ApplyPass -> ApplyPasses; Refactored infer pass; (apache#43)

* ApplyPass -> ApplyPasses; Refactored infer pass;

* lint fix

Author: jermainewang

nnvm/include/nnvm/c_api.h

@@ -329,16 +329,16 @@ NNVM_DLL int NNGraphSetNodeEntryListAttr_(GraphHandle handle,
                                           const char* key,
                                           SymbolHandle list);
 /*!
- * \brief Apply pass on the src graph.
+ * \brief Apply passes on the src graph.
  * \param src The source graph handle.
  * \param num_pass The number of pass to be applied.
  * \param pass_names The names of the pass.
  * \param dst The result graph.
  * \return 0 when success, -1 when failure happens
  */
-NNVM_DLL int NNGraphApplyPass(GraphHandle src,
-                              nn_uint num_pass,
-                              const char** pass_names,
-                              GraphHandle *dst);
+NNVM_DLL int NNGraphApplyPasses(GraphHandle src,
+                                nn_uint num_pass,
+                                const char** pass_names,
+                                GraphHandle *dst);
 
 #endif  // NNVM_C_API_H_

nnvm/include/nnvm/graph.h

@@ -179,11 +179,11 @@ class IndexedGraph {
    * \param other The source graph.
    */
   explicit IndexedGraph(const Graph& other);
-  // node pointers in CSR structure.
+  // Node pointers in CSR structure.
   std::vector<Node> nodes_;
-  // index all to input nodes
+  // Index to all input nodes.
   std::vector<uint32_t> input_nodes_;
-  // index to mutable input nodes
+  // Index to all mutable input nodes.
   std::unordered_set<uint32_t> mutable_input_nodes_;
   // space to store the outputs entries
   std::vector<NodeEntry> outputs_;

nnvm/include/nnvm/graph_attr_types.h

@@ -18,7 +18,7 @@ namespace nnvm {
  * \note Stored under ret.attrs["json"], provided by Pass "SaveJSON"
 
  * \code
- *  Graph ret = ApplyPass(src_graph, {"SaveJSON"});
+ *  Graph ret = ApplyPass(src_graph, "SaveJSON");
  *  const JSONString& json = ret.GetAttr<JSONString>("shape");
  * \endcode
  */
@@ -29,7 +29,7 @@ using JSONString = std::string;
  * \note Stored under graph.attrs["shape"], provided by Pass "InferShape"
  *
  * \code
- *  Graph g = ApplyPass(src_graph, {"InferShape"});
+ *  Graph g = ApplyPass(src_graph, "InferShape");
  *  const ShapeVector& shapes = g.GetAttr<ShapeVector>("shape");
  *  // get shape by entry id
  *  TShape entry_shape = shapes[g.indexed_graph().entry_id(my_entry)];
@@ -44,7 +44,7 @@ using ShapeVector = std::vector<TShape>;
  * \note Stored under graph.attrs["dtype"], provided by Pass "InferType"
  *
  * \code
- *  Graph g = ApplyPass(src_graph, {"InferType"});
+ *  Graph g = ApplyPass(src_graph, "InferType");
  *  const DTypeVector& types = g.GetAttr<DTypeVector>("dtype");
  *  // get shape by entry id
  *  int entry_type = dtypes[g.indexed_graph().entry_id(my_entry)];
@@ -59,7 +59,7 @@ using DTypeVector = std::vector<int>;
  * \note Stored under graph.attrs["device"], provided by Pass "PlaceDevice"
  *
  * \code
- *  Graph g = ApplyPass(src_graph, {"PlaceDevice"});
+ *  Graph g = ApplyPass(src_graph, "PlaceDevice");
  *  const &device = g.GetAttr<DeviceVector>("device");
  *  // get device by node_id
  *  int device_type = device[g.indexed_graph().node_id(my_node)];
@@ -83,7 +83,7 @@ using DeviceAssignMap = std::unordered_map<std::string, int>;
  *  If the storage id is -1 then the storage is not assigned.
  *
  * \code
- *  Graph g = ApplyPass(src_graph, {"PlanMemory"});
+ *  Graph g = ApplyPass(src_graph, "PlanMemory");
  *  const &storage = g.GetAttr<StorageVector>("storage");
  *  // get storage id by entry
  *  int storage_id = storage[g.indexed_graph().entry_id(my_entry)];

nnvm/include/nnvm/pass.h

@@ -29,11 +29,22 @@ typedef std::function<Graph (Graph src)> PassFunction;
 /*!
  * \brief Apply a series of pass transformations on the input graph.
  * \param src The graph to be transformed.
+ * \param passes A list of pass names to be applied.
+ * \return The transformed graph
+ */
+Graph ApplyPasses(Graph src,
+                  const std::vector<std::string>& passes);
+
+/*!
+ * \brief Apply one pass to the graph.
+ * \param src The graph to be transformed.
  * \param pass The name of pass to be applied.
  * \return The transformed graph.
  */
-Graph ApplyPass(Graph src,
-                const std::vector<std::string>& pass);
+inline Graph ApplyPass(Graph src, const std::string& pass) {
+  return ApplyPasses(src, {pass});
+}
+
 
