Merge branch 'feature/change_proto_to_desc' into feature/complete_variable_bind

doc/design/python_api.md

@@ -22,7 +22,7 @@ Whenever we create a block, we need to set its parent block to the current block
 ```python
 class Program(objects):
     def __init__(self):
-        self.proto = core.NewProgram() # a C++ ProgramDesc pointer.
+        self.desc = core.NewProgram() # a C++ ProgramDesc pointer.
         self.blocks = vector<Block>()
         self.blocks.append(Block(self, -1)) # the global block
         self.current_block = 0          # initialized to the global block
@@ -57,7 +57,7 @@ A [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.m
 ```python
 class Block(objects):
     def __init__(self, program, parent_idx):
-        self.proto = core.NewBlock(program.proto)
+        self.desc = core.NewBlock(program.desc)
         self.program = program
         self.vars = map<string, Variable>()
         self.ops = vector<Operator>()
@@ -98,11 +98,11 @@ class Operator(object):
                  outputs,# dict<stirng, Variable>
                  attrs   # dict<string, Any>
                  ):
-        self.proto = core.NewOpDesc(block.proto, type, inputs, outputs, attrs)
-        core.infer_shape(self.proto, inputs, outputs)
+        self.desc = core.NewOpDesc(block.desc, type, inputs, outputs, attrs)
+        core.infer_shape(self.desc, inputs, outputs)
 
     def type(self):
-        return self.proto.type()
+        return self.desc.type()
 ```
 
 `Operator` creates the `OpDesc` message in C++ space, so that it can call the `InferShape` function, which is in C++.
@@ -124,7 +124,7 @@ class Variable(object):
             name = unique_name_generator()
         self.name = name
         self.block = block
-        self.proto = core.NewVarDesc(block.proto, name, shape, lod_level)
+        self.desc = core.NewVarDesc(block.desc, name, shape, lod_level)
         self.writer = None
 ```
 

paddle/framework/CMakeLists.txt

@@ -19,7 +19,7 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope)
 proto_library(framework_proto SRCS framework.proto)
 
 cc_library(attribute SRCS attribute.cc DEPS framework_proto)
-cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute)
+cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim)
 cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute)
 cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
 cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc)

paddle/framework/op_desc.cc

@@ -13,7 +13,10 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/framework/op_desc.h"
+#include <functional>
+#include <unordered_map>
 #include "paddle/framework/block_desc.h"
+#include "paddle/framework/operator.h"
 
 namespace paddle {
 namespace framework {
@@ -185,5 +188,38 @@ void OpDescBind::Sync() {
     need_update_ = false;
   }
 }
+
+using InferShapeFuncMap =
+    std::unordered_map<std::string /*op_type*/,
+                       std::function<void(InferShapeContext *)>>;
+
+static InferShapeFuncMap &InferShapeFuncs() {
+  static InferShapeFuncMap *g_map = nullptr;
+  if (g_map == nullptr) {
+    g_map = new InferShapeFuncMap();
+    auto &info_map = OpInfoMap::Instance();
+    // all registered kernels
+    for (auto &pair : OperatorWithKernel::AllOpKernels()) {
+      auto &info = info_map.Get(pair.first);
+      // use empty type here to avoid runtime checks.
+      auto op =
+          static_cast<OperatorWithKernel *>(info.Creator()("", {}, {}, {}));
+      g_map->insert(
+          {pair.first, [op](InferShapeContext *ctx) { op->InferShape(ctx); }});
+    }
+  }
+  return *g_map;
+}
+
+void OpDescBind::InferShape(const BlockDescBind &block) const {
+  auto &funcs = InferShapeFuncs();
+  auto it = funcs.find(this->Type());
+  if (it == funcs.end()) {
+    PADDLE_THROW("Operator %s has not been registered", this->Type());
+  }
+  CompileTimeInferShapeContext ctx(*this, block);
+  it->second(&ctx);
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/framework/op_desc.h

@@ -100,6 +100,8 @@ class OpDescBind {
     return &this->attrs_;
   }
 
+  void InferShape(const BlockDescBind &block) const;
+
  private:
   template <typename MapType>
   static std::vector<typename MapType::key_type> MapKeys(const MapType &map) {

paddle/pybind/protobuf.cc

@@ -198,7 +198,8 @@ void BindOpDesc(py::module &m) {
       .def("set_attr", &OpDescBind::SetAttr)
       .def("attr", &OpDescBind::GetAttr)
       .def("set_block_attr", &OpDescBind::SetBlockAttr)
-      .def("get_block_attr", &OpDescBind::GetBlockAttr);
+      .def("get_block_attr", &OpDescBind::GetBlockAttr)
+      .def("infer_shape", &OpDescBind::InferShape);
 }
 
 }  // namespace pybind

paddle/pybind/pybind.cc

@@ -231,21 +231,6 @@ All parameter, weight, gradient are variables in Paddle.
                                    desc.InitializationErrorString());
                     return OpRegistry::CreateOp(desc);
                   })
-      .def_static("infer_shape",
-                  [](OpDescBind &op_desc, BlockDescBind &block) {
-                    auto op = OpRegistry::CreateOp(*op_desc.Proto());
-                    auto *op_with_kernel =
-                        dynamic_cast<OperatorWithKernel *>(op.get());
-                    if (op_with_kernel != nullptr) {
-                      auto ctx = CompileTimeInferShapeContext(op_desc, block);
-                      op_with_kernel->InferShape(&ctx);
-                    } else {
-                      PADDLE_THROW(
-                          "OP(%s) is not type of OperatorWithKernel, "
-                          "should not call this function",
-                          op_desc.Type());
-                    }
-                  })
       .def("backward",
            [](const OperatorBase &forwardOp,
               const std::unordered_set<std::string> &no_grad_vars) {

