alchemy-cat 0.0.9

Alchemy Cat —— 🔥Config System for SOTA

Alchemy Cat —— 🔥Config System for SOTA

English | 中文

🚀Introduction | 📦Installation | 🚚Migration | 📖Documentation

🚀 Introduction

When developing machine learning algorithms, we often bother with:

• Config files (YAML / YACS / MMCV) are lengthy and complex. If entries are interdependent, extra caution is needed to avoid errors when modifying them.
• Parameter tuning requires rewriting the config for each parameter set, leading to code duplication and difficulty in tracking changes.
• Manually traversing the parameter space and summarizing results during parameter tuning is time-consuming and inefficient.
• Insufficient parameter tuning can obscure effective designs.
• Effective methods may not achieve SOTA due to insufficient parameter tuning, reducing persuasiveness.

AlchemyCat is a config system designed for machine learning research to address such issues. It helps researchers to fully explore the parameter tuning potential by simplifying repetitive tasks like reproduction, modifying configs, and hyperparameter tuning

The table below compares AlchemyCat with existing config systems (😡 not support, 🤔 limited support, 🥳 supported):

Feature	argparse	yaml	YACS	mmcv	AlchemyCat
Reproducible	😡	🥳	🥳	🥳	🥳
IDE Jump	😡	😡	🥳	🥳	🥳
Inheritance	😡	😡	🤔	🤔	🥳
Composition	😡	😡	🤔	🤔	🥳
dependency	😡	😡	😡	😡	🥳
Automatic Parameter Tuning	😡	😡	😡	😡	🥳

AlchemyCat implements all features of current popular config systems, while fully considering various special cases, ensuring stability. AlchemyCat distinguishes itself by:

• Readable: The syntax is simple, elegant, and Pythonic.
• Reusable: Supports inheritance and composition of configs, reducing redundancy and enhancing reusability.
• Maintainable: Allows for establishing dependency between config items, enabling global synchronization with a single change.
• Supports auto parameter tuning and result summarization without needing to modify original configs or training codes.

Migrate from config systems listed above to AlchemyCat is effortless. Just spend 15 minutes reading the documentation and apply AlchemyCat to your project, and your GPU will never be idle again!

Quick Glance

Deep learning relies on numerous empirical hyperparameters, such as learning rate, loss weights, max iterations, sliding window size, drop probability, thresholds, and even random seeds.

The relationship between hyperparameters and performance is hard to predict theoretically. The only certainty is that arbitrarily chosen hyperparameters are unlikely to be optimal. Practice has shown that grid search through the hyperparameter space can significantly enhance model performance; sometimes its effect even surpasses so-called "contributions." Achieving SOTA often depends on this!

AlchemyCat offers an auto parameter-tuner that seamlessly integrates with existing config systems to explore the hyperparameter space and summarize experiment results automatically. Using this tool requires no modifications to the original config or training code.

For example, with MMSeg users only need to write a tunable config inherited from MMSeg's base config and define the parameter search space:

# -- configs/deeplabv3plus/tune_bs,iter/cfg.py --
from alchemy_cat import Cfg2Tune, Param2Tune

# Inherit from standard mmcv config.
cfg = Cfg2Tune(caps='configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py')

# Inherit and override
cfg.model.auxiliary_head.loss_decode.loss_weight = 0.2

# Tuning parameters: grid search batch_size and max_iters
cfg.train_dataloader.batch_size = Param2Tune([4, 8])
cfg.train_cfg.max_iters = Param2Tune([20_000, 40_000])
# ... 

Next, write a script specifying how to run a single config and read its results:

# -- tools/tune_dist_train.py --
import argparse, subprocess
from alchemy_cat.dl_config import Cfg2TuneRunner, Config
from alchemy_cat.dl_config.examples.read_mmcv_metric import get_metric

parser = argparse.ArgumentParser()
parser.add_argument('--cfg2tune', type=str)            # Path to the tunable config
parser.add_argument('--num_gpu', type=int, default=2)  # Number of GPUs for each task
args = parser.parse_args()

runner = Cfg2TuneRunner(args.cfg2tune, experiment_root='work_dirs', work_gpu_num=args.num_gpu)

@runner.register_work_fn  # Run experiment for each param combination with mmcv official train script
def work(pkl_idx: int, cfg: Config, cfg_pkl: str, cfg_rslt_dir: str, cuda_env: dict[str, str]):
    mmcv_cfg = cfg.save_mmcv(cfg_rslt_dir + '/mmcv_config.py')
    subprocess.run(f'./tools/dist_train.sh {mmcv_cfg} {args.num_gpu}', env=cuda_env, shell=True)

@runner.register_gather_metric_fn    # Optional, gather metric of each config
def gather_metric(cfg: Config, cfg_rslt_dir: str, run_rslt, param_comb) -> dict[str, float]:
    return get_metric(cfg_rslt_dir)  # {'aAcc': xxx, 'mIoU': xxx, 'mAcc': xxx}

runner.tuning()

Run CUDA_VISIBLE_DEVICES=0,1,2,3 python tools/tune_dist_train.py --cfg2tune configs/deeplabv3plus/tune_bs,iter/cfg.py, which will automatically search the parameter space in parallel and summarize the experiment results as follows:

In fact, the above config is still incomplete for some hyperparameters are interdependent and need to be adjusted together. For instance, the learning rate should scale with the batch size. AlchemyCat uses dependency to manage these relationships; when a dependency source changes, related dependencies automatically update for consistency. The complete config with dependencies is:

# -- configs/deeplabv3plus/tune_bs,iter/cfg.py --
from alchemy_cat import Cfg2Tune, Param2Tune, P_DEP

# Inherit from standard mmcv config.
cfg = Cfg2Tune(caps='configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb4-40k_voc12aug-512x512.py')

# Inherit and override
cfg.model.auxiliary_head.loss_decode.loss_weight = 0.2

# Tuning parameters: grid search batch_size and max_iters
cfg.train_dataloader.batch_size = Param2Tune([4, 8])
cfg.train_cfg.max_iters = Param2Tune([20_000, 40_000])

# Dependencies:
# 1) learning rate increase with batch_size
cfg.optim_wrapper.optimizer.lr = P_DEP(lambda c: (c.train_dataloader.batch_size / 8) * 0.01)

# 2) end of param_scheduler increase with max_iters
@cfg.set_DEP()
def param_scheduler(c):
    return dict(
        type='PolyLR',
        eta_min=1e-4,
        power=0.9,
        begin=0,
        end=c.train_cfg.max_iters,
        by_epoch=False)

[!NOTE] In the example above, defining dependencies might seem needless since they can be computed directly. However, when combined with inheritance, setting dependencies in the base config allows tunable configs to focus on key hyperparameters without worrying about trivial dependency details. Refer to the documentation for details.

