# Copyright (c) 2020 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 os import collections import pickle import six import warnings import paddle # deprecated module import from paddle import fluid from paddle.fluid import core from paddle.fluid.framework import Variable, _varbase_creator, _dygraph_tracer from paddle.fluid.dygraph.jit import _SaveLoadConfig from paddle.fluid.dygraph.io import _construct_program_holders, _construct_params_and_buffers from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX, INFER_PARAMS_INFO_SUFFIX __all__ = [ 'save', 'load', ] def _build_saved_state_dict(state_dict): save_dict = {} name_table = {} for key, value in state_dict.items(): if isinstance(value, (Variable, core.VarBase)): save_dict[key] = value.numpy() name_table[key] = value.name else: save_dict[key] = value save_dict["StructuredToParameterName@@"] = name_table return save_dict def _load_state_dict_from_save_inference_model(model_path, config): # 1. load program desc & construct _ProgramHolder programs = _construct_program_holders(model_path, config.model_filename) # 2. load layer parameters & buffers with fluid.dygraph.guard(): persistable_var_dict = _construct_params_and_buffers( model_path, programs, config.params_filename, append_suffix=False) # 3. construct state_dict load_param_dict = dict() for var_name in persistable_var_dict: load_param_dict[var_name] = persistable_var_dict[var_name].numpy() # if *.info exists, we can recover structured_name var_info_filename = str(config.params_filename) + ".info" var_info_path = os.path.join(model_path, var_info_filename) if os.path.exists(var_info_path): with open(var_info_path, 'rb') as f: extra_var_info = pickle.load(f) structured_para_dict = dict() for var_name in load_param_dict: structured_name = extra_var_info[var_name].get( 'structured_name', None) assert structured_name is not None, "Cannot find saved variable (%s)'s structured name in saved model." % var_name structured_para_dict[structured_name] = load_param_dict[ var_name] load_param_dict = structured_para_dict return load_param_dict def _load_state_dict_from_save_params(model_path): # Try to load all the files in the directory in VarBase format, # the file name is used as the name of VarBase load_var_list = [] # 1. load file names var_name_list = [] for root, _, files in os.walk(model_path): for filename in files: file_path = os.path.join(root, filename) tmp_var_name = os.path.relpath(file_path, model_path) var_name = tmp_var_name.replace("\\", "/") var_name_list.append(var_name) # 2. create and load VarBase with fluid.dygraph.guard(): for name in var_name_list: new_var = _varbase_creator(name=name, persistable=True) _dygraph_tracer().trace_op( type='load', inputs={}, outputs={'Out': new_var}, attrs={'file_path': os.path.join(model_path, name)}) load_var_list.append(new_var) # 3. construct state_dict load_param_dict = dict() for var in load_var_list: load_param_dict[var.name] = var.numpy() return load_param_dict # NOTE(chenweihang): [ Handling of use cases of API paddle.load ] # `paddle.load` may be used to load saved results of: # 1. Expected cases: # - need [full filename] when loading # - paddle.save # - paddle.static.save # - paddle.fluid.save_dygraph # - need [prefix] when loading [compatible for paddle 2.x] # - paddle.jit.save # - paddle.static.save_inference_model # - need [directory] when loading [compatible for paddle 1.x] # - paddle.fluid.io.save_inference_model # - paddle.fluid.io.save_params/save_persistable # 2. Error cases: # - no error case def _build_load_path_and_config(path, config): # NOTE(chenweihang): If both [prefix save format] and [directory save format] exist, # raise error, avoid confusing behavior prefix_format_path = path + INFER_MODEL_SUFFIX prefix_format_exist = os.path.exists(prefix_format_path) directory_format_exist = os.path.isdir(path) if prefix_format_exist and directory_format_exist: raise ValueError( "The %s.pdmodel and %s directory exist at the same time, " "don't know which one to load, please make sure that the specified target " "of ``path`` is unique." % (path, path)) elif not prefix_format_exist and not directory_format_exist: error_msg = "The ``path`` (%s) to load model not exists." # if current path is a prefix, and