Merge pull request #2893 from reyoung/feature/op_creation_methods

Python Generate OpCreation Methods by OpProto

paddle/framework/op_registry.h

@@ -1,6 +1,7 @@
 #pragma once
 
 #include <algorithm>
+#include <atomic>
 #include <type_traits>
 #include <unordered_map>
 #include <unordered_set>
@@ -214,11 +215,14 @@ class OpRegistry {
   }
 
   static OperatorPtr CreateOp(const OpDesc& op_desc) {
+    //! Create a OpPtr by type.
     std::string op_type = op_desc.type();
     OperatorPtr op(creators().at(op_type)());
+    //! Fill op's data member. Not use constructor because it will be noising
+    //! for Op developer.
     const OpProto& op_proto = protos().at(op_type);
-    // set op's inputs_ from desc.
     op->type_ = op_desc.type();
+    // set op's inputs_ from desc.
     op->inputs_.reserve((size_t)op_desc.inputs_size());
     std::copy(op_desc.inputs().begin(), op_desc.inputs().end(),
               std::back_inserter(op->inputs_));
@@ -226,13 +230,20 @@ class OpRegistry {
     op->outputs_.reserve((size_t)op_desc.outputs_size());
     std::copy(op_desc.outputs().begin(), op_desc.outputs().end(),
               std::back_inserter(op->outputs_));
-    // set op's attr;
+
+    //! Fill attrs, and validate attrs.
     for (auto& attr : op_desc.attrs()) {
       op->attrs_[attr.name()] = AttrTypeHelper::GetAttrValue(attr);
     }
     op_checkers().at(op_type).Check(op->attrs_);
+
+    //! Convert Temporary variable name to an unique variable name.
+    GenerateTempVariableName(op.get());
+
     // set argument offsets stored in op.
     CreateInOutOffsetMap(op, op_proto);
+    //! Other op's custom Init for a complex Op. For simple Op, the Init
+    //! method do nothing.
     op->Init();
     return op;
   }
@@ -248,6 +259,17 @@ class OpRegistry {
   };
 
  private:
+  static void GenerateTempVariableName(OperatorBase* op) {
+    static std::atomic<size_t> gUniqId(0UL);
+    for (auto& outname : op->outputs_) {
+      if (outname == OperatorBase::TMP_VAR_NAME()) {
+        outname += op->type_;
+        outname += "@";
+        outname += std::to_string(gUniqId.fetch_add(1));
+      }
+    }
+  }
+
   static std::unordered_map<std::string, OpCreator>& creators() {
     static std::unordered_map<std::string, OpCreator> creators_;
     return creators_;

paddle/framework/operator.cc

@@ -77,23 +77,21 @@ std::vector<std::string> OperatorBase::Outputs(const std::string& name) const {
 
 std::string OperatorBase::DebugString() const {
   std::stringstream ss;
-  ss << "=================\n";
-  ss << "type = " << type_ << "\n";
-  ss << "inputs = [";
-  for (auto& ipt : inputs_) {
-    ss << ipt << ", ";
+  ss << "Op(" << type_ << "), inputs:(";
+  for (size_t i = 0; i < inputs_.size(); ++i) {
+    ss << inputs_[i];
+    if (i != inputs_.size() - 1) {
+      ss << ", ";
     }
-  ss << "]\n";
-  ss << "outputs = [";
-  for (auto& opt : outputs_) {
-    ss << opt << ", ";
   }
-  ss << "]\n";
-  ss << "attr_keys = [";
-  for (auto& attr : attrs_) {
-    ss << attr.first << ", ";
+  ss << "), outputs:(";
+  for (size_t i = 0; i < outputs_.size(); ++i) {
+    ss << outputs_[i];
+    if (i != outputs_.size() - 1) {
+      ss << ", ";
     }
-  ss << "]\n";
+  }
+  ss << ").";
   return ss.str();
 }
 

