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return_transformer.py
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return_transformer.py
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# 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
from paddle.utils import gast
from paddle.fluid import unique_name
from paddle.fluid.dygraph.dygraph_to_static.utils import index_in_list
from paddle.fluid.dygraph.dygraph_to_static.break_continue_transformer import ForToWhileTransformer
from paddle.fluid.dygraph.dygraph_to_static.variable_trans_func import create_fill_constant_node
from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_source_code
__all__ = [
'RETURN_NO_VALUE_MAGIC_NUM', 'RETURN_NO_VALUE_VAR_NAME', 'ReturnTransformer'
]
# Constant for the name of the variable which stores the boolean state that we
# should return
RETURN_PREFIX = '__return'
# Constant for the name of the variable which stores the final return value
RETURN_VALUE_PREFIX = '__return_value'
# Constant for the name of variables to initialize the __return_value
RETURN_VALUE_INIT_NAME = '__return_value_init'
# Constant magic number representing returning no value. This constant amis to
# support returning various lengths of variables. Static graph must have fixed
# size of fetched output while dygraph can have flexible lengths of output, to
# solve it in dy2stat, we put float64 value with this magic number at Static
# graph as a place holder to indicate the returning placeholder means no value
# should return.
RETURN_NO_VALUE_MAGIC_NUM = 1.77113e+279
RETURN_NO_VALUE_VAR_NAME = "__no_value_return_var"
def get_return_size(return_node):
assert isinstance(return_node, gast.Return), "Input is not gast.Return node"
return_length = 0
if return_node.value is not None:
if isinstance(return_node.value, gast.Tuple):
return_length = len(return_node.value.elts)
else:
return_length = 1
return return_length
class ReplaceReturnNoneTransformer(gast.NodeTransformer):
"""
Replace 'return None' to 'return' because 'None' cannot be a valid input
in control flow. In ReturnTransformer single 'Return' will be appended no
value placeholder
"""
def __init__(self, root_node):
self.root = root_node
def transform(self):
self.visit(self.root)
def visit_Return(self, node):
if isinstance(node.value, gast.Name) and node.value.id == 'None':
node.value = None
return node
if isinstance(node.value, gast.Constant) and node.value.value == None:
node.value = None
return node
return node
class ReturnAnalysisVisitor(gast.NodeVisitor):
"""
Visits gast Tree and analyze the information about 'return'.
"""
def __init__(self, root_node):
self.root = root_node
# A list to store where the current function is.
self.function_def = []
# Mapping from gast.FunctionDef node to the number of return statements
# Python allows define function inside function so we have to handle it
self.count_return = {}
# Mapping from gast.FunctionDef node to the maximum number of variables
# returned by the function's return statement
self.max_return_length = {}
self.visit(self.root)
def visit_FunctionDef(self, node):
self.function_def.append(node)
self.count_return[node] = 0
self.max_return_length[node] = 0
self.generic_visit(node)
self.function_def.pop()
return node
def visit_Return(self, node):
assert len(
self.function_def) > 0, "Found 'return' statement out of function."
cur_func = self.function_def[-1]
if cur_func in self.count_return:
self.count_return[cur_func] += 1
else:
self.count_return[cur_func] = 1
return_length = get_return_size(node)
if cur_func in self.max_return_length:
self.max_return_length[cur_func] = max(
self.max_return_length[cur_func], return_length)
else:
self.max_return_length[cur_func] = return_length
self.generic_visit(node)
def get_func_return_count(self, func_node):
return self.count_return[func_node]
def get_func_max_return_length(self, func_node):
return self.max_return_length[func_node]
class ReturnTransformer(gast.NodeTransformer):
"""
Transforms return statements into equivalent python statements containing
only one return statement at last. The basics idea is using a return value
variable to store the early return statements and boolean states with
if-else to skip the statements after the return.
