Tensor.shape support control flow if/for/while and bugfix (#22866)

* Support Tensor.shape in control flow if/for/while and separate TensorShapeTransformer from BasicApiTransformer. test=develop

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

@@ -27,13 +27,14 @@ import gast
 
 from paddle.fluid import unique_name
 from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_func
-from paddle.fluid.dygraph.dygraph_to_static.utils import is_paddle_api, is_dygraph_api, is_to_variable
+from paddle.fluid.dygraph.dygraph_to_static.utils import is_dygraph_api, is_to_variable
 from paddle.fluid.dygraph.dygraph_to_static.utils import to_assign_node, to_static_ast, update_args_of_func
-from paddle.fluid.dygraph.dygraph_to_static.utils import dygraph_class_to_static_api, create_api_shape_node
+from paddle.fluid.dygraph.dygraph_to_static.utils import dygraph_class_to_static_api
+from paddle.fluid.dygraph.dygraph_to_static.tensor_shape_transformer import TensorShapeTransformer
 from paddle.fluid.dygraph.dygraph_to_static.list_transformer import ListTransformer
 from paddle.fluid.dygraph.dygraph_to_static.loop_transformer import LoopTransformer
 from paddle.fluid.dygraph.dygraph_to_static.ifelse_transformer import IfElseTransformer
-from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper, NodeVarType
+from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper
 from paddle.fluid.dygraph.dygraph_to_static.static_analysis import StaticAnalysisVisitor
 
 __all__ = ['DygraphToStaticAst', 'convert_to_static']
@@ -62,16 +63,21 @@ class DygraphToStaticAst(gast.NodeTransformer):
         # Generic transformation
         self.visit(node_wrapper.node)
 
-        # Transform basic api of dygraph to static graph
-        basic_api_trans = BasicApiTransformer(node_wrapper,
-                                              self.static_analysis_visitor)
-        basic_api_trans.ast_visit()
+        # Transform basic api of dygraph to static graph and get feed_name_to_arg_name
+        basic_api_trans = BasicApiTransformer(node_wrapper)
+        basic_api_trans.transform()
         self.feed_name_to_arg_name = basic_api_trans.get_feed_name_to_arg_id()
 
+        # Transform Tensor.shape into fluid.layers.shape(Tensor)
+        TensorShapeTransformer(node_wrapper).transform()
+
+        # Transform list used in control flow
         ListTransformer(node_wrapper).transform()
+
         # Transform all if/else statement of Dygraph into Static Graph.
         IfElseTransformer(node_wrapper).transform()
 
+        # Transform for loop and while loop
         LoopTransformer(node_wrapper).transform()
 
     def visit_FunctionDef(self, node):
@@ -110,7 +116,7 @@ class BasicApiTransformer(gast.NodeTransformer):
     Class to transform basic API from dygraph to static graph.
     """
 
-    def __init__(self, wrapper_root, static_analysis_visitor):
+    def __init__(self, wrapper_root):
         assert isinstance(
             wrapper_root, AstNodeWrapper
         ), "Input non-AstNodeWrapper node for the initialization of BasicApiTransformer."
@@ -123,20 +129,7 @@ class BasicApiTransformer(gast.NodeTransformer):
         self.feed_name_to_arg_id = {}
         self.name_to_tensor_shape = {}
 
-        # Used for transformation of Tensor.shape
-        self.static_analysis_visitor = static_analysis_visitor
-        self.node_to_wrapper_map = self.static_analysis_visitor.get_node_to_wrapper_map(
-        )
-        self.scope_var_type_dict = {}
-        self._run_static_visitor()
-
-    def _run_static_visitor(self):
-        var_env = copy.deepcopy(self.static_analysis_visitor.get_var_env())
-        # TODO: Consider that Tensor.shape is used in sub function and sub_scopes is empty
-        var_env.cur_scope = var_env.cur_scope.sub_scopes[0]
-        self.scope_var_type_dict = var_env.get_scope_var_type()
-
-    def ast_visit(self):
+    def transform(self):
         self.visit(self.root)
         return self.wrapper_root
 
@@ -153,9 +146,6 @@ class BasicApiTransformer(gast.NodeTransformer):
         if self._update_class_node_dict(node):
             return None
 
-        if self._update_name_to_tensor_shape(node):
-            return node
-
         for child_node in gast.walk(node.value):
             if isinstance(child_node, gast.Call):
                 self._visit_Call(child_node)
@@ -171,25 +161,6 @@ class BasicApiTransformer(gast.NodeTransformer):
                     self._visit_Call(child_node)
         return node
 
