# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # 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 six import os import errno import warnings import six import numpy as np import paddle from paddle.fluid import layers from paddle.fluid import core from paddle.fluid import unique_name from paddle.fluid.executor import global_scope from paddle.fluid.compiler import CompiledProgram from paddle.fluid.framework import Program, Parameter, default_main_program, default_startup_program, Variable, \ program_guard __all__ = ["save_mask_inference_model"] def _get_valid_program(main_program): if main_program is None: main_program = default_main_program() elif isinstance(main_program, CompiledProgram): main_program = main_program._program if main_program is None: raise TypeError("program should be as Program type or None") warnings.warn( "The input is a CompiledProgram, this is not recommended.") if not isinstance(main_program, Program): raise TypeError("program should be as Program type or None") return main_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 _clone_var_in_block_(block, var): assert isinstance(var, Variable) if var.desc.type() == core.VarDesc.VarType.LOD_TENSOR: return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=True) else: return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, persistable=True) def save_vars(executor, dirname, main_program=None, vars=None, predicate=None, filename=None): """ This API saves specific variables in the `Program` to files. There are two ways to specify the variables to be saved: set variables in a list and assign it to the `vars`, or use the `predicate` function to select variables that make `predicate(variable) == True`. The first way has a higher priority. The `dirname` is used to specify the folder where to save variables. If you prefer to save variables in separate files in the `dirname` floder, do not set `filename`. 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, optional): The folder where to save variables. When you need to save the parameter to the memory, set it to None. main_program(Program, optional): The program whose variables will be saved. If it is None, the default main program will be used automatically. Default: None vars(list[Variable], optional): The list contains all variables to be saved. Default: None predicate(function, optional): The function selects the variables that make `predicate(variable) == True`. Default: None filename(str, optional): If you prefer to save all variables in a single file, use `filename` to specify it. Otherwise, let `filename` be None. Default: None Returns: str: When saving parameters to a file, returns None. When saving parameters to memory, returns a binary string containing parameters. Raises: TypeError: If `main_program` is not an instance of Program nor None. Examples: .. code-block:: python import paddle.fluid as fluid main_prog = fluid.Program() startup_prog = fluid.Program() with fluid.program_guard(main_prog, startup_prog): data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False) w = fluid.layers.create_parameter(shape=[784, 200], dtype='float32', name='fc_w') b = fluid.layers.create_parameter(shape=[200], dtype='float32', name='fc_b') hidden_w = fluid.layers.matmul(x=data, y=w) hidden_b = fluid.layers.elementwise_add(hidden_w, b) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) # The first usage: use `vars` to set the saved variables. var_list = [w, b] path = "./my_paddle_vars" fluid.io.save_vars(executor=exe, dirname=path, vars=var_list, filename="vars_file") # w and b will be save in a file named "var_file". # The second usage: use `predicate` to select the saved variable. def name_has_fc(var): res = "fc" in var.name return res param_path = "./my_paddle_model" fluid.io.save_vars(executor=exe, dirname=param_path, main_program=main_prog, vars=None, predicate = name_has_fc) # all variables whose names contain "fc " are saved. """ save_to_memory = False if dirname is None and filename is None: save_to_memory = True main_program = _get_valid_program(main_program) if vars is None: return save_vars( executor, main_program=main_program, dirname=dirname, vars=list(filter(predicate, main_program.list_vars())), filename=filename) else: params_var_name = unique_name.generate("saved_params") # give warning when there is no var in model if len(list(vars)) == 0: warnings.warn( "no variable in your model, please ensure there are any variables in your model to save" ) return None 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 and save_to_memory is False: save_file_path = os.path.join( os.path.normpath(dirname), new_var.name) save_block.append_op( type='save', inputs={'X': [new_var]}, outputs={}, attrs={'file_path': os.path.normpath(save_file_path)}) else: save_var_map[new_var.name] = new_var if filename is not None or save_to_memory: save_var_list = [] for name in sorted(save_var_map.keys()): save_var_list.append(save_var_map[name]) save_path = str() if save_to_memory is False: save_path = os.path.join(os.path.normpath(dirname), filename) saved_params = save_block.create_var( type=core.VarDesc.VarType.RAW, name=params_var_name) saved_params.desc.set_persistable(True) save_block.append_op( type='save_combine', inputs={'X': save_var_list}, outputs={'Y': saved_params}, attrs={ 'file_path': save_path, 'save_to_memory': save_to_memory }) #NOTE(zhiqiu): save op will add variable kLookupTablePath in save_program.desc, # which leads to diff on save_program and its desc. Call _sync_with_cpp # to keep consistency. save_program._sync_with_cpp() executor.run(save_program) if save_to_memory: return global_scope().find_var(params_var_name).get_bytes() def _save_distributed_persistables(executor, dirname, main_program): """ save_persistables for distributed training. the method will do things listed below: 1.save part of persistable variables on trainer. 