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“24509f4af942bb250564756ad636691c7921e1df”上不存在“paddle/legacy/function/RowConvOp.cpp”
未验证 提交 d926b30b 编写于 作者: A Aurelius84 提交者： GitHub
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[Fluid Clean]Migrate if/while/return/break transformer into paddle.jit (#48449) 

* [Fluid Clean]Migrate if/while/return/break transformer into paddle.jit

* migrate call_transformer

* migrate call_transformer
上级 f41ccbd5
隐藏空白更改
内联 并排
Showing with 1179 addition and 1256 deletion
python/paddle/fluid/compiler.py
浏览文件 @ d926b30b
......@@ -697,7 +697,7 @@ class IpuDynamicPatcher:
MAX_TRACED_PROGRAM_COUNT,
)
from ..fluid.dygraph.dygraph_to_static import logging_utils
from ..fluid.dygraph.dygraph_to_static.partial_program import (
from paddle.jit.dy2static.partial_program import (
partial_program_from,
)
......
python/paddle/fluid/dygraph/dygraph_to_static/__init__.py
浏览文件 @ d926b30b
......@@ -15,23 +15,14 @@
from . import static_analysis
from .static_analysis import *
from . import loop_transformer
from .loop_transformer import *
from . import variable_trans_func
from .variable_trans_func import *
from . import convert_call_func
from .convert_call_func import *
from . import convert_operators
from . import logging_utils
from .logging_utils import *
__all__ = []
__all__ += loop_transformer.__all__
__all__ += static_analysis.__all__
__all__ += variable_trans_func.__all__
__all__ += convert_call_func.__all__
__all__ += logging_utils.__all__
python/paddle/fluid/dygraph/dygraph_to_static/call_transformer.py
浏览文件 @ d926b30b
......@@ -51,7 +51,7 @@ class CallTransformer(BaseTransformer):
func_str = ast_to_source_code(node.func).strip()
try:
from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import (
from paddle.jit.dy2static.convert_call_func import (
is_builtin,
)
......
python/paddle/fluid/dygraph/dygraph_to_static/convert_call_func.py 已删除 100644 → 0
浏览文件 @ f41ccbd5
# 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.
import collections
import copy
import functools
import logging
import inspect
import pdb
import re
import types
import numpy
import builtins
from paddle.fluid.dygraph.container import Sequential
from paddle.fluid.dygraph.dygraph_to_static.convert_operators import (
convert_len,
convert_zip,
)
from paddle.fluid.dygraph.dygraph_to_static.convert_operators import (
convert_range,
convert_enumerate,
)
from paddle.fluid.dygraph.dygraph_to_static.logging_utils import (
TranslatorLogger,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import is_paddle_func, unwrap
from paddle.fluid.dygraph.layers import Layer
__all__ = ["convert_call"]
# The api(s) should be considered as plain function and convert
# them into static layer code.
PADDLE_NEED_CONVERT_APIS = [Sequential]
translator_logger = TranslatorLogger()
CONVERSION_OPTIONS = "An attribute for a function that indicates conversion flags of the function in dynamic-to-static."
class ConversionOptions:
"""
A container for conversion flags of a function in dynamic-to-static.
Attributes:
not_convert(bool): An attribute indicates that the function won't be converted in dynamic-to-static.
NOTE(liym27): More attributes and methods can be added in this class.
"""
def __init__(self, not_convert=False):
self.not_convert = not_convert
def is_builtin(func, name=None):
"""predict whether a function is a builtin function with name={name}.
if name == None, then any builtin function will return True
"""
def name_judge():
return name is None or func.__name__ == name
if isinstance(func, types.BuiltinFunctionType) and name_judge():
return True
elif func in builtins.__dict__.values() and name_judge():
return True
else:
return False
def builtin_modules():
"""
Return builtin modules.
"""
modules = [
collections,
pdb,
copy,
inspect,
re,
numpy,
logging,
]
try:
import six
modules.append(six)
except ImportError:
pass # do nothing
return modules
BUILTIN_LIKELY_MODULES = builtin_modules()
def is_unsupported(func):
"""
Checks whether the func is supported by dygraph to static graph.
"""
for m in BUILTIN_LIKELY_MODULES:
for v in m.__dict__.values():
func_in_dict = func == v
if isinstance(func_in_dict, (list, numpy.ndarray)):
func_in_dict = numpy.array(func_in_dict).any()
if func_in_dict:
translator_logger.log(
2,
"Whitelist: {} is part of built-in module and does not have to be transformed.".format(
func
),
)
return True
# NOTE: should be placed before `is_paddle_func`
if type(func) in PADDLE_NEED_CONVERT_APIS:
return False
if is_paddle_func(func):
translator_logger.log(
2,
"Whitelist: {} is part of Paddle module and does not have to be transformed.".format(
func
),
)
return True
def convert_call(func):
"""
Converts a function call which needs to be transformed to static function.
Args:
func (callable): A callable function or method to convert.
Returns:
Callable: A converted function.
Examples:
.. code-block:: python
import paddle
from paddle.jit.dy2static import convert_call
paddle.enable_static()
def dyfunc(x):
if paddle.mean(x) < 0:
x_v = x - 1
else:
x_v = x + 1
return x_v
new_func = convert_call(dyfunc)
x = paddle.tensor.manipulation.fill_constant(shape=[3, 3], value=0, dtype='float64')
x_v = new_func(x)
exe = paddle.static.Executor(paddle.CPUPlace())
out = exe.run(fetch_list=[x_v])
print(out[0])
# [[1. 1. 1.]
# [1. 1. 1.]
# [1. 1. 1.]]
"""
# NOTE(Aurelius84): Fix it after all files migrating into jit.
from paddle.jit.dy2static.program_translator import (
convert_to_static,
unwrap_decorators,
StaticFunction,
)
translator_logger.log(
1, "Convert callable object: convert {}.".format(func)
)
func_self = None
converted_call = None
# Function in convert_call may be decorated by another `@to_static`,
# in this case, unwraps it into a raw method or function.
_, func = unwrap_decorators(func)
options = getattr(func, CONVERSION_OPTIONS, None)
if options is not None and options.not_convert:
translator_logger.log(
2,
"{} is not converted when it is decorated by 'paddle.jit.not_to_static'.".format(
func
),
)
return func
if is_builtin(func, "len"):
return convert_len
if is_builtin(func, "zip"):
return convert_zip
if is_builtin(func, "range"):
return convert_range
if is_builtin(func, "enumerate"):
return convert_enumerate
if is_builtin(func) or is_unsupported(func):
return func
if inspect.isgeneratorfunction(func):
# NOTE(xiongkun03): inspect.isfunction() will return True even though func is a generator function.
# If we don't deal generatorfunction here, we will regard it as normal function and get errors in some
# occasion.
number_of_stars = 30
translator_logger.warn(
"\n\n"
+ "*" * number_of_stars
+ "\nYour function:`{}` doesn't support to transform to static function because it is a generator function, it will be run as-is.".format(
func.__name__
)
+ "\n"
+ "*" * number_of_stars
+ "\n\n"
)
return func
if inspect.isfunction(func):
# TODO(liym27): If func is a lambda function, special conversion is needed.
if func.__name__ == '<lambda>':
return func
try:
# Note(Aurelius84): Because `@declarative` returns a class instance instead of
# a function. This will modify the value referring to itself in `__globals__`.
# For example:
#
# @declarative
# def foo(x):
# return x
#
# `foo` will be converted into a wrapper class, suppose as `StaticFunction`.
# And `foo.__globals__['foo']` will still return this `StaticFunction` instead of
# `foo` function. So `isinstance(fn, StaticFunction)` is added here.
_origfunc = unwrap(func)
global_functions = set()
for fn in _origfunc.__globals__.values():
if inspect.isfunction(fn):
global_functions.add(fn)
elif isinstance(fn, StaticFunction):
_, fn = unwrap_decorators(fn)
global_functions.add(fn)
elif inspect.isclass(fn):
if isinstance(
fn.__dict__.get(func.__name__, None), staticmethod
):
global_functions.add(
func
) # Add func to ensure that we will convert
if func in global_functions:
converted_call = convert_to_static(func)
func_self = getattr(func, '__self__', None)
else:
# NOTE:
# If func is not in __globals__, it does not need to be transformed
# because it has been transformed before.
translator_logger.warn(
"{} doesn't have to be transformed to static function because it has been transformed before, it will be run as-is.".format(
func
)
)
converted_call = func
except AttributeError:
# NOTE:
# If func is not in __globals__, it does not need to be transformed
# because it has been transformed before.
converted_call = None
except (IOError, OSError):
# NOTE:
# If func has been decorated, its source code can not be get
# so that it can not be transformed to static function.
converted_call = None
elif inspect.ismethod(func):
try:
converted_call = convert_to_static(func)
func_self = getattr(func, '__self__', None)
except (IOError, OSError):
# NOTE: func may have been decorated.
converted_call = None
elif hasattr(func, '__class__') and hasattr(func.__class__, '__call__'):
if hasattr(func, 'forward') and isinstance(func, Layer):
try:
_, forward_func = unwrap_decorators(func.forward)
func._original_funcs['forward'] = forward_func.__func__
forward_func = convert_to_static(forward_func)
