Merge pull request #4691 from Canpio/dev_opdesc_in_python

Complete `Operator` of Python API

paddle/framework/block_desc.cc

@@ -91,9 +91,5 @@ BlockDescBind *BlockDescBind::ParentBlock() const {
   return prog_->Block(static_cast<size_t>(this->desc_->parent_idx()));
 }
 
-void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
-  BlockDesc *desc = block.RawPtr();
-  this->attrs_[name] = desc;
-}
 }  // namespace framework
 }  // namespace paddle

paddle/framework/op_desc.cc

@@ -100,6 +100,12 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
   need_update_ = true;
 }
 
+void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
+  BlockDesc *desc = block.RawPtr();
+  this->attrs_[name] = desc;
+  need_update_ = true;
+}
+
 void OpDescBind::SetAttrMap(
     const std::unordered_map<std::string, Attribute> &attr_map) {
   attrs_ = attr_map;

paddle/framework/var_desc.h

@@ -34,6 +34,7 @@ inline std::vector<T> RepeatedToVector(
 template <typename T, typename RepeatedField>
 inline void VectorToRepeated(const std::vector<T> &vec,
                              RepeatedField *repeated_field) {
+  repeated_field->Clear();
   repeated_field->Reserve(vec.size());
   for (const auto &elem : vec) {
     *repeated_field->Add() = elem;
@@ -44,6 +45,7 @@ inline void VectorToRepeated(const std::vector<T> &vec,
 template <typename RepeatedField>
 inline void VectorToRepeated(const std::vector<bool> &vec,
                              RepeatedField *repeated_field) {
+  repeated_field->Clear();
   repeated_field->Reserve(vec.size());
   for (auto elem : vec) {
     *repeated_field->Add() = elem;

paddle/operators/uniform_random_op.cc

@@ -54,7 +54,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(
         ctx->Attrs().Get<float>("min") < ctx->Attrs().Get<float>("max"),
         "uniform_random's min must less then max");
-    auto dims = Attr<std::vector<int>>("dims");
+    auto& dims = ctx->Attrs().Get<std::vector<int>>("dims");
     std::vector<int64_t> temp;
     temp.reserve(dims.size());
     for (auto dim : dims) {

paddle/pybind/protobuf.cc

@@ -204,7 +204,7 @@ 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("block_attr", &OpDescBind::GetBlockAttr)
       .def("check_attrs", &OpDescBind::CheckAttrs)
       .def("infer_shape", &OpDescBind::InferShape);
 }

python/paddle/v2/framework/graph.py

 import paddle.v2.framework.core as core
+import paddle.v2.framework.proto.framework_pb2 as framework_pb2
 import collections
 import numpy as np
 import copy
@@ -106,6 +107,40 @@ class Variable(object):
             raise ValueError("Not supported numpy dtype " + str(dtype))
 
 
+def get_all_op_protos():
+    """
+    Get all registered op proto from PaddlePaddle C++ end.
+    :return: A list of registered OpProto.
+    """
+    protostrs = core.get_all_op_protos()
+    ret_values = []
+    for pbstr in protostrs:
+        op_proto = framework_pb2.OpProto.FromString(str(pbstr))
+        ret_values.append(op_proto)
+    return ret_values
+
+
+class OpProtoHolder(object):
+    @classmethod
+    def instance(cls):
+        if not hasattr(cls, '_instance'):
+            cls._instance = cls()
+        return cls._instance
+
+    def __init__(self):
+        assert not hasattr(
+            self.__class__,
+            '_instance'), 'Please use `instance()` to get OpProtoHolder opject!'
+        op_protos = get_all_op_protos()
+        self.op_proto_map = {}
+        for proto in op_protos:
+            self.op_proto_map[proto.type] = proto
+
+    def get_op_proto(self, type):
+        assert type in self.op_proto_map, "Operator \"%s\" has not been registered." % type
+        return self.op_proto_map[type]
+
+
 class Operator(object):
     def __init__(self,
                  block,
@@ -116,20 +151,89 @@ class Operator(object):
                  attrs=None):
         self.block = block
         self.desc = desc
-        if type is not None:
-            # TODO.
-            pass
+        if len(self.desc.type()) != 0:
+            return
+        if type is None:
+            raise ValueError(
+                "`type` to initilized an Operator can not be None.")
+        self.desc.set_type(type)
+        proto = OpProtoHolder.instance().get_op_proto(type)
+
         if inputs is not None:
-            # TODO
-            pass
+            for in_proto in proto.inputs:
+                in_argus = inputs[in_proto.name]
+                if not isinstance(in_argus, list):
+                    in_argus = [in_argus]
+                if not in_proto.duplicable and len(in_argus) > 1:
+                    raise ValueError(
+                        "Input %s expects only one input, but %d are given." %
+                        (in_proto.name, len(in_argus)))
+                in_argu_names = []
+                for argu in in_argus:
+                    in_argu_names.append(argu.name)
+                self.desc.set_input(in_proto.name, in_argu_names)
+
         if outputs is not None:
-            # TODO
-            pass
+            for out_proto in proto.outputs:
+                out_argus = outputs[out_proto.name]
+                if not isinstance(out_argus, list):
+                    out_argus = [out_argus]
+                if not out_proto.duplicable and len(out_argus) > 1:
+                    raise ValueError(
+                        "Output %s expects only one output, but %d are given." %
+                        (out_proto.name, len(out_argus)))
+                out_argu_names = []
+                for argu in out_argus:
+                    out_argu_names.append(argu.name)
+                    argu.op = self
+                self.desc.set_output(out_proto.name, out_argu_names)
+
         if attrs is not None:
-            # TODO
-            pass
+            for attr in proto.attrs:
+                attr_name = attr.name
+                if not attr_name in attrs:
+                    continue
+                if not isinstance(attrs[attr_name], Block):
+                    self.desc.set_attr(attr_name, attrs[attr_name])
+                else:
+                    self.desc.set_block_attr(attr_name, attrs[attr_name].desc)
+
+        self.desc.check_attrs()
+        self.desc.infer_shape(self.block.desc)
+
+    @property
+    def type(self):
+        return self.desc.type()
+
+    def input(self, name):
+        return self.desc.input(name)
+
+    @property
+    def input_names(self):
+        return self.desc.input_names()
+
+    def output(self, name):
+        return self.desc.output(name)
+
+    @property
+    def output_names(self):
+        return self.desc.output_names()
+
+    def has_attr(self, name):
+        return self.desc.has_attr(name)
+
+    def attr_type(self, name):
+        return self.desc.attr_type(name)
+
+    @property
+    def attr_names(self):
+        return self.desc.attr_names()
+
+    def attr(self, name):
+        return self.desc.attr(name)
 
