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提交 e8cb97b8 编写于 作者: Y Yu Yang
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Moving unique_name to python 

* Add reset and guard to unique_name
上级 78cc64a5
隐藏空白更改
内联 并排
Showing with 134 addition and 86 deletion
paddle/fluid/pybind/pybind.cc
浏览文件 @ e8cb97b8
......@@ -48,11 +48,6 @@ PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);
namespace paddle {
namespace pybind {
static size_t UniqueIntegerGenerator(const std::string &prefix) {
static std::unordered_map<std::string, std::atomic<size_t>> generators;
return generators[prefix].fetch_add(1);
}
bool IsCompiledWithCUDA() {
#ifndef PADDLE_WITH_CUDA
return false;
......@@ -409,7 +404,6 @@ All parameter, weight, gradient are variables in Paddle.
(void (Executor::*)(const ProgramDesc &, Scope *, int, bool, bool)) &
Executor::Run);
m.def("unique_integer", UniqueIntegerGenerator);
m.def("init_gflags", framework::InitGflags);
m.def("init_glog", framework::InitGLOG);
m.def("init_devices", &framework::InitDevices);
......
python/paddle/v2/fluid/__init__.py
浏览文件 @ e8cb97b8
......@@ -39,30 +39,16 @@ from concurrency import (Go, make_channel, channel_send, channel_recv,
import clip
from memory_optimization_transpiler import memory_optimize
import profiler
import unique_name
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + [
'io',
'initializer',
'layers',
'nets',
'optimizer',
'learning_rate_decay',
'backward',
'regularizer',
'LoDTensor',
'CPUPlace',
'CUDAPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
'DataFeeder',
'clip',
'SimpleDistributeTranspiler',
'DistributeTranspiler',
'memory_optimize',
'profiler',
'io', 'initializer', 'layers', 'nets', 'optimizer', 'learning_rate_decay',
'backward', 'regularizer', 'LoDTensor', 'CPUPlace', 'CUDAPlace', 'Tensor',
'ParamAttr', 'WeightNormParamAttr', 'DataFeeder', 'clip',
'SimpleDistributeTranspiler', 'DistributeTranspiler', 'memory_optimize',
'profiler', 'unique_name'
]
......
python/paddle/v2/fluid/backward.py
浏览文件 @ e8cb97b8
......@@ -16,6 +16,7 @@ from paddle.v2.fluid import framework as framework
from . import core
import collections
import copy
import unique_name
__all__ = [
'append_backward',
......@@ -388,7 +389,7 @@ def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map):
for name in op_desc.output_arg_names():
if block.desc.find_var(name.encode("ascii")):
new_name = "%s_%s" % (name, core.unique_integer(name))
new_name = unique_name.generate(name)
op_desc.rename_output(name, new_name)
var_map[name] = new_name
......
python/paddle/v2/fluid/evaluator.py
浏览文件 @ e8cb97b8
......@@ -15,7 +15,8 @@
import numpy as np
import layers
from framework import Program, unique_name, Variable, program_guard
from framework import Program, Variable, program_guard
import unique_name
from layer_helper import LayerHelper
__all__ = [
......@@ -96,7 +97,7 @@ class Evaluator(object):
"""
state = self.helper.create_variable(
name="_".join([unique_name(self.helper.name), suffix]),
name="_".join([unique_name.generate(self.helper.name), suffix]),
persistable=True,
dtype=dtype,
shape=shape)
......
python/paddle/v2/fluid/framework.py
浏览文件 @ e8cb97b8
......@@ -20,6 +20,7 @@ import numpy as np
import proto.framework_pb2 as framework_pb2
from . import core
import unique_name
__all__ = [
'Block',
......@@ -47,20 +48,6 @@ def grad_var_name(var_name):
return var_name + GRAD_VAR_SUFFIX
def unique_name(prefix):
"""
Generate unique names with prefix
Args:
prefix(str): The prefix of return string
Returns(str): A unique string with the prefix
"""
uid = core.unique_integer(prefix) # unique during whole process.
return "_".join([prefix, str(uid)])
def convert_np_dtype_to_dtype_(np_dtype):
"""
Convert the data type in numpy to the data type in Paddle
......@@ -175,7 +162,7 @@ class Variable(object):
self.error_clip = error_clip
if name is None:
name = Variable._unique_var_name_()
name = unique_name.generate('_generated_var')
is_new_var = False
self.desc = self.block.desc.find_var(name)
......@@ -303,12 +290,6 @@ class Variable(object):
def type(self):
return self.desc.type()
@staticmethod
def _unique_var_name_():
prefix = "_generated_var"
uid = core.unique_integer(prefix) # unique during whole process.
return "_".join([prefix, str(uid)])
def set_error_clip(self, error_clip):
self.error_clip = error_clip
......
