Update according to the reviewers' suggestion. test=develop

paddle/fluid/pybind/ir.cc

@@ -148,8 +148,8 @@ void BindNode(py::module *m) {
            })
       .def("outputs_append",
            [](Node &self, Node &node) { self.outputs.push_back(&node); })
-      .def_readonly("inputs", &Node::inputs)
-      .def_readonly("outputs", &Node::outputs);
+      .def_readwrite("inputs", &Node::inputs)
+      .def_readwrite("outputs", &Node::outputs);
 
   py::enum_<Node::Type>(node, "Type")
       .value("Operation", Node::Type::kOperation)

paddle/fluid/pybind/pybind.cc

@@ -797,17 +797,17 @@ All parameter, weight, gradient are variables in Paddle.
   py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
   pass.def(py::init())
       .def("has", &ir::Pass::Has)
-      .def("set_program",
+      .def("set",
            [](ir::Pass &self, const std::string &attr_name,
               const ProgramDesc &attr) {
              return self.Set(attr_name, new ProgramDesc(attr));
            })
       .def(
-          "set_str",
+          "set",
           [](ir::Pass &self, const std::string &name, const std::string &attr) {
             self.Set<std::string>(name, new std::string(attr));
           })
-      .def("set_int", [](ir::Pass &self, const std::string &name,
+      .def("set", [](ir::Pass &self, const std::string &name,
                      int val) { self.Set<const int>(name, new int(val)); })
       .def("get_program", &ir::Pass::Get<ProgramDesc>)
       .def("type", &ir::Pass::Type)

python/paddle/fluid/contrib/slim/graph/graph.py

@@ -18,140 +18,7 @@ from ....framework import Program
 from ....framework import Block
 from .... import core
 
