[dy2static] Add print transformer and unify print format (#24068)

* add print transformer & unify print format, test=develop

* remove using of dygraph_to_static_func, test=develop

* remove python stdout capture, test=develop

* fix compatibility problems for PY2, test=develop

* fix detail error, test=develop

* fix type analysis bug, test=develop

* fix print tuple compatible error in PY2, test=develop

* replace get_func to declarative, test=develop

* fix detail bug, test=develop

* fix some detail problems, test=develop

* change visit_call in print transformer, test=develop

paddle/fluid/framework/lod_tensor.cc

@@ -50,9 +50,10 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
 }
 
 std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
-  os << "\tlod: " << t.lod() << "\n";
-  os << static_cast<Tensor>(t) << "\n";
-
+  if (t.lod().size() > 0) {
+    os << "  - lod: " << t.lod() << "\n";
+  }
+  os << static_cast<Tensor>(t);
   return os;
 }
 

paddle/fluid/framework/tensor_util.cc

@@ -639,7 +639,7 @@ std::ostream& print_tensor(std::ostream& os, const framework::Tensor& tensor) {
   auto inspect = tensor.data<T>();
   auto element_num = tensor.numel();
 
-  os << "\tdata: [";
+  os << "  - data: [";
   if (element_num > 0) {
     os << inspect[0];
     for (int j = 1; j < element_num; ++j) {
@@ -651,8 +651,9 @@ std::ostream& print_tensor(std::ostream& os, const framework::Tensor& tensor) {
 }
 
 std::ostream& operator<<(std::ostream& os, const Tensor& t) {
-  os << "\tdim: " << t.dims() << "\n";
-  os << "\tlayout: " << DataLayoutToString(t.layout()) << "\n";
+  os << "  - place: " << t.place() << "\n";
+  os << "  - shape: [" << t.dims() << "]\n";
+  os << "  - layout: " << DataLayoutToString(t.layout()) << "\n";
 
   Tensor tensor;
   tensor.Resize(t.dims());
@@ -669,7 +670,7 @@ std::ostream& operator<<(std::ostream& os, const Tensor& t) {
 #define PrintTensorCallback(cpp_type, proto_type) \
   do {                                            \
     if (tensor.type() == proto_type) {            \
-      os << "\tdtype: " << proto_type << "\n";    \
+      os << "  - dtype: " << proto_type << "\n";  \
       print_tensor<cpp_type>(os, tensor);         \
       return os;                                  \
     }                                             \

paddle/fluid/operators/print_op.cc

@@ -13,6 +13,7 @@
    limitations under the License. */
 
 #include <algorithm>
+#include "paddle/fluid/framework/data_layout.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/operators/assign_op.h"
@@ -43,8 +44,9 @@ class LogGuard {
 struct Formater {
   std::string message;
   std::string name;
-  std::vector<int> dims;
+  std::string dims;
   std::type_index dtype{typeid(const char)};
+  std::string layout;
   framework::LoD lod;
   int summarize;
   void *data{nullptr};
@@ -52,50 +54,62 @@ struct Formater {
   std::stringstream logs;
 
   void operator()(size_t size) {
-    PrintMessage();
-    PrintPlaceInfo();
     PrintName();
+    PrintMessage();
+    PrintLod();
+    PrintPlace();
     PrintDims();
+    PrintLayout();
     PrintDtype();
-    PrintLod();
     PrintData(size);
     LogGuard guard;
     CLOG << logs.str();
   }
 
