# 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 ast import astor import atexit import copy import collections import gast import inspect import os import six import tempfile import textwrap import numpy as np from paddle.fluid import unique_name # imp is deprecated in python3 if six.PY2: import imp else: from importlib.machinery import SourceFileLoader dygraph_class_to_static_api = { "CosineDecay": "cosine_decay", "ExponentialDecay": "exponential_decay", "InverseTimeDecay": "inverse_time_decay", "NaturalExpDecay": "natural_exp_decay", "NoamDecay": "noam_decay", "PiecewiseDecay": "piecewise_decay", "PolynomialDecay": "polynomial_decay", } FOR_ITER_INDEX_PREFIX = '__for_loop_var_index' FOR_ITER_VAR_LEN_PREFIX = '__for_loop_var_len' # FullArgSpec is valid from Python3. Defined a Namedtuple to # to make it available in Python2. FullArgSpec = collections.namedtuple('FullArgSpec', [ 'args', 'varargs', 'varkw', 'defaults', 'kwonlyargs', 'kwonlydefaults', 'annotations' ]) def getfullargspec(target): if hasattr(inspect, "getfullargspec"): return inspect.getfullargspec(target) else: argspec = inspect.getargspec(target) return FullArgSpec( args=argspec.args, varargs=argspec.varargs, varkw=argspec.keywords, defaults=argspec.defaults, kwonlyargs=[], kwonlydefaults=None, annotations={}) def parse_arg_and_kwargs(function): """ Returns full argument names as list. e.g ['x', 'y', 'z'] """ fullargspec = getfullargspec(function) arg_names = fullargspec.args if arg_names and 'self' == arg_names[0]: arg_names = fullargspec.args[1:] # parse default kwargs default_kwargs = {} default_values = fullargspec.defaults if default_values: assert len(default_values) <= len(arg_names) default_kwarg_names = arg_names[-len(default_values):] default_kwargs = dict(zip(default_kwarg_names, default_values)) return arg_names, default_kwargs def type_name(v): return type(v).__name__ def make_hashable(x, error_msg=None): """ Makes input `x` hashable. For some unhashable objects, such as `dict/list/np.ndarray`,applying hash function by using their values. """ if isinstance(x, (tuple, list)): return tuple(map(make_hashable, x)) try: hash(x) except TypeError: if isinstance(x, np.ndarray): # Note: `tostring()` will return the binary data from np.ndarray that # means different value will lead to different hash code. return hash(x.tostring()) elif isinstance(x, dict): return tuple(map(make_hashable, x.values())) error_msg = error_msg or "Requires a hashable object." raise ValueError(error_msg + " But received type: %s" % type_name(x)) return x def _is_api_in_module_helper(obj, module_prefix): m = inspect.getmodule(obj) return m is not None and m.__name__.startswith(module_prefix) def is_api_in_module(node, module_prefix): assert isinstance(node, gast.Call), "Input non-Call node for is_dygraph_api" func_str = astor.to_source(gast.gast_to_ast(node.func)) try: # TODO(liym27): # Consider a better to import modules like: # source_file = inspect.getfile(dyfunc) # import_statements = ImportVisitor(source_file).transform() # import_str = "".join(import_statements) import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers from paddle.fluid.dygraph import to_variable import paddle.fluid.dygraph as dygraph return eval("_is_api_in_module_helper({}, '{}')".format(func_str, module_prefix)) except NameError: return False def is_dygraph_api(node): # Note: A api in module dygraph_to_static is not a real dygraph api. if is_api_in_module(node, "paddle.fluid.dygraph.dygraph_to_static"): return False return is_api_in_module(node, "paddle.fluid.dygraph") def is_paddle_api(node): return is_api_in_module(node, "paddle.fluid") # Is numpy_api cannot reuse is_api_in_module because of numpy module problem def is_numpy_api(node): assert isinstance(node, gast.Call), "Input non-Call node for is_numpy_api" func_str = astor.to_source(gast.gast_to_ast(node.func)) try: import numpy as np module_result = eval("_is_api_in_module_helper({}, '{}')".format( func_str, "numpy")) # 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 def is_control_flow_to_transform(node, static_analysis_visitor=None, var_name_to_type=None): """ Determines whether the node is a PaddlePaddle control flow statement which needs to be transformed into a static graph control flow statement. """ assert isinstance(node, gast.AST), \ "The type of