2.receive "remote prefetch variables" from parameter servers and merge them. 3.save "distributed lookup table" on parameter servers. 4.receive "optimizer variables" from parameter servers and merge them. Args: executor(Executor): The executor to run for saving parameters. dirname(str): The saving directory path. main_program(Program): The program whose parameters will be saved. the main_program must be the trainer_program get after transpiler. Returns: None Examples: .. code-block:: python import paddle.fluid as fluid exe = fluid.Executor(fluid.CPUPlace()) param_path = "./my_paddle_model" t = distribute_transpiler.DistributeTranspiler() t.transpile(...) train_program = t.get_trainer_program() _save_distributed_persistables(executor=exe, dirname=param_path, main_program=train_program) """ def __save_remote_params(executor, dirname, remote_params_map): """ recive params on pserver through rpc. if the params are be sliced, will concat them to one, then save it. """ if not remote_params_map: return prog = Program() block = prog.global_block() # recv optimize vars from pserver for name, remote_params in remote_params_map.items(): origin = remote_params[0].origin is_slice = remote_params[0].is_slice slices = [None] * len(remote_params) slice_varnames = [None] * len(remote_params) remote_varnames = [None] * len(remote_params) endpoints = [None] * len(remote_params) for idx, optimizer in enumerate(remote_params): block_id = optimizer.block_id slice = optimizer.slice endpoint = optimizer.endpoint index = block_id if is_slice else idx slices[index] = slice slice_varnames[index] = "{}.slice.{}".format(slice.name, idx) remote_varnames[index] = slice.name endpoints[index] = endpoint slice_shapes = [] for slice in slices: tmp = [str(dim) for dim in slice.shape] slice_shapes.append(",".join(tmp)) block.append_op( type='recv_save', attrs={ "trainer_id": 0, "shape": origin.shape, "slice_shapes": slice_shapes, "slice_varnames": slice_varnames, "remote_varnames": remote_varnames, "endpoints": endpoints, "file_path": os.path.join(dirname, origin.name) }) executor.run(prog) 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 import paddle.fluid as fluid param = fluid.default_main_program().global_block().var('fc.b') res = fluid.io.is_persistable(param) """ 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.READER: return False return var.persistable def save_persistables(executor, dirname, main_program=None, filename=None): """ This operator saves all persistable variables from :code:`main_program` to the folder :code:`dirname` or file :code:`filename`. You can refer to :ref:`api_guide_model_save_reader_en` for more details. And then saves these persistables variables to the folder :code:`dirname` or file :code:`filename`. The :code:`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 :code:`filename` None; if you would like to save all variables in a single file, use :code:`filename` to specify the file name. Args: executor(Executor): The executor to run for saving persistable variables. You can refer to :ref:`api_guide_executor_en` for more details. dirname(str, optional): The saving directory path. When you need to save the parameter to the memory, set it to None. main_program(Program, optional): The program whose persistbale variables will be saved. You can refer to :ref:`api_guide_Program_en` for more details. If it is None, the default main program will be used. Default: None. filename(str, optional): The file to save all variables. If you prefer to save variables in different files, set it to None. Default: None. Returns: str: When saving parameters to a file, returns None. When saving parameters to memory, returns a binary string containing parameters. Examples: .. code-block:: python import paddle.fluid as fluid dir_path = "./my_paddle_model" file_name = "persistables" image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') feeder = fluid.DataFeeder(feed_list=[image, label], place=fluid.CPUPlace()) predict = fluid.layers.fc(input=image, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=predict, label=label) avg_loss = fluid.layers.mean(loss) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) fluid.io.save_persistables(executor=exe, dirname=dir_path, filename=file_name) # The persistables variables weights and bias in the fc layer of the network # are going to be saved in the same file named "persistables" in the path # "./my_paddle_model" """ if main_program and main_program._is_distributed: return _save_distributed_persistables( executor, dirname=dirname, main_program=main_program) else: return save_vars( executor, dirname=dirname, main_program=main_program, vars=None, predicate=is_persistable, filename=filename) def save_mask_inference_model(dirname, feeded_var_names, target_vars, executor, main_program=None, model_filename=None, params_filename=None, export_for_deployment=True, program_only=False): """ Prune the given `main_program` to build a new program especially for inference, and then save it and all related parameters to given `dirname` . If you just want to save parameters of your trained model, please use the :ref:`api_fluid_io_save_params` . You can refer to :ref:`api_guide_model_save_reader_en` for more details. Note: The :code:`dirname` is used to specify the folder where inference model structure and parameters are going to be saved. If you would like to save params of Program in separate files, set `params_filename` None; if you would like to save all params of Program in a single file, use `params_filename` to specify the file name. Args: dirname(str): The directory path to save the inference model. feeded_var_names(list[str]): list