# 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 contextlib import os import errno import shutil import time import core import data_feeder import executor import framework import io # optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module import optimizer as opt_module 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): # 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) @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()) == range(len(feed_order)): raise ValueError( "The values of 'feed_order' should be a permutation of [0, len(feed_order))" ) sorted_pair_list = sorted(feed_order.items(), 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 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