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提交 1840bd75 编写于 作者: M minqiyang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_is_taged

上级 f8e83196 72ce4d56
隐藏空白更改
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Showing with 773 addition and 689 deletion
python/paddle/fluid/__init__.py
浏览文件 @ 1840bd75
......@@ -65,7 +65,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
'io',
'initializer',
'layers',
'transpiler'
'transpiler',
'nets',
'optimizer',
'learning_rate_decay',
......
python/paddle/fluid/io.py
浏览文件 @ 1840bd75
......@@ -24,10 +24,7 @@ from . import core
__all__ = [
'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
'load_persistables', 'save_inference_model', 'load_inference_model',
'get_inference_program', 'save_checkpoint', 'load_checkpoint',
'clean_checkpoint', 'load_persist_vars_without_grad',
'load_lookup_table_vars', 'save_persist_vars_without_grad',
'get_latest_checkpoint_serial'
'get_inference_program'
]
......@@ -794,588 +791,6 @@ def get_parameter_value_by_name(name, executor, program=None):
return get_parameter_value(var, executor)
SUCCESS_MARK_FILENAME = "_SUCCESS"
CHECKPOINT_PREFIX = "checkpoint"
MODEL_DIR = "__model__"
LOOKUP_TABLE_DIR = "__lookup_table__"
TRAINER_PREFIX = "trainer"
CHECKPOINT_SEPARATOR = "_"
def save_checkpoint(executor,
checkpoint_dir,
trainer_id,
trainer_args=None,
main_program=None,
max_num_checkpoints=3,
lookup_table=None,
ps_endpoint_list=None):
"""
This function filters out all checkpoint variables from the give
main_program and then saves these variables to the `checkpoint_dir`
directory.
In the training precess, we generally save a checkpoint in each
iteration. So there might be a lot of checkpoints in the
`checkpoint_dir`. To avoid them taking too much disk space, the
`max_num_checkpoints` are introduced to limit the total number of
checkpoints. If the number of existing checkpints is greater than
the `max_num_checkpoints`, oldest ones will be scroll deleted.
A variable is a checkpoint variable and will be saved if it meets
all following conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for save checkpoint.
checkpoint_dir(str): The folder where to save checkpoints.
trainer_id(int): currect trainer id, if id is equal to 0, the trainer
is chief.
trainer_args(dict|None): Current training arguments. Such as 'epoch_id'
and 'step_id'.
Defaut: None
main_program(Program|None): The program whose checkpoint variables will
be saved. If it is None, the default main program will be used.
max_num_checkpoints(int): The max number of total number of existing
checkpoints.
Default: 3
lookup_table(string|None): the lookup table name, when use distribute
lookup table, we can get lookup table name by DistributeTranspiler.
table_name
ps_endpoint_list(list|None): the parameter server ip:port list.
when use distribute lookup table, we can get ps_endpoint_list by
distribute arguments.
Returns:
None
Raises:
ValueError: If `checkpoint_dir` is None.
AssertionError: If `trainer_args` is not a dict.
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
path = "./checkpoints"
prog = fluid.default_main_program()
trainer_args = {"epoch_id": 200,
"step_id": 20} # just an example
table_name = "share_w"
ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
fluid.io.save_checkpoint(executor=exe,
checkpoint_dir=path,
trainer_id=0,
trainer_args=trainer_args,
main_program=prog,
max_num_checkpoints=3,
lookup_table=table_name,
ps_endpoint_list = ps_endpoints)
"""
if checkpoint_dir is None:
raise ValueError("'checkpoint_dir' should not be None")
assert checkpoint_dir
if trainer_args:
assert isinstance(trainer_args, dict)
is_chief = trainer_id == 0
_make_chekcpoint_dirs(checkpoint_dir)
serial = get_latest_checkpoint_serial(checkpoint_dir) + 1
cur_dir = _get_serial_dir(checkpoint_dir, serial)
save_trainer_args(cur_dir, trainer_id, trainer_args)
if is_chief:
save_persist_vars_without_grad(executor, cur_dir, main_program)
if is_chief and lookup_table and ps_endpoint_list:
save_pserver_vars_by_notify(executor, cur_dir, lookup_table,
ps_endpoint_list)
_scroll_delete(checkpoint_dir, max_num_checkpoints)
def load_checkpoint(executor, checkpoint_dir, serial, main_program):
"""
This function filters out all checkpoint variables from the give
main_program and then try to load these variables from the
`checkpoint_dir` directory.
In the training precess, we generally save a checkpoint in each
iteration. So there are more than one checkpoint in the
`checkpoint_dir` (each checkpoint has its own sub folder), use
`serial` to specify which serial of checkpoint you would like to
load.
A variable is a checkpoint variable and will be loaded if it meets
all following conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for loading checkpoint.
checkpoint_dir(str): The folder where all checkpoints are.
serial(int): The serial of checkpoint you would like to load.
main_program(Program): The program whose checkpoint variables will
be loaded.
Returns:
None
Raises:
ValueError: If `checkpoint_dir` is None.
ValueError: If `serial` is None or `serial` is less than 0.
ValueError: If `main_program` is None.