-__all__ = ['Graph', 'ImitationGraph', 'IRGraph', 'PyGraph']
-
-
-class PyGraph(object):
-    """
-    PyGraph uses core.Graph as the delegation to accomplish the manipulation.
-    """
-
-    def __init__(self, graph, for_test=False):
-        """
-        Construct the PyGraph using core.Graph.
-        Args:
-            graph(core.Graph): C++ Graph.
-            for_test(bool): True for the test graph and false for the train graph.
-        """
-        assert isinstance(
-            graph, core.Graph), 'graph must be the instance of core.Graph.'
-        self.graph = graph
-        self.for_test = for_test
-
-    def is_test(self):
-        return self.for_test
-
-    def all_parameters(self):
-        param_nodes = set()
-        for node in self.graph.nodes():
-            if node.is_var() and node.var() is not None and node.var(
-            ).persistable():
-                param_nodes.add(node)
-        return param_nodes
-
-    def all_vars(self):
-        return {node for node in self.graph.nodes() if node.is_var()}
-
-    def all_ops(self):
-        return {node for node in self.graph.nodes() if node.is_op()}
-
-    def create_param_node(self, name, var_type, shape, var_dtype):
-        var_desc = core.VarDesc(name)
-        var_desc.set_type(var_type)
-        var_desc.set_shape(shape)
-        var_desc.set_dtype(var_dtype)
-        var_desc.set_persistable(True)
-        return self.graph.create_var_node(var_desc)
-
-    def create_var_node(self, name, var_type, shape, var_dtype):
-        var_desc = core.VarDesc(name)
-        var_desc.set_type(var_type)
-        var_desc.set_shape(shape)
-        var_desc.set_dtype(var_dtype)
-        return self.graph.create_var_node(var_desc)
-
-    def create_var_node_from_desc(self, var_desc):
-        return self.graph.create_var_node(var_desc)
-
-    def create_op_node(self, op_type, attrs, inputs, outputs):
-        op_desc = core.OpDesc()
-        op_desc.set_type(op_type)
-        for attr, value in attrs.iteritems():
-            self._update_desc_attr(op_desc, attr, value)
-        for input_name, var_nodes in inputs.iteritems():
-            if not isinstance(var_nodes, list):
-                var_nodes = [var_nodes]
-            op_desc.set_input(input_name,
-                              [var_node.name() for var_node in var_nodes])
-        for output_name, var_nodes in outputs.iteritems():
-            if not isinstance(var_nodes, list):
-                var_nodes = [var_nodes]
-            op_desc.set_output(output_name,
-                               [var_node.name() for var_node in var_nodes])
-        return self.graph.create_op_node(op_desc)
-
-    def create_op_node_from_desc(self, op_desc):
-        return self.graph.create_op_node(op_desc)
-
-    def _update_desc_attr(self, desc, name, val):
-        """
-        Update the value of desc's attribute by attribute's name.
-        """
-        if isinstance(val, Block):
-            desc.set_block_attr(name, val.desc)
-        elif isinstance(val, list) and val and all(
-                isinstance(v, Block) for v in val):
-            desc.set_blocks_attr(name, [v.desc for v in val])
-        elif isinstance(val, core.BlockDesc) or \
-                isinstance(val, core.ProgramDesc):
-            desc.set_serialized_attr(name, val.serialize_to_string())
-        else:
-            desc._set_attr(name, val)
-
-    def safe_remove_nodes(self, remove_nodes):
-        if not isinstance(remove_nodes, set):
-            remove_nodes = set(remove_nodes)
-        core.graph_safe_remove_nodes(self.graph, remove_nodes)
-
-    def draw(self, save_path, name, marked_nodes=None):
-        def _convert_to_pdf(dot_file_path):
-            pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
-            exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
-                            + ' -o ' + pdf_save_path, shell=True)
-            if exited_code != 0:
-                print('The dot command is needed for creating pdf files.')
-                print('The {} is saved as the dot filetype.'.format(
-                    dot_file_path))
-
-        remove_ctr_vars = set()
-        ops_num = 0
-        for node in self.graph.nodes():
-            if node.is_ctrl_var():
-                remove_ctr_vars.add(node)
-            elif node.is_op():
-                ops_num += 1
-        print('Total ops num = {}.'.format(ops_num))
-        self.safe_remove_nodes(remove_ctr_vars)
-        if marked_nodes is not None:
-            if not isinstance(marked_nodes, set):
-                marked_nodes = set(marked_nodes)
-            marked_nodes = marked_nodes - remove_ctr_vars
-            if self.graph.has('__graphviz__marked_node__'):
-                self.graph.erase('__graphviz__marked_node__')
-            self.graph.set('__graphviz__marked_node__', marked_nodes)
-        viz_dot_path = os.path.join(save_path, name) + '.dot'
-        viz_pass = core.get_pass('graph_viz_pass')
-        viz_pass.set_str('graph_viz_path', viz_dot_path)
-        viz_pass.apply(self.graph)
-        _convert_to_pdf(viz_dot_path)
-
-    def to_program(self):
-        convert_pass = core.get_pass('graph_to_program_pass')
-        convert_pass.set_program('program', Program().desc)
-        convert_pass.apply(self.graph)
-        desc = convert_pass.get_program('program')
-        program = Program.construct_from_desc(desc)
-        return program
+__all__ = ['Graph', 'ImitationGraph', 'IRGraph']
 
 
 class Graph(object):

python/paddle/fluid/contrib/slim/quantization/quantization_pass.py

@@ -13,13 +13,12 @@
 # limitations under the License.
 
 import collections
-import numpy as np
 from .... import core
+from ....framework import IrGraph
 from ....framework import Program
 from ....framework import Variable
 from ....initializer import Constant
 from .... import unique_name
-from ..graph import PyGraph
 
 __all__ = ['QuantizationTransformPass']
 
@@ -34,7 +33,7 @@ class QuantizationTransformPass(object):
                  weight_quantize_type='abs_max',
                  window_size=10000):
         """
-        Convert and rewrite the PyGraph according to weight and
+        Convert and rewrite the IrGraph according to weight and
         activation quantization type.
         Args:
             weight_bits (int): quantization bit number for weights,
@@ -56,19 +55,19 @@ class QuantizationTransformPass(object):
             import paddle.fluid as fluid
             from paddle.fluid.contrib.slim.quantization \
                 import QuantizationTransformPass
-            from paddle.fluid.contrib.slim.graph import PyGraph
+            from paddle.fluid.contrib.slim.graph import IrGraph
             from paddle.fluid import core
 