 /*!
  * \brief Registry entry for DataIterator factory functions.

nnvm/include/nnvm/pass_functions.h

@@ -28,7 +28,7 @@ namespace pass {
 inline Graph LoadJSON(const std::string& json_str) {
   Graph ret;
   ret.attrs["json"] = std::make_shared<any>(json_str);
-  return ApplyPass(ret, {"LoadJSON"});
+  return ApplyPass(ret, "LoadJSON");
 }
 
 /*!
@@ -37,7 +37,7 @@ inline Graph LoadJSON(const std::string& json_str) {
  * \return The json string.
  */
 inline std::string SaveJSON(Graph graph) {
-  Graph ret = ApplyPass(std::move(graph), {"SaveJSON"});
+  Graph ret = ApplyPass(std::move(graph), "SaveJSON");
   return ret.GetAttr<std::string>("json");
 }
 
@@ -52,7 +52,7 @@ inline std::string SaveJSON(Graph graph) {
  * \return A graph with proper control flow dependencies added.
  */
 inline Graph OrderMutation(Graph src) {
-  return ApplyPass(std::move(src), {"OrderMutation"});
+  return ApplyPass(std::move(src), "OrderMutation");
 }
 
 /*!
@@ -73,7 +73,7 @@ inline Graph InferShape(Graph graph,
   if (shape_attr_key.length() != 0) {
     graph.attrs["shape_attr_key"] = std::make_shared<any>(std::move(shape_attr_key));
   }
-  return ApplyPass(std::move(graph), {"InferShape"});
+  return ApplyPass(std::move(graph), "InferShape");
 }
 
 /*!
@@ -94,7 +94,7 @@ inline Graph InferType(Graph graph,
   if (dtype_attr_key.length() != 0) {
     graph.attrs["dtype_attr_key"] = std::make_shared<any>(std::move(dtype_attr_key));
   }
-  return ApplyPass(std::move(graph), {"InferType"});
+  return ApplyPass(std::move(graph), "InferType");
 }
 
 /*!
@@ -118,7 +118,7 @@ inline Graph PlaceDevice(Graph graph,
   graph.attrs["device_group_attr_key"] = std::make_shared<any>(std::move(device_group_attr_key));
   graph.attrs["device_assign_map"] = std::make_shared<any>(std::move(device_assign_map));
   graph.attrs["device_copy_op"] = std::make_shared<any>(std::move(device_copy_op));
-  return ApplyPass(std::move(graph), {"PlaceDevice"});
+  return ApplyPass(std::move(graph), "PlaceDevice");
 }
 
 /*!
@@ -149,7 +149,7 @@ inline Graph Gradient(
     graph.attrs["grad_mirror_fun"] = std::make_shared<any>(mirror_fun);
   }
 
-  return ApplyPass(std::move(graph), {"Gradient"});
+  return ApplyPass(std::move(graph), "Gradient");
 }
 
 }  // namespace pass

nnvm/python/nnvm/graph.py

@@ -113,7 +113,7 @@ def apply(self, passes):
         cpass = c_array(ctypes.c_char_p, [c_str(key) for key in passes])
         ghandle = GraphHandle()
         npass = nn_uint(len(passes))
-        check_call(_LIB.NNGraphApplyPass(self.handle, npass, cpass, ctypes.byref(ghandle)))
+        check_call(_LIB.NNGraphApplyPasses(self.handle, npass, cpass, ctypes.byref(ghandle)))
         return Graph(ghandle)
 
 

nnvm/src/c_api/c_api_graph.cc

@@ -82,17 +82,17 @@ int NNGraphGetJSONAttr(GraphHandle handle,
   API_END();
 }
 
-int NNGraphApplyPass(GraphHandle src,
-                     nn_uint num_pass,
-                     const char** pass_names,
-                     GraphHandle *dst) {
+int NNGraphApplyPasses(GraphHandle src,
+                       nn_uint num_pass,
+                       const char** pass_names,
+                       GraphHandle *dst) {
   Graph* g = new Graph();
   API_BEGIN();
   std::vector<std::string> vpass;
   for (nn_uint i = 0; i < num_pass; ++i) {
     vpass.emplace_back(std::string(pass_names[i]));
   }
-  *g = ApplyPass(*static_cast<Graph*>(src), vpass);
+  *g = ApplyPasses(*static_cast<Graph*>(src), vpass);
   *dst = g;
   API_END_HANDLE_ERROR(delete g);
 }

nnvm/src/core/pass.cc

@@ -22,8 +22,8 @@ const PassFunctionReg* FindPassDep(const std::string&attr_name) {
   return nullptr;
 }
 
