chanfig 0.0.91

Easier Configuration

CHANfiG

Introduction

CHANfiG aims to make your configuration easier.

There are tons of configurable parameters in training a Machine Learning model. To configure all these parameters, researchers usually need to write gigantic config files, sometimes even thousands of lines. Most of the configs are just replicates of the default arguments of certain functions, resulting in many unnecessary declarations. It is also very hard to alter the configurations. One needs to navigate and open the right configuration file, make changes, save and exit. These had wasted an uncountable[^uncountable] amount of precious time and are no doubt a crime. Using argparse could relieve the burdens to some extent. However, it takes a lot of work to make it compatible with existing config files, and its lack of nesting limits its potential.

CHANfiG would like to make a change.

You just type the alternations in the command line, and leave everything else to CHANfiG.

CHANfiG is highly inspired by YACS. Different from the paradigm of YACS( your code + a YACS config for experiment E (+ external dependencies + hardware + other nuisance terms ...) = reproducible experiment E), The paradigm of CHANfiG is:

your code + command line arguments (+ optional CHANfiG config + external dependencies + hardware + other nuisance terms ...) = reproducible experiment E (+ optional CHANfiG config for experiment E)

Components

A Config is basically a nested dict structure.

However, the default Python dict is hard to manipulate.

The only way to access a dict member is through dict['name'], which is obviously extremely complex. Even worse, if the dict is nested like a config, member access could be something like dict['parent']['children']['name'].

Enough is enough, it is time to make a change.

We need attribute-style access, and we need it now. dict.name and dict.parent.children.name is all you need.

Although there have been some other works that achieve a similar functionality of attribute-style access to dict members. Their Config object either uses a separate dict to store information from attribute-style access (EasyDict), which may lead to inconsistency between attribute-style access and dict-style access; or re-use the existing __dict__ and redirect dict-style access (ml_collections), which may result in confliction between attributes and members of Config.

To overcome the aforementioned limitations, we inherit the Python built-in dict to create FlatDict, DefaultDict, NestedDict, Config, and Registry. We also introduce Variable to allow sharing a value across multiple places, and ConfigParser to parse command line arguments.

FlatDict

FlatDict improves the default dict in 3 aspects.

Dict Operations

FlatDict incorporates a merge method that allows you to merge a Mapping, an Iterable, or a path to the FlatDict. Different from built-in update, merge assign values instead of replace, which makes it works better with DefaultDict.

dict in Python is ordered since Python 3.7, but there isn't a built-in method to help you sort a dict. FlatDictsupportssort to help you manage your dict.

Moreover, FlatDict comes with difference and intersect, which makes it very easy to compare a FlatDict with other Mapping, Iterable, or a path.

ML Operations

FlatDict supports to method similar to PyTorch Tensors. You can simply convert all member values of FlatDict to a certain type or pass to a device in the same way.

FlatDict also integrates cpu, gpu (cuda), and tpu (xla) methods for easier access.

IO Operations

FlatDict provides json, jsons, yaml and yamls methods to dump FlatDict to a file or string. It also provides from_json, from_jsons, from_yaml and from_yamls methods to build a FlatDict from a string or file.

FlatDict also includes dump and load methods which determines the type by its extension and dump/load FlatDict to/from a file.

DefaultDict

To facility the needs of default values, we incorporate DefaultDict which accepts default_factory and works just like a collections.defaultdict.

NestedDict

Since most Configs are in a nested structure, we further propose a NestedDict.

Based on DefaultDict, NestedDict provides all_keys, all_values, and all_items methods to allow iterating over the whole nested structure at once.

NestedDict also comes with apply and apply_ methods, which made it easier to manipulate the nested structure.

Config

Config extends the functionality by supporting freeze and defrost, and by adding a built-in ConfigParser to pare command line arguments.

Note that Config also has default_factory=Config() by default for convenience.

Registry

Registry extends the NestedDict and supports register, lookup, and build to help you register constructors and build objects from a Config.

Variable

Have one value for multiple names at multiple places? We got you covered.

Just wrap the value with Variable, and one alteration will be reflected everywhere.

Variable also supports type, choices, validator, and required to ensure the correctness of the value.

To make it even easier, Variable also supports help to provide a description when using ConfigParser.

ConfigParser

ConfigParser extends ArgumentParser and provides parse and parse_config to parse command line arguments.

