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提交 82b8a3c5 编写于 作者: Y yuyang
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Move trainer to contrib

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python/paddle/fluid/__init__.py
浏览文件 @ 82b8a3c5
...@@ -19,17 +19,8 @@ from .framework import * ...@@ -19,17 +19,8 @@ from .framework import *
# import all class inside executor into fluid module # import all class inside executor into fluid module
from . import executor from . import executor
from .executor import * from .executor import *
from . import trainer from . import trainer
from .trainer import Trainer
from .trainer import BeginEpochEvent
from .trainer import EndEpochEvent
from .trainer import BeginStepEvent
from .trainer import EndStepEvent
from .trainer import CheckpointConfig
from . import inferencer from . import inferencer
from .inferencer import Inferencer
from . import io from . import io
from . import evaluator from . import evaluator
......
python/paddle/fluid/contrib/inferencer.py 0 → 100644
浏览文件 @ 82b8a3c5
# 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.
from __future__ import print_function
import contextlib
from .. import core
from .. import executor
from .. import framework
from .. import io
from .. import parallel_executor
from .. import unique_name
from .trainer import check_and_get_place
__all__ = ['Inferencer', ]
class Inferencer(object):
"""
Inferencer High Level API.
Args:
infer_func (Python func): Infer function that will return predict Variable
param_path (str): The path where the inference model is saved by fluid.io.save_params
place (Place): place to do the inference
parallel (bool): use parallel_executor to run the inference, it will use multi CPU/GPU.
Examples:
.. code-block:: python
def inference_program():
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
return y_predict
place = fluid.CPUPlace()
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path="/tmp/model", place=place)
"""
def __init__(self, infer_func, param_path, place=None, parallel=False):
self.param_path = param_path
self.scope = core.Scope()
self.parallel = parallel
self.place = check_and_get_place(place)
self.inference_program = framework.Program()
with framework.program_guard(self.inference_program):
with unique_name.guard():
self.predict_var = infer_func()
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_params(executor.Executor(self.place), param_path)
if parallel:
with self._prog_and_scope_guard():
self.exe = parallel_executor.ParallelExecutor(
use_cuda=isinstance(self.place, core.CUDAPlace),
loss_name=self.predict_var.name)
else:
self.exe = executor.Executor(self.place)
self.inference_program = self.inference_program.clone(for_test=True)
def infer(self, inputs, return_numpy=True):
"""
Do Inference for Inputs
Args:
inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
return_numpy (bool): transform return value into numpy or not
Returns:
Tensor or Numpy: the predict value of the inference model for the inputs
Examples:
.. code-block:: python
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
"""
if not isinstance(inputs, dict):
raise ValueError(
"inputs should be a map of {'input_name': input_var}")
with self._prog_and_scope_guard():
results = self.exe.run(feed=inputs,
fetch_list=[self.predict_var.name],
return_numpy=return_numpy)
return results
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
yield
python/paddle/fluid/contrib/trainer.py 0 → 100644
浏览文件 @ 82b8a3c5
# 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.
from __future__ import print_function
import contextlib
import os
import errno
import shutil
import six
import time
from .. import core
from .. import data_feeder
from .. import executor
from .. import framework
from .. import io
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
from .. import optimizer as opt_module
from .. import parallel_executor
from ..transpiler import distribute_transpiler
__all__ = [
'Trainer', 'BeginEpochEvent', 'EndEpochEvent', 'BeginStepEvent',
'EndStepEvent', 'CheckpointConfig'
]
class BeginEpochEvent(object):
"""
The begin of a training epoch.
Args:
epoch_id(int): The current epoch ID.
"""
def __init__(self, epoch_id):
self.epoch = epoch_id
class EndEpochEvent(object):
"""
The end of a training epoch.
Args:
epoch_id(int): The current epoch ID.
"""
def __init__(self, epoch_id):
self.epoch = epoch_id
class BeginStepEvent(object):
"""
The begin of a training epoch.
Args:
epoch_id(int): The current epoch ID.
step_id(int): The current step ID.
"""
def __init__(self, epoch_id, step_id):
self.epoch = epoch_id
self.step = step_id
self.fetch_metrics = True
"""
If fetch_metrics is true, the metrics will be fetched at the
EndStepEvent. Default is True.
"""
class EndStepEvent(object):
"""
The end of a training step.
Args:
epoch_id(int): The current epoch ID.
step_id(int): The current step ID.
metrics(list): A list of fetched tensor. The order of this list is same
as the :code:`train_func` returns.
"""
def __init__(self, epoch_id, step_id, metrics):
self.epoch = epoch_id
self.step = step_id
self.metrics = metrics
class CheckpointConfig(object):
"""
Parameter object for :code:`save_checkpoint` and
:code:`fluid.Trainer`. Used to configuration how to save checkpoint.
Args:
checkpoint_dir(str): Directory path to save check point. Default is the
current directory.
max_num_checkpoints(int): The max number of local check points.
epoch_interval(int): Every number of epoch to save check point.
step_interval(int): Every number of step to save check point.