"""
def __init__(self, wrapper_root):
self.wrapper_root = wrapper_root
self.root = wrapper_root.node
pre_transformer = ReplaceReturnNoneTransformer(self.root)
pre_transformer.transform()
self.ancestor_nodes = []
# The name of the variable which stores the final return value
# Mapping from FunctionDef node to string
self.return_value_name = {}
# The names of the variable which stores the boolean state that skip
# statments. Mapping from FunctionDef node to list
self.return_name = {}
# The names of the variable which is placeholder to handle various-
# length return. Mapping from FunctionDef node to list
self.return_no_value_name = {}
# A list of FunctionDef to store where the current function is.
self.function_def = []
self.pre_analysis = None
def transform(self):
self.visit(self.root)
def generic_visit(self, node):
# Because we change ancestor nodes during visit_Return, not current
# node, original generic_visit of NodeTransformer will visit node
# which may be deleted. To prevent that node being added into
# transformed AST, We self-write a generic_visit and visit
for field, value in gast.iter_fields(node):
if isinstance(value, list):
for item in value:
if isinstance(item, gast.AST):
self.visit(item)
elif isinstance(value, gast.AST):
self.visit(value)
def visit(self, node):
"""
Self-defined visit for appending ancestor
"""
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_FunctionDef(self, node):
self.function_def.append(node)
self.return_value_name[node] = None
self.return_name[node] = []
self.return_no_value_name[node] = []
self.pre_analysis = ReturnAnalysisVisitor(node)
max_return_length = self.pre_analysis.get_func_max_return_length(node)
while self.pre_analysis.get_func_return_count(node) > 1:
self.generic_visit(node)
self.pre_analysis = ReturnAnalysisVisitor(node)
if max_return_length == 0:
self.function_def.pop()
return node
# Prepend initialization of final return and append final return statement
value_name = self.return_value_name[node]
if value_name is not None:
node.body.append(
gast.Return(value=gast.Name(
id=value_name,
ctx=gast.Load(),
annotation=None,
type_comment=None)))
init_names = [
unique_name.generate(RETURN_VALUE_INIT_NAME)
for i in range(max_return_length)
]
assign_zero_nodes = [
create_fill_constant_node(iname, 0.0) for iname in init_names
]
if len(init_names) == 1:
return_value_nodes = gast.Name(
id=init_names[0],
ctx=gast.Load(),
annotation=None,
type_comment=None)
else:
# We need to initialize return value as a tuple because control
# flow requires some inputs or outputs have same structure
return_value_nodes = gast.Tuple(
elts=[
gast.Name(
id=iname,
ctx=gast.Load(),
annotation=None,
type_comment=None) for iname in init_names
],
ctx=gast.Load())
assign_return_value_node = gast.Assign(
targets=[
gast.Name(
id=value_name,
ctx=gast.Store(),
annotation=None,
type_comment=None)
],
value=return_value_nodes)
node.body.insert(0, assign_return_value_node)
node.body[:0] = assign_zero_nodes
# Prepend no value placeholders
for name in self.return_no_value_name[node]:
assign_no_value_node = create_fill_constant_node(
name, RETURN_NO_VALUE_MAGIC_NUM)
node.body.insert(0, assign_no_value_node)
self.function_def.pop()
return node
def visit_Return(self, node):
cur_func_node = self.function_def[-1]
return_name = unique_name.generate(RETURN_PREFIX)
self.return_name[cur_func_node].append(return_name)
max_return_length = self.pre_analysis.get_func_max_return_length(
cur_func_node)
parent_node_of_return = self.ancestor_nodes[-2]
for ancestor_index in reversed(range(len(self.ancestor_nodes) - 1)):
ancestor = self.ancestor_nodes[ancestor_index]
cur_node = self.ancestor_nodes[ancestor_index + 1]
if hasattr(ancestor,
"body") and index_in_list(ancestor.body, cur_node) != -1:
if cur_node == node:
self._replace_return_in_stmt_list(
ancestor.body, cur_node, return_name, max_return_length,
parent_node_of_return)
self._replace_after_node_to_if_in_stmt_list(
ancestor.body, cur_node, return_name, parent_node_of_return)
elif hasattr(ancestor, "orelse") and index_in_list(ancestor.orelse,
cur_node) != -1:
if cur_node == node:
self._replace_return_in_stmt_list(
ancestor.orelse, cur_node, return_name,
max_return_length, parent_node_of_return)
self._replace_after_node_to_if_in_stmt_list(
ancestor.orelse, cur_node, return_name,
parent_node_of_return)
