def to_graph(entity,
recursive=True,
arg_values=None,
arg_types=None,
experimental_optional_features=converter.Feature.ALL,
experimental_strip_decorators=None,
experimental_verbose=converter.Verbosity.BRIEF,
experimental_partial_types=None):
"""Converts a Python entity into a TensorFlow graph.
Also see: `tf.autograph.to_code`, `tf.function`.
Unlike `tf.function`, `to_graph` is a low-level transpiler that converts
Python code to TensorFlow graph code. It does not implement any caching,
variable management or create any actual ops, and is best used where greater
control over the generated TensorFlow graph is desired. Another difference
from `tf.function` is that `to_graph` will not wrap the graph into a
TensorFlow function or a Python callable. Internally, `tf.function` uses
`to_graph`.
_Example Usage_
```python
def foo(x):
if x > 0:
y = x * x
else:
y = -x
return y
converted_foo = to_graph(foo)
x = tf.constant(1)
y = converted_foo(x) # converted_foo is a TensorFlow Op-like.
assert is_tensor(y)
```
Supported Python entities include:
* functions
* classes
* object methods
Functions are converted into new functions with converted code.
Classes are converted by generating a new class whose methods use converted
code.
Methods are converted into unbound function that have an additional first
argument called `self`.
Args:
entity: Python callable or class to convert.
recursive: Whether to recursively convert any functions that the
converted function may call.
arg_values: Optional dict of value hints for symbols including
function arguments mapping string names to actual values. For example,
`arg_values={'a': 1}` will map the variable `a` to the value `1`.
arg_types: Optional dict of type hints for symbols including function
arguments. Type hints allow specifying just the type of a variable, rather
than a specific value.
experimental_optional_features: `None`, a tuple of, or a single
`tf.autograph.experimental.Feature` value. Controls the use of
optional features in the conversion process.
experimental_strip_decorators: A tuple specifying decorators that should be
excluded from the compiled output. By default, when converting a function
before the decorators are applied, the compiled output will include those
decorators.
experimental_verbose: The level of printing verbosity to use, as a
`tf.autograph.experimental.Verbosity` value.
experimental_partial_types: A `set` of `type` values, reserved for internal
use.
Returns:
Same as `entity`, the converted Python function or class.
Raises:
ValueError: If the entity could not be converted.
"""
try:
if experimental_strip_decorators is None:
experimental_strip_decorators = ()
experimental_strip_decorators += (convert, do_not_convert, converted_call)
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
verbose=experimental_verbose,
strip_decorators=experimental_strip_decorators,
optional_features=experimental_optional_features),
partial_types=experimental_partial_types,
autograph_module=tf_inspect.getmodule(to_graph),
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
_, name, namespace = conversion.entity_to_graph(entity, program_ctx,
arg_values, arg_types)
nodes = []
for dep in reversed(program_ctx.conversion_order):
nodes.extend(program_ctx.dependency_cache[dep])
compiled_module, _ = compiler.ast_to_object(
nodes,
source_prefix=program_ctx.required_imports,
include_source_map=True)
# The compiled code should see everything the entry entity saw.
# TODO(mdan): This might not work well if the call tree spans modules?
for key, val in namespace.items():
# Avoid overwriting entities that have been transformed.
if key not in compiled_module.__dict__:
compiled_module.__dict__[key] = val
for key, val in program_ctx.additional_symbols.items():
if key not in compiled_module.__dict__:
compiled_module.__dict__[key] = val
compiled = getattr(compiled_module, name)
if hasattr(entity, '__defaults__'):
logging.log(3, 'Default args mapping: %s has: %s', entity,
entity.__defaults__)
compiled.__defaults__ = entity.__defaults__
else:
logging.log(3, 'Default args mapping: %s has no __defaults__', entity)
logging.log(3, 'Namespace of %s includes: %s', compiled,
compiled_module.__dict__.keys())
if hasattr(compiled, '__globals__'):
# Remove self to avoid circular references. This will probably only work
# so long as the function is not reentrant.
del compiled.__globals__[name]
# Need this so the source_mapping attribute is available for the context
# manager to access for runtime errors.
#
# Note that compiler.ast_to_object attaches the source map 'ag_source_map__'
# symbol to the compiled module.
# TODO(mdan): Record this statically in the generated code.
# TODO(mdan): Rename this attribute to 'autograph_info__'
source_map_attribute_name = 'ag_source_map'
if getattr(compiled, source_map_attribute_name, None) is not None:
# TODO(znado): change input problem errors into TransformError
raise ValueError('cannot convert %s because is has an attribute '
'"%s", which is reserved for AutoGraph.' %
(compiled, source_map_attribute_name))
setattr(compiled, source_map_attribute_name,
compiled_module.__dict__['ag_source_map__'])
return compiled
except (ValueError, AttributeError, KeyError, NameError, AssertionError) as e:
errors.report_internal_error(entity, e)
def to_graph(entity,
recursive=True,
arg_values=None,
arg_types=None,
experimental_optional_features=converter.Feature.ALL):
"""Converts a Python entity into a TensorFlow graph.