  private:
-  void PrintPlaceInfo() { logs << "The place is:" << place << std::endl; }
-  void PrintMessage() { logs << std::time(nullptr) << "\t" << message << "\t"; }
+  void PrintPlace() { logs << "  - place: " << place << std::endl; }
+  void PrintMessage() {
+    if (!message.empty()) {
+      logs << "  - message: " << message << std::endl;
+    }
+  }
   void PrintName() {
     if (!name.empty()) {
-      logs << "Tensor[" << name << "]" << std::endl;
+      logs << "Variable: " << name << std::endl;
     }
   }
   void PrintDims() {
     if (!dims.empty()) {
-      logs << "\tshape: [";
-      for (auto i : dims) {
-        logs << i << ",";
-      }
-      logs << "]" << std::endl;
+      logs << "  - shape: " << dims << std::endl;
     }
   }
   void PrintDtype() {
     if (!framework::IsType<const char>(dtype)) {
-      logs << "\tdtype: " << dtype.name() << std::endl;
+      logs << "  - dtype: " << platform::demangle(dtype.name()) << std::endl;
+    }
+  }
+  void PrintLayout() {
+    if (!layout.empty()) {
+      logs << "  - layout: " << layout << std::endl;
     }
   }
   void PrintLod() {
     if (!lod.empty()) {
-      logs << "\tLoD: [";
+      logs << "  - lod: {";
       for (auto level : lod) {
-        logs << "[ ";
+        logs << "{";
+        bool is_first = true;
         for (auto i : level) {
-          logs << i << ",";
+          if (is_first) {
+            logs << i;
+            is_first = false;
+          } else {
+            logs << ", " << i;
+          }
         }
-        logs << " ]";
+        logs << "}";
       }
-      logs << "]" << std::endl;
+      logs << "}" << std::endl;
     }
   }
 
@@ -113,25 +127,31 @@ struct Formater {
     } else if (framework::IsType<const bool>(dtype)) {
       Display<bool>(size);
     } else {
-      logs << "\tdata: unprintable type: " << dtype.name() << std::endl;
+      logs << "  - data: unprintable type: " << dtype.name() << std::endl;
     }
   }
 
   template <typename T>
   void Display(size_t size) {
     auto *d = reinterpret_cast<T *>(data);
-    logs << "\tdata: ";
+    logs << "  - data: [";
     if (summarize != -1) {
       summarize = std::min(size, (size_t)summarize);
-      for (int i = 0; i < summarize; i++) {
-        logs << d[i] << ",";
+      if (summarize > 0) {
+        logs << d[0];
+        for (int i = 1; i < summarize; ++i) {
+          logs << " " << d[i];
+        }
       }
     } else {
-      for (size_t i = 0; i < size; i++) {
-        logs << d[i] << ",";
+      if (size > 0) {
+        logs << d[0];
+        for (size_t i = 1; i < size; ++i) {
+          logs << " " << d[i];
+        }
       }
     }
-    logs << std::endl;
+    logs << "]" << std::endl;
   }
 };
 
@@ -201,13 +221,14 @@ class PrintOp : public framework::OperatorBase {
       formater.dtype = framework::ToTypeIndex(printed_tensor.type());
     }
     if (Attr<bool>("print_tensor_shape")) {
-      auto &dims = printed_tensor.dims();
-      formater.dims.resize(dims.size());
-      for (int i = 0; i < dims.size(); ++i) formater.dims[i] = dims[i];
+      formater.dims = printed_tensor.dims().to_str();
     }
     if (Attr<bool>("print_tensor_lod")) {
       formater.lod = printed_tensor.lod();
     }
+    if (Attr<bool>("print_tensor_layout")) {
+      formater.layout = framework::DataLayoutToString(printed_tensor.layout());
+    }
     formater.summarize = Attr<int>("summarize");
     formater.data = reinterpret_cast<void *>(printed_tensor.data<void>());
     formater(printed_tensor.numel());
@@ -225,10 +246,17 @@ class PrintOpProtoAndCheckMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<int>("first_n", "Only log `first_n` number of times.");
     AddAttr<std::string>("message", "A string message to print as a prefix.");
     AddAttr<int>("summarize", "Number of elements printed.");
-    AddAttr<bool>("print_tensor_name", "Whether to print the tensor name.");
-    AddAttr<bool>("print_tensor_type", "Whether to print the tensor's dtype.");
-    AddAttr<bool>("print_tensor_shape", "Whether to print the tensor's shape.");
-    AddAttr<bool>("print_tensor_lod", "Whether to print the tensor's lod.");
+    AddAttr<bool>("print_tensor_name", "Whether to print the tensor name.")
+        .SetDefault(true);
+    AddAttr<bool>("print_tensor_type", "Whether to print the tensor's dtype.")
+        .SetDefault(true);
+    AddAttr<bool>("print_tensor_shape", "Whether to print the tensor's shape.")
+        .SetDefault(true);
+    AddAttr<bool>("print_tensor_layout",
+                  "Whether to print the tensor's layout.")
+        .SetDefault(true);
+    AddAttr<bool>("print_tensor_lod", "Whether to print the tensor's lod.")
+        .SetDefault(true);
     AddAttr<std::string>("print_phase",
                          "(string, default 'FORWARD') Which phase to display "
                          "including 'FORWARD' "