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
path = "./checkpoints"
prog = fluid.default_main_program()
fluid.io.load_checkpoint(executor=exe, checkpoint_dir=path,
serial=9, main_program=prog)
# In this example, `load_checkpoint` function
# will first filters out all checkpoint variables in the default
# main program, and then try to load these variables form the
# folder "./checkpoints/checkpoint_9/__model__".
"""
if checkpoint_dir is None:
raise ValueError("'checkpoint_dir' should not be None")
if serial is None or serial < 0:
raise ValueError("'serial' should not be None or <0 ")
if main_program is None:
raise ValueError('main_program should not be None.')
cur_dir = _get_serial_dir(checkpoint_dir, serial)
load_persist_vars_without_grad(executor, cur_dir, main_program, True)
def clean_checkpoint(checkpoint_dir, delete_dir=False):
"""
clean the checkpoint dir, when the train exits normally,
the trainer will call clean_checkpoint to delete checkpoint directory saved before.
delete_dir only works when the directory is empty, otherwise, OSError is raised.
: param checkpoint_dir
: param delete_dir
"""
if checkpoint_dir is None:
raise ValueError("'checkpoint_dir' should not be None")
_scroll_delete(checkpoint_dir, max_num_checkpoints=0)
if delete_dir and not os.listdir(checkpoint_dir):
os.rmdir(checkpoint_dir)
def load_persist_vars_without_grad(executor,
dirname,
program,
has_model_dir=False):
"""
This function filters out all checkpoint variables from the give
program and then trys to load these variables from the given directory.
A variable is a checkpoint variable if it meets all following
conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for loading variables.
dirname(str): The directory path.
program(Program): The program whose checkpoint variables will
be loaded.
has_model_dir(bool): if True, the function loads variables
from a sub directory named '__model__'.
Default: False
Returns:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
fluid.io.load_persist_vars_without_grad(executor=exe,
dirname=param_path, program=prog, has_model_dir=True)
# In this example, `load_persist_vars_without_grad` function
# will first filters out all checkpoint variables in the default
# main program, and then trys to load these variables form the
# folder "./my_paddle_model/__model__".
"""
if has_model_dir:
dirname = _get_model_dir(dirname)
load_vars(
executor,
dirname=dirname,
main_program=program,
predicate=_is_checkpoint_var,
filename=None)
def load_lookup_table_vars(executor, dirname, program, pserver_id, table_name):
"""
The parameter server will load lookup table's local file in
selectedrows variable.
Args:
executor(Executor): The executor to run for loading persistable variables
dirname(str): The directory path
main_program(Program): Find the variable named table_name in main_program
pserver_id(int): the serial number in pserver_endpoints list
table_name(str): lookup table name
Returns:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
dirname = "./checkpoints/checkpoint_9/__model__"
prog = fluid.default_main_program()
pserver_id = 1
table_name = "share_w"
fluid.io.load_lookup_table_vars(executor=exe,
dirname=dirname, program=prog, pserver_id=pserver_id,
table_name=table_name)
"""
for var in program.list_vars():
if var.name == table_name:
lookup_table_var = var
break
assert lookup_table_var is not None
lookup_table_dir = os.path.join(dirname, LOOKUP_TABLE_DIR)
table_file = table_name + CHECKPOINT_SEPARATOR + str(pserver_id)
load_prog = Program()
load_block = load_prog.global_block()
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [lookup_table_var]},
attrs={'file_path': os.path.join(lookup_table_dir, table_file)})
executor.run(load_prog)
def save_persist_vars_without_grad(executor, dirname, program):
"""
This function filters out all checkpoint variables from the give
program and then save these variables to a sub-folder '__model__' of
the given directory.
A variable is a checkpoint variable if it meets all following
conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for saving variables.
dirname(str): The directory path.
program(Program): The program whose checkpoint variables will
be saved.
Returns:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
fluid.io.save_persist_vars_without_grad(executor=exe,
dirname=param_path, program=prog)
# In this example, `save_persist_vars_without_grad` function
# will first filters out all checkpoint variables in the default
# main program, and then saves these variables to the folder
# "./my_paddle_model/__model__".
"""
cur_dir = _get_model_dir(dirname)
save_vars(
executor,
dirname=cur_dir,
main_program=program,
vars=None,
predicate=_is_checkpoint_var,
filename=None)
_write_success(cur_dir)
def save_pserver_vars_by_notify(executor, dirname, lookup_table,
ps_endpoint_list):
"""
This function will send checkpoint notify message from Trainer 0
to all the pservers.
The checkpoint notify message contains lookup table name,
the absolute path on pserver to save lookup_table.
Args:
executor(Executor): The executor to run for send checkpoint notify.
dirname(str): The folder where to save checkpoints.
lookup_table(string): the lookup table name, when use distribute
lookup table, we can get lookup table name by DistributeTranspiler.
table_name
ps_endpoint_list(list): the parameter server ip:port list.
when use distribute lookup table, we can get ps_endpoint_list by
distribute arguments.