paddle/framework/operator.h

@@ -41,6 +41,13 @@ using OperatorPtr = std::shared_ptr<OperatorBase>;
  */
 class OperatorBase {
  public:
+  /// If a variable is a empty variable, that name will be used.
+  static std::string EMPTY_VAR_NAME() { return "@EMPTY@"; }
+
+  /// If a variable is a temporary variable, that name will be set in Python,
+  /// but it will be convert to a unique name in scope after OpCreator.
+  static std::string TMP_VAR_NAME() { return "@TEMP@"; }
+
   virtual ~OperatorBase() {}
 
   template <typename T>

paddle/pybind/pybind.cc

@@ -63,6 +63,23 @@ All parameter, weight, gradient are variables in Paddle.
     }
     return ret_values;
   });
+  m.def_submodule(
+       "var_names",
+       "The module will return special predefined variable name in Paddle")
+      .def("empty", pd::OperatorBase::EMPTY_VAR_NAME)
+      .def("temp", pd::OperatorBase::TMP_VAR_NAME);
+
+  py::class_<pd::OperatorBase, pd::OperatorPtr>(m, "Operator")
+      .def("__str__", &pd::OperatorBase::DebugString)
+      .def_static("create", [](const std::string& protobin) {
+        pd::OpDesc desc;
+        PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
+                       "Cannot parse user input to OpDesc");
+        PADDLE_ENFORCE(desc.IsInitialized(),
+                       "User OpDesc is not initialized, reason %s",
+                       desc.InitializationErrorString());
+        return pd::OpRegistry::CreateOp(desc);
+      });
 
   return m.ptr();
 }

python/paddle/v2/framework/create_op_creation_methods.py

@@ -1,11 +1,246 @@
 import paddle.v2.framework.core as core
 import paddle.v2.framework.proto.op_proto_pb2 as op_proto_pb2
+import paddle.v2.framework.proto.op_desc_pb2 as op_desc_pb2
+import paddle.v2.framework.proto.attr_type_pb2 as attr_type_pb2
+import cStringIO
 