input node must be gast.AST, but received %s." % type(node) visitor = IsControlFlowVisitor( node, static_analysis_visitor, node_var_type_map=var_name_to_type) need_to_transform = visitor.transform() return need_to_transform def _delete_keywords_from(node): assert isinstance(node, gast.Call) func_src = astor.to_source(gast.gast_to_ast(node.func)) import paddle.fluid as fluid full_args = eval("inspect.getargspec({})".format(func_src)) full_args_name = full_args[0] node.keywords = [k for k in node.keywords if k.arg in full_args_name] return def to_static_api(dygraph_class): if dygraph_class in dygraph_class_to_static_api: return dygraph_class_to_static_api[dygraph_class] else: raise NotImplementedError("Paddle dygraph API {} cannot be converted " "to static graph at present.".format( dygraph_class)) def _add_keywords_to(node, dygraph_api_name): assert isinstance(node, gast.Call) if dygraph_api_name == "Linear": for ast_keyword in node.keywords: if ast_keyword.arg == "output_dim": ast_keyword.arg = "size" node.keywords.append( gast.keyword( arg="num_flatten_dims", value=gast.Constant( value=-1, kind=None))) if dygraph_api_name == "BilinearTensorProduct": for ast_keyword in node.keywords: if ast_keyword.arg == "output_dim": ast_keyword.arg = "size" if dygraph_api_name == "PRelu": for ast_keyword in node.keywords: if ast_keyword.arg == "input": ast_keyword.arg = "x" return def is_to_variable(node): assert isinstance(node, gast.Call) if is_dygraph_api(node): api_name = ast_to_source_code(node.func).strip() return api_name.endswith("to_variable") return False def to_static_ast(node, class_node): assert isinstance(node, gast.Call) assert isinstance(class_node, gast.Call) static_api = to_static_api(class_node.func.attr) node.func = gast.Attribute( attr=static_api, ctx=gast.Load(), value=gast.Attribute( attr='layers', ctx=gast.Load(), value=gast.Name( ctx=gast.Load(), id='fluid', annotation=None, type_comment=None))) update_args_of_func(node, class_node, 'forward') node.args.extend(class_node.args) node.keywords.extend(class_node.keywords) _add_keywords_to(node, class_node.func.attr) _delete_keywords_from(node) gast.fix_missing_locations(node) return node def to_assign_node(node): # Transform dygraph api `fluid.dygraph.to_variable` to static api `fluid.layers.assign`. # NOTE: # 1. Api `to_variable` supports data type {float16, float32, float64, int16, int32, int64, uint8, uint16}, # but api `assign` only supports {float32, float64, int32, int64, bool}; # 2. If the input of api `assign` is numpy.ndarray, its size cannot be greater than 1024 * 1024. assert isinstance(node, gast.Call) assign_api = gast.parse('fluid.layers.assign').body[0].value node.func = assign_api if node.args: node.args = [node.args[0]] node.keywords = [] else: for idx, kw in enumerate(node.keywords): if kw.arg == 'value': node.keywords[idx].arg = 'input' node.keywords = [node.keywords[idx]] node.args = [] break return node def update_args_of_func(node, dygraph_node, method_name): assert isinstance(node, gast.Call) if method_name not in ["__init__", "forward"]: raise ValueError( "The method name of class to update args should be '__init__' or 'forward'" ) class_src = astor.to_source(gast.gast_to_ast(dygraph_node.func)) import paddle.fluid as fluid if method_name == "__init__" or eval( "issubclass({}, fluid.dygraph.Layer)".format(class_src)): full_args = eval("inspect.getargspec({}.{})".format(class_src, method_name)) full_args_name = [ arg_name for arg_name in full_args[0] if arg_name != "self" ] else: full_args_name = [] added_keywords = [] for idx, arg in enumerate(node.args): added_keywords.append(gast.keyword(arg=full_args_name[idx], value=arg)) node.args = [] node.keywords = added_keywords + node.keywords def create_api_shape_node(tensor_shape_node): assert isinstance(tensor_shape_node, (gast.Name, gast.Attribute, gast.Subscript)) if isinstance(tensor_shape_node, gast.Name): api_shape_node = gast.Call( func=gast.parse('fluid.layers.shape').body[0].value, args=[tensor_shape_node], keywords=[]) return api_shape_node 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'): return gast.parse('%s = fluid.layers.fill_constant(%s, "%s", %s)' % (name, str(shape), dtype, str(value))) def get_attribute_full_name(node): assert isinstance( node, gast.Attribute), "Input non-Attribute node to get attribute