Return:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
table_name = "share_w"
ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
fluid.io.save_pserver_vars_by_notify(executor=exe,
dirname=param_path, lookup_table=table_name,
ps_endpoint_list=ps_endpoints)
"""
cur_dir = _get_lookuptable_dir(dirname)
checkpoint_notify_program = Program()
checkpoint_notify_block = checkpoint_notify_program.global_block()
attrs = {}
attrs['epmap'] = ps_endpoint_list
attrs['dir'] = cur_dir
attrs['lookup_table'] = lookup_table
checkpoint_notify_block.append_op(
type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)
executor.run(checkpoint_notify_program)
def save_trainer_args(dirname, trainer_id, trainer_args):
assert isinstance(trainer_args, dict)
cur_dir = _get_trainer_dir(dirname, trainer_id)
for name, value in trainer_args.iteritems():
args_file = os.path.join(cur_dir, name)
with open(args_file, 'w') as f:
f.write(str(value))
_write_success(cur_dir)
def load_trainer_args(checkpoint_dir, serial, trainer_id, trainer_args):
"""
trainer will load some args from it's independent directory,
such as epoch_id and step_id.
Args:
checkpoint_dir(str): The folder where all checkpoints are.
serial(int): The serial of checkpoint you would like to load.
trainer_id(int): current trainer id.
trainer_args(list): list about load trainer args
Return:
None
Examples:
.. code-block:: python
param_path = "./checkpoint/"
serial = 7
trainer_id = 2
trainer_args = ["epoch_id", "step_id"]
fluid.io.load_trainer_args(checkpoint_dir=param_path, serial=serial,
trainer_id=trainer_id, trainer_args=trainer_args)
"""
assert isinstance(trainer_args, list)
cur_dir = _get_serial_dir(checkpoint_dir, serial)
cur_dir = _get_trainer_dir(cur_dir, trainer_id)
ret_values = []
for arg in trainer_args:
cur_file = os.path.join(cur_dir, arg)
with open(cur_file, 'r') as f:
contents = f.read()
ret_values.append(contents.strip())
return ret_values
def _is_checkpoint_var(var):
"""
the checkpoint will not save or load all the variables.
var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
: param var(Variable)
"""
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.RAW:
return False
# @GRAD are named for gradient variables, checkpoint will not save it.
if "@GRAD" in var.name:
return False
# .trainer_ are named for distribute train variables, checkpoint will not save it.
if ".trainer_" in var.name:
return False
# .block is named for distribute train variables, checkpoint will not save it.
if ".block" in var.name:
return False
return var.persistable
def _make_chekcpoint_dirs(dirs):
"""
_make_chekcpoint_dirs will makdir local directory directly, when the directory is exist, it will igore it.
"""
assert dirs is not None
if os.path.isfile(dirs):
raise OSError(errno.ENOTDIR, "dirs path shoule be a Directory.", dirs)
if not os.path.isdir(dirs):
try:
os.makedirs(dirs)
except OSError as err:
if err.errno != errno.EEXIST:
raise err
def _get_dir_serial(dirname):
_, serial = dirname.split(CHECKPOINT_SEPARATOR)
try:
serial_num = int(serial)
except ValueError:
serial_num = -1
return serial_num
def _get_serial_dir(dirname, serial):
serial_folder = CHECKPOINT_PREFIX + CHECKPOINT_SEPARATOR + str(serial)
serial_dir = os.path.join(dirname, serial_folder)
_make_chekcpoint_dirs(serial_dir)
return serial_dir
def _get_model_dir(dirname):
model_dir = os.path.join(dirname, MODEL_DIR)
_make_chekcpoint_dirs(model_dir)
return model_dir
def _get_lookuptable_dir(dirname):
lookuptable_dir = os.path.join(dirname, LOOKUP_TABLE_DIR)
_make_chekcpoint_dirs(lookuptable_dir)
return lookuptable_dir
def _get_trainer_dir(dirname, trainer_id):
trainer_folder = TRAINER_PREFIX + CHECKPOINT_SEPARATOR + str(trainer_id)
trainer_dir = os.path.join(dirname, trainer_folder)
_make_chekcpoint_dirs(trainer_dir)
return trainer_dir
def _scroll_delete(dirname, max_num_checkpoints=3):
dirs = os.listdir(dirname)
serial_map = {}
for serial in dirs:
serial_num = _get_dir_serial(serial)
serial_map[serial_num] = serial
if len(serial_map.keys()) <= max_num_checkpoints:
return
serials = serial_map.keys()
serials.sort(reverse=True)
serials = serials[max_num_checkpoints:]
for serial in serials:
cur_dir = _get_serial_dir(dirname, serial)
try:
shutil.rmtree(cur_dir)
except OSError as err:
if err.errno != errno.ENOENT:
raise err
def _write_success(dirname):
"""
write an empty file named "_SUCCESS" in checkpoint dir, indicate this checkpoint is correct.