Also see: `tf.autograph.to_code`, `tf.function`.
Unlike `tf.function`, `to_graph` is a low-level transpiler that converts
Python code to TensorFlow graph code. It does not implement any caching,
variable management or create any actual ops, and is best used where greater
control over the generated TensorFlow graph is desired. Another difference
from `tf.function` is that `to_graph` will not wrap the graph into a
TensorFlow function or a Python callable. Internally, `tf.function` uses
`to_graph`.
_Example Usage_
```python
def foo(x):
if x > 0:
y = x * x
else:
y = -x
return y
converted_foo = to_graph(foo)
x = tf.constant(1)
y = converted_foo(x) # converted_foo is a TensorFlow Op-like.
assert is_tensor(y)
```
Supported Python entities include:
* functions
* classes
* object methods
Functions are converted into new functions with converted code.
Classes are converted by generating a new class whose methods use converted
code.
Methods are converted into unbound function that have an additional first
argument called `self`.
Args:
entity: Python callable or class to convert.
recursive: Whether to recursively convert any functions that the
converted function may call.
arg_values: Optional dict of value hints for symbols including
function arguments mapping string names to actual values. For example,
`arg_values={'a': 1}` will map the variable `a` to the value `1`.
arg_types: Optional dict of type hints for symbols including function
arguments. Type hints allow specifying just the type of a variable, rather
than a specific value.
experimental_optional_features: `None`, a tuple of, or a single
`tf.autograph.experimental.Feature` value. Controls the use of
optional features in the conversion process.
Returns:
Same as `entity`, the converted Python function or class.
Raises:
ValueError: If the entity could not be converted.
"""
try:
# TODO(b/129431421): Remove these args.
del arg_values
del arg_types
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
optional_features=experimental_optional_features),
autograph_module=tf_inspect.getmodule(to_graph))
return conversion.convert(entity, program_ctx)
except (ValueError, AttributeError, KeyError, NameError,
AssertionError) as e:
errors.report_internal_error(entity, e)
def to_graph(entity,
recursive=True,
arg_values=None,
arg_types=None,
experimental_optional_features=converter.Feature.ALL,
experimental_strip_decorators=None,
experimental_verbose=converter.Verbosity.BRIEF,
experimental_partial_types=None):
"""Converts a Python entity into a TensorFlow graph.
Also see: `tf.autograph.to_code`, `tf.function`.
Unlike `tf.function`, `to_graph` is a low-level transpiler that converts
Python code to TensorFlow graph code. It does not implement any caching,
variable management or create any actual ops, and is best used where greater
control over the generated TensorFlow graph is desired. Another difference
from `tf.function` is that `to_graph` will not wrap the graph into a
TensorFlow function or a Python callable. Internally, `tf.function` uses
`to_graph`.
_Example Usage_
```python
def foo(x):
if x > 0:
y = x * x
else:
y = -x
return y
converted_foo = to_graph(foo)
x = tf.constant(1)
y = converted_foo(x) # converted_foo is a TensorFlow Op-like.
assert is_tensor(y)
```
Supported Python entities include:
* functions
* classes
* object methods
Functions are converted into new functions with converted code.
Classes are converted by generating a new class whose methods use converted
code.
Methods are converted into unbound function that have an additional first
argument called `self`.
Args:
entity: Python callable or class to convert.
recursive: Whether to recursively convert any functions that the
converted function may call.
arg_values: Optional dict of value hints for symbols including
function arguments mapping string names to actual values. For example,
`arg_values={'a': 1}` will map the variable `a` to the value `1`.
arg_types: Optional dict of type hints for symbols including function
arguments. Type hints allow specifying just the type of a variable, rather
than a specific value.
experimental_optional_features: `None`, a tuple of, or a single
`tf.autograph.experimental.Feature` value. Controls the use of
optional features in the conversion process.
experimental_strip_decorators: A tuple specifying decorators that should be
excluded from the compiled output. By default, when converting a function
before the decorators are applied, the compiled output will include those
decorators.
experimental_verbose: The level of printing verbosity to use, as a
`tf.autograph.experimental.Verbosity` value.
experimental_partial_types: A `set` of `type` values, reserved for internal
use.
Returns:
Same as `entity`, the converted Python function or class.
Raises:
ValueError: If the entity could not be converted.