Examples:
>>> config = fluid.CheckpointConfig("./checkpoints")
>>> trainer = fluid.Trainer(train_func=train_program,
>>> place=place,
>>> optimizer_func=optimizer_func,
>>> checkpoint_config=config)
>>> trainer.train(...)
"""
def __init__(self,
checkpoint_dir=None,
max_num_checkpoints=3,
epoch_interval=1,
step_interval=10):
assert epoch_interval >= 1
assert step_interval >= 1
self.checkpoint_dir = checkpoint_dir \
if checkpoint_dir is not None else os.getcwd()
self.max_num_checkpoints = max_num_checkpoints
self.epoch_interval = epoch_interval
self.step_interval = step_interval
self.epoch_id = 0
self.step_id = 0
self.load_serial = None
self.pserver_id = None
self.lookup_table_name = None
def check_and_get_place(place):
"""
Check the type of place or get the default place
Args:
place(None|core.CUDAPlace|core.CPUPlace): the place that trainer will be executed on.
Raises:
TypeError if the type mismatched.
Returns:
the original place if it is not None.
if fluid is compiled with CUDA, returns CUDAPlace(0) by default.
Otherwise returns CPUPlace by default.
"""
if place is None:
if core.is_compiled_with_cuda():
return core.CUDAPlace(0)
else:
return core.CPUPlace()
else:
if not isinstance(place, core.CUDAPlace) and not isinstance(
place, core.CPUPlace):
raise TypeError("Place should be either CUDAPlace or CPUPlace")
return place
class Trainer(object):
"""
A trainer wraps MultiGPU/MultiNode training loops and can be used to train a
simple neural network easily.
This API takes a :code:`train_func`. A :code:`train_func` is a function that
return loss as it first return value. The reset value can be fetched by
EndStepEvent.metrics
This API also takes a :code:`optimizer_func` that will return an optimizer
instance.
For example, to train a MLP for MNIST dataset, the sample program is
>>> import paddle.fluid as fluid
>>>
>>> def mlp(image, layer_sizes=[200, 100], activation="relu", num_classes=10):
>>> hidden = image
>>> for layer_size in layer_sizes:
>>> hidden = fluid.layers.fc(input=hidden, size=layer_size, act=activation)
>>> return fluid.layers.fc(input=hidden, size=num_classes, act="softmax")
>>>
>>> def train_mnist_mlp():
>>> img = fluid.layers.data(name='image', shape=[784])
>>> label = fluid.layers.data(name='label', shape=[1], dtype='int64')
>>> prediction = mlp(img)
>>> return fluid.layers.mean(fluid.layers.cross_entropy(prediction, label))
>>>
>>> def optimizer():
>>> return fluid.optimizer.Adam()
>>>
>>> trainer = Trainer(train_func=train_mnist_mlp,
>>> optimizer_func=optimizer,
>>> place=fluid.CUDAPlace(0),
>>> parallel=True)
>>>
>>> def train_callback(event):
>>> if isinstance(event, fluid.EndStepEvent):
>>> print "Epoch ID", event.epoch, "Step ID",\
>>> event.step, "AvgLoss", event.metrics[0]
>>> elif isinstance(event, fluid.EndEpochEvent):
>>> trainer.save_params("./model_{0}".format(event.epoch))
>>>
>>> trainer.train(num_epochs=100, event_handler=train_callback)
For more example, please see :ref:`api_guide_high_level_api`.
Args:
train_func(callable): A function which will return loss. The loss must be
a scalar tensor.
optimizer_func(callable): A function that returns an Optimizer object.
place(CUDAPlace|CPUPlace): The device place of this trainer. If
:code:`parallel=True,` all CUDA Places will be used if :code:`place`
is a :code:`CUDAPlace`.
parallel(bool): True if use multiple devices.
checkpoint_config(CheckpointConfig): Configuration about how to save
checkpoints.
"""
def __init__(self,
train_func,
optimizer_func,
param_path=None,
place=None,
parallel=False,
checkpoint_config=None):
self.__stop = False
self.parallel = parallel
# config for checkpoint
# only chief worker will save variables
self.trainer_id = 0
self.checkpoint_cfg = checkpoint_config
if self.checkpoint_cfg:
assert isinstance(self.checkpoint_cfg, CheckpointConfig)
serial = _get_latest_checkpoint_serial(
self.checkpoint_cfg.checkpoint_dir)
self.checkpoint_cfg.load_serial = serial if serial >= 0 else None
self.scope = core.Scope()
# 1. we need to generate a framework.Program by calling
# program_func. Reference: fluid.program_guard in
# test_word2vec.py
self.startup_program = framework.Program()
self.train_program = framework.Program()
with framework.program_guard(self.train_program, self.startup_program):
program_func_outs = train_func()
self.train_func_outputs = program_func_outs if isinstance(
program_func_outs, list) else [program_func_outs]
self.test_program = self.train_program.clone(for_test=True)