: param dirname
"""
success_file = os.path.join(dirname, SUCCESS_MARK_FILENAME)
with open(success_file, 'a') as f:
now = time.ctime()
f.write(now)
def get_latest_checkpoint_serial(checkpoint_dir):
"""
get the latest file in checkpoint directory, the _SUCCESS file must exist in the directory
: param checkpoint_dir
"""
if not checkpoint_dir:
return -1
def has_success(checkpoint_dir, cur_dir):
"""
is _SUCCESS in this dir
"""
serial = _get_dir_serial(cur_dir)
if serial == -1 or not os.path.isdir(
os.path.join(checkpoint_dir, cur_dir)):
return -1
success_path = os.path.join(
_get_serial_dir(checkpoint_dir, serial), MODEL_DIR,
SUCCESS_MARK_FILENAME)
if os.path.isfile(success_path):
return serial
if not os.path.isdir(checkpoint_dir):
return -1
current_dir = -1
dirs = os.listdir(checkpoint_dir)
for cur_dir in dirs:
success_num = has_success(checkpoint_dir, cur_dir)
if success_num > current_dir:
current_dir = success_num
return current_dir
def get_test_program(filelist, program=None, startup_program=None):
"""
Transpile current train program to a program to read test dataset
......
python/paddle/fluid/tests/unittests/test_checkpoint.py 已删除 100644 → 0
浏览文件 @ f8e83196
# 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 paddle.fluid as fluid
import unittest
import os
import tempfile
class TestCheckpoint(unittest.TestCase):
def setUp(self):
self.dirname = tempfile.mktemp()
self.max_num_checkpoints = 3
self.epoch_interval = 1
self.step_interval = 1
self.trainer_id = 0
self.chief = self.trainer_id == 0
self.place = fluid.CPUPlace()
self.epoch_id = 100
self.step_id = 20
def test_checkpoint(self):
self.save_checkpoint()
serial = fluid.io.get_latest_checkpoint_serial(self.dirname)
self.assertTrue(serial >= 0)
trainer_args = ["epoch_id", "step_id"]
epoch_id, step_id = fluid.io.load_trainer_args(
self.dirname, serial, self.trainer_id, trainer_args)
self.assertEqual(self.step_id, int(step_id))
self.assertEqual(self.epoch_id, int(epoch_id))
program = fluid.Program()
with fluid.program_guard(program):
exe = fluid.Executor(self.place)
fluid.io.load_checkpoint(exe, self.dirname, serial, program)
fluid.io.clean_checkpoint(self.dirname, delete_dir=True)
self.assertFalse(os.path.isdir(self.dirname))
def save_checkpoint(self):
config = fluid.CheckpointConfig(self.dirname, self.max_num_checkpoints,
self.epoch_interval, self.step_interval)
trainer_args = {}
trainer_args["epoch_id"] = self.epoch_id
trainer_args["step_id"] = self.step_id
program = fluid.Program()
with fluid.program_guard(program):
program.global_block().create_var(
name="scale_0",
psersistable=True,
dtype="float32",
shape=[32, 32])
exe = fluid.Executor(self.place)
for i in xrange(10):
fluid.io.save_checkpoint(exe, config.checkpoint_dir,
self.trainer_id, trainer_args, program,
config.max_num_checkpoints)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_reader_reset.py 0 → 100644
浏览文件 @ 1840bd75
# 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 paddle.fluid as fluid
import paddle.v2 as paddle
import numpy as np
import unittest
class TestReaderReset(unittest.TestCase):
def prepare_data(self):
def fake_data_generator():
for n in xrange(self.total_ins_num):
yield np.ones(self.ins_shape) * n, n
# Prepare data
with fluid.program_guard(fluid.Program(), fluid.Program()):
reader = paddle.batch(fake_data_generator, batch_size=1)
feeder = fluid.DataFeeder(
feed_list=[
fluid.layers.data(
name='data', shape=[3], dtype='float32'),
fluid.layers.data(
name='label', shape=[1], dtype='int64'),
],
place=fluid.CPUPlace())
fluid.recordio_writer.convert_reader_to_recordio_file(
self.data_file_name, reader, feeder)
def setUp(self):
self.use_cuda = fluid.core.is_compiled_with_cuda()
self.data_file_name = './reader_reset_test.recordio'
self.ins_shape = [3]
self.batch_size = 5
self.total_ins_num = self.batch_size * 20
self.test_pass_num = 100
self.prepare_data()
def main(self, with_double_buffer):
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
data_reader_handle = fluid.layers.io.open_files(
filenames=[self.data_file_name],
shapes=[[-1] + self.ins_shape, [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'],
thread_num=1,
pass_num=1)
data_reader = fluid.layers.io.batch(data_reader_handle,
self.batch_size)
if with_double_buffer:
data_reader = fluid.layers.double_buffer(data_reader)
image, label = fluid.layers.read_file(data_reader)
fetch_list = [image.name, label.name]
place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
build_strategy = fluid.BuildStrategy()
if with_double_buffer:
build_strategy.enable_data_balance = True
exec_strategy = fluid.ExecutionStrategy()
parallel_exe = fluid.ParallelExecutor(
use_cuda=self.use_cuda,
main_program=main_prog,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
data_appeared = [False] * self.total_ins_num
pass_count = 0
while (True):
try:
data_val, label_val = parallel_exe.run(fetch_list,
return_numpy=True)
ins_num = data_val.shape[0]
broadcasted_label = np.ones((ins_num, ) + tuple(
self.ins_shape)) * label_val.reshape((ins_num, 1))
self.assertEqual(data_val.all(), broadcasted_label.all())
for l in label_val:
self.assertFalse(data_appeared[l[0]])
data_appeared[l[0]] = True
except fluid.core.EOFException:
pass_count += 1
if with_double_buffer:
data_appeared = data_appeared[:-parallel_exe.device_count *
self.batch_size]
for i in data_appeared:
self.assertTrue(i)
if pass_count < self.test_pass_num:
data_appeared = [False] * self.total_ins_num
data_reader_handle.reset()
else:
break
def test_all(self):
self.main(with_double_buffer=False)
self.main(with_double_buffer=True)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/trainer.py
浏览文件 @ 1840bd75
......@@ -14,6 +14,9 @@
import contextlib
import os
import errno
import shutil
import time
import core
......@@ -94,7 +97,7 @@ class EndStepEvent(object):
class CheckpointConfig(object):
"""
Parameter object for :code:`fluid.io.save_checkpoint` and
Parameter object for :code:`save_checkpoint` and
:code:`fluid.Trainer`. Used to configuration how to save checkpoint.