python/paddle/fluid/dygraph/dygraph_to_static/ast_transformer.py

@@ -33,6 +33,7 @@ from paddle.fluid.dygraph.dygraph_to_static.list_transformer import ListTransfor
 from paddle.fluid.dygraph.dygraph_to_static.loop_transformer import LoopTransformer
 from paddle.fluid.dygraph.dygraph_to_static.tensor_shape_transformer import TensorShapeTransformer
 from paddle.fluid.dygraph.dygraph_to_static.call_transformer import CallTransformer
+from paddle.fluid.dygraph.dygraph_to_static.print_transformer import PrintTransformer
 
 from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper
 from paddle.fluid.dygraph.dygraph_to_static.static_analysis import NodeVarType
@@ -88,6 +89,9 @@ class DygraphToStaticAst(gast.NodeTransformer):
         # Transform all if/else statement of Dygraph into Static Graph.
         IfElseTransformer(node_wrapper).transform()
 
+        # Transform all python print statement
+        PrintTransformer(node_wrapper).transform()
+
         # Transform call recursively
         CallTransformer(node_wrapper).transform()
 

python/paddle/fluid/dygraph/dygraph_to_static/print_transformer.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import gast
+import astor
+
+from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper, NodeVarType, StaticAnalysisVisitor
+
+
+class PrintTransformer(gast.NodeTransformer):
+    """
+    This class transforms python print function to fluid.layers.Print.
+    """
+
+    def __init__(self, wrapper_root):
+        assert isinstance(
+            wrapper_root, AstNodeWrapper
+        ), "Input non-AstNodeWrapper node for the initialization of PrintTransformer."
+        self.wrapper_root = wrapper_root
+        self.root = wrapper_root.node
+
+        self.static_analysis_visitor = StaticAnalysisVisitor(self.root)
+        self.node_to_wrapper_map = self.static_analysis_visitor.get_node_to_wrapper_map(
+        )
+
+    def transform(self):
+        self.visit(self.root)
+
+    # NOTE: deal with print in PY3
+    def visit_Call(self, node):
+        assert isinstance(node, gast.Call)
+        if isinstance(node.func, gast.Name) and node.func.id == 'print':
+            var = self._get_print_var(node)
+            return self._construct_print_node(var)
+        return node
+
+    # NOTE: deal with print in PY2
+    def visit_Print(self, node):
+        var = self._get_print_var(node)
+        print_call_node = self._construct_print_node(var)
+        return gast.Expr(value=print_call_node)
+
+    def _get_print_var(self, node):
+        if isinstance(node, gast.Call):
+            var_list = node.args
+        elif isinstance(node, gast.Print):
+            var_list = node.values
+            if isinstance(var_list[0], gast.Tuple):
+                var_list = var_list[0].elts
+        # TODO: support print multiple Var
+        assert len(var_list) == 1, "Now only support print one Variable."
+        return var_list[0]
+
+    def _construct_print_node(self, node):
+        if isinstance(node, gast.Name):
+            if self._is_tensor_node(node):
+                print_node = gast.Call(
+                    func=gast.parse('fluid.layers.Print').body[0].value,
+                    args=[node],
+                    keywords=[])
+                return print_node
+            else:
+                raise TypeError(
+                    "print object type error, only support print Variable now.")
+        else:
+            # TODO: may not only print with format
+            raise NotImplementedError(
+                "cannot transform print with format temporarily.")
+
+    def _is_tensor_node(self, node):
+        tensor_types = {NodeVarType.TENSOR, NodeVarType.PADDLE_RETURN_TYPES}
+        wrapper_node = self.node_to_wrapper_map.get(node, None)
+        if wrapper_node is not None:
+            if wrapper_node.node_var_type & tensor_types:
+                return True
+        return False