full name" return astor.to_source(gast.gast_to_ast(node)).strip() def generate_name_node(name_ids, ctx=gast.Load()): """ Generate list or gast.Tuple of ast.Name for Return statement. """ if isinstance(name_ids, six.string_types): name_ids = [name_ids] if not isinstance(name_ids, (list, tuple, set)): raise TypeError('name_ids must be list or tuple or set, but received %s' % type(type(name_ids))) gast_names = [ gast.Name( id=name_id, ctx=ctx, annotation=None, type_comment=None) for name_id in name_ids ] if len(gast_names) == 1: name_node = gast_names[0] else: name_node = gast.Tuple(elts=gast_names, ctx=ctx) return name_node def create_funcDef_node(nodes, name, input_args, return_name_ids): """ Wrapper all statements of nodes into one ast.FunctionDef, which can be called by ast.Call. """ nodes = copy.copy(nodes) # 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, body=nodes, decorator_list=[], returns=None, type_comment=None) return func_def_node def index_in_list(array_list, item): try: return array_list.index(item) except ValueError: # Item not in array_list return -1 def create_assign_node(name, node): """ Creates a `gast.Assign` node by given name_id as target and node as value. """ targets = generate_name_node(name, ctx=gast.Store()) assign_node = gast.Assign(targets=[targets], value=node) return targets, assign_node class RenameTransformer(gast.NodeTransformer): def __init__(self, node): assert isinstance( node, gast.AST), "RenameTransformer only accepts gast.AST as input" self.root = node self.old_name = "" self.new_name = "" def rename(self, old_name, new_name): self.old_name = old_name self.new_name = new_name self.visit(self.root) def visit_Name(self, node): self.generic_visit(node) if node.id == self.old_name: node.id = self.new_name return node def visit_Attribute(self, node): self.generic_visit(node) attr_full_name = get_attribute_full_name(node) if attr_full_name == self.old_name: new_name_node = gast.parse(self.new_name).body[0].value return new_name_node return node def ast_to_func(ast_root, dyfunc, delete_on_exit=True): """ Transform modified AST of decorated function into python callable object. TODO: If only decorate one of inner function instead of decorating the main function, the other inner functions are invisible for the decorated function. """ def remove_if_exit(filepath): if os.path.exists(filepath): os.remove(filepath) source = ast_to_source_code(ast_root) import_fluid = "import paddle.fluid as fluid\n" source = import_fluid + source if six.PY2: source = source.encode('utf-8') f = tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) else: f = tempfile.NamedTemporaryFile( mode='w', suffix='.py', delete=False, encoding='utf-8') with f: module_name = os.path.basename(f.name[:-3]) f.write(source) if delete_on_exit: atexit.register(lambda: remove_if_exit(f.name)) atexit.register(lambda: remove_if_exit(f.name[:-3] + ".pyc")) if six.PY2: module = imp.load_source(module_name, f.name) else: module = SourceFileLoader(module_name, f.name).load_module() func_name = dyfunc.__name__ if not hasattr(module, func_name): raise ValueError( 'Function: %s doesn\'t exist in the Module transformed from AST.' % func_name) callable_func = getattr(module, func_name) # After transform dygraph function into callable_func saved in tmp file, # it lost the global variables from imported statements or defined in source file. # Recovers the necessary variables by `__globals__`. recover_globals_attribute(dyfunc, callable_func) return callable_func, f.name def recover_globals_attribute(src_obj, dst_obj): attr_name = '__globals__' src_globals = getattr(src_obj, attr_name, {}) dst_globals = getattr(dst_obj, attr_name, {}) for k, v in src_globals.items(): # ignore builtin attribute. if not (k.startswith('__') and k.endswith('__')): dst_globals[k] = v def func_to_source_code(function, dedent=True): """ Transforms function into raw string of source code. """ if not (inspect.isfunction(function) or inspect.ismethod(function)): raise TypeError( "The type of 'function' should be a function or method, but received {}.". format(type(function).