hpman 0.13.0

A hyperparameter manager for deep learning experiments.

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hpman (超参侠): The uncompromising hyperparameter manager.

English | 简体中文

hpman is a hyperparameter manager (HPM) library that truly make sense. It enables a Distributed-Centralized HPM experience in deep learning experiment. You can define hyperparameters anywhere, but manage them as a whole.

hpman is intended to be used as a basic building blocks for downstream tools, such as command line interface, IDE integration, experiment management system, etc.

hpman supports Python version greater equal than 3.5.

• hpman (超参侠): The uncompromising hyperparameter manager.
• Story / Background
  • Centralized HPM
  • However ...
  • Distributed HPM
  • Distributed-Centralized HPM
• Installation
• Usage
• Examples
• Features
  • Design principles
  • Aribitrary Imports
  • Define Hyperparameters
  • Static Parsing
  • Runtime Value Getter/Setter
  • Hints
  • Nested Hyperparameters
• Contributing
• License

Story / Background

lib.py:

# File: lib.py
from hpman.m import _


def add():
    return _("a", 0) + _("b", 0)


def mult():
    return _("a") * _("b")

main.py:

#!/usr/bin/env python3
import os
import argparse

from hpman.m import _

import lib


def main():
    basedir = os.path.dirname(os.path.realpath(__file__))
    _.parse_file(basedir)

    parser = argparse.ArgumentParser()
    parser.add_argument("-a", default=_.get_value("a"), type=int)
    parser.add_argument("-b", default=_.get_value("b"), type=int)
    args = parser.parse_args()

    _.set_value("a", args.a)
    _.set_value("b", args.b)

    print("a = {}".format(_.get_value("a")))
    print("b = {}".format(_.get_value("b")))
    print("lib.add() = {}".format(lib.add()))
    print("lib.mult() = {}".format(lib.mult()))


if __name__ == "__main__":
    main()

Results:

$ ./main.py
a = 0
b = 0
lib.add() = 0
lib.mult() = 0

$ ./main.py -a 2 -b 3
a = 2
b = 3
lib.add() = 5
lib.mult() = 6

The core library is designed as a backend for hyperparameter data manipulation, rather than an end-to-end solution. It is highly recommend to start with a better frontend:

• CLI frontend: hpargparse

Installation

python3 -m pip install hpman

Story

Managing ever-changing hyperparameters is a pain in the a**. From the practice of performing enormous amount of deep learning experiments, we found two existing hyperparameter managing patterns of the utmost prevalence.

Centralized HPM

We call the first type "centralized HPM". It follows the way of configuration management in traditional software, regardless of using a python file or json or yaml or whatever that can store some key-value mapping (may remind you of settings.ini, nginx.conf, config.yaml etc.):

# File: config.py
BATCH_SIZE = 256
NUM_EPOCH = 120
LEARNING_RATE = 1e-1
WEIGHT_DECAY = 4e-5
OPTIMIZER = 'SGD'
LR_DECAY_EPOCHS = [30, 60, 90]
HIDDEN_CHANNELS = 128
NUM_LAYERS = 5
INPUT_CHANNELS = 784
OUTPUT_CHANNELS = 10

# File: model.py
from torch import nn
import config

def build_model():
    return nn.Sequence(
    [
        nn.Sequence(nn.Linear(config.INPUT_CHANNELS, config.HIDDEN_CHANNELS),
            nn.BatchNorm1d(config.HIDDEN_CHANNELS),
            nn.ReLU())
    ] + [
        nn.Sequence(nn.Linear(config.HIDDEN_CHANNELS, config.HIDDEN_CHANNELS),
            nn.BatchNorm1d(config.HIDDEN_CHANNELS),
            nn.ReLU())
        for i in range(config.NUM_LAYERS - 1)
    ] + [
        nn.Linear(config.HIDDEN_CHANNELS, config.OUTPUT_CHANNELS)
    ]
    )

This way of managing hyperparameters is widely seen in machine learning libraries, e.g., xgboost, whose hyperparameters are fairly stable compare than that in deep learning research.

However ...

However, it is quite common for researchers to add some hyperparameters at their inspiration (e.g., suddenly come up with a "Temperature" parameter in softmax.). They found pleasure in tweaking the hyperparameters, but quickly abandon it if the experiment goes wrong. These acts are called Non-Recurring Engineering (NRE).

