未验证 提交 1c9c8e8d 编写于 作者: Y Yu Yang 提交者: GitHub

Merge pull request #12065 from reyoung/feature/hide_apis

Add deprecated annotation and hide a lot of APIs
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import sys
__all__ = ['deprecated']
def deprecated(since, instead, extra_message=""):
def decorator(func):
err_msg = "API {0} is deprecated since {1}. Please use {2} instead.".format(
func.__name__, since, instead)
if len(extra_message) != 0:
err_msg += "\n"
err_msg += extra_message
@functools.wraps(func)
def wrapper(*args, **kwargs):
print >> sys.stderr, err_msg
return func(*args, **kwargs)
wrapper.__doc__ += "\n "
wrapper.__doc__ += err_msg
return wrapper
return decorator
......@@ -18,10 +18,7 @@ import collections
import copy
import unique_name
__all__ = [
'append_backward',
'calc_gradient',
]
__all__ = ['append_backward']
def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None):
......
......@@ -18,10 +18,12 @@ All util layers.
from layer_function_generator import autodoc
from ..framework import unique_name
from ..layer_helper import LayerHelper
from ..annotations import deprecated
__all__ = ['get_places']
__all__ = []
@deprecated(since='0.15.0', instead="ParallelExecutor")
@autodoc()
def get_places(device_count=None, device_type=None):
helper = LayerHelper('get_places', **locals())
......
......@@ -29,7 +29,7 @@ __all__ = [
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer',
'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'Optimizer', 'RMSPropOptimizer'
'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'RMSPropOptimizer'
]
......@@ -67,7 +67,7 @@ class Optimizer(object):
self._LARS_weight_decay = LARS_weight_decay
def _create_global_learning_rate(self):
lr = self.global_learning_rate()
lr = self._global_learning_rate()
if isinstance(lr, framework.Variable):
return
......@@ -86,7 +86,7 @@ class Optimizer(object):
dtype='float32' if self._dtype == None else self._dtype,
persistable=True)
def global_learning_rate(self, program=None):
def _global_learning_rate(self, program=None):
"""
get global decayed learning rate
:return:
......@@ -110,9 +110,9 @@ class Optimizer(object):
return param_lr
else:
if param_lr == 1.0:
return self.global_learning_rate()
return self._global_learning_rate()
else:
return self.global_learning_rate() * param_lr
return self._global_learning_rate() * param_lr
def _create_accumulators(self, block, parameters):
"""Create all accumulators needed by the parameters
......@@ -185,10 +185,10 @@ class Optimizer(object):
format(name, param.name))
return self._accumulators[name][param.name]
def create_optimization_pass(self,
parameters_and_grads,
loss,
startup_program=None):
def _create_optimization_pass(self,
parameters_and_grads,
loss,
startup_program=None):
"""Add optimization operators to update gradients to variables.
Args:
......@@ -221,7 +221,7 @@ class Optimizer(object):
self._create_global_learning_rate()
if self._LARS_weight_decay > 0.0:
layers.append_LARS(parameters_and_grads,
self.global_learning_rate(),
self._global_learning_rate(),
self._LARS_weight_decay)
optimize_ops = []
......@@ -262,8 +262,8 @@ class Optimizer(object):
params_grads = append_regularization_ops(params_grads,
self.regularization)
optimize_ops = self.create_optimization_pass(params_grads, loss,
startup_program)
optimize_ops = self._create_optimization_pass(params_grads, loss,
startup_program)
return optimize_ops, params_grads
......
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from paddle.fluid.layers.device import get_places
import unittest
import paddle.fluid as fluid
import paddle
......@@ -144,7 +144,7 @@ def train(word_dict,
cost, acc_out, prediction = net_method(
data, label, input_dim=dict_dim, class_dim=class_dim)
else:
places = fluid.layers.get_places()
places = get_places()
pd = fluid.layers.ParallelDo(places)
with pd.do():
cost, acc, _ = net_method(
......
......@@ -12,15 +12,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import argparse
import paddle.fluid as fluid
import paddle
import sys
import numpy
import unittest
import math
import sys
import os
import sys
import unittest
import numpy
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
BATCH_SIZE = 64
......@@ -76,7 +78,7 @@ def train(nn_type,
net_conf = conv_net
if parallel:
places = fluid.layers.get_places()
places = get_places()
pd = fluid.layers.ParallelDo(places)
with pd.do():
img_ = pd.read_input(img)
......
......@@ -14,6 +14,7 @@
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
import unittest
import os
import numpy as np
......@@ -80,7 +81,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
avg_cost, predict_word = __network__(
[first_word, second_word, third_word, forth_word, next_word])
else:
places = fluid.layers.get_places()
places = get_places()
pd = fluid.layers.ParallelDo(places)
with pd.do():
avg_cost, predict_word = __network__(
......