__name__)) source_code = inspect.getsource(function) if dedent: source_code = textwrap.dedent(source_code) return source_code def ast_to_source_code(ast_node): """ Transforms ast node into source code. """ if not isinstance(ast_node, (gast.AST, ast.AST)): raise TypeError( "Type of ast_root should be gast.AST or ast.AST, but received %s." % type(ast_node)) if isinstance(ast_node, gast.AST): ast_node = gast.gast_to_ast(ast_node) source_code = astor.to_source(ast_node) return source_code def is_candidate_node(node): """ Nodes with specified type will be dependent on tensor. """ is_compare_node = isinstance(node, (gast.Compare, gast.BoolOp, gast.UnaryOp, gast.For, gast.If, gast.While)) # TODO(Aurelius84): `.numpy()` may be an customized function, # and should consider a more elegant way to solve this problem. has_numpy_attr = ".numpy()" in ast_to_source_code(node) return is_compare_node or has_numpy_attr def compare_with_none(node): """ Whether the comparator of `gast.Compare` node is `None`. """ if isinstance(node, gast.Compare): for child in [node.left, node.comparators]: # node.comparators is a list. if isinstance(child, list): child = child[0] if (isinstance(child, gast.Constant) and child.value is None) or ( isinstance(child, gast.Name) and child.id == 'None'): return True return False class IsControlFlowVisitor(gast.NodeVisitor): """ Judge whether the ast_node of control flow from Dygraph code dependent on paddle Tensor. `ast_node` can be gast.If, gast.For, gast.While, gast.If.test(gast.Compare, gast.BoolOp, gast.UnaryOp). If returns True, gast.If.test must meet at least one of the following requirements: 1. involves at least one var whose type is Tensor. 2. the Tensor var calls `.numpy()[]` interface or Tensor.shape is [1]. 3. involves Tensor.shape[i] and the shape[i] is unknown in compile time. gast.While must meet at least one of the requirements 1 to 5: 4. has `break` statement. 5. has `continue` statement. gast.For must meet at least one of the requirements 4 to 8: 6. calls `range` function in `for` statement and the argument of range is Tensor. 7. calls `enumerate` function in `for` statement and the argument of enumerate is Tensor. 8. the iterable varaible in `for` statement is Tensor. TODO: Support non-range case The following examples should not be considered as control_flow_if: 1. `if Tensor_var` or `if Tensor_var is None` 2. if Tensor.shape[i] is determined with fixed value (not -1 or None) Note: pred in ConditionalBlock require variable, which means all vars should be Tensor or transformed into Tensor, like fill_constant(shape=[1], dtype='int32', value=Tensor.shape[i]). TODO: 1. need to deal with `tensor.shape[i]` which need to eval the data of shape[i], because reshape_op may be called before this statement. """ def __init__(self, ast_node, static_analysis_visitor=None, node_var_type_map=None): assert isinstance( ast_node, gast.AST ), "Type of input node should be gast.AST, but received %s." % type( ast_node) self.ast_root = ast_node if static_analysis_visitor is None: from .static_analysis import StaticAnalysisVisitor static_analysis_visitor = StaticAnalysisVisitor(ast_node) self.static_analysis_visitor = static_analysis_visitor self.node_to_wrapper_map = self.static_analysis_visitor.get_node_to_wrapper_map( ) self.node_var_type_map = node_var_type_map self.is_control_flow_num = 0 self._compare_node_tenor_set = set() def transform(self): node = self.ast_root if isinstance(node, gast.If): self._visit_If(node) elif isinstance(node, gast.For): self._visit_For(node) elif isinstance(node, gast.While): self._visit_While(node) else: self.visit(node) return self.is_control_flow_num > 0 def _visit_If(self, node): assert isinstance(node, gast.If) self.visit(node.test) return def _visit_For(self, node): assert isinstance(node, gast.For) if isinstance(node.iter, gast.Call): # for in range(var[0]|var.numpy()[0]) or for in enumerate(var|var.numpy()) if isinstance(node.iter.func, gast.Name): if node.iter.func.id == "range" or node.iter.func.id == "enumerate": for arg in node.iter.args: self.visit(arg) else: return # for in var.numpy() elif isinstance(node.iter.func, gast.Attribute): if node.iter.func.attr == 'numpy': self._visit_Call(node.iter) else: return else: return elif isinstance(node.iter, gast.Name): # for in var self.visit(node.iter) else: return for child_node in gast.walk(node): if isinstance(child_node, (gast.Continue, gast.Break)): self._visit_break_continue(child_node) return def _visit_While(self, node): assert isinstance(node, gast.While) test = node.test