-    def visit_Attribute(self, node):
-        if self._used_by_paddle_api(node):
-            if self.is_tensor_shape(node):
-                return create_api_shape_node(node)
-        return node
-
-    def visit_Name(self, node):
-        if node.id in self.name_to_tensor_shape:
-            if self._used_by_paddle_api(node):
-                tensor_shape_node = self.name_to_tensor_shape[node.id]
-                if isinstance(tensor_shape_node, gast.Attribute):
-                    return create_api_shape_node(tensor_shape_node)
-                elif isinstance(tensor_shape_node, gast.Subscript):
-                    result_node = copy.deepcopy(tensor_shape_node)
-                    result_node.value = create_api_shape_node(
-                        tensor_shape_node.value)
-                    return result_node
-        return node
-
     def _visit_Call(self, node):
         assert isinstance(node, gast.Call)
         # Replace API `to_variable` with `fluid.layers.assign`
@@ -198,10 +169,6 @@ class BasicApiTransformer(gast.NodeTransformer):
             node = to_assign_node(node)
             return node
 
-        if is_paddle_api(node):
-            # Visit gast.Attribute and gast.Name to replace tensor.shape if necessary
-            self.generic_visit(node)
-
         func_name = astor.to_source(gast.gast_to_ast(node.func))
 
         if self._is_dygraph_forward(func_name):
@@ -211,53 +178,6 @@ class BasicApiTransformer(gast.NodeTransformer):
         else:
             return node
 
-    def is_tensor_shape(self, node):
-        """
-        Return True if node is like `x.shape` and x is Tensor, return False otherwise.
-        """
-        assert isinstance(node, gast.Attribute)
-        if node.attr != 'shape':
-            return False
-
-        try:
-            value_id = node.value.id
-        except AttributeError:
-            return False
-
-        if value_id in self.name_to_tensor_shape:
-            return True
-
-        # TODO: `value_id` may be not in scope_var_type_dict if `value_id` is the arg of decorated function
-        # Need a better way to confirm whether `value_id` is a Tensor.
-        try:
-            var_type_set = self.scope_var_type_dict[value_id]
-        except KeyError:
-            return False
-
-        if NodeVarType.NUMPY_NDARRAY in var_type_set:
-            return False
-        if NodeVarType.TENSOR not in var_type_set and NodeVarType.PADDLE_RETURN_TYPES not in var_type_set:
-            return False
-
-        return True
-
-    def _used_by_paddle_api(self, node):
-        assert isinstance(node, (gast.Attribute, gast.Name))
-        wrapper_node = self.node_to_wrapper_map.get(node)
-        if not wrapper_node:
-            # Transformed node is not in node_to_wrapper_map
-            return False
-        while wrapper_node.parent:
-            parent_node = wrapper_node.parent.node
-            if isinstance(parent_node, gast.Call):
-                if is_paddle_api(parent_node):
-                    return True
-                else:
-                    return False
-            wrapper_node = wrapper_node.parent
-
-        return False
-
     def _is_dygraph_forward(self, func_id):
         return func_id in self.class_node_dict
 
@@ -304,32 +224,6 @@ class BasicApiTransformer(gast.NodeTransformer):
     def get_feed_name_to_arg_id(self):
         return self.feed_name_to_arg_id
 
-    def _update_name_to_tensor_shape(self, node):
-        assert isinstance(node, gast.Assign)
-        # TODO: Consider node has more than one target. eg: x, y = a, Tensor.shape[1]
-        target_node = node.targets[0]
-        try:
-            target_id = target_node.id
-        except AttributeError:
-            return False
-        value_node = node.value
-
-        if isinstance(value_node, gast.Name):
-            if value_node.id in self.name_to_tensor_shape:
-                self.name_to_tensor_shape[
-                    target_id] = self.name_to_tensor_shape[value_node.id]
-                return True
-        if isinstance(value_node, gast.Attribute):
-            if self.is_tensor_shape(value_node):  # eg: x.shape
-                self.name_to_tensor_shape[target_id] = value_node
-                return True
-        if isinstance(value_node, gast.Subscript):
-            if isinstance(value_node.value, gast.Attribute):
-                if self.is_tensor_shape(value_node.value):  # eg: x.shape[0]
-                    self.name_to_tensor_shape[target_id] = value_node
-                    return True
-        return False
-
 
 def convert_to_static(dyfunc):
     """