-            graph = PyGraph(core.Graph(program.desc), for_test=False)
+            graph = IrGraph(core.Graph(program.desc), for_test=False)
             exe = fluid.Executor(fluid.CPUPlace())
             transform_pass = QuantizationTransformPass(fluid.global_scope(),
             exe)
             transform_pass.apply(graph)
         """
-        self.scope = scope
-        self.program_exe = program_exe
-        self.weight_bits = weight_bits
-        self.activation_bits = activation_bits
+        self._scope = scope
+        self._program_exe = program_exe
+        self._weight_bits = weight_bits
+        self._activation_bits = activation_bits
 
         quant_type = ['abs_max', 'range_abs_max']
         if activation_quantize_type not in quant_type:
@@ -80,27 +79,27 @@ class QuantizationTransformPass(object):
                 "Unknown weight_quantize_type: '%s'. It can only be ",
                 "'abs_max' or 'range_abs_max'.", str(weight_quantize_type))
 
-        self.activation_quantize_type = activation_quantize_type
-        self.weight_quantize_type = weight_quantize_type
-        self.window_size = window_size
+        self._activation_quantize_type = activation_quantize_type
+        self._weight_quantize_type = weight_quantize_type
+        self._window_size = window_size
 
-        self.need_initialized = collections.OrderedDict()
-        self.quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
-        self.quantizable_grad_ops = [
-            '%s_grad' % (op) for op in self.quantizable_ops
+        self._need_initialized = collections.OrderedDict()
+        self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
+        self._quantizable_grad_ops = [
+            '%s_grad' % (op) for op in self._quantizable_ops
         ]
-        self.fake_quant_op_types = [
+        self._fake_quant_op_types = [
             'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
         ]
-        self.fake_dequant_op_types = ['fake_dequantize_max_abs']
-        self.is_test = None
-        self.global_step = None
+        self._fake_dequant_op_types = ['fake_dequantize_max_abs']
+        self._is_test = None
+        self._global_step = None
 
     def apply(self, graph):
         assert isinstance(graph,
-                          PyGraph), 'graph must be the instance of PyGraph.'
-        self.need_initialized.clear()
-        self.is_test = graph.is_test()
+                          IrGraph), 'graph must be the instance of IrGraph.'
+        self._need_initialized.clear()
+        self._is_test = graph.is_test()
         # marked the variable which has been dequantized.
         dequantized_vars = collections.OrderedDict()
         params = [p.name() for p in graph.all_parameters()]
@@ -110,72 +109,69 @@ class QuantizationTransformPass(object):
                 if var_node.name() in dequantized_vars:
                     dequant_var_node = dequantized_vars[var_node.name()]
                 else:
-                    quant_bits = self.weight_bits if var_node.name() in params \
-                    else self.activation_bits
-                    quant_type = self.weight_quantize_type if var_node.name() \
-                        in params else self.activation_quantize_type
+                    quant_bits = self._weight_bits if var_node.name() in params \
+                    else self._activation_bits
+                    quant_type = self._weight_quantize_type if var_node.name() \
+                        in params else self._activation_quantize_type
                     quant_var_node, scale_var_node = self._insert_quant_op(
                         graph, var_node, quant_bits, quant_type)
                     dequant_var_node = self._insert_dequant_op(
                         graph, quant_var_node, scale_var_node, quant_bits)
                     dequantized_vars[var_node.name()] = dequant_var_node
-                self._update_input(var_node, dequant_var_node, op)
-                op.op()._rename_input(var_node.name(), dequant_var_node.name())
+                graph.update_input_link(var_node, dequant_var_node, op)
 
         def _transform_backward(graph, op):
             no_dequanted_input_vars = True
             for var_node in op.inputs:
                 if var_node.name() in dequantized_vars:
                     dequant_var_node = dequantized_vars[var_node.name()]
-                    self._update_input(var_node, dequant_var_node, op)
-                    op.op()._rename_input(var_node.name(),
-                                          dequant_var_node.name())
+                    graph.update_input_link(var_node, dequant_var_node, op)
                     no_dequanted_input_vars = False
             if no_dequanted_input_vars:
                 raise ValueError("There is no dequanted inputs for op %s." %
                                  (op.name()))
 
-        if not self.is_test:
+        if not self._is_test:
             self._create_global_step(graph)
         ops = graph.all_ops()
         # The process of _transform_forward and _transform_backward is needed in two for loops.
         # The loop for transforming the forward graph:
         for op in ops:
-            if op.name() in self.quantizable_ops:
+            if op.name() in self._quantizable_ops:
                 _transform_forward(graph, op)
         # The loop for renaming the inputs of backward op.
         for op in ops:
-            if op.name() in self.quantizable_grad_ops:
+            if op.name() in self._quantizable_grad_ops:
                 _transform_backward(graph, op)
 
