Add cpp_package build option in CI

Jenkinsfile

@@ -81,6 +81,7 @@ stage('Build') {
         init_git()
         def flag = """ \
 USE_PROFILER=1                \
+USE_CPP_PACKAGE=1             \
 USE_BLAS=openblas             \
 -j\$(nproc)
 """
@@ -99,6 +100,7 @@ USE_BLAS=openblas             \
 USE_CUDA=1                    \
 USE_CUDA_PATH=/usr/local/cuda \
 USE_CUDNN=1                   \
+USE_CPP_PACKAGE=1             \
 -j\$(nproc)
 """
         make('gpu', flag)
@@ -126,6 +128,7 @@ USE_MKL2017_EXPERIMENTAL=1    \
 USE_CUDA=1                    \
 USE_CUDA_PATH=/usr/local/cuda \
 USE_CUDNN=1                   \
+USE_CPP_PACKAGE=1             \
 -j\$(nproc)
 """
         make('mklml_gpu', flag)

cpp-package/example/charRNN.cpp

@@ -194,7 +194,8 @@ class Shuffler {
 
 class BucketSentenceIter : public DataIter {
   Shuffler* random;
-  int batch, current, end, sequence_length;
+  int batch, current, end;
+  unsigned int sequence_length;
   Context device;
   vector<vector<mx_float>> sequences;
   vector<wchar_t> index2chars;
@@ -582,7 +583,7 @@ void predict(wstring* ptext, int sequence_length, const string param_file,
   LoadCheckpoint(param_file, exe);
 
   mx_float index;
-  wchar_t next;
+  wchar_t next = 0;
   vector<mx_float> softmax;
   softmax.resize(input_dim);
   for (auto c : *ptext) {
@@ -642,7 +643,7 @@ void predictWithBuiltInRNNOp(wstring* ptext, int sequence_length, const string p
   LoadCheckpoint(param_file, exe);
 
   mx_float index;
-  wchar_t next;
+  wchar_t next = 0;
   vector<mx_float> softmax;
   softmax.resize(input_dim);
   for (auto c : *ptext) {

cpp-package/include/mxnet-cpp/op_suppl.h

@@ -119,48 +119,7 @@ inline Symbol Crop(const std::string& symbol_name,
 
 
 /*!
- * \breif Slice input equally along specified axis.
- * \param data input symbol.
- * \param num_outputs Number of outputs to be sliced.
- * \param axis Dimension along which to slice.
- * \param squeeze_axis If true AND the sliced dimension becomes 1, squeeze that dimension.
- * \return new symbol
- */
-inline Symbol SliceChannel(Symbol data,
-                           int num_outputs,
-                           int axis = 1,
-                           bool squeeze_axis = false) {
-  return Operator("SliceChannel")
-           .SetParam("num_outputs", num_outputs)
-           .SetParam("axis", axis)
-           .SetParam("squeeze_axis", squeeze_axis) (data)
-           .CreateSymbol();
-}
-
-
-/*!
- * \breif Slice input equally along specified axis.
- * \param symbol_name name of the resulting symbol.
- * \param data input symbol.
- * \param num_outputs Number of outputs to be sliced.
- * \param axis Dimension along which to slice.
- * \param squeeze_axis If true AND the sliced dimension becomes 1, squeeze that dimension.
- * \return new symbol
- */
-inline Symbol SliceChannel(const std::string& symbol_name,
-                           Symbol data,
-                           int num_outputs,
-                           int axis = 1,
-                           bool squeeze_axis = false) {
-  return Operator("SliceChannel")
-           .SetParam("num_outputs", num_outputs)
-           .SetParam("axis", axis)
-           .SetParam("squeeze_axis", squeeze_axis) (data)
-           .CreateSymbol(symbol_name);
-}
-
-/*!
- * \breif Apply activation function to input.
+ * \brief Apply activation function to input.
  *        Softmax Activation is only available with CUDNN on GPUand will be
  *        computed at each location across channel if input is 4D.
  * \param symbol_name name of the resulting symbol.

src/operator/tensor/matrix_op-inl.h

@@ -32,6 +32,10 @@ struct ReshapeParam : public dmlc::Parameter<ReshapeParam> {
     DMLC_DECLARE_FIELD(shape)
     .set_default(nnvm::Tuple<int>())
     .describe("The target shape");
+    DMLC_DECLARE_FIELD(keep_highest).set_default(false)
+    .describe("(Deprecated! Use ``shape`` instead.) Whether keep the highest dim unchanged."
+              "If set to true, then the first dim in target_shape is ignored,"
+              "and always fixed as input");
     DMLC_DECLARE_FIELD(reverse)
     .set_default(false)
     .describe("If true then the special values are inferred from right to left");
@@ -40,10 +44,6 @@ struct ReshapeParam : public dmlc::Parameter<ReshapeParam> {
     .describe("(Deprecated! Use ``shape`` instead.) "
               "Target new shape. One and only one dim can be 0, "
               "in which case it will be inferred from the rest of dims");
-    DMLC_DECLARE_FIELD(keep_highest).set_default(false)
-    .describe("(Deprecated! Use ``shape`` instead.) Whether keep the highest dim unchanged."
-              "If set to true, then the first dim in target_shape is ignored,"
-              "and always fixed as input");
   }
 };
 

src/operator/tensor/multisample_op.h

@@ -45,7 +45,7 @@ inline bool MultiSampleOpShape(const nnvm::NodeAttrs& attrs,
   // Get shape to be sampled for each parameter set.
   const MultiSampleParam& param = nnvm::get<MultiSampleParam>(attrs.parsed);
   TShape sshape = param.shape;
-  for ( int i = 0; i < sshape.ndim(); ++i ) {
+  for ( size_t i = 0; i < sshape.ndim(); ++i ) {
     CHECK_GT((int)sshape[i], 0) << "shape parameter must be non-zero within each dimension";
   }
   // Examine output shape whether it is already defined.
@@ -57,10 +57,10 @@ inline bool MultiSampleOpShape(const nnvm::NodeAttrs& attrs,
     tshape = TShape(tshape.begin(), tshape.begin()+(tshape.ndim()-sshape.ndim()));
   }
   // Shape assignemnt/checking for inputs.
-  for ( int i = 0; i < in_attrs->size(); ++i ) {
+  for ( size_t i = 0; i < in_attrs->size(); ++i ) {
     if ( !shape_assign(&tshape, (*in_attrs)[i])) return false;
   }
-  for ( int i = 0; i < in_attrs->size(); ++i ) {
+  for ( size_t i = 0; i < in_attrs->size(); ++i ) {
     SHAPE_ASSIGN_CHECK(*in_attrs, i, tshape);
   }
   if ( tshape.ndim() > 0 ) {
@@ -84,10 +84,10 @@ inline bool MultiSampleOpType(const nnvm::NodeAttrs& attrs,
 
   // All inputs must have same type.
   int dtype = -1;
-  for ( int i = 0; i < in_attrs->size(); ++i ) {
+  for ( size_t i = 0; i < in_attrs->size(); ++i ) {
     if (!type_assign(&dtype, (*in_attrs)[i])) return false;
   }
-  for ( int i = 0; i < in_attrs->size(); ++i ) {
+  for ( size_t i = 0; i < in_attrs->size(); ++i ) {
     TYPE_ASSIGN_CHECK(*in_attrs, i, dtype);
   }
   if (-1 == dtype) return false;