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Refine ProgramTranslator API English Doc for 2.0rc (#27849)

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my_2.0rc
Huihuang Zheng 5 years ago committed by GitHub
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python/paddle/fluid/dygraph/dygraph_to_static/program_translator.py
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@ -702,11 +702,11 @@ class ProgramTranslator(object):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
# Two methods get same object because ProgramTranslator is a singleton # Two methods get same object because ProgramTranslator is a singleton
fluid.dygraph.ProgramTranslator() paddle.jit.ProgramTranslator()
fluid.dygraph.ProgramTranslator.get_instance() paddle.jit.ProgramTranslator.get_instance()
""" """
@ -743,11 +743,11 @@ class ProgramTranslator(object):
def enable(self, enable_to_static): def enable(self, enable_to_static):
""" """
Enable or disable the converting from imperative to declarative by Enable or disable the converting from imperative to static graph by
ProgramTranslator globally. ProgramTranslator globally.
Args: Args:
enable_to_static (bool): True or False to enable or disable declarative. enable_to_static (bool): True or False to enable or disable converting to static.
Returns: Returns:
None. None.
@ -755,25 +755,24 @@ class ProgramTranslator(object):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
import numpy as np
@fluid.dygraph.jit.declarative
def func(x):
x = fluid.dygraph.to_variable(x)
if fluid.layers.mean(x) > 0:
x_v = x - 1
else:
x_v = x + 1
return x_v
prog_trans = fluid.dygraph.ProgramTranslator() @paddle.jit.to_static
prog_trans.enable(False) def func(x):
if paddle.mean(x) > 0:
x_v = x - 1
else:
x_v = x + 1
return x_v
x = np.ones([1, 2])
# The declarative is disabled so the func is run in dygraph prog_trans = paddle.jit.ProgramTranslator()
with fluid.dygraph.guard(): prog_trans.enable(False)
print(func(x).numpy()) # [[2. 2.]]
x = paddle.ones([1, 2])
# ProgramTranslator is disabled so the func is run in dygraph
print(func(x).numpy()) # [[0. 0.]]
""" """
check_type(enable_to_static, "enable_to_static", bool, check_type(enable_to_static, "enable_to_static", bool,
@ -782,38 +781,37 @@ class ProgramTranslator(object):
def get_output(self, dygraph_func, *args, **kwargs): def get_output(self, dygraph_func, *args, **kwargs):
""" """
Returns the output dygraph VarBase for dygraph function. The dygraph Returns the output dygraph Tensor for dygraph function. The dygraph
function will be translated into static graph function so the under function will be translated into static graph function so the under
beneath numerical result will be calculated by declarative mode. beneath numerical result will be calculated by static graph mode.
Args: Args:
dygraph_func (callable): the dygraph function. dygraph_func (callable): the dygraph function.
*args, **kwargs : the input argument of dygraph_func. *args (tuple): the input argument of dygraph_func.
**kwargs (dict): the input argument of dygraph_func.
Returns: Returns:
VarBase or tuple of VarBase: the dygraph VarBase containing digital Tensor or tuple of Tensors: the dygraph Tensor containing digital result.
result.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
import numpy as np
def func(x): def func(x):
x = fluid.dygraph.to_variable(x) if paddle.mean(x) > 0:
if fluid.layers.mean(x) > 0:
x_v = x - 1 x_v = x - 1
else: else:
x_v = x + 1 x_v = x + 1
return x_v return x_v
prog_trans = fluid.dygraph.ProgramTranslator()
with fluid.dygraph.guard(): prog_trans = paddle.jit.ProgramTranslator()
x = np.ones([1, 2])
x_v = prog_trans.get_output(func, x) x = paddle.ones([1, 2])
print(x_v.numpy()) # [[0. 0.]] x_v = prog_trans.get_output(func, x)
print(x_v.numpy()) # [[0. 0.]]
