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checkpoint.py
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checkpoint.py
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# Copyright (c) 2019 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 functools
from ..framework import Variable, default_main_program, in_dygraph_mode, dygraph_only, Parameter, ParamBase, _varbase_creator, _dygraph_tracer
import pickle
import six
from . import learning_rate_scheduler
import warnings
from .. import core
from .base import guard
from paddle.fluid.dygraph.jit import _SaveLoadConfig
from paddle.fluid.dygraph.io import _construct_program_holders, _construct_params_and_buffers
__all__ = [
'save_dygraph',
'load_dygraph',
]
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.fluid.load_dygraph` 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
@dygraph_only
def save_dygraph(state_dict, model_path):
'''
:api_attr: imperative
Save Layer's state_dict to disk. This will generate a file with suffix ".pdparams"
The state_dict is get from Layers.state_dict function
Args:
state_dict(dict) : The state dict to be saved.
model_path(str) : the file prefix to save the state_dict. The format is "dirname/file_prefix". If file_prefix is empty str. A exception will be raised
Returns:
None
Examples:
.. code-block:: python
import paddle.fluid as fluid
with fluid.dygraph.guard():
emb = fluid.dygraph.Embedding([10, 10])
state_dict = emb.state_dict()
fluid.save_dygraph( state_dict, "paddle_dy")
adam = fluid.optimizer.Adam( learning_rate = fluid.layers.noam_decay( 100, 10000),
parameter_list = emb.parameters() )
state_dict = adam.state_dict()
fluid.save_dygraph( state_dict, "paddle_dy")
'''
base_name = os.path.basename(model_path)
assert base_name != "", "The input model_path MUST be format of dirname/filename [dirname\\filename in Windows system], but received filename is empty string."
suffix = ".pdparams"
assert len(state_dict) > 0, "state_dict is empty, no need to save"
param_num = 0
for k, v in state_dict.items():
if isinstance(v, ParamBase):
param_num += 1
if param_num == 0:
suffix = ".pdopt"
model_dict = {}
name_table = {}
for k, v in state_dict.items():
if isinstance(v, (Variable, core.VarBase)):
model_dict[k] = v.numpy()
name_table[k] = v.name
else:
model_dict[k] = v
model_dict["StructuredToParameterName@@"] = name_table
file_name = model_path + suffix
dir_name = os.path.dirname(file_name)
if dir_name and not os.path.exists(dir_name):
os.makedirs(dir_name)
with open(file_name, 'wb') as f:
pickle.dump(model_dict, f, protocol=2)
# NOTE(chenweihang): load_dygraph will deprecated in future, we don't
# support new loading features for it
# TODO(qingqing01): remove dygraph_only to support loading static model.
# maybe need to unify the loading interface after 2.0 API is ready.
# @dygraph_only
def load_dygraph(model_path, **configs):
'''
:api_attr: imperative
Load parameter state dict from disk.
.. note::
Due to some historical reasons, if you load ``state_dict`` from the saved
result of `paddle.static.save_inference_model`, the structured variable name
will cannot be restored. You need to set the argument `use_structured_name=False`
when using `Layer.set_state_dict` later.
Args:
model_path(str) : The file prefix store the state_dict.
(The path should Not contain suffix '.pdparams')
**configs (dict, optional): Other load configuration options for compatibility. We do not
recommend using these configurations, 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:
state_dict(dict) : the dict store the state_dict
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.disable_static()
emb = paddle.nn.Embedding(10, 10)
state_dict = emb.state_dict()
fluid.save_dygraph(state_dict, "paddle_dy")
scheduler = paddle.optimizer.lr.NoamDecay(
d_model=0.01, warmup_steps=100, verbose=True)
adam = paddle.optimizer.Adam(
learning_rate=scheduler,
parameters=emb.parameters())
state_dict = adam.state_dict()
fluid.save_dygraph(state_dict, "paddle_dy")
para_state_dict, opti_state_dict = fluid.load_dygraph("paddle_dy")
'''
# deal with argument `model_path`
model_prefix = model_path
if model_prefix.endswith(".pdparams"):
model_prefix = model_prefix[:-9]
elif model_prefix.endswith(".pdopt"):
model_prefix = model_prefix[:-6]
para_dict = None
opti_dict = None
params_file_path = model_prefix + ".pdparams"
opti_file_path = model_prefix + ".pdopt"
# deal with argument `config`
config = _parse_load_config(configs)
if os.path.exists(params_file_path) or os.path.exists(opti_file_path):
# Load state dict by `save_dygraph` save format
para_dict = {}
if os.path.exists(params_file_path):
with open(params_file_path, 'rb') as f:
para_dict = pickle.load(f) if six.PY2 else pickle.load(
f, encoding='latin1')
if not config.keep_name_table and "StructuredToParameterName@@" in para_dict:
del para_dict["StructuredToParameterName@@"]
if os.path.exists(opti_file_path):
with open(opti_file_path, 'rb') as f:
opti_dict = pickle.load(f) if six.PY2 else pickle.load(
f, encoding='latin1')
else:
# check model path
if not os.path.isdir(model_prefix):
raise ValueError("Model saved directory '%s' is not exists." %
model_prefix)
# 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
# 1. load program desc & construct _ProgramHolder
programs = _construct_program_holders(model_path,
config.model_filename)
# 2. load layer parameters & buffers
# NOTE: using fluid.dygraph.guard() here will cause import error in py2
with guard():
persistable_var_dict = _construct_params_and_buffers(
model_prefix,
programs,
config.params_filename,
append_suffix=False)
# 3. construct state_dict
para_dict = dict()
for var_name in persistable_var_dict:
para_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_prefix, 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 para_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] = para_dict[
var_name]
para_dict = structured_para_dict
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
# 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 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
para_dict = dict()
for var in load_var_list:
para_dict[var.name] = var.numpy()
return para_dict, opti_dict