[SYMBOL] Change list_input->list_input_names, add list_input_variables (

#59)

* [SYMBOL] Change list_input->list_input_names, add list_input_variables

* fix

include/nnvm/c_api.h

@@ -205,8 +205,25 @@ NNVM_DLL int NNSymbolListAttrs(SymbolHandle symbol,
                                int recursive_option,
                                nn_uint *out_size,
                                const char*** out);
+
+/*!
+ * \brief List inputs variables in the symbol.
+ * \param symbol the symbol
+ * \param option The option to list the inputs
+ *   option=0 means list all arguments.
+ *   option=1 means list arguments that are readed only by the graph.
+ *   option=2 means list arguments that are mutated by the graph.
+ * \param out_size output size
+ * \param out_sym_array the output array.
+ * \return 0 when success, -1 when failure happens
+ */
+NNVM_DLL int NNSymbolListInputVariables(SymbolHandle symbol,
+                                        int option,
+                                        nn_uint *out_size,
+                                        SymbolHandle** out_sym_array);
+
 /*!
- * \brief List inputs in the symbol.
+ * \brief List input names in the symbol.
  * \param symbol the symbol
  * \param option The option to list the inputs
  *   option=0 means list all arguments.

python/nnvm/symbol.py

@@ -108,7 +108,7 @@ def __deepcopy__(self, _=None):
     def __getitem__(self, index):
         if isinstance(index, _base.string_types):
             idx = None
-            for i, name in enumerate(self.list_outputs()):
+            for i, name in enumerate(self.list_output_names()):
                 if name == index:
                     if idx is not None:
                         raise ValueError('There are multiple outputs with name \"%s\"' % index)
@@ -177,7 +177,40 @@ def get_internals(self):
             self.handle, _ctypes.byref(handle)))
         return Symbol(handle=handle)
 
-    def list_inputs(self, option='all'):
+    def _get_list_copt(self, option):
+        """internal function to get list option"""
+        if option == 'all':
+            return _ctypes.c_int(0)
+        elif option == 'read_only':
+            return _ctypes.c_int(1)
+        elif option == 'aux_state':
+            return _ctypes.c_int(2)
+        else:
+            raise ValueError("option need to be in {'all', 'read_only, 'aux_state'}")
+
+    def list_input_variables(self, option='all'):
+        """List all the input variables in the symbol.
+
+        Parameters
+        ----------
+        option : {'all', 'read_only', 'aux_state'}, optional
+           The listing option
+           - 'all' will list all the arguments.
+           - 'read_only' lists arguments that are readed by the graph.
+           - 'aux_state' lists arguments that are mutated by the graph as state.
+        Returns
+        -------
+        vars : list of symbol
+            List of all the variables
+        """
+        size = _ctypes.c_uint()
+        sarr = _ctypes.POINTER(_base.SymbolHandle)()
+        _check_call(_LIB.NNSymbolListInputVariables(
+            self.handle, self._get_list_copt(option),
+            _ctypes.byref(size), _ctypes.byref(sarr)))
+        return [Symbol(_base.SymbolHandle(sarr[i])) for i in range(size.value)]
+
+    def list_input_names(self, option='all'):
         """List all the inputs in the symbol.
 
         Parameters
@@ -194,19 +227,12 @@ def list_inputs(self, option='all'):
         """
         size = _ctypes.c_uint()
         sarr = _ctypes.POINTER(_ctypes.c_char_p)()
-        if option == 'all':
-            copt = _ctypes.c_int(0)
-        elif option == 'read_only':
-            copt = _ctypes.c_int(1)
-        elif option == 'aux_state':
-            copt = _ctypes.c_int(2)
-        else:
-            raise ValueError("option need to be in {'all', 'read_only, 'aux_state'}")
         _check_call(_LIB.NNSymbolListInputNames(
-            self.handle, copt, _ctypes.byref(size), _ctypes.byref(sarr)))
+            self.handle, self._get_list_copt(option),
+            _ctypes.byref(size), _ctypes.byref(sarr)))
         return [_base.py_str(sarr[i]) for i in range(size.value)]
 
-    def list_outputs(self):
+    def list_output_names(self):
         """List all outputs in the symbol.
 
         Returns

src/c_api/c_api_symbolic.cc

@@ -221,6 +221,25 @@ int NNSymbolListAttrs(SymbolHandle symbol,
   API_END();
 }
 
+int NNSymbolListInputVariables(SymbolHandle symbol,
+                               int option,
+                               nn_uint *out_size,
+                               SymbolHandle** out_sym_array) {
+  Symbol *s = static_cast<Symbol*>(symbol);
+  NNAPIThreadLocalEntry *ret = NNAPIThreadLocalStore::Get();
+  API_BEGIN();
+  std::vector<NodePtr> vs = s->ListInputs(Symbol::ListInputOption(option));
+  ret->ret_handles.clear();
+  for (size_t i = 0; i < vs.size(); ++i) {
+    nnvm::Symbol* rs = new nnvm::Symbol();
+    rs->outputs.push_back(NodeEntry{vs[i], 0, 0});
+    ret->ret_handles.push_back(rs);
+  }
+  *out_size = static_cast<nn_uint>(vs.size());
+  *out_sym_array = dmlc::BeginPtr(ret->ret_handles);
+  API_END();
+}
+
 int NNSymbolListInputNames(SymbolHandle symbol,
                            int option,
                            nn_uint *out_size,