 
 def get_all_op_protos():
+    """
+    Get all registered op proto from Paddle C++
+    :return: list of OpProto
+    """
     protostrs = core.get_all_op_protos()
     ret_values = []
     for pbstr in protostrs:
         op_proto = op_proto_pb2.OpProto.FromString(str(pbstr))
         ret_values.append(op_proto)
     return ret_values
+
+
+class OpDescCreationMethod(object):
+    """
+    A Functor object to convert user input(use key word args) to OpDesc based on
+    OpProto.
+    
+    :param op_proto: The OpProto object.
+    :type op_proto: op_proto_pb2.OpProto
+    """
+
+    def __init__(self, op_proto):
+        if not isinstance(op_proto, op_proto_pb2.OpProto):
+            raise TypeError("Argument should be OpProto")
+        self.__op_proto__ = op_proto
+
+    def __call__(self, *args, **kwargs):
+        """
+        Convert user input to OpDesc. Only key-word args are supported. 
+        :return: OpDesc based on user input
+        :rtype: op_desc_pb2.OpDesc
+        """
+        if len(args) != 0:
+            raise ValueError("Only keyword arguments is supported by Paddle")
+        op_desc = op_desc_pb2.OpDesc()
+
+        # Inputs
+        ipts, ipt_format, _ = OpDescCreationMethod.extract_input_or_output(
+            "input", kwargs, self.__op_proto__.inputs)
+        op_desc.inputs.extend(ipts)
+        if ipt_format is not None:
+            op_desc.attrs.extend([ipt_format])
+
+        # Outputs
+        outs, out_format, tmp_index = OpDescCreationMethod.extract_input_or_output(
+            "output", kwargs, self.__op_proto__.outputs)
+        op_desc.outputs.extend(outs)
+        if out_format is not None:
+            op_desc.attrs.extend([out_format])
+        if len(tmp_index) != 0:
+            tmp_index_attr = op_desc.attrs.add()
+            tmp_index_attr.type = attr_type_pb2.INTS
+            tmp_index_attr.name = "temporary_index"
+            tmp_index_attr.ints.extend(tmp_index)
+
+        # Types
+        op_desc.type = self.__op_proto__.type
+
+        # Attrs
+        for attr in self.__op_proto__.attrs:
+            if attr.generated:
+                continue
+            user_defined_attr = kwargs.get(attr.name, None)
+            if user_defined_attr is not None:
+                new_attr = op_desc.attrs.add()
+                new_attr.name = attr.name
+                new_attr.type = attr.type
+                if attr.type == attr_type_pb2.INT:
+                    new_attr.i = user_defined_attr
+                elif attr.type == attr_type_pb2.FLOAT:
+                    new_attr.f = user_defined_attr
+                elif attr.type == attr_type_pb2.STRING:
+                    new_attr.s = user_defined_attr
+                elif attr.type == attr_type_pb2.INTS:
+                    new_attr.ints.extend(user_defined_attr)
+                elif attr.type == attr_type_pb2.FLOATS:
+                    new_attr.floats.extend(user_defined_attr)
+                elif attr.type == attr_type_pb2.STRINGS:
+                    new_attr.strings.extend(user_defined_attr)
+                else:
+                    raise NotImplementedError("Not support attribute type " +
+                                              attr.type)
+
+        return op_desc
+
+    @staticmethod
+    def extract_input_or_output(in_out, kwargs, meta):
+        """
+        Extract input variable names or output variable names from key-word 
+        arguments, which base on VarProtos.
+        
+        :param in_out: "input" or "output"
+        :param kwargs: key-word arguments that user inputted.
+        :param meta: a list of VarProto
+        :return: The three object will be return. The variable names. The 
+        input_format or output_format attribute(None if the input or output is 
+        not multiple). The temporary variable index list.
+        """
+        multiple = OpDescCreationMethod.any_is_true((m.multiple for m in meta))
+        tmp_index = []
+        retv = []
+        if multiple:
+            var_format = op_desc_pb2.AttrDesc()
+            var_format.type = attr_type_pb2.INTS
+            var_format.name = "%s_format" % in_out
+            var_format.ints.append(0)
+
+            for var in meta:
+                var_name = var.name
+
+                if var.temporary:
+                    var_name = [core.var_names.temp()]
+                    tmp_index.append(len(retv))
+                else:
+                    var_name = kwargs.get(var_name, [])
+                if not isinstance(var_name, list):
+                    var_name = [var_name]
+                retv.extend(var_name)
+                var_format.ints.append(len(var_name) + var_format.ints[-1])
+            return retv, var_format, tmp_index
+        else:
+            for var in meta:
+                if var.temporary:
+                    retv.append(kwargs.get(var.name, core.var_names.temp()))
+                    tmp_index.append(len(retv))
+                else:
+                    retv.append(kwargs.get(var.name, core.var_names.empty()))
+            return retv, None, tmp_index
+
+    @staticmethod
+    def any_is_true(generator):
+        """
+        Reduce a bool array to one. If any of them is True, then return True.
+        """
+        for flag in generator:
+            if flag:
+                return True
+        return False
+
+
+def get_docstring_from_op_proto(op_proto):
+    """
+    Generate docstring from a OpProto
+    :param op_proto: a OpProto instance.
+    :type op_proto: op_proto_pb2.OpProto
+    :return: docstring
+    """
+    if not isinstance(op_proto, op_proto_pb2.OpProto):
+        raise TypeError("Input must be OpProto")
+    f = cStringIO.StringIO()
+    f.write(op_proto.comment)
+    f.write("\n")
+
+    def __append_param__(name, comment, type):
+        # Maybe replace the following line with template engine is better.
+        f.write(":param ")
+        f.write(name)
+        f.write(": ")
+        f.write(comment)
+        f.write("\n")
+        f.write(":type ")
+        f.write(name)
+        f.write(": ")
+        f.write(type)
+        f.write("\n")
+
+    for ipt in op_proto.inputs:
+        __append_param__(ipt.name, ipt.comment, "list | basestr"
+                         if ipt.multiple else "basestr")
+
+    temp_var_prefix = \
+        "This is a temporary variable. It does not have to set by user. "
+    for opt in op_proto.outputs:
+        __append_param__(opt.name, opt.comment if not opt.temporary else
+                         temp_var_prefix + opt.comment, "list | basestr"
+                         if opt.multiple else "basestr")
+
+    for attr in op_proto.attrs:
+        attr_type = None
+        if attr.type == attr_type_pb2.INT:
+            attr_type = "int"
+        elif attr.type == attr_type_pb2.FLOAT:
+            attr_type = "float"
+        elif attr.type == attr_type_pb2.STRING:
+            attr_type = "basestr"
+        elif attr.type == attr_type_pb2.INTS:
+            attr_type = "list of int"
+        elif attr.type == attr_type_pb2.FLOATS:
+            attr_type = "list of float"
+        elif attr.type == attr_type_pb2.STRINGS:
+            attr_type = "list of basestr"
+
+        if attr_type is None:
+            raise RuntimeError("Not supported attribute type " + attr.type)
+
+        __append_param__(attr.name, attr.comment, attr_type)
+
+    return f.getvalue()
+
+
+def create_op_creation_method(op_proto):
+    """
+    Generate op creation method for an OpProto
+    """
+    method = OpDescCreationMethod(op_proto)
+
+    def __impl__(*args, **kwargs):
+        opdesc = method(*args, **kwargs)
+        return core.Operator.create(opdesc.SerializeToString())
+
+    __impl__.__doc__ = get_docstring_from_op_proto(op_proto)
+    return __impl__
+
+
+class OpCreationsHolder(object):
+    """
+    A object will holds all op creation methods.
+    
+    Use `op_creations.xxx_op` to access them.
+    """
+    pass
+
+
+op_creations = OpCreationsHolder()
+
+
+def __bootstrap__():
+    """
+    Bootstrap function for this module. It will dynamic create all op creation
+    methods in runtime.
+    """
+    for op_proto in get_all_op_protos():
+        func = create_op_creation_method(op_proto)
+        func.__name__ = str(op_proto.type)
+        setattr(op_creations, func.__name__, func)
+
+
+__bootstrap__()