📦 Installation

pip install alchemy-cat

🚚 Migration

How to migrate from YAML / YACS / MMCV

σ`∀´)σ Just kidding! No migration is needed. AlchemyCat can direct read and write YAML / YACS / MMCV config files:

from alchemy_cat.dl_config import load_config, Config

# READ YAML / YACS / MMCV config to alchemy_cat.Config
cfg = load_config('path/to/yaml_config.yaml or yacs_config.py or mmcv_config.py')
# Init alchemy_cat.Config with YAML / YACS / MMCV config
cfg = Config('path/to/yaml_config.yaml or yacs_config.py or mmcv_config.py')
# alchemy_cat.Config inherits from YAML / YACS / MMCV config
cfg = Config(caps='path/to/yaml_config.yaml or yacs_config.py or mmcv_config.py')

print(cfg.model.backbone)  # Access config item

cfg.save_yaml('path/to/save.yaml')  # Save to YAML config
cfg.save_mmcv('path/to/save.py')    # Save to MMCV config
cfg.save_py('path/to/save.py')      # Save to AlchemyCat config

We also provide a script to convert between different config formats:

python -m alchemy_cat.dl_config.from_x_to_y --x X --y Y --y_type=yaml/mmcv/alchemy-cat

where:

• --x: Source config file path, can be YAML / YACS / MMCV / AlchemyCat config.
• --y: Target config file path.
• --y_type: Target config format, can be yaml, mmcv, or alchemy-cat.

📖 Documentation

Basic Usage

AlchemyCat ensures a one-to-one correspondence between each configuration and its unique experimental record, with the bijective relationship ensuring the experiment's reproducibility.

config C + algorithm code A ——> reproducible experiment E(C, A)

The experimental directory is automatically generated, mirroring the relative path of the configuration file. This path can include multi-level directories and special characters such as spaces, commas, and equal signs. Such flexibility aids in categorizing experiments for clear management. For instance:

.
├── configs
│   ├── MNIST
│   │   ├── resnet18,wd=1e-5@run2
│   │   │   └── cfg.py
│   │   └── vgg,lr=1e-2
│   │       └── cfg.py
│   └── VOC2012
│       └── swin-T,γ=10
│           └── 10 epoch
│               └── cfg.py
└── experiment
    ├── MNIST
    │   ├── resnet18,wd=1e-5@run2
    │   │   └── xxx.log
    │   └── vgg,lr=1e-2
    │       └── xxx.log
    └── VOC2012
        └── swin-T,γ=10
            └── 10 epoch
                └── xxx.log

[!TIP] Best Practice: Avoid having '.' in the path. By following this best practice, relative imports can be used in cfg.py, and functions and classes defined within it can be pickled.

Let's begin with an incomplete example to demonstrate writing and loading a config. First, create the config file:

# -- [INCOMPLETE] configs/mnist/plain_usage/cfg.py --

from torchvision.datasets import MNIST
from alchemy_cat.dl_config import Config

cfg = Config()

cfg.rand_seed = 0

cfg.dt.cls = MNIST
cfg.dt.ini.root = '/tmp/data'
cfg.dt.ini.train = True

# ... Code Omitted.

Here, we first instantiate a Config object cfg, and then add config items through attribute operator .. Config items can be any Python objects, including functions, methods, and classes.

[!TIP] Best Practice: We prefer specifying functions or classes directly in config over using strings/semaphores to control the program behavior. This enables IDE navigation, simplifying reading and debugging.

Config is a subclass of Python's dict. The above code defines a nested dictionary with a tree structure:

>>> print(cfg.to_dict())
{'rand_seed': 0,
 'dt': {'cls': <class 'torchvision.datasets.mnist.MNIST'>,
        'ini': {'root': '/tmp/data', 'train': True}}}

Config implements all API of Python dict:

>>> cfg.keys()
dict_keys(['rand_seed', 'dt'])

>>> cfg['dt']['ini']['root']
'/tmp/data'

>>> {**cfg['dt']['ini'], 'download': True}
{'root': '/tmp/data', 'train': True, 'download': True}

You can initialize a Config object using dict (yaml, json) or its subclasses (YACS, mmcv.Config).

>>> Config({'rand_seed': 0, 'dt': {'cls': MNIST, 'ini': {'root': '/tmp/data', 'train': True}}})
{'rand_seed': 0, 'dt': {'cls': <class 'torchvision.datasets.mnist.MNIST'>, 'ini': {'root': '/tmp/data', 'train': True}}}

Using operator . to read and write cfg will be clearer. For instance, the following code creates and initializes the MNIST dataset based on the config:

>>> dataset = cfg.dt.cls(**cfg.dt.ini)
>>> dataset
Dataset MNIST
    Number of datapoints: 60000
    Root location: /tmp/data
    Split: Train

Accessing a non-existent key returns an empty dictionary, which should be treated as False:

>>> cfg.not_exit
{}

In the main code, use the following code to load the config:

# # [INCOMPLETE] -- train.py --

from alchemy_cat.dl_config import load_config
cfg = load_config('configs/mnist/base/cfg.py', experiments_root='/tmp/experiment', config_root='configs')
# ... Code Omitted.
torch.save(model.state_dict(), f"{cfg.rslt_dir}/model_{epoch}.pth")  # Save all experiment results to cfg.rslt_dir.

The load_config imports cfg from configs/mnist/base/cfg.py, handling inheritance and dependencies. Given the experiment root directory experiments_root and config root directory config_root, it auto creates an experiment directory at experiment/mnist/base and assign it to cfg.rslt_dir. All experimental results should be saved to cfg.rslt_dir.