......@@ -12,12 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import paddle
import paddle.fluid as fluid
import math
import sys
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
......@@ -34,7 +35,7 @@ if fluid.core.is_compiled_with_cuda():
use_nccl = False
place = fluid.CUDAPlace(0)
places = fluid.layers.get_places(device_count=0, device_type=device_type)
places = get_places(device_count=0, device_type=device_type)
pd = fluid.layers.ParallelDo(places, use_nccl=use_nccl)
with pd.do():
x_ = pd.read_input(x)
......
......@@ -16,8 +16,6 @@ import unittest
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.framework as framework
import paddle.fluid.optimizer as optimizer
from paddle.fluid.backward import calc_gradient
......
......@@ -13,6 +13,7 @@
# limitations under the License.
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
import decorators
import unittest
......@@ -20,7 +21,7 @@ import unittest
class TestGetPlaces(unittest.TestCase):
@decorators.prog_scope()
def test_get_places(self):
places = fluid.layers.get_places()
places = get_places()
cpu = fluid.CPUPlace()
exe = fluid.Executor(cpu)
exe.run(fluid.default_main_program())
......
......@@ -16,6 +16,7 @@ from __future__ import print_function
import unittest
import paddle.fluid.layers as layers
from paddle.fluid.layers.device import get_places
import paddle.fluid.nets as nets
from paddle.fluid.framework import Program, program_guard, default_main_program
from paddle.fluid.param_attr import ParamAttr
......@@ -238,7 +239,7 @@ class TestBook(unittest.TestCase):
def test_get_places(self):
program = Program()
with program_guard(program):
x = layers.get_places(device_count=4)
x = get_places(device_count=4)
self.assertIsNotNone(x)
print(str(program))
......
......@@ -97,7 +97,7 @@ class TestMomentumOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
opts = momentum_optimizer.create_optimization_pass(
opts = momentum_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
self.assertEqual(len(opts), 3)
sgd_op = opts[-1]
......@@ -151,7 +151,7 @@ class TestMomentumOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(momentum_optimizer.get_accumulators()), 0)
opts = momentum_optimizer.create_optimization_pass(
opts = momentum_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
self.assertEqual(len(opts), 3)
sgd_op = opts[-1]
......@@ -214,8 +214,8 @@ class TestAdagradOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adagrad_optimizer.get_accumulators()), 0)
opts = adagrad_optimizer.create_optimization_pass(params_grads, mul_out,
init_program)
opts = adagrad_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
self.assertEqual(len(opts), 3)
self.assertEqual([op.type for op in opts],
["fill_constant", "elementwise_mul", "adagrad"])
......@@ -278,8 +278,8 @@ class TestAdamOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adam_optimizer.get_accumulators()), 0)
opts = adam_optimizer.create_optimization_pass(params_grads, mul_out,
init_program)
opts = adam_optimizer._create_optimization_pass(params_grads, mul_out,
init_program)
self.assertEqual(len(opts), 5)
self.assertEqual(
[op.type for op in opts],
......@@ -345,8 +345,8 @@ class TestAdamaxOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(adamax_optimizer.get_accumulators()), 0)
opts = adamax_optimizer.create_optimization_pass(params_grads, mul_out,
init_program)
opts = adamax_optimizer._create_optimization_pass(params_grads, mul_out,
init_program)
self.assertEqual(len(opts), 4)
self.assertEqual(
[op.type for op in opts],
......@@ -409,7 +409,7 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(decayed_adagrad_optimizer.get_accumulators()), 0)
opts = decayed_adagrad_optimizer.create_optimization_pass(
opts = decayed_adagrad_optimizer._create_optimization_pass(
params_grads, mul_out, init_program)
self.assertEqual(len(opts), 3)
self.assertEqual(
......@@ -475,8 +475,8 @@ class TestFtrlOptimizer(unittest.TestCase):
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(ftrl_optimizer.get_accumulators()), 0)
opts = ftrl_optimizer.create_optimization_pass(params_grads, mul_out,
init_program)
opts = ftrl_optimizer._create_optimization_pass(params_grads, mul_out,
init_program)
self.assertEqual(len(opts), 3)
self.assertEqual([op.type for op in opts],
["fill_constant", "elementwise_mul", "ftrl"])
......
......@@ -15,6 +15,7 @@
import unittest
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
import paddle.fluid.profiler as profiler
import numpy
......@@ -115,7 +116,7 @@ class BaseParallelForTest(unittest.TestCase):
if use_parallel:
thread_num = fluid.core.get_cuda_device_count(
) if use_gpu else 8
places = fluid.layers.get_places(thread_num)
places = get_places(thread_num)
pd = fluid.layers.ParallelDo(places, use_nccl=use_nccl)
data = next(generator)
......
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