3RNN/Lib/site-packages/tensorflow/python/autograph/converters/call_trees.py
2024-05-26 19:49:15 +02:00

222 lines
7.2 KiB
Python

# Copyright 2016 The TensorFlow 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.
# ==============================================================================
"""Handles function calls, by generating compiled function names and calls.
Note: this transformer does not rename the top level object being converted;
that is the caller's responsibility.
Requires function_scopes.
"""
import gast
from tensorflow.python.autograph.core import converter
from tensorflow.python.autograph.pyct import anno
from tensorflow.python.autograph.pyct import parser
from tensorflow.python.autograph.pyct import qual_names
from tensorflow.python.autograph.pyct import templates
from tensorflow.python.autograph.utils import ag_logging
# TODO(mdan): Rename to FunctionCallsTransformer.
class _Function(object):
no_root = True
def __init__(self):
self.context_name = None
set_trace_warned = False
class _ArgTemplateBuilder(object):
"""Constructs a tuple representing the positional arguments in a call.
Example (yes, it's legal Python 3):
f(*args1, b, *args2, c, d) -> args1 + (b,) + args2 + (c, d)
"""
def __init__(self):
self._arg_accumulator = []
self._argspec = []
self._finalized = False
def _consume_args(self):
if self._arg_accumulator:
self._argspec.append(
gast.Tuple(elts=self._arg_accumulator, ctx=gast.Load()))
self._arg_accumulator = []
def add_arg(self, a):
self._arg_accumulator.append(a)
def add_stararg(self, a):
self._consume_args()
self._argspec.append(
gast.Call(
gast.Name(
'tuple', ctx=gast.Load(), annotation=None, type_comment=None),
args=[a],
keywords=()))
def finalize(self):
self._consume_args()
self._finalized = True
def to_ast(self):
assert self._finalized
if self._argspec:
result = self._argspec[0]
for i in range(1, len(self._argspec)):
result = gast.BinOp(result, gast.Add(), self._argspec[i])
return result
return gast.Tuple([], gast.Load())
class CallTreeTransformer(converter.Base):
"""Transforms the call tree by renaming transformed symbols."""
def visit_Lambda(self, node):
if not anno.hasanno(node, 'function_context_name'):
# Lambda functions created during the conversion process have no
# context manager.
return self.generic_visit(node)
with self.state[_Function] as fn_scope:
fn_scope.context_name = anno.getanno(node, 'function_context_name')
return self.generic_visit(node)
def visit_FunctionDef(self, node):
# Decorators and arg defaults are part of the outer scope.
node.decorator_list = self.visit_block(node.decorator_list)
node.args.defaults = self.visit_block(node.args.defaults)
for i, d in enumerate(node.args.kw_defaults):
if d is not None:
node.args.kw_defaults[i] = self.visit(d)
with self.state[_Function] as fn_scope:
# Note: if the conversion process ever creates helper functions, this
# assumption will no longer hold.
assert anno.hasanno(node, 'function_context_name'), (
'The function_scopes converter always creates a scope for functions.')
fn_scope.context_name = anno.getanno(node, 'function_context_name')
node.body = self.visit_block(node.body)
if node.returns:
node.returns = self.visit(node.returns)
return node
def visit_With(self, node):
# Context manager calls (in node.items) are not converted.
node.body = self.visit_block(node.body)
return node
def _args_to_tuple(self, node):
"""Ties together all positional and *arg arguments in a single tuple."""
# TODO(mdan): We could rewrite this to just a call to tuple(). Maybe better?
# For example for
# f(a, b, *args)
# instead of writing:
# (a, b) + args
# just write this?
# tuple(a, b, *args)
builder = _ArgTemplateBuilder()
for a in node.args:
if isinstance(a, gast.Starred):
builder.add_stararg(a.value)
else:
builder.add_arg(a)
builder.finalize()
return builder.to_ast()
def _kwargs_to_dict(self, node):
"""Ties together all keyword and **kwarg arguments in a single dict."""
if node.keywords:
return gast.Call(
gast.Name(
'dict', ctx=gast.Load(), annotation=None, type_comment=None),
args=(),
keywords=node.keywords)
else:
return parser.parse_expression('None')
def visit_Call(self, node):
full_name = str(anno.getanno(node.func, anno.Basic.QN, default=''))
function_context_name = self.state[_Function].context_name
node = self.generic_visit(node)
# TODO(mdan): Refactor converted_call as a 'Call' operator.
# Calls to the internal 'ag__' module are never converted (though their
# arguments might be).
if full_name.startswith('ag__.'):
return node
# Calls to the function context manager (inserted by function_scopes) are
# also safe.
if full_name.startswith(function_context_name + '.'):
return node
# Calls to pdb.set_trace or ipdb.set_trace are never converted. We don't use
# the normal mechanisms to bypass these literals because they are sensitive
# to the frame they are being called from.
# TODO(mdan): Generalize this to a "static allowlist" config.
if full_name in ('pdb.set_trace', 'ipdb.set_trace', 'breakpoint'):
global set_trace_warned
if not set_trace_warned:
# TODO(mdan): Update and shorten once available on tensorflow.org.
ag_logging.warning(
'Detected `pdb.set_trace()` in user code. The code'
' generated by AutoGraph is not optimized for step-by-step'
' debugging. See https://github.com/tensorflow/tensorflow/'
'blob/master/tensorflow/python/autograph/g3doc/reference/'
'debugging.md.')
set_trace_warned = True
return node
if (full_name == 'print' and
not self.ctx.user.options.uses(converter.Feature.BUILTIN_FUNCTIONS)):
return node
template = """
ag__.converted_call(func, args, kwargs, function_ctx)
"""
new_call = templates.replace_as_expression(
template,
func=node.func,
args=self._args_to_tuple(node),
kwargs=self._kwargs_to_dict(node),
function_ctx=function_context_name)
return new_call
def transform(node, ctx):
"""Transform function call to the compiled counterparts.
Args:
node: AST
ctx: EntityContext
Returns:
A tuple (node, new_names):
node: The transformed AST
new_names: set(string), containing any newly-generated names
"""
node = qual_names.resolve(node)
node = CallTreeTransformer(ctx).visit(node)
return node