The loaded cfg is read-only by default (cfg.is_frozen == True). To modify, unfreeze cfg with cfg.unfreeze().

Summary of This Chapter

• The config file offers a Config object cfg, a nested dictionary with a tree structure, allowing read and write via the . operator.
• Accessing non-existent keys in cfg returns a one-time empty dictionary considered as False.
• Use load_config to load the config file. The experiment path will be auto created and assigned to cfg.rslt_dir.

Inheritance

The new config can inherit the existing base config, written as cfg = Config(caps='base_cfg.py'). The new config only needs to override or add items, with rest items reusing the base config. For example, with base config:

# -- [INCOMPLETE] configs/mnist/plain_usage/cfg.py --

# ... Code Omitted.

cfg.loader.ini.batch_size = 128
cfg.loader.ini.num_workers = 2

cfg.opt.cls = optim.AdamW
cfg.opt.ini.lr = 0.01

# ... Code Omitted.

To double the batch size, new config can be written as:

# -- configs/mnist/plain_usage,2xbs/cfg.py --

from alchemy_cat.dl_config import Config

cfg = Config(caps='configs/mnist/plain_usage/cfg.py')  # Inherit from base config.

cfg.loader.ini.batch_size = 128 * 2  # Double batch size.

cfg.opt.ini.lr = 0.01 * 2  # Linear scaling rule, see https://arxiv.org/abs/1706.02677

Inheritance behaves like dict.update. The key difference is that if both config have keys with the same name and their values are Config instance (naming config subtree), we recursively update within these subtrees. Thus, the new config can modify cfg.loader.ini.batch_size while inheriting cfg.loader.ini.num_workers.

>>> base_cfg = load_config('configs/mnist/plain_usage/cfg.py', create_rslt_dir=False)
>>> new_cfg = load_config('configs/mnist/plain_usage,2xbs/cfg.py', create_rslt_dir=False)
>>> base_cfg.loader.ini
{'batch_size': 128, 'num_workers': 2}
>>> new_cfg.loader.ini
{'batch_size': 256, 'num_workers': 2}

To overwrite the entire config subtree in the new config, set this subtree to "override", e.g. :

# -- configs/mnist/plain_usage,override_loader/cfg.py --

from alchemy_cat.dl_config import Config

cfg = Config(caps='configs/mnist/plain_usage/cfg.py')  # Inherit from base config.

cfg.loader.ini.override()  # Set subtree as whole.
cfg.loader.ini.shuffle = False
cfg.loader.ini.drop_last = False

cfg.loader.ini will now be solely defined by the new config:

>>> base_cfg = load_config('configs/mnist/plain_usage/cfg.py', create_rslt_dir=False)
>>> new_cfg = load_config('configs/mnist/plain_usage,2xbs/cfg.py', create_rslt_dir=False)
>>> base_cfg.loader.ini
{'batch_size': 128, 'num_workers': 2}
>>> new_cfg.loader.ini
{'shuffle': False, 'drop_last': False}

Naturally, a base config can inherit from another base config, known as chain inheritance.

Multiple inheritance is also supported, written as cfg = Config(caps=('base.py', 'patch1.py', 'patch2.py', ...)), creating an inheritance chain of base -> patch1 -> patch2 -> current cfg. The base configs on the right are often used patches to batch add config items. For example, this patch includes CIFAR10 dataset configurations:

# -- configs/patches/cifar10.py --

import torchvision.transforms as T
from torchvision.datasets import CIFAR10

from alchemy_cat.dl_config import Config

cfg = Config()

cfg.dt.override()
cfg.dt.cls = CIFAR10
cfg.dt.ini.root = '/tmp/data'
cfg.dt.ini.transform = T.Compose([T.ToTensor(), T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

To switch to CIFAR10, new config only need to inherit the patch:

# -- configs/mnist/plain_usage,cifar10/cfg.py --

from alchemy_cat.dl_config import Config

cfg = Config(caps=('configs/mnist/plain_usage/cfg.py', 'alchemy_cat/dl_config/examples/configs/patches/cifar10.py'))

>>> cfg = load_config('configs/mnist/plain_usage,cifar10/cfg.py', create_rslt_dir=False)
>>> cfg.dt
{'cls': torchvision.datasets.cifar.CIFAR10,
 'ini': {'root': '/tmp/data',
  'transform': Compose(
      ToTensor()
      Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
  )}}

Inheritance Implementation Details

We copy the base config tree and update it with the new config, ensuring isolation between them. This means changes to the new config do not affect the base. Complex inheritance like diamond inheritance is supported but not recommended due to readability issues.
Note that leaf node values are passed by reference; modifying them inplace will affect the entire inheritance chain.

Summary of This Chapter

• The new config can leverage inheritance to reuse the base config and modifies or adds some items.
• The new config updates the base config recursively. Use Config.override to revert to the dict.update method for updates.
• Config supports chain and multiple inheritance, allowing for more fine-grained reuse.

Dependency

In the previous example, changing the batch size in the new configuration also alters the learning rate. This interdependence is called "dependency."

When modifying a config item, it's common to forget its dependencies. AlchemyCat lets you define dependencies, changing the dependency source updates all dependent items automatically. For example:

# -- [INCOMPLETE] configs/mnist/base/cfg.py --

from alchemy_cat.dl_config import Config, DEP
# ... Code Omitted.

cfg.loader.ini.batch_size = 128
# ... Code Omitted.
cfg.opt.ini.lr = DEP(lambda c: c.loader.ini.batch_size // 128 * 0.01)  # Linear scaling rule.

# ... Code Omitted.

The learning rate cfg.opt.ini.lr is calculated as a dependency DEP using the batch size cfg.loader.ini.batch_size. DEP takes a function with cfg as an argument and returns the dependency value.

In the new config, we only need to modify the batch size, and the learning rate will update automatically:

# -- configs/mnist/base,2xbs/cfg.py --

from alchemy_cat.dl_config import Config

cfg = Config(caps='configs/mnist/base/cfg.py')

cfg.loader.ini.batch_size = 128 * 2  # Double batch size, learning rate will be doubled automatically.

