# 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 os import errno import time import shutil from paddle.fluid.evaluator import Evaluator from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable from . import core __all__ = [ 'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params', 'load_persistables', 'save_inference_model', 'load_inference_model', 'get_inference_program', 'save_checkpoint', 'load_checkpoint', 'clean_checkpoint', 'load_persist_vars_without_grad', 'load_lookup_table_vars', 'save_persist_vars_without_grad', 'get_latest_checkpoint_serial' ] def is_parameter(var): """ Check whether the given variable is an instance of Parameter. Args: var(Variable): The variable to be checked. Returns: bool: True if the given `var` is an instance of Parameter, False if not. Examples: .. code-block:: python param = fluid.default_main_program().global_block().var('fc.w') res = fluid.io.is_parameter(param) """ return isinstance(var, Parameter) def is_persistable(var): """ Check whether the given variable is persistable. Args: var(Variable): The variable to be checked. Returns: bool: True if the given `var` is persistable False if not. Examples: .. code-block:: python param = fluid.default_main_program().global_block().var('fc.w') res = fluid.io.is_persistable(param) """ if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \ var.desc.type() == core.VarDesc.VarType.FETCH_LIST: return False return var.persistable def _clone_var_in_block_(block, var): assert isinstance(var, Variable) return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=True) def save_vars(executor, dirname, main_program=None, vars=None, predicate=None, filename=None): """ Save variables to the given directory by executor. There are two ways to specify variables to be saved: The first way, list variables in a list and assign it to the `vars`. The second way, assign the `main_program` with an existing program, then all variables in the program will be saved. The first way has a higher priority. In other words, if `vars` are assigned, the `main_program` and the `predicate` will be ignored. The `dirname` are used to specify the folder where to save variables. If you prefer to save variables in separate files in the folder `dirname`, set `filename` None; if you prefer to save all variables in a single file, use `filename` to specify it. Args: executor(Executor): The executor to run for saving variables. dirname(str): The directory path. main_program(Program|None): The program whose variables will be saved. If it is None, the default main program will be used automatically. Default: None vars(list[Variable]|None): The list that contains all variables to save. It has a higher priority than the `main_program`. Default: None predicate(function|None): If it is not None, only variables in the `main_program` that makes predicate(variable)==True will be saved. It only works when we are using the `main_program` to specify variables (In other words `vars` is None). Default: None filename(str|None): The file which to save all variables. If you prefer to save variables separately, set it to None. Default: None Returns: None Raises: TypeError: If `main_program` is not an instance of Program nor None. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" # The first usage: using `main_program` to specify variables def name_has_fc(var): res = "fc" in var.name return res prog = fluid.default_main_program() fluid.io.save_vars(executor=exe, dirname=path, main_program=prog, vars=None) # All variables in `main_program` whose name includes "fc" will be saved. # And variables are going to be saved separately. # The second usage: using `vars` to specify variables var_list = [var_a, var_b, var_c] fluid.io.save_vars(executor=exe, dirname=path, vars=var_list, filename="vars_file") # var_a, var_b and var_c will be saved. And they are going to be # saved in the same file named 'var_file' in the path "./my_paddle_model". """ if vars is None: if main_program is None: main_program = default_main_program() if not isinstance(main_program, Program): raise TypeError("program should be as Program type or None") save_vars( executor, dirname=dirname, vars=filter(predicate, main_program.list_vars()), filename=filename) else: save_program = Program() save_block = save_program.global_block() save_var_map = {} for each_var in vars: # NOTE: don't save the variable which type is RAW if each_var.type == core.VarDesc.VarType.RAW: continue new_var = _clone_var_in_block_(save_block, each_var) if filename is None: save_block.append_op( type='save', inputs={'X': [new_var]}, outputs={}, attrs={'file_path': os.path.join(dirname, new_var.name)}) else: save_var_map[new_var.name] = new_var if filename is not None: save_var_list = [] for name in sorted(save_var_map.keys()): save_var_list.append(save_var_map[name]) save_block.append_op( type='save_combine', inputs={'X': save_var_list}, outputs={}, attrs={'file_path': os.path.join(dirname, filename)}) executor.run(save_program) def save_params(executor, dirname, main_program=None, filename=None): """ This function filters out all