the path.pdparams or path.pdopt # is exist, users may want use `paddle.load` load the result of # `fluid.save_dygraph`, we raise error here for users params_file_path = path + ".pdparams" opti_file_path = path + ".pdopt" if os.path.exists(params_file_path) or os.path.exists(opti_file_path): error_msg += " If you want to load the results saved by `fluid.save_dygraph`, " \ "please specify the full file name, not just the file name prefix. For " \ "example, it should be written as `paddle.load('model.pdparams')` instead of " \ "`paddle.load('model')`." raise ValueError(error_msg % path) else: if prefix_format_exist: file_prefix = os.path.basename(path) model_path = os.path.dirname(path) if config.model_filename is not None: warnings.warn( "When loading the result saved with the " "specified file prefix, the ``model_filename`` config does " "not take effect.") config.model_filename = file_prefix + INFER_MODEL_SUFFIX if config.params_filename is not None: warnings.warn( "When loading the result saved with the " "specified file prefix, the ``params_filename`` config does " "not take effect.") config.params_filename = file_prefix + INFER_PARAMS_SUFFIX else: # Compatible with the old save_inference_model format model_path = path return model_path, config def _parse_load_config(configs): supported_configs = ['model_filename', 'params_filename', 'keep_name_table'] # input check for key in configs: if key not in supported_configs: raise ValueError( "The additional config (%s) of `paddle.load` is not supported." % key) # construct inner config inner_config = _SaveLoadConfig() inner_config.model_filename = configs.get('model_filename', None) inner_config.params_filename = configs.get('params_filename', None) inner_config.keep_name_table = configs.get('keep_name_table', None) return inner_config def save(obj, path): ''' Save an object to the specified path. .. note:: Now only supports save ``state_dict`` of Layer or Optimizer. .. note:: Different from ``paddle.jit.save``, since the save result of ``paddle.save`` is a single file, there is no need to distinguish multiple saved files by adding a suffix. The argument ``path`` of ``paddle.save`` will be directly used as the saved file name instead of a prefix. In order to unify the saved file name format, we recommend using the paddle standard suffix: 1. for ``Layer.state_dict`` , recommend to use ``.pdparams`` ; 2. for ``Optimizer.state_dict`` , recommend to use ``.pdopt`` . For specific examples, please refer to API code examples. Args: obj(Object) : The object to be saved. path(str) : The path of the object to be saved. If saved in the current directory, the input path string will be used as the file name. Returns: None Examples: .. code-block:: python import paddle paddle.disable_static() emb = paddle.nn.Embedding(10, 10) layer_state_dict = emb.state_dict() paddle.save(layer_state_dict, "emb.pdparams") scheduler = paddle.optimizer.lr.NoamDecay( d_model=0.01, warmup_steps=100, verbose=True) adam = paddle.optimizer.Adam( learning_rate=scheduler, parameters=emb.parameters()) opt_state_dict = adam.state_dict() paddle.save(opt_state_dict, "adam.pdopt") ''' # 1. input check if not isinstance(obj, dict): raise NotImplementedError( "Now only supports save state_dict of Layer or Optimizer, " "expect dict, but received %s." % type(obj)) if len(obj) == 0: warnings.warn("The input state dict is empty, no need to save.") filename = os.path.basename(path) if filename == "": raise ValueError("The input path MUST be format of dirname/filename " "[dirname\\filename in Windows system], but received " "filename is empty string.") # 2. save object dirname = os.path.dirname(path) if dirname and not os.path.exists(dirname): os.makedirs(dirname) # TODO(chenweihang): supports save other object saved_obj = _build_saved_state_dict(obj) with open(path, 'wb') as f: pickle.dump(saved_obj, f, protocol=2) def load(path, **configs): ''' Load an object can be used in paddle from specified path. .. note:: Now only supports load ``state_dict`` of Layer or Optimizer. .. note:: In order to use the model parameters saved by paddle more efficiently, ``paddle.load`` supports loading ``state_dict`` of Layer from the result of other save APIs except ``paddle.save`` , but the