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

@@ -20,7 +20,7 @@ import gast
 from collections import defaultdict
 from paddle.fluid import unique_name
 from paddle.fluid.dygraph.dygraph_to_static.utils import generate_name_node
-from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper
+from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper, StaticAnalysisVisitor
 from paddle.fluid.dygraph.dygraph_to_static.utils import get_constant_variable_node
 from paddle.fluid.dygraph.dygraph_to_static.utils import get_attribute_full_name
 from paddle.fluid.dygraph.dygraph_to_static.variable_trans_func import create_static_variable_gast_node
@@ -70,8 +70,6 @@ class NameVisitor(gast.NodeVisitor):
     def __init__(self, root_node):
         # Set of gast.Name or gast.Attribute for variables
         self.current_seen_vars = set()
-        # list of nodes of current visit node
-        self.ancestor_nodes = []
 
         # List of gast.While/gast.For nodes
         self.current_loop = []
@@ -80,6 +78,10 @@ class NameVisitor(gast.NodeVisitor):
         self.before_loop_body_vars = defaultdict(set)
         self.in_loop_vars = defaultdict(set)
 
+        self.static_analysis_visitor = StaticAnalysisVisitor(root_node)
+        self.node_to_wrapper_map = self.static_analysis_visitor.get_node_to_wrapper_map(
+        )
+
         self.visit(root_node)
 
     def is_control_flow_loop(self, node):
@@ -123,11 +125,9 @@ class NameVisitor(gast.NodeVisitor):
         self.generic_visit(node)
 
     def visit(self, node):
-        self.ancestor_nodes.append(node)
         method = 'visit_' + node.__class__.__name__
         visitor = getattr(self, method, self.generic_visit)
         ret = visitor(node)
-        self.ancestor_nodes.pop()
         return ret
 
     def visit_Attribute(self, node):
@@ -166,10 +166,9 @@ class NameVisitor(gast.NodeVisitor):
         return ret
 
     def _is_call_func_name_node(self, node):
-        if self.ancestor_nodes:
-            parent_node = self.ancestor_nodes[-1]
-            if isinstance(parent_node, gast.Call) and parent_node.func == node:
-                return True
+        parent_node = self.node_to_wrapper_map[node].parent.node
+        if isinstance(parent_node, gast.Call) and parent_node.func == node:
+            return True
         return False
 
 

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

@@ -313,6 +313,10 @@ class StaticAnalysisVisitor(object):
             return self.var_env.get_var_type(node.id)
 
         if isinstance(node, gast.Return):
+            # If return nothing:
+            if node.value is None:
+                return {NodeVarType.NONE}
+
             return_type = self.node_to_wrapper_map[node.value].node_var_type
             assert self.var_env.cur_scope.scope_type == AstVarScope.SCOPE_TYPE_FUNCTION, "Return at non-function scope"
             func_name = self.var_env.cur_scope.scope_name