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ e8cb97b8
......@@ -15,8 +15,8 @@
import copy
import itertools
from framework import Variable, Parameter, default_main_program, default_startup_program, \
unique_name, dtype_is_floating
from framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
import unique_name
from paddle.v2.fluid.initializer import Constant, Xavier
from param_attr import ParamAttr, WeightNormParamAttr
......@@ -27,7 +27,7 @@ class LayerHelper(object):
self.layer_type = layer_type
name = self.kwargs.get('name', None)
if name is None:
self.kwargs['name'] = unique_name(self.layer_type)
self.kwargs['name'] = unique_name.generate(self.layer_type)
@property
def name(self):
......@@ -117,17 +117,20 @@ class LayerHelper(object):
block=self.startup_program.global_block()):
if out is None:
out = block.create_var(
name=unique_name(".".join([self.name, 'weight_norm_norm'])),
name=unique_name.generate(".".join(
[self.name, 'weight_norm_norm'])),
dtype=dtype,
persistable=False)
abs_out = block.create_var(
name=unique_name(".".join([self.name, 'weight_norm_abs'])),
name=unique_name.generate(".".join(
[self.name, 'weight_norm_abs'])),
dtype=dtype,
persistable=False)
block.append_op(
type='abs', inputs={'X': x}, outputs={'Out': abs_out})
pow_out = block.create_var(
name=unique_name(".".join([self.name, 'weight_norm_pow'])),
name=unique_name.generate(".".join(
[self.name, 'weight_norm_pow'])),
dtype=dtype,
persistable=False)
block.append_op(
......@@ -136,7 +139,8 @@ class LayerHelper(object):
outputs={'Out': pow_out},
attrs={'factor': float(p)})
sum_out = block.create_var(
name=unique_name(".".join([self.name, 'weight_norm_sum'])),
name=unique_name.generate(".".join(
[self.name, 'weight_norm_sum'])),
dtype=dtype,
persistable=False)
block.append_op(
......@@ -161,7 +165,7 @@ class LayerHelper(object):
block=self.startup_program.global_block()):
if out is None:
out = block.create_var(
name=unique_name(".".join(
name=unique_name.generate(".".join(
[self.name, 'weight_norm_reshape'])),
dtype=dtype,
persistable=False)
......@@ -178,7 +182,7 @@ class LayerHelper(object):
block=self.startup_program.global_block()):
if out is None:
out = block.create_var(
name=unique_name(".".join(
name=unique_name.generate(".".join(
[self.name, 'weight_norm_transpose'])),
dtype=dtype,
persistable=False)
......@@ -196,7 +200,8 @@ class LayerHelper(object):
"""Computes the norm over all dimensions except dim"""
if out is None:
out = block.create_var(
name=unique_name(".".join([self.name, 'weight_norm_norm'])),
name=unique_name.generate(".".join(
[self.name, 'weight_norm_norm'])),
dtype=dtype,
persistable=False)
if dim is None:
......@@ -286,7 +291,7 @@ class LayerHelper(object):
assert isinstance(attr, ParamAttr)
suffix = 'b' if is_bias else 'w'
if attr.name is None:
attr.name = unique_name(".".join([self.name, suffix]))
attr.name = unique_name.generate(".".join([self.name, suffix]))
if default_initializer is None and attr.initializer is None:
if is_bias:
......@@ -316,7 +321,7 @@ class LayerHelper(object):
def create_tmp_variable(self, dtype, stop_gradient=False):
return self.main_program.current_block().create_var(
name=unique_name(".".join([self.name, 'tmp'])),
name=unique_name.generate(".".join([self.name, 'tmp'])),
dtype=dtype,
persistable=False,
stop_gradient=stop_gradient)
......