argument ``path`` format is different: 1. loading from ``paddle.static.save`` or ``paddle.Model().save(training=True)`` , ``path`` needs to be a complete file name, such as ``model.pdparams`` or ``model.pdopt`` ; 2. loading from ``paddle.jit.save`` or ``paddle.static.save_inference_model`` or ``paddle.Model().save(training=False)`` , ``path`` need to be a file prefix, such as ``model/mnist``, and ``paddle.load`` will get information from ``mnist.pdmodel`` and ``mnist.pdiparams`` ; 3. loading from paddle 1.x APIs ``paddle.fluid.io.save_inference_model`` or ``paddle.fluid.io.save_params/save_persistables`` , ``path`` need to be a directory, such as ``model`` and model is a directory. .. note:: If you load ``state_dict`` from the saved result of static mode API such as ``paddle.static.save`` or ``paddle.static.save_inference_model`` , the structured variable name in dynamic mode will cannot be restored. You need to set the argument ``use_structured_name=False`` when using ``Layer.set_state_dict`` later. Args: path(str) : The path to load the target object. Generally, the path is the target file path. When loading state_dict from the saved result of the API used to save the inference model, the path may be a file prefix or directory. **configs (dict, optional): other load configuration options for compatibility. We do not recommend using these configurations, they may be removed in the future. If not necessary, DO NOT use them. Default None. The following options are currently supported: (1) model_filename (str): The inference model file name of the paddle 1.x ``save_inference_model`` save format. Default file name is :code:`__model__` . (2) params_filename (str): The persistable variables file name of the paddle 1.x ``save_inference_model`` save format. No default file name, save variables separately by default. Returns: Object(Object): a target object can be used in paddle Examples: .. code-block:: python import paddle paddle.disable_static() emb = paddle.nn.Embedding(10, 10) layer_state_dict = emb.state_dict() paddle.save(layer_state_dict, "emb.pdparams") scheduler = paddle.optimizer.lr.NoamDecay( d_model=0.01, warmup_steps=100, verbose=True) adam = paddle.optimizer.Adam( learning_rate=scheduler, parameters=emb.parameters()) opt_state_dict = adam.state_dict() paddle.save(opt_state_dict, "adam.pdopt") load_layer_state_dict = paddle.load("emb.pdparams") load_opt_state_dict = paddle.load("adam.pdopt") ''' load_result = None config = _parse_load_config(configs) if os.path.isfile(path): # we think path is file means this file is created by paddle.save with open(path, 'rb') as f: load_result = pickle.load(f) if six.PY2 else pickle.load( f, encoding='latin1') if not config.keep_name_table and "StructuredToParameterName@@" in load_result: del load_result["StructuredToParameterName@@"] else: # file prefix and directory are compatible cases model_path, config = _build_load_path_and_config(path, config) # check whether model file exists if config.model_filename is None: model_filename = '__model__' else: model_filename = config.model_filename model_file_path = os.path.join(model_path, model_filename) if os.path.exists(model_file_path): # Load state dict by `jit.save/io.save_inference_model` save format # NOTE(chenweihang): [ Compatibility of save_inference_model save format ] # The model saved by `save_inference_model` does not completely correspond to # the information required by the `state_dict` under the dygraph. # `save_inference_model` not save structured name, we need to remind # the user to configure the `use_structured_name` argument when `set_state_dict` # NOTE(chenweihang): `jit.save` doesn't save optimizer state load_result = _load_state_dict_from_save_inference_model(model_path, config) else: # load state dict by `io.save_params/persistables` save format # TODO(chenweihang): [ Now only supports loading parameters seperately ] # If users save all parameters as one file, the [ variable.name -> variable ] # mapping info will lost, so users need to give variable list, but users build # variable list in dygraph mode is difficult, we recommend users to use # paddle.static.load_program_state in this case load_result = _load_state_dict_from_save_params(model_path) return load_result