In these cases, the "centralized HPM" reveals obvious drawbacks:

1. Whenever you need to introduce a new hyperparameter, you must kind of "declare" it in the configuration file, while using it in some deeply-nested easy-to-forget files.
2. Whenever you need to abandon an existing hyperparameter, you must not only remove all the appearances of that hyperparameter in some deeply-nested easy-to-forget files, but also remove it in the centralized configuration file.
3. There's a "Heisenberg uncertainty principle" on hyperparameters: you cannot know both what and where the hyperparameters are at the same time. The context around where the hyperparameter are used conveys valuable information of the precise usecase of that hyperparameter. You can either look it up in the code, or in the centralized config file.

These drawbacks essentially requires the user to maintain a distributed data structure, which not only induces great mental burden doing experiments, but also be error-prone to bugs.

Distributed HPM

So researchers come to another solution: forget about config files; define and use whatever hyperparameters whenever you need, anywhere in the project. We call this "Distributed HPM". However, this is hardly called "management"; it is more like anarchism: no management is the best management. This makes add a hyperparameter cheap: let yourself free and do whatever you want.

Let it go, let it go

from torch import nn

def build_model():
    hidden_channels = 128  # <-- hyperparameter
    return nn.Sequence(
    [
        nn.Sequence(nn.Linear(784, hidden_channels), # <-- hyperparameter
            nn.BatchNorm1d(hidden_channels),
            nn.ReLU())
    ] + [
        nn.Sequence(nn.Linear(hidden_channels, hidden_channels),
            nn.BatchNorm1d(hidden_channels),
            nn.ReLU())
        for i in range(4)  # <-- hyperparameter
    ] + [
        nn.Linear(hidden_channels, 10)  # <-- hyperparameter
    ]
    )

However, barbaric growth of hyperparameters of different names in different places without governance would soon run into a disaster in knowledge sharing, communication, reproduction, and engineering. Nobody knows what happened, when did it happen, and nobody knows how to know easily. You know nothing, unless you read and diff through all the source codes.

You know nothing, Jon Snow.

咱也不知道，咱也不敢问呀

Distributed-Centralized HPM

Now we have two ways of managing hyperparameters: one is good for engineering but inconvenient for researchers, another one is convenient for researchers, but bad for engineering.

We are uncompromising. We did not want to make a decision between these two choices; we want the best of both worlds.

Only children make choices, adults want them all.

小孩子才做选择，大人全都要

After some trial and error, we came up with a design like this:

main.py

#!/usr/bin/env python3

from hpman.m import _
import hpargparse

import argparse


def func():
    weight_decay = _("weight_decay", 1e-5)
    print("weight decay is {}".format(weight_decay))


def main():
    parser = argparse.ArgumentParser()
    _.parse_file(__file__)
    hpargparse.bind(parser, _)
    parser.parse_args()

    func()


if __name__ == "__main__":
    main()

and you can:

$ ./main.py
weight decay is 1e-05
$ ./main.py --weight-decay 1e-4
weight decay is 0.0001
$ ./main.py --weight-decay 1e-4 --hp-list
weight_decay: 0.0001
$ ./main.py --weight-decay 1e-4 --hp-list detail
All hyperparameters:
    ['weight_decay']
Details:
+--------------+--------+---------+--------------------------------------------------------------+
| name         | type   |   value | details                                                      |
+==============+========+=========+==============================================================+
| weight_decay | float  |  0.0001 | occurrence[0]:                                               |
|              |        |         |   ./main.py:10                                               |
|              |        |         |      5:                                                      |
|              |        |         |      6: import argparse                                      |
|              |        |         |      7:                                                      |
|              |        |         |      8:                                                      |
|              |        |         |      9: def func():                                          |
|              |        |         | ==> 10:     weight_decay = _("weight_decay", 1e-5)           |
|              |        |         |     11:     print("weight decay is {}".format(weight_decay)) |
|              |        |         |     12:                                                      |
|              |        |         |     13:                                                      |
|              |        |         |     14: def main():                                          |
|              |        |         |     15:     parser = argparse.ArgumentParser()               |
+--------------+--------+---------+--------------------------------------------------------------+
$ ./main.py -h
usage: main.py [-h] [--weight-decay WEIGHT_DECAY] [--hp-save HP_SAVE]
               [--hp-load HP_LOAD] [--hp-list [{detail,yaml}]]
               [--hp-serial-format {auto,yaml,pickle}] [--hp-exit]