of string. Names of variables that need to be feeded data during inference. target_vars(list[Variable]): list of Variable. Variables from which we can get inference results. executor(Executor): The executor that saves the inference model. You can refer to :ref:`api_guide_executor_en` for more details. main_program(Program, optional): The original program, which will be pruned to build the inference model. If is setted None, the global default :code:`_main_program_` will be used. Default: None. model_filename(str, optional): The name of file to save the inference program itself. If is setted None, a default filename :code:`__model__` will be used. params_filename(str, optional): The name of file to save all related parameters. If it is setted None, parameters will be saved in separate files . export_for_deployment(bool): If True, programs are modified to only support direct inference deployment. Otherwise, more information will be stored for flexible optimization and re-training. Currently, only True is supported. Default: True. program_only(bool, optional): If True, It will save inference program only, and do not save params of Program. Default: False. Returns: The fetch variables' name list Return Type: list Raises: ValueError: If `feed_var_names` is not a list of basestring, an exception is thrown. ValueError: If `target_vars` is not a list of Variable, an exception is thrown. Examples: .. code-block:: python import paddle.fluid as fluid path = "./infer_model" # User defined network, here a softmax regresssion example image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32') label = fluid.data(name='label', shape=[None, 1], dtype='int64') feeder = fluid.DataFeeder(feed_list=[image, label], place=fluid.CPUPlace()) predict = fluid.layers.fc(input=image, size=10, act='softmax') loss = fluid.layers.cross_entropy(input=predict, label=label) avg_loss = fluid.layers.mean(loss) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) # Feed data and train process # Save inference model. Note we don't save label and loss in this example fluid.io.save_inference_model(dirname=path, feeded_var_names=['img'], target_vars=[predict], executor=exe) # In this example, the save_inference_mode inference will prune the default # main program according to the network's input node (img) and output node(predict). # 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, six.string_types): feeded_var_names = [feeded_var_names] elif export_for_deployment: if len(feeded_var_names) > 0: # TODO(paddle-dev): polish these code blocks if not (bool(feeded_var_names) and all( isinstance(name, six.string_types) 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] elif export_for_deployment: 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.") main_program = _get_valid_program(main_program) # remind user to set auc_states to zeros if the program contains auc op all_ops = main_program.global_block().ops for op in all_ops: if op.type == 'auc': warnings.warn( "please ensure that you have set the auc states to zeros before saving inference model" ) break # fix the bug that the activation op's output as target will be pruned. # will affect the inference performance. # TODO(Superjomn) add an IR pass to remove 1-scale op. with program_guard(main_program): uniq_target_vars = [] for i, var in enumerate(target_vars): if isinstance(var, Variable): var = layers.scale( var, 1., name="save_infer_model/scale_{}".format(i)) uniq_target_vars.append(var) target_vars = uniq_target_vars target_var_name_list = [var.name for var in target_vars] # when a pserver and a trainer running on the same machine, mkdir may conflict save_dirname = dirname try: save_dirname = os.path.normpath(dirname) os.makedirs(save_dirname) except OSError as e: if e.errno != errno.EEXIST: raise if model_filename is not None: model_basename = os.path.basename(model_filename) else: model_basename = "__model__" model_basename = os.path.join(save_dirname, model_basename) # When export_for_deployment is true, we modify the program online so that # it can only be loaded for inference directly. If it's false, the whole # original program and related meta are saved so that future usage can be # more flexible. origin_program = main_program.clone() if export_for_deployment: main_program = main_program.clone() global_block = main_program.global_block() need_to_remove_op_index = [] for i, op in enumerate(global_block.ops): op.desc.set_is_target(False) if op.type == "feed" or op.type == "fetch": need_to_remove_op_index.append(i) for index in need_to_remove_op_index[::-1]: global_block._remove_op(index) main_program.desc.flush() main_program = main_program._prune_with_input( feeded_var_names=feeded_var_names, targets=target_vars) main_program = main_program._inference_optimize(prune_read_op=True) fetch_var_names = [v.name for v in target_vars] prepend_feed_ops(main_program, feeded_var_names) append_fetch_ops(main_program, fetch_var_names) main_program.desc._set_version() paddle.fluid.core.save_op_compatible_info(main_program.desc) with open(model_basename, "wb") as f: f.write(main_program.desc.serialize_to_string()) else: # TODO(panyx0718): Save more information so that it can also be used # for training and more flexible post-processing. with open(model_basename + ".main_program", "wb") as f: f.write(main_program.desc.serialize_to_string()) if program_only: warnings.warn( "save_inference_model specified the param `program_only` to True, It will not save params of Program." ) return target_var_name_list main_program._copy_dist_param_info_from(origin_program) if params_filename is not None: params_filename = os.path.basename(params_filename) save_persistables(executor, save_dirname, main_program, params_filename) return target_var_name_list