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @ e8cb97b8
......@@ -428,7 +428,8 @@ class StaticRNN(object):
raise ValueError(
"if init is None, memory at least need shape and batch_ref")
parent_block = self.parent_block()
var_name = unique_name("@".join([self.helper.name, "memory_boot"]))
var_name = unique_name.generate("@".join(
[self.helper.name, "memory_boot"]))
boot_var = parent_block.create_var(
name=var_name,
shape=shape,
......@@ -450,7 +451,7 @@ class StaticRNN(object):
return self.memory(init=boot_var)
else:
pre_mem = self.helper.create_variable(
name=unique_name("@".join([self.helper.name, "mem"])),
name=unique_name.generate("@".join([self.helper.name, "mem"])),
dtype=init.dtype,
shape=init.shape)
self.memories[pre_mem.name] = StaticRNNMemoryLink(
......@@ -709,7 +710,7 @@ def lod_rank_table(x, level=0):
helper = LayerHelper("lod_rank_table", **locals())
table = helper.create_variable(
type=core.VarDesc.VarType.LOD_RANK_TABLE,
name=unique_name("lod_rank_table"))
name=unique_name.generate("lod_rank_table"))
helper.append_op(
type='lod_rank_table',
inputs={'X': x},
......@@ -807,7 +808,7 @@ def lod_tensor_to_array(x, table):
"""
helper = LayerHelper("lod_tensor_to_array", **locals())
array = helper.create_variable(
name=unique_name("lod_tensor_to_array"),
name=unique_name.generate("lod_tensor_to_array"),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.dtype)
helper.append_op(
......@@ -1264,11 +1265,11 @@ class IfElse(object):
if id(x) not in self.input_table:
parent_block = self.parent_block()
out_true = parent_block.create_var(
name=unique_name('ifelse_input' + self.helper.name),
name=unique_name.generate('ifelse_input' + self.helper.name),
dtype=x.dtype)
out_false = parent_block.create_var(
name=unique_name('ifelse_input' + self.helper.name),
name=unique_name.generate('ifelse_input' + self.helper.name),
dtype=x.dtype)
parent_block.append_op(
type='split_lod_tensor',
......@@ -1310,7 +1311,8 @@ class IfElse(object):
raise TypeError("Each output should be a variable")
# create outside tensor
outside_out = parent_block.create_var(
name=unique_name("_".join([self.helper.name, 'output'])),
name=unique_name.generate("_".join(
[self.helper.name, 'output'])),
dtype=each_out.dtype)
out_table.append(outside_out)
......@@ -1373,7 +1375,7 @@ class DynamicRNN(object):
parent_block = self._parent_block_()
if self.lod_rank_table is None:
self.lod_rank_table = parent_block.create_var(
name=unique_name('lod_rank_table'),
name=unique_name.generate('lod_rank_table'),
type=core.VarDesc.VarType.LOD_RANK_TABLE)
self.lod_rank_table.stop_gradient = True
parent_block.append_op(
......@@ -1381,7 +1383,8 @@ class DynamicRNN(object):
inputs={"X": x},
outputs={"Out": self.lod_rank_table})
self.max_seq_len = parent_block.create_var(
name=unique_name('dynamic_rnn_max_seq_len'), dtype='int64')
name=unique_name.generate('dynamic_rnn_max_seq_len'),
dtype='int64')
self.max_seq_len.stop_gradient = False
parent_block.append_op(
type='max_sequence_len',
......@@ -1395,7 +1398,7 @@ class DynamicRNN(object):
outputs={'Out': self.cond})
input_array = parent_block.create_var(
name=unique_name('dynamic_rnn_input_array'),
name=unique_name.generate('dynamic_rnn_input_array'),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.dtype)
self.input_array.append((input_array, x.dtype))
......@@ -1416,7 +1419,7 @@ class DynamicRNN(object):
"static_input() must be called after step_input().")
parent_block = self._parent_block_()
x_reordered = parent_block.create_var(
name=unique_name("dynamic_rnn_static_input_reordered"),
name=unique_name.generate("dynamic_rnn_static_input_reordered"),
type=core.VarDesc.VarType.LOD_TENSOR,
dtype=x.dtype)
parent_block.append_op(
......@@ -1478,7 +1481,7 @@ class DynamicRNN(object):
'invoked before '
'memory(init=init, need_reordered=True, ...).')
init_reordered = parent_block.create_var(
name=unique_name('dynamic_rnn_mem_init_reordered'),
name=unique_name.generate('dynamic_rnn_mem_init_reordered'),
type=core.VarDesc.VarType.LOD_TENSOR,
dtype=init.dtype)
parent_block.append_op(
......@@ -1490,7 +1493,7 @@ class DynamicRNN(object):
outputs={'Out': [init_reordered]})
init_tensor = init_reordered
mem_array = parent_block.create_var(
name=unique_name('dynamic_rnn_mem_array'),
name=unique_name.generate('dynamic_rnn_mem_array'),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=init.dtype)
parent_block.append_op(
......@@ -1510,9 +1513,10 @@ class DynamicRNN(object):
)
parent_block = self._parent_block_()
init = parent_block.create_var(
name=unique_name('mem_init'), dtype=dtype)
name=unique_name.generate('mem_init'), dtype=dtype)
arr, dtype = self.input_array[0]
in0 = parent_block.create_var(name=unique_name('in0'), dtype=dtype)
in0 = parent_block.create_var(
name=unique_name.generate('in0'), dtype=dtype)
parent_block.append_op(
type='read_from_array',
inputs={'X': [arr],
......@@ -1551,7 +1555,7 @@ class DynamicRNN(object):
parent_block = self._parent_block_()
for each in outputs:
outside_array = parent_block.create_var(
name=unique_name("_".join(
name=unique_name.generate("_".join(
[self.helper.name, "output_array", each.name])),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=each.dtype)
......
