utils.py

# 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 gast
import imp
import inspect
import os
import six
import tempfile

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",
}


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.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'):
    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.
    """
    source = ast_to_source_code(ast_root)
    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: os.remove(f.name))
    module = imp.load_source(module_name, f.name)
    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 ast_to_source_code(ast_node):
    """
    Transformers 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 6:
        6. calls `range` function in `for` statement and the argument of range 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 is_candidate_node(node):
            if isinstance(node, gast.If):
                self._visit_If(node)
            if 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)
        # TODO
        # self.is_control_flow_num += 1
        if not isinstance(node.iter, gast.Call):
            return
        if not isinstance(node.iter.func, gast.Name):
            return
        if node.iter.func.id != "range":
            return
        for arg in node.iter.args:
            self.visit(arg)

        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):
            if is_candidate_node(child):
                self.visit(child)
        return node

    def visit_Compare(self, node):
        # Ignores child node with `if x` or `if x is None`
        # TODO(Aurelius84): `if tensor` will be supported in dygraph
        # and should be considered as is_control_flow.
        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

        tensor_types = {NodeVarType.TENSOR, NodeVarType.PADDLE_RETURN_TYPES}
        # 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 & tensor_types:
                    return True
        # if not found, look up the node_to_wrapper_map by node.
        node_to_wrapper_map = self.static_analysis_visitor.get_node_to_wrapper_map(
        )
        wrapper_node = node_to_wrapper_map.get(node, None)
        if wrapper_node is not None:
            if wrapper_node.node_var_type & tensor_types:
                return True

        return False

    def get_compare_nodes_with_tensor(self):
        return self._compare_node_tenor_set