>>> cfg = load_config('configs/mnist/base,2xbs/cfg.py', create_rslt_dir=False)
>>> cfg.loader.ini.batch_size
256
>>> cfg.opt.ini.lr
0.02

Below is a more complex example:

# -- configs/mnist/base/cfg.py --

# ... Code Omitted.

cfg.sched.epochs = 30
@cfg.sched.set_DEP(name='warm_epochs', priority=0)  # kwarg `name` is not necessary
def warm_epochs(c: Config) -> int:  # warm_epochs = 10% of total epochs
    return round(0.1 * c.sched.epochs)

cfg.sched.warm.cls = sched.LinearLR
cfg.sched.warm.ini.total_iters = DEP(lambda c: c.sched.warm_epochs, priority=1)
cfg.sched.warm.ini.start_factor = 1e-5
cfg.sched.warm.ini.end_factor = 1.

cfg.sched.main.cls = sched.CosineAnnealingLR
cfg.sched.main.ini.T_max = DEP(lambda c: c.sched.epochs - c.sched.warm.ini.total_iters,
                               priority=2)  # main_epochs = total_epochs - warm_epochs

# ... Code Omitted.

>>> print(cfg.sched.to_txt(prefix='cfg.sched.'))  # A pretty print of the config tree.
cfg.sched = Config()
# ------- ↓ LEAVES ↓ ------- #
cfg.sched.epochs = 30
cfg.sched.warm_epochs = 3
cfg.sched.warm.cls = <class 'torch.optim.lr_scheduler.LinearLR'>
cfg.sched.warm.ini.total_iters = 3
cfg.sched.warm.ini.start_factor = 1e-05
cfg.sched.warm.ini.end_factor = 1.0
cfg.sched.main.cls = <class 'torch.optim.lr_scheduler.CosineAnnealingLR'>
cfg.sched.main.ini.T_max = 27

In the code, cfg.sched.epochs determines total training epochs, which is also the dependency source. Warm-up epochs cfg.sched.warm_epochs are 10% of this total, and main epochs cfg.sched.main.ini.T_max is the remainder. Adjusting total training epochs updates both warm-up and main epochs automatically.

The dependency cfg.sched.warm_epochs is defined using the Config.set_DEP decorator. The decorated function, passed as the first parameter of DEP, computes the dependency. The key name of dependency can be specified via the keyword argument name; if omitted, it defaults to the function's name. For complex computations, using a decorator for definition is recommended.

When a dependency relies on another dependency, they must be computed in the correct order. By default, this is the defined order. The priority parameter can specify computation order: smaller priority compute earlier. For instance, cfg.sched.warm_epochs depended by cfg.sched.warm.ini.total_iters, which is depended by cfg.sched.main.ini.T_max, so their priority increase sequentially.

Summary of This Chapter

• A dependency is defined when one config item relies on another. Changing the dependency source will automatically recalculate the dependency based on the calculation function.
• Dependencies can be defined by DEP(...) or the Config.set_DEP decorator.
• If dependencies are interdependent, use the priority parameter to specify the computation order; otherwise, they resolve in the order of definition.

Composition

Composition allows reusing configs by compose predefined config subtrees to form a complete config. For instance, the following config subtree defines a learning rate strategy:

# -- configs/addons/linear_warm_cos_sched.py --
import torch.optim.lr_scheduler as sched

from alchemy_cat.dl_config import Config, DEP

cfg = Config()

cfg.epochs = 30

@cfg.set_DEP(priority=0)  # warm_epochs = 10% of total epochs
def warm_epochs(c: Config) -> int:
    return round(0.1 * c.epochs)

cfg.warm.cls = sched.LinearLR
cfg.warm.ini.total_iters = DEP(lambda c: c.warm_epochs, priority=1)
cfg.warm.ini.start_factor = 1e-5
cfg.warm.ini.end_factor = 1.

cfg.main.cls = sched.CosineAnnealingLR
cfg.main.ini.T_max = DEP(lambda c: c.epochs - c.warm.ini.total_iters,
                         priority=2)  # main_epochs = total_epochs - warm_epochs

In the final config, we compose this set of learning rate strategy:

# -- configs/mnist/base,sched_from_addon/cfg.py --
# ... Code Omitted.

cfg.sched = Config('configs/addons/linear_warm_cos_sched.py')

# ... Code Omitted.

>>> print(cfg.sched.to_txt(prefix='cfg.sched.'))  # A pretty print of the config tree.
cfg.sched = Config()
# ------- ↓ LEAVES ↓ ------- #
cfg.sched.epochs = 30
cfg.sched.warm_epochs = 3
cfg.sched.warm.cls = <class 'torch.optim.lr_scheduler.LinearLR'>
cfg.sched.warm.ini.total_iters = 3
cfg.sched.warm.ini.start_factor = 1e-05
cfg.sched.warm.ini.end_factor = 1.0
cfg.sched.main.cls = <class 'torch.optim.lr_scheduler.CosineAnnealingLR'>
cfg.sched.main.ini.T_max = 27

It looks very simple! Just assign/mount the predefined config sub-subtree to the final config. Config('path/to/cfg.py') returns a copy of the cfg object in the config file, ensuring modifications before and after copying are isolated.

Implementation Details of Composition and Dependency

Attentive readers might wonder how DEP determines the parameter c for the dependency computation function, specifically which Config object is passed. In this chapter's example, c is the config subtree of learning rate; thus, the calculation function for cfg.warm.ini.total_iters is lambda c: c.warm_epochs. However, in the previous chapter's example, c is the final config; hence, the calculation function for cfg.sched.warm.ini.total_iters is lambda c: c.sched.warm_epochs.

In fact, c is the root node of the configuration tree where DEP was first mounted. The Config is a bidirectional tree. When DEP is first mounted, it records its relative distance to the root. During computation, it traces back this distance to find and pass the corresponding config tree into the computation function.

To prevent this default behavior, set DEP(lambda c: ..., rel=False), ensuring c is always the complete configuration.

Best Practice: Both composition and inheritance aim to reuse config. Composition is more flexible and loosely coupled, so it should be prioritized over inheritance.