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

@@ -0,0 +1,210 @@
+#   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
+import copy
+from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper, NodeVarType, StaticAnalysisVisitor
+from paddle.fluid.dygraph.dygraph_to_static.utils import is_control_flow_to_transform
+from paddle.fluid import unique_name
+from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_func
+from paddle.fluid.dygraph.dygraph_to_static.utils import is_paddle_api, is_dygraph_api, is_to_variable
+from paddle.fluid.dygraph.dygraph_to_static.utils import to_assign_node, to_static_ast, update_args_of_func
+from paddle.fluid.dygraph.dygraph_to_static.utils import dygraph_class_to_static_api, create_api_shape_node
+from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper, NodeVarType
+from paddle.fluid.dygraph.dygraph_to_static.static_analysis import StaticAnalysisVisitor
+
+
+class TensorShapeTransformer(gast.NodeTransformer):
+    """
+    This class transforms Tensor.shape used in Paddle Apis and control flow conditions into Static Graph Ast.
+    """
+
+    def __init__(self, wrapper_root):
+        assert isinstance(
+            wrapper_root, AstNodeWrapper
+        ), "Input non-AstNodeWrapper node for the initialization of TensorShapeTransformer."
+        self.wrapper_root = wrapper_root
+        self.root = wrapper_root.node
+        self.name_to_tensor_shape = {}
+
+        self.static_analysis_visitor = StaticAnalysisVisitor(self.root)
+        self.node_to_wrapper_map = self.static_analysis_visitor.get_node_to_wrapper_map(
+        )
+        var_env = self.static_analysis_visitor.get_var_env()
+        var_env.cur_scope = var_env.cur_scope.sub_scopes[0]
+        self.scope_var_type_dict = var_env.get_scope_var_type()
+
+    def transform(self):
+        self.visit(self.root)
+
+    def visit_Assign(self, node):
+        if self._update_name_to_tensor_shape(node):
+            return node
+        self.generic_visit(node)
+        return node
+
+    def visit_Attribute(self, node):
+        if self._used_by_paddle_api(node):
+            if self.is_tensor_shape(node):
+                return create_api_shape_node(node)
+        return node
+
+    def visit_Name(self, node):
+        if node.id in self.name_to_tensor_shape:
+            if self._used_by_paddle_api(node):
+                tensor_shape_node = self.name_to_tensor_shape[node.id]
+                return create_api_shape_node(tensor_shape_node)
+        return node
+
+    def visit_Call(self, node):
+        assert isinstance(node, gast.Call)
+        if is_paddle_api(node):
+            # Visit gast.Attribute and gast.Name to replace tensor.shape if necessary.
+            self.generic_visit(node)
+
+        return node
+
+    def visit_If(self, node):
+        # Call generic_visit first to transform Tensor.shape that is used in Paddle Api.
+        self.generic_visit(node)
+        cond = node.test
+        self._transform_tensor_shape_if_necessary(cond)
+        return node
+
+    def visit_While(self, node):
+        self.generic_visit(node)
+        cond = node.test
+        self._transform_tensor_shape_if_necessary(cond)
+        return node
+
+    def visit_For(self, node):
+        self.generic_visit(node)
+        iter = node.iter
+        self._transform_tensor_shape_if_necessary(iter)
+
+        # If tensor.shape is a gast.Name and it is used in range function, transform it
+        self._transform_tensor_shape_in_range(node)
+        return node
+
+    def _transform_tensor_shape_in_range(self, node):
+        assert isinstance(node, gast.For)
+        if not isinstance(node.iter, gast.Call):
+            return False
+        if not isinstance(node.iter.func, gast.Name):
+            return False
+        if node.iter.func.id != "range":
+            return False
+        args = node.iter.args
+        for idx, arg in enumerate(args):
+            if isinstance(arg,
+                          gast.Name) and arg.id in self.name_to_tensor_shape:
+                args[idx] = create_api_shape_node(self.name_to_tensor_shape[
+                    arg.id])
+
+        return True
+
+    def _transform_tensor_shape_if_necessary(self, cond):
+        for child_node in gast.walk(cond):
+            tensor_shape_node = None
+            if isinstance(child_node, (gast.Attribute)):
+                if self.is_tensor_shape(child_node):
+                    tensor_shape_node = child_node
+            elif isinstance(child_node, (gast.Name)):
+                if child_node.id in self.name_to_tensor_shape:
+                    tensor_shape_node = self.name_to_tensor_shape[child_node.id]
+
+            if tensor_shape_node:
+                wrapper_node = self.node_to_wrapper_map.get(child_node)
+                parent_node = wrapper_node.parent.node
+                for field, value in gast.iter_fields(parent_node):
+                    if child_node is value:
+                        setattr(parent_node, field,
+                                create_api_shape_node(tensor_shape_node))
+                        break
+
+    def _used_by_paddle_api(self, node):
+        assert isinstance(node, (gast.Attribute, gast.Name))
+        wrapper_node = self.node_to_wrapper_map.get(node)
+        if not wrapper_node:
+            # Transformed node is not in node_to_wrapper_map
+            return False
+        while wrapper_node.parent:
+            parent_node = wrapper_node.parent.node
+            if isinstance(parent_node, gast.Call):
+                if is_paddle_api(parent_node):
+                    return True
+                else:
+                    return False
+            wrapper_node = wrapper_node.parent
+
+        return False
+
+    def is_tensor_shape(self, node):
+        """
+        Return True if node is like `x.shape` and x is Tensor, return False otherwise.
+        """
+        assert isinstance(node, gast.Attribute)
+        if node.attr != 'shape':
+            return False
+
+        try:
+            value_id = node.value.id
+        except AttributeError:
+            return False
+
+        if value_id in self.name_to_tensor_shape:
+            return True
+
+        # TODO: `value_id` may be not in scope_var_type_dict if `value_id` is the arg of decorated function
+        # Need a better way to confirm whether `value_id` is a Tensor.
+        try:
+            var_type_set = self.scope_var_type_dict[value_id]
+        except KeyError:
+            return False
+
+        if NodeVarType.NUMPY_NDARRAY in var_type_set:
+            return False
+        if NodeVarType.TENSOR not in var_type_set and NodeVarType.PADDLE_RETURN_TYPES not in var_type_set:
+            return False
+
+        return True
+
+    def _update_name_to_tensor_shape(self, node):
+        assert isinstance(node, gast.Assign)
+        # TODO: Consider node has more than one target. eg: x, y = a, Tensor.shape[1]
+        target_node = node.targets[0]
+        try:
+            target_id = target_node.id
+        except AttributeError:
+            return False
+        value_node = node.value
+
+        if isinstance(value_node, gast.Name):
+            if value_node.id in self.name_to_tensor_shape:
+                self.name_to_tensor_shape[
+                    target_id] = self.name_to_tensor_shape[value_node.id]
+                return True
+        if isinstance(value_node, gast.Attribute):
+            if self.is_tensor_shape(value_node):  # eg: x.shape
+                self.name_to_tensor_shape[target_id] = value_node
+                return True
+        if isinstance(value_node, gast.Subscript):
+            if isinstance(value_node.value, gast.Attribute):
+                if self.is_tensor_shape(value_node.value):  # eg: x.shape[0]
+                    self.name_to_tensor_shape[target_id] = value_node
+                    return True
+        return False