python/paddle/v2/framework/graph.py

@@ -13,15 +13,15 @@ class Variable(object):
         if name is None:
             name = Variable._unique_var_name_()
         try:
-            self.proto = self.block.proto.var(name)
+            self.desc = self.block.desc.var(name)
             is_new_var = False
         except core.EnforceNotMet:
-            self.proto = self.block.proto.new_var(name)
+            self.desc = self.block.desc.new_var(name)
             is_new_var = True
 
         if shape is not None:
             if is_new_var:
-                self.proto.set_shape(shape)
+                self.desc.set_shape(shape)
             else:
                 old_shape = self.shape
                 shape = tuple(shape)
@@ -34,7 +34,7 @@ class Variable(object):
             if not isinstance(dtype, core.DataType):
                 dtype = Variable._convert_np_dtype_to_dtype_(dtype)
             if is_new_var:
-                self.proto.set_data_type(dtype)
+                self.desc.set_data_type(dtype)
             else:
                 old_dtype = self.data_type()
                 if dtype != old_shape:
@@ -46,7 +46,7 @@ class Variable(object):
 
         if lod_level is not None:
             if is_new_var:
-                self.proto.set_lod_level(lod_level)
+                self.desc.set_lod_level(lod_level)
             else:
                 if lod_level != self.lod_level:
                     raise ValueError("Variable {0} has been created before. "
@@ -54,26 +54,25 @@ class Variable(object):
                                      "lod_level is {2}. They are not "
                                      "matched".format(self.name, self.lod_level,
                                                       lod_level))
-
         self.block.vars[name] = self
         self.op = None
 
     @property
     def name(self):
-        return self.proto.name()
+        return self.desc.name()
 
     @property
     def shape(self):
         # convert to tuple, make it as same as numpy API.
-        return tuple(self.proto.shape())
+        return tuple(self.desc.shape())
 
     @property
     def data_type(self):
-        return self.proto.data_type()
+        return self.desc.data_type()
 
     @property
     def lod_level(self):
-        return self.proto.lod_level()
+        return self.desc.lod_level()
 
     @staticmethod
     def _unique_var_name_():
@@ -104,13 +103,13 @@ class Variable(object):
 class Operator(object):
     def __init__(self,
                  block,
-                 proto,
+                 desc,
                  type=None,
                  inputs=None,
                  outputs=None,
                  attrs=None):
         self.block = block
-        self.proto = proto
+        self.desc = desc
         if type is not None:
             # TODO.
             pass
@@ -129,31 +128,31 @@ class Operator(object):
 
 class Block(object):
     def __init__(self, program, idx):
-        self.proto = program.proto.block(idx)
+        self.desc = program.desc.block(idx)
         self.vars = dict()  # var_name --> var
         self.ops = collections.deque()  # operator list
         self.program = program
 
     @property
     def parent_idx(self):
-        return self.proto.parent
+        return self.desc.parent
 
     @property
     def idx(self):
-        return self.proto.id
+        return self.desc.id
 
     def create_var(self, *args, **kwargs):
         return Variable(self, *args, **kwargs)
 
     def append_op(self, *args, **kwargs):
-        op_proto = self.proto.append_op()
-        op = Operator(self, op_proto, *args, **kwargs)
+        op_desc = self.desc.append_op()
+        op = Operator(self, op_desc, *args, **kwargs)
         self.ops.append(op)
         return op
 
     def prepend_op(self, *args, **kwargs):
-        op_proto = self.proto.prepend_op()
-        op = Operator(self, op_proto, *args, **kwargs)
+        op_desc = self.desc.prepend_op()
+        op = Operator(self, op_desc, *args, **kwargs)
         self.ops.appendleft(op)
         return op
 
@@ -170,7 +169,7 @@ class Program(object):
     def __init__(self):
         assert not hasattr(self.__class__,
                            '_instance'), 'Do not call constructor directly!'
-        self.proto = core.ProgramDesc.instance()
+        self.desc = core.ProgramDesc.instance()
         self.blocks = [Block(self, 0)]
         self.current_block_idx = 0
 
@@ -182,7 +181,7 @@ class Program(object):
 
     def create_block(self):
         new_block_idx = len(self.blocks)
-        self.proto.append_block(self.current_block().proto)
+        self.desc.append_block(self.current_block().desc)
         self.current_block_idx = new_block_idx
         self.blocks.append(Block(self, self.current_block_idx))
         return self.current_block()

python/paddle/v2/framework/tests/test_infer_shape.py

 import unittest
+
 import paddle.v2.framework.core as core
-from paddle.v2.framework.op import Operator
 
 
 class TestInferShape(unittest.TestCase):
@@ -26,7 +26,7 @@ class TestInferShape(unittest.TestCase):
         sum_op_desc.set_input("X", ["x1", "x2"])
         sum_op_desc.set_output("Out", ["out"])
 
-        core.Operator.infer_shape(sum_op_desc, block)
+        sum_op_desc.infer_shape(block)
         self.assertEqual(out.shape(), shape)
 
     def test_mul_op(self):
@@ -55,7 +55,7 @@ class TestInferShape(unittest.TestCase):
         mul_op_desc.set_attr("x_num_col_dims", 1)
         mul_op_desc.set_attr("y_num_col_dims", 1)
 
-        core.Operator.infer_shape(mul_op_desc, block)
+        mul_op_desc.infer_shape(block)
         self.assertEqual(out.shape(), [x_shape[0], y_shape[1]])