Usage

CHANfiG has great backward compatibility with previous configs.

No matter if your old config is json or yaml, you could directly read from them.

And if you are using yacs, just replace CfgNode with Config and enjoy all the additional benefits that CHANfiG provides.

Moreover, if you find a name in the config is too long for command-line, you could simply call self.add_argument with proper dest to use a shorter name in command-line, as you do with argparse.

from chanfig import Config, Variable


class Model:
    def __init__(self, encoder, dropout=0.1, activation='ReLU'):
        self.encoder = Encoder(**encoder)
        self.dropout = Dropout(dropout)
        self.activation = getattr(Activation, activation)

def main(config):
    model = Model(**config.model)
    optimizer = Optimizer(**config.optimizer)
    scheduler = Scheduler(**config.scheduler)
    dataset = Dataset(**config.dataset)
    dataloader = Dataloader(**config.dataloader)


class TestConfig(Config):
    def __init__(self):
        super().__init__()
        dropout = Variable(0.1)
        self.name = "CHANfiG"
        self.seed = 1013
        self.data.batch_size = 64
        self.model.encoder.num_layers = 6
        self.model.decoder.num_layers = 6
        self.model.dropout = dropout
        self.model.encoder.dropout = dropout
        self.model.decoder.dropout = dropout
        self.activation = "GELU"
        self.optim.lr = 1e-3
        self.add_argument("--batch_size", dest="data.batch_size")
        self.add_argument("--lr", dest="optim.lr")

    def post(self):
        self.id = f"{self.name}_{self.seed}"


if __name__ == '__main__':
    # config = Config.load('config.yaml')  # in case you want to read from a yaml
    # config = Config.load('config.json')  # in case you want to read from a json
    # existing_configs = {'data.batch_size': 64, 'model.encoder.num_layers': 8}
    # config = Config(**existing_configs)  # in case you have some config in dict to load
    config = TestConfig()
    config = config.parse()
    # config.merge('dataset.yaml')  # in case you want to merge a yaml
    # config.merge('dataset.json')  # in case you want to merge a json
    # note that the value of merge will override current values
    config.model.decoder.num_layers = 8
    config.freeze()
    print(config)
    # main(config)
    # config.yaml('config.yaml')  # in case you want to save a yaml
    # config.json('config.json')  # in case you want to save a json

All you need to do is just run a line:

python main.py --model.encoder.num_layers 8 --model.dropout=0.2 --lr 5e-3

You could also load a default configure file and make changes based on it:

Note, you must specify config.parse(default_config='config') to correctly load the default config.

python main.py --config meow.yaml --model.encoder.num_layers 8 --model.dropout=0.2 --lr 5e-3

If you have made it dump current configurations, this should be in the written file:

activation: GELU
data:
  batch_size: 64
id: CHANfiG_1013
model:
  decoder:
    dropout: 0.1
    num_layers: 6
  dropout: 0.1
  encoder:
    dropout: 0.1
    num_layers: 6
name: CHANfiG
optim:
  lr: 0.005
seed: 1013

{
  "name": "CHANfiG",
  "seed": 1013,
  "data": {
    "batch_size": 64
  },
  "model": {
    "encoder": {
      "num_layers": 6,
      "dropout": 0.1
    },
    "decoder": {
      "num_layers": 6,
      "dropout": 0.1
    },
    "dropout": 0.1
  },
  "activation": "GELU",
  "optim": {
    "lr": 0.005
  },
  "id": "CHANfiG_1013"
}

Define the default arguments in function, put alterations in CLI, and leave the rest to CHANfiG.

Installation

Install the most recent stable version on pypi:

pip install chanfig

Install the latest version from source:

pip install git+https://github.com/ZhiyuanChen/CHANfiG

It works the way it should have worked.

License

CHANfiG is multi-licensed under the following licenses:

• The Unlicense
• GNU Affero General Public License v3.0 or later
• GNU General Public License v2.0 or later
• BSD 4-Clause "Original" or "Old" License
• MIT License
• Apache License 2.0

You can choose any (one or more) of these licenses if you use this work.

SPDX-License-Identifier: Unlicense OR AGPL-3.0-or-later OR GPL-2.0-or-later OR BSD-4-Clause OR MIT OR Apache-2.0

[^uncountable]: fun fact: time is always uncountable.