def from_func_spec(func_spec, input_spec, class_instance):
"""
Builds the main_program with specialized inputs and returns outputs
of program as fetch_list.
Args:
func_spec(FunctionSpec): A FunctionSpec instance for decorated function.
input_spec(list[InputSpec]):
"""
# Transforms dygraph function into static function and caches it.
dygraph_function = func_spec.dygraph_function
static_func = convert_to_static(dygraph_function)
main_program, startup_program = framework.Program(), framework.Program()
# Note: The random seed should be synchronized into cached program
# if set in `fluid.dygraph_guard` because some ops rely on it, such as
# `fluid.layers.dropout`.
main_program.random_seed = framework.default_main_program().random_seed
startup_program.random_seed = framework.default_startup_program(
).random_seed
with framework.program_guard(main_program, startup_program):
with _switch_declarative_mode_guard_(is_declarative=True):
# 1. Adds `fluid.data` layers for input if needed
inputs = func_spec.to_static_inputs_with_spec(input_spec,
main_program)
if class_instance:
inputs = tuple([class_instance] + list(inputs))
# 2. Gets all ParamBases and buffered VarBases in the function
all_parameters_and_buffers = list(
get_parameters(class_instance).values()) + list(
get_buffers(class_instance).values())
# 3. Builds program only once and returns the output Variables.
with param_guard(get_parameters(
class_instance, False)), param_guard(
get_buffers(class_instance, False)):
try:
outputs = static_func(*inputs)
except BaseException as e:
# NOTE: If e is raised in compile time, e should be attached to ERROR_DATA here.
attach_error_data(e)
raise
if not isinstance(outputs,
(tuple, list)) and outputs is not None:
outputs = [outputs]
main_program = update_op_callstack_with_origin_info(main_program)
return ConcreteProgram(
inputs=inputs,
outputs=outputs,
parameters=all_parameters_and_buffers,
function=dygraph_function,
main_program=main_program,
startup_program=startup_program)
def _extract_indeed_params_buffers(class_instance):
"""
To filter not initialzed buffers.
"""
params = list(get_parameters(class_instance).values())
buffers = list(get_buffers(class_instance).values())
buffers = [buffer for buffer in buffers if len(buffer.shape) != 0]
return params + buffers
def from_func_spec(func_spec, input_spec, input_kwargs_spec,
class_instance, **kwargs):
"""
Builds the main_program with specialized inputs and returns outputs
of program as fetch_list.
Args:
func_spec(FunctionSpec): A FunctionSpec instance for decorated function.
input_spec(list[InputSpec]):
"""
# verify the instance is initialized in imperative mode.
_verify_init_in_dynamic_mode(class_instance)
# Transforms dygraph function into static function and caches it.
dygraph_function = func_spec.dygraph_function
static_func = convert_to_static(dygraph_function)
# apply pre\post hook for outermost layer
hook_helper = HookHelper(dygraph_function, class_instance,
kwargs.get("with_hook", False))
main_program, startup_program = framework.Program(), framework.Program(
)
# Note: The random seed should be synchronized into cached program
# if set in `fluid.dygraph_guard` because some ops rely on it, such as
# `fluid.layers.dropout`.
main_program.random_seed = framework.default_main_program().random_seed
startup_program.random_seed = framework.default_startup_program(
).random_seed
from paddle.fluid.dygraph.base import _switch_declarative_mode_guard_
with framework.program_guard(main_program, startup_program):
with _switch_declarative_mode_guard_(is_declarative=True):
# 1. Adds `fluid.data` layers for input if needed
static_inputs = func_spec.to_static_inputs_with_spec(
input_spec, main_program)
_kwargs = func_spec.to_static_inputs_with_spec(
input_kwargs_spec, main_program)
if class_instance:
static_inputs = tuple([class_instance] +
list(static_inputs))
# 2. Gets all ParamBases and buffered VarBases in the function
all_parameters_and_buffers = _extract_indeed_params_buffers(
class_instance)
# 3. Builds program only once and returns the output Variables.
with param_guard(get_parameters(
class_instance, False)), param_guard(
get_buffers(class_instance, False)):
try:
# only for jit.save, do nothing while train and eval process
inputs = hook_helper.apply_pre_hooks(static_inputs)
if _kwargs:
outputs = static_func(*inputs, **_kwargs)
else:
outputs = static_func(*inputs)
outputs = hook_helper.apply_post_hooks(inputs, outputs)
except BaseException as e:
# NOTE: If e is raised in compile time, e should be attached to ERROR_DATA here.
error.attach_error_data(e)
error_data = getattr(e, error.ERROR_DATA, None)
if error_data:
error_data.raise_new_exception()
raise
if outputs is not None:
need_wrap_into_list = not isinstance(
outputs, (tuple, list)) or len(outputs) == 1
if need_wrap_into_list:
outputs = [outputs]
main_program = update_op_callstack_with_origin_info(main_program)
return ConcreteProgram(inputs=static_inputs,
outputs=outputs,
parameters=all_parameters_and_buffers,
function=dygraph_function,
main_program=main_program,
startup_program=startup_program,
**kwargs)