self.generic_visit(test) for child_node in gast.walk(node): if isinstance(child_node, (gast.Continue, gast.Break)): self._visit_break_continue(child_node) return def _visit_break_continue(self, node): assert isinstance(node, (gast.Break, gast.Continue)) wrapper_node = self.node_to_wrapper_map.get(node) if not wrapper_node: # Transformed node is not in node_to_wrapper_map return while wrapper_node.parent: parent_node = wrapper_node.parent.node if isinstance(parent_node, (gast.For, gast.While)): if parent_node is self.ast_root: self.is_control_flow_num += 1 return else: return wrapper_node = wrapper_node.parent return def visit_BoolOp(self, node): for i, child in enumerate(node.values): self.visit(child) return node def visit_Compare(self, node): pre_control_flow_num = self.is_control_flow_num if not compare_with_none(node): self.generic_visit(node) for child in gast.walk(node): if isinstance(child, gast.Subscript): self._visit_Subscript(child) if self.is_control_flow_num > pre_control_flow_num: self._compare_node_tenor_set.add(node) return node def _visit_Subscript(self, node): self.generic_visit(node) if hasattr(node, 'value') and isinstance(node.value, gast.Call): self._visit_Call(node.value) return node def _visit_Call(self, node): assert isinstance(node, gast.Call) if isinstance(node.func, gast.Attribute): attr_node = node.func if attr_node.attr == 'numpy': self.is_control_flow_num += 1 def visit_Call(self, node): self._visit_Call(node) if is_paddle_api(node): self.is_control_flow_num += 1 return node def visit_Name(self, node): if self._is_node_with_tensor(node, node.id): self.is_control_flow_num += 1 return node def visit_Constant(self, node): if self._is_node_with_tensor(node, node.value): self.is_control_flow_num += 1 return node def _is_node_with_tensor(self, node, name_id): from paddle.fluid.dygraph.dygraph_to_static.static_analysis import NodeVarType # Look up the node_var_type_map by name_id. if self.node_var_type_map: if name_id and isinstance(name_id, six.string_types): var_type = self.node_var_type_map.get(name_id, None) if var_type and var_type & NodeVarType.TENSOR_TYPES: return True # if not found, look up the node_to_wrapper_map by node. wrapper_node = self.node_to_wrapper_map.get(node, None) if wrapper_node is not None: if wrapper_node.node_var_type & NodeVarType.TENSOR_TYPES: return True return False def get_compare_nodes_with_tensor(self): return self._compare_node_tenor_set class NameNodeReplaceTransformer(gast.NodeTransformer): """ This class replaces specified gast.Name node by replace_node. """ def __init__(self, root_node, target_name, replace_node): assert isinstance(target_name, str) self.target_name = target_name self.replace_node = replace_node self.visit(root_node) def visit_Name(self, node): if node.id == self.target_name: return self.replace_node return node class ForNodeVisitor(object): """ This class parses python for statement, get transformed 3 statement components of for node three key statements: 1). init_stmts: list[node], prepare nodes of for loop, may not only one 2). cond_stmt: node, condition node to judge whether continue loop 3). body_stmts: list[node], updated loop body, sometimes we should change the original statement in body, not just append new statement In this process, the semantics of for does not change. Now only can parse 3 type statements (Here var is VarBase(Tensor) or python variable): 1). for x in range(var[*]|var.numpy()[*]) 2). for x in var|var.numpy() 3). for i, x enumerate(var|var.numpy()) """ def __init__(self, for_node): assert isinstance( for_node, gast.For ), "Input node for the initialization of ForNodeVisitor is not gast.For node." # 1. original for node self.node = for_node # 2. gast.For node main parts self.target = for_node.target # NOTE: type may be Node or list[Node] self.iter_args = for_node.iter if self.is_for_iter( ) else for_node.iter.args self.body = for_node.body # 3. key shared node or names # - x: # - for x in range(***) # - for x in var|var.numpy() # - for i, x enumerate(var|var.numpy()) self.iter_var_name = self._get_iter_var_name() # - created index var to slice Variable: __for_loop_var_index_0 # - for x in var|var.numpy() # - for i, x enumerate(var|var.numpy()) self.iter_idx_name = unique_name.generate(FOR_ITER_INDEX_PREFIX) # - created shape var to build loop