# Bound mothod will be convert into plain function after `convert_to_static`.
# So descriptor mechanism is used to bound `self` instance on function to
# keep it as bound method.
setattr(func, 'forward', forward_func.__get__(func))
except (IOError, OSError, TypeError):
# NOTE: func.forward may have been decorated.
func_self = None if func_self else func_self
converted_call = func
else:
try:
call_func = func.__class__.__call__
converted_call = convert_to_static(call_func)
func_self = func
except (IOError, OSError, TypeError):
# NOTE:
# If `func` is a class which is being initialized, for example `convert_call(Foo)()`,
# it doesn't need to be transformed
func_self = None if func_self else func_self
else:
raise NotImplementedError(
"Callable {} can not be transformed at present.".format(func)
)
if converted_call is None:
translator_logger.warn(
"{} doesn't have to be transformed to static function, and it will be run as-is.".format(
func
)
)
return func
if func_self:
converted_call = functools.partial(converted_call, func_self)
return converted_call
python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py 已删除 100644 → 0
浏览文件 @ f41ccbd5
# 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.
import re
import paddle
from paddle.fluid.data_feeder import convert_dtype
from paddle.fluid.dygraph.dygraph_to_static.variable_trans_func import (
to_static_variable,
)
from paddle.fluid.framework import core, Variable
from paddle.fluid.layers import Assert, Print
from paddle.fluid.layers import (
array_length,
array_read,
array_write,
create_array,
)
from paddle.fluid.layers import (
assign,
fill_constant,
reduce_all,
reduce_any,
)
from paddle.fluid.layers import (
cast,
control_flow,
nn,
)
from paddle.fluid.layers.control_flow import (
cond,
while_loop,
less_than,
increment,
)
from paddle.fluid.dygraph.dygraph_to_static.return_transformer import (
RETURN_NO_VALUE_VAR_NAME,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import (
UndefinedVar,
Dygraph2StaticException,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import GetterSetterHelper
from paddle.fluid.layers.utils import copy_mutable_vars
def convert_attr(x, attr):
if isinstance(x, Variable) and attr == "size":
return x.size()
else:
return getattr(x, attr)
def indexable(x, code=None):
if isinstance(x, Variable):
return x
if hasattr(x, '__len__') and hasattr(x, '__getitem__'):
return x
if hasattr(x, '__iter__'):
return [i for i in x]
else:
raise RuntimeError("X can't be convert into indexable.")
def unpack_by_structure(target, structure):
"""unified unpack interface for paddle and python."""
if isinstance(target, Variable):
return _unpack_by_structure_paddle(target, structure)
else:
return _unpack_by_structure_python(target, structure)
def _unpack_by_structure_python(target, structure):
"""TODO(xiongkun): analysis the differences between python and paddle unpack."""
return _unpack_by_structure_paddle(target, structure)
def _unpack_by_structure_paddle(target, structure):
if structure == 1:
return target
ret = []
for idx, ele in enumerate(structure):
if ele == 1:
ret.append(target[idx])
continue
if isinstance(ele, list):
ret.append(unpack_by_structure(target[idx], ele))
continue
assert False, "structure element must be 1 or list"
return ret
def convert_while_loop(
cond, body, getter, setter, return_name_ids=None, push_pop_names=None
):
"""
A function representation of a Python ``while`` statement.
Args:
cond(Callable): A callable object that returns a boolean variable to control whether to execute the loop body. It takes ``loop_vars`` as arguments.
body(Callable): A callable object that returns a tuple or list of variables with the same arguments ``loops_vars`` as ``cond`` .
get_args(callable): Get all arguments that needed in true_fn and false_fn.
set_args(callable): Update arguments that modified in trure_fn and false_fn.
return_name_ids(list[string], optional): the returned names.
push_pop_names(list[string], optional): the names on which called .append() or .pop().
Returns:
A list or tuple of variables which returned by ``body``.
"""
# NOTE: It may be slower if cond is very expensive, but usually cond is just O(1).
# If loop_vars is changed during cond callable, then it causes bug, but current logical_and/logical_not/... doesn't change the loop_vars.
pred = cond()
if isinstance(pred, Variable):
_run_paddle_while(
cond, body, getter, setter, return_name_ids, push_pop_names
)
else:
_run_py_while(cond, body, getter, setter)
def _convert_tensor_arrray_if_necessary(setterhelper, push_pop_names):
push_pop_vars = setterhelper.get(push_pop_names)
if push_pop_vars is None:
return
def maybe_to_tensor_array(v):
if isinstance(v, list):
return create_array("float32", initialized_list=v)
else:
return v
setterhelper.set(
push_pop_names, [maybe_to_tensor_array(v) for v in push_pop_vars]
)
def _run_paddle_while(
cond, body, getter, setter, return_name_ids, push_pop_names
):
# NOTE: loop_vars of Paddle op `control_flow.while_loop` must be Paddle Tensors.
helper = GetterSetterHelper(getter, setter, return_name_ids, push_pop_names)
_convert_tensor_arrray_if_necessary(helper, push_pop_names)
def new_body_fn(*args):
"""wrap the body() and add return value for `while_loop`
the args may be differ from getter().
"""
mutable_loop_vars = args
helper.set(return_name_ids, mutable_loop_vars)
body()
return helper.get(return_name_ids)
def new_cond_fn(*args):
"""cond is a zero-args function, which is not
compatible with `while_loop`.
"""
return cond()
# UndefinedVar will become data layer not check variable with value=NO_VALUE_MAGIC.
loop_vars = [
to_static_variable(var) if not isinstance(var, UndefinedVar) else var
for var in helper.get(return_name_ids)
]
helper.set(
return_name_ids, loop_vars
) # change the non-local var to variable
# variable maybe modified to inner var. change it into
loop_vars = control_flow.while_loop(new_cond_fn, new_body_fn, loop_vars)
helper.set(return_name_ids, loop_vars)
return loop_vars
def _run_py_while(cond, body, getter, setter):
while True:
pred = cond()
if isinstance(pred, Variable):
raise Dygraph2StaticException(
"python while pred change from bool to variable."
)
if not pred:
break
body()
def convert_logical_and(x_func, y_func):
"""
A function representation of a Python ``and`` statement.
Args:
x_func(callable): x_func() is the left hand operand of ``and`` operator. x_func() is bool or Tensor.
y_func(callable): y_func() is the right hand operand of ``and`` operator. y_func() is bool or Tensor.
Returns:
A python bool variable or a bool Tensor.
NOTE(liym27):
1) The operands are executed sequentially according to the running logic of Python. So here the arguments
should be callable.
2) If the left hand operand is False, the right hand operand should be executed.
For example:
a = x > 1 and y < 1
Transformed code:
a = paddle.jit.dy2static.convert_logical_and(lambda:x>1, lambda:y<1)
In `convert_logical_and(lambda:x>1, lambda:y<1)`, `lambda:y<1` must be run after `lambda:x>1`. And
if `x>1` is False, `y<1` should NOT be run.
"""
x_value = x_func()
if not isinstance(x_value, Variable):
return _run_py_logical_and(lambda: x_value, y_func)
y_value = y_func()
if not isinstance(y_value, Variable):
return _run_py_logical_and(lambda: y_value, lambda: x_value)
return _run_paddle_logical_and(x_value, y_value)
def _run_paddle_logical_and(x, y):
x = cast_bool_if_necessary(x)
y = cast_bool_if_necessary(y)
return paddle.logical_and(x, y)
def _run_py_logical_and(x_func, y_func):
x_value = x_func()
assert not isinstance(x_value, Variable)
# NOTE(liym27):
# 1. Returns y_func() if x_value is False;
# 2. If x_value is False, y_func() should not be run.
return x_value and y_func()
def convert_logical_or(x_func, y_func):
"""
A function representation of a Python ``or`` statement.
Args:
x_func(callable): x_func() is the left hand operand of ``or`` operator. x_func() is bool or Tensor.
y_func(callable): y_func() is the right hand operand of ``or`` operator. y_func() is bool or Tensor.
Returns:
A python bool variable or a bool Tensor.
NOTE(liym27):
1) The operands are executed sequentially according to the running logic of Python. So here the arguments
should be callable.
2) If the left hand operand is True, the right hand operand should be executed.
For example:
a = x > 1 or y < 1
Transformed code:
a = paddle.jit.dy2static.convert_logical_or(lambda:x>1, lambda:y<1)
In `convert_logical_or(lambda:x>1, lambda:y<1)`, `lambda:y<1` must be run after `lambda:x>1`. And
if `x>1` is True, `y<1` should NOT be run.
"""
x_value = x_func()
if not isinstance(x_value, Variable):
return _run_py_logical_or(lambda: x_value, y_func)
y_value = y_func()
if not isinstance(y_value, Variable):
return _run_py_logical_or(lambda: y_value, lambda: x_value)
return _run_paddle_logical_or(x_value, y_value)
def _run_paddle_logical_or(x, y):
x = cast_bool_if_necessary(x)
y = cast_bool_if_necessary(y)
return paddle.logical_or(x, y)
def _run_py_logical_or(x_func, y_func):
x_value = x_func()
assert not isinstance(x_value, Variable)
# NOTE(liym27):
# 1. Returns y_func() if x_value is False;
# 2. If x_value is True, y_func() should not be run.
return x_value or y_func()
def convert_logical_not(x):
"""
A function representation of a Python ``not`` statement.