python/paddle/fluid/dygraph/dygraph_to_static/program_translator.py

@@ -181,6 +181,9 @@ class ProgramCache(object):
                     # func just returns one reuslt
                     fetch_list = [fetch_list]
                 fetch_list = list(fetch_list)
+                # NOTE: avoid fetch_list is [None]
+                if len(fetch_list) == 1 and fetch_list[0] is None:
+                    fetch_list = None
                 self._outputs = fetch_list
             else:
                 fetch_list = func(*args, **kwargs)
@@ -188,6 +191,9 @@ class ProgramCache(object):
                     # func just returns one reuslt
                     fetch_list = [fetch_list]
                 fetch_list = list(fetch_list)
+                # NOTE: avoid fetch_list is [None]
+                if len(fetch_list) == 1 and fetch_list[0] is None:
+                    fetch_list = None
 
         return fetch_list
 

python/paddle/fluid/dygraph/dygraph_to_static/static_analysis.py

@@ -66,7 +66,8 @@ class NodeVarType(object):
 
         supported_types = [
             NodeVarType.BOOLEAN, NodeVarType.INT, NodeVarType.FLOAT,
-            NodeVarType.NUMPY_NDARRAY, NodeVarType.TENSOR
+            NodeVarType.NUMPY_NDARRAY, NodeVarType.TENSOR,
+            NodeVarType.PADDLE_RETURN_TYPES
         ]
 
         if in_type1 not in supported_types:

python/paddle/fluid/dygraph/varbase_patch_methods.py

@@ -198,11 +198,9 @@ def monkey_patch_varbase():
             # TODO(panyx0718): add more dygraph debug info.
             tensor = self.value().get_tensor()
             if tensor._is_initialized():
-                return 'name %s, dtype: %s shape: %s %s' % (
-                    self.name, self.dtype, self.shape, str(tensor))
+                return 'Variable: %s\n%s' % (self.name, str(tensor))
             else:
-                return 'name %s, shape: %s, not inited' % (self.name,
-                                                           self.shape)
+                return 'Variable: %s, not initialized' % (self.name)
 
     def __nonzero__(self):
         numel = np.prod(self.shape)

python/paddle/fluid/framework.py

@@ -5134,10 +5134,9 @@ class ParamBase(core.VarBase):
                                                                bool)
         tensor = self.value().get_tensor()
         if tensor._is_initialized():
-            return 'name %s, dtype: %s shape: %s %s' % (self.name, self.dtype,
-                                                        self.shape, str(tensor))
+            return 'Parameter: %s\n%s' % (self.name, str(tensor))
         else:
-            return 'name %s, shape: %s, not inited' % (self.name, self.shape)
+            return 'Parameter: %s, not initialized' % (self.name)
 
     __repr__ = __str__
 

python/paddle/fluid/layers/control_flow.py

@@ -218,6 +218,7 @@ def Print(input,
           print_tensor_name=True,
           print_tensor_type=True,
           print_tensor_shape=True,
+          print_tensor_layout=True,
           print_tensor_lod=True,
           print_phase='both'):
     '''
@@ -238,6 +239,7 @@ def Print(input,
         print_tensor_name (bool, optional): Print the tensor name. Default: True.
         print_tensor_type (bool, optional): Print the tensor type. Defaultt: True.
         print_tensor_shape (bool, optional): Print the tensor shape. Default: True.
+        print_tensor_layout (bool, optional): Print the tensor layout. Default: True.
         print_tensor_lod (bool, optional): Print the tensor lod. Default: True.
         print_phase (str): Which phase to displace, including 'forward',
                 'backward' and 'both'. Default: 'both'. If set to 'backward', will 
@@ -291,6 +293,7 @@ def Print(input,
             'print_tensor_name': print_tensor_name,
             'print_tensor_type': print_tensor_type,
             'print_tensor_shape': print_tensor_shape,
+            'print_tensor_layout': print_tensor_layout,
             'print_tensor_lod': print_tensor_lod,
             'print_phase': print_phase.upper()
         })