-        if len(self.need_initialized) > 0:
-            assert self.scope is not None, \
+        if len(self._need_initialized) > 0:
+            assert self._scope is not None, \
             'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
-            assert self.program_exe is not None, \
+            assert self._program_exe is not None, \
             'The program_exe cannot be set None when activation_quantize_type equals to range_abs_max.'
             init_program = Program()
-            for var_desc, initializer in self.need_initialized.iteritems():
-                var = Variable.construct_from_desc(init_program.global_block(),
-                                                   var_desc)
+            for var_desc, initializer in self._need_initialized.iteritems():
+                var = Variable(init_program.global_block())
+                var._set_desc(var_desc)
                 initializer(var, init_program.global_block())
-            self.program_exe.run(program=init_program, scope=self.scope)
+            self._program_exe.run(program=init_program, scope=self._scope)
 
         return graph
 
     def _create_global_step(self, graph):
-        if self.weight_quantize_type == 'range_abs_max' or \
-                self.activation_quantize_type == 'range_abs_max':
+        if self._weight_quantize_type == 'range_abs_max' or \
+                self._activation_quantize_type == 'range_abs_max':
             counter_name = '@STEP_COUNTER@'
             for node in graph.all_vars():
                 if node.name() == counter_name:
-                    self.global_step = node
-            if self.global_step is None:
+                    self._global_step = node
+            if self._global_step is None:
                 global_step_in = graph.create_param_node(
                     name=counter_name,
                     var_type=core.VarDesc.VarType.LOD_TENSOR,
                     shape=[1],
                     var_dtype=core.VarDesc.VarType.INT64)
-                self.need_initialized[global_step_in.var()] = \
+                self._need_initialized[global_step_in.var()] = \
                     Constant(value=0, force_cpu=True)
                 global_step_out = graph.create_var_node_from_desc(
                     global_step_in.var())
@@ -184,9 +180,9 @@ class QuantizationTransformPass(object):
                     attrs={'step': 1.0},
                     inputs={'X': global_step_in},
                     outputs={'Out': global_step_out})
-                self._link_to(global_step_in, increment_op)
-                self._link_to(increment_op, global_step_out)
-                self.global_step = global_step_out
+                graph.link_to(global_step_in, increment_op)
+                graph.link_to(increment_op, global_step_out)
+                self._global_step = global_step_out
 
     def _insert_quant_op(self, graph, var_node, quant_bits, quant_type):
         """
@@ -220,9 +216,9 @@ class QuantizationTransformPass(object):
             inputs={'X': var_node},
             outputs={'Out': quant_var_node,
                      'OutScale': scale_var_node})
-        self._link_to(var_node, quant_op_node)
-        self._link_to(quant_op_node, quant_var_node)
-        self._link_to(quant_op_node, scale_var_node)
+        graph.link_to(var_node, quant_op_node)
+        graph.link_to(quant_op_node, quant_var_node)
+        graph.link_to(quant_op_node, scale_var_node)
         return quant_var_node, scale_var_node
 
     def _insert_quant_range_abs_max_op(self, graph, var_node, quant_bits):
@@ -242,26 +238,26 @@ class QuantizationTransformPass(object):
             var_type=core.VarDesc.VarType.LOD_TENSOR,
             shape=[1],
             var_dtype=var_node.var().dtype())
-        self.need_initialized[scale_in_node.var()] = Constant(value=0.001)
+        self._need_initialized[scale_in_node.var()] = Constant(value=0.001)
 
         scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
         inputs = {'X': var_node, 'InScale': scale_in_node}
         outputs = {'Out': quant_var_node, 'OutScale': scale_out_node}
 
