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

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# Copyright (c) 2019 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 astor
import gast
import inspect
import six
import warnings
__all__ = ['AstNodeWrapper', 'NodeVarType', 'StaticAnalysisVisitor']
# TODO: _is_paddle_dygraph_api is duplicated in Yamei's utils.py. Merge the two
# function code together when Yamei finish her PR.
def _is_paddle_dygraph_api(obj):
m = inspect.getmodule(obj)
return m is not None and m.__name__.startswith("paddle.fluid.dygraph")
# TODO: is_dygraph_api is duplicated in Yamei's utils.py. Merge the two
# function code together when Yamei finish her PR.
def is_dygraph_api(node):
assert isinstance(node, gast.Call), "Input non-Call node for is_dygraph_api"
func_src = astor.to_source(node.func)
try:
import paddle.fluid as fluid
return eval("_is_paddle_dygraph_api({})".format(func_src))
except NameError:
return False
def _is_numpy_api_helper(obj):
m = inspect.getmodule(obj)
return m is not None and m.__name__.startswith("numpy")
def is_numpy_api(node):
assert isinstance(node, gast.Call), "Input non-Call node for is_numpy_api"
func_str = astor.to_source(node.func)
try:
import numpy as np
module_result = eval("_is_numpy_api_helper({})".format(func_str))
# BUG: np.random.uniform doesn't have module and cannot be analyzed
# TODO: find a better way
if not module_result:
return func_str.startswith("numpy.") or func_str.startswith("np.")
except NameError:
return False
class NodeVarType(object):
"""
Enum class of python variable types. We have to know some variable types
during compile time to transfer AST. For example, a string variable and a
tensor variable in if clause may lead to different conversion from dygraph
to static graph.
"""
ERROR = -1 # Returns when static analysis gets error
UNKNOWN = 0 # Reserve for AST nodes have not known the type
STATEMENT = 1 # For nodes representing statement (non-variable type)
CALLABLE = 2
# python data types
NONE = 100
BOOLEAN = 101
INT = 102
FLOAT = 103
STRING = 104
TENSOR = 105
NUMPY_NDARRAY = 106
# python collections
LIST = 200
SET = 201
DICT = 202
PADDLE_DYGRAPH_API = 300
PADDLE_CONTROL_IF = 301
PADDLE_CONTROL_WHILE = 302
PADDLE_CONTROL_FOR = 303
@staticmethod
def binary_op_output_type(in_type1, in_type2):
if in_type1 == in_type2:
return in_type1
if in_type1 == NodeVarType.UNKNOWN:
return in_type2
if in_type2 == NodeVarType.UNKNOWN:
return in_type1
supported_types = [
NodeVarType.BOOLEAN, NodeVarType.INT, NodeVarType.FLOAT,
NodeVarType.NUMPY_NDARRAY, NodeVarType.TENSOR
]
if in_type1 not in supported_types:
warnings.warn("Binary Op on un supported in_type1 = %d " %
(in_type1))
return NodeVarType.UNKNOWN
if in_type2 not in supported_types:
warnings.warn("Binary Op on un supported in_type2 = %d " %
(in_type2))
return NodeVarType.UNKNOWN
forbidden_types = [NodeVarType.NUMPY_NDARRAY, NodeVarType.TENSOR]
if in_type1 in forbidden_types and in_type2 in forbidden_types:
warnings.warn(
"Binary Op on un supported types: in_type1 = %d, in_type2 = %d"
% (in_type1, in_type2))
return NodeVarType.UNKNOWN
return max(in_type1, in_type2)
class AstNodeWrapper(object):
"""
Wrapper for python gast.node. We need a node wrapper because gast.node
doesn't store all required information when we are transforming AST.
We should collect additional information which the actual transformation
needs.
"""
def __init__(self, node):
self.node = node
self.parent = None
self.children = []
self.node_var_type = NodeVarType.UNKNOWN
class AstVarScope(object):
"""
AstVarScope is a class holding the map from current scope variable to its
type.
"""
def __init__(self, parent_scope=None):
self.sub_scopes = []
self.name_to_id = {}
self.id_to_type = {}
self.cur_id = 0
self.parent_scope = parent_scope
if parent_scope is not None:
parent_scope.sub_scopes.append(self)
def set_var_type(self, var_name, node_var_type):
if var_name in self.name_to_id:
num_id = self.name_to_id[var_name]
else:
num_id = self.cur_id
self.cur_id += 1
self.name_to_id[var_name] = num_id
self.id_to_type[num_id] = node_var_type
def get_var_type(self, var_name):
if var_name in self.name_to_id:
num_id = self.name_to_id[var_name]
return self.id_to_type[num_id]
if self.parent_scope is None:
return NodeVarType.UNKNOWN
return self.parent_scope.get_var_type(var_name)
class AstVarEnv(object):
"""
A class maintains scopes and mapping from variable name to type.