python/paddle/fluid/tests/unittests/dygraph_to_static/test_print.py

+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy
+import unittest
+
+import paddle.fluid as fluid
+from paddle.fluid.dygraph.jit import declarative
+
+
+# 1. print VarBase
+@declarative
+def dyfunc_print_variable(x):
+    """
+    PY2:
+    Print(dest=None, values=[Name(id='x_v', annotation=None, type_comment=None)], nl=True)],
+    PY3:
+    Expr(
+        value=Call(func=Name(id='print', annotation=None, type_comment=None),
+            args=[Name(id='x_v', annotation=None, type_comment=None)],
+            keywords=[]))
+    """
+    # NOTE: transform to static code, var name will be changed
+    x_v = fluid.dygraph.to_variable(x)
+    print(x_v)
+
+
+# 2. print ndarray
+@declarative
+def dyfunc_print_ndarray(x):
+    """
+    PY2:
+    Print(dest=None, values=[Name(id='x', annotation=None, type_comment=None)
+    PY3:
+    Expr(
+        value=Call(func=Name(id='print', annotation=None, type_comment=None),
+            args=[Name(id='x', annotation=None, type_comment=None)],
+            keywords=[]))
+    """
+    print(x)
+
+
+# 3. print VarBase with format
+@declarative
+def dyfunc_print_with_format(x):
+    """
+    PY2:
+    Print(dest=None,
+        values=[
+            Call(
+                func=Attribute(value=Constant(value='PrintVariable: {}', kind=None), attr='format'),
+                args=[Name(id='x_v', annotation=None, type_comment=None)],
+                keywords=[])],
+        nl=True)
+    PY3:
+    Expr(
+        value=Call(func=Name(id='print', annotation=None, type_comment=None),
+            args=[
+                Call(
+                    func=Attribute(value=Constant(value='PrintVariable: {}', kind=None), attr='format'),
+                    args=[Name(id='x_v', annotation=None, type_comment=None)],
+                    keywords=[])],
+            keywords=[]))
+    """
+    x_v = fluid.dygraph.to_variable(x)
+    print("PrintVariable: {}".format(x_v))
+
+
+# 4. print VarBase with format 2
+@declarative
+def dyfunc_print_with_format2(x):
+    """
+    PY2:
+    Print(dest=None,
+        values=[
+            BinOp(left=Constant(value='PrintVariable: %s', kind=None),
+                op=Mod,
+                right=Name(id='x_v', annotation=None, type_comment=None))],
+        nl=True)
+    PY3:
+    Expr(
+        value=Call(func=Name(id='print', annotation=None, type_comment=None),
+            args=[
+                BinOp(left=Constant(value='PrintVariable: %s', kind=None),
+                    op=Mod,
+                    right=Name(id='x_v', annotation=None, type_comment=None))],
+            keywords=[]))
+    """
+    x_v = fluid.dygraph.to_variable(x)
+    print("PrintVariable: %s" % (x_v))
+
+
+# 5. print VarBase in control flow1
+@declarative
+def dyfunc_print_with_ifelse(x):
+    x_v = fluid.dygraph.to_variable(x)
+    if len(x_v.shape) > 1:
+        print(x_v)
+    else:
+        print(x_v)
+
+
+# 6. print mutiple VarBases
+@declarative
+def dyfunc_print_multi_vars(x):
+    """
+    # NOTE: y_v type is error before cur PR in this case
+    Assign(targets=[Name(id='y_v', annotation=None, type_comment=None)],
+        value=BinOp(left=Name(id='x_v', annotation=None, type_comment=None), op=Mult, right=Constant(value=2, kind=None)))
+    """
+    x_v = fluid.dygraph.to_variable(x)
+    y_v = x_v * 2
+    print(x_v)
+    print(y_v)
+
+
+# 7. print continue VarBase
+@declarative
+def dyfunc_print_continue_vars(x):
+    """
+    PY3:
+    Expr(
