def _parse_and_analyze(f):
"""Performs preliminary analyses and transformations.
The goal is to massage the source program into a form on which
the `_AutoBatchingTransformer` below will be successful.
Args:
f: Function to analyze
Returns:
node: A Python AST node representing the function, suitable for
passing to `_AutoBatchingTransformer.visit`
entity_info: An AutoGraph `EntityInfo` object, with some information
about `f`. Required for initializing `_AutoBatchingTransformer`.
"""
namespace = {}
# Get the AST of the function
future_features = inspect_utils.getfutureimports(f)
node, _ = parser.parse_entity(f, future_features=future_features)
# Boilerplate for AutoGraph transforms
entity_info = transformer.EntityInfo(source_code='',
source_file=None,
future_features=future_features,
namespace=namespace)
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(recursive=True),
autograph_module=None)
ctx = converter.EntityContext(namer=naming.Namer(namespace),
entity_info=entity_info,
program_ctx=program_ctx)
# Canonicalize away break statements
node = converter.standard_analysis(node, ctx, is_initial=True)
node = break_statements.transform(node, ctx)
# Canonicalize away continue statements
node = converter.standard_analysis(node, ctx, is_initial=False)
node = continue_statements.transform(node, ctx)
# Force single returns
node = converter.standard_analysis(node, ctx, is_initial=False)
node = return_statements.transform(node, ctx, default_to_null_return=False)
# Transform into ANF
node = anf.transform(node, ctx)
node = converter.standard_analysis(node, ctx, is_initial=False)
return node, ctx
def prepare(self,
test_fn,
namespace,
namer=None,
arg_types=None,
owner_type=None,
recursive=True,
strip_decorators=()):
namespace['ConversionOptions'] = converter.ConversionOptions
node, source = parser.parse_entity(test_fn)
node = node.body[0]
if namer is None:
namer = FakeNamer()
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive, strip_decorators=strip_decorators),
partial_types=None,
autograph_module=None,
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
entity_info = transformer.EntityInfo(source_code=source,
source_file='<fragment>',
namespace=namespace,
arg_values=None,
arg_types=arg_types,
owner_type=owner_type)
ctx = converter.EntityContext(namer, entity_info, program_ctx)
node = converter.standard_analysis(node, ctx, is_initial=True)
return node, ctx
def prepare(self,
test_fn,
namespace,
namer=None,
arg_types=None,
owner_type=None,
recursive=True,
strip_decorators=()):
namespace['ConversionOptions'] = converter.ConversionOptions
node, source = parser.parse_entity(test_fn)
node = node.body[0]
if namer is None:
namer = FakeNamer()
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
strip_decorators=strip_decorators,
verbose=True),
partial_types=None,
autograph_module=None,
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
entity_info = transformer.EntityInfo(
source_code=source,
source_file='<fragment>',
namespace=namespace,
arg_values=None,
arg_types=arg_types,
owner_type=owner_type)
ctx = converter.EntityContext(namer, entity_info, program_ctx)
origin_info.resolve(node, source, test_fn)
node = converter.standard_analysis(node, ctx, is_initial=True)
return node, ctx
def converted(self, entity, converter_module, namespace, *tf_symbols):
node, ctx = self.prepare(entity, namespace)
if not isinstance(converter_module, (list, tuple)):
converter_module = (converter_module, )
for m in converter_module:
node = m.transform(node, ctx)
node = converter.standard_analysis(node, ctx, is_initial=True)
with self.compiled(node, namespace, *tf_symbols) as result:
yield result
def converted(self, entity, converter_module, namespace, *tf_symbols):
node, ctx = self.prepare(entity, namespace)
if not isinstance(converter_module, (list, tuple)):
converter_module = (converter_module,)
for i, m in enumerate(converter_module):
node = converter.standard_analysis(node, ctx, is_initial=not i)
node = m.transform(node, ctx)
with self.compiled(node, namespace, *tf_symbols) as result:
yield result
def node_to_graph(node, context):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: AST, the code to convert.
context: converter.EntityContext
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Insert list_comprehensions somewhere.