src/core/symbolic.cc

@@ -87,7 +87,8 @@ inline std::vector<std::string> GetKeys(
 
 // whether the symbol is atomic functor
 inline bool IsAtomic(const std::vector<NodeEntry>& outputs) {
-  return outputs[0].node->inputs.size() == 0;
+  return outputs[0].node->inputs.size() == 0 &&
+      outputs[0].node->control_deps.size() == 0;
 }
 
 // public functions
@@ -118,7 +119,9 @@ Symbol Symbol::Copy() const {
 }
 
 void Symbol::Print(std::ostream &os) const {
-  if (outputs.size() == 1 && outputs[0].node->inputs.size() == 0) {
+  if (outputs.size() == 1 &&
+      outputs[0].node->inputs.size() == 0 &&
+      outputs[0].node->control_deps.size() == 0) {
     if (outputs[0].node->is_variable()) {
       os << "Variable:" << outputs[0].node->attrs.name << '\n';
     } else {

src/pass/gradient.cc

@@ -69,6 +69,7 @@ Graph Gradient(Graph src) {
   // topo sort
   std::vector<NodePtr> topo_order;
   std::unordered_map<Node*, std::vector<GradEntry> > output_grads;
+
   DFSVisit(ys, [&](const NodePtr& node) {
       if (output_grads.count(node.get()) == 0) {
         output_grads[node.get()].resize(node->num_outputs());
@@ -113,13 +114,15 @@ Graph Gradient(Graph src) {
       e.sum = agg_fun(std::move(e.grads));
       out_agg_grads.push_back(e.sum);
     }
-    std::vector<NodeEntry> input_grads = grad_fun_map[ptr->op()]
-        (mirror_map.size() == 0 ? ptr : mirror_map.at(ptr.get()), out_agg_grads);
-    CHECK_EQ((*rit)->inputs.size(), input_grads.size())
-        << "Gradient function not returning enough gradient";
-    auto git = input_grads.begin();
-    for (auto it = (*rit)->inputs.begin(); it != (*rit)->inputs.end(); ++it, ++git) {
-      output_grads[it->node.get()][it->index].grads.emplace_back(std::move(*git));
+    if ((*rit)->inputs.size() != 0) {
+      std::vector<NodeEntry> input_grads = grad_fun_map[ptr->op()]
+          (mirror_map.size() == 0 ? ptr : mirror_map.at(ptr.get()), out_agg_grads);
+      CHECK_EQ((*rit)->inputs.size(), input_grads.size())
+          << "Gradient function not returning enough gradient";
+      auto git = input_grads.begin();
+      for (auto it = (*rit)->inputs.begin(); it != (*rit)->inputs.end(); ++it, ++git) {
+        output_grads[it->node.get()][it->index].grads.emplace_back(std::move(*git));
+      }
     }
   }
   // take out the xs' grads

tests/python/test_graph.py

@@ -42,8 +42,8 @@ def test_list_args():
     y = sym.add(y, z, name='add1')
     # write after read
     z = sym.assign(x, y, name='assign')
-    assert z.list_inputs('read_only') == ['conv_weight', 'z']
-    assert z.list_inputs('aux_state') == ['x']
+    assert z.list_input_names('read_only') == ['conv_weight', 'z']
+    assert z.list_input_names('aux_state') == ['x']
 
 def test_infer_shape():
     x = sym.Variable('x', shape=(4, 2))

tests/python/test_symbol.py

@@ -7,17 +7,19 @@ def test_compose():
     y = sym.exp(sym.add(x, x, name='add', gpu=2),
                 name='exp', gpu=1, attr={"kk": "1"})
 
-    assert y.list_inputs() == ['x']
-    assert y.list_outputs() == ["exp_output"]
+    assert y.list_input_names() == ['x']
+    assert y.list_output_names() == ["exp_output"]
     assert y.list_attr()['gpu'] == '1'
     z = y.get_internals()
-    assert z['add_output'].list_outputs() == ['add_output']
+    assert z['add_output'].list_output_names() == ['add_output']
     assert y.list_attr(recursive=True)['add_gpu'] == '2'
 
 def test_default_input():
     x = sym.Variable('x')
     y = sym.conv2d(data=x, name='conv')
-    assert y.list_inputs() == ['x', 'conv_weight']
+    assert y.list_input_names() == ['x', 'conv_weight']
+    tname = [z.list_output_names()[0] for z in y.list_input_variables()]
+    assert tname == y.list_input_names()
     try:
         z = sym.add(x)
         assert False