Args:
......@@ -237,7 +240,7 @@ class Trainer(object):
self.checkpoint_cfg = checkpoint_config
if self.checkpoint_cfg:
assert isinstance(self.checkpoint_cfg, CheckpointConfig)
serial = io.get_latest_checkpoint_serial(
serial = _get_latest_checkpoint_serial(
self.checkpoint_cfg.checkpoint_dir)
self.checkpoint_cfg.load_serial = serial if serial >= 0 else None
......@@ -276,32 +279,15 @@ class Trainer(object):
exe = executor.Executor(place)
exe.run(self.startup_program)
if self.checkpoint_cfg and self.checkpoint_cfg.load_serial:
with self._prog_and_scope_guard():
exe = executor.Executor(place)
io.load_checkpoint(exe, self.checkpoint_cfg.checkpoint_dir,
self.checkpoint_cfg.load_serial,
self.startup_program)
if not self.checkpoint_cfg.pserver_id:
epoch_id, step_id = io.load_trainer_args(
self.checkpoint_cfg.checkpoint_dir,
self.checkpoint_cfg.load_serial, self.trainer_id,
self._get_checkpoint_load_args())
self.checkpoint_cfg.epoch_id = int(epoch_id)
self.checkpoint_cfg.step_id = int(step_id)
else:
if self.checkpoint_cfg.lookup_table_name:
io.load_lookup_table_vars(
exe, self.checkpoint_cfg.checkpoint_dir,
self.startup_program,
self.checkpoint_cfg.pserver_id,
self.checkpoint_cfg.lookup_table_name)
if self.checkpoint_cfg and self.checkpoint_cfg.load_serial is not None:
self._load_checkpoint()
if param_path and os.path.isdir(param_path):
# load params from param_path into scope
io.load_persist_vars_without_grad(
exe, dirname=param_path, program=self.startup_program)
io.load_persistables(
executor=exe,
dirname=param_path,
main_program=self.startup_program)
def _transpile_nccl2_dist(self):
# PADDLE_TRAINER_IPS
......@@ -549,7 +535,7 @@ class Trainer(object):
def _clean_checkpoint(self):
assert self.checkpoint_cfg
io.clean_checkpoint(checkpoint_dir=self.checkpoint_cfg.checkpoint_dir)
clean_checkpoint(checkpoint_dir=self.checkpoint_cfg.checkpoint_dir)
def _get_checkpoint_load_args(self):
"""
......@@ -572,7 +558,7 @@ class Trainer(object):
if epoch_id % self.checkpoint_cfg.epoch_interval == 0 \
and step_id % self.checkpoint_cfg.step_interval == 0:
exe = executor.Executor(self.place)
io.save_checkpoint(
save_checkpoint(
executor=exe,
checkpoint_dir=self.checkpoint_cfg.checkpoint_dir,
trainer_id=self.trainer_id,
......@@ -580,6 +566,41 @@ class Trainer(object):
main_program=self.train_program,
max_num_checkpoints=self.checkpoint_cfg.max_num_checkpoints)
def _load_checkpoint(self):
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
load_checkpoint(
executor=exe,
checkpoint_dir=self.checkpoint_cfg.checkpoint_dir,
main_program=self.startup_program)
if not self.checkpoint_cfg.pserver_id:
load_trainer_args = self._get_checkpoint_load_args()
trainer_args = load_checkpoint(
executor=exe,
checkpoint_dir=self.checkpoint_cfg.checkpoint_dir,
main_program=self.startup_program,
role_id=self.trainer_id,
is_trainer=True,
load_trainer_args=load_trainer_args)
if len(trainer_args) != 2:
raise ValueError(
"the return trainer_args length do not equal _get_checkpoint_load_args"
)
self.checkpoint_cfg.epoch_id = int(trainer_args[0])
self.checkpoint_cfg.step_id = int(trainer_args[1])
else:
if self.checkpoint_cfg.lookup_table_name:
load_checkpoint(
executor=exe,
checkpoint_dir=self.checkpoint_cfg.checkpoint_dir,
main_program=self.startup_program,
role_id=self.checkpoint_cfg.pserver_id,
is_trainer=False,
load_trainer_args=None,
load_lookup_table=self.checkpoint_cfg.lookup_table_name)
def build_feed_var_list(program, feed_order):
if not isinstance(program, framework.Program):
......@@ -602,3 +623,610 @@ def build_feed_var_list(program, feed_order):
program.global_block().var(pair[0]) for pair in sorted_pair_list
]
return feed_var_list
# move Checkpoint APIs from io.py to trainer.py, make all of them are private.
SUCCESS_MARK_FILENAME = "_SUCCESS"
CHECKPOINT_PREFIX = "checkpoint"
MODEL_DIR = "__model__"
LOOKUP_TABLE_DIR = "__lookup_table__"
TRAINER_PREFIX = "trainer"
CHECKPOINT_SEPARATOR = "_"
def save_checkpoint(executor,
checkpoint_dir,
trainer_id,
main_program,
trainer_args=None,
max_num_checkpoints=3,
lookup_table=None,
pserver_endpoints=None):
"""
This function filters out all checkpoint variables from the give
main_program and then saves these variables to the `checkpoint_dir`
directory.