unsupported_features_checker.verify(node)
node = converter.standard_analysis(node, context, is_initial=True)
# Past this point, line numbers are no longer accurate so we ignore the
# source.
# TODO(mdan): Is it feasible to reconstruct intermediate source code?
context.info.source_code = None
node = converter.apply_(node, context, arg_defaults)
node = converter.apply_(node, context, directives)
node = converter.apply_(node, context, break_statements)
if context.program.options.uses(converter.Feature.ASSERT_STATEMENTS):
node = converter.apply_(node, context, asserts)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = converter.apply_(node, context, continue_statements)
node = converter.apply_(node, context, return_statements)
if context.program.options.uses(converter.Feature.LISTS):
node = converter.apply_(node, context, lists)
node = converter.apply_(node, context, slices)
if context.program.options.uses(converter.Feature.BUILTIN_FUNCTIONS):
node = converter.apply_(node, context, builtin_functions)
node = converter.apply_(node, context, call_trees)
node = converter.apply_(node, context, control_flow)
node = converter.apply_(node, context, conditional_expressions)
if context.program.options.uses(converter.Feature.LOGICAL_EXPRESSIONS):
node = converter.apply_(node, context, logical_expressions)
if context.program.options.uses(converter.Feature.AUTO_CONTROL_DEPS):
node = converter.apply_(node, context, side_effect_guards)
# TODO(mdan): If function scopes ever does more, the toggle will need moving.
if context.program.options.uses(converter.Feature.NAME_SCOPES):
node = converter.apply_(node, context, function_scopes)
if context.program.options.uses(converter.Feature.ERROR_REWRITING):
node = converter.apply_(node, context, error_handlers)
return node
def node_to_graph(node, context):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: AST, the code to convert.
context: converter.EntityContext
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Insert list_comprehensions somewhere.
unsupported_features_checker.verify(node)
node = converter.standard_analysis(node, context, is_initial=True)
# Past this point, line numbers are no longer accurate so we ignore the
# source.
# TODO(mdan): Is it feasible to reconstruct intermediate source code?
context.info.source_code = None
node = converter.apply_(node, context, arg_defaults)
node = converter.apply_(node, context, directives)
node = converter.apply_(node, context, break_statements)
if context.program.options.uses(converter.Feature.ASSERT_STATEMENTS):
node = converter.apply_(node, context, asserts)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = converter.apply_(node, context, continue_statements)
node = converter.apply_(node, context, return_statements)
if context.program.options.uses(converter.Feature.LISTS):
node = converter.apply_(node, context, lists)
node = converter.apply_(node, context, slices)
if context.program.options.uses(converter.Feature.BUILTIN_FUNCTIONS):
node = converter.apply_(node, context, builtin_functions)
node = converter.apply_(node, context, call_trees)
node = converter.apply_(node, context, control_flow)
node = converter.apply_(node, context, conditional_expressions)
if context.program.options.uses(converter.Feature.LOGICAL_EXPRESSIONS):
node = converter.apply_(node, context, logical_expressions)
if context.program.options.uses(converter.Feature.AUTO_CONTROL_DEPS):
node = converter.apply_(node, context, side_effect_guards)
# TODO(mdan): If function scopes ever does more, the toggle will need moving.
if context.program.options.uses(converter.Feature.NAME_SCOPES):
node = converter.apply_(node, context, function_scopes)
if context.program.options.uses(converter.Feature.ERROR_REWRITING):
node = converter.apply_(node, context, error_handlers)
return node
def node_to_graph(node, context, rewrite_errors=True):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: AST, the code to convert.
context: converter.EntityContext
rewrite_errors: Boolean, whether or not to rewrite the error traceback.
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Insert list_comprehensions somewhere.
node = converter.standard_analysis(node, context, is_initial=True)