# If return node in while loop, add `not return_name` in gast.While.test
if isinstance(ancestor, gast.While):
cond_var_node = gast.UnaryOp(
op=gast.Not(),
operand=gast.Name(
id=return_name,
ctx=gast.Load(),
annotation=None,
type_comment=None))
ancestor.test = gast.BoolOp(
op=gast.And(), values=[ancestor.test, cond_var_node])
continue
# If return node in for loop, add `not return_name` in gast.While.test
if isinstance(ancestor, gast.For):
cond_var_node = gast.UnaryOp(
op=gast.Not(),
operand=gast.Name(
id=return_name,
ctx=gast.Load(),
annotation=None,
type_comment=None))
parent_node = self.ancestor_nodes[ancestor_index - 1]
for_to_while = ForToWhileTransformer(parent_node, ancestor,
cond_var_node)
new_stmts = for_to_while.transform()
while_node = new_stmts[-1]
self.ancestor_nodes[ancestor_index] = while_node
if ancestor == cur_func_node:
break
# return_node is replaced so we shouldn't return here
def _replace_return_in_stmt_list(self, stmt_list, return_node, return_name,
max_return_length, parent_node_of_return):
assert max_return_length >= 0, "Input illegal max_return_length"
i = index_in_list(stmt_list, return_node)
if i == -1:
return False
assign_nodes = []
# Here assume that the parent node of return is gast.If
if isinstance(parent_node_of_return, gast.If):
# Prepend control flow boolean nodes such as '__return@1 = True'
node_str = "{} = paddle.jit.dy2static.create_bool_as_type({}, True)".format(
return_name,
ast_to_source_code(parent_node_of_return.test).strip())
assign_true_node = gast.parse(node_str).body[0]
assign_nodes.append(assign_true_node)
cur_func_node = self.function_def[-1]
return_length = get_return_size(return_node)
if return_length < max_return_length:
# In this case we should append RETURN_NO_VALUE placeholder
#
# max_return_length must be >= 1 here because return_length will be
# 0 at least.
if self.return_value_name[cur_func_node] is None:
self.return_value_name[cur_func_node] = unique_name.generate(
RETURN_VALUE_PREFIX)
no_value_names = [
unique_name.generate(RETURN_NO_VALUE_VAR_NAME)
for j in range(max_return_length - return_length)
]
self.return_no_value_name[cur_func_node].extend(no_value_names)
# Handle tuple/non-tuple case
if max_return_length == 1:
assign_nodes.append(
gast.Assign(
targets=[
gast.Name(
id=self.return_value_name[cur_func_node],
ctx=gast.Store(),
annotation=None,
type_comment=None)
],
value=gast.Name(
id=no_value_names[0],
ctx=gast.Load(),
annotation=None,
type_comment=None)))
else:
# max_return_length > 1 which means we should assign tuple
fill_tuple = [
gast.Name(
id=n,
ctx=gast.Load(),
annotation=None,
type_comment=None) for n in no_value_names
]
if return_node.value is not None:
if isinstance(return_node.value, gast.Tuple):
fill_tuple[:0] = return_node.value.elts
else:
fill_tuple.insert(0, return_node.value)
assign_nodes.append(
gast.Assign(
targets=[
gast.Name(
id=self.return_value_name[cur_func_node],
ctx=gast.Store(),
annotation=None,
type_comment=None)
],
value=gast.Tuple(
elts=fill_tuple, ctx=gast.Load())))
else:
# In this case we should NOT append RETURN_NO_VALUE placeholder
if return_node.value is not None:
cur_func_node = self.function_def[-1]
if self.return_value_name[cur_func_node] is None:
self.return_value_name[
cur_func_node] = unique_name.generate(
RETURN_VALUE_PREFIX)
assign_nodes.append(
gast.Assign(
targets=[
gast.Name(
id=self.return_value_name[cur_func_node],
ctx=gast.Store(),
annotation=None,
type_comment=None)
],
value=return_node.value))
stmt_list[i:] = assign_nodes
return True
def _replace_after_node_to_if_in_stmt_list(
self, stmt_list, node, return_name, parent_node_of_return):
i = index_in_list(stmt_list, node)
if i < 0 or i >= len(stmt_list):
return False
if i == len(stmt_list) - 1:
# No need to add, we consider this as added successfully
return True
if_stmt = gast.If(test=gast.UnaryOp(
op=gast.Not(),
operand=gast.Name(
id=return_name,
ctx=gast.Store(),
annotation=None,
type_comment=None)),
body=stmt_list[i + 1:],
orelse=[])
stmt_list[i + 1:] = [if_stmt]
# Here assume that the parent node of return is gast.If
if isinstance(parent_node_of_return, gast.If):
# Prepend control flow boolean nodes such as '__return@1 = False'
node_str = "{} = paddle.jit.dy2static.create_bool_as_type({}, False)".format(
return_name,
ast_to_source_code(parent_node_of_return.test).strip())
assign_false_node = gast.parse(node_str).body[0]
stmt_list[i:i] = [assign_false_node]
return True