allennlp-optuna 0.1.7

AllenNLP integration for hyperparameter optimization

allennlp-optuna: Hyperparameter Optimization Library for AllenNLP

allennlp-optuna is AllenNLP plugin for hyperparameter optimization using Optuna.

Supported environments

Machine \ Device	Single GPU	Multi GPUs
Single Node	:white_check_mark:	Partial
Multi Nodes	:white_check_mark:	Partial

AllenNLP provides a way of distributed training (https://medium.com/ai2-blog/c4d7c17eb6d6). Unfortunately, allennlp-optuna doesn't fully support this feature. With multiple GPUs, you can run hyperparameter optimization. But you cannot enable a pruning feature. (For more detail, please see himkt/allennlp-optuna#20 and optuna/optuna#1990)

Alternatively, allennlp-optuna supports distributed optimization with multiple machines. Please read the tutorial about distributed optimization in allennlp-optuna. You can also learn about a mechanism of Optuna in the paper or documentation.

Documentation

You can read the documentation on readthedocs.

1. Installation

pip install allennlp_optuna

# Create .allennlp_plugins at the top of your repository or $HOME/.allennlp/plugins
# For more information, please see https://github.com/allenai/allennlp#plugins
echo 'allennlp_optuna' >> .allennlp_plugins

2. Optimization

2.1. AllenNLP config

Model configuration written in Jsonnet.

You have to replace values of hyperparameters with jsonnet function std.extVar. Remember casting external variables to desired types by std.parseInt, std.parseJson.

local lr = 0.1;  // before
↓↓↓
local lr = std.parseJson(std.extVar('lr'));  // after

For more information, please refer to AllenNLP Guide.

2.2. Define hyperparameter search speaces

You can define search space in Json.

Each hyperparameter config must have type and keyword. You can see what parameters are available for each hyperparameter in Optuna API reference.

[
  {
    "type": "int",
    "attributes": {
      "name": "embedding_dim",
      "low": 64,
      "high": 128
    }
  },
  {
    "type": "int",
    "attributes": {
      "name": "max_filter_size",
      "low": 2,
      "high": 5
    }
  },
  {
    "type": "int",
    "attributes": {
      "name": "num_filters",
      "low": 64,
      "high": 256
    }
  },
  {
    "type": "int",
    "attributes": {
      "name": "output_dim",
      "low": 64,
      "high": 256
    }
  },
  {
    "type": "float",
    "attributes": {
      "name": "dropout",
      "low": 0.0,
      "high": 0.5
    }
  },
  {
    "type": "float",
    "attributes": {
      "name": "lr",
      "low": 5e-3,
      "high": 5e-1,
      "log": true
    }
  }
]

Parameters for suggest_#{type} are available for config of type=#{type}. (e.g. when type=float, you can see the available parameters in suggest_float

Please see the example in detail.

2.3. Optimize hyperparameters by allennlp cli

allennlp tune \
    config/imdb_optuna.jsonnet \
    config/hparams.json \
    --serialization-dir result/hpo \
    --study-name test

Optionally, you can specify the metrics and direction you are optimizing for:

allennlp tune \
    config/imdb_optuna.jsonnet \
    config/hparams.json \
    --serialization-dir result/hpo \
    --study-name test \
    --metrics best_validation_accuracy \
    --direction maximize

2.4. [Optional] Specify Optuna configurations

You can choose a pruner/sample implemented in Optuna. To specify a pruner/sampler, create a JSON config file

The example of optuna.json looks like:

{
  "pruner": {
    "type": "HyperbandPruner",
    "attributes": {
      "min_resource": 1,
      "reduction_factor": 5
    }
  },
  "sampler": {
    "type": "TPESampler",
    "attributes": {
      "n_startup_trials": 5
    }
  }
}

And add a epoch callback to your configuration. (https://guide.allennlp.org/hyperparameter-optimization#6)

  callbacks: [
    {
      type: 'optuna_pruner',
    }
  ],

• config/imdb_optuna.jsonnet is a simple configuration for allennlp-optuna
• config/imdb_optuna_with_pruning.jsonnet is a configuration using Optuna pruner (and TPEsampler)

$ diff config/imdb_optuna.jsonnet config/imdb_optuna_with_pruning.jsonnet
32d31
<   datasets_for_vocab_creation: ['train'],
58a58,62
>     callbacks: [
>       {
>         type: 'optuna_pruner',
>       }
>     ],

Then, you can use a pruning callback by running following:

allennlp tune \
    config/imdb_optuna_with_pruning.jsonnet \
    config/hparams.json \
    --optuna-param-path config/optuna.json \
    --serialization-dir result/hpo_with_optuna_config \
    --study-name test_with_pruning

3. Get best hyperparameters

allennlp best-params \
    --study-name test

4. Retrain a model with optimized hyperparameters

allennlp retrain \
    config/imdb_optuna.jsonnet \
    --serialization-dir retrain_result \
    --study-name test

5. Hyperparameter optimization at scale!

you can run optimizations in parallel. You can easily run distributed optimization by adding an option --skip-if-exists to allennlp tune command.

allennlp tune \
    config/imdb_optuna.jsonnet \
    config/hparams.json \
    --optuna-param-path config/optuna.json \
    --serialization-dir result \
    --study-name test \
    --skip-if-exists

allennlp-optuna uses SQLite as a default storage for storing results. You can easily run distributed optimization over machines by using MySQL or PostgreSQL as a storage.

For example, if you want to use MySQL as a storage, the command should be like following:

allennlp tune \
    config/imdb_optuna.jsonnet \
    config/hparams.json \
    --optuna-param-path config/optuna.json \
    --serialization-dir result \
    --study-name test \
    --storage mysql://<user_name>:<passwd>@<db_host>/<db_name> \
    --skip-if-exists

You can run the above command on each machine to run multi-node distributed optimization.

If you want to know about a mechanism of Optuna distributed optimization, please see the official documentation: https://optuna.readthedocs.io/en/latest/tutorial/10_key_features/004_distributed.html

Reference

• Cookpad Techlife (in Japanese): https://techlife.cookpad.com/entry/2020/11/06/110000
  • allennlp-optuna is used for optimizing hyperparameter of NER model