"""
def __init__(self):
self.cur_scope = AstVarScope()
def enter_scope(self):
self.cur_scope = AstVarScope(parent_scope=self.cur_scope)
return self.cur_scope
def exit_scope(self):
assert self.cur_scope.parent_scope is not None, "Call exit_scope in "\
"AstVarEnv when current scope doens't have parent scope."
self.cur_scope = self.cur_scope.parent_scope
return self.cur_scope
def set_var_type(self, var_name, node_var_type):
self.cur_scope.set_var_type(var_name, node_var_type)
def get_var_type(self, var_name):
return self.cur_scope.get_var_type(var_name)
def get_scope_var_type(self):
'''
Returns a dict mapping from variable name to type. Used for debug and
test.
'''
cur_scope_dict = {}
for name in self.cur_scope.name_to_id:
node_var_type = self.cur_scope.get_var_type(name)
cur_scope_dict[name] = node_var_type
return cur_scope_dict
class StaticAnalysisVisitor(object):
"""
A class that does static analysis
"""
def __init__(self, ast_root=None):
if ast_root is not None:
self.run(ast_root)
def run(self, ast_root):
self.node_wrapper_root = None
self.ancestor_wrappers = []
self.node_to_wrapper_map = {}
self.var_env = AstVarEnv()
self.dfs_visit(ast_root)
def dfs_visit(self, node):
# AST reuses some gast.nodes, such as Param node of expr_context
if node not in self.node_to_wrapper_map:
cur_wrapper = AstNodeWrapper(node)
self.node_to_wrapper_map[node] = cur_wrapper
else:
cur_wrapper = self.node_to_wrapper_map[node]
if self.node_wrapper_root is None:
self.node_wrapper_root = cur_wrapper
if len(self.ancestor_wrappers) != 0:
last_wrapper = self.ancestor_wrappers[-1]
last_wrapper.children.append(cur_wrapper)
cur_wrapper.parent = last_wrapper
self.ancestor_wrappers.append(cur_wrapper)
for child in gast.iter_child_nodes(node):
self.dfs_visit(child)
self.ancestor_wrappers.pop()
cur_wrapper.node_var_type = self._get_node_var_type(cur_wrapper)
return cur_wrapper.node_var_type
def get_node_wrapper_root(self):
return self.node_wrapper_root
def get_node_to_wrapper_map(self):
return self.node_to_wrapper_map
def get_var_env(self):
return self.var_env
def _get_node_var_type(self, cur_wrapper):
node = cur_wrapper.node
if isinstance(node, gast.Constant):
# singleton: None, True or False
if node.value is None:
return NodeVarType.NONE
if isinstance(node.value, bool):
return NodeVarType.BOOLEAN
if isinstance(node.value, int):
return NodeVarType.INT
if isinstance(node.value, float):
return NodeVarType.FLOAT
if isinstance(node.value, str):
return NodeVarType.STRING
if isinstance(node, gast.BoolOp):
return NodeVarType.BOOLEAN
if isinstance(node, gast.Compare):
return NodeVarType.BOOLEAN
if isinstance(node, gast.Dict):
return NodeVarType.DICT
if isinstance(node, gast.Set):
return NodeVarType.SET
if isinstance(node, gast.UnaryOp):
return self.node_to_wrapper_map[node.operand].node_var_type
if isinstance(node, gast.BinOp):
left_type = self.node_to_wrapper_map[node.left].node_var_type
right_type = self.node_to_wrapper_map[node.right].node_var_type
return NodeVarType.binary_op_output_type(left_type, right_type)
if isinstance(node, gast.Assign):
ret_type = self.node_to_wrapper_map[node.value].node_var_type
for target in node.targets:
if isinstance(target, gast.Name):
self.node_to_wrapper_map[target].node_var_type = ret_type
self.var_env.set_var_type(target.id, ret_type)
return ret_type
if isinstance(node, gast.Name):
if node.id == "None":
return NodeVarType.NONE
if node.id == "True" or node.id == "False":
return NodeVarType.BOOLEAN
return self.var_env.get_var_type(node.id)
if isinstance(node, gast.Call):
if is_dygraph_api(node):
api_name = node.func.attr
if api_name == "to_variable":
return NodeVarType.TENSOR
if is_numpy_api(node):
# In this simple version we assume numpy api returns nd-array
return NodeVarType.NUMPY_NDARRAY
return NodeVarType.STATEMENT