-            # TODO: Getters
+    def block_attr(self, name):
+        return self.desc.block_attr(name)
 
 
 class Block(object):

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

+import unittest
+from paddle.v2.framework.graph import Variable, g_program
+import paddle.v2.framework.core as core
+
+
+class TestOperator(unittest.TestCase):
+    def test_error_type(self):
+        block = g_program.create_block()
+        try:
+            block.append_op()
+            self.assertFail()
+        except ValueError as v_err:
+            self.assertEqual(
+                v_err.message,
+                "`type` to initilized an Operator can not be None.")
+        try:
+            block.append_op(type="no_such_op")
+            self.assertFail()
+        except AssertionError as a_err:
+            self.assertEqual(a_err.message,
+                             "Operator \"no_such_op\" has not been registered.")
+
+    def test_op_desc_creation(self):
+        block = g_program.current_block()
+        mul_x = block.create_var(
+            dtype="float32", shape=[5, 10], lod_level=0, name="mul.x")
+        mul_y = block.create_var(
+            dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
+        mul_out = block.create_var(
+            dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
+        mul_op = block.append_op(
+            type="mul",
+            inputs={"X": [mul_x],
+                    "Y": mul_y},
+            outputs={"Out": [mul_out]},
+            attrs={"x_num_col_dims": 1})
+        self.assertEqual(mul_op.type, "mul")
+        self.assertEqual(mul_op.input_names, ["X", "Y"])
+        self.assertEqual(mul_op.input("X"), ["mul.x"])
+        self.assertEqual(mul_op.input("Y"), ["mul.y"])
+        self.assertEqual(mul_op.output_names, ["Out"])
+        self.assertEqual(mul_op.output("Out"), ["mul.out"])
+        self.assertEqual(
+            set(mul_op.attr_names), set(["x_num_col_dims", "y_num_col_dims"]))
+        self.assertEqual(mul_op.has_attr("x_num_col_dims"), True)
+        self.assertEqual(mul_op.attr_type("x_num_col_dims"), core.AttrType.INT)
+        self.assertEqual(mul_op.attr("x_num_col_dims"), 1)
+        self.assertEqual(mul_op.has_attr("y_num_col_dims"), True)
+        self.assertEqual(mul_op.attr_type("y_num_col_dims"), core.AttrType.INT)
+        self.assertEqual(mul_op.attr("y_num_col_dims"), 1)
+        self.assertEqual(mul_out.op, mul_op)
+
+    def test_mult_input(self):
+        block = g_program.current_block()
+        sum_x1 = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.x1")
+        sum_x2 = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.x2")
+        sum_x3 = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.x3")
+        sum_out = block.create_var(
+            dtype="int", shape=[3, 4], lod_level=0, name="sum.out")
+        sum_op = block.append_op(
+            type="sum",
+            inputs={"X": [sum_x1, sum_x2, sum_x3]},
+            outputs={"Out": sum_out})
+        self.assertEqual(sum_op.type, "sum")
+        self.assertEqual(sum_op.input_names, ["X"])
+        self.assertEqual(sum_op.input("X"), ["sum.x1", "sum.x2", "sum.x3"])
+        self.assertEqual(sum_op.output_names, ["Out"])
+        self.assertEqual(sum_op.output("Out"), ["sum.out"])
+        self.assertEqual(sum_out.op, sum_op)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -53,7 +53,7 @@ class TestOpDesc(unittest.TestCase):
         self.assertEqual(8, len(op.attr_names()))
 
         op.set_block_attr("block_attr", prog.block(0))
-        self.assertEqual(0, op.get_block_attr("block_attr"))
+        self.assertEqual(0, op.block_attr("block_attr"))
 
         mul_op = block.append_op()
         mul_op.set_type("mul")