"""
try:
if experimental_strip_decorators is None:
experimental_strip_decorators = ()
experimental_strip_decorators += (convert, do_not_convert, converted_call)
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
verbose=experimental_verbose,
strip_decorators=experimental_strip_decorators,
optional_features=experimental_optional_features),
partial_types=experimental_partial_types,
autograph_module=tf_inspect.getmodule(to_graph),
uncompiled_modules=config.DEFAULT_UNCOMPILED_MODULES)
_, name, namespace = conversion.entity_to_graph(entity, program_ctx,
arg_values, arg_types)
nodes = []
for dep in reversed(program_ctx.conversion_order):
nodes.extend(program_ctx.dependency_cache[dep])
compiled_module, _ = compiler.ast_to_object(
nodes,
source_prefix=program_ctx.required_imports,
include_source_map=True)
# The compiled code should see everything the entry entity saw.
# TODO(mdan): This might not work well if the call tree spans modules?
for key, val in namespace.items():
# Avoid overwriting entities that have been transformed.
if key not in compiled_module.__dict__:
compiled_module.__dict__[key] = val
for key, val in program_ctx.additional_symbols.items():
if key not in compiled_module.__dict__:
compiled_module.__dict__[key] = val
compiled = getattr(compiled_module, name)
if tf_inspect.isfunction(entity):
compiled.__defaults__ = entity.__defaults__
if hasattr(compiled, '__globals__'):
# Remove self to avoid circular references. This will probably only work
# so long as the function is not reentrant.
del compiled.__globals__[name]
# Need this so the source_mapping attribute is available for the context
# manager to access for runtime errors.
#
# Note that compiler.ast_to_object attaches the source map 'ag_source_map__'
# symbol to the compiled module.
# TODO(mdan): Record this statically in the generated code.
# TODO(mdan): Rename this attribute to 'autograph_info__'
source_map_attribute_name = 'ag_source_map'
if getattr(compiled, source_map_attribute_name, None) is not None:
# TODO(znado): change input problem errors into TransformError
raise ValueError('cannot convert %s because is has an attribute '
'"%s", which is reserved for AutoGraph.' %
(compiled, source_map_attribute_name))
setattr(compiled, source_map_attribute_name,
compiled_module.__dict__['ag_source_map__'])
return compiled
except (ValueError, AttributeError, KeyError, NameError, AssertionError) as e:
errors.report_internal_error(entity, e)
def to_graph(entity,
recursive=True,
arg_values=None,
arg_types=None,
experimental_optional_features=None):
"""Converts a Python entity into a TensorFlow graph.
Also see: `tf.autograph.to_code`, `tf.function`.
Unlike `tf.function`, `to_graph` is a low-level transpiler that converts
Python code to TensorFlow graph code. It does not implement any caching,
variable management or create any actual ops, and is best used where greater
control over the generated TensorFlow graph is desired. Another difference
from `tf.function` is that `to_graph` will not wrap the graph into a
TensorFlow function or a Python callable. Internally, `tf.function` uses
`to_graph`.
_Example Usage_
```python
def foo(x):
if x > 0:
y = x * x
else:
y = -x
return y
converted_foo = to_graph(foo)
x = tf.constant(1)
y = converted_foo(x) # converted_foo is a TensorFlow Op-like.
assert is_tensor(y)
```
Supported Python entities include:
* functions
* classes
* object methods
Functions are converted into new functions with converted code.
Classes are converted by generating a new class whose methods use converted
code.
Methods are converted into unbound function that have an additional first
argument called `self`.
Args:
entity: Python callable or class to convert.
recursive: Whether to recursively convert any functions that the
converted function may call.
arg_values: Optional dict of value hints for symbols including
function arguments mapping string names to actual values. For example,
`arg_values={'a': 1}` will map the variable `a` to the value `1`.
arg_types: Optional dict of type hints for symbols including function
arguments. Type hints allow specifying just the type of a variable, rather
than a specific value.
experimental_optional_features: `None`, a tuple of, or a single
`tf.autograph.experimental.Feature` value. Controls the use of
optional features in the conversion process.
Returns:
Same as `entity`, the converted Python function or class.
Raises:
ValueError: If the entity could not be converted.
"""
try:
# TODO(b/129431421): Remove these args.
del arg_values
del arg_types
program_ctx = converter.ProgramContext(
options=converter.ConversionOptions(
recursive=recursive,
optional_features=experimental_optional_features),
autograph_module=tf_inspect.getmodule(to_graph))
return conversion.convert(entity, program_ctx)
except (ValueError, AttributeError, KeyError, NameError, AssertionError) as e:
errors.report_internal_error(entity, e)