# The first element of program_func_outs is loss.
loss = self.train_func_outputs[0]
optimizer = optimizer_func()
if not isinstance(optimizer, opt_module.Optimizer):
raise TypeError(
"The optimizer should be an instance of Optimizer")
optimize_ops, params_grads = optimizer.minimize(loss)
self.place = check_and_get_place(place)
self._dist_transpile_if_necessary(optimize_ops, params_grads)
# 2. move the default_main_program to self.program and run the
# default_startup program on an empty core.Scope()
# Run startup program
with self._prog_and_scope_guard():
exe = executor.Executor(place)
exe.run(self.startup_program)
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):
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_persistables(
executor=exe,
dirname=param_path,
main_program=self.startup_program)
def _transpile_nccl2_dist(self):
# PADDLE_TRAINER_IPS
if "PADDLE_TRAINER_IPS" not in os.environ:
self.nccl_id_var = None
else:
self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
port = os.getenv("PADDLE_PSERVER_PORT")
worker_ips = os.getenv("PADDLE_TRAINER_IPS")
worker_endpoints = []
for ip in worker_ips.split(","):
worker_endpoints.append(':'.join([ip, port]))
self.num_trainers = len(worker_endpoints)
current_endpoint = os.getenv("PADDLE_CURRENT_IP") + ":" + port
worker_endpoints.remove(current_endpoint)
# TODO(wuyi): use self.nccl_id_var, self.num_trainers and self.trainer_id
# in ParallelExecutor to start
# distributed training using NCCL2
self.nccl_id_var = self.startup_program.global_block().create_var(
name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)
self.startup_program.global_block().append_op(
type="gen_nccl_id",
inputs={},
outputs={"NCCLID": self.nccl_id_var},
attrs={
"endpoint": current_endpoint,
"endpoint_list": worker_endpoints,
"trainer_id": self.trainer_id
})
def _dist_transpile_if_necessary(self, optimize_ops, params_grads):
self._transpile_nccl2_dist()
if self.nccl_id_var != None:
return
if "PADDLE_TRAINING_ROLE" not in os.environ:
return
# the port of all pservers, needed by both trainer and pserver
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
# comma separated ips of all pservers, needed by trainer and
# pserver
pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist)
# total number of workers/trainers in the job, needed by
# trainer and pserver
trainers = int(os.getenv("PADDLE_TRAINERS"))
# the IP of the local machine, needed by pserver only
current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
# the unique trainer id, starting from 0, needed by trainer
# only
self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
# the role, should be either PSERVER or TRAINER
training_role = os.getenv("PADDLE_TRAINING_ROLE")
with self._prog_and_scope_guard():
t = distribute_transpiler.DistributeTranspiler()
t.transpile(
self.trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
if self.checkpoint_cfg:
pserver_id = eplist.index(current_endpoint)
self.checkpoint_cfg.pserver_id = pserver_id
if t.has_distributed_lookup_table:
self.checkpoint_cfg.lookup_table_name = t.table_name
self.train_program = t.get_pserver_program(current_endpoint)
self.startup_program = t.get_startup_program(current_endpoint,
self.train_program)
elif training_role == "TRAINER":
self.train_program = t.get_trainer_program()
else:
raise ValueError(
'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
def stop(self):
"""
stop training
"""
self.__stop = True
def train(self, num_epochs, event_handler, reader=None, feed_order=None):
"""
Start the train loop to train the model.
Args:
num_epochs(int): The number of epoch. An epoch will process all data in reader
event_handler(callable): The event handler. A function with type (ev:Event)->void
reader(callable): A reader creator object. See also
:ref:`api_guide_python_reader` .
feed_order(list): Feeding order of reader. None will following the defining
order in program
Returns:
None
"""
training_role = os.getenv("PADDLE_TRAINING_ROLE", "")
if training_role == "PSERVER":
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
exe.run()
return
if self.parallel:
self._train_by_parallel_executor(num_epochs, event_handler, reader,
feed_order)
else:
self._train_by_executor(num_epochs, event_handler, reader,
feed_order)
def test(self, reader, feed_order):
"""
Test the model on given test data
Args:
reader(callable): The reader that yields test data.
feed_order(list): Feeding order of reader. None will following the
defining order in program
"""
return self._test_by_executor(reader, feed_order,
self.train_func_outputs)
def save_params(self, param_path):
"""
Save all parameters into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path)
def save_inference_model(self, param_path, feeded_var_names,
target_var_indexes):
"""
Save model for cpp inference into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
feeded_var_names(list(str)): The name of the vars that you
need to feed in before run program.
target_var_indexes(list(int)): the index of target var that
you need to return in trainer.train_func.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
target_vars = [
self.train_func_outputs[index] for index in target_var_indexes
]
io.save_inference_model(param_path, feeded_var_names, target_vars,
exe)
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(
main_program=self.train_program,
startup_program=self.startup_program):
with executor.scope_guard(self.scope):
yield
def _train_by_executor(self, num_epochs, event_handler, reader, feed_order):
"""
Train by Executor and single device.
