How does Hydra’s _partial_ affect runtime seeding during object instantiation?

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In the Hydra configuration management library, you can partially instantiate classes using the _partial_ keyword, which results in a standard Python functools.partial object.

This led me to wonder whether using _partial_ with all parameters specified for __init__ essentially creates a factory that delays seeding until instantiation-time. For instance, if I use seeding functions like PyTorch’s torch.manual_seed() or PyTorch Lightning’s seed_everything(), will the seed be applied only when the factory is called? Or does the _partial_ operator somehow bake in a seed if provided?

My assumption is that _partial_ only stores the provided parameters without binding any dynamic elements such as randomly generated seeds, thus allowing for different seeding at runtime. Is this understanding correct?

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4 Answers

Answer by StackOverflow TychoAI Agent • 8 months ago

Your understanding is correct. When you use Hydra’s _partial_ to instantiate a class (or function) it simply creates a standard Python functools.partial object. This means:

The partial object stores the arguments you specify in the configuration.
It does not “bake in” any dynamic elements like randomly generated seeds unless those seeds themselves are fixed values in your configuration.
If you postpone seeding (e.g., using torch.manual_seed() or seed_everything()) until after the factory has been created, those calls will affect randomness in subsequent instantiations and method calls.

For example, suppose you have the following class defined in your configuration:

PYTHON
class MyComponent:
    def __init__(self, a, b):
        self.a = a
        self.b = b

And your Hydra configuration uses _partial_ like this:

YAML
_my_component_:
  _target_: my_module.MyComponent
  a: 10
  b: 20

Hydra will create a partial function essentially equivalent to:

PYTHON
from functools import partial
my_component_factory = partial(my_module.MyComponent, a=10, b=20)

When you later set the seed and call the factory:

PYTHON
import torch
torch.manual_seed(42)

component = my_component_factory()

The seed setting (and any subsequent seeding calls) are applied at runtime, not at the time the partial is created. This makes partial instantiation a convenient way to create a factory that you can then call in different seeding contexts without the seed being “baked in.”

So, as long as you leave the seed out of the parameters provided via _partial_ (or override it later), multiple runs with different seeds will behave as expected.

Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?

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Answer by NebularWatcher298 • 9 months ago

Before using hydra.utils.instantiate no third party code is not run by hydra. So you can set your seeds before each use of instantiate; or if a partial before each call to the partial.

Here a complete toy example, based on Hydra's doc overview, which creates a partial to instantiate an optimizer or a model, that takes a callable optim_partial as an argument.

YAML
# config.yaml
model:
  _target_: "__main.__.MyModel"
  optim_partial:
    _partial_: true
    _target_: __main__.MyOptimizer
    algo: SGD
  lr: 0.01

PYTHON
from functools import partial
from typing import Callable
import random
from pprint import pprint

import hydra
from omegaconf import DictConfig, OmegaConf


class MyModel:
    def __init__(self, lr, optim_partial: Callable[..., "MyOptimizer"):
        self.optim_partial = optim_partial
        self.optim1 = self.optim_partial()
        self.optim2 = self.optim_partial()


class MyOptimizer:
    def __init__(self, algo):
        print(algo, random.randint(0, 10000))


@hydra.main(config_name="config", config_path="./", version_base=None)
def main(cfg: DictConfig):
    # Check out the config
    pprint(OmegaConf.to_container(cfg, resolve=False))
    print(type(cfg.model.optim_partial))
    
    # Create the functools.partial
    optim_partial: partial[MyOptimizer] = hydra.utils.instantiate(cfg.model.optim_partial)
    # Set the seed before you call the a partial
    random.seed(42)
    optimizer1: MyOptimizer = optim_partial()
    optimizer2: MyOptimizer = optim_partial()
    random.seed(42)
    optimizer1b: MyOptimizer = optim_partial()
    optimizer2b: MyOptimizer = optim_partial()
    
    # model is not a partial; use seed before creation
    random.seed(42)
    model: MyModel = hydra.utils.instantiate(cfg.model)


if __name__ == "__main__":
    main()

BASH
# Output
{'model': {'_target_': '__main__.MyModel',
           'lr': 0.01,
           'optim_partial': {'_partial_': True,
                             '_target_': '__main__.MyOptimizer',
                             'algo': 'SGD'}}}
type of cfg.model.optim_partial <class 'omegaconf.dictconfig.DictConfig'>
SGD 1824
SGD 409
SGD 1824
SGD 409
SGD 1824
SGD 409

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Answer by StarKeeper090 • 9 months ago

Generally speaking Hydra is independent of PyTorch and does not directly interact with (except via plugins).
_partial_ has nothing at all to do with PyTorch or seeding.

At a glance what you are suggesting should work, but it's best if you just verify it.

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Answer by NeptunianTracker101 • 9 months ago

Your understanding is correct. Using partial in Hydra simply returns functools.partial object and doesn't immediately execute class constructor or otherwise "bake in" seed. As a result seed is not fixed at the time of creating that partial. You can safely call torch.manual_seed(...) or any other seed-setting functions just before you invoke the partial object multiple times for reproducible runs.

A common pattern is something like:

PYTHON
import torch
from hydra import compose, initialize

# For example your Hydra config defines partial for your model
with initialize(config_path="conf", version_base=None):
    cfg = compose(config_name="config")

model_partial = cfg.model  # A functools.partial(MyModel, ...)

# Then in your experiment loop :
for seed in [123, 456, 789]:
    torch.manual_seed(seed)
    model = model_partial()  # Actually instantiate model with given seed
    # train or evaluate your model

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How does Hydra’s _partial_ affect runtime seeding during object instantiation?

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