diff --git a/python/paddle/fluid/__init__.py b/python/paddle/fluid/__init__.py index 9aac3c7fc16ae1ded2700662764895385b043130..7acaeb5f33ce0afcede9fa35e6cbcd066c2036c1 100644 --- a/python/paddle/fluid/__init__.py +++ b/python/paddle/fluid/__init__.py @@ -19,17 +19,8 @@ from .framework import * # import all class inside executor into fluid module from . import executor from .executor import * - 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 .inferencer import Inferencer from . import io from . import evaluator diff --git a/python/paddle/fluid/contrib/inferencer.py b/python/paddle/fluid/contrib/inferencer.py new file mode 100644 index 0000000000000000000000000000000000000000..b8d5f4ffeadca0a7b103682f175d50dc46fa258a --- /dev/null +++ b/python/paddle/fluid/contrib/inferencer.py @@ -0,0 +1,112 @@ +# 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 diff --git a/python/paddle/fluid/contrib/trainer.py b/python/paddle/fluid/contrib/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..8569e486f91786b5562e84dcdccf6d91da0612cc --- /dev/null +++ b/python/paddle/fluid/contrib/trainer.py @@ -0,0 +1,1258 @@ +# 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 diff --git a/python/paddle/fluid/inferencer.py b/python/paddle/fluid/inferencer.py index a9b94a20720615dbfca97749463f27dbc88ac64f..7bdd430f985bd0b3818f6ef305ce2d7d8976106b 100644 --- a/python/paddle/fluid/inferencer.py +++ b/python/paddle/fluid/inferencer.py @@ -12,101 +12,5 @@ # 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 +# NOTE: inferencer is moved into fluid.contrib.inferencer. +__all__ = [] diff --git a/python/paddle/fluid/trainer.py b/python/paddle/fluid/trainer.py index 30cdfe4ad2c9892184862b70ff49417ce5a08516..b495b6699b5d02ca8c466c984820be5c497d626e 100644 --- a/python/paddle/fluid/trainer.py +++ b/python/paddle/fluid/trainer.py @@ -12,1247 +12,5 @@ # 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 +# NOTE: Trainer is moved into fluid.contrib.trainer. +__all__ = []