Summary of This Chapter

• Define config subtree and compose them to create a complete config.

Full Example

Expand full example

Config subtree related to learning rate:

# -- configs/addons/linear_warm_cos_sched.py --

import torch.optim.lr_scheduler as sched

from alchemy_cat.dl_config import Config, DEP

cfg = Config()

cfg.epochs = 30

@cfg.set_DEP(priority=0)  # warm_epochs = 10% of total epochs
def warm_epochs(c: Config) -> int:
    return round(0.1 * c.epochs)

cfg.warm.cls = sched.LinearLR
cfg.warm.ini.total_iters = DEP(lambda c: c.warm_epochs, priority=1)
cfg.warm.ini.start_factor = 1e-5
cfg.warm.ini.end_factor = 1.

cfg.main.cls = sched.CosineAnnealingLR
cfg.main.ini.T_max = DEP(lambda c: c.epochs - c.warm.ini.total_iters,
                         priority=2)  # main_epochs = total_epochs - warm_epochs

The composed base config:

# -- configs/mnist/base/cfg.py --

import torchvision.models as model
import torchvision.transforms as T
from torch import optim
from torchvision.datasets import MNIST

from alchemy_cat.dl_config import Config, DEP

cfg = Config()

cfg.rand_seed = 0

# -* Set datasets.
cfg.dt.cls = MNIST
cfg.dt.ini.root = '/tmp/data'
cfg.dt.ini.transform = T.Compose([T.Grayscale(3), T.ToTensor(), T.Normalize((0.1307,), (0.3081,)),])

# -* Set data loader.
cfg.loader.ini.batch_size = 128
cfg.loader.ini.num_workers = 2

# -* Set model.
cfg.model.cls = model.resnet18
cfg.model.ini.num_classes = DEP(lambda c: len(c.dt.cls.classes))

# -* Set optimizer.
cfg.opt.cls = optim.AdamW
cfg.opt.ini.lr = DEP(lambda c: c.loader.ini.batch_size // 128 * 0.01)  # Linear scaling rule.

# -* Set scheduler.
cfg.sched = Config('configs/addons/linear_warm_cos_sched.py')

# -* Set logger.
cfg.log.save_interval = DEP(lambda c: c.sched.epochs // 5, priority=1)  # Save model at every 20% of total epochs.

Inherited from the base config, batch size doubled, number of epochs halved new config:

# -- configs/mnist/base,sched_from_addon,2xbs,2÷epo/cfg.py --

from alchemy_cat.dl_config import Config

cfg = Config(caps='configs/mnist/base,sched_from_addon/cfg.py')

cfg.loader.ini.batch_size = 256

cfg.sched.epochs = 15

Note that dependencies such as learning rate, warm-up epochs, and main epochs will be automatically updated:

>>> cfg = load_config('configs/mnist/base,sched_from_addon,2xbs,2÷epo/cfg.py', create_rslt_dir=False)
>>> print(cfg)
cfg = Config()
cfg.override(False).set_attribute('_cfgs_update_at_parser', ('configs/mnist/base,sched_from_addon/cfg.py',))
# ------- ↓ LEAVES ↓ ------- #
cfg.rand_seed = 0
cfg.dt.cls = <class 'torchvision.datasets.mnist.MNIST'>
cfg.dt.ini.root = '/tmp/data'
cfg.dt.ini.transform = Compose(
    Grayscale(num_output_channels=3)
    ToTensor()
    Normalize(mean=(0.1307,), std=(0.3081,))
)
cfg.loader.ini.batch_size = 256
cfg.loader.ini.num_workers = 2
cfg.model.cls = <function resnet18 at 0x7f5bcda68a40>
cfg.model.ini.num_classes = 10
cfg.opt.cls = <class 'torch.optim.adamw.AdamW'>
cfg.opt.ini.lr = 0.02
cfg.sched.epochs = 15
cfg.sched.warm_epochs = 2
cfg.sched.warm.cls = <class 'torch.optim.lr_scheduler.LinearLR'>
cfg.sched.warm.ini.total_iters = 2
cfg.sched.warm.ini.start_factor = 1e-05
cfg.sched.warm.ini.end_factor = 1.0
cfg.sched.main.cls = <class 'torch.optim.lr_scheduler.CosineAnnealingLR'>
cfg.sched.main.ini.T_max = 13
cfg.log.save_interval = 3
cfg.rslt_dir = 'mnist/base,sched_from_addon,2xbs,2÷epo'

Training code:

# -- train.py --
import argparse
import json

import torch
import torch.nn.functional as F
from rich.progress import track
from torch.optim.lr_scheduler import SequentialLR

from alchemy_cat.dl_config import load_config
from utils import eval_model

parser = argparse.ArgumentParser(description='AlchemyCat MNIST Example')
parser.add_argument('-c', '--config', type=str, default='configs/mnist/base,sched_from_addon,2xbs,2÷epo/cfg.py')
args = parser.parse_args()

# Folder 'experiment/mnist/base' will be auto created by `load` and assigned to `cfg.rslt_dir`
cfg = load_config(args.config, experiments_root='/tmp/experiment', config_root='configs')
print(cfg)

torch.manual_seed(cfg.rand_seed)  # Use `cfg` to set random seed

dataset = cfg.dt.cls(**cfg.dt.ini)  # Use `cfg` to set dataset type and its initial parameters

# Use `cfg` to set changeable parameters of loader,
# other fixed parameter like `shuffle` is set in main code
loader = torch.utils.data.DataLoader(dataset, shuffle=True, **cfg.loader.ini)

model = cfg.model.cls(**cfg.model.ini).train().to('cuda')  # Use `cfg` to set model

# Use `cfg` to set optimizer, and get `model.parameters()` in run time
opt = cfg.opt.cls(model.parameters(), **cfg.opt.ini, weight_decay=0.)