-        if not self.is_test:
+        if not self._is_test:
             # The name of scales_var_node maybe 'scales_0', 'scales_1', etc.
             scales_node = graph.create_param_node(
                 name=unique_name.generate('scales'),
                 var_type=core.VarDesc.VarType.LOD_TENSOR,
-                shape=[self.window_size],
+                shape=[self._window_size],
                 var_dtype=var_node.var().dtype())
-            self.need_initialized[scales_node.var()] = Constant(value=0)
-            inputs['Iter'] = self.global_step
+            self._need_initialized[scales_node.var()] = Constant(value=0)
+            inputs['Iter'] = self._global_step
             outputs['OutScales'] = scales_node
         attrs = {
-            'window_size': self.window_size,
+            'window_size': self._window_size,
             'bit_length': quant_bits,
-            'is_test': self.is_test
+            'is_test': self._is_test
         }
         quant_op_node = graph.create_op_node(
             op_type='fake_quantize_range_abs_max',
@@ -269,14 +265,14 @@ class QuantizationTransformPass(object):
             inputs=inputs,
             outputs=outputs)
 
-        self._link_to(var_node, quant_op_node)
-        self._link_to(scale_in_node, quant_op_node)
-        self._link_to(quant_op_node, quant_var_node)
-        self._link_to(quant_op_node, scale_out_node)
+        graph.link_to(var_node, quant_op_node)
+        graph.link_to(scale_in_node, quant_op_node)
+        graph.link_to(quant_op_node, quant_var_node)
+        graph.link_to(quant_op_node, scale_out_node)
 
-        if not self.is_test:
-            self._link_to(self.global_step, quant_op_node)
-            self._link_to(quant_op_node, scales_node)
+        if not self._is_test:
+            graph.link_to(self._global_step, quant_op_node)
+            graph.link_to(quant_op_node, scales_node)
 
         return quant_var_node, scale_out_node
 
@@ -298,21 +294,11 @@ class QuantizationTransformPass(object):
             inputs={'X': var_node,
                     'Scale': scale_var_node},
             outputs={'Out': dequant_var_node})
-        self._link_to(var_node, dequant_op_node)
-        self._link_to(scale_var_node, dequant_op_node)
-        self._link_to(dequant_op_node, dequant_var_node)
+        graph.link_to(var_node, dequant_op_node)
+        graph.link_to(scale_var_node, dequant_op_node)
+        graph.link_to(dequant_op_node, dequant_var_node)
         return dequant_var_node
 
-    def _update_input(self, old_input_node, new_input_node, op_node):
-        old_input_node.outputs_remove(op_node)
-        op_node.inputs_remove(old_input_node)
-        new_input_node.outputs_append(op_node)
-        op_node.inputs_append(new_input_node)
-
-    def _link_to(self, node_in, node_out):
-        node_in.outputs_append(node_out)
-        node_out.inputs_append(node_in)
-
     def _quantized_var_name(self, var_name):
         """
         Return quantized variable name for the input `var_name`.
@@ -330,25 +316,3 @@ class QuantizationTransformPass(object):
         Return quantized variable name for the input `var_name`.
         """
         return "%s.scale" % (var_name)
-
-    def _original_var_name(self, var_name):
-        """
-        Return the original variable name.
-        """
-        if var_name.endswith('.quantized.dequantized'):
-            return var_name[:-len('.quantized.dequantized')]
-        if var_name.endswith('.quantized'):
-            return var_name[:-len('.quantized')]
-        if var_name.endswith('.dequantized'):
-            return var_name[:-len('.dequantized')]
-        if var_name.endswith('.scale'):
-            return var_name[:-len('.scale')]
-        else:
-            return var_name
-
-    def _is_float(self, v):
-        return isinstance(v, float) or isinstance(v, np.float32)
-
-    def _quant(self, x, scale, num_bits):
-        y = np.round(x / scale * ((1 << (num_bits - 1)) - 1))
-        return y

python/paddle/fluid/contrib/slim/unitest/test_quantization_pass.py

@@ -18,8 +18,8 @@ import numpy as np
 import paddle.fluid as fluid
 import six
 from paddle.fluid.framework import Program
+from paddle.fluid.framework import IrGraph
 from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
-from paddle.fluid.contrib.slim.graph import PyGraph
 from paddle.fluid import core
 