In the training precess, we generally save a checkpoint in each
iteration. So there might be a lot of checkpoints in the
`checkpoint_dir`. To avoid them taking too much disk space, the
`max_num_checkpoints` are introduced to limit the total number of
checkpoints. If the number of existing checkpints is greater than
the `max_num_checkpoints`, oldest ones will be scroll deleted.
A variable is a checkpoint variable and will be saved if it meets
all following conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for save checkpoint.
checkpoint_dir(str): The folder where to save checkpoints.
trainer_id(int): currect trainer id, if id is equal to 0, the trainer
is chief.
trainer_args(dict|None): Current training arguments. Such as 'epoch_id'
and 'step_id'.
Defaut: None
main_program(Program): The program whose checkpoint variables will
be saved.
max_num_checkpoints(int): The max number of total number of existing
checkpoints.
Default: 3
lookup_table(string|None): the lookup table name, when use distribute
lookup table, we can get lookup table name by DistributeTranspiler.
table_name
pserver_endpoints(list|None): the parameter server ip:port list.
when use distribute lookup table, we can get pserver_endpoints by
distribute arguments.
Returns:
None
Raises:
ValueError: If `checkpoint_dir` is None.
AssertionError: If `trainer_args` is not a dict.
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
path = "./checkpoints"
prog = fluid.default_main_program()
trainer_args = {"epoch_id": 200,
"step_id": 20} # just an example
table_name = "share_w"
ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
save_checkpoint(executor=exe,
checkpoint_dir=path,
trainer_id=0,
trainer_args=trainer_args,
main_program=prog,
max_num_checkpoints=3,
lookup_table=table_name,
pserver_endpoints = ps_endpoints)
"""
if checkpoint_dir is None:
raise ValueError("'checkpoint_dir' should not be None")
if main_program is None:
raise ValueError('main_program should not be None.')
if trainer_args:
assert isinstance(trainer_args, dict)
is_chief = trainer_id == 0
_make_chekcpoint_dirs(checkpoint_dir)
serial = _get_latest_checkpoint_serial(checkpoint_dir) + 1
cur_dir = _get_serial_dir(checkpoint_dir, serial)
_save_trainer_args(cur_dir, trainer_id, trainer_args)
if is_chief:
_save_persist_vars_without_grad(executor, cur_dir, main_program)
if is_chief and lookup_table and pserver_endpoints:
_save_pserver_vars_by_notify(executor, cur_dir, lookup_table,
pserver_endpoints)
_scroll_delete(checkpoint_dir, max_num_checkpoints)
def load_checkpoint(executor,
checkpoint_dir,
main_program,
role_id=0,
is_trainer=True,
load_trainer_args=None,
load_lookup_table=None):
"""
This function filters out all checkpoint variables from the give
main_program and then try to load these variables from the
`checkpoint_dir` directory.
In the training precess, we generally save a checkpoint in each
iteration. So there are more than one checkpoint in the
`checkpoint_dir` (each checkpoint has its own sub folder), use
`serial` to specify which serial of checkpoint you would like to
load.
A variable is a checkpoint variable and will be loaded if it meets
all following conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for loading checkpoint.
checkpoint_dir(str): The folder where all checkpoints are.
serial(int): The serial of checkpoint you would like to load.
main_program(Program): The program whose checkpoint variables will
be loaded.
role_id(int): the trainer id or the parameter server id.
is_trainer(bool): trainer is True and parameter server is False.
load_trainer_args(list|None): list about load trainer args.
load_lookup_table(str|None): the lookup table name
Returns:
None
Raises:
ValueError: If `checkpoint_dir` is None.
ValueError: If `main_program` is None.
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
path = "./checkpoints"
prog = fluid.default_main_program()
load_checkpoint(executor=exe, checkpoint_dir=path,
serial=9, main_program=prog)
# In this example, `load_checkpoint` function
# will first filters out all checkpoint variables in the default
# main program, and then try to load these variables form the
# folder "./checkpoints/checkpoint_9/__model__".
"""
if checkpoint_dir is None:
raise ValueError("'checkpoint_dir' should not be None")
serial = _get_latest_checkpoint_serial(checkpoint_dir)
# there are nothing need to be loaded
if serial is None or serial < 0:
return
if main_program is None:
raise ValueError('main_program should not be None.')
if is_trainer and load_trainer_args is None:
cur_dir = _get_serial_dir(checkpoint_dir, serial)
_load_persist_vars_without_grad(executor, cur_dir, main_program, True)
return
if is_trainer and load_trainer_args:
return _load_trainer_args(checkpoint_dir, serial, role_id,
load_trainer_args)
if not is_trainer and load_lookup_table:
_load_lookup_table_vars(executor, checkpoint_dir, main_program, role_id,
load_lookup_table)
def clean_checkpoint(checkpoint_dir, delete_dir=False):
"""
clean the checkpoint dir, when the train exits normally,
the trainer will call clean_checkpoint to delete checkpoint directory saved before.
delete_dir only works when the directory is empty, otherwise, OSError is raised.