# Use `cfg` to set warm and main scheduler, and `SequentialLR` to combine them
warm_sched = cfg.sched.warm.cls(opt, **cfg.sched.warm.ini)
main_sched = cfg.sched.main.cls(opt, **cfg.sched.main.ini)
sched = SequentialLR(opt, [warm_sched, main_sched], [cfg.sched.warm_epochs])

for epoch in range(1, cfg.sched.epochs + 1):  # train `cfg.sched.epochs` epochs
    for data, target in track(loader, description=f"Epoch {epoch}/{cfg.sched.epochs}"):
        F.cross_entropy(model(data.to('cuda')), target.to('cuda')).backward()
        opt.step()
        opt.zero_grad()

    sched.step()

    # If cfg.log is defined, save model to `cfg.rslt_dir` at every `cfg.log.save_interval`
    if cfg.log and epoch % cfg.log.save_interval == 0:
        torch.save(model.state_dict(), f"{cfg.rslt_dir}/model_{epoch}.pth")

    eval_model(model)

if cfg.log:
    eval_ret = eval_model(model)
    with open(f"{cfg.rslt_dir}/eval.json", 'w') as json_f:
        json.dump(eval_ret, json_f)

Run python train.py --config 'configs/mnist/base,sched_from_addon,2xbs,2÷epo/cfg.py', and it will use the settings in the config file to train with train.py and save the results to the /tmp/experiment/mnist/base,sched_from_addon,2xbs,2÷epo directory.

Auto Parameter Tuning

In the example above, running python train.py --config path/to/cfg.py each time yields an experimental result for a set of parameters.

However, we often need to perform grid search over the parameter space to find the optimal parameter combination. Writing a config for each combination is laborious and error-prone. Can we define the entire parameter space in a "tunable config"? Then let the program automatically traverse all combinations, generate configs, run them, and summarize results for comparison.

The auto-tuner traverses through tunable config's parameter combinations, generates N sub-configs, runs them to obtain N experimental records, and summarizes all experimental results into an Excel sheet:

config to be tuned T ───> config C1 + algorithm code A ───> reproducible experiment E1(C1, A) ───> summary table S(T,A)
                     │                                                                          │  
                     ├──> config C2 + algorithm code A ───> reproducible experiment E1(C2, A) ──│ 
                    ...                                                                         ...

Tunable Config

To use the auto-tuner, we first need to write a tunable config:

# -- configs/tune/tune_bs_epoch/cfg.py --

from alchemy_cat.dl_config import Cfg2Tune, Param2Tune

cfg = Cfg2Tune(caps='configs/mnist/base,sched_from_addon/cfg.py')

cfg.loader.ini.batch_size = Param2Tune([128, 256, 512])

cfg.sched.epochs = Param2Tune([5, 15])

Its writing style is similar to the normal configuration in the previous chapter. It supports attribute reading and writing, inheritance, dependency, and combination. The difference lies in:

• The type of config is Cfg2Tune, a subclass of Config.
• For grid search parameters, use Param2Tune([v1, v2, ...]) with optional values v1, v2, ....

The tunable config above will search a parameter space of size 3×2=6 and generate these 6 sub-configs:

batch_size  epochs  child_configs            
128         5       configs/tune/tune_bs_epoch/batch_size=128,epochs=5/cfg.pkl
            15      configs/tune/tune_bs_epoch/batch_size=128,epochs=15/cfg.pkl
256         5       configs/tune/tune_bs_epoch/batch_size=256,epochs=5/cfg.pkl
            15      configs/tune/tune_bs_epoch/batch_size=256,epochs=15/cfg.pkl
512         5       configs/tune/tune_bs_epoch/batch_size=512,epochs=5/cfg.pkl
            15      configs/tune/tune_bs_epoch/batch_size=512,epochs=15/cfg.pkl

Set the priority parameter of Param2Tune to specify the search order. The default is the defined order. Use optional_value_names to assign readable names to parameter values. For example:

# -- configs/tune/tune_bs_epoch,pri,name/cfg.py --

from alchemy_cat.dl_config import Cfg2Tune, Param2Tune

cfg = Cfg2Tune(caps='configs/mnist/base,sched_from_addon/cfg.py')

cfg.loader.ini.batch_size = Param2Tune([128, 256, 512], optional_value_names=['1xbs', '2xbs', '4xbs'], priority=1)

cfg.sched.epochs = Param2Tune([5, 15], priority=0)

whose search space is:

epochs batch_size  child_configs                    
5      1xbs        configs/tune/tune_bs_epoch,pri,name/epochs=5,batch_size=1xbs/cfg.pkl
       2xbs        configs/tune/tune_bs_epoch,pri,name/epochs=5,batch_size=2xbs/cfg.pkl
       4xbs        configs/tune/tune_bs_epoch,pri,name/epochs=5,batch_size=4xbs/cfg.pkl
15     1xbs        configs/tune/tune_bs_epoch,pri,name/epochs=15,batch_size=1xbs/cfg.pkl
       2xbs        configs/tune/tune_bs_epoch,pri,name/epochs=15,batch_size=2xbs/cfg.pkl
       4xbs        configs/tune/tune_bs_epoch,pri,name/epochs=15,batch_size=4xbs/cfg.pk

We can set constraints between parameters to eliminate unnecessary combinations. For example, the following example limits total iterations to a maximum of 15×128:

# -- configs/tune/tune_bs_epoch,subject_to/cfg.py --

from alchemy_cat.dl_config import Cfg2Tune, Param2Tune

cfg = Cfg2Tune(caps='configs/mnist/base,sched_from_addon/cfg.py')

cfg.loader.ini.batch_size = Param2Tune([128, 256, 512])

cfg.sched.epochs = Param2Tune([5, 15],
                              subject_to=lambda cur_val: cur_val * cfg.loader.ini.batch_size.cur_val <= 15 * 128)

whose search space is:

batch_size epochs  child_configs                 
128        5       configs/tune/tune_bs_epoch,subject_to/batch_size=128,epochs=5/cfg.pkl  
           15      configs/tune/tune_bs_epoch,subject_to/batch_size=128,epochs=15/cfg.pkl
256        5       configs/tune/tune_bs_epoch,subject_to/batch_size=256,epochs=5/cfg.pkl

Running auto-tuner

We also need to write a small script to run the auto-tuner:

# -- tune_train.py --
import argparse, json, os, subprocess, sys
from alchemy_cat.dl_config import Config, Cfg2TuneRunner

parser = argparse.ArgumentParser(description='Tuning AlchemyCat MNIST Example')
parser.add_argument('-c', '--cfg2tune', type=str)
args = parser.parse_args()