 
@@ -106,7 +106,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
             opt = fluid.optimizer.Adam(learning_rate=0.001)
             opt.minimize(loss)
         exe = fluid.Executor(fluid.CPUPlace())
-        graph = PyGraph(core.Graph(main.desc), for_test=False)
+        graph = IrGraph(core.Graph(main.desc), for_test=False)
         transform_pass = QuantizationTransformPass(
             scope=fluid.global_scope(),
             program_exe=exe,
@@ -119,7 +119,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
         graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
         program = graph.to_program()
         self.check_program(transform_pass, program)
-        val_graph = PyGraph(core.Graph(program.desc), for_test=False)
+        val_graph = IrGraph(core.Graph(program.desc), for_test=False)
         val_marked_nodes = set()
         for op in val_graph.all_ops():
             if op.name().find('quantize') > -1:
@@ -142,7 +142,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
             opt = fluid.optimizer.Adam(learning_rate=0.001)
             opt.minimize(loss)
         exe = fluid.Executor(fluid.CPUPlace())
-        graph = PyGraph(core.Graph(main.desc), for_test=False)
+        graph = IrGraph(core.Graph(main.desc), for_test=False)
         transform_pass = QuantizationTransformPass(
             scope=fluid.global_scope(),
             program_exe=exe,
@@ -155,7 +155,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
         graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
         program = graph.to_program()
         self.check_program(transform_pass, program)
-        val_graph = PyGraph(core.Graph(program.desc), for_test=False)
+        val_graph = IrGraph(core.Graph(program.desc), for_test=False)
         val_marked_nodes = set()
         for op in val_graph.all_ops():
             if op.name().find('quantize') > -1:

python/paddle/fluid/framework.py

@@ -23,6 +23,7 @@ import traceback
 import six
 
 import numpy as np
+import subprocess
 
 from .. import compat as cpt
 from .proto import framework_pb2
@@ -381,27 +382,6 @@ class Variable(object):
             self._ivar.desc = self.desc
             self._ivar.stop_gradient = stop_gradient
 
-    @staticmethod
-    def construct_from_desc(block, desc):
-        """
-        Construct a Variable from variable desc.
-        Args:
-            desc(core.VarDesc): The  variable desc for constructing.
-
-        Returns:
-            Variable: A variable.
-        """
-        v = Variable(
-            block=block,
-            type=desc.type(),
-            name=desc.name(),
-            shape=desc.shape(),
-            dtype=desc.dtype(),
-            lod_level=desc.lod_level(),
-            persistable=desc.persistable())
-        v.desc = desc
-        return v
-
     def _numpy(self):
         tensor = self._ivar.value().get_tensor()
         return np.array(tensor)
@@ -1533,6 +1513,154 @@ class Block(object):
         return ret_var
 