python/paddle/v2/fluid/layers/device.py
浏览文件 @ e8cb97b8
......@@ -25,7 +25,8 @@ __all__ = ['get_places']
@autodoc()
def get_places(device_count=None, device_type=None):
helper = LayerHelper('get_places', **locals())
out_places = helper.create_variable(name=unique_name(helper.name + ".out"))
out_places = helper.create_variable(
name=unique_name.generate(helper.name + ".out"))
attrs = dict()
if device_count is not None:
attrs['device_count'] = int(device_count)
......
python/paddle/v2/fluid/layers/math_op_patch.py
浏览文件 @ e8cb97b8
......@@ -21,7 +21,7 @@ __all__ = ['monkey_patch_variable']
def monkey_patch_variable():
def unique_tmp_name():
return unique_name("tmp")
return unique_name.generate("tmp")
def safe_get_dtype(var):
try:
......
python/paddle/v2/fluid/optimizer.py
浏览文件 @ e8cb97b8
......@@ -17,7 +17,8 @@ from collections import defaultdict
import framework
import layers
from backward import append_backward
from framework import unique_name, program_guard
from framework import program_guard
import unique_name
from initializer import Constant
from layer_helper import LayerHelper
from regularizer import append_regularization_ops
......@@ -49,7 +50,7 @@ class Optimizer(object):
def _create_global_learning_rate(self):
if isinstance(self._global_learning_rate, float):
self._global_learning_rate = layers.create_global_var(
name=unique_name("learning_rate"),
name=unique_name.generate("learning_rate"),
shape=[1],
value=float(self._global_learning_rate),
dtype='float32',
......@@ -118,7 +119,7 @@ class Optimizer(object):
assert isinstance(self.helper, LayerHelper)
var = self.helper.create_global_variable(
name=unique_name(name),
name=unique_name.generate(name),
persistable=True,
dtype=dtype or param.dtype,
type=param.type,
......@@ -379,7 +380,7 @@ class AdamOptimizer(Optimizer):
# Create beta1 and beta2 power tensors
beta_shape = [1]
self._beta1_pow_acc = self.helper.create_global_variable(
name=unique_name('beta1_pow_acc'),
name=unique_name.generate('beta1_pow_acc'),
dtype='float32',
shape=beta_shape,
lod_level=0,
......@@ -388,7 +389,7 @@ class AdamOptimizer(Optimizer):
self._beta1_pow_acc, initializer=Constant(self._beta1))
self._beta2_pow_acc = self.helper.create_global_variable(
name=unique_name('beta2_pow_acc'),
name=unique_name.generate('beta2_pow_acc'),
dtype='float32',
shape=beta_shape,
lod_level=0,
......@@ -481,7 +482,7 @@ class AdamaxOptimizer(Optimizer):
# Create beta1 power accumulator tensor
beta_shape = [1]
self._beta1_pow_acc = self.helper.create_global_variable(
name=unique_name('beta1_pow_acc'),
name=unique_name.generate('beta1_pow_acc'),
dtype='float32',
shape=beta_shape,
lod_level=0,
......
python/paddle/v2/fluid/unique_name.py 0 → 100644
浏览文件 @ e8cb97b8
# 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 collections
import contextlib
import sys
__all__ = ['generate', 'switch', 'guard', 'UniqueNameGenerator']
class UniqueNameGenerator(object):
"""
Generate unique name with prefix.
Args:
prefix(str): The generated name prefix. All generated name will be
started with this prefix.
"""
def __init__(self, prefix=None):
self.ids = collections.defaultdict(int)
if prefix is None:
prefix = ""
self.prefix = prefix
def __call__(self, key):
"""
Generate unique names with prefix
Args:
key(str): The key of return string.
Returns(str): A unique string with the prefix
"""
tmp = self.ids[key]
self.ids[key] += 1
return self.prefix + "_".join([key, str(tmp)])
generator = UniqueNameGenerator()
def generate(prefix):
return generator(prefix)
def switch(new_generator=None):
global generator
old = generator
if new_generator is None:
generator = UniqueNameGenerator()
else:
generator = new_generator
return old
@contextlib.contextmanager
def guard(new_generator=None):
if isinstance(new_generator, basestring):
new_generator = UniqueNameGenerator(new_generator)
old = switch(new_generator)
yield
switch(old)
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