# Past this point, line numbers are no longer accurate so we ignore the
# source.
# TODO(mdan): Is it feasible to reconstruct intermediate source code?
context.info.source_code = None
if context.program.options.uses(converter.Feature.DECORATORS):
node = converter.apply_(node, context, decorators)
node = converter.apply_(node, context, arg_defaults)
node = converter.apply_(node, context, directives)
node = converter.apply_(node, context, break_statements)
node = converter.apply_(node, context, asserts)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = converter.apply_(node, context, continue_statements)
node = converter.apply_(node, context, return_statements)
if context.program.options.uses(converter.Feature.LISTS):
node = converter.apply_(node, context, lists)
node = converter.apply_(node, context, slices)
node = converter.apply_(node, context, builtin_functions)
node = converter.apply_(node, context, call_trees)
node = converter.apply_(node, context, control_flow)
node = converter.apply_(node, context, conditional_expressions)
node = converter.apply_(node, context, logical_expressions)
if context.program.options.uses(converter.Feature.AUTO_CONTROL_DEPS):
node = converter.apply_(node, context, side_effect_guards)
node = converter.apply_(node, context, function_scopes)
if rewrite_errors:
node = converter.apply_(node, context, error_handlers)
return node
def prepare(self, test_fn, namespace, recursive=True):
namespace['ConversionOptions'] = converter.ConversionOptions
future_features = ('print_function', 'division')
node, source = parser.parse_entity(test_fn,
future_features=future_features)
namer = naming.Namer(namespace)
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(recursive=recursive),
autograph_module=None)
entity_info = transformer.EntityInfo(source_code=source,
source_file='<fragment>',
future_features=future_features,
namespace=namespace)
ctx = converter.EntityContext(namer, entity_info, program_ctx)
node = converter.standard_analysis(node, ctx, is_initial=True)
return node, ctx
def prepare(self, test_fn, namespace, arg_types=None, recursive=True):
namespace['ConversionOptions'] = converter.ConversionOptions
node, source, _ = parser.parse_entity(test_fn)
namer = naming.Namer(namespace)
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(recursive=recursive),
autograph_module=None)
entity_info = transformer.EntityInfo(source_code=source,
source_file='<fragment>',
namespace=namespace,
arg_values=None,
arg_types=arg_types)
ctx = converter.EntityContext(namer, entity_info, program_ctx)
origin_info.resolve(node, source, test_fn)
node = converter.standard_analysis(node, ctx, is_initial=True)
return node, ctx
def node_to_graph(node, context, rewrite_errors=True):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: AST, the code to convert.
context: converter.EntityContext
rewrite_errors: Boolean, whether or not to rewrite the error traceback.
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Insert list_comprehensions somewhere.
node = converter.standard_analysis(node, context, is_initial=True)
# Past this point, line numbers are no longer accurate so we ignore the
# source.
# TODO(mdan): Is it feasible to reconstruct intermediate source code?
context.info.source_code = None
node = converter.apply_(node, context, decorators)
node = converter.apply_(node, context, directives)
node = converter.apply_(node, context, break_statements)
node = converter.apply_(node, context, asserts)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = converter.apply_(node, context, continue_statements)
node = converter.apply_(node, context, return_statements)
if context.program.options.uses(converter.Feature.LISTS):
node = converter.apply_(node, context, lists)
node = converter.apply_(node, context, slices)
node = converter.apply_(node, context, builtin_functions)
node = converter.apply_(node, context, call_trees)
node = converter.apply_(node, context, control_flow)
node = converter.apply_(node, context, conditional_expressions)
node = converter.apply_(node, context, logical_expressions)
if context.program.options.uses(converter.Feature.AUTO_CONTROL_DEPS):
node = converter.apply_(node, context, side_effect_guards)
node = converter.apply_(node, context, function_scopes)
if rewrite_errors:
node = converter.apply_(node, context, error_handlers)
return node
def prepare(self, test_fn, namespace, recursive=True):
namespace['ConversionOptions'] = converter.ConversionOptions
future_features = ('print_function', 'division')
node, source = parser.parse_entity(test_fn, future_features=future_features)
namer = naming.Namer(namespace)
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(recursive=recursive),
autograph_module=None)
entity_info = transformer.EntityInfo(
source_code=source,
source_file='<fragment>',
future_features=future_features,
namespace=namespace)
ctx = converter.EntityContext(namer, entity_info, program_ctx)
origin_info.resolve(node, source, test_fn)
node = converter.standard_analysis(node, ctx, is_initial=True)
return node, ctx
def transform_ast(self, node, ctx):
# TODO(mdan): Insert list_comprehensions somewhere.
unsupported_features_checker.verify(node)
node = converter.standard_analysis(node, ctx, is_initial=True)
node = converter.apply_(node, ctx, functions)
node = converter.apply_(node, ctx, directives)
node = converter.apply_(node, ctx, break_statements)
if ctx.user.options.uses(converter.Feature.ASSERT_STATEMENTS):
node = converter.apply_(node, ctx, asserts)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = converter.apply_(node, ctx, continue_statements)
node = converter.apply_(node, ctx, return_statements)
if ctx.user.options.uses(converter.Feature.LISTS):
node = converter.apply_(node, ctx, lists)
node = converter.apply_(node, ctx, slices)
node = converter.apply_(node, ctx, call_trees)
node = converter.apply_(node, ctx, control_flow)
node = converter.apply_(node, ctx, conditional_expressions)
node = converter.apply_(node, ctx, logical_expressions)
return node
def node_to_graph(node, context):
"""Convert Python code to equivalent TF graph mode code.