python/paddle/v2/optimizer.py

@@ -1,4 +1,3 @@
-import py_paddle.swig_paddle as swig_api
 import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
 import paddle.trainer_config_helpers.optimizers as v1_optimizers
 """
@@ -17,6 +16,7 @@ __all__ = [
 
 class Optimizer(object):
     def __init__(self, **kwargs):
+        import py_paddle.swig_paddle as swig_api
         if 'batch_size' in kwargs:
             del kwargs['batch_size']  # not important for python library.
 
@@ -35,18 +35,22 @@ class Optimizer(object):
         For each optimizer(SGD, Adam), GradientMachine should enable different
         buffers.
         """
+        import py_paddle.swig_paddle as swig_api
         tmp = swig_api.ParameterOptimizer.create(self.__opt_conf__)
         assert isinstance(tmp, swig_api.ParameterOptimizer)
         return tmp.getParameterTypes()
 
     def __create_local_updater__(self):
+        import py_paddle.swig_paddle as swig_api
         return swig_api.ParameterUpdater.createLocalUpdater(self.__opt_conf__)
 
     def __create_remote_updater__(self, pass_num, use_sparse_updater):
+        import py_paddle.swig_paddle as swig_api
         return swig_api.ParameterUpdater.createRemoteUpdater(
             self.__opt_conf__, pass_num, use_sparse_updater)
 
     def __create_new_remote_updater__(self, pserver_spec, use_etcd):
+        import py_paddle.swig_paddle as swig_api
         return swig_api.ParameterUpdater.createNewRemoteUpdater(
             self.__opt_conf__, pserver_spec, use_etcd)