parameters from the give `main_program` and then save them to the folder `dirname` or the file `filename`. Use the `dirname` to specify the saving folder. If you would like to save parameters in separate files, set `filename` None; if you would like to save all parameters in a single file, use `filename` to specify the file name. NOTICE: Some variables are not Parameter while they are necessary for training. So you can NOT save and continue your training just by `save_params()` and `load_params()`. Please use `save_persistables()` and `load_persistables()` instead. Args: executor(Executor): The executor to run for saving parameters. dirname(str): The saving directory path. main_program(Program|None): The program whose parameters will be saved. If it is None, the default main program will be used automatically. Default: None filename(str|None): The file to save all parameters. If you prefer to save parameters in differnet files, set it to None. Default: None Returns: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" prog = fluid.default_main_program() fluid.io.save_params(executor=exe, dirname=param_path, main_program=None) """ save_vars( executor, dirname=dirname, main_program=main_program, vars=None, predicate=is_parameter, filename=filename) def save_persistables(executor, dirname, main_program=None, filename=None): """ This function filters out all variables with `persistable==True` from the give `main_program` and then saves these variables to the folder `dirname` or file `filename`. The `dirname` is used to specify the folder where persistable variables are going to be saved. If you would like to save variables in separate files, set `filename` None; if you would like to save all variables in a single file, use `filename` to specify the file name. Args: executor(Executor): The executor to run for saving persistable variables. dirname(str): The directory path. main_program(Program|None): The program whose persistbale variables will be saved. If it is None, the default main program will be used automatically. Default: None filename(str|None): The file to saved all variables. If you prefer to save variables in differnet files, set it to None. Default: None Returns: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" prog = fluid.default_main_program() fluid.io.save_persistables(executor=exe, dirname=param_path, main_program=None) """ save_vars( executor, dirname=dirname, main_program=main_program, vars=None, predicate=is_persistable, filename=filename) def load_vars(executor, dirname, main_program=None, vars=None, predicate=None, filename=None): """ Load variables from the given directory by executor. There are two ways to specify variables to be loaded: The first way, list variables in a list and assign it to the `vars`. The second way, assign the `main_program` with an existing program, then all variables in the program will be loaded. The first way has a higher priority. In other words if `vars` are assigned, the `main_program` and the `predicate` will be ignored. The `dirname` are used to specify the folder where to load variables. If variables were saved in separate files in the folder `dirname`, set `filename` None; if all variables were saved in a single file, use `filename` to specify it. Args: executor(Executor): The executor to run for loading variables. dirname(str): The directory path. main_program(Program|None): The program whose variables will be loaded. If it is None, the default main program will be used automatically. Default: None vars(list[Variable]|None): The list that contains all variables to load. It has a higher priority than the `main_program`. Default: None predicate(function|None): If it is not None, only variables in the `main_program` that makes predicate(variable)==True will be loaded. It only works when we are using the `main_program` to specify variables (In other words `vars` is None). Default: None filename(str|None): The file which saved all required variables. If variables were saved in differnet files, set it to None. Default: None Returns: None Raises: TypeError: If `main_program` is not an instance of Program nor None. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" # The first usage: using `main_program` to specify variables def name_has_fc(var): res = "fc" in var.name return res prog = fluid.default_main_program() fluid.io.load_vars(executor=exe, dirname=path, main_program=prog, vars=None) # All variables in `main_program` whose name includes "fc" will be loaded. # And all the variables are supposed to have been saved in differnet files. # The second usage: using `vars` to specify variables var_list = [var_a, var_b, var_c] fluid.io.load_vars(executor=exe, dirname=path, vars=var_list, filename="vars_file") # var_a, var_b and var_c will be loaded. And they are supposed to haven # been saved in the same file named 'var_file' in the path "./my_paddle_model". """ if vars is None: if main_program is None: main_program = default_main_program() if not isinstance(main_program, Program): raise TypeError("program's type should be