: param checkpoint_dir
: param delete_dir
"""
if checkpoint_dir is None:
raise ValueError("'checkpoint_dir' should not be None")
_scroll_delete(checkpoint_dir, max_num_checkpoints=0)
if delete_dir and not os.listdir(checkpoint_dir):
os.rmdir(checkpoint_dir)
def _load_persist_vars_without_grad(executor,
dirname,
program,
has_model_dir=False):
"""
This function filters out all checkpoint variables from the give
program and then trys to load these variables from the given directory.
A variable is a checkpoint variable if it meets all following
conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for loading variables.
dirname(str): The directory path.
program(Program): The program whose checkpoint variables will
be loaded.
has_model_dir(bool): if True, the function loads variables
from a sub directory named '__model__'.
Default: False
Returns:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
_load_persist_vars_without_grad(executor=exe,
dirname=param_path, program=prog, has_model_dir=True)
# In this example, `_load_persist_vars_without_grad` function
# will first filters out all checkpoint variables in the default
# main program, and then trys to load these variables form the
# folder "./my_paddle_model/__model__".
"""
if has_model_dir:
dirname = _get_model_dir(dirname)
io.load_vars(
executor,
dirname=dirname,
main_program=program,
predicate=_is_checkpoint_var,
filename=None)
def _load_lookup_table_vars(executor, dirname, program, pserver_id, table_name):
"""
The parameter server will load lookup table's local file in
selectedrows variable.
Args:
executor(Executor): The executor to run for loading persistable variables
dirname(str): The directory path
main_program(Program): Find the variable named table_name in main_program
pserver_id(int): the serial number in pserver_endpoints list
table_name(str): lookup table name
Returns:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
dirname = "./checkpoints/checkpoint_9/"
prog = fluid.default_main_program()
pserver_id = 1
table_name = "share_w"
_load_lookup_table_vars(executor=exe,
dirname=dirname, program=prog, pserver_id=pserver_id,
table_name=table_name)
"""
for var in program.list_vars():
if var.name == table_name:
lookup_table_var = var
break
assert lookup_table_var is not None
lookup_table_dir = os.path.join(dirname, LOOKUP_TABLE_DIR)
table_file = table_name + CHECKPOINT_SEPARATOR + str(pserver_id)
load_prog = framework.Program()
load_block = load_prog.global_block()
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [lookup_table_var]},
attrs={'file_path': os.path.join(lookup_table_dir, table_file)})
executor.run(load_prog)
def _save_persist_vars_without_grad(executor, dirname, program):
"""
This function filters out all checkpoint variables from the give
program and then save these variables to a sub-folder '__model__' of
the given directory.
A variable is a checkpoint variable if it meets all following
conditions:
1. It's persistable.
2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW.
3. It's name contains no "@GRAD" nor ".trainer_" nor ".block".
Args:
executor(Executor): The executor to run for saving variables.
dirname(str): The directory path.
program(Program): The program whose checkpoint variables will
be saved.
Returns:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
_save_persist_vars_without_grad(executor=exe,
dirname=param_path, program=prog)
# In this example, `_save_persist_vars_without_grad` function
# will first filters out all checkpoint variables in the default
# main program, and then saves these variables to the folder
# "./my_paddle_model/__model__".
"""
cur_dir = _get_model_dir(dirname)
io.save_vars(
executor,
dirname=cur_dir,
main_program=program,
vars=None,
predicate=_is_checkpoint_var,
filename=None)
_write_success(cur_dir)
def _save_pserver_vars_by_notify(executor, dirname, lookup_table,
ps_endpoint_list):
"""
This function will send checkpoint notify message from Trainer 0
to all the pservers.
The checkpoint notify message contains lookup table name,
the absolute path on pserver to save lookup_table.
Args:
executor(Executor): The executor to run for send checkpoint notify.
dirname(str): The folder where to save checkpoints.
lookup_table(string): the lookup table name, when use distribute
lookup table, we can get lookup table name by DistributeTranspiler.
table_name
ps_endpoint_list(list): the parameter server ip:port list.
when use distribute lookup table, we can get ps_endpoint_list by
distribute arguments.
Return:
None
Examples:
.. code-block:: python
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
table_name = "share_w"
ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"]
_save_pserver_vars_by_notify(executor=exe,
dirname=param_path, lookup_table=table_name,
ps_endpoint_list=ps_endpoints)
"""
cur_dir = _get_lookuptable_dir(dirname)
checkpoint_notify_program = framework.Program()
checkpoint_notify_block = checkpoint_notify_program.global_block()
attrs = {}
attrs['epmap'] = ps_endpoint_list
attrs['dir'] = cur_dir
attrs['lookup_table'] = lookup_table
checkpoint_notify_block.append_op(
type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs)
executor.run(checkpoint_notify_program)
def _save_trainer_args(dirname, trainer_id, trainer_args):
assert isinstance(trainer_args, dict)
cur_dir = _get_trainer_dir(dirname, trainer_id)
for name, value in trainer_args.iteritems():
args_file = os.path.join(cur_dir, name)
with open(args_file, 'w') as f:
f.write(str(value))
_write_success(cur_dir)
def _load_trainer_args(checkpoint_dir, serial, trainer_id, trainer_args):
"""
trainer will load some args from it's independent directory,
such as epoch_id and step_id.
