_program(),
-                       feed={"words": tensor_words,
-                             "label": tensor_label},
-                       fetch_list=[cost, acc])
-        cost_val = np.array(outs[0])
-        acc_val = np.array(outs[1])
-
-        print("cost=" + str(cost_val) + " acc=" + str(acc_val))
-        if acc_val > 0.9:
-            break
+    for pass_id in xrange(PASS_NUM):
+        for data in train_data():
+            chopped_data = chop_data(data)
+            tensor_words, tensor_label = prepare_feed_data(chopped_data, place)
+
+            outs = exe.run(framework.default_main_program(),
+                           feed={"words": tensor_words,
+                                 "label": tensor_label},
+                           fetch_list=[cost, acc])
+            cost_val = np.array(outs[0])
+            acc_val = np.array(outs[1])
+
+            print("cost=" + str(cost_val) + " acc=" + str(acc_val))
+            if acc_val > 0.7:
+                exit(0)
+    exit(1)
 
 
 if __name__ == '__main__':

python/paddle/v2/fluid/tests/test_initializer.py

@@ -223,5 +223,109 @@ class TestXavierInitializer(unittest.TestCase):
         self.assertEqual(init_op.attr('seed'), 134)
 
 
+class TestMSRAInitializer(unittest.TestCase):
+    def test_uniform_msra_initializer(self):
+        """Test MSRA initializer with uniform distribution on
+           for matrix multiply.
+        """
+        program = framework.Program()
+        block = program.global_block()
+        param = block.create_parameter(
+            dtype="float32",
+            shape=[5, 10],
+            lod_level=0,
+            name="param",
+            initializer=initializer.MSRAInitializer())
+        self.assertEqual(len(block.ops), 1)
+        init_op = block.ops[0]
+        self.assertEqual(init_op.type, 'uniform_random')
+        limit = np.sqrt(6.0 / param.shape[0])
+        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
+        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
+        self.assertEqual(init_op.attr('seed'), 0)
+
+    def test_uniform_msra_initializer_conv(self):
+        """Test MSRA initializer with uniform distribution on
+           for convolutions.
+        """
+        program = framework.Program()
+        block = program.global_block()
+        param = block.create_parameter(
+            dtype="float32",
+            shape=[5, 10, 15, 20],
+            lod_level=0,
+            name="param",
+            initializer=initializer.MSRAInitializer())
+        self.assertEqual(len(block.ops), 1)
+        init_op = block.ops[0]
+        self.assertEqual(init_op.type, 'uniform_random')
+        receptive_field_size = float(15 * 20)
+        limit = np.sqrt(6.0 / (param.shape[1] * receptive_field_size))
+        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
+        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
+        self.assertEqual(init_op.attr('seed'), 0)
+
+    def test_normal_msra_initializer(self):
+        """Test MSRA initializer with normal distribution on
+           for matrix multiply.
+        """
+        program = framework.Program()
+        block = program.global_block()
+        param = block.create_parameter(
+            dtype="float32",
+            shape=[5, 10],
+            lod_level=0,
+            name="param",
+            initializer=initializer.MSRAInitializer(uniform=False))
+        self.assertEqual(len(block.ops), 1)
+        init_op = block.ops[0]
+        self.assertEqual(init_op.type, 'gaussian_random')
+        std = np.sqrt(2.0 / param.shape[0])
+        self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
+        self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
+        self.assertEqual(init_op.attr('seed'), 0)
+
+    def test_normal_msra_initializer_conv(self):
+        """Test MSRA initializer with normal distribution on
+           for convolutions.
+        """
+        program = framework.Program()
+        block = program.global_block()
+        param = block.create_parameter(
+            dtype="float32",
+            shape=[5, 10, 15, 20],
+            lod_level=0,
+            name="param",
+            initializer=initializer.MSRAInitializer(uniform=False))
+        self.assertEqual(len(block.ops), 1)
+        init_op = block.ops[0]
+        self.assertEqual(init_op.type, 'gaussian_random')
+        receptive_field_size = float(15 * 20)
+        std = np.sqrt(2.0 / (param.shape[1] * receptive_field_size))
+        self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
+        self.assertAlmostEqual(init_op.attr('std'), std, delta=DELTA)
+        self.assertEqual(init_op.attr('seed'), 0)
+
+    def test_msra_initializer_supplied_arguments(self):
+        """Test the MSRA initializer with supplied arguments
+        """
+        program = framework.Program()
+        block = program.global_block()
+        block.create_parameter(
+            dtype="float32",
+            shape=[5, 10],
+            lod_level=0,
+            name="param",
+            initializer=initializer.MSRAInitializer(
+                fan_in=12, seed=134))
+        self.assertEqual(len(block.ops), 1)
+        init_op = block.ops[0]
+        self.assertEqual(init_op.type, 'uniform_random')
+        limit = np.sqrt(6.0 / 12)
+        self.assertAlmostEqual(init_op.attr('min'), -limit, delta=DELTA)
+        self.assertAlmostEqual(init_op.attr('max'), limit, delta=DELTA)
+        self.assertEqual(init_op.attr('seed'), 134)
+
+
 if __name__ == '__main__':
     unittest.main()