Program") load_vars( executor, dirname=dirname, vars=filter(predicate, main_program.list_vars()), filename=filename) else: load_prog = Program() load_block = load_prog.global_block() load_var_map = {} for each_var in vars: assert isinstance(each_var, Variable) if each_var.type == core.VarDesc.VarType.RAW: continue new_var = _clone_var_in_block_(load_block, each_var) if filename is None: load_block.append_op( type='load', inputs={}, outputs={'Out': [new_var]}, attrs={'file_path': os.path.join(dirname, new_var.name)}) else: load_var_map[new_var.name] = new_var if filename is not None: load_var_list = [] for name in sorted(load_var_map.keys()): load_var_list.append(load_var_map[name]) load_block.append_op( type='load_combine', inputs={}, outputs={"Out": load_var_list}, attrs={'file_path': os.path.join(dirname, filename)}) executor.run(load_prog) def load_params(executor, dirname, main_program=None, filename=None): """ This function filters out all parameters from the give `main_program` and then trys to load these parameters from the folder `dirname` or the file `filename`. Use the `dirname` to specify the folder where parameters were saved. If parameters were saved in separate files in the folder `dirname`, set `filename` None; if all parameters were saved in a single file, use `filename` to specify the file name. NOTICE: Some variables are not Parameter while they are necessary for training. So you can NOT save and continue your training just by `save_params()` and `load_params()`. Please use `save_persistables()` and `load_persistables()` instead. Args: executor(Executor): The executor to run for loading parameters. dirname(str): The directory path. main_program(Program|None): The program whose parameters will be loaded. If it is None, the default main program will be used automatically. Default: None filename(str|None): The file which saved all parameters. If parameters were saved in differnet files, set it to None. Default: None Returns: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" prog = fluid.default_main_program() fluid.io.load_params(executor=exe, dirname=param_path, main_program=None) """ load_vars( executor, dirname=dirname, main_program=main_program, predicate=is_parameter, filename=filename) def load_persistables(executor, dirname, main_program=None, filename=None): """ This function filters out all variables with `persistable==True` from the give `main_program` and then trys to load these variables from the folder `dirname` or the file `filename`. Use the `dirname` to specify the folder where persistable variables were saved. If variables were saved in separate files, set `filename` None; if all variables were saved in a single file, use `filename` to specify the file name. Args: executor(Executor): The executor to run for loading persistable variables. dirname(str): The directory path. main_program(Program|None): The program whose persistbale variables will be loaded. If it is None, the default main program will be used automatically. Default: None filename(str|None): The file which saved all variables. If variables were saved in differnet files, set it to None. Default: None Returns: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" prog = fluid.default_main_program() fluid.io.load_persistables(executor=exe, dirname=param_path, main_program=None) """ load_vars( executor, dirname=dirname, main_program=main_program, predicate=is_persistable, filename=filename) def get_inference_program(target_vars, main_program=None): if main_program is None: main_program = default_main_program() if not isinstance(target_vars, list): target_vars = [target_vars] vars = [] for var in target_vars: if isinstance(var, Evaluator): vars.extend(var.states) vars.extend(var.metrics) else: vars.append(var) pruned_program = main_program.prune(targets=vars) inference_program = pruned_program.inference_optimize() return inference_program def prepend_feed_ops(inference_program, feed_target_names, feed_holder_name='feed'): if len(feed_target_names) == 0: return global_block = inference_program.global_block() feed_var = global_block.create_var( name=feed_holder_name, type=core.VarDesc.VarType.FEED_MINIBATCH, persistable=True) for i, name in enumerate(feed_target_names): out = global_block.var(name) global_block.prepend_op( type='feed', inputs={'X': [feed_var]}, outputs={'Out': [out]}, attrs={'col': i}) def append_fetch_ops(inference_program, fetch_target_names, fetch_holder_name='fetch'): global_block = inference_program.global_block() fetch_var = global_block.create_var( name=fetch_holder_name, type=core.VarDesc.VarType.FETCH_LIST, persistable=True) for i, name in enumerate(fetch_target_names): global_block.append_op( type='fetch', inputs={'X': [name]}, outputs={'Out': [fetch_var]}, attrs={'col': i}) def save_inference_model(dirname, feeded_var_names, target_vars, executor, main_program=None, model_filename=None, params_filename=None): """ Prune the given `main_program` to build a new program especially for inference, and then save it and all related parameters to given `dirname` by the `executor`. Args: dirname(str): The directory