Args:
x(bool|Tensor): Operand of ``not`` operator.
Returns:
A python bool variable or a bool Tensor.
"""
if isinstance(x, Variable):
return _run_paddle_logical_not(x)
else:
return _run_py_logical_not(x)
def _run_paddle_logical_not(x):
x = cast_bool_if_necessary(x)
return paddle.logical_not(x)
def _run_py_logical_not(x):
return not x
def convert_ifelse(
pred,
true_fn,
false_fn,
get_args,
set_args,
return_name_ids,
push_pop_names=None,
):
"""
A function representation of a Python ``if/else`` statement.
Args:
pred(bool|Tensor): A boolean Tensor which determines whether to return the result of ``true_fn`` or ``false_fn`` .
true_fn(callable): A callable to be performed if ``pred`` is true.
false_fn(callable): A callable to be performed if ``pred`` is false.
get_args(callable): Get all arguments that needed in true_fn and false_fn.
set_args(callable): Update arguments that modified in trure_fn and false_fn.
return_name_ids(list[string], optional): the returned names.
push_pop_names(list[string], optional): the names on which called .append() or .pop().
Returns:
``true_fn()`` if the predicate ``pred`` is true else ``false_fn()`` .
"""
if isinstance(pred, Variable):
out = _run_paddle_cond(
pred,
true_fn,
false_fn,
get_args,
set_args,
return_name_ids,
push_pop_names,
)
else:
out = _run_py_ifelse(
pred, true_fn, false_fn, get_args, set_args, return_name_ids
)
return out
def _run_paddle_cond(
pred, true_fn, false_fn, get_args, set_args, return_name_ids, push_pop_names
):
"""
Paddle cond API will evaluate both true_fn and false_fn codes.
"""
helper = GetterSetterHelper(
get_args, set_args, return_name_ids, push_pop_names
)
_convert_tensor_arrray_if_necessary(helper, push_pop_names)
pred = cast_bool_if_necessary(pred)
init_args = helper.get(return_name_ids)
def new_true_fn():
# init args may contain mutable python container like [var, 2], we copy then like in while_loop
helper.set(return_name_ids, copy_mutable_vars(init_args))
ret = true_fn()
# IfExpr will return a non-None return value, so we just return ret.
# We assume normal return has no return value.
if ret is None:
return helper.get(return_name_ids)
else:
return ret
def new_false_fn():
# init args may contain mutable python container like [var, 2], we copy then like in while_loop
helper.set(return_name_ids, copy_mutable_vars(init_args))
ret = false_fn()
if ret is None:
return helper.get(return_name_ids)
else:
return ret
try:
cond_outs = control_flow.cond(
pred, new_true_fn, new_false_fn, None, return_name_ids
)
except Exception as e:
if re.search(
"Unsupported return type of true_fn and false_fn in cond", str(e)
):
raise Dygraph2StaticException(
"Your if/else have different return type. TODO: add link to modifty. {}".format(
str(e)
)
)
if re.search("Incompatible return values of", str(e)):
raise Dygraph2StaticException(
"Your if/else have different number of return value. TODO: add link to modifty. {}".format(
str(e)
)
)
raise e
get_args = lambda: helper.get(return_name_ids)
set_args = lambda vs: helper.set(return_name_ids, vs)
return _recover_args_state(cond_outs, get_args, set_args, return_name_ids)
def _run_py_ifelse(
pred, true_fn, false_fn, get_args, set_args, return_name_ids
):
"""
Evaluate python original branch function if-else.
"""
py_outs = true_fn() if pred else false_fn()
return py_outs
def _remove_no_value_return_var(out):
if isinstance(out, tuple) and len(out) > 0:
processed_out = out
align_ret = out[0]
if isinstance(align_ret, tuple):
for index, item in enumerate(align_ret):
if isinstance(item, Variable) and (
RETURN_NO_VALUE_VAR_NAME in item.name
):
# return None
if index == 0:
processed_out = (None,) + out[1:]
elif index == 1:
processed_out = align_ret[:1] + out[1:]
else:
processed_out = (align_ret[:index],) + out[1:]
break
for index, item in enumerate(processed_out):
if isinstance(item, Variable) and (
RETURN_NO_VALUE_VAR_NAME in item.name
):
processed_out = processed_out[:index]
if not processed_out:
return None
elif len(processed_out) == 1:
return processed_out[0]
else:
return processed_out
else:
return out
def _check_no_undefined_var(outs, names, branch_name):
if names is None:
return
if not isinstance(outs, (list, tuple)):
outs = [outs]
for var, name in zip(list(outs), names):
if isinstance(var, UndefinedVar):
raise ValueError(
"Required '{}' must be initialized both in if-else branch, but found it not initialized in '{}'.".format(
name, branch_name
)
)
def _recover_args_state(outs, get_args, set_args, return_name_ids):
"""
Currently we support variant length of early return statement by padding
_no_return_value.
# TODO(dev): We shall consider to evaluate whether should support this for Python if-else?
"""
# IfExpr's return_name_ids maybe None
if return_name_ids is None:
return outs
init_args = get_args()
# recover args state
num_outs = len(return_name_ids)
num_args = len(init_args)
assert num_outs <= num_args
if num_args == 1:
final_outs = (
(outs,) if not isinstance(outs, (list, tuple)) else tuple(outs)
)
else:
outs = (outs,) if num_outs == 1 else tuple(outs)
final_outs = outs + init_args[num_outs:]
set_args(final_outs)
return final_outs
def convert_len(var):
"""
Returns variable(length) from shape ops based on var.type
Note: In addition to some ast transformations, some block-related
operations are added in `len` transformation, such as appending
`shape_op` in var.block.
"""
if isinstance(var, Variable):
assert var.ndim > 0, "len() of a 0D tensor is wrong"
if var.type in [
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.SELECTED_ROWS,
]:
# Note: Length of var may be known ahead of time in dygraph,
# but it probably represents batch size which can be variant.
# so we return a variable dynamically inferred from var.shape.
if var.shape[0] > 0 and var.type == core.VarDesc.VarType.LOD_TENSOR:
return var.shape[0]
return nn.shape(var)[0]
elif var.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
return control_flow.array_length(var)
else:
raise TypeError(
'len(var) only supports LoDTensor/LoDTensorArray/SelectedRows, but received %s.'
% type(var)
)
else:
if isinstance(var, VariableTuple):
return var.__len__()
return len(var)
def convert_zip(*args):
for i, arg in enumerate(args):
if isinstance(arg, Variable) and arg.shape[0] == -1:
raise RuntimeError(
"Not support zip(tensor, ...) when tensor.shape[0] == -1, "
"but found args[{}].shape[0] == -1 in 'zip'".format(str(i))
)
return zip(*args)
# TODO(xiongkun): delete when list<variable> is ready.
class VariableTuple:
"""
this class will cause enumerate can't be wrapped by other iterator change function.
this will be fixed when list<Variable> is producted.
VariableTuple can only deal with variables which is fixed.
"""
def __init__(self, var, start=0):
self.var = var
self.len = convert_len(var)
if isinstance(self.len, Variable):
self.rag = paddle.arange(start, start + self.len, 1, paddle.int64)
else:
self.rag = range(start, start + self.len)
def __getitem__(self, idx):
return self.rag[idx], self.var[idx]
def __len__(self):
return self.len
def convert_enumerate(*args):
has_variable = any(map(lambda x: isinstance(x, Variable), args))
if has_variable:
return VariableTuple(*args)
return enumerate(*args)
def convert_range(*args):
has_variable = any(map(lambda x: isinstance(x, Variable), args))
if has_variable:
if len(args) == 1:
return paddle.arange(0, args[0], 1, paddle.int64)
if len(args) == 2:
return paddle.arange(args[0], args[1], 1, paddle.int64)
if len(args) == 3:
return paddle.arange(args[0], args[1], args[2], paddle.int64)
return range(*args)
def convert_shape(x):
"""
A function representation of the shape of variable.
"""
def has_negative(list_shape):
return any([x < 0 for x in list_shape])
# When `x` is Variable:
# (1) if x.shape contains -1, such as [2, -1, 64], returns [2, var, 64],
# where var = paddle.shape(x)[1]
# (2) if x.shape does not contains -1, return lsit(x.shape) directly
if isinstance(x, Variable):
values = list(x.shape)
if has_negative(values):
shape_tensor = nn.shape(x)
for i, v in enumerate(values):
if v is None or v < 0:
values[i] = shape_tensor[i]
return values
else:
return x.shape
def convert_shape_compare(left, *args):
"""
A function handles comparison difference between Paddle and Python.
For example, if x and y are Tensors, x.shape == y.shape will return single
boolean Value (True/False). However, paddle.shape(x) == paddle.shape(y) is
an element-wise comparison. The difference can cause dy2stat error. So we
create this function to handle the difference.
Args:
left: variable
*args: compare_op(str), variable, compare_op(str), variable, where
compare_op means "<", ">", "==", "!=", etc.
Returns:
If the variables to compare are NOT Paddle Variables, we will return as
Python like "a op1 b and b op2 c and ... ".
If the variables to compare are Paddle Variables, we will do elementwise
comparsion first and then reduce to a boolean whose numel is 1.