""" """
assert callable( assert callable(
@ -875,19 +873,18 @@ class ProgramTranslator(object):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
import numpy as np
def func(x): def func(x):
x = fluid.dygraph.to_variable(x) if paddle.mean(x) > 0:
if fluid.layers.mean(x) > 0:
x_v = x - 1 x_v = x - 1
else: else:
x_v = x + 1 x_v = x + 1
return x_v return x_v
prog_trans = fluid.dygraph.ProgramTranslator()
prog_trans = paddle.jit.ProgramTranslator()
static_func = prog_trans.get_func(func) static_func = prog_trans.get_func(func)
print(callable(static_func)) # True print(callable(static_func)) # True
@ -908,43 +905,43 @@ class ProgramTranslator(object):
def get_program(self, dygraph_func, *args, **kwargs): def get_program(self, dygraph_func, *args, **kwargs):
""" """
Returns the translated static program and input/output variables from Returns the translated static program and input/output Tensors from
dygraph function. The users can use the program to run by executor. dygraph function. The users can use the program to run by executor.
Args: Args:
dygraph_func (callable): the dygraph function. dygraph_func (callable): the dygraph function.
*args, **kwargs : the input argument of dygraph_func. *args (tuple): the input argument of dygraph_func.
**kwargs (dict): the input argument of dygraph_func.
Returns: Returns:
tuple of (main_program, startup_program, inputs, outputs) whose tuple of (main_program, startup_program, inputs, outputs) whose
types are (Program, Program, list of Variable, list of Variable). types are (Program, Program, list of Tensors, list of Tensors).
main_program: the converted main program. main_program: the converted main program.
startup_program: the converted startup program. startup_program: the converted startup program.
inputs: list of input Variables which need to be fed. inputs: list of input Tensors which need to be fed.
outputs: list of output Variables which users can fetch. outputs: list of output Tensors which users can fetch.
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
import numpy as np
def func(x): def func(x):
x = fluid.dygraph.to_variable(x) if paddle.mean(x) > 0:
if fluid.layers.mean(x) > 0:
x_v = x - 1 x_v = x - 1
else: else:
x_v = x + 1 x_v = x + 1
return x_v return x_v
prog_trans = fluid.dygraph.ProgramTranslator()
x = np.ones([1, 2]) prog_trans = paddle.jit.ProgramTranslator()
x = paddle.ones([1, 2])
main_prog, start_prog, inputs, outputs = prog_trans.get_program(func, x) main_prog, start_prog, inputs, outputs = prog_trans.get_program(func, x)
print([i.name for i in inputs]) print([i.name for i in inputs])
# ['feed_0'] the feed input variable name representing x # [u'generated_tensor_0'] the feed input Tensor name representing x
print([o.name for o in outputs]) print([o.name for o in outputs])
# ['_generated_var_4'] the fetch output variable name representing x_v # [u'_generated_var_4'] the fetch output Tensor name representing x_v
""" """
assert callable( assert callable(
@ -993,21 +990,21 @@ class ProgramTranslator(object):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
import numpy as np
def func(x):
if paddle.mean(x) > 0:
x_v = x - 1
else:
x_v = x + 1
return x_v
def func(x):
x = fluid.dygraph.to_variable(x)
if fluid.layers.mean(x) > 0:
x_v = x - 1
else:
x_v = x + 1
return x_v
prog_trans = fluid.dygraph.ProgramTranslator() prog_trans = paddle.jit.ProgramTranslator()
code = prog_trans.get_code(func) code = prog_trans.get_code(func)
print(type(code)) # <class 'str'> print(type(code)) # <class 'str'>
""" """
assert callable( assert callable(
@ -1040,9 +1037,9 @@ class ProgramTranslator(object):
Examples: Examples:
.. code-block:: python .. code-block:: python
import paddle.fluid as fluid import paddle
prog_trans = fluid.dygraph.ProgramTranslator() prog_trans = paddle.jit.ProgramTranslator()
prog_cache = prog_trans.get_program_cache() prog_cache = prog_trans.get_program_cache()
""" """

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