Support Tensor.shape in dygraph_to_static (#22830)

* support basic tensor.shape. 

* Support tensor.shape with dependencies.

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

@@ -14,19 +14,21 @@
 
 from __future__ import print_function
 
+import copy
+import inspect
+import textwrap
+
 import astor
 # gast is a generic AST to represent Python2 and Python3's Abstract Syntax Tree(AST).
 # It provides a compatibility layer between the AST of various Python versions,
 # as produced by ast.parse from the standard ast module.
 # See details in https://github.com/serge-sans-paille/gast/
 import gast
-import textwrap
-import inspect
 
 from paddle.fluid import unique_name
 from paddle.fluid.dygraph.dygraph_to_static.loop_transformer import LoopTransformer
 from .ast_utils import is_control_flow_if, create_cond_node, transform_if_else, ast_to_func
-from .static_analysis import AstNodeWrapper, StaticAnalysisVisitor
+from .static_analysis import AstNodeWrapper, NodeVarType, StaticAnalysisVisitor
 from .utils import *
 
 __all__ = ['DygraphToStaticAst', 'convert_to_static']
@@ -121,8 +123,10 @@ class DygraphToStaticAst(gast.NodeTransformer):
     def get_static_ast(self, root):
         # save root for some analysis may need global AST
         self.root = root
-        self.static_analysis_root = StaticAnalysisVisitor(
-            root).get_node_wrapper_root()
+        self.static_analysis_visitor = StaticAnalysisVisitor(root)
+        self.static_analysis_root = self.static_analysis_visitor.get_node_wrapper_root(
+        )
+
         self.decorate_func_name = None
         self.arg_name_to_idx = {}
         self.transfer_from_node_type(self.static_analysis_root)
@@ -133,7 +137,8 @@ class DygraphToStaticAst(gast.NodeTransformer):
         self.visit(node_wrapper.node)
 
         # Transform basic api of dygraph to static graph
-        basic_api_trans = BasicApiTransformer(node_wrapper)
+        basic_api_trans = BasicApiTransformer(node_wrapper,
+                                              self.static_analysis_visitor)
         basic_api_trans.ast_visit()
         self.feed_name_to_arg_name = basic_api_trans.get_feed_name_to_arg_id()
 
@@ -178,14 +183,31 @@ class BasicApiTransformer(gast.NodeTransformer):
     Class to transform basic API from dygraph to static graph.
     """
 
-    def __init__(self, wrapper_root):
+    def __init__(self, wrapper_root, static_analysis_visitor):
         assert isinstance(
             wrapper_root, AstNodeWrapper
         ), "Input non-AstNodeWrapper node for the initialization of BasicApiTransformer."
+
         self.wrapper_root = wrapper_root
         self.root = wrapper_root.node
         self.class_node_dict = {}
+
+        # Used for transformation of data feed
         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):
         self.visit(self.root)
@@ -204,11 +226,12 @@ class BasicApiTransformer(gast.NodeTransformer):
         if self._update_class_node_dict(node):
             return None
 
-        value_node = node.value
-        for child_node in gast.walk(value_node):
+        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)
-
         return node
 
     def visit_Expr(self, node):
@@ -219,19 +242,41 @@ class BasicApiTransformer(gast.NodeTransformer):
                     return
                 else:
                     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`
         if is_to_variable(node):
             self._update_feed_dict(node)
             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):
             class_node = self._get_class_node(func_name)
             static_node = to_static_ast(node, class_node)
@@ -239,6 +284,53 @@ 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
 
@@ -280,6 +372,32 @@ 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/ast_utils.py

@@ -360,7 +360,9 @@ def ast_to_func(ast_root, func_name, delete_on_exit=True):
     # TODO(Aurelius84): more elegant way to transform ast into callable object
     import_str = "import paddle\n" \
                  "import paddle.fluid as fluid\n" \
-                 "import paddle.fluid.layers as layers\n"
+                 "import paddle.fluid.layers as layers\n" \
+                 "import numpy as np\n" \
+                 "import numpy\n"
     with f:
         module_name = os.path.basename(f.name[:-3])
         f.write(import_str)

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

@@ -181,3 +181,12 @@ def update_args_of_func(node, dygraph_node, method_name):
 
     node.args = []
     node.keywords = added_keywords + node.keywords
+
+
+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

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

@@ -1,4 +1,4 @@
-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# 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.

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

@@ -0,0 +1,100 @@
+#   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 dygraph_to_static_graph
+
+
+def dyfunc_tensor_shape_1(x):
+    x = fluid.dygraph.to_variable(x)
+    res = fluid.layers.reshape(x, shape=x.shape)
+    return res
+
+
+def dyfunc_tensor_shape_2(x):
+    x = fluid.dygraph.to_variable(x)
+    shape = x.shape
+    shape2 = shape
+    res = fluid.layers.reshape(x, shape2)
+    return res
+
+
+def dyfunc_tensor_shape_3(x):
+    # Don't transform y.shape because y is numpy.ndarray
+    x = fluid.dygraph.to_variable(x)
+    y = numpy.ones(5)
+    res = fluid.layers.reshape(x, shape=y.shape)
+    return res
+
+
+def dyfunc_tensor_shape_4(x):
+    x = fluid.dygraph.to_variable(x)
+    res = fluid.layers.reshape(x, shape=(-1, x.shape[0], len(x.shape)))
+    return res
+
+
+def dyfunc_tensor_shape_5(x):
+    # `res = fluid.layers.reshape(x, shape=(-1, s))` to
+    # `res = fluid.layers.reshape(x, shape=(-1, fluid.layers.shape(x)[0]))`
+    x = fluid.dygraph.to_variable(x)
+    s = x.shape[0]
+    res = fluid.layers.reshape(x, shape=(-1, s))
+    return res
+
+
+test_funcs = [
+    dyfunc_tensor_shape_1, dyfunc_tensor_shape_2, dyfunc_tensor_shape_3,
+    dyfunc_tensor_shape_4, dyfunc_tensor_shape_5
+]
+
+
+class TestTensorShape(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()
+
+    def get_dygraph_output(self):
+        with fluid.dygraph.guard():
+            res = self.dygraph_func(self.input).numpy()
+            return res
+
+    def get_static_output(self):
+        main_program = fluid.Program()
+        with fluid.program_guard(main_program):
+            static_out = dygraph_to_static_graph(self.dygraph_func)(self.input)
+
+        exe = fluid.Executor(self.place)
+        static_res = exe.run(main_program, fetch_list=static_out)
+
+        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))
+
+
+if __name__ == '__main__':
+    unittest.main()