+        value=Call(func=Name(id='print', annotation=None, type_comment=None),
+            args=[Name(id='x_v', annotation=None, type_comment=None),
+                Name(id='y_v', annotation=None, type_comment=None)],
+            keywords=[]))
+    PY2:
+    Print(dest=None,
+        values=[
+            Tuple(
+                elts=[Name(id='x_v', annotation=None, type_comment=None),
+                    Name(id='y_v', annotation=None, type_comment=None)])],
+        nl=True)
+    """
+    x_v = fluid.dygraph.to_variable(x)
+    y_v = x_v * 2
+    print(x_v, y_v)
+
+
+class TestPrintBase(unittest.TestCase):
+    def setUp(self):
+        self.input = numpy.ones(5).astype("int32")
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        self.set_test_func()
+
+    def set_test_func(self):
+        raise NotImplementedError("Print test should implement set_test_func")
+
+    def get_dygraph_output(self):
+        with fluid.dygraph.guard():
+            self.dygraph_func(self.input)
+
+    def get_static_output(self):
+        with fluid.program_guard(fluid.Program()):
+            # TODO: How to catch C++ stdout to python
+            self.dygraph_func(self.input)
+
+
+class TestPrintVariable(TestPrintBase):
+    def set_test_func(self):
+        self.dygraph_func = dyfunc_print_variable
+
+    def test_transformed_static_result(self):
+        self.get_dygraph_output()
+        self.get_static_output()
+
+
+class TestPrintNdArray(TestPrintBase):
+    def set_test_func(self):
+        self.dygraph_func = dyfunc_print_ndarray
+
+    def test_transform_static_error(self):
+        with self.assertRaises(TypeError):
+            self.get_dygraph_output()
+            self.get_static_output()
+
+
+class TestPrintWithFormat(TestPrintBase):
+    def set_test_func(self):
+        self.dygraph_func = dyfunc_print_with_format
+
+    def test_transform_static_error(self):
+        with self.assertRaises(NotImplementedError):
+            self.get_dygraph_output()
+            self.get_static_output()
+
+
+class TestPrintWithFormat2(TestPrintBase):
+    def set_test_func(self):
+        self.dygraph_func = dyfunc_print_with_format2
+
+    def test_transform_static_error(self):
+        with self.assertRaises(NotImplementedError):
+            self.get_dygraph_output()
+            self.get_static_output()
+
+
+class TestPrintWithIfElse(TestPrintVariable):
+    def set_test_func(self):
+        self.dygraph_func = dyfunc_print_with_ifelse
+
+
+class TestPrintMultipleVar(TestPrintVariable):
+    def set_test_func(self):
+        self.dygraph_func = dyfunc_print_multi_vars
+
+
+class TestPrintContinueVar(TestPrintBase):
+    def set_test_func(self):
+        self.dygraph_func = dyfunc_print_continue_vars
+
+    def test_transform_static_error(self):
+        with self.assertRaises(AssertionError):
+            self.get_dygraph_output()
+            self.get_static_output()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_print_op.py

@@ -81,6 +81,14 @@ class TestPrintOpCPU(unittest.TestCase):
                        fetch_list=[loss],
                        return_numpy=False)
 
+    def test_no_summarize(self):
+        switch_main_program(Program())
+        printed = self.build_network(True, summarize=-1, print_phase='forward')
+        exe = Executor(self.place)
+        outs = exe.run(feed={'x': self.x_tensor},
+                       fetch_list=[printed],
+                       return_numpy=False)
+
 
 class TestPrintOpError(unittest.TestCase):
     def test_errors(self):