# Will run `torch.cuda.device_count() // work_gpu_num`  of configs in parallel
runner = Cfg2TuneRunner(args.cfg2tune, experiment_root='/tmp/experiment', work_gpu_num=1)

@runner.register_work_fn  # How to run config
def work(pkl_idx: int, cfg: Config, cfg_pkl: str, cfg_rslt_dir: str, cuda_env: dict[str, str]) -> ...:
    subprocess.run([sys.executable, 'train.py', '-c', cfg_pkl], env=cuda_env)

@runner.register_gather_metric_fn  # How to gather metric for summary
def gather_metric(cfg: Config, cfg_rslt_dir: str, run_rslt: ..., param_comb: dict[str, tuple[..., str]]) -> dict[str, ...]:
    return json.load(open(os.path.join(cfg_rslt_dir, 'eval.json')))

runner.tuning()

The script performs these operations:

• Instantiates the auto-tuner with runner = Cfg2TuneRunner(...), passing in the tunable config path. By default, it runs sub-configs sequentially. Set the parameter work_gpu_num to run len(os.environ['CUDA_VISIBLE_DEVICES']) // work_gpu_num sub-configs in parallel.

• Registers a worker that executes each sub-config. The function parameters are:

  • pkl_idx: index of the sub-config
  • cfg: the sub-config
  • cfg_pkl: pickle save path for this sub-config
  • cfg_rslt_dir: experiment directory.
  • cuda_env: If work_gpu_num is set, then cuda_env will allocate non-overlapping CUDA_VISIBLE_DEVICES environment variables for parallel sub-configs.

  Commonly, we only need to pass cfg_pkl as the config file into the training script, since load_cfg supports reading config in pickle format. For deep learning tasks, different CUDA_VISIBLE_DEVICES are needed for each sub-config.

• Registers a summary function that returns an experimental result as a {metric_name: metric_value} dictionary. The auto-tunner will traverse all experimental results and summary into a table. The summary function accepts these parameters:

  • cfg: the sub-configuration
  • cfg_rslt_dir: experiment directory
  • run_rslt: returned from working functions
  • param_comb: parameter combinations for that particular sub-configuration.

  Generally, only need to read results from cfg_rslt_dir and return them.

• Calls runner.tuning() to start automatic tuning.

After tuning, the tuning results will be printed:

Metric Frame: 
                  test_loss    acc
batch_size epochs                 
128        5       1.993285  32.63
           15      0.016772  99.48
256        5       1.889874  37.11
           15      0.020811  99.49
512        5       1.790593  41.74
           15      0.024695  99.33

Saving Metric Frame at /tmp/experiment/tune/tune_bs_epoch/metric_frame.xlsx

As the prompt says, the tuning results will also be saved to the /tmp/experiment/tune/tune_bs_epoch/metric_frame.xlsx table:

[!TIP] Best Practice: The auto-tuner is separate from the standard workflow. Write configs and code without considering it. When tuning, add extra code to define parameter space, specify invocation and result methods. After tuning, remove the auto-tuner, keeping only the best config and algorithm.

Summary of This Chapter

• Define a tunable config Cfg2Tune with Param2Tune to specify the parameter space.
• Use the auto-tuner Cfg2TuneRunner to traverse the parameter space, generate sub-configs, run them, and summarize the results.

Advanced Usage

Expand advanced usage

Pretty Print

The __str__ method of Config is overloaded to print the tree structure with keys separated by .:

>>> cfg = Config()
>>> cfg.foo.bar.a = 1
>>> cfg.bar.foo.b = ['str1', 'str2']
>>> cfg.whole.override()
>>> print(cfg)
cfg = Config()
cfg.whole.override(True)
# ------- ↓ LEAVES ↓ ------- #
cfg.foo.bar.a = 1
cfg.bar.foo.b = ['str1', 'str2']

When all leaf nodes are built-in types, the pretty print output of Config can be executed as Python code to get the same configuration:

>>> exec(cfg.to_txt(prefix='new_cfg.'), globals(), (l_dict := {}))
>>> l_dict['new_cfg'] == cfg
True

For invalid attribute names, Config will fall back to the print format of dict:

>>> cfg = Config()
>>> cfg['Invalid Attribute Name'].foo = 10
>>> cfg.bar['def'] = {'a': 1, 'b': 2}
>>> print(cfg)
cfg = Config()
# ------- ↓ LEAVES ↓ ------- #
cfg['Invalid Attribute Name'].foo = 10
cfg.bar['def'] = {'a': 1, 'b': 2}

Auto Capture Experiment Logs

For deep learning tasks, we recommend using init_env instead of load_config. In addition to loading the config, init_env can also initialize the deep learning environment, such as setting the torch device, gradient, random seed, and distributed training:

from alchemy_cat.torch_tools import init_env

if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument('-c', '--config', type=str)
    parser.add_argument('--local_rank', type=int, default=-1)
    args = parser.parse_args()
    
    device, cfg = init_env(config_path=args.config,             # config file path，read to `cfg`
                           is_cuda=True,                        # if True，`device` is cuda，else cpu
                           is_benchmark=bool(args.benchmark),   # torch.backends.cudnn.benchmark = is_benchmark
                           is_train=True,                       # torch.set_grad_enabled(is_train)
                           experiments_root="experiment",       # root of experiment dir
                           rand_seed=True,                      # set python, numpy, torch rand seed. If True, read cfg.rand_seed as seed, else use actual parameter as rand seed. 
                           cv2_num_threads=0,                   # set cv2 num threads
                           verbosity=True,                      # print more env init info
                           log_stdout=True,                     # where fork stdout to log file
                           loguru_ini=True,                     # config a pretty loguru format
                           reproducibility=False,               # set pytorch to reproducible mode
                           local_rank=...,                      # dist.init_process_group(..., local_rank=local_rank)
                           silence_non_master_rank=True,        # if True, non-master rank will not print to stdout, but only log to file
                           is_debug=bool(args.is_debug))        # is debug mode

If log_stdout=True, init_env will fork sys.stdout and sys.stderr to the log file cfg.rslt_dir/{local-time}.log. This will not interfere with normal print, but all screen output will be recorded in the log. Therefore, there is no need to manually write logs, what you see on the screen is what you get in the log.