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

@@ -230,12 +230,19 @@ def update_args_of_func(node, dygraph_node, method_name):
 
 
 def create_api_shape_node(tensor_shape_node):
-    assert isinstance(tensor_shape_node, gast.Attribute)
-    api_shape_node = gast.Call(
-        func=gast.parse('fluid.layers.shape').body[0].value,
-        args=[tensor_shape_node.value],
-        keywords=[])
-    return api_shape_node
+    assert isinstance(tensor_shape_node, (gast.Attribute, gast.Subscript))
+
+    if isinstance(tensor_shape_node, gast.Attribute):
+        api_shape_node = gast.Call(
+            func=gast.parse('fluid.layers.shape').body[0].value,
+            args=[tensor_shape_node.value],
+            keywords=[])
+        return api_shape_node
+
+    if isinstance(tensor_shape_node, gast.Subscript):
+        result_node = copy.deepcopy(tensor_shape_node)
+        result_node.value = create_api_shape_node(result_node.value)
+        return result_node
 
 
 def get_constant_variable_node(name, value, shape=[1], dtype='int64'):
@@ -280,6 +287,8 @@ def create_funcDef_node(nodes, name, input_args, return_name_ids):
     # add return statement
     if return_name_ids:
         nodes.append(gast.Return(value=generate_name_node(return_name_ids)))
+    else:
+        nodes.append(gast.Return(value=None))
     func_def_node = gast.FunctionDef(
         name=name,
         args=input_args,