Args:
num_epochs:
event_handler:
reader:
feed_order:
Returns:
"""
with self._prog_and_scope_guard():
feed_var_list = build_feed_var_list(self.train_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
exe = executor.Executor(self.place)
reader = feeder.decorate_reader(reader, multi_devices=False)
self._train_by_any_executor(event_handler, exe, num_epochs, reader)
def _train_by_any_executor(self, event_handler, exe, num_epochs, reader):
if self.checkpoint_cfg:
epochs = [
epoch_id for epoch_id in range(num_epochs)
if epoch_id >= self.checkpoint_cfg.epoch_id
]
else:
epochs = [epoch_id for epoch_id in range(num_epochs)]
for epoch_id in epochs:
event_handler(BeginEpochEvent(epoch_id))
for step_id, data in enumerate(reader()):
if self.__stop:
if self.checkpoint_cfg:
self._clean_checkpoint()
return
if self.checkpoint_cfg and self.checkpoint_cfg.load_serial \
and self.checkpoint_cfg.step_id >= step_id and self.checkpoint_cfg.epoch_id == epoch_id:
continue
begin_event = BeginStepEvent(epoch_id, step_id)
event_handler(begin_event)
if begin_event.fetch_metrics:
metrics = exe.run(feed=data,
fetch_list=[
var.name
for var in self.train_func_outputs
])
else:
metrics = exe.run(feed=data, fetch_list=[])
if self.checkpoint_cfg:
self._save_checkpoint(epoch_id, step_id)
event_handler(EndStepEvent(epoch_id, step_id, metrics))
event_handler(EndEpochEvent(epoch_id))
if self.checkpoint_cfg:
self._clean_checkpoint()
def _test_by_executor(self, reader, feed_order, fetch_list):
with executor.scope_guard(self.scope):
feed_var_list = build_feed_var_list(self.test_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
exe = executor.Executor(self.place)
accumulated = len(fetch_list) * [0]
count = 0
for data in reader():
outs = exe.run(program=self.test_program,
feed=feeder.feed(data),
fetch_list=fetch_list)
accumulated = [x[0] + x[1][0] for x in zip(accumulated, outs)]
count += 1
return [x / count for x in accumulated]
def _train_by_parallel_executor(self, num_epochs, event_handler, reader,
feed_order):
with self._prog_and_scope_guard():
pe = self._get_or_create_parallel_executor()
feed_var_list = build_feed_var_list(self.train_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
reader = feeder.decorate_reader(reader, multi_devices=True)
self._train_by_any_executor(event_handler, pe, num_epochs, reader)
def _get_parallel_executor(self):
return getattr(self, 'parallel_executor', None)
def _get_or_create_parallel_executor(self):
if self._get_parallel_executor() is None:
self.parallel_executor = parallel_executor.ParallelExecutor(
use_cuda=isinstance(self.place, core.CUDAPlace),
loss_name=self.train_func_outputs[0].name)
return self._get_parallel_executor()
def _clean_checkpoint(self):
assert self.checkpoint_cfg
clean_checkpoint(checkpoint_dir=self.checkpoint_cfg.checkpoint_dir)
def _get_checkpoint_load_args(self):
"""
epoch_id and step_id are runtime arguments, they are not variables, will load them independently.
"""
return ["epoch_id", "step_id"]
def _get_checkpoint_save_args(self, epoch_id, step_id):
"""
epoch_id and step_id are runtime arguments, they are not variables, will save them independently.
"""
trainer_args = {}
trainer_args["epoch_id"] = epoch_id
trainer_args["step_id"] = step_id
return trainer_args
def _save_checkpoint(self, epoch_id, step_id):
assert self.checkpoint_cfg
if epoch_id % self.checkpoint_cfg.epoch_interval == 0 \
and step_id % self.checkpoint_cfg.step_interval == 0:
exe = executor.Executor(self.place)
save_checkpoint(
executor=exe,
checkpoint_dir=self.checkpoint_cfg.checkpoint_dir,
trainer_id=self.trainer_id,
trainer_args=self._get_checkpoint_save_args(epoch_id, step_id),
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):
raise TypeError("The 'program' should be an object of Program")
if isinstance(feed_order, list):
feed_var_list = [
program.global_block().var(var_name) for var_name in feed_order
]
else:
if not isinstance(feed_order, dict):
raise TypeError(
"The 'feed_order' should be either None, list or dict.")
if not sorted(feed_order.values()) == list(range(len(feed_order))):
raise ValueError(
"The values of 'feed_order' should be a permutation of [0, len(feed_order))"
)
sorted_pair_list = sorted(
six.iteritems(feed_order), key=lambda item: item[1])
feed_var_list = [
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 six.iteritems(trainer_args):
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(list(serial_map.keys())) <= max_num_checkpoints:
return
serials = list(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
python/paddle/fluid/inferencer.py
浏览文件 @ 82b8a3c5
...@@ -12,101 +12,5 @@ ...@@ -12,101 +12,5 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from __future__ import print_function # NOTE: inferencer is moved into fluid.contrib.inferencer.