path to save the inference model. feeded_var_names(list[str]): Names of variables that need to be feeded data during inference. target_vars(list[Variable]): Variables from which we can get inference results. executor(Executor): The executor that saves the inference model. main_program(Program|None): The original program, which will be pruned to build the inference model. If is setted None, the default main program will be used. Default: None. model_filename(str|None): The name of file to save the inference program itself. If is setted None, a default filename `__model__` will be used. params_filename(str|None): The name of file to save all related parameters. If it is setted None, parameters will be saved in separate files . Returns: None Raises: ValueError: If `feed_var_names` is not a list of basestring. ValueError: If `target_vars` is not a list of Variable. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) path = "./infer_model" fluid.io.save_inference_model(dirname=path, feeded_var_names=['img'], target_vars=[predict_var], executor=exe) # In this exsample, the function will prune the default main program # to make it suitable for infering the `predict_var`. The pruned # inference program is going to be saved in the "./infer_model/__model__" # and parameters are going to be saved in separate files under folder # "./infer_model". """ if isinstance(feeded_var_names, basestring): feeded_var_names = [feeded_var_names] else: if len(feeded_var_names) > 0: if not (bool(feeded_var_names) and all( isinstance(name, basestring) for name in feeded_var_names)): raise ValueError("'feed_var_names' should be a list of str.") if isinstance(target_vars, Variable): target_vars = [target_vars] else: if not (bool(target_vars) and all( isinstance(var, Variable) for var in target_vars)): raise ValueError("'target_vars' should be a list of Variable.") if main_program is None: main_program = default_main_program() copy_program = main_program.clone() if not os.path.isdir(dirname): os.makedirs(dirname) # Clear the is_target information and remove the existed feed and fetch op global_block = copy_program.global_block() for i, op in enumerate(global_block.ops): op.desc.set_is_target(False) if op.type == "feed" or op.type == "fetch": global_block.remove_op(i) copy_program.desc.flush() pruned_program = copy_program.prune(targets=target_vars) inference_program = pruned_program.inference_optimize() fetch_var_names = [v.name for v in target_vars] prepend_feed_ops(inference_program, feeded_var_names) append_fetch_ops(inference_program, fetch_var_names) if model_filename is not None: model_filename = os.path.basename(model_filename) else: model_filename = "__model__" model_filename = os.path.join(dirname, model_filename) if params_filename is not None: params_filename = os.path.basename(params_filename) with open(model_filename, "wb") as f: f.write(inference_program.desc.serialize_to_string()) save_persistables(executor, dirname, inference_program, params_filename) def load_inference_model(dirname, executor, model_filename=None, params_filename=None): """ Load inference model from a directory Args: dirname(str): The directory path executor(Executor): The executor to run for loading inference model. model_filename(str|None): The name of file to load inference program. If it is None, the default filename '__model__' will be used. Default: None params_filename(str|None): The name of file to load all parameters. It is only used for the case that all parameters were saved in a single binary file. If parameters were saved in separate files, set it as 'None'. Returns: tuple: The return of this function is a tuple with three elements: (program, feed_target_names, fetch_targets). The `program` is a Program, it's the program for inference. The `feed_target_names` is a list of str, it contains Names of variables that need to feed data in the inference program. The `fetch_targets` is a list of Variable. It contains variables from which we can get inference results. Raises: ValueError: If `dirname` is not a existing directory. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) path = "./infer_model" [inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(dirname=path, executor=exe) results = exe.run(inference_program, feed={feed_target_names[0]: tensor_img}, fetch_list=fetch_targets) # In this exsample, the inference program was saved in the # "./infer_model/__model__" and parameters were saved in # separate files in ""./infer_model". # After getting inference program, feed target names and # fetch targets, we can use an Executor to run the inference # program to get the inference result. """ if not os.path.isdir(dirname): raise ValueError("There is no directory named '%s'", dirname) if model_filename is not None: model_filename = os.path.basename(model_filename) else: model_filename = "__model__" model_filename = os.path.join(dirname, model_filename) if params_filename is not None: params_filename = os.path.basename(params_filename) with open(model_filename, "rb") as f: program_desc_str = f.read() program = Program.parse_from_string(program_desc_str) load_persistables(executor, dirname, program, params_filename) feed_target_names = program.desc.get_feed_target_names() fetch_target_names = program.desc.get_fetch_target_names() fetch_targets = [ program.global_block().var(name) for name in fetch_target_names ] return [program, feed_target_names, fetch_targets] def get_parameter_value(para, executor): """ Get the LoDTensor value of the given parameter. Args: para(Parameter): The parameter to get value from. executor(Executor): The executor to run for retrieving the value. Returns: numpy.array: The given parameter's values. Raises: AssertionError: If the `para` is not an instance of Parameter. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param = fluid.default_main_program().global_block().var('fc.w') p = fluid.io.get_parameter_value(param, exe) """ assert is_parameter(para) get_program = Program() block = get_program.global_block() new_var = _clone_var_in_block_(block, para) return executor.run(get_program, feed={}, fetch_list=[new_var])[0] def get_parameter_value_by_name(name, executor, program=None): """ Get the LoDTensor value of a certain parameter by its name. Args: name(str): The parameter's name. executor(Executor): The executor to run for retrieving the value. program(Program | None): The program where to find the parameter. If it's set to be None, the function will try to find the parameter in the default main program. Returns: numpy.array: The parameter's values. Raises: TypeError: If given `name` is not an instance of basestring. TypeError: If the parameter with the given name doesn't exist. AssertionError: If there is a varibale named `name` in the given program but it is not a Parameter. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) p = fluid.io.get_parameter_value('fc.w', exe) """ if program is None: program = default_main_program() var = program.global_block().var(name) return get_parameter_value(var, executor) SUCCESS_MARK_FILENAME = "_SUCCESS" CHECKPOINT_PREFIX = "checkpoint" MODEL_DIR = "__model__" LOOKUP_TABLE_DIR = "__lookup_table__" TRAINER_PREFIX = "trainer" CHECKPOINT_SEPARATOR = "_" def save_checkpoint(executor, checkpoint_dir, trainer_id, trainer_args=None, main_program=None, max_num_checkpoints=3, lookup_table=None, ps_endpoint_list=None): """ This function filters out all checkpoint variables from the give main_program and then saves these variables to the `checkpoint_dir` directory. In the training precess, we generally save a checkpoint in each iteration. So there might be a lot of checkpoints in the `checkpoint_dir`. To avoid them taking too much disk space, the `max_num_checkpoints` are introduced to limit the total number of checkpoints. If the number of existing checkpints is greater than the `max_num_checkpoints`, oldest ones will be scroll deleted. A variable is a checkpoint variable and will be saved if it meets all following conditions: 1. It's persistable. 2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW. 3. It's name contains no "@GRAD" nor ".trainer_" nor ".block". Args: executor(Executor): The executor to run for save checkpoint. checkpoint_dir(str): The folder where to save checkpoints. trainer_id(int): currect trainer id, if id is equal to 0, the trainer is chief. trainer_args(dict|None): Current training arguments. Such as 'epoch_id' and 'step_id'. Defaut: None main_program(Program|None): The program whose checkpoint variables will be saved. If it is None, the default main program will be used. max_num_checkpoints(int): The max number of total number of existing checkpoints. Default: 3 lookup_table(string|None): the lookup table name, when use distribute lookup table, we can get lookup table name by DistributeTranspiler. table_name ps_endpoint_list(list|None): the parameter server ip:port list. when use distribute lookup table, we can get ps_endpoint_list by distribute arguments. Returns: None Raises: ValueError: If `checkpoint_dir` is None. AssertionError: If `trainer_args` is not a dict. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) path = "./checkpoints" prog = fluid.default_main_program() trainer_args = {"epoch_id": 200, "step_id": 20} # just an example table_name = "share_w" ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"] fluid.io.save_checkpoint(executor=exe, checkpoint_dir=path, trainer_id=0, trainer_args=trainer_args, main_program=prog, max_num_checkpoints=3, lookup_table=table_name, ps_endpoint_list = ps_endpoints) """ if checkpoint_dir is None: raise ValueError("'checkpoint_dir' should not be None") assert checkpoint_dir if trainer_args: assert isinstance(trainer_args, dict) is_chief = trainer_id == 0 _make_chekcpoint_dirs(checkpoint_dir) serial = get_latest_checkpoint_serial(checkpoint_dir) + 1 cur_dir = _get_serial_dir(checkpoint_dir, serial) save_trainer_args(cur_dir, trainer_id, trainer_args) if is_chief: save_persist_vars_without_grad(executor, cur_dir, main_program) if is_chief and lookup_table and ps_endpoint_list: save_pserver_vars_by_notify(executor, cur_dir, lookup_table, ps_endpoint_list) _scroll_delete(checkpoint_dir, max_num_checkpoints) def load_checkpoint(executor, checkpoint_dir, serial, main_program): """ This function filters out all checkpoint variables from the give main_program and then try to load these variables from the `checkpoint_dir` directory. In the training precess, we generally save a checkpoint in each iteration. So there are more than one checkpoint in the `checkpoint_dir` (each checkpoint has its own sub folder), use `serial` to specify which serial of checkpoint you would like to load. A variable is a checkpoint variable and will be loaded if it meets all following conditions: 1. It's persistable. 