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

@@ -58,17 +58,118 @@ def dyfunc_tensor_shape_5(x):
     return res
 
 
-test_funcs = [
-    dyfunc_tensor_shape_1, dyfunc_tensor_shape_2, dyfunc_tensor_shape_3,
-    dyfunc_tensor_shape_4, dyfunc_tensor_shape_5
-]
+def dyfunc_with_if_1(x):
+    x = fluid.dygraph.to_variable(x)
+    res = fluid.layers.reshape(x, [-1, 1])
+    x_shape_0 = x.shape[0]
+    if x_shape_0 < 1:
+        # `res.shape[0] > 1` is transformed into `if fluid.layers.shape(res)[0] > 1`
+        if res.shape[0] > 1:
+            res = fluid.layers.fill_constant(
+                value=2, shape=x.shape, dtype="int32")
+        else:
+            res = fluid.layers.fill_constant(
+                value=3, shape=x.shape, dtype="int32")
+    return res
+
+
+def dyfunc_with_if_2(x):
+    x = fluid.dygraph.to_variable(x)
+    # `len(x.shape)` will not be transformed.
+    if len(x.shape) < 1:
+        res = x
+    else:
+        res = fluid.layers.fill_constant(value=8, shape=x.shape, dtype="int32")
+
+    return res
+
+
+def dyfunc_with_for_1(x):
+    x = fluid.dygraph.to_variable(x)
+    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    # `x.shape[0]` is transformed into `fluid.layers.shape(x)[0]`
+    for i in range(x.shape[0]):
+        res += 1
+    return res
+
+
+def dyfunc_with_for_2(x):
+    x = fluid.dygraph.to_variable(x)
+    x_shape_0 = x.shape[0]
+    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+
+    # `x_shape_0` is transformed into `fluid.layers.shape(x)[0]`
+    for i in range(x_shape_0):
+        res += 1
+    return res
+
+
+def dyfunc_with_for_3(x):
+    # TODO(liym27):
+    #  It will fail to run because `for i in range(len(x.shape))` will be transformed into Paddle while_loop.
+    #  Here the python list x.shape will be added to loop_vars. However, loop_vars doesn't support python list.
+    #  And the condition of `for i in range(len(x.shape))` only uses the length of x.shape, so it doesn't have to be transformed into Paddle while_loop.
+    #  After the AST tranformation of for loop is improved, add TestTensorShapeInFor3.
+    x = fluid.dygraph.to_variable(x)
+    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    # `len(x.shape)` is not transformed.
+    for i in range(len(x.shape)):
+        res += 1
+
+    return res
+
+
+def dyfunc_with_while_1(x):
+    x = fluid.dygraph.to_variable(x)
+    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    # `x.shape[0]` is transformed into `fluid.layers.shape(x)[0]`
+    i = 1
+    while i < x.shape[0]:
+        res += 1
+        i = i + 2
+    return res
+
+
+def dyfunc_with_while_2(x):
+    x = fluid.dygraph.to_variable(x)
+    x_shape_0 = x.shape[0]
+    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    i = 1
+    # `x_shape_0` is transformed into `fluid.layers.shape(x)[0]`
+    # TODO(liym27): If `x_shape_0` is at right like `while i < x_shape_0`, it will not be transformed.
+    #  Fix this bug next PR.
+    while x_shape_0 > i:
+        res += 1
+        i = i + 2
+    return res
 
 
-class TestTensorShape(unittest.TestCase):
+def dyfunc_with_while_3(x):
+    # TODO(liym27):
+    #  It will fail to run because the same problem as `dyfunc_with_for_3`.
+    #  After the AST tranformation of for loop is improved, add TestTensorShapeInWhile3.
+    x = fluid.dygraph.to_variable(x)
+    x_shape = x.shape
+    res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32")
+    i = 1
+
+    # `len(x.shape)` is not transformed.
+    while len(x_shape) > i:
+        res += 1
+        i += 1
+    return res
+
+
+# 1. Basic tests without control flow
+class TestTensorShapeBasic(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.init_test_func()
+
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_tensor_shape_1
 
     def get_dygraph_output(self):
         with fluid.dygraph.guard():
@@ -86,14 +187,65 @@ class TestTensorShape(unittest.TestCase):
         return static_res[0]
 
     def test_transformed_static_result(self):
-        for func in test_funcs:
-            self.dygraph_func = func
-            static_res = self.get_static_output()
-            dygraph_res = self.get_dygraph_output()
-            self.assertTrue(
-                numpy.allclose(dygraph_res, static_res),
-                msg='dygraph res is {}\nstatic_res is {}'.format(dygraph_res,
-                                                                 static_res))
+        static_res = self.get_static_output()
+        dygraph_res = self.get_dygraph_output()
+        self.assertTrue(
+            numpy.allclose(dygraph_res, static_res),
+            msg='dygraph res is {}\nstatic_res is {}'.format(dygraph_res,
+                                                             static_res))
+
+
+class TestTensorShapeBasic2(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_tensor_shape_2
+
+
+class TestTensorShapeBasic3(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_tensor_shape_3
+
+
+class TestTensorShapeBasic4(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_tensor_shape_4
+
+
+class TestTensorShapeBasic5(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_tensor_shape_5
+
+
+# 2. Tests with control flow if
+class TestTensorShapeInIf1(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_with_if_1
+
+
+class TestTensorShapeInIf2(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_with_if_2
+
+
+# 3. Tests with control flow for loop
+class TestTensorShapeInFor1(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_with_for_1
+
+
+class TestTensorShapeInFor2(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_with_for_2
+
+
+# 4. Tests with control flow while loop
+class TestTensorShapeInWhile1(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_with_while_1
+
+
+class TestTensorShapeInWhile2(TestTensorShapeBasic):
+    def init_test_func(self):
+        self.dygraph_func = dyfunc_with_while_2
 
 
 if __name__ == '__main__':