condition: __for_loop_var_len_0 # - for x in var|var.numpy() # - for i, x enumerate(var|var.numpy()) # - for x in var self.iter_var_len_name = unique_name.generate(FOR_ITER_VAR_LEN_PREFIX) # - var.numpy()/var # - for x in var|var.numpy() # - for i, x enumerate(var|var.numpy()) self.iter_node = self._get_iter_node() # - enumeate i: # - for i, x enumerate(var|var.numpy()) self.enum_idx_name = self._get_enum_idx_name() # - range/enumerate args length self.args_length = None def parse(self): self._args_check() if self.is_for_range_iter(): return self._parse_for_range_stmts() elif self.is_for_iter(): return self._parse_for_stmts() elif self.is_for_enumerate_iter(): return self._parse_for_enumerate_stmts() else: return None def is_for_range_iter(self): return isinstance(self.node.iter, gast.Call) and isinstance( self.node.iter.func, gast.Name) and self.node.iter.func.id == "range" def is_for_iter(self): if isinstance(self.node.iter, (gast.Name, gast.Attribute)): return True elif isinstance(self.node.iter, gast.Call) and isinstance( self.node.iter.func, gast.Attribute) and self.node.iter.func.attr == 'numpy': return True else: return False def is_for_enumerate_iter(self): return isinstance(self.node.iter, gast.Call) and isinstance( self.node.iter.func, gast.Name) and self.node.iter.func.id == "enumerate" def _args_check(self): if self.is_for_range_iter(): self.args_length = len(self.iter_args) assert self.args_length >= 1 and self.args_length <= 3, "range() function takes 1 to 3 arguments" elif self.is_for_enumerate_iter(): self.args_length = len(self.iter_args) assert self.args_length >= 1 and self.args_length <= 2, "enumerate() function takes 1 to 2 arguments" else: self.args_length = None def _parse_for_range_stmts(self): init_stmts = [] init_stmts.append(self._build_index_init_node()) compare_node = self._build_compare_node() step_node = self._build_step_node() cond_stmt = self._build_cond_stmt(step_node, compare_node) body_stmts = self.body body_stmts.append(self._build_index_increase_node(step_node)) return init_stmts, cond_stmt, body_stmts def _parse_for_stmts(self): init_stmts = [] init_stmts.append(self._build_index_init_node()) init_stmts.append(self._build_var_len_assign_node()) compare_node = self._build_compare_node() step_node = self._build_step_node() cond_stmt = self._build_cond_stmt(step_node, compare_node) body_stmts = self.body var_slice_node = self._build_var_slice_node() for body_node in body_stmts: NameNodeReplaceTransformer(body_node, self.iter_var_name, var_slice_node) body_stmts.append(self._build_index_increase_node(step_node)) return init_stmts, cond_stmt, body_stmts def _parse_for_enumerate_stmts(self): init_stmts = [] init_stmts.append(self._build_index_init_node()) init_stmts.append(self._build_var_len_assign_node()) init_stmts.append(self._build_enum_init_node()) compare_node = self._build_compare_node() step_node = self._build_step_node() cond_stmt = self._build_cond_stmt(step_node, compare_node) body_stmts = self.body var_slice_node = self._build_var_slice_node() for body_node in body_stmts: NameNodeReplaceTransformer(body_node, self.iter_var_name, var_slice_node) body_stmts.append(self._build_index_increase_node(step_node)) body_stmts.append(self._build_enum_increase_node()) return init_stmts, cond_stmt, body_stmts def _build_index_init_node(self): if self.is_for_range_iter(): if self.args_length == 1: index_init_value_str = '0' else: index_init_value_str = ast_to_source_code(self.iter_args[ 0]).strip() index_init_var_name = self.iter_var_name else: index_init_value_str = '0' index_init_var_name = self.iter_idx_name index_init_node_source_str = "{target} = {value}".format( target=index_init_var_name, value=index_init_value_str) index_init_node = gast.parse(index_init_node_source_str).body[0] return index_init_node def _build_var_len_assign_node(self): # get the length of iterable variable if isinstance(self.iter_node, gast.Call) and isinstance( self.iter_node.func, gast.Attribute) and self.iter_node.func.attr == 'numpy': iter_var_name = ast_to_source_code(self.iter_node.func.value).strip( ) else: iter_var_name = ast_to_source_code(self.iter_node).strip() convert_len_node_source_str = '{} = fluid.dygraph.dygraph_to_static.convert_operators.convert_len({})'.format( self.iter_var_len_name, iter_var_name) convert_len_node = gast.parse(convert_len_node_source_str).body[0] return convert_len_node def _build_enum_init_node(self): if self.is_for_enumerate_iter() and self.args_length != 1: init_value_str = ast_to_source_code(self.iter_args[1]).strip() else: init_value_str = '0' enum_init_node_source_str = "{} = {}".format(self.enum_idx_name, init_value_str) enum_init_node = gast.parse(enum_init_node_source_str).body[0] return enum_init_node def _build_compare_node(self): if self.is_for_range_iter(): compare_node = self.iter_args[ 0] if self.args_length == 1 else self.iter_args[1] else: compare_node = gast.Name( id=self.iter_var_len_name, ctx=gast.Load(), annotation=None, type_comment=None) return compare_node def _build_step_node(self): if self.is_for_range_iter(): step_node = self.iter_args[ 2] if self.args_length == 3 else gast.Constant( value=1, kind=None) else: step_node = gast.Constant(value=1, kind=None) return step_node def _build_cond_stmt(self, step_node, compare_node): return gast.Compare( left=gast.BinOp( left=gast.Name( id=self.iter_var_name if self.is_for_range_iter() else self.iter_idx_name, ctx=gast.Load(), annotation=None, type_comment=None), op=gast.Add(), right=step_node), ops=[gast.LtE()], comparators=[compare_node]) def _build_index_increase_node(self, step_node): return gast.AugAssign( target=gast.Name( id=self.iter_var_name if self.is_for_range_iter() else self.iter_idx_name, ctx=gast.Store(), annotation=None, type_comment=None), op=gast.Add(), value=step_node) def _build_var_slice_node(self): return gast.Subscript( value=self.iter_node, slice=gast.Index(value=gast.Name( id=self.iter_idx_name, ctx=gast.Load(), annotation=None, type_comment=None)), ctx=gast.Load()) def _build_enum_increase_node(self): return gast.AugAssign( target=gast.Name( id=self.enum_idx_name, ctx=gast.Store(), annotation=None, type_comment=None), op=gast.Add(), value=gast.Constant( value=1, kind=None)) def _get_iter_var_name(self): if self.is_for_range_iter(): return self.target.id elif self.is_for_iter(): return self.target.id elif self.is_for_enumerate_iter(): return self.target.elts[1].id return None def _get_iter_node(self): if self.is_for_iter(): return self.iter_args elif self.is_for_enumerate_iter(): return self.iter_args[0] return None def _get_enum_idx_name(self): if self.is_for_enumerate_iter(): return self.target.elts[0].id return None class SplitAssignTransformer(gast.NodeTransformer): """ This class transforms sequence assignments and multi-target assignments to normal assignments. """ def __init__(self, ast_node): assert isinstance(ast_node, gast.AST) self.ast_root = ast_node def transform(self): self.visit(self.ast_root) def visit_Assign(self, node): target_nodes = node.targets if len(target_nodes) == 1: node = self._parse_sequence_assign(node) else: node = self._parse_multi_target_assign(node) return node def _parse_sequence_assign(self, node): """ a, b = c, d -> a = c b = d """ assert isinstance(node, gast.Assign) target_nodes = node.targets value_node = node.value if not isinstance(target_nodes[0], (gast.List, gast.Tuple)): return node if not isinstance(value_node, (gast.List, gast.Tuple)): return node targets = node.targets[0].elts values = node.value.elts if len(targets) != len(values): return node new_nodes = [] for target, value in zip(targets, values): assign_node = gast.Assign(targets=[target], value=value) new_nodes.append(assign_node) return new_nodes def _parse_multi_target_assign(self, node): """ Example 1: a = b = c -> b = c a = b Example 2: a, b = c, d = x -> c,d = x a = c b = d """ assert isinstance(node, gast.Assign) target_nodes = node.targets value_node = node.value new_nodes = [] for target in reversed(target_nodes): assign_node = gast.Assign(targets=[target], value=value_node) # NOTE: Because assign_node can be sequence assign statement like `a,b = c,d`, # it's necessary to visit this new assign_node parsed_node = self.visit_Assign(assign_node) if not isinstance(parsed_node, list): parsed_node = [parsed_node] new_nodes.extend(parsed_node) value_node = target return new_nodes # NOTE: inspect.unwrap() exits in PY3 but not in PY2. def unwrap(func): """ Returns the object wrapped by decorators. """ def _is_wrapped(f): return hasattr(f, '__wrapped__') unwrapped_f = func while (_is_wrapped(unwrapped_f)): unwrapped_f = unwrapped_f.__wrapped__ return unwrapped_f