optional arguments:
  -h, --help            show this help message and exit
  --weight-decay WEIGHT_DECAY
  --hp-save HP_SAVE     Save hyperparameters to a file. The hyperparameters
                        are saved after processing of all other options
  --hp-load HP_LOAD     Load hyperparameters from a file. The hyperparameters
                        are loaded before any other options are processed
  --hp-list [{detail,yaml}]
                        List all available hyperparameters. If `--hp-list
                        detail` is specified, a verbose table will be print
  --hp-serial-format {auto,yaml,pickle}
                        Format of the saved config file. Defaults to auto. Can
                        be set to override auto file type deduction.
  --hp-exit             process all hpargparse actions and quit

(Example taken from hpargparse)

We are now both distributed (write anywhere) and centralized (manage them as a whole).

Our design is inspired by the underscore function commonly used in gettext in software translation. We deem "hyperparameters" as slots of text to be translated, while different hyperparameter values correspond to different "language" of the same text.

We achieve the above things by parsing your source code statically and extract where and how you are defining your hyperparameters. It follows the thoughts of Code as Data.

Also, expression evaluation in hpman is quite safe as we are using ast.literal_eval.

Features

Design principles

1. Low runtime overhead.

2. Values of hyperparameter can be any type.

Arbitrary Imports

Hyperparameter managers are the most important objects of hpman. We are using from hpman.m import _ throughout the tutorial, as well as recommend using underscore ("_", courtesy of gettext) as the name of imports in practice, but you can actually use anything name you want.

The hpman.m module is configured to allow arbitrary imports. Whatever you import will always be an object of hyperparameter manager and works the same as "_":

from hpman.m import _, hpm, hp, ddd, abc, hello
ddd('a', 1)
abc('a', 2)
_('hello', 3)

Hyperparameter managers imported by different names work independently and work in parallel. Imports of the same name are cached in the sense that, imports of the same name in the same process will return always the same object.

There are caveats:

• Assignment of these imported objects to variables will not work in static parsing (will be addressed later), but works at runtime (if you skipped parsing stage). e.g.:

# XXX: BAD EXAMPLE
from hpman.m import _
hello = _  # this breaks the rule
hello('a', 1)  # <-- hpman will not be aware of this 'a' hyperparameter.

• Variables share the same name with hpman.m imports will be statically parsed by hpman, but will not work as expected at runtime. e.g.:

def func(*args, **kargs):
    pass

_ = func

_("a", 1)  # <-- hpman can do nothing with "_" at runtime

from hpman.m import _

print(_.parse_file(__file__).get_values())
# Will output "{'a': 1}", which is a "false positive" of hyperparameter
# occurrence.

Define Hyperparameters

The most basic (and the most frequently used) function of hpman is to define a hyperparameter.

from hpman.m import _

def training_loop():
    # training settings
    batch_size = _('batch_size', 128)

    # first use of `num_layer` is recommend to come with default value
    print('num_layers = {}'.format(_('num_layers', 50)))

    # use it directly without storing the values
    if _('use_resnet', True):
    # second use of `num_layer` should not provide default value
    for i in range(_('num_layers')):
        pass

There are a few caveats:

1. Among all the occurrence of the same hyperparameter, one and only one occurrence should come with a default value. Nonetheless, which one has the default value does not matter (you can surely first use, then define the default value in later occurrence).
2. The name of the hyperparameter must be a literal string.
3. The value of the hyperparameter can be an arbitrary object (variable, lambda, string, whatever), but it is highly recommended to use only literal values, which is precisely defined by what ast.literal_eval function accepts. It not only makes the serialization of hyperparameters in downstream frameworks (such as hpargparse) easier but also improves the interoperability of hyperparameter settings among different programming languages and frameworks. The readability of dumped hyperparameters will be better as well.

Static Parsing

We employ static parsing to retrieve information on where and how you are using the hyperparameters in your source codes. It is employed by _.parse_file and _.parse_source.

• _.parse_file accepts file paths, directory names, or a list of both. It internally calls _.parse_source.
• _.parse_source accepts only a piece of source code string.