__all__ = []
import contextlib
from . import core
from . import executor
from . import framework
from . import io
from . import parallel_executor
from . import unique_name
from .trainer import check_and_get_place
__all__ = ['Inferencer', ]
class Inferencer(object):
"""
Inferencer High Level API.
Args:
infer_func (Python func): Infer function that will return predict Variable
param_path (str): The path where the inference model is saved by fluid.io.save_params
place (Place): place to do the inference
parallel (bool): use parallel_executor to run the inference, it will use multi CPU/GPU.
Examples:
.. code-block:: python
def inference_program():
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
return y_predict
place = fluid.CPUPlace()
inferencer = fluid.Inferencer(
infer_func=inference_program, param_path="/tmp/model", place=place)
"""
def __init__(self, infer_func, param_path, place=None, parallel=False):
self.param_path = param_path
self.scope = core.Scope()
self.parallel = parallel
self.place = check_and_get_place(place)
self.inference_program = framework.Program()
with framework.program_guard(self.inference_program):
with unique_name.guard():
self.predict_var = infer_func()
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_params(executor.Executor(self.place), param_path)
if parallel:
with self._prog_and_scope_guard():
self.exe = parallel_executor.ParallelExecutor(
use_cuda=isinstance(self.place, core.CUDAPlace),
loss_name=self.predict_var.name)
else:
self.exe = executor.Executor(self.place)
self.inference_program = self.inference_program.clone(for_test=True)
def infer(self, inputs, return_numpy=True):
"""
Do Inference for Inputs
Args:
inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
return_numpy (bool): transform return value into numpy or not
Returns:
Tensor or Numpy: the predict value of the inference model for the inputs
Examples:
.. code-block:: python
tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
results = inferencer.infer({'x': tensor_x})
"""
if not isinstance(inputs, dict):
raise ValueError(
"inputs should be a map of {'input_name': input_var}")
with self._prog_and_scope_guard():
results = self.exe.run(feed=inputs,
fetch_list=[self.predict_var.name],
return_numpy=return_numpy)
return results
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(main_program=self.inference_program):
with executor.scope_guard(self.scope):
yield
python/paddle/fluid/trainer.py
浏览文件 @ 82b8a3c5
...@@ -12,1247 +12,5 @@ ...@@ -12,1247 +12,5 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from __future__ import print_function # NOTE: Trainer is moved into fluid.contrib.trainer.
__all__ = []
import contextlib
import os
import errno
import shutil
import six
import time
from . import core
from . import data_feeder
from . import executor
from . import framework
from . import io
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
from . import optimizer as opt_module
from . import parallel_executor
from .transpiler import distribute_transpiler
__all__ = [
'Trainer', 'BeginEpochEvent', 'EndEpochEvent', 'BeginStepEvent',
'EndStepEvent', 'CheckpointConfig'
]
class BeginEpochEvent(object):
"""
The begin of a training epoch.
Args:
epoch_id(int): The current epoch ID.
"""
def __init__(self, epoch_id):
self.epoch = epoch_id
class EndEpochEvent(object):
"""
The end of a training epoch.
Args:
epoch_id(int): The current epoch ID.
"""
def __init__(self, epoch_id):
self.epoch = epoch_id
class BeginStepEvent(object):
"""
The begin of a training epoch.
Args:
epoch_id(int): The current epoch ID.
step_id(int): The current step ID.
"""
def __init__(self, epoch_id, step_id):
self.epoch = epoch_id
self.step = step_id
self.fetch_metrics = True
"""
If fetch_metrics is true, the metrics will be fetched at the
EndStepEvent. Default is True.
"""
class EndStepEvent(object):
"""
The end of a training step.
Args:
epoch_id(int): The current epoch ID.
step_id(int): The current step ID.
metrics(list): A list of fetched tensor. The order of this list is same
as the :code:`train_func` returns.
"""
def __init__(self, epoch_id, step_id, metrics):
self.epoch = epoch_id
self.step = step_id
self.metrics = metrics
class CheckpointConfig(object):
"""
Parameter object for :code:`save_checkpoint` and
:code:`fluid.Trainer`. Used to configuration how to save checkpoint.
Args:
checkpoint_dir(str): Directory path to save check point. Default is the
current directory.
max_num_checkpoints(int): The max number of local check points.
epoch_interval(int): Every number of epoch to save check point.
step_interval(int): Every number of step to save check point.
Examples:
>>> config = fluid.CheckpointConfig("./checkpoints")
>>> trainer = fluid.Trainer(train_func=train_program,
>>> place=place,
>>> optimizer_func=optimizer_func,
>>> checkpoint_config=config)
>>> trainer.train(...)
