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

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my_2.0rc
Huihuang Zheng 4 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:
.. code-block:: python
import paddle.fluid as fluid
import paddle
# Two methods get same object because ProgramTranslator is a singleton
fluid.dygraph.ProgramTranslator()
fluid.dygraph.ProgramTranslator.get_instance()
paddle.jit.ProgramTranslator()
paddle.jit.ProgramTranslator.get_instance()
"""
@ -743,11 +743,11 @@ class ProgramTranslator(object):
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.
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:
None.
@ -755,25 +755,24 @@ class ProgramTranslator(object):
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
import paddle
@fluid.dygraph.jit.declarative
@paddle.jit.to_static
def func(x):
x = fluid.dygraph.to_variable(x)
if fluid.layers.mean(x) > 0:
if paddle.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()
prog_trans.enable(False)
x = np.ones([1, 2])
# The declarative is disabled so the func is run in dygraph
with fluid.dygraph.guard():
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,
@ -782,36 +781,35 @@ class ProgramTranslator(object):
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
beneath numerical result will be calculated by declarative mode.
beneath numerical result will be calculated by static graph mode.
Args:
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:
VarBase or tuple of VarBase: the dygraph VarBase containing digital
result.
Tensor or tuple of Tensors: the dygraph Tensor containing digital result.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
import paddle
def func(x):
x = fluid.dygraph.to_variable(x)
if fluid.layers.mean(x) > 0:
if paddle.mean(x) > 0:
x_v = x - 1
else:
x_v = x + 1
return x_v
prog_trans = fluid.dygraph.ProgramTranslator()
with fluid.dygraph.guard():
x = np.ones([1, 2])
prog_trans = paddle.jit.ProgramTranslator()
x = paddle.ones([1, 2])
x_v = prog_trans.get_output(func, x)
print(x_v.numpy()) # [[0. 0.]]
@ -875,19 +873,18 @@ class ProgramTranslator(object):
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
import paddle
def func(x):
x = fluid.dygraph.to_variable(x)
if fluid.layers.mean(x) > 0:
if paddle.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()
static_func = prog_trans.get_func(func)
print(callable(static_func)) # True
@ -908,43 +905,43 @@ class ProgramTranslator(object):
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.
Args:
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:
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.
startup_program: the converted startup program.
inputs: list of input Variables which need to be fed.
outputs: list of output Variables which users can fetch.
inputs: list of input Tensors which need to be fed.
outputs: list of output Tensors which users can fetch.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
import paddle
def func(x):
x = fluid.dygraph.to_variable(x)
if fluid.layers.mean(x) > 0:
if paddle.mean(x) > 0:
x_v = x - 1
else:
x_v = x + 1
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)
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])
# ['_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(
@ -993,18 +990,18 @@ class ProgramTranslator(object):
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
import paddle
def func(x):
x = fluid.dygraph.to_variable(x)
if fluid.layers.mean(x) > 0:
if paddle.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)
print(type(code)) # <class 'str'>
@ -1040,9 +1037,9 @@ class ProgramTranslator(object):
Examples:
.. 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()
"""

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