"""
args_len = len(args)
assert (
args_len >= 2
), "convert_shape_compare needs at least one right compare variable"
assert (
args_len % 2 == 0
), "Illegal input for convert_shape_compare, *args should be op(str), var, op(str), var ..."
num_cmp = args_len // 2
if isinstance(left, Variable):
def reduce_compare(x, op_str, y):
element_wise_result = eval("x " + op_str + " y")
if op_str == "!=":
return reduce_any(element_wise_result)
elif (
op_str == "is"
or op_str == "is not"
or op_str == "in"
or op_str == "not in"
):
return element_wise_result
else:
return reduce_all(element_wise_result)
final_result = reduce_compare(left, args[0], args[1])
for i in range(1, num_cmp):
cmp_left = args[i * 2 - 1]
cmp_op = args[i * 2]
cmp_right = args[i * 2 + 1]
cur_result = reduce_compare(cmp_left, cmp_op, cmp_right)
final_result = convert_logical_and(
lambda: final_result, lambda: cur_result
)
return final_result
else:
cmp_left = left
final_result = None
for i in range(num_cmp):
cmp_op = args[i * 2]
cmp_right = args[i * 2 + 1]
cur_result = eval("cmp_left " + cmp_op + " cmp_right")
if final_result is None:
final_result = cur_result
else:
final_result = final_result and cur_result
if final_result is False:
return False
cmp_left = cmp_right
return final_result
def cast_bool_if_necessary(var):
assert isinstance(var, Variable)
if convert_dtype(var.dtype) not in ['bool']:
var = cast(var, dtype="bool")
return var
def convert_var_dtype(var, dtype):
if isinstance(var, Variable):
src_dtype = convert_dtype(var.dtype)
assert src_dtype in [
'bool',
'float16',
'float32',
'float64',
'int32',
'int64',
'uint8',
], "The dtype of var {} is {}, which is not supported in the cast op.".format(
var.name, src_dtype
)
assert dtype in [
'bool',
'int',
'float',
], "The casted target dtype is {}, which is not supported in type casting.".format(
dtype
)
cast_map = {
'bool': 'bool',
'int': 'int32',
'float': 'float32',
}
return cast(var, dtype=cast_map[dtype])
else:
return eval('{}(var)'.format(dtype))
def convert_assert(cond, message=""):
"""
A function representation of a Python ``assert`` statement.
"""
if isinstance(cond, Variable):
cond = cast(cond, "bool")
# NOTE: message is not used because Paddle Assert has no corresponding parameter to use.
return Assert(cond)
else:
assert cond, message
def convert_print(*args):
"""
A function representing Python ``print`` statement. Note: this is a basic
python function so we haven't handle sep, end, file and flush parameters of
python function.
"""
for var in args:
if isinstance(var, Variable):
var = Print(var)
else:
print(var)
def convert_pop(target, *args):
"""
A function representation of a Python pop statement for a list or dict.
Args:
target(list|dict|Tensor): A variable to pop item from.
*args(tuple): index or default value to parse.
Returns:
A item poped from target.
"""
is_variable = isinstance(target, Variable)
if is_variable:
is_tensor_array = target.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY
if is_variable and is_tensor_array:
return _run_paddle_pop(target, *args)
else:
return _run_python_pop(target, *args)
def _run_paddle_pop(array, *args):
if len(args) == 0:
idx = -1
else:
idx = args[0]
assert isinstance(idx, int)
def cond(i, new_array):
return less_than(i, arr_len)
def body(i, new_array):
item = array_read(array=array, i=i)
array_write(item, array_length(new_array), new_array)
i = increment(i)
return i, new_array
arr_len = array_length(array)
if idx < 0:
idx = idx + arr_len
else:
idx = fill_constant(shape=[1], dtype="int64", value=idx)
pop_item = array_read(array, idx)
new_array = _slice_tensor_array(array, 0, idx)
i = idx + 1
_, new_array = while_loop(cond, body, [i, new_array])
assign(input=new_array, output=array)
return pop_item
# TODO(liym27): A better way to slice tensor array.
# Maybe support start == end for slice op.
def _slice_tensor_array(array, start, end):
def true_fn():
null_array = create_array("float32")
return null_array
def false_fn(array, start, end):
new_array = paddle.slice(array, starts=[start], ends=[end], axes=[0])
return new_array
new_array = cond(start == end, true_fn, lambda: false_fn(array, start, end))
return new_array
def _run_python_pop(target, *args):
# 1. pop for a dict
if len(args) == 2:
idx, default = args
return target.pop(idx, default)
# 2. pop for a list or dict
else:
idx = args[0] if args else -1
return target.pop(idx)
python/paddle/fluid/dygraph/dygraph_to_static/utils.py
浏览文件 @ d926b30b
......@@ -145,7 +145,7 @@ def data_layer_not_check(name, shape, dtype='float32', lod_level=0):
def create_undefined_variable():
from paddle.fluid.dygraph.dygraph_to_static.return_transformer import (
from paddle.jit.dy2static.return_transformer import (
RETURN_NO_VALUE_MAGIC_NUM,
)
......@@ -1212,13 +1212,13 @@ class FunctionNameLivenessAnalysis(gast.NodeVisitor):
"""NOTE: why we need merge w_vars and push_pop_vars here ?
because we do ifelse_transformer after loop_transformer. Loops will changed into functioons. but we know this function will be called in if. so we add w_vars to father function scope.
"""
from paddle.fluid.dygraph.dygraph_to_static.loop_transformer import (
from paddle.jit.dy2static.loop_transformer import (
WHILE_CONDITION_PREFIX,
WHILE_BODY_PREFIX,
FOR_CONDITION_PREFIX,
FOR_BODY_PREFIX,
)
from paddle.fluid.dygraph.dygraph_to_static.ifelse_transformer import (
from paddle.jit.dy2static.ifelse_transformer import (
TRUE_FUNC_PREFIX,
FALSE_FUNC_PREFIX,
)
......
python/paddle/fluid/dygraph/io.py
浏览文件 @ d926b30b
......@@ -30,7 +30,7 @@ from paddle.fluid.executor import (
_is_enable_standalone_executor,
_is_dy2st_enable_standalone_executor,
)
from paddle.fluid.dygraph.dygraph_to_static.partial_program import (
from paddle.jit.dy2static.partial_program import (
add_build_strategy_for,
LazyInitialized,
)
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ d926b30b
......@@ -2589,7 +2589,7 @@ def expand_undefined_var(nest1, nest2, names):
In this case, we should not expand recursively.
"""
from paddle.fluid.dygraph.dygraph_to_static.utils import UndefinedVar
from paddle.fluid.dygraph.dygraph_to_static.return_transformer import (
from paddle.jit.dy2static.return_transformer import (
RETURN_VALUE_PREFIX,
)
......
python/paddle/fluid/layers/math_op_patch.py
浏览文件 @ d926b30b
......@@ -275,7 +275,7 @@ def monkey_patch_variable():
Returns:
Variable: self[index]
"""
from paddle.fluid.dygraph.dygraph_to_static.convert_operators import (
from paddle.jit.dy2static.convert_operators import (
_run_paddle_pop,
)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_convert_call.py
浏览文件 @ d926b30b
......@@ -20,7 +20,7 @@ import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.jit import ProgramTranslator
from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import (
from paddle.jit.dy2static.convert_call_func import (
CONVERSION_OPTIONS,
)
from test_program_translator import get_source_code
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_len.py
浏览文件 @ d926b30b
......@@ -18,7 +18,7 @@ import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.jit.api import declarative
from paddle.fluid.dygraph.dygraph_to_static import convert_call
from paddle.jit.dy2static import Call
SEED = 2020
np.random.seed(SEED)
......@@ -90,11 +90,11 @@ def len_with_selected_rows(place):
)
# y is Variable(SelectedRows)
y = fluid.layers.merge_selected_rows(var)
y_len = convert_call(len)(y)
y_len = Call(len)(y)
# z is inner tensor with shape [4, 2]
z = fluid.layers.get_tensor_from_selected_rows(y)
z_len = convert_call(len)(z)
z_len = Call(len)(z)
# set data for selected_rows
x_rows = [0, 2, 2, 4, 19]
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py
浏览文件 @ d926b30b
......@@ -19,7 +19,7 @@ import paddle
import paddle.fluid as fluid
import unittest
from paddle.fluid.dygraph.dygraph_to_static.loop_transformer import NameVisitor
from paddle.jit.dy2static.loop_transformer import NameVisitor
from paddle.jit.api import declarative
SEED = 2020
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_save_inference_model.py
浏览文件 @ d926b30b
......@@ -21,7 +21,7 @@ import paddle
import paddle.fluid as fluid
from paddle.jit import ProgramTranslator
from paddle.jit.api import declarative
from paddle.fluid.dygraph.dygraph_to_static.partial_program import (
from paddle.jit.dy2static.partial_program import (
partial_program_from,
)
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
......
python/paddle/jit/api.py
浏览文件 @ d926b30b
......@@ -35,7 +35,7 @@ from paddle.fluid.dygraph.base import (
switch_to_static_graph,
)
from paddle.fluid.dygraph.dygraph_to_static import logging_utils
from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import (
from paddle.jit.dy2static.convert_call_func import (
ConversionOptions,
CONVERSION_OPTIONS,
)
......