"""
def __init__(self,
checkpoint_dir=None,
max_num_checkpoints=3,
epoch_interval=1,
step_interval=10):
assert epoch_interval >= 1
assert step_interval >= 1
self.checkpoint_dir = checkpoint_dir \
if checkpoint_dir is not None else os.getcwd()
self.max_num_checkpoints = max_num_checkpoints
self.epoch_interval = epoch_interval
self.step_interval = step_interval
self.epoch_id = 0
self.step_id = 0
self.load_serial = None
self.pserver_id = None
self.lookup_table_name = None
def check_and_get_place(place):
"""
Check the type of place or get the default place
Args:
place(None|core.CUDAPlace|core.CPUPlace): the place that trainer will be executed on.
Raises:
TypeError if the type mismatched.
Returns:
the original place if it is not None.
if fluid is compiled with CUDA, returns CUDAPlace(0) by default.
Otherwise returns CPUPlace by default.
"""
if place is None:
if core.is_compiled_with_cuda():
return core.CUDAPlace(0)
else:
return core.CPUPlace()
else:
if not isinstance(place, core.CUDAPlace) and not isinstance(
place, core.CPUPlace):
raise TypeError("Place should be either CUDAPlace or CPUPlace")
return place
class Trainer(object):
"""
A trainer wraps MultiGPU/MultiNode training loops and can be used to train a
simple neural network easily.
This API takes a :code:`train_func`. A :code:`train_func` is a function that
return loss as it first return value. The reset value can be fetched by
EndStepEvent.metrics
This API also takes a :code:`optimizer_func` that will return an optimizer
instance.
For example, to train a MLP for MNIST dataset, the sample program is
>>> import paddle.fluid as fluid
>>>
>>> def mlp(image, layer_sizes=[200, 100], activation="relu", num_classes=10):
>>> hidden = image
>>> for layer_size in layer_sizes:
>>> hidden = fluid.layers.fc(input=hidden, size=layer_size, act=activation)
>>> return fluid.layers.fc(input=hidden, size=num_classes, act="softmax")
>>>
>>> def train_mnist_mlp():
>>> img = fluid.layers.data(name='image', shape=[784])
>>> label = fluid.layers.data(name='label', shape=[1], dtype='int64')
>>> prediction = mlp(img)
>>> return fluid.layers.mean(fluid.layers.cross_entropy(prediction, label))
>>>
>>> def optimizer():
>>> return fluid.optimizer.Adam()
>>>
>>> trainer = Trainer(train_func=train_mnist_mlp,
>>> optimizer_func=optimizer,
>>> place=fluid.CUDAPlace(0),
>>> parallel=True)
>>>
>>> def train_callback(event):
>>> if isinstance(event, fluid.EndStepEvent):
>>> print "Epoch ID", event.epoch, "Step ID",\
>>> event.step, "AvgLoss", event.metrics[0]
>>> elif isinstance(event, fluid.EndEpochEvent):
>>> trainer.save_params("./model_{0}".format(event.epoch))
>>>
>>> trainer.train(num_epochs=100, event_handler=train_callback)
For more example, please see :ref:`api_guide_high_level_api`.
Args:
train_func(callable): A function which will return loss. The loss must be
a scalar tensor.
optimizer_func(callable): A function that returns an Optimizer object.
place(CUDAPlace|CPUPlace): The device place of this trainer. If
:code:`parallel=True,` all CUDA Places will be used if :code:`place`
is a :code:`CUDAPlace`.
parallel(bool): True if use multiple devices.
checkpoint_config(CheckpointConfig): Configuration about how to save
checkpoints.
"""
def __init__(self,
train_func,
optimizer_func,
param_path=None,
place=None,
parallel=False,
checkpoint_config=None):
self.__stop = False
self.parallel = parallel
# config for checkpoint
# only chief worker will save variables
self.trainer_id = 0
self.checkpoint_cfg = checkpoint_config
if self.checkpoint_cfg:
assert isinstance(self.checkpoint_cfg, CheckpointConfig)
serial = _get_latest_checkpoint_serial(
self.checkpoint_cfg.checkpoint_dir)
self.checkpoint_cfg.load_serial = serial if serial >= 0 else None
self.scope = core.Scope()
# 1. we need to generate a framework.Program by calling
# program_func. Reference: fluid.program_guard in
# test_word2vec.py
self.startup_program = framework.Program()
self.train_program = framework.Program()
with framework.program_guard(self.train_program, self.startup_program):
program_func_outs = train_func()
self.train_func_outputs = program_func_outs if isinstance(
program_func_outs, list) else [program_func_outs]
self.test_program = self.train_program.clone(for_test=True)