2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW. 3. It's name contains no "@GRAD" nor ".trainer_" nor ".block". Args: executor(Executor): The executor to run for loading checkpoint. checkpoint_dir(str): The folder where all checkpoints are. serial(int): The serial of checkpoint you would like to load. main_program(Program): The program whose checkpoint variables will be loaded. Returns: None Raises: ValueError: If `checkpoint_dir` is None. ValueError: If `serial` is None or `serial` is less than 0. ValueError: If `main_program` is None. Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) path = "./checkpoints" prog = fluid.default_main_program() fluid.io.load_checkpoint(executor=exe, checkpoint_dir=path, serial=9, main_program=prog) # In this example, `load_checkpoint` function # will first filters out all checkpoint variables in the default # main program, and then try to load these variables form the # folder "./checkpoints/checkpoint_9/__model__". """ if checkpoint_dir is None: raise ValueError("'checkpoint_dir' should not be None") if serial is None or serial < 0: raise ValueError("'serial' should not be None or <0 ") if main_program is None: raise ValueError('main_program should not be None.') cur_dir = _get_serial_dir(checkpoint_dir, serial) load_persist_vars_without_grad(executor, cur_dir, main_program, True) def clean_checkpoint(checkpoint_dir, delete_dir=False): """ clean the checkpoint dir, when the train exits normally, the trainer will call clean_checkpoint to delete checkpoint directory saved before. delete_dir only works when the directory is empty, otherwise, OSError is raised. : param checkpoint_dir : param delete_dir """ if checkpoint_dir is None: raise ValueError("'checkpoint_dir' should not be None") _scroll_delete(checkpoint_dir, max_num_checkpoints=0) if delete_dir and not os.listdir(checkpoint_dir): os.rmdir(checkpoint_dir) def load_persist_vars_without_grad(executor, dirname, program, has_model_dir=False): """ This function filters out all checkpoint variables from the give program and then trys to load these variables from the given directory. A variable is a checkpoint variable if it meets all following conditions: 1. It's persistable. 2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW. 3. It's name contains no "@GRAD" nor ".trainer_" nor ".block". Args: executor(Executor): The executor to run for loading variables. dirname(str): The directory path. program(Program): The program whose checkpoint variables will be loaded. has_model_dir(bool): if True, the function loads variables from a sub directory named '__model__'. Default: False Returns: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" prog = fluid.default_main_program() fluid.io.load_persist_vars_without_grad(executor=exe, dirname=param_path, program=prog, has_model_dir=True) # In this example, `load_persist_vars_without_grad` function # will first filters out all checkpoint variables in the default # main program, and then trys to load these variables form the # folder "./my_paddle_model/__model__". """ if has_model_dir: dirname = _get_model_dir(dirname) load_vars( executor, dirname=dirname, main_program=program, predicate=_is_checkpoint_var, filename=None) def load_lookup_table_vars(executor, dirname, program, pserver_id, table_name): """ The parameter server will load lookup table's local file in selectedrows variable. Args: executor(Executor): The executor to run for loading persistable variables dirname(str): The directory path main_program(Program): Find the variable named table_name in main_program pserver_id(int): the serial number in pserver_endpoints list table_name(str): lookup table name Returns: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) dirname = "./checkpoints/checkpoint_9/__model__" prog = fluid.default_main_program() pserver_id = 1 table_name = "share_w" fluid.io.load_lookup_table_vars(executor=exe, dirname=dirname, program=prog, pserver_id=pserver_id, table_name=table_name) """ for var in program.list_vars(): if var.name == table_name: lookup_table_var = var break assert lookup_table_var is not None lookup_table_dir = os.path.join(dirname, LOOKUP_TABLE_DIR) table_file = table_name + CHECKPOINT_SEPARATOR + str(pserver_id) load_prog = Program() load_block = load_prog.global_block() load_block.append_op( type='load', inputs={}, outputs={'Out': [lookup_table_var]}, attrs={'file_path': os.path.join(lookup_table_dir, table_file)}) executor.run(load_prog) def save_persist_vars_without_grad(executor, dirname, program): """ This function filters out all checkpoint variables from the give program and then save these variables to a sub-folder '__model__' of the given directory. A variable is a checkpoint variable if it meets all following conditions: 1. It's persistable. 