Args:
checkpoint_dir(str): The folder where all checkpoints are.
serial(int): The serial of checkpoint you would like to load.
trainer_id(int): current trainer id.
trainer_args(list): list about load trainer args
Return:
None
Examples:
.. code-block:: python
param_path = "./checkpoint/"
serial = 7
trainer_id = 2
trainer_args = ["epoch_id", "step_id"]
_load_trainer_args(checkpoint_dir=param_path, serial=serial,
trainer_id=trainer_id, trainer_args=trainer_args)
"""
assert isinstance(trainer_args, list)
cur_dir = _get_serial_dir(checkpoint_dir, serial)
cur_dir = _get_trainer_dir(cur_dir, trainer_id)
ret_values = []
for arg in trainer_args:
cur_file = os.path.join(cur_dir, arg)
with open(cur_file, 'r') as f:
contents = f.read()
ret_values.append(contents.strip())
return ret_values
def _is_checkpoint_var(var):
"""
the checkpoint will not save or load all the variables.
var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
: param var(Variable)
"""
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.RAW:
return False
# @GRAD are named for gradient variables, checkpoint will not save it.
if "@GRAD" in var.name:
return False
# .trainer_ are named for distribute train variables, checkpoint will not save it.
if ".trainer_" in var.name:
return False
# .block is named for distribute train variables, checkpoint will not save it.
if ".block" in var.name:
return False
return var.persistable
def _make_chekcpoint_dirs(dirs):
"""
_make_chekcpoint_dirs will makdir local directory directly, when the directory is exist, it will igore it.
"""
assert dirs is not None
if os.path.isfile(dirs):
raise OSError(errno.ENOTDIR, "dirs path shoule be a Directory.", dirs)
if not os.path.isdir(dirs):
try:
os.makedirs(dirs)
except OSError as err:
if err.errno != errno.EEXIST:
raise err
def _get_dir_serial(dirname):
_, serial = dirname.split(CHECKPOINT_SEPARATOR)
try:
serial_num = int(serial)
except ValueError:
serial_num = -1
return serial_num
def _get_serial_dir(dirname, serial):
serial_folder = CHECKPOINT_PREFIX + CHECKPOINT_SEPARATOR + str(serial)
serial_dir = os.path.join(dirname, serial_folder)
_make_chekcpoint_dirs(serial_dir)
return serial_dir
def _get_model_dir(dirname):
model_dir = os.path.join(dirname, MODEL_DIR)
_make_chekcpoint_dirs(model_dir)
return model_dir
def _get_lookuptable_dir(dirname):
lookuptable_dir = os.path.join(dirname, LOOKUP_TABLE_DIR)
_make_chekcpoint_dirs(lookuptable_dir)
return lookuptable_dir
def _get_trainer_dir(dirname, trainer_id):
trainer_folder = TRAINER_PREFIX + CHECKPOINT_SEPARATOR + str(trainer_id)
trainer_dir = os.path.join(dirname, trainer_folder)
_make_chekcpoint_dirs(trainer_dir)
return trainer_dir
def _scroll_delete(dirname, max_num_checkpoints=3):
dirs = os.listdir(dirname)
serial_map = {}
for serial in dirs:
serial_num = _get_dir_serial(serial)
serial_map[serial_num] = serial
if len(serial_map.keys()) <= max_num_checkpoints:
return
serials = serial_map.keys()
serials.sort(reverse=True)
serials = serials[max_num_checkpoints:]
for serial in serials:
cur_dir = _get_serial_dir(dirname, serial)
try:
shutil.rmtree(cur_dir)
except OSError as err:
if err.errno != errno.ENOENT:
raise err
def _write_success(dirname):
"""
write an empty file named "_SUCCESS" in checkpoint dir, indicate this checkpoint is correct.
: param dirname
"""
success_file = os.path.join(dirname, SUCCESS_MARK_FILENAME)
with open(success_file, 'a') as f:
now = time.ctime()
f.write(now)
def _get_latest_checkpoint_serial(checkpoint_dir):
"""
get the latest file in checkpoint directory, the _SUCCESS file must exist in the directory
: param checkpoint_dir
"""
if not checkpoint_dir:
return -1
def has_success(checkpoint_dir, cur_dir):
"""
is _SUCCESS in this dir
"""
serial = _get_dir_serial(cur_dir)
if serial == -1 or not os.path.isdir(
os.path.join(checkpoint_dir, cur_dir)):
return -1
success_path = os.path.join(
_get_serial_dir(checkpoint_dir, serial), MODEL_DIR,
SUCCESS_MARK_FILENAME)
if os.path.isfile(success_path):
return serial
if not os.path.isdir(checkpoint_dir):
return -1
current_dir = -1
dirs = os.listdir(checkpoint_dir)
for cur_dir in dirs:
success_num = has_success(checkpoint_dir, cur_dir)
if success_num > current_dir:
current_dir = success_num
return current_dir
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