# The first element of program_func_outs is loss.
loss = self.train_func_outputs[0]
optimizer = optimizer_func()
if not isinstance(optimizer, opt_module.Optimizer):
raise TypeError(
"The optimizer should be an instance of Optimizer")
optimize_ops, params_grads = optimizer.minimize(loss)
self.place = check_and_get_place(place)
self._dist_transpile_if_necessary(optimize_ops, params_grads)
# 2. move the default_main_program to self.program and run the
# default_startup program on an empty core.Scope()
# Run startup program
with self._prog_and_scope_guard():
exe = executor.Executor(place)
exe.run(self.startup_program)
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):
with self._prog_and_scope_guard():
# load params from param_path into scope
io.load_persistables(
executor=exe,
dirname=param_path,
main_program=self.startup_program)
def _transpile_nccl2_dist(self):
# PADDLE_TRAINER_IPS
if "PADDLE_TRAINER_IPS" not in os.environ:
self.nccl_id_var = None
else:
self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
port = os.getenv("PADDLE_PSERVER_PORT")
worker_ips = os.getenv("PADDLE_TRAINER_IPS")
worker_endpoints = []
for ip in worker_ips.split(","):
worker_endpoints.append(':'.join([ip, port]))
self.num_trainers = len(worker_endpoints)
current_endpoint = os.getenv("PADDLE_CURRENT_IP") + ":" + port
worker_endpoints.remove(current_endpoint)
# TODO(wuyi): use self.nccl_id_var, self.num_trainers and self.trainer_id
# in ParallelExecutor to start
# distributed training using NCCL2
self.nccl_id_var = self.startup_program.global_block().create_var(
name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)
self.startup_program.global_block().append_op(
type="gen_nccl_id",
inputs={},
outputs={"NCCLID": self.nccl_id_var},
attrs={
"endpoint": current_endpoint,
"endpoint_list": worker_endpoints,
"trainer_id": self.trainer_id
})
def _dist_transpile_if_necessary(self, optimize_ops, params_grads):
self._transpile_nccl2_dist()
if self.nccl_id_var != None:
return
if "PADDLE_TRAINING_ROLE" not in os.environ:
return
# the port of all pservers, needed by both trainer and pserver
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
# comma separated ips of all pservers, needed by trainer and
# pserver
pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist)
# total number of workers/trainers in the job, needed by
# trainer and pserver
trainers = int(os.getenv("PADDLE_TRAINERS"))
# the IP of the local machine, needed by pserver only
current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
# the unique trainer id, starting from 0, needed by trainer
# only
self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
# the role, should be either PSERVER or TRAINER
training_role = os.getenv("PADDLE_TRAINING_ROLE")
with self._prog_and_scope_guard():
t = distribute_transpiler.DistributeTranspiler()
t.transpile(
self.trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
if self.checkpoint_cfg:
pserver_id = eplist.index(current_endpoint)
self.checkpoint_cfg.pserver_id = pserver_id
if t.has_distributed_lookup_table:
self.checkpoint_cfg.lookup_table_name = t.table_name
self.train_program = t.get_pserver_program(current_endpoint)
self.startup_program = t.get_startup_program(current_endpoint,
self.train_program)
elif training_role == "TRAINER":
self.train_program = t.get_trainer_program()
else:
raise ValueError(
'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
def stop(self):
"""
stop training
"""
self.__stop = True
def train(self, num_epochs, event_handler, reader=None, feed_order=None):
"""
Start the train loop to train the model.
Args:
num_epochs(int): The number of epoch. An epoch will process all data in reader
event_handler(callable): The event handler. A function with type (ev:Event)->void
reader(callable): A reader creator object. See also
:ref:`api_guide_python_reader` .
feed_order(list): Feeding order of reader. None will following the defining
order in program
Returns:
None
"""
training_role = os.getenv("PADDLE_TRAINING_ROLE", "")
if training_role == "PSERVER":
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
exe.run()
return
if self.parallel:
self._train_by_parallel_executor(num_epochs, event_handler, reader,
feed_order)
else:
self._train_by_executor(num_epochs, event_handler, reader,
feed_order)
def test(self, reader, feed_order):
"""
Test the model on given test data
Args:
reader(callable): The reader that yields test data.
feed_order(list): Feeding order of reader. None will following the
defining order in program
"""
return self._test_by_executor(reader, feed_order,
self.train_func_outputs)
def save_params(self, param_path):
"""
Save all parameters into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path)
def save_inference_model(self, param_path, feeded_var_names,
target_var_indexes):
"""
Save model for cpp inference into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
feeded_var_names(list(str)): The name of the vars that you
need to feed in before run program.
target_var_indexes(list(int)): the index of target var that
you need to return in trainer.train_func.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
target_vars = [
self.train_func_outputs[index] for index in target_var_indexes
]
io.save_inference_model(param_path, feeded_var_names, target_vars,
exe)
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(
main_program=self.train_program,
startup_program=self.startup_program):
with executor.scope_guard(self.scope):
yield
def _train_by_executor(self, num_epochs, event_handler, reader, feed_order):
"""
Train by Executor and single device.