python/paddle/jit/dy2static/assert_transformer.py
浏览文件 @ d926b30b
......@@ -22,6 +22,8 @@ from paddle.fluid.dygraph.dygraph_to_static.base_transformer import (
BaseTransformer,
)
__all__ = ['AssertTransformer']
class AssertTransformer(BaseTransformer):
"""
......
python/paddle/jit/dy2static/ast_transformer.py
浏览文件 @ d926b30b
......@@ -21,7 +21,7 @@ import os
from paddle.fluid.dygraph.dygraph_to_static.base_transformer import (
BaseTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.early_return_transformer import (
from .early_return_transformer import (
EarlyReturnTransformer,
)
from .assert_transformer import (
......@@ -30,10 +30,8 @@ from .assert_transformer import (
from paddle.fluid.dygraph.dygraph_to_static.basic_api_transformer import (
BasicApiTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.break_continue_transformer import (
from .break_continue_transformer import (
BreakContinueTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.break_continue_transformer import (
BreakTransformOptimizer,
)
from paddle.fluid.dygraph.dygraph_to_static.call_transformer import (
......@@ -45,19 +43,19 @@ from paddle.fluid.dygraph.dygraph_to_static.cast_transformer import (
from paddle.fluid.dygraph.dygraph_to_static.typehint_transformer import (
TypeHintTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.ifelse_transformer import (
from .ifelse_transformer import (
IfElseTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.logical_transformer import (
LogicalTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.loop_transformer import (
from .loop_transformer import (
LoopTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.print_transformer import (
PrintTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.return_transformer import (
from .return_transformer import (
ReturnTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.create_variable_transformer import (
......
python/paddle/jit/dy2static/convert_call_func.py
浏览文件 @ d926b30b
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2022 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.
......@@ -12,8 +12,330 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from ...fluid.dygraph.dygraph_to_static.convert_call_func import ( # noqa: F401
convert_call,
import collections
import copy
import functools
import logging
import inspect
import pdb
import re
import types
import numpy
import builtins
from paddle.fluid.dygraph.container import Sequential
from .convert_operators import (
convert_len,
convert_zip,
convert_range,
convert_enumerate,
)
from paddle.fluid.dygraph.dygraph_to_static.logging_utils import (
TranslatorLogger,
)
__all__ = []
from paddle.fluid.dygraph.dygraph_to_static.utils import is_paddle_func, unwrap
from paddle.fluid.dygraph.layers import Layer
__all__ = ["convert_call"]
# The api(s) should be considered as plain function and convert
# them into static layer code.
PADDLE_NEED_CONVERT_APIS = [Sequential]
translator_logger = TranslatorLogger()
CONVERSION_OPTIONS = "An attribute for a function that indicates conversion flags of the function in dynamic-to-static."
class ConversionOptions:
"""
A container for conversion flags of a function in dynamic-to-static.
Attributes:
not_convert(bool): An attribute indicates that the function won't be converted in dynamic-to-static.
NOTE(liym27): More attributes and methods can be added in this class.
"""
def __init__(self, not_convert=False):
self.not_convert = not_convert
def is_builtin(func, name=None):
"""predict whether a function is a builtin function with name={name}.
if name == None, then any builtin function will return True
"""
def name_judge():
return name is None or func.__name__ == name
if isinstance(func, types.BuiltinFunctionType) and name_judge():
return True
elif func in builtins.__dict__.values() and name_judge():
return True
else:
return False
def builtin_modules():
"""
Return builtin modules.
"""
modules = [
collections,
pdb,
copy,
inspect,
re,
numpy,
logging,
]
try:
import six
modules.append(six)
except ImportError:
pass # do nothing
return modules
BUILTIN_LIKELY_MODULES = builtin_modules()
def is_unsupported(func):
"""
Checks whether the func is supported by dygraph to static graph.
"""
for m in BUILTIN_LIKELY_MODULES:
for v in m.__dict__.values():
func_in_dict = func == v
if isinstance(func_in_dict, (list, numpy.ndarray)):
func_in_dict = numpy.array(func_in_dict).any()
if func_in_dict:
translator_logger.log(
2,
"Whitelist: {} is part of built-in module and does not have to be transformed.".format(
func
),
)
return True
# NOTE: should be placed before `is_paddle_func`
if type(func) in PADDLE_NEED_CONVERT_APIS:
return False
if is_paddle_func(func):
translator_logger.log(
2,
"Whitelist: {} is part of Paddle module and does not have to be transformed.".format(
func
),
)
return True
def convert_call(func):
"""
Converts a function call which needs to be transformed to static function.
Args:
func (callable): A callable function or method to convert.
Returns:
Callable: A converted function.
Examples:
.. code-block:: python
import paddle
from paddle.jit.dy2static import Call
paddle.enable_static()
def dyfunc(x):
if paddle.mean(x) < 0:
x_v = x - 1
else:
x_v = x + 1
return x_v
new_func = Call(dyfunc)
x = paddle.tensor.manipulation.fill_constant(shape=[3, 3], value=0, dtype='float64')
x_v = new_func(x)
exe = paddle.static.Executor(paddle.CPUPlace())
out = exe.run(fetch_list=[x_v])
print(out[0])
# [[1. 1. 1.]
# [1. 1. 1.]
# [1. 1. 1.]]
"""
# NOTE(Aurelius84): Fix it after all files migrating into jit.
from paddle.jit.dy2static.program_translator import (
convert_to_static,
unwrap_decorators,
StaticFunction,
)
translator_logger.log(
1, "Convert callable object: convert {}.".format(func)
)
func_self = None
converted_call = None
# Function in convert_call may be decorated by another `@to_static`,
# in this case, unwraps it into a raw method or function.
_, func = unwrap_decorators(func)
options = getattr(func, CONVERSION_OPTIONS, None)
if options is not None and options.not_convert:
translator_logger.log(
2,
"{} is not converted when it is decorated by 'paddle.jit.not_to_static'.".format(
func
),
)
return func
if is_builtin(func, "len"):
return convert_len
if is_builtin(func, "zip"):
return convert_zip
if is_builtin(func, "range"):
return convert_range
if is_builtin(func, "enumerate"):
return convert_enumerate
if is_builtin(func) or is_unsupported(func):
return func
if inspect.isgeneratorfunction(func):
# NOTE(xiongkun03): inspect.isfunction() will return True even though func is a generator function.
# If we don't deal generatorfunction here, we will regard it as normal function and get errors in some
# occasion.
number_of_stars = 30
translator_logger.warn(
"\n\n"
+ "*" * number_of_stars
+ "\nYour function:`{}` doesn't support to transform to static function because it is a generator function, it will be run as-is.".format(
func.__name__
)
+ "\n"
+ "*" * number_of_stars
+ "\n\n"
)
return func
if inspect.isfunction(func):
# TODO(liym27): If func is a lambda function, special conversion is needed.
if func.__name__ == '<lambda>':
return func
try:
# Note(Aurelius84): Because `@declarative` returns a class instance instead of
# a function. This will modify the value referring to itself in `__globals__`.
# For example:
#
# @declarative
# def foo(x):
# return x
#
# `foo` will be converted into a wrapper class, suppose as `StaticFunction`.
# And `foo.__globals__['foo']` will still return this `StaticFunction` instead of
# `foo` function. So `isinstance(fn, StaticFunction)` is added here.
_origfunc = unwrap(func)
global_functions = set()
for fn in _origfunc.__globals__.values():
if inspect.isfunction(fn):
global_functions.add(fn)
elif isinstance(fn, StaticFunction):
_, fn = unwrap_decorators(fn)
global_functions.add(fn)
elif inspect.isclass(fn):
if isinstance(
fn.__dict__.get(func.__name__, None), staticmethod
):
global_functions.add(
func
) # Add func to ensure that we will convert
if func in global_functions:
converted_call = convert_to_static(func)
func_self = getattr(func, '__self__', None)
else:
# NOTE:
# If func is not in __globals__, it does not need to be transformed
# because it has been transformed before.
translator_logger.warn(
"{} doesn't have to be transformed to static function because it has been transformed before, it will be run as-is.".format(
func
)
)
converted_call = func
except AttributeError:
# NOTE:
# If func is not in __globals__, it does not need to be transformed
# because it has been transformed before.
converted_call = None
except (IOError, OSError):
# NOTE:
# If func has been decorated, its source code can not be get
# so that it can not be transformed to static function.
converted_call = None
elif inspect.ismethod(func):
try:
converted_call = convert_to_static(func)
func_self = getattr(func, '__self__', None)
except (IOError, OSError):
# NOTE: func may have been decorated.
converted_call = None
elif hasattr(func, '__class__') and hasattr(func.__class__, '__call__'):
if hasattr(func, 'forward') and isinstance(func, Layer):
try:
_, forward_func = unwrap_decorators(func.forward)
func._original_funcs['forward'] = forward_func.__func__
forward_func = convert_to_static(forward_func)
# Bound mothod will be convert into plain function after `convert_to_static`.
# So descriptor mechanism is used to bound `self` instance on function to
# keep it as bound method.
setattr(func, 'forward', forward_func.__get__(func))
except (IOError, OSError, TypeError):
# NOTE: func.forward may have been decorated.
func_self = None if func_self else func_self
converted_call = func
else:
try:
call_func = func.__class__.__call__
converted_call = convert_to_static(call_func)
func_self = func
except (IOError, OSError, TypeError):
# NOTE:
# If `func` is a class which is being initialized, for example `convert_call(Foo)()`,
# it doesn't need to be transformed
func_self = None if func_self else func_self
else:
raise NotImplementedError(
"Callable {} can not be transformed at present.".format(func)
)
if converted_call is None:
translator_logger.warn(
"{} doesn't have to be transformed to static function, and it will be run as-is.".format(
func
)
)
return func
if func_self:
converted_call = functools.partial(converted_call, func_self)
return converted_call
python/paddle/jit/dy2static/convert_operators.py
浏览文件 @ d926b30b
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2022 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.
