How to properly generate a multi-level composition? - fb-hydra

Question

Currently, my hydra config is organized as follows:
configs/
├── config.yaml
├── data
│   ├── IMDB.yaml
│   └── REUT.yaml
└── model
├── BERT.yaml
├── GPT.yaml
└── loss
├── CrossEntropyLoss.yaml
└── TripletMarginLoss.yaml
config.yaml:
defaults:
- model: BERT
- data: IMDB
tasks: [ "fit", "eval" ]
The dataset (IMDB.yaml and REUT.yaml) settings are in the format:
name: IMDB
dir: resource/dataset/imdb_reviews/
folds: [0,1,2,3,4]
max_length: 256
num_classes: 10
The model (BERT.yaml and GPT.yaml) settings are in the format:
defaults:
- loss: TripletMarginLoss
name: BERT
architecture: bert-base-uncased
lr: 5e-5
tokenizer:
architecture: ${model.architecture}
And finally, the loss function settings (CrossEntropyLoss.yaml and TripletMarginLoss.yam) adopt the following structure:
_target_: source.loss.TripletMarginLoss.TripletMarginLoss
params:
name: TripletMarginLoss
margin: 1.0
eps: 1e-6
reduction: mean
Running the following entry point:
#hydra.main(config_path="configs/", config_name="config.yaml")
def my_app(params):
OmegaConf.resolve(params)
print(
f"Params:\n"
f"{OmegaConf.to_yaml(params)}\n")
if __name__ == '__main__':
my_app()
# python main.py
generates the correct config composition:
tasks:
- fit
- eval
model:
loss:
_target_: source.loss.TripletMarginLoss.TripletMarginLoss
params:
name: TripletMarginLoss
margin: 1.0
eps: 1.0e-06
reduction: mean
name: BERT
architecture: bert-base-uncased
lr: 5.0e-05
tokenizer:
architecture: bert-base-uncased
data:
name: IMDB
dir: resource/dataset/imdb_reviews/
folds:
- 0
- 1
- 2
- 3
- 4
max_length: 256
num_classes: 10
However, overriding the loss function generates the wrong config:
python main.py model.loss=CrossEntropyLoss
tasks:
- fit
- eval
model:
loss: CrossEntropyLoss
name: BERT
architecture: bert-base-uncased
lr: 5.0e-05
tokenizer:
architecture: bert-base-uncased
data:
name: IMDB
dir: resource/dataset/imdb_reviews/
folds:
- 0
- 1
- 2
- 3
- 4
max_length: 256
num_classes: 10
Therefore, how to correctly generate a multi-level composition?

Answer 1

Overriding nested config groups is done with / as separator as documented in the config group description here.
Try:
$ python main.py model/loss=CrossEntropyLoss