 
+class IrGraph(object):
+    """
+    IrGraph uses core.Graph as the delegation to accomplish the manipulation.
+    """
+
+    def __init__(self, graph, for_test=False):
+        """
+        Construct the IrGraph using core.Graph.
+        Args:
+            graph(core.Graph): C++ Graph.
+            for_test(bool): True for the test graph and false for the train graph.
+        """
+        assert isinstance(
+            graph, core.Graph), 'graph must be the instance of core.Graph.'
+        self.graph = graph
+        self._for_test = for_test
+
+    def is_test(self):
+        return self._for_test
+
+    def all_parameters(self):
+        param_nodes = set()
+        for node in self.graph.nodes():
+            if node.is_var() and node.var() is not None and node.var(
+            ).persistable():
+                param_nodes.add(node)
+        return param_nodes
+
+    def all_vars(self):
+        return {node for node in self.graph.nodes() if node.is_var()}
+
+    def all_ops(self):
+        return {node for node in self.graph.nodes() if node.is_op()}
+
+    def create_param_node(self, name, var_type, shape, var_dtype):
+        var_desc = core.VarDesc(name)
+        var_desc.set_type(var_type)
+        var_desc.set_shape(shape)
+        var_desc.set_dtype(var_dtype)
+        var_desc.set_persistable(True)
+        return self.graph.create_var_node(var_desc)
+
+    def create_var_node(self, name, var_type, shape, var_dtype):
+        var_desc = core.VarDesc(name)
+        var_desc.set_type(var_type)
+        var_desc.set_shape(shape)
+        var_desc.set_dtype(var_dtype)
+        return self.graph.create_var_node(var_desc)
+
+    def create_var_node_from_desc(self, var_desc):
+        return self.graph.create_var_node(var_desc)
+
+    def create_op_node(self, op_type, attrs, inputs, outputs):
+        op_desc = core.OpDesc()
+        op_desc.set_type(op_type)
+        for attr, value in attrs.iteritems():
+            self._update_desc_attr(op_desc, attr, value)
+        for input_name, var_nodes in inputs.iteritems():
+            if not isinstance(var_nodes, list):
+                var_nodes = [var_nodes]
+            op_desc.set_input(input_name,
+                              [var_node.name() for var_node in var_nodes])
+        for output_name, var_nodes in outputs.iteritems():
+            if not isinstance(var_nodes, list):
+                var_nodes = [var_nodes]
+            op_desc.set_output(output_name,
+                               [var_node.name() for var_node in var_nodes])
+        return self.graph.create_op_node(op_desc)
+
+    def create_op_node_from_desc(self, op_desc):
+        return self.graph.create_op_node(op_desc)
+
+    def update_input_link(self, old_input_node, new_input_node, op_node):
+        assert old_input_node in self.graph.nodes() and new_input_node in self.graph.nodes() and \
+            op_node in self.graph.nodes(), 'Th three arguments must be in the graph nodes.'
+        old_input_node.outputs_remove(op_node)
+        op_node.inputs_remove(old_input_node)
+        new_input_node.outputs_append(op_node)
+        op_node.inputs_append(new_input_node)
+        op_node.op()._rename_input(old_input_node.name(), new_input_node.name())
+
+    def link_to(self, node_in, node_out):
+        assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \
+            'Th two arguments must be in the graph nodes.'
+        node_in.outputs_append(node_out)
+        node_out.inputs_append(node_in)
+
+    def safe_remove_nodes(self, remove_nodes):
+        if not isinstance(remove_nodes, set):
+            remove_nodes = set(remove_nodes)
+        core.graph_safe_remove_nodes(self.graph, remove_nodes)
+
+    def draw(self, save_path, name, marked_nodes=None):
+        def _convert_to_pdf(dot_file_path):
+            pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
+            exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
+                            + ' -o ' + pdf_save_path, shell=True)
+            if exited_code != 0:
+                print('The dot command is needed for creating pdf files.')
+                print('The {} is saved as the dot filetype.'.format(
+                    dot_file_path))
+
+        remove_ctr_vars = set()
+        ops_num = 0
+        for node in self.graph.nodes():
+            if node.is_ctrl_var():
+                remove_ctr_vars.add(node)
+            elif node.is_op():
+                ops_num += 1
+        print('Total ops num = {}.'.format(ops_num))
+        self.safe_remove_nodes(remove_ctr_vars)
+        if marked_nodes is not None:
+            if not isinstance(marked_nodes, set):
+                marked_nodes = set(marked_nodes)
+            marked_nodes = marked_nodes - remove_ctr_vars
+            if self.graph.has('__graphviz__marked_node__'):
+                self.graph.erase('__graphviz__marked_node__')
+            self.graph.set('__graphviz__marked_node__', marked_nodes)
+        viz_dot_path = os.path.join(save_path, name) + '.dot'
+        viz_pass = core.get_pass('graph_viz_pass')
+        viz_pass.set('graph_viz_path', viz_dot_path)
+        viz_pass.apply(self.graph)
+        _convert_to_pdf(viz_dot_path)
+
+    def to_program(self):
+        convert_pass = core.get_pass('graph_to_program_pass')
+        convert_pass.set('program', Program().desc)
+        convert_pass.apply(self.graph)
+        desc = convert_pass.get_program('program')
+        program = Program._construct_from_desc(desc)
+        return program
+
+    def _update_desc_attr(self, desc, name, val):
+        """
+        Update the value of desc's attribute by attribute's name.
+        """
+        if isinstance(val, Block):
+            desc.set_block_attr(name, val.desc)
+        elif isinstance(val, list) and val and all(
+                isinstance(v, Block) for v in val):
+            desc.set_blocks_attr(name, [v.desc for v in val])
+        elif isinstance(val, core.BlockDesc) or \
+                isinstance(val, core.ProgramDesc):
+            desc.set_serialized_attr(name, val.serialize_to_string())
+        else:
+            desc._set_attr(name, val)
+
+
 class Program(object):
     """
     Python Program. Beneath it is a ProgramDesc, which is used for
@@ -1958,12 +2086,10 @@ class Program(object):
         return p
 
     @staticmethod
-    def construct_from_desc(desc):
+    def _construct_from_desc(desc):
         """
         Construct a program from program desc.
 
-        Notes: All information about parameters will be lost.
-
         Args:
             desc(core.ProgramDesc): The program desc for constructing.