......@@ -12,23 +12,826 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from ...fluid.dygraph.dygraph_to_static.convert_operators import ( # noqa: F401
cast_bool_if_necessary,
convert_assert,
convert_ifelse,
convert_len,
convert_logical_and,
convert_logical_not,
convert_logical_or,
convert_pop,
convert_print,
convert_shape_compare,
convert_var_dtype,
convert_shape,
convert_while_loop,
unpack_by_structure,
indexable,
convert_attr,
import re
import paddle
from paddle.fluid.data_feeder import convert_dtype
from paddle.fluid.dygraph.dygraph_to_static.variable_trans_func import (
to_static_variable,
)
from paddle.fluid.framework import core, Variable
from paddle.fluid.layers import Assert, Print
from paddle.fluid.layers import (
array_length,
array_read,
array_write,
create_array,
)
from paddle.fluid.layers import (
assign,
fill_constant,
reduce_all,
reduce_any,
)
from paddle.fluid.layers import (
cast,
control_flow,
nn,
)
from paddle.fluid.layers.control_flow import (
cond,
while_loop,
less_than,
increment,
)
from .return_transformer import (
RETURN_NO_VALUE_VAR_NAME,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import (
UndefinedVar,
Dygraph2StaticException,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import GetterSetterHelper
from paddle.fluid.layers.utils import copy_mutable_vars
def convert_attr(x, attr):
if isinstance(x, Variable) and attr == "size":
return x.size()
else:
return getattr(x, attr)
def indexable(x, code=None):
if isinstance(x, Variable):
return x
if hasattr(x, '__len__') and hasattr(x, '__getitem__'):
return x
if hasattr(x, '__iter__'):
return [i for i in x]
else:
raise RuntimeError("X can't be convert into indexable.")
def unpack_by_structure(target, structure):
"""unified unpack interface for paddle and python."""
if isinstance(target, Variable):
return _unpack_by_structure_paddle(target, structure)
else:
return _unpack_by_structure_python(target, structure)
def _unpack_by_structure_python(target, structure):
"""TODO(xiongkun): analysis the differences between python and paddle unpack."""
return _unpack_by_structure_paddle(target, structure)
def _unpack_by_structure_paddle(target, structure):
if structure == 1:
return target
ret = []
for idx, ele in enumerate(structure):
if ele == 1:
ret.append(target[idx])
continue
if isinstance(ele, list):
ret.append(unpack_by_structure(target[idx], ele))
continue
assert False, "structure element must be 1 or list"
return ret
def convert_while_loop(
cond, body, getter, setter, return_name_ids=None, push_pop_names=None
):
"""
A function representation of a Python ``while`` statement.
Args:
cond(Callable): A callable object that returns a boolean variable to control whether to execute the loop body. It takes ``loop_vars`` as arguments.
body(Callable): A callable object that returns a tuple or list of variables with the same arguments ``loops_vars`` as ``cond`` .
get_args(callable): Get all arguments that needed in true_fn and false_fn.
set_args(callable): Update arguments that modified in trure_fn and false_fn.
return_name_ids(list[string], optional): the returned names.
push_pop_names(list[string], optional): the names on which called .append() or .pop().
Returns:
A list or tuple of variables which returned by ``body``.
"""
# NOTE: It may be slower if cond is very expensive, but usually cond is just O(1).
# If loop_vars is changed during cond callable, then it causes bug, but current logical_and/logical_not/... doesn't change the loop_vars.
pred = cond()
if isinstance(pred, Variable):
_run_paddle_while(
cond, body, getter, setter, return_name_ids, push_pop_names
)
else:
_run_py_while(cond, body, getter, setter)
def _convert_tensor_arrray_if_necessary(setterhelper, push_pop_names):
push_pop_vars = setterhelper.get(push_pop_names)
if push_pop_vars is None:
return
def maybe_to_tensor_array(v):
if isinstance(v, list):
return create_array("float32", initialized_list=v)
else:
return v
setterhelper.set(
push_pop_names, [maybe_to_tensor_array(v) for v in push_pop_vars]
)
def _run_paddle_while(
cond, body, getter, setter, return_name_ids, push_pop_names
):
# NOTE: loop_vars of Paddle op `control_flow.while_loop` must be Paddle Tensors.
helper = GetterSetterHelper(getter, setter, return_name_ids, push_pop_names)
_convert_tensor_arrray_if_necessary(helper, push_pop_names)
def new_body_fn(*args):
"""wrap the body() and add return value for `while_loop`
the args may be differ from getter().
"""
mutable_loop_vars = args
helper.set(return_name_ids, mutable_loop_vars)
body()
return helper.get(return_name_ids)
def new_cond_fn(*args):
"""cond is a zero-args function, which is not
compatible with `while_loop`.
"""
return cond()
# UndefinedVar will become data layer not check variable with value=NO_VALUE_MAGIC.
loop_vars = [
to_static_variable(var) if not isinstance(var, UndefinedVar) else var
for var in helper.get(return_name_ids)
]
helper.set(
return_name_ids, loop_vars
) # change the non-local var to variable
# variable maybe modified to inner var. change it into
loop_vars = control_flow.while_loop(new_cond_fn, new_body_fn, loop_vars)
helper.set(return_name_ids, loop_vars)
return loop_vars
def _run_py_while(cond, body, getter, setter):
while True:
pred = cond()
if isinstance(pred, Variable):
raise Dygraph2StaticException(
"python while pred change from bool to variable."
)
if not pred:
break
body()
def convert_logical_and(x_func, y_func):
"""
A function representation of a Python ``and`` statement.
Args:
x_func(callable): x_func() is the left hand operand of ``and`` operator. x_func() is bool or Tensor.
y_func(callable): y_func() is the right hand operand of ``and`` operator. y_func() is bool or Tensor.
Returns:
A python bool variable or a bool Tensor.
NOTE(liym27):
1) The operands are executed sequentially according to the running logic of Python. So here the arguments
should be callable.
2) If the left hand operand is False, the right hand operand should be executed.
For example:
a = x > 1 and y < 1
Transformed code:
a = paddle.jit.dy2static.convert_logical_and(lambda:x>1, lambda:y<1)
In `convert_logical_and(lambda:x>1, lambda:y<1)`, `lambda:y<1` must be run after `lambda:x>1`. And
if `x>1` is False, `y<1` should NOT be run.
"""
x_value = x_func()
if not isinstance(x_value, Variable):
return _run_py_logical_and(lambda: x_value, y_func)
y_value = y_func()
if not isinstance(y_value, Variable):
return _run_py_logical_and(lambda: y_value, lambda: x_value)
return _run_paddle_logical_and(x_value, y_value)
def _run_paddle_logical_and(x, y):
x = cast_bool_if_necessary(x)
y = cast_bool_if_necessary(y)
return paddle.logical_and(x, y)
def _run_py_logical_and(x_func, y_func):
x_value = x_func()
assert not isinstance(x_value, Variable)
# NOTE(liym27):
# 1. Returns y_func() if x_value is False;
# 2. If x_value is False, y_func() should not be run.
return x_value and y_func()
def convert_logical_or(x_func, y_func):
"""
A function representation of a Python ``or`` statement.
Args:
x_func(callable): x_func() is the left hand operand of ``or`` operator. x_func() is bool or Tensor.
y_func(callable): y_func() is the right hand operand of ``or`` operator. y_func() is bool or Tensor.
Returns:
A python bool variable or a bool Tensor.
NOTE(liym27):
1) The operands are executed sequentially according to the running logic of Python. So here the arguments
should be callable.
2) If the left hand operand is True, the right hand operand should be executed.
For example:
a = x > 1 or y < 1
Transformed code:
a = paddle.jit.dy2static.convert_logical_or(lambda:x>1, lambda:y<1)
In `convert_logical_or(lambda:x>1, lambda:y<1)`, `lambda:y<1` must be run after `lambda:x>1`. And
if `x>1` is True, `y<1` should NOT be run.
"""
x_value = x_func()
if not isinstance(x_value, Variable):
return _run_py_logical_or(lambda: x_value, y_func)
y_value = y_func()
if not isinstance(y_value, Variable):
return _run_py_logical_or(lambda: y_value, lambda: x_value)
return _run_paddle_logical_or(x_value, y_value)
def _run_paddle_logical_or(x, y):
x = cast_bool_if_necessary(x)
y = cast_bool_if_necessary(y)
return paddle.logical_or(x, y)
def _run_py_logical_or(x_func, y_func):
x_value = x_func()
assert not isinstance(x_value, Variable)
# NOTE(liym27):
# 1. Returns y_func() if x_value is False;
# 2. If x_value is True, y_func() should not be run.
return x_value or y_func()
def convert_logical_not(x):
"""
A function representation of a Python ``not`` statement.