-Graph ApplyPass(Graph g,
-                const std::vector<std::string>& pass) {
+Graph ApplyPasses(Graph g,
+                  const std::vector<std::string>& pass) {
   std::vector<const PassFunctionReg*> fpass;
   for (auto& name : pass) {
     auto* reg = dmlc::Registry<PassFunctionReg>::Find(name);

nnvm/src/pass/infer_shape_type.cc

@@ -13,7 +13,7 @@ namespace {
 
 template<typename AttrType, typename IsNone>
 Graph InferAttr(Graph &&ret,
-                const AttrType def_value,
+                const AttrType default_val,
                 const char* infer_name,
                 const char* input_name,
                 const char* attr_key_name,
@@ -23,16 +23,16 @@ Graph InferAttr(Graph &&ret,
   using AttrVector = std::vector<AttrType>;
   const IndexedGraph& idx = ret.indexed_graph();
   static auto& finfer_shape =
-      Op::GetAttr<FInferNodeEntryAttr<AttrType> >(infer_name);
+      Op::GetAttr<FInferNodeEntryAttr<AttrType>>(infer_name);
   static auto& backward_map =
       Op::GetAttr<FBackwardOutToInIndex>("FBackwardOutToInIndex");
   // reshape shape vector
-  AttrVector rshape(idx.num_node_entries(), def_value);
+  AttrVector rshape(idx.num_node_entries(), default_val);
 
   if (ret.attrs.count(input_name) != 0) {
     const AttrVector& shape_args = ret.GetAttr<AttrVector>(input_name);
     CHECK_LE(shape_args.size(), idx.input_nodes().size())
-        << "shape args is more than number of arguments";
+        << "More provided shapes than number of arguments.";
     for (size_t i = 0; i < shape_args.size(); ++i) {
       rshape[idx.entry_id(idx.input_nodes()[i], 0)] = shape_args[i];
     }
@@ -46,47 +46,54 @@ Graph InferAttr(Graph &&ret,
     ret.attrs.erase(attr_key_name);
   }
 
-  // temp space for shape inference.
+  // Temp space for shape inference.
   std::vector<AttrType> ishape, oshape;
   // number of completed nodes
   size_t num_unknown = 0;
   for (uint32_t nid = 0; nid < idx.num_nodes(); ++nid) {
     const auto& inode = idx[nid];
-    uint32_t num_inputs = inode.inputs.size();
-    uint32_t num_outputs = inode.source->num_outputs();
+    const uint32_t num_inputs = inode.inputs.size();
+    const uint32_t num_outputs = inode.source->num_outputs();
     if (inode.source->is_variable()) {
-      if (shape_attr_key.length() != 0 && fis_none(rshape[idx.entry_id(nid, 0)])) {
+      // Variable node. No operator. Only one output entry.
+      CHECK(inode.source->op() == nullptr);
+      CHECK_EQ(num_outputs, 1);
+      const uint32_t out_ent_id = idx.entry_id(nid, 0);
+      if (shape_attr_key.length() != 0 && fis_none(rshape[out_ent_id])) {
         auto it = inode.source->attrs.dict.find(shape_attr_key);
         if (it != inode.source->attrs.dict.end()) {
-          CHECK_EQ(num_outputs, 1);
           std::istringstream is(it->second);
-          CHECK(is >> rshape[idx.entry_id(nid, 0)]) << "Invalid attribute";
+          CHECK(is >> rshape[out_ent_id]) << "Invalid attribute";
         }
       }
-      continue;
-    }
-    if (finfer_shape.count(inode.source->op())) {
-      ishape.resize(num_inputs, def_value);
+    } else if (finfer_shape.count(inode.source->op())) {
+      // Forward operator inference.
+      ishape.resize(num_inputs, default_val);
       for (uint32_t i = 0; i < ishape.size(); ++i) {
         ishape[i] = rshape[idx.entry_id(inode.inputs[i])];
       }
-      oshape.resize(num_outputs, def_value);
+      oshape.resize(num_outputs, default_val);
       for (uint32_t i = 0; i < oshape.size(); ++i) {
         oshape[i] = rshape[idx.entry_id(nid, i)];
       }
-      num_unknown +=
-          !(finfer_shape[inode.source->op()](inode.source->attrs, &ishape, &oshape));
+      // Call inference function of the operator.
+      bool forward_known = finfer_shape[inode.source->op()](
+          inode.source->attrs, &ishape, &oshape);
+      num_unknown += !forward_known;
+      // Save to the result map.
       for (uint32_t i = 0; i < num_inputs; ++i) {
         rshape[idx.entry_id(inode.inputs[i])] = ishape[i];
       }
       for (uint32_t i = 0; i < num_outputs; ++i) {
         rshape[idx.entry_id(nid, i)] = oshape[i];
       }
     } else if (backward_map.count(inode.source->op())) {
-      // backward operator inference.
+      // Backward operator inference.
       CHECK_GE(inode.control_deps.size(), 1)
           << "BackwardOp need to have control_deps to its forward op";
-      const auto& fnode = idx[inode.control_deps[0]];
+      const IndexedGraph::Node& fnode = idx[inode.control_deps[0]];
+      // Inference the outputs of backward operator (equal to the inputs
+      // of its corresponding forward operator).
       std::vector<uint32_t> out_map =
           backward_map[inode.source->op()](inode.source->attrs);
       bool known = true;