2. It's type is not FEED_MINIBATCH nor FETCH_LIST nor RAW. 3. It's name contains no "@GRAD" nor ".trainer_" nor ".block". Args: executor(Executor): The executor to run for saving variables. dirname(str): The directory path. program(Program): The program whose checkpoint variables will be saved. Returns: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" prog = fluid.default_main_program() fluid.io.save_persist_vars_without_grad(executor=exe, dirname=param_path, program=prog) # In this example, `save_persist_vars_without_grad` function # will first filters out all checkpoint variables in the default # main program, and then saves these variables to the folder # "./my_paddle_model/__model__". """ cur_dir = _get_model_dir(dirname) save_vars( executor, dirname=cur_dir, main_program=program, vars=None, predicate=_is_checkpoint_var, filename=None) _write_success(cur_dir) def save_pserver_vars_by_notify(executor, dirname, lookup_table, ps_endpoint_list): """ This function will send checkpoint notify message from Trainer 0 to all the pservers. The checkpoint notify message contains lookup table name, the absolute path on pserver to save lookup_table. Args: executor(Executor): The executor to run for send checkpoint notify. dirname(str): The folder where to save checkpoints. lookup_table(string): the lookup table name, when use distribute lookup table, we can get lookup table name by DistributeTranspiler. table_name ps_endpoint_list(list): the parameter server ip:port list. when use distribute lookup table, we can get ps_endpoint_list by distribute arguments. Return: None Examples: .. code-block:: python exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" prog = fluid.default_main_program() table_name = "share_w" ps_endpoints = ["127.0.0.1:6000","127.0.0.1:6001"] fluid.io.save_pserver_vars_by_notify(executor=exe, dirname=param_path, lookup_table=table_name, ps_endpoint_list=ps_endpoints) """ cur_dir = _get_lookuptable_dir(dirname) checkpoint_notify_program = Program() checkpoint_notify_block = checkpoint_notify_program.global_block() attrs = {} attrs['epmap'] = ps_endpoint_list attrs['dir'] = cur_dir attrs['lookup_table'] = lookup_table checkpoint_notify_block.append_op( type='checkpoint_notify', inputs={}, outputs={}, attrs=attrs) executor.run(checkpoint_notify_program) def save_trainer_args(dirname, trainer_id, trainer_args): assert isinstance(trainer_args, dict) cur_dir = _get_trainer_dir(dirname, trainer_id) for name, value in trainer_args.iteritems(): args_file = os.path.join(cur_dir, name) with open(args_file, 'w') as f: f.write(str(value)) _write_success(cur_dir) def load_trainer_args(checkpoint_dir, serial, trainer_id, trainer_args): """ trainer will load some args from it's independent directory, such as epoch_id and step_id. Args: checkpoint_dir(str): The folder where all checkpoints are. serial(int): The serial of checkpoint you would like to load. trainer_id(int): current trainer id. trainer_args(list): list about load trainer args Return: None Examples: .. code-block:: python param_path = "./checkpoint/" serial = 7 trainer_id = 2 trainer_args = ["epoch_id", "step_id"] fluid.io.load_trainer_args(checkpoint_dir=param_path, serial=serial, trainer_id=trainer_id, trainer_args=trainer_args) """ assert isinstance(trainer_args, list) cur_dir = _get_serial_dir(checkpoint_dir, serial) cur_dir = _get_trainer_dir(cur_dir, trainer_id) ret_values = [] for arg in trainer_args: cur_file = os.path.join(cur_dir, arg) with open(cur_file, 'r') as f: contents = f.read() ret_values.append(contents.strip()) return ret_values def _is_checkpoint_var(var): """ the checkpoint will not save or load all the variables. var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded. : param var(Variable) """ if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \ var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \ var.desc.type() == core.VarDesc.VarType.RAW: return False # @GRAD are named for gradient variables, checkpoint will not save it. if "@GRAD" in var.name: return False # .trainer_ are named for distribute train variables, checkpoint will not save it. if ".trainer_" in var.name: return False # .block is named for distribute train variables, checkpoint will not save it. if ".block" in var.name: return False return var.persistable def _make_chekcpoint_dirs(dirs): """ _make_chekcpoint_dirs will makdir local directory directly, when the directory is exist, it will igore it. """ assert dirs is not None if os.path.isfile(dirs): raise OSError(errno.ENOTDIR, "dirs path shoule be a Directory.", dirs) if not os.path.isdir(dirs): try: os.makedirs(dirs) except OSError as err: if err.errno != errno.EEXIST: raise err def _get_dir_serial(dirname): _, serial = dirname.split(CHECKPOINT_SEPARATOR) try: serial_num = int(serial) except ValueError: serial_num = -1 return serial_num def _get_serial_dir(dirname, serial): serial_folder = CHECKPOINT_PREFIX + CHECKPOINT_SEPARATOR + str(serial) serial_dir = os.path.join(dirname, serial_folder) _make_chekcpoint_dirs(serial_dir) return serial_dir def _get_model_dir(dirname): model_dir = os.path.join(dirname, MODEL_DIR) _make_chekcpoint_dirs(model_dir) return model_dir def _get_lookuptable_dir(dirname): lookuptable_dir = os.path.join(dirname, LOOKUP_TABLE_DIR) _make_chekcpoint_dirs(lookuptable_dir) return lookuptable_dir def _get_trainer_dir(dirname, trainer_id): trainer_folder = TRAINER_PREFIX + CHECKPOINT_SEPARATOR + str(trainer_id) trainer_dir = os.path.join(dirname, trainer_folder) _make_chekcpoint_dirs(trainer_dir) return trainer_dir def _scroll_delete(dirname, max_num_checkpoints=3): dirs = os.listdir(dirname) serial_map = {} for serial in dirs: serial_num = _get_dir_serial(serial) serial_map[serial_num] = serial if len(serial_map.keys()) <= max_num_checkpoints: return serials = serial_map.keys() serials.sort(reverse=True) serials = serials[max_num_checkpoints:] for serial in serials: cur_dir = _get_serial_dir(dirname, serial) try: shutil.rmtree(cur_dir) except OSError as err: if err.errno != errno.ENOENT: raise err def _write_success(dirname): """ write an empty file named "_SUCCESS" in checkpoint dir, indicate this checkpoint is correct. : param dirname """ success_file = os.path.join(dirname, SUCCESS_MARK_FILENAME) with open(success_file, 'a') as f: now = time.ctime() f.write(now) def get_latest_checkpoint_serial(checkpoint_dir): """ get the latest file in checkpoint directory, the _SUCCESS file must exist in the directory : param checkpoint_dir """ if not checkpoint_dir: return -1 def has_success(checkpoint_dir, cur_dir): """ is _SUCCESS in this dir """ serial = _get_dir_serial(cur_dir) if serial == -1 or not os.path.isdir( os.path.join(checkpoint_dir, cur_dir)): return -1 success_path = os.path.join( _get_serial_dir(checkpoint_dir, serial), MODEL_DIR, SUCCESS_MARK_FILENAME) if os.path.isfile(success_path): return serial if not os.path.isdir(checkpoint_dir): return -1 current_dir = -1 dirs = os.listdir(checkpoint_dir) for cur_dir in dirs: success_num = has_success(checkpoint_dir, cur_dir) if success_num > current_dir: current_dir = success_num return current_dir def get_test_program(filelist, program=None, startup_program=None): """ Transpile current train program to a program to read test dataset if the program is using reader ops like "open_files_op". """ def _copy_reader_var_(block, var, new_name=None): if new_name == None: new_name = var.name new_var = block.create_var( name=str(new_name), type=core.VarDesc.VarType.READER) new_var.desc.set_shapes(var.desc.shapes()) new_var.desc.set_dtypes(var.desc.dtypes()) new_var.persistable = True return new_var def _get_test_reader_name(train_reader_name): return train_reader_name + "_test" def _is_reader_op(op): block = op.block if "Out" in op.output_names: reader_out = block.vars[op.output("Out")[0]] if reader_out.type == core.VarDesc.VarType.READER: return True return False if program == None: program = default_main_program() if startup_program == None: startup_program = default_startup_program() startup_block = startup_program.global_block() # 1. find out the orignal reader var name startup_reader_op_list = [] for op in startup_block.ops: if _is_reader_op(op): startup_reader_op_list.append(op) if len(startup_reader_op_list) == 0: return program root_reader_op = startup_reader_op_list[0] train_test_reader_map = {} # 2. add operators to startup to read open and read test data files for op in startup_reader_op_list: assert (len(op.output("Out")) == 1) train_reader_name = op.output("Out")[0] train_reader = startup_block.vars[train_reader_name] test_reader = _copy_reader_var_( startup_block, train_reader, new_name=_get_test_reader_name(train_reader_name)) train_test_reader_map[train_reader.name] = test_reader test_op_inputs = {} for name in op.input_names: train_arg_names = op.input(name) test_arg_vars = [] for arg_name in train_arg_names: arg_var = train_test_reader_map[ arg_name] if name == "UnderlyingReader" else startup_block.vars[ arg_name] test_arg_vars.append(arg_var) test_op_inputs[name] = test_arg_vars test_op = startup_block.append_op( type=op.type, inputs=test_op_inputs, outputs={'Out': [test_reader]}, attrs=op.attrs) # root reader op's filelist attr for read test files if op.type == root_reader_op.type: test_op.set_attr("file_names", filelist) if op.type == "create_multi_pass_reader": test_op.set_attr("pass_num", 1) # 3. rename reader vars in inference program to different name # to avoid read from train data. main_block = program.global_block() for var in main_block.vars.values(): if var.type == core.VarDesc.VarType.READER: main_block.rename_var( str(var.name), str(_get_test_reader_name(var.name))) for op in main_block.ops: if op.type == root_reader_op.type: test_op.set_attr("file_names", filelist) if op.type == "create_multi_pass_reader": test_op.set_attr("pass_num", 1) startup_program.sync_with_cpp() program.sync_with_cpp() return program