Args:
x(bool|Tensor): Operand of ``not`` operator.
Returns:
A python bool variable or a bool Tensor.
"""
if isinstance(x, Variable):
return _run_paddle_logical_not(x)
else:
return _run_py_logical_not(x)
def _run_paddle_logical_not(x):
x = cast_bool_if_necessary(x)
return paddle.logical_not(x)
def _run_py_logical_not(x):
return not x
def convert_ifelse(
pred,
true_fn,
false_fn,
get_args,
set_args,
return_name_ids,
push_pop_names=None,
):
"""
A function representation of a Python ``if/else`` statement.
Args:
pred(bool|Tensor): A boolean Tensor which determines whether to return the result of ``true_fn`` or ``false_fn`` .
true_fn(callable): A callable to be performed if ``pred`` is true.
false_fn(callable): A callable to be performed if ``pred`` is false.
get_args(callable): Get all arguments that needed in true_fn and false_fn.
set_args(callable): Update arguments that modified in trure_fn and false_fn.
return_name_ids(list[string], optional): the returned names.
push_pop_names(list[string], optional): the names on which called .append() or .pop().
Returns:
``true_fn()`` if the predicate ``pred`` is true else ``false_fn()`` .
"""
if isinstance(pred, Variable):
out = _run_paddle_cond(
pred,
true_fn,
false_fn,
get_args,
set_args,
return_name_ids,
push_pop_names,
)
else:
out = _run_py_ifelse(
pred, true_fn, false_fn, get_args, set_args, return_name_ids
)
return out
def _run_paddle_cond(
pred, true_fn, false_fn, get_args, set_args, return_name_ids, push_pop_names
):
"""
Paddle cond API will evaluate both true_fn and false_fn codes.
"""
helper = GetterSetterHelper(
get_args, set_args, return_name_ids, push_pop_names
)
_convert_tensor_arrray_if_necessary(helper, push_pop_names)
pred = cast_bool_if_necessary(pred)
init_args = helper.get(return_name_ids)
def new_true_fn():
# init args may contain mutable python container like [var, 2], we copy then like in while_loop
helper.set(return_name_ids, copy_mutable_vars(init_args))
ret = true_fn()
# IfExpr will return a non-None return value, so we just return ret.
# We assume normal return has no return value.
if ret is None:
return helper.get(return_name_ids)
else:
return ret
def new_false_fn():
# init args may contain mutable python container like [var, 2], we copy then like in while_loop
helper.set(return_name_ids, copy_mutable_vars(init_args))
ret = false_fn()
if ret is None:
return helper.get(return_name_ids)
else:
return ret
try:
cond_outs = control_flow.cond(
pred, new_true_fn, new_false_fn, None, return_name_ids
)
except Exception as e:
if re.search(
"Unsupported return type of true_fn and false_fn in cond", str(e)
):
raise Dygraph2StaticException(
"Your if/else have different return type. TODO: add link to modifty. {}".format(
str(e)
)
)
if re.search("Incompatible return values of", str(e)):
raise Dygraph2StaticException(
"Your if/else have different number of return value. TODO: add link to modifty. {}".format(
str(e)
)
)
raise e
get_args = lambda: helper.get(return_name_ids)
set_args = lambda vs: helper.set(return_name_ids, vs)
return _recover_args_state(cond_outs, get_args, set_args, return_name_ids)
def _run_py_ifelse(
pred, true_fn, false_fn, get_args, set_args, return_name_ids
):
"""
Evaluate python original branch function if-else.
"""
py_outs = true_fn() if pred else false_fn()
return py_outs
def _remove_no_value_return_var(out):
if isinstance(out, tuple) and len(out) > 0:
processed_out = out
align_ret = out[0]
if isinstance(align_ret, tuple):
for index, item in enumerate(align_ret):
if isinstance(item, Variable) and (
RETURN_NO_VALUE_VAR_NAME in item.name
):
# return None
if index == 0:
processed_out = (None,) + out[1:]
elif index == 1:
processed_out = align_ret[:1] + out[1:]
else:
processed_out = (align_ret[:index],) + out[1:]
break
for index, item in enumerate(processed_out):
if isinstance(item, Variable) and (
RETURN_NO_VALUE_VAR_NAME in item.name
):
processed_out = processed_out[:index]
if not processed_out:
return None
elif len(processed_out) == 1:
return processed_out[0]
else:
return processed_out
else:
return out
def _check_no_undefined_var(outs, names, branch_name):
if names is None:
return
if not isinstance(outs, (list, tuple)):
outs = [outs]
for var, name in zip(list(outs), names):
if isinstance(var, UndefinedVar):
raise ValueError(
"Required '{}' must be initialized both in if-else branch, but found it not initialized in '{}'.".format(
name, branch_name
)
)
def _recover_args_state(outs, get_args, set_args, return_name_ids):
"""
Currently we support variant length of early return statement by padding
_no_return_value.
# TODO(dev): We shall consider to evaluate whether should support this for Python if-else?
"""
# IfExpr's return_name_ids maybe None
if return_name_ids is None:
return outs
init_args = get_args()
# recover args state
num_outs = len(return_name_ids)
num_args = len(init_args)
assert num_outs <= num_args
if num_args == 1:
final_outs = (
(outs,) if not isinstance(outs, (list, tuple)) else tuple(outs)
)
else:
outs = (outs,) if num_outs == 1 else tuple(outs)
final_outs = outs + init_args[num_outs:]
set_args(final_outs)
return final_outs
def convert_len(var):
"""
Returns variable(length) from shape ops based on var.type
Note: In addition to some ast transformations, some block-related
operations are added in `len` transformation, such as appending
`shape_op` in var.block.
"""
if isinstance(var, Variable):
assert var.ndim > 0, "len() of a 0D tensor is wrong"
if var.type in [
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.SELECTED_ROWS,
]:
# Note: Length of var may be known ahead of time in dygraph,
# but it probably represents batch size which can be variant.
# so we return a variable dynamically inferred from var.shape.
if var.shape[0] > 0 and var.type == core.VarDesc.VarType.LOD_TENSOR:
return var.shape[0]
return nn.shape(var)[0]
elif var.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
return control_flow.array_length(var)
else:
raise TypeError(
'len(var) only supports LoDTensor/LoDTensorArray/SelectedRows, but received %s.'
% type(var)
)
else:
if isinstance(var, VariableTuple):
return var.__len__()
return len(var)
def convert_zip(*args):
for i, arg in enumerate(args):
if isinstance(arg, Variable) and arg.shape[0] == -1:
raise RuntimeError(
"Not support zip(tensor, ...) when tensor.shape[0] == -1, "
"but found args[{}].shape[0] == -1 in 'zip'".format(str(i))
)
return zip(*args)
# TODO(xiongkun): delete when list<variable> is ready.
class VariableTuple:
"""
this class will cause enumerate can't be wrapped by other iterator change function.
this will be fixed when list<Variable> is producted.
VariableTuple can only deal with variables which is fixed.
"""
def __init__(self, var, start=0):
self.var = var
self.len = convert_len(var)
if isinstance(self.len, Variable):
self.rag = paddle.arange(start, start + self.len, 1, paddle.int64)
else:
self.rag = range(start, start + self.len)
def __getitem__(self, idx):
return self.rag[idx], self.var[idx]
def __len__(self):
return self.len
def convert_enumerate(*args):
has_variable = any(map(lambda x: isinstance(x, Variable), args))
if has_variable:
return VariableTuple(*args)
return enumerate(*args)
def convert_range(*args):
has_variable = any(map(lambda x: isinstance(x, Variable), args))
if has_variable:
if len(args) == 1:
return paddle.arange(0, args[0], 1, paddle.int64)
if len(args) == 2:
return paddle.arange(args[0], args[1], 1, paddle.int64)
if len(args) == 3:
return paddle.arange(args[0], args[1], args[2], paddle.int64)
return range(*args)
def convert_shape(x):
"""
A function representation of the shape of variable.
"""
def has_negative(list_shape):
return any([x < 0 for x in list_shape])
# When `x` is Variable:
# (1) if x.shape contains -1, such as [2, -1, 64], returns [2, var, 64],
# where var = paddle.shape(x)[1]
# (2) if x.shape does not contains -1, return lsit(x.shape) directly
if isinstance(x, Variable):
values = list(x.shape)
if has_negative(values):
shape_tensor = nn.shape(x)
for i, v in enumerate(values):
if v is None or v < 0:
values[i] = shape_tensor[i]
return values
else:
return x.shape
def convert_shape_compare(left, *args):
"""
A function handles comparison difference between Paddle and Python.
For example, if x and y are Tensors, x.shape == y.shape will return single
boolean Value (True/False). However, paddle.shape(x) == paddle.shape(y) is
an element-wise comparison. The difference can cause dy2stat error. So we
create this function to handle the difference.
Args:
left: variable
*args: compare_op(str), variable, compare_op(str), variable, where
compare_op means "<", ">", "==", "!=", etc.
Returns:
If the variables to compare are NOT Paddle Variables, we will return as
Python like "a op1 b and b op2 c and ... ".
If the variables to compare are Paddle Variables, we will do elementwise
comparsion first and then reduce to a boolean whose numel is 1.
