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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
- Installation
- Usage
- Examples
- Features
- Contributing
- License
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() = 6The 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
python3 -m pip install hpman
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.
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, 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:
- 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.
- 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.
- 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.
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
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.
-
Low runtime overhead.
-
Values of hyperparameter can be any type.
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.mimports 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.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')):
passThere are a few caveats:
- 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).
- The name of the hyperparameter must be a literal string.
- 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_evalfunction 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.
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_fileaccepts file paths, directory names, or a list of both. It internally calls_.parse_source._.parse_sourceaccepts 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.
Value of a hyperparameter can be retrieved by two ways in runtime:
- use
__call__syntax:_('varname') - 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 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
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.").
It is advised that
- DO use hpman when global hyperparameters are needed (e.g., config.{py,yml,json}). hpman can substitute a global config file theoretically.
- DO NOT use hpman in python libraries share among projects, unless you fully aware what the consequences are.
- Install requirements:
python3 -m pip install -r requirements.dev.txt- Activate git commit template
git config commit.template .git-commit-template.txt
- Install pre-commit hook
pre-commit install- To format your source code
make format- To check the coding style
make style-check- To run the tests
make testThis project is still in its early stage. API may subject to radical changes (until version 1.0.0).
MIT © MEGVII Research