Examples:

_.parse_file(__file__)
_.parse_file('main.py')
_.parse_file('library_dir')
_.parse_file(['main.py', 'library_dir'])

_.parse_source('_("a", 1)')

Parsing is done using the ast module provided in the python standard library. We match all function calls with required syntax to detect proper calls to hyperparameter manager.

Runtime Value Getter/Setter

Value of a hyperparameter can be retrieved by two ways in runtime:

1. use __call__ syntax: _('varname')
2. use dedicated function: _.get_value('varname')

A dict of all hyperparameters can be retrieved by _.get_values()

Setting a hyperparameter can only be done with

_.set_value('varname', value)

Hints

Hints is intended to provide a mechanism for extending hpman.

It provides an interface to store and retrieve arbitrary information provided at hyperparameter definition. Downstream libraries and frameworks could utilize this provided information to better serve its purpose.

For example, say we would like to create an argparse interface for setting hyperparameters from the command line, a user could write something like

_('optimizer', 'adam', choices=['adam', 'sgd'])

in their codebase, and the entry point of the program, we could retrieve this information and provide better argparse options:

# File: hints_example.py
from hpman.m import _
from hpman.hpm_db import L

import argparse

_('optimizer', 'adam', choices=['adam', 'sgd'])


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    _.parse_file(__file__)
    occurrences = _.db.select(lambda row: row.name == 'optimizer')
    oc = [
        oc
        for oc in occurrences
        if oc['hints'] is not None
    ][0]
    choices = oc['hints']['choices']
    value = oc['value']

    parser.add_argument('--optimizer', default=value, choices=choices)
    args = parser.parse_args()

    print('optimizer: {}'.format(args.optimizer))

usecase is as follows:

$ python3 hints_example.py
optimizer: adam
$ python3 hints_example.py -h
usage: hints_example.py [-h] [--optimizer {adam,sgd}]

optional arguments:
  -h, --help            show this help message and exit
  --optimizer {adam,sgd}
$ python3 hints_example.py --optimizer sgd
optimizer: sgd
$ python3 hints_example.py --optimizer rmsprop
usage: hints_example.py [-h] [--optimizer {adam,sgd}]
hints_example.py: error: argument --optimizer: invalid choice: 'rmsprop' (choose from 'adam', 'sgd')

The example can be found at examples/02-hints

Nested Hyperparameters

当超参数数量增多时，会带来管理压力。我们经常将超参数分为若干族，使用相同的前缀方便管理。

你可以批量操作同一族的超参数。如将超参数导出成如下结构的yaml，提高了可读性。也可以直接导入树状结构的yaml。

discriminator:
  in_channels: 3
  spectral: true
  norm: 'instance'
  activation: 'leaky_relu'
  residual: true
  input_size: [512, 512]

Notice: 一个key不能同时指向一棵树和一个值，你可以通过set_value和set_tree分别指明超参数的类型是value还是tree。当你通过下划线函数定义默认值时，会被视为是value。 所以如下代码

_('a', {'b': 1})    # 被视为name='a'的超参数，默认值为{'b': 1}。此时a是value。
_('a.b')            # 被视为超参树a中的b，此时a是tree。

在运行时会抛出异常：

KeyError: '`a.b` not found'

在静态解析时会抛出异常：

hpman.primitives.ImpossibleTree: node `a` has is both a leaf and a tree.

缺点 and 兼容性破坏：你不能使用两个超参数，一个是另一个的前缀 (split by '.')。 因为tree的name允许为空，所以你仍然可以在超参数的name中使用.，包括以.开头，以.结尾，或连续的.都是合法的。Like _(".hpman is a good...man.").

Best Practices

It is advised that

1. DO use hpman when global hyperparameters are needed (e.g., config.{py,yml,json}). hpman can substitute a global config file theoretically.
2. DO NOT use hpman in python libraries share among projects, unless you fully aware what the consequences are.

Development

1. Install requirements:

python3 -m pip install -r requirements.dev.txt

2. Activate git commit template

git config commit.template .git-commit-template.txt

3. Install pre-commit hook

pre-commit install

4. To format your source code

make format

5. To check the coding style

make style-check

6. To run the tests

make test

CAVEAT

This project is still in its early stage. API may subject to radical changes (until version 1.0.0).

Contributing

License

MIT © MEGVII Research