"""
args_len = len(args)
assert (
args_len >= 2
), "convert_shape_compare needs at least one right compare variable"
assert (
args_len % 2 == 0
), "Illegal input for convert_shape_compare, *args should be op(str), var, op(str), var ..."
num_cmp = args_len // 2
if isinstance(left, Variable):
def reduce_compare(x, op_str, y):
element_wise_result = eval("x " + op_str + " y")
if op_str == "!=":
return reduce_any(element_wise_result)
elif (
op_str == "is"
or op_str == "is not"
or op_str == "in"
or op_str == "not in"
):
return element_wise_result
else:
return reduce_all(element_wise_result)
final_result = reduce_compare(left, args[0], args[1])
for i in range(1, num_cmp):
cmp_left = args[i * 2 - 1]
cmp_op = args[i * 2]
cmp_right = args[i * 2 + 1]
cur_result = reduce_compare(cmp_left, cmp_op, cmp_right)
final_result = convert_logical_and(
lambda: final_result, lambda: cur_result
)
return final_result
else:
cmp_left = left
final_result = None
for i in range(num_cmp):
cmp_op = args[i * 2]
cmp_right = args[i * 2 + 1]
cur_result = eval("cmp_left " + cmp_op + " cmp_right")
if final_result is None:
final_result = cur_result
else:
final_result = final_result and cur_result
if final_result is False:
return False
cmp_left = cmp_right
return final_result
def cast_bool_if_necessary(var):
assert isinstance(var, Variable)
if convert_dtype(var.dtype) not in ['bool']:
var = cast(var, dtype="bool")
return var
def convert_var_dtype(var, dtype):
if isinstance(var, Variable):
src_dtype = convert_dtype(var.dtype)
assert src_dtype in [
'bool',
'float16',
'float32',
'float64',
'int32',
'int64',
'uint8',
], "The dtype of var {} is {}, which is not supported in the cast op.".format(
var.name, src_dtype
)
assert dtype in [
'bool',
'int',
'float',
], "The casted target dtype is {}, which is not supported in type casting.".format(
dtype
)
cast_map = {
'bool': 'bool',
'int': 'int32',
'float': 'float32',
}
return cast(var, dtype=cast_map[dtype])
else:
return eval('{}(var)'.format(dtype))
def convert_assert(cond, message=""):
"""
A function representation of a Python ``assert`` statement.
"""
if isinstance(cond, Variable):
cond = cast(cond, "bool")
# NOTE: message is not used because Paddle Assert has no corresponding parameter to use.
return Assert(cond)
else:
assert cond, message
def convert_print(*args):
"""
A function representing Python ``print`` statement. Note: this is a basic
python function so we haven't handle sep, end, file and flush parameters of
python function.
"""
for var in args:
if isinstance(var, Variable):
var = Print(var)
else:
print(var)
def convert_pop(target, *args):
"""
A function representation of a Python pop statement for a list or dict.
Args:
target(list|dict|Tensor): A variable to pop item from.
*args(tuple): index or default value to parse.
Returns:
A item poped from target.
"""
is_variable = isinstance(target, Variable)
if is_variable:
is_tensor_array = target.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY
if is_variable and is_tensor_array:
return _run_paddle_pop(target, *args)
else:
return _run_python_pop(target, *args)
def _run_paddle_pop(array, *args):
if len(args) == 0:
idx = -1
else:
idx = args[0]
assert isinstance(idx, int)
def cond(i, new_array):
return less_than(i, arr_len)
def body(i, new_array):
item = array_read(array=array, i=i)
array_write(item, array_length(new_array), new_array)
i = increment(i)
return i, new_array
arr_len = array_length(array)
if idx < 0:
idx = idx + arr_len
else:
idx = fill_constant(shape=[1], dtype="int64", value=idx)
pop_item = array_read(array, idx)
new_array = _slice_tensor_array(array, 0, idx)
i = idx + 1
_, new_array = while_loop(cond, body, [i, new_array])
assign(input=new_array, output=array)
return pop_item
# TODO(liym27): A better way to slice tensor array.
# Maybe support start == end for slice op.
def _slice_tensor_array(array, start, end):
def true_fn():
null_array = create_array("float32")
return null_array
def false_fn(array, start, end):
new_array = paddle.slice(array, starts=[start], ends=[end], axes=[0])
return new_array
new_array = cond(start == end, true_fn, lambda: false_fn(array, start, end))
return new_array
def _run_python_pop(target, *args):
# 1. pop for a dict
if len(args) == 2:
idx, default = args
return target.pop(idx, default)
__all__ = []
# 2. pop for a list or dict
else:
idx = args[0] if args else -1
return target.pop(idx)
python/paddle/fluid/dygraph/dygraph_to_static/early_return_transformer.py → python/paddle/jit/dy2static/early_return_transformer.py
浏览文件 @ d926b30b
......@@ -20,6 +20,8 @@ from paddle.fluid.dygraph.dygraph_to_static.base_transformer import (
BaseTransformer,
)
__all__ = ['EarlyReturnTransformer']
class EarlyReturnTransformer(BaseTransformer):
"""
......
python/paddle/fluid/dygraph/dygraph_to_static/ifelse_transformer.py → python/paddle/jit/dy2static/ifelse_transformer.py
浏览文件 @ d926b30b
......@@ -13,7 +13,6 @@
# limitations under the License.
import copy
import textwrap
from collections import defaultdict
# gast is a generic AST to represent Python2 and Python3's Abstract Syntax Tree(AST).
......@@ -28,18 +27,11 @@ from paddle.fluid.dygraph.dygraph_to_static.utils import (
ast_to_source_code,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import (
create_assign_node,
FunctionNameLivenessAnalysis,
)
from paddle.fluid.dygraph.dygraph_to_static.static_analysis import (
StaticAnalysisVisitor,
)
from paddle.fluid.dygraph.dygraph_to_static.static_analysis import (
AstNodeWrapper,
)
from paddle.fluid.dygraph.dygraph_to_static.variable_trans_func import (
create_undefined_var,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import (
create_nonlocal_stmt_nodes,
)
......@@ -65,6 +57,8 @@ from paddle.fluid.dygraph.dygraph_to_static.utils import (
create_name_str,
)
__all__ = ['IfElseTransformer']
TRUE_FUNC_PREFIX = 'true_fn'
FALSE_FUNC_PREFIX = 'false_fn'
GET_ARGS_FUNC_PREFIX = 'get_args'
......
python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py → python/paddle/jit/dy2static/loop_transformer.py
浏览文件 @ d926b30b
......@@ -25,11 +25,7 @@ from paddle.fluid.dygraph.dygraph_to_static.static_analysis import (
StaticAnalysisVisitor,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_source_code
from paddle.fluid.dygraph.dygraph_to_static.utils import generate_name_node
from paddle.fluid.dygraph.dygraph_to_static.utils import get_attribute_full_name
from paddle.fluid.dygraph.dygraph_to_static.variable_trans_func import (
create_undefined_var,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import (
create_nonlocal_stmt_nodes,
create_get_args_node,
......@@ -38,13 +34,10 @@ from paddle.fluid.dygraph.dygraph_to_static.utils import (
from paddle.fluid.dygraph.dygraph_to_static.utils import (
FunctionNameLivenessAnalysis,
)
from paddle.fluid.dygraph.dygraph_to_static.ifelse_transformer import ARGS_NAME
from .ifelse_transformer import ARGS_NAME
from paddle.fluid.dygraph.dygraph_to_static.base_transformer import (
BaseTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.base_transformer import (
RenameTransformer,
)
from paddle.fluid.dygraph.dygraph_to_static.base_transformer import (
ForLoopTuplePreTransformer,
)
......@@ -217,7 +210,7 @@ class NameVisitor(gast.NodeVisitor):
# If this var is a basic variable and read-only and not
# condition var, it may not be loop_var else it should
# be in loop_var as input
if (not name in condition_names) and (not name in write_names):
if (name not in condition_names) and (name not in write_names):
continue
loop_var_names.add(name)
......
python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py → python/paddle/jit/dy2static/partial_program.py
浏览文件 @ d926b30b
......@@ -23,7 +23,7 @@ from paddle.fluid.executor import (
from paddle.fluid.dygraph import layers
from paddle.fluid.dygraph.base import switch_to_static_graph
from paddle.fluid.dygraph.dygraph_to_static import logging_utils
from paddle.fluid.dygraph.dygraph_to_static.return_transformer import (
from .return_transformer import (
RETURN_NO_VALUE_MAGIC_NUM,
)
from paddle.fluid.layers.utils import flatten
......
python/paddle/jit/dy2static/program_translator.py
浏览文件 @ d926b30b
......@@ -37,7 +37,7 @@ from paddle.fluid.dygraph.dygraph_to_static.origin_info import (
from paddle.fluid.dygraph.dygraph_to_static.origin_info import (
update_op_callstack_with_origin_info,
)
from paddle.fluid.dygraph.dygraph_to_static.partial_program import (
from .partial_program import (
partial_program_from,
)
from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_func
......
python/paddle/fluid/dygraph/dygraph_to_static/return_transformer.py → python/paddle/jit/dy2static/return_transformer.py
浏览文件 @ d926b30b
......@@ -16,12 +16,9 @@ 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 (
from .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
from paddle.fluid.dygraph.dygraph_to_static.base_transformer import (
BaseTransformer,
......
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