Args:
num_epochs:
event_handler:
reader:
feed_order:
Returns:
"""
with self._prog_and_scope_guard():
feed_var_list = build_feed_var_list(self.train_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
exe = executor.Executor(self.place)
reader = feeder.decorate_reader(reader, multi_devices=False)
self._train_by_any_executor(event_handler, exe, num_epochs, reader)
def _train_by_any_executor(self, event_handler, exe, num_epochs, reader):
if self.checkpoint_cfg:
epochs = [
epoch_id for epoch_id in range(num_epochs)
if epoch_id >= self.checkpoint_cfg.epoch_id
]
else:
epochs = [epoch_id for epoch_id in range(num_epochs)]
for epoch_id in epochs:
event_handler(BeginEpochEvent(epoch_id))
for step_id, data in enumerate(reader()):
if self.__stop:
if self.checkpoint_cfg:
self._clean_checkpoint()
return
if self.checkpoint_cfg and self.checkpoint_cfg.load_serial \
and self.checkpoint_cfg.step_id >= step_id and self.checkpoint_cfg.epoch_id == epoch_id:
continue
begin_event = BeginStepEvent(epoch_id, step_id)
event_handler(begin_event)
if begin_event.fetch_metrics:
metrics = exe.run(feed=data,
fetch_list=[
var.name
for var in self.train_func_outputs
])
else:
metrics = exe.run(feed=data, fetch_list=[])
if self.checkpoint_cfg:
self._save_checkpoint(epoch_id, step_id)
event_handler(EndStepEvent(epoch_id, step_id, metrics))
event_handler(EndEpochEvent(epoch_id))
if self.checkpoint_cfg:
self._clean_checkpoint()
def _test_by_executor(self, reader, feed_order, fetch_list):
with executor.scope_guard(self.scope):
feed_var_list = build_feed_var_list(self.test_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
exe = executor.Executor(self.place)
accumulated = len(fetch_list) * [0]
count = 0
for data in reader():
outs = exe.run(program=self.test_program,
feed=feeder.feed(data),
fetch_list=fetch_list)
accumulated = [x[0] + x[1][0] for x in zip(accumulated, outs)]
count += 1
return [x / count for x in accumulated]
def _train_by_parallel_executor(self, num_epochs, event_handler, reader,
feed_order):
with self._prog_and_scope_guard():
pe = self._get_or_create_parallel_executor()
feed_var_list = build_feed_var_list(self.train_program, feed_order)
feeder = data_feeder.DataFeeder(
feed_list=feed_var_list, place=self.place)
reader = feeder.decorate_reader(reader, multi_devices=True)
self._train_by_any_executor(event_handler, pe, num_epochs, reader)
def _get_parallel_executor(self):
return getattr(self, 'parallel_executor', None)
def _get_or_create_parallel_executor(self):
if self._get_parallel_executor() is None:
self.parallel_executor = parallel_executor.ParallelExecutor(
use_cuda=isinstance(self.place, core.CUDAPlace),
loss_name=self.train_func_outputs[0].name)
return self._get_parallel_executor()
def _clean_checkpoint(self):
assert self.checkpoint_cfg
clean_checkpoint(checkpoint_dir=self.checkpoint_cfg.checkpoint_dir)
def _get_checkpoint_load_args(self):
"""
epoch_id and step_id are runtime arguments, they are not variables, will load them independently.
"""
return ["epoch_id", "step_id"]
def _get_checkpoint_save_args(self, epoch_id, step_id):
"""
epoch_id and step_id are runtime arguments, they are not variables, will save them independently.
"""
trainer_args = {}
trainer_args["epoch_id"] = epoch_id
trainer_args["step_id"] = step_id
return trainer_args
def _save_checkpoint(self, epoch_id, step_id):
assert self.checkpoint_cfg
if epoch_id % self.checkpoint_cfg.epoch_interval == 0 \
and step_id % self.checkpoint_cfg.step_interval == 0:
exe = executor.Executor(self.place)
save_checkpoint(
executor=exe,
checkpoint_dir=self.checkpoint_cfg.checkpoint_dir,
trainer_id=self.trainer_id,
trainer_args=self._get_checkpoint_save_args(epoch_id, step_id),
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):
raise TypeError("The 'program' should be an object of Program")
if isinstance(feed_order, list):
feed_var_list = [
program.global_block().var(var_name) for var_name in feed_order
]
else:
if not isinstance(feed_order, dict):
raise TypeError(
"The 'feed_order' should be either None, list or dict.")
if not sorted(feed_order.values()) == list(range(len(feed_order))):
raise ValueError(
"The values of 'feed_order' should be a permutation of [0, len(feed_order))"
)
sorted_pair_list = sorted(
six.iteritems(feed_order), key=lambda item: item[1])
feed_var_list = [
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 six.iteritems(trainer_args):
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(list(serial_map.keys())) <= max_num_checkpoints:
return
serials = list(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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