Args:
node: AST, the code to convert.
context: converter.EntityContext
Returns:
A tuple (node, deps):
* node: A Python ast node, representing the converted code.
* deps: A set of strings, the fully qualified names of entity
dependencies that this node has.
"""
# TODO(mdan): Insert list_comprehensions somewhere.
unsupported_features_checker.verify(node)
node = converter.standard_analysis(node, context, is_initial=True)
node = converter.apply_(node, context, function_scopes)
node = converter.apply_(node, context, arg_defaults)
node = converter.apply_(node, context, directives)
node = converter.apply_(node, context, break_statements)
if context.program.options.uses(converter.Feature.ASSERT_STATEMENTS):
node = converter.apply_(node, context, asserts)
# Note: sequencing continue canonicalization before for loop one avoids
# dealing with the extra loop increment operation that the for
# canonicalization creates.
node = converter.apply_(node, context, continue_statements)
node = converter.apply_(node, context, return_statements)
if context.program.options.uses(converter.Feature.LISTS):
node = converter.apply_(node, context, lists)
node = converter.apply_(node, context, slices)
node = converter.apply_(node, context, call_trees)
node = converter.apply_(node, context, control_flow)
node = converter.apply_(node, context, conditional_expressions)
node = converter.apply_(node, context, logical_expressions)
return node
def _parse_and_analyze(f, autobatch_functions):
"""Performs preliminary analyses and transformations.
The goal is to massage the source program into a form on which
the `_AutoBatchingTransformer` below will be successful.
Args:
f: Function to analyze
autobatch_functions: List of Python `str` names of autobatched functions.
Arguments to these functions will be canonicalized to variable references,
but others will not.
Returns:
node: A Python AST node representing the function, suitable for
passing to `_AutoBatchingTransformer.visit`
entity_info: An AutoGraph `EntityInfo` object, with some information
about `f`. Required for initializing `_AutoBatchingTransformer`.
"""
namespace = {}
# Get the AST of the function
future_features = inspect_utils.getfutureimports(f)
node, _ = parser.parse_entity(f, future_features=future_features)
# Boilerplate for AutoGraph transforms
entity_info = transformer.EntityInfo(source_code='',
source_file=None,
future_features=future_features,
namespace=namespace)
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(recursive=True),
autograph_module=None)
ctx = converter.EntityContext(namer=naming.Namer(namespace),
entity_info=entity_info,
program_ctx=program_ctx)
# Canonicalize away break statements
node = converter.standard_analysis(node, ctx, is_initial=True)
node = break_statements.transform(node, ctx)
# Canonicalize away continue statements
node = converter.standard_analysis(node, ctx, is_initial=False)
node = continue_statements.transform(node, ctx)
# Force single returns
node = converter.standard_analysis(node, ctx, is_initial=False)
node = return_statements.transform(node, ctx, default_to_null_return=False)
# Transform into ANF
# Replacing if tests and autobatched function call arguments because
# that's where divergence can happen.
# Replacing all function calls because the downstream transformation
# expects calls to lead directly to assignments.
def maybe_replace_function_argument(parent, field_name, child):
del field_name, child
if not anno.hasanno(parent.func, anno.Basic.QN):
return False
func_name = anno.getanno(parent.func, anno.Basic.QN)
if str(func_name) in autobatch_functions:
return True
return False
anf_config = [
(anf.ASTEdgePattern(gast.If, 'test', anf.ANY), anf.REPLACE),
(anf.ASTEdgePattern(anf.ANY, anf.ANY, gast.Call), anf.REPLACE),
(anf.ASTEdgePattern(gast.Call, 'args',
anf.ANY), maybe_replace_function_argument),
]
node = anf.transform(node, ctx, config=anf_config)
node = converter.standard_analysis(node, ctx, is_initial=False)
return node, ctx