Details can be found in the docstring of init_env.

Attribute Dict

If you are a user of addict, our ADict can be used as a drop-in replacement for addict.Dict: from alchemy_cat.dl_config import ADict as Dict.

ADict has all the interfaces of addict.Dict. However, all methods are re-implemented to optimize execution efficiency and cover more corner cases (such as circular references). Config is actually a subclass of ADict.

If you haven't used addict before, read this documentation. Research code often involves complex dictionaries. addict.Dict or ADict supports attribute-style access for nested dictionaries.

Circular References

The initialization, inheritance, and composition of ADict and Config require a branch_copy operation, which is between shallow and deep copy, that is, copying the tree structure but not the leaf nodes. ADict.copy, Config.copy, and copy.copy(cfg) all call branch_copy, not the copy method of dict.

In theory, ADict.branch_copy can handle circular references, such as:

>>> dic = {'num': 0,
           'lst': [1, 'str'],
           'sub_dic': {'sub_num': 3}}
>>> dic['lst'].append(dic['sub_dic'])
>>> dic['sub_dic']['parent'] = dic
>>> dic
{'num': 0,
 'lst': [1, 'str', {'sub_num': 3, 'parent': {...}}],
 'sub_dic': {'sub_num': 3, 'parent': {...}}}

>>> adic = ADict(dic)
>>> adic.sub_dic.parent is adic is not dic
True
>>> adic.lst[-1] is adic.sub_dic is not dic['sub_dic']
True

Different from ADict, the data model of Config is a bidirectional tree, and circular references will form a cycle. To avoid cycles, if a subtree is mounted to different parent configs multiple times, the subtree will be copied to an independent config tree before mounting. In normal use, circular references should not appear in the config tree.

In summary, although circular references are supported, they are neither necessary nor recommended.

Traverse the Config Tree

Config.named_branchs and Config.named_ckl respectively traverse all branches and leaves of the config tree (the branch, key name, and value they are in):

>>> list(cfg.named_branches) 
[('', {'foo': {'bar': {'a': 1}},  
       'bar': {'foo': {'b': ['str1', 'str2']}},  
       'whole': {}}),
 ('foo', {'bar': {'a': 1}}),
 ('foo.bar', {'a': 1}),
 ('bar', {'foo': {'b': ['str1', 'str2']}}),
 ('bar.foo', {'b': ['str1', 'str2']}),
 ('whole', {})]
 
>>> list(cfg.ckl)
[({'a': 1}, 'a', 1), ({'b': ['str1', 'str2']}, 'b', ['str1', 'str2'])]

Lazy Inheritance

>>> from alchemy_cat.dl_config import Config
>>> cfg = Config(caps='configs/mnist/base,sched_from_addon/cfg.py')
>>> cfg.loader.ini.batch_size = 256
>>> cfg.sched.epochs = 15
>>> print(cfg)

cfg = Config()
cfg.override(False).set_attribute('_cfgs_update_at_parser', ('configs/mnist/base,sched_from_addon/cfg.py',))
# ------- ↓ LEAVES ↓ ------- #
cfg.loader.ini.batch_size = 256
cfg.sched.epochs = 15

When inheriting, the parent configs caps is not immediately updated, but is loaded when load_config is called. Lazy inheritance allows the config system to have an eager-view of the entire inheritance chain, and a few features rely on this.

Work with Git

For config C + algorithm code A ——> reproducible experiment E(C, A), meaning that when the config C and the algorithm code A are determined, the experiment E can always be reproduced. Therefore, it is recommended to submit the configuration file and algorithm code to the Git repository together for reproducibility.

We also provide a script that runs pyhon -m alchemy_cat.torch_tools.scripts.tag_exps -s commit_ID -a commit_ID, interactively lists the new configs added by the commit, and tags the commit according to the config path. This helps quickly trace back the config and algorithm of a historical experiment.

Automatically Allocate idle GPUs

The work function receives the idle GPU automatically allocated by Cfg2TuneRunner through the cuda_env parameter. We can further control the definition of 'idle GPU':

runner = Cfg2TuneRunner(args.cfg2tune, experiment_root='/tmp/experiment', work_gpu_num=1, 
                        block=True,             # Try to allocate idle GPU
                        memory_need=10 * 1024,  # Need 10 GB memory
                        max_process=2)          # Max 2 process already ran on each GPU

where:

• block: Defaults is True. If set to False, GPUs are allocated sequentially, regardless of whether they are idle.
• memory_need: The amount of GPU memory required for each sub-config, in MB. The free memory on an idle GPU must be ≥ memory_need. Default is -1., indicating need all memory.
• max_process: Maximum number of existing processes. The number of existing processes on an idle GPU must be ≤ max_process. Default value is -1, indicating no limit.

Pickling Lambda Functions

Sub-configs generated by Cfg2Tune will be saved using pickle. However, if Cfg2Tune defines dependencies as DEP(lambda c: ...), these lambda functions cannot be pickled. Workarounds include:

• Using the decorator @Config.set_DEP to define the dependency's computation function.
• Defining the dependency's calculation function in a separate module and passing it to DEP.
• Defining dependencies in the parent configs since inheritance is handled lazily, so sub-configs temporarily exclude dependencies.
• If the dependency source is a tunable parameter, use P_DEP, which resolves after generating sub-configs of Cfg2Tune but before saving them as pickle.

More Inheritance Tricks

Deleting During Inheritance

The Config.empty_leaf() combines Config.clear() and Config.override() to get an empty and "override" subtree. This is commonly used to represent the "delete" semantics during inheritance, that is, using an empty config to override a subtree of the base config.

update Method

Let cfg be a Config instance and base_cfg be a dict instance. The effects of cfg.dict_update(base_cfg), cfg.update(base_cfg), and cfg |= base_cfg are similar to inheriting Config(base_cfg) from cfg.

Run cfg.dict_update(base_cfg, incremental=True